From a850d5f6ebd90550084c117a7b069ec9ef38f43c Mon Sep 17 00:00:00 2001 From: themattinthehatt Date: Mon, 12 Oct 2020 19:57:35 -0400 Subject: [PATCH 01/50] allow masking of labels --- behavenet/data/data_generator.py | 15 ++++++--------- behavenet/data/utils.py | 10 ++++++++++ behavenet/models/vaes.py | 11 +++++++++-- configs/data_default.json | 2 ++ docs/source/data_structure.rst | 12 +++++------- docs/source/glossary.rst | 1 + tests/test_data/test_utils_data.py | 31 +++++++++++++++++++++++++++++- 7 files changed, 63 insertions(+), 19 deletions(-) diff --git a/behavenet/data/data_generator.py b/behavenet/data/data_generator.py index d593894..b8702a8 100644 --- a/behavenet/data/data_generator.py +++ b/behavenet/data/data_generator.py @@ -274,7 +274,8 @@ def __getitem__(self, idx): else: sample[signal] = f[signal][str('trial_%04i' % idx)][()].astype(dtype) - elif signal == 'neural' or signal == 'labels' or signal == 'labels_sc': + elif signal == 'neural' or signal == 'labels' or signal == 'labels_sc' \ + or signal == 'labels_masks': dtype = 'float32' with h5py.File(self.paths[signal], 'r', libver='latest', swmr=True) as f: if idx is None: @@ -288,23 +289,19 @@ def __getitem__(self, idx): elif signal == 'ae_latents': dtype = 'float32' - sample[signal] = self._try_to_load( - signal, key='latents', idx=idx, dtype=dtype) + sample[signal] = self._try_to_load(signal, key='latents', idx=idx, dtype=dtype) elif signal == 'ae_predictions': dtype = 'float32' - sample[signal] = self._try_to_load( - signal, key='predictions', idx=idx, dtype=dtype) + sample[signal] = self._try_to_load(signal, key='predictions', idx=idx, dtype=dtype) elif signal == 'arhmm' or signal == 'arhmm_states': dtype = 'int32' - sample[signal] = self._try_to_load( - signal, key='states', idx=idx, dtype=dtype) + sample[signal] = self._try_to_load(signal, key='states', idx=idx, dtype=dtype) elif signal == 'arhmm_predictions': dtype = 'float32' - sample[signal] = self._try_to_load( - signal, key='predictions', idx=idx, dtype=dtype) + sample[signal] = self._try_to_load(signal, key='predictions', idx=idx, dtype=dtype) else: raise ValueError('"%s" is an invalid signal type' % signal) diff --git a/behavenet/data/utils.py b/behavenet/data/utils.py index cc00248..10654d1 100644 --- a/behavenet/data/utils.py +++ b/behavenet/data/utils.py @@ -75,6 +75,12 @@ def get_data_generator_inputs(hparams, sess_ids, check_splits=True): signals.append('masks') transforms.append(None) paths.append(os.path.join(data_dir, 'data.hdf5')) + if hparams.get('use_label_mask', False) and ( + hparams['model_class'] == 'cond-ae-msp' or hparams['model_class'] == 'sss-vae' + ): + signals.append('labels_masks') + transforms.append(None) + paths.append(os.path.join(data_dir, 'data.hdf5')) if hparams.get('conditional_encoder', False): from behavenet.data.transforms import MakeOneHot2D signals.append('labels_sc') @@ -253,6 +259,10 @@ def get_data_generator_inputs(hparams, sess_ids, check_splits=True): signals = [hparams['model_class']] transforms = [None] paths = [os.path.join(data_dir, 'data.hdf5')] + if hparams.get('use_label_mask', False): + signals.append('labels_masks') + transforms.append(None) + paths.append(os.path.join(data_dir, 'data.hdf5')) else: raise ValueError('"%s" is an invalid model_class' % hparams['model_class']) diff --git a/behavenet/models/vaes.py b/behavenet/models/vaes.py index 342876f..5b93c7b 100644 --- a/behavenet/models/vaes.py +++ b/behavenet/models/vaes.py @@ -631,6 +631,7 @@ def loss(self, data, dataset=0, accumulate_grad=True, chunk_size=200): x = data['images'][0] y = data['labels'][0] m = data['masks'][0] if 'masks' in data else None + n = data['labels_masks'][0] if 'labels_masks' in data else None batch_size = x.shape[0] n_chunks = int(np.ceil(batch_size / chunk_size)) n_labels = self.hparams['n_labels'] @@ -659,6 +660,7 @@ def loss(self, data, dataset=0, accumulate_grad=True, chunk_size=200): x_in = x[idx_beg:idx_end] y_in = y[idx_beg:idx_end] m_in = m[idx_beg:idx_end] if m is not None else None + n_in = n[idx_beg:idx_end] if n is not None else None x_hat, sample, mu, logvar, y_hat = self.forward(x_in, dataset=dataset, use_mean=False) # reset losses @@ -669,7 +671,7 @@ def loss(self, data, dataset=0, accumulate_grad=True, chunk_size=200): loss_dict_torch['loss'] -= loss_dict_torch['loss_data_ll'] # label log-likelihood - loss_dict_torch['loss_label_ll'] = losses.gaussian_ll(y_in, y_hat) + loss_dict_torch['loss_label_ll'] = losses.gaussian_ll(y_in, y_hat, n_in) loss_dict_torch['loss'] -= alpha * loss_dict_torch['loss_label_ll'] # supervised latents kl @@ -717,7 +719,12 @@ def loss(self, data, dataset=0, accumulate_grad=True, chunk_size=200): # use variance-weighted r2s to ignore small-variance latents y_hat_all = np.concatenate(y_hat_all, axis=0) - r2 = r2_score(y.cpu().detach().numpy(), y_hat_all, multioutput='variance_weighted') + y_all = y.cpu().detach().numpy() + if n is not None: + n_np = n.cpu().detach().numpy() + r2 = r2_score(y_all[n_np == 1], y_hat_all[n_np == 1], multioutput='variance_weighted') + else: + r2 = r2_score(y_all, y_hat_all, multioutput='variance_weighted') # compile (properly weighted) loss terms for key in loss_dict_vals.keys(): diff --git a/configs/data_default.json b/configs/data_default.json index a39d4be..514d65b 100644 --- a/configs/data_default.json +++ b/configs/data_default.json @@ -31,6 +31,8 @@ "use_output_mask": false, # type: boolean +"use_label_mask": false, # type: boolean + ######################## ## Neural data params ## diff --git a/docs/source/data_structure.rst b/docs/source/data_structure.rst index 0f676a2..c14d900 100644 --- a/docs/source/data_structure.rst +++ b/docs/source/data_structure.rst @@ -37,14 +37,12 @@ does not require all trials to be of the same length, but does require that for images and neural activity have the same number of frames. This may require you to interpolate/bin video or neural data differently than the rate at which it was acquired. -**Note 1**: for large experiments having all of this data in memory might be infeasible, and more -sophisticated processing will be required +**Notes**: -**Note 2**: neural data is only required for fitting decoding models; it is still possible to fit -autoencoders and ARHMMs when the HDF5 file only contains images - -**Note 3**: the python package ``h5py`` is required for creating the HDF5 file, and is -automatically installed with the BehaveNet package. +* for large experiments, having all of this (video) data in memory to create the HDF5 file might be infeasible, and more sophisticated processing will be required +* neural data is only required for fitting decoding models; it is still possible to fit autoencoders and ARHMMs when the HDF5 file only contains images +* masks should be the same size as images; a value of 0 excludes the pixel from the loss function, a value of 1 includes it +* the python package ``h5py`` is required for creating the HDF5 file, and is automatically installed with the BehaveNet package. .. code-block:: python diff --git a/docs/source/glossary.rst b/docs/source/glossary.rst index 97941dc..ec35952 100644 --- a/docs/source/glossary.rst +++ b/docs/source/glossary.rst @@ -21,6 +21,7 @@ Data * **y_pixels** (*int*): number of behavioral video pixels in y dimension * **x_pixels** (*int*): number of behavioral video pixels in x dimension * **use_output_mask** (*bool*): `True`` to apply frame-wise output masks (must be a key ``masks`` in data HDF5 file) +* **use_label_mask** (*bool*): `True`` to apply frame-wise masks to labels in conditional ae models (must be a key ``labels_masks`` in data HDF5 file) * **neural_bin_size** (*float*): bin size of neural/video data (ms) * **neural_type** (*str*): 'spikes' | 'ca' * **approx_batch_size** (*str*): approximate batch size (number of frames) for gpu memory calculation diff --git a/tests/test_data/test_utils_data.py b/tests/test_data/test_utils_data.py index 2509325..c87b4f0 100644 --- a/tests/test_data/test_utils_data.py +++ b/tests/test_data/test_utils_data.py @@ -94,6 +94,15 @@ def test_get_data_generator_inputs(): assert paths[0] == [hdf5_path, hdf5_path, hdf5_path] hparams['use_output_mask'] = False + hparams['model_class'] = 'sss-vae' + hparams['use_label_mask'] = True + hparams_, signals, transforms, paths = utils.get_data_generator_inputs( + hparams, sess_ids, check_splits=False) + assert signals[0] == ['images', 'labels', 'labels_masks'] + assert transforms[0] == [None, None, None] + assert paths[0] == [hdf5_path, hdf5_path, hdf5_path] + hparams['use_label_mask'] = False + # ----------------- # cond-vae # ----------------- @@ -114,7 +123,7 @@ def test_get_data_generator_inputs(): hparams['use_output_mask'] = False # ----------------- - # cond-ae [-msp] + # cond-ae # ----------------- hparams['model_class'] = 'cond-ae' hparams_, signals, transforms, paths = utils.get_data_generator_inputs( @@ -145,6 +154,9 @@ def test_get_data_generator_inputs(): assert paths[0] == [hdf5_path, hdf5_path, hdf5_path] hparams['conditional_encoder'] = False + # ----------------- + # cond-ae-msp + # ----------------- hparams['model_class'] = 'cond-ae-msp' hparams_, signals, transforms, paths = utils.get_data_generator_inputs( hparams, sess_ids, check_splits=False) @@ -152,6 +164,15 @@ def test_get_data_generator_inputs(): assert transforms[0] == [None, None] assert paths[0] == [hdf5_path, hdf5_path] + hparams['model_class'] = 'cond-ae-msp' + hparams['use_label_mask'] = True + hparams_, signals, transforms, paths = utils.get_data_generator_inputs( + hparams, sess_ids, check_splits=False) + assert signals[0] == ['images', 'labels', 'labels_masks'] + assert transforms[0] == [None, None, None] + assert paths[0] == [hdf5_path, hdf5_path, hdf5_path] + hparams['use_label_mask'] = False + # ----------------- # ae_latents # ----------------- @@ -377,6 +398,14 @@ def test_get_data_generator_inputs(): assert transforms[0] == [None] assert paths[0] == [hdf5_path] + hparams['use_label_mask'] = True + hparams_, signals, transforms, paths = utils.get_data_generator_inputs( + hparams, sess_ids, check_splits=False) + assert signals[0] == ['labels', 'labels_masks'] + assert transforms[0] == [None, None] + assert paths[0] == [hdf5_path, hdf5_path] + hparams['use_label_mask'] = False + # ----------------- # other # ----------------- From f00d2db2e046cbf45a9156063ed23bd654aad352 Mon Sep 17 00:00:00 2001 From: themattinthehatt Date: Wed, 14 Oct 2020 12:18:14 -0400 Subject: [PATCH 02/50] load labels_masks from data generator --- behavenet/data/data_generator.py | 2 +- behavenet/data/transforms.py | 2 ++ behavenet/data/utils.py | 6 ++++++ behavenet/plotting/cond_ae_utils.py | 13 ++++++++++--- tests/test_data/test_transforms.py | 13 +++++++++++++ tests/test_data/test_utils_data.py | 10 ++++++++++ 6 files changed, 42 insertions(+), 4 deletions(-) diff --git a/behavenet/data/data_generator.py b/behavenet/data/data_generator.py index b8702a8..83f9bae 100644 --- a/behavenet/data/data_generator.py +++ b/behavenet/data/data_generator.py @@ -192,7 +192,7 @@ def __init__( self.n_trials = None for i, signal in enumerate(signals): if signal == 'images' or signal == 'neural' or signal == 'labels' or \ - signal == 'labels_sc': + signal == 'labels_sc' or signal == 'labels_masks': data_file = paths[i] with h5py.File(data_file, 'r', libver='latest', swmr=True) as f: self.n_trials = len(f[signal]) diff --git a/behavenet/data/transforms.py b/behavenet/data/transforms.py index 8e6b537..6c58bef 100644 --- a/behavenet/data/transforms.py +++ b/behavenet/data/transforms.py @@ -295,11 +295,13 @@ def __call__(self, sample): labels_2d = np.zeros((time, n_labels, self.y_pixels, self.x_pixels)) x_vals = sample[:, :n_labels] + x_vals[np.isnan(x_vals)] = -1 # set nans to 0 x_vals[x_vals > self.x_pixels - 1] = self.x_pixels - 1 x_vals[x_vals < 0] = 0 x_vals = np.round(x_vals).astype(np.int) y_vals = sample[:, n_labels:] + y_vals[np.isnan(y_vals)] = -1 # set nans to 0 y_vals[y_vals > self.y_pixels - 1] = self.y_pixels - 1 y_vals[y_vals < 0] = 0 y_vals = np.round(y_vals).astype(np.int) diff --git a/behavenet/data/utils.py b/behavenet/data/utils.py index 10654d1..a58a701 100644 --- a/behavenet/data/utils.py +++ b/behavenet/data/utils.py @@ -264,6 +264,12 @@ def get_data_generator_inputs(hparams, sess_ids, check_splits=True): transforms.append(None) paths.append(os.path.join(data_dir, 'data.hdf5')) + elif hparams['model_class'] == 'labels_masks': + + signals = [hparams['model_class']] + transforms = [None] + paths = [os.path.join(data_dir, 'data.hdf5')] + else: raise ValueError('"%s" is an invalid model_class' % hparams['model_class']) diff --git a/behavenet/plotting/cond_ae_utils.py b/behavenet/plotting/cond_ae_utils.py index 8da6167..f019547 100644 --- a/behavenet/plotting/cond_ae_utils.py +++ b/behavenet/plotting/cond_ae_utils.py @@ -53,7 +53,7 @@ def get_crop(im, y_0, y_ext, x_0, x_ext): def get_input_range( input_type, hparams, sess_ids=None, sess_idx=0, model=None, data_gen=None, version=0, - min_p=5, max_p=95): + min_p=5, max_p=95, apply_label_masks=False): """Helper function to compute input range for a variety of data types. Parameters @@ -110,6 +110,13 @@ def get_input_range( inputs = labels_sc['latents'] else: raise NotImplementedError + + if apply_label_masks: + masks = load_labels_like_latents( + hparams, sess_ids, sess_idx=sess_idx, data_key='labels_masks') + for i, m in zip(inputs, masks): + i[m == 0] = np.nan + input_range = compute_range(inputs, min_p=min_p, max_p=max_p) return input_range @@ -141,8 +148,8 @@ def compute_range(values_list, min_p=5, max_p=95): else: values = np.vstack(values_list) ranges = { - 'min': np.percentile(values, min_p, axis=0), - 'max': np.percentile(values, max_p, axis=0)} + 'min': np.nanpercentile(values, min_p, axis=0), + 'max': np.nanpercentile(values, max_p, axis=0)} return ranges diff --git a/tests/test_data/test_transforms.py b/tests/test_data/test_transforms.py index bc5393a..018fde1 100644 --- a/tests/test_data/test_transforms.py +++ b/tests/test_data/test_transforms.py @@ -119,6 +119,19 @@ def test_makeonehot2d(): s = t(signal) assert np.all(s == sp) + # correct one-hotting with nans in signal + t = transforms.MakeOneHot2D(4, 4) + signal = np.array([[1, 2, 0, np.nan], [0, 2, 1, 1], [3, 0, np.nan, 2]]) + sp = np.zeros((3, 2, 4, 4)) + sp[0, 0, 0, 1] = 1 + sp[0, 1, 0, 2] = 1 + sp[1, 0, 1, 0] = 1 + sp[1, 1, 1, 2] = 1 + sp[2, 0, 0, 3] = 1 + sp[2, 1, 2, 0] = 1 + s = t(signal) + assert np.all(s == sp) + def test_blockshuffle(): diff --git a/tests/test_data/test_utils_data.py b/tests/test_data/test_utils_data.py index c87b4f0..e277bbb 100644 --- a/tests/test_data/test_utils_data.py +++ b/tests/test_data/test_utils_data.py @@ -406,6 +406,16 @@ def test_get_data_generator_inputs(): assert paths[0] == [hdf5_path, hdf5_path] hparams['use_label_mask'] = False + # ----------------- + # labels_masks + # ----------------- + hparams['model_class'] = 'labels_masks' + hparams_, signals, transforms, paths = utils.get_data_generator_inputs( + hparams, sess_ids, check_splits=False) + assert signals[0] == ['labels_masks'] + assert transforms[0] == [None] + assert paths[0] == [hdf5_path] + # ----------------- # other # ----------------- From 2f0dac7356c016d9537b128afab9faf115b5ea21 Mon Sep 17 00:00:00 2001 From: themattinthehatt Date: Wed, 14 Oct 2020 16:38:37 -0400 Subject: [PATCH 03/50] more nan updates --- behavenet/plotting/ae_utils.py | 4 ++-- behavenet/plotting/cond_ae_utils.py | 2 ++ 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/behavenet/plotting/ae_utils.py b/behavenet/plotting/ae_utils.py index 79b0c67..d6d1cad 100644 --- a/behavenet/plotting/ae_utils.py +++ b/behavenet/plotting/ae_utils.py @@ -581,8 +581,8 @@ def plot_neural_reconstruction_traces( sns.set_style('white') sns.set_context('poster') - means = np.mean(traces_ae, axis=0) - std = np.std(traces_ae) / scale # scale for better visualization + means = np.nanmean(traces_ae, axis=0) + std = np.nanstd(traces_ae) / scale # scale for better visualization traces_ae_sc = (traces_ae - means) / std traces_neural_sc = (traces_neural - means) / std diff --git a/behavenet/plotting/cond_ae_utils.py b/behavenet/plotting/cond_ae_utils.py index f019547..4d5c30d 100644 --- a/behavenet/plotting/cond_ae_utils.py +++ b/behavenet/plotting/cond_ae_utils.py @@ -77,6 +77,8 @@ def get_input_range( defines lower end of range; percentile in [0, 100] max_p : :obj:`int`, optional defines upper end of range; percentile in [0, 100] + apply_label_masks : :obj:`bool`, optional + `True` to set masked values to NaN in labels Returns ------- From 50b3ba2fbc4ce7ab64c7997453983f2af0faba16 Mon Sep 17 00:00:00 2001 From: themattinthehatt Date: Wed, 14 Oct 2020 21:31:18 -0400 Subject: [PATCH 04/50] doc updates --- behavenet/models/base.py | 3 + docs/source/adv_user_guide.load_model.rst | 136 ++++++++++++++++++++++ docs/source/adv_user_guide.rst | 1 + docs/source/user_guide.intro.rst | 1 - 4 files changed, 140 insertions(+), 1 deletion(-) create mode 100644 docs/source/adv_user_guide.load_model.rst diff --git a/behavenet/models/base.py b/behavenet/models/base.py index dacaad2..06a5f17 100644 --- a/behavenet/models/base.py +++ b/behavenet/models/base.py @@ -3,6 +3,9 @@ import math from torch import nn, save, Tensor +# to ignore imports for sphix-autoapidoc +__all__ = ['BaseModule', 'BaseModel', 'DiagLinear', 'CustomDataParallel'] + class BaseModule(nn.Module): """Template for PyTorch modules.""" diff --git a/docs/source/adv_user_guide.load_model.rst b/docs/source/adv_user_guide.load_model.rst new file mode 100644 index 0000000..13a3777 --- /dev/null +++ b/docs/source/adv_user_guide.load_model.rst @@ -0,0 +1,136 @@ +Loading a trained model +======================= + +After you've fit one or more models, often you'll want to load these models and their associated data generator to perform further analyses. BehaveNet provides three methods for doing so: + +* :ref:`Method 1`: load the "best" model from a test-tube experiment +* :ref:`Method 2`: specify the model version in a test-tube experiment +* :ref:`Method 3`: specify the model hyperparameters in a test-tube experiment + +To illustrate these three methods we'll use an autoencoder as an example. Let's assume that we've trained 5 convolutional autoencoders with 10 latents, each with a different random seed for initializing the weights, and these have all been saved in the test-tube experiment ``ae-example``. + +.. _load_best_model: + +Method 1: load best model +------------------------- +The first option is to load the best model from ``ae-example``. The "best" model is defined as the one with the smallest loss value computed on validation data. If you set the parameter ``val_check_interval`` in the ae training json to a nonzero value before fitting, this information has already been computed and saved in a csv file, so this is a relatively fast option. The following code block shows how to load the best model, as well as the associated data generator, from ``ae-example``. + +.. code-block:: python + + # imports + from behavenet import get_user_dir + from behavenet.fitting.utils import get_best_model_and_data + from behavenet.fitting.utils import get_expt_dir + from behavenet.fitting.utils import get_lab_example + from behavenet.fitting.utils import get_session_dir + from behavenet.models import AE as Model + + # define necessary hyperparameters + hparams = { + 'data_dir': get_user_dir('data'), + 'save_dir': get_user_dir('save'), + 'experiment_name': 'ae-example', + 'model_class': 'ae', + 'model_type': 'conv', + 'n_ae_latents': 10, + } + + # programmatically fill out other hparams options + get_lab_example(hparams, 'musall', 'vistrained') + hparams['session_dir'], sess_ids = get_session_dir(hparams) + hparams['expt_dir'] = get_expt_dir(hparams) + + # use helper function to load model and data generator + model, data_generator = get_best_model_and_data(hparams, Model, version='best') + + +.. _specify_version: + +Method 2: specify the model version +----------------------------------- +The next option requires that you know in advance which test-tube version you want to load. In this example, we'll load version 3. All you need to do is replace ``version='best'`` with ``version=3`` in the final line above. + +.. code-block:: python + + # use helper function to load model and data generator + model, data_generator = get_best_model_and_data(hparams, Model, version=3) + + +.. _specify_hparams: + +Method 3: specify model hyperparameters +--------------------------------------- +The final option gives you the most control - you can specify all relevant hyperparameters needed to define the model and the data generator, and load that specific model. + +.. code-block:: python + + # imports + from behavenet import get_user_dir + from behavenet.fitting.utils import experiment_exists + from behavenet.fitting.utils import get_best_model_and_data + from behavenet.fitting.utils import get_expt_dir + from behavenet.fitting.utils import get_lab_example + from behavenet.fitting.utils import get_session_dir + from behavenet.models import AE as Model + + # define necessary hyperparameters + hparams = { + 'data_dir': get_user_dir('data'), + 'save_dir': get_user_dir('save'), + 'experiment_name': 'ae-example', + 'model_class': 'ae', + 'model_type': 'conv', + 'n_ae_latents': 10, + 'rng_seed_data': 0, + 'trial_splits': '8;1;1;0', + 'train_frac': 1, + 'rng_seed_model': 0, + 'fit_sess_io_layers': False, + 'learning_rate': 1e-4, + 'l2_reg': 0, + } + + # programmatically fill out other hparams options + get_lab_example(hparams, 'musall', 'vistrained') + hparams['session_dir'], sess_ids = get_session_dir(hparams) + hparams['expt_dir'] = get_expt_dir(hparams) + + # find the version for these hyperparameters; returns None for version if it doesn't exist + exists, version = experiment_exists(hparams, which_version=True) + + # use helper function to load model and data generator + model, data_generator = get_best_model_and_data(hparams, Model, version=version) + +You will need to specify the following entries in ``hparams`` regardless of the model class: + +* 'rng_seed_data' +* 'trial_splits' +* 'train_frac' +* 'rng_seed_model' +* 'model_class' +* 'model_type' + +For the autencoder, we need to additionally specify ``n_ae_latents``, ``fit_sess_io_layers``, ``learning_rate``, and ``l2_reg``. Check out the source code for :py:func:`behavenet.fitting.utils.get_model_params` to see which entries are required for other model classes. + + +Iterating through the data +-------------------------- + +Below is an example of how to iterate through the data generator and load batches of data: + +.. code-block:: python + + # select data type to load + dtype = 'train' # 'train' | 'val' | 'test' + + # reset data iterator for this data type + data_generator.reset_iterators(dtype) + + # loop through all batches for this data type + for i in range(data_generator.n_tot_batches[dtype]): + + batch, sess = data_generator.next_batch(dtype) + # "batch" is a dict with keys for the relevant signal, e.g. 'images', 'neural', etc. + # "sess" is an integer denoting the dataset this batch comes from + + # ... perform analyses ... diff --git a/docs/source/adv_user_guide.rst b/docs/source/adv_user_guide.rst index 5c5c408..c158037 100644 --- a/docs/source/adv_user_guide.rst +++ b/docs/source/adv_user_guide.rst @@ -7,4 +7,5 @@ Advanced user guide :caption: Contents: adv_user_guide.slurm + adv_user_guide.load_model diff --git a/docs/source/user_guide.intro.rst b/docs/source/user_guide.intro.rst index ae56305..58eba8b 100644 --- a/docs/source/user_guide.intro.rst +++ b/docs/source/user_guide.intro.rst @@ -136,4 +136,3 @@ Finally, multiple hyperparameters can be searched over simultaneously; for examp } This job would then fit a total of 4 latent values x 3 regularization values = 12 models. - From 9b1f0101304c4a68f7ad6b72bbae9b7f5eb0d506 Mon Sep 17 00:00:00 2001 From: themattinthehatt Date: Fri, 16 Oct 2020 16:50:27 -0400 Subject: [PATCH 05/50] multisession docs --- docs/source/adv_user_guide.load_model.rst | 3 +- docs/source/adv_user_guide.multisession.rst | 125 ++++++++++++++++++++ 2 files changed, 127 insertions(+), 1 deletion(-) create mode 100644 docs/source/adv_user_guide.multisession.rst diff --git a/docs/source/adv_user_guide.load_model.rst b/docs/source/adv_user_guide.load_model.rst index 13a3777..a0486c0 100644 --- a/docs/source/adv_user_guide.load_model.rst +++ b/docs/source/adv_user_guide.load_model.rst @@ -1,7 +1,8 @@ Loading a trained model ======================= -After you've fit one or more models, often you'll want to load these models and their associated data generator to perform further analyses. BehaveNet provides three methods for doing so: +After you've fit one or more models, often you'll want to load these models and their associated +data generator to perform further analyses. BehaveNet provides three methods for doing so: * :ref:`Method 1`: load the "best" model from a test-tube experiment * :ref:`Method 2`: specify the model version in a test-tube experiment diff --git a/docs/source/adv_user_guide.multisession.rst b/docs/source/adv_user_guide.multisession.rst new file mode 100644 index 0000000..d3816a5 --- /dev/null +++ b/docs/source/adv_user_guide.multisession.rst @@ -0,0 +1,125 @@ +Training a model with multiple datasets +======================================= + +The statistical models that comprise BehaveNet - autoencoders, ARHMMs, neural network decoders - +often require large amounts of data to avoid overfitting. While the amount of data collected in an +hour long experimental session may suffice, every one of these models will benefit from additional +data. If data is collected from multiple experimental sessions, and these data are similar enough +(e.g. same camera placement/contrast across sessions), then you can train BehaveNet models on all +of this data simultaneously. + +BehaveNet provides two methods for specifying the experimental sessions used to train a model: + +* :ref:`Method 1`: use all sessions from a specified animal, experiment, or lab +* :ref:`Method 2`: specify the sessions in a csv file + +The first method is simpler, while the second method offers greater control. Both of these methods +require modifying the data configuration json before training. We'll use the Musall dataset as an +example; below is the relevant section of the json file located in +``behavenet/configs/data_default.json`` that we will modify below. + +.. code-block:: json + + "lab": "musall", # type: str + + "expt": "vistrained", # type: str + + "animal": "mSM30", # type: str + + "session": "10-Oct-2017", # type: str + + "sessions_csv": "", # type: str, help: specify multiple sessions + + "all_source": "save", # type: str, help: "save" or "data" + +The Musall dataset provided with the repo (see ``behavenet/example/00_data.ipynb``) contains +autoencoders trained on two sessions individually, as well as a single autoencoder trained on both +sessions as an example of this. + + +.. _all_keyword: + +Method 1: the "all" keyword +--------------------------- +This method is appropriate if you want to fit a model on all sessions from a specified animal, +experiment, or lab. For example, if we want to fit a model on all sessions from animal +``mSM30``, we would modify the ``session`` parameter value to ``all``: + +.. code-block:: json + + "lab": "musall", # type: str + + "expt": "vistrained", # type: str + + "animal": "mSM30", # type: str + + "session": "all", # type: str + + "sessions_csv": "", # type: str, help: specify multiple sessions + + "all_source": "save", # type: str, help: "save" or "data" + +In this case the resulting models will be stored in the directory +``save_dir/musall/vistrained/mSM30/multisession-xx``, where ``xx`` can change. BehaveNet will +create a csv file named ``session_info.csv`` inside the multisession directory that lists the +lab, expt, animal, and session for all sessions in that multisession. + + +If we want to fit a model on all sessions from all animals in the ``vistrained`` experiment, we +would modify the ``animal`` parameter value to ``all``: + +.. code-block:: json + + "lab": "musall", # type: str + + "expt": "vistrained", # type: str + + "animal": "all", # type: str + + "session": "all", # type: str + + "sessions_csv": "", # type: str, help: specify multiple sessions + + "all_source": "save", # type: str, help: "save" or "data" + +In this case the resulting models will be stored in the directory +``save_dir/musall/vistrained/multisession-xx``. The string value for ``session`` does not +matter; BehaveNet searches for the ``all`` +keyword starting at the lab level and moves down; once it finds the ``all`` keyword it ignores all +further entries. + +.. note:: + + The ``all_source`` parameter in the json file is included to resolve an ambiguity with the + "all" keyword. For example, let's assume you use ``all`` at the session level for a single + animal. If data for 6 sessions exist for that animal, and BehaveNet models have been fit to 4 + of those 6 sessions, then setting ``"all_source": "data"`` will use all 6 sessions with data. + On the other hand, setting ``"all_source": "save"`` will use all 4 sessions that have been + previously used to fit models. + +.. _sessions_csv: + +Method 2: session_info.csv file +-------------------------------- +This method is appropriate if you want finer control over which sessions are included; for example, +if you want all sessions from one animal, as well as all but one session from another animal. To +specify these sessions, you can construct a csv file with the four column headers ``lab``, +``expt``, ``animal``, and ``session``. You can then provide this csv file (let's say it's called +``data_dir/example_sessions.csv``) as the value for the ``sessions_csv`` parameter: + +.. code-block:: json + + "lab": "musall", # type: str + + "expt": "vistrained", # type: str + + "animal": "all", # type: str + + "session": "all", # type: str + + "sessions_csv": "data_dir/example_sessions.csv", # type: str, help: specify multiple sessions + + "all_source": "save", # type: str, help: "save" or "data" + +The ``sessions_csv`` parameter takes precedence over any values supplied for ``lab``, ``expt``, +``animal``, ``session``, and ``all_source``. From 1a899fc37d2cef5bd1dda3945368bf27e24b7a0b Mon Sep 17 00:00:00 2001 From: themattinthehatt Date: Fri, 16 Oct 2020 17:15:24 -0400 Subject: [PATCH 06/50] more multisession docs --- docs/source/adv_user_guide.load_model.rst | 2 + docs/source/adv_user_guide.multisession.rst | 92 +++++++++++++++++++-- docs/source/adv_user_guide.rst | 2 +- 3 files changed, 89 insertions(+), 7 deletions(-) diff --git a/docs/source/adv_user_guide.load_model.rst b/docs/source/adv_user_guide.load_model.rst index a0486c0..5e2e921 100644 --- a/docs/source/adv_user_guide.load_model.rst +++ b/docs/source/adv_user_guide.load_model.rst @@ -1,3 +1,5 @@ +.. _load_model: + Loading a trained model ======================= diff --git a/docs/source/adv_user_guide.multisession.rst b/docs/source/adv_user_guide.multisession.rst index d3816a5..f8f8485 100644 --- a/docs/source/adv_user_guide.multisession.rst +++ b/docs/source/adv_user_guide.multisession.rst @@ -34,7 +34,7 @@ example; below is the relevant section of the json file located in The Musall dataset provided with the repo (see ``behavenet/example/00_data.ipynb``) contains autoencoders trained on two sessions individually, as well as a single autoencoder trained on both -sessions as an example of this. +sessions as an example of this feature. .. _all_keyword: @@ -60,9 +60,9 @@ experiment, or lab. For example, if we want to fit a model on all sessions from "all_source": "save", # type: str, help: "save" or "data" In this case the resulting models will be stored in the directory -``save_dir/musall/vistrained/mSM30/multisession-xx``, where ``xx`` can change. BehaveNet will -create a csv file named ``session_info.csv`` inside the multisession directory that lists the -lab, expt, animal, and session for all sessions in that multisession. +``save_dir/musall/vistrained/mSM30/multisession-xx``, where ``xx`` is selected automatically. +BehaveNet will create a csv file named ``session_info.csv`` inside the multisession directory that +lists the lab, expt, animal, and session for all sessions in that multisession. If we want to fit a model on all sessions from all animals in the ``vistrained`` experiment, we @@ -99,8 +99,8 @@ further entries. .. _sessions_csv: -Method 2: session_info.csv file --------------------------------- +Method 2: specify sessions in a csv file +---------------------------------------- This method is appropriate if you want finer control over which sessions are included; for example, if you want all sessions from one animal, as well as all but one session from another animal. To specify these sessions, you can construct a csv file with the four column headers ``lab``, @@ -123,3 +123,83 @@ specify these sessions, you can construct a csv file with the four column header The ``sessions_csv`` parameter takes precedence over any values supplied for ``lab``, ``expt``, ``animal``, ``session``, and ``all_source``. + + +Loading a trained multisession model +------------------------------------ + +The approach is almost identical to that laid out in :ref:`Loading a trained model`; +namely, you can either specify the "best" model, the model version, or fully specify all the model +hyperparameters. The one necessary change is to alert BehaveNet that you want to load a +multisession model. As above, you can do this by either using the "all" keyword or a csv file. +The code snippets below illustrate both of these methods when loading the "best" model. + +Method 1: use the "all" keyword to specify all sessions for a particular animal: + +.. code-block:: python + + # imports + from behavenet import get_user_dir + from behavenet.fitting.utils import get_best_model_and_data + from behavenet.fitting.utils import get_expt_dir + from behavenet.fitting.utils import get_lab_example + from behavenet.fitting.utils import get_session_dir + from behavenet.models import AE as Model + + # define necessary hyperparameters + hparams = { + 'data_dir': get_user_dir('data'), + 'save_dir': get_user_dir('save'), + 'lab': 'musall', + 'expt': 'vistrained', + 'animal': 'mSM30', + 'session': 'all', # use all sessions for animal mSM30 + 'experiment_name': 'ae-example', + 'model_class': 'ae', + 'model_type': 'conv', + 'n_ae_latents': 10, + } + + # programmatically fill out other hparams options + hparams['session_dir'], sess_ids = get_session_dir(hparams) + hparams['expt_dir'] = get_expt_dir(hparams) + + # use helper function to load model and data generator + model, data_generator = get_best_model_and_data(hparams, Model, version='best') + +As above, the ``all`` keyword can also be used at the animal or expt level, though not currently at +the lab level. + +Method 2: use a sessions csv file: + +.. code-block:: python + + # imports + from behavenet import get_user_dir + from behavenet.fitting.utils import get_best_model_and_data + from behavenet.fitting.utils import get_expt_dir + from behavenet.fitting.utils import get_lab_example + from behavenet.fitting.utils import get_session_dir + from behavenet.models import AE as Model + + # define necessary hyperparameters + hparams = { + 'data_dir': get_user_dir('data'), + 'save_dir': get_user_dir('save'), + 'sessions_csv': '/path/to/csv/file', + 'experiment_name': 'ae-example', + 'model_class': 'ae', + 'model_type': 'conv', + 'n_ae_latents': 10, + } + + # programmatically fill out other hparams options + hparams['session_dir'], sess_ids = get_session_dir(hparams) + hparams['expt_dir'] = get_expt_dir(hparams) + + # use helper function to load model and data generator + model, data_generator = get_best_model_and_data(hparams, Model, version='best') + +In both cases, iterating through the data proceeds exactly as when using a single session, and the +second return value from ``data_generator.next_batch()`` identifies which session the batch belongs +to. diff --git a/docs/source/adv_user_guide.rst b/docs/source/adv_user_guide.rst index c158037..9d3c643 100644 --- a/docs/source/adv_user_guide.rst +++ b/docs/source/adv_user_guide.rst @@ -8,4 +8,4 @@ Advanced user guide adv_user_guide.slurm adv_user_guide.load_model - + adv_user_guide.multisession From f6bc8bedc08552f45a511c6a1c295a6b10ece7f7 Mon Sep 17 00:00:00 2001 From: themattinthehatt Date: Fri, 16 Oct 2020 17:23:05 -0400 Subject: [PATCH 07/50] make model class optional arg in load_best_model --- behavenet/fitting/utils.py | 32 +++++++++++++++++++++++++++++--- behavenet/models/README.md | 1 + 2 files changed, 30 insertions(+), 3 deletions(-) diff --git a/behavenet/fitting/utils.py b/behavenet/fitting/utils.py index 09882f6..e5bc2d5 100644 --- a/behavenet/fitting/utils.py +++ b/behavenet/fitting/utils.py @@ -967,14 +967,14 @@ def get_best_model_version(expt_dir, measure='val_loss', best_def='min', n_best= return best_versions -def get_best_model_and_data(hparams, Model, load_data=True, version='best', data_kwargs=None): +def get_best_model_and_data(hparams, Model=None, load_data=True, version='best', data_kwargs=None): """Load the best model (and data) defined by hparams out of all available test-tube versions. Parameters ---------- hparams : :obj:`dict` needs to contain enough information to specify both a model and the associated data - Model : :obj:`behavenet.models` object + Model : :obj:`behavenet.models` object, optional model type load_data : :obj:`bool`, optional if `False` then data generator is not returned @@ -1042,7 +1042,33 @@ def get_best_model_and_data(hparams, Model, load_data=True, version='best', data else: data_generator = None - # build models + # build model + if Model is None: + if hparams['model_class'] == 'ae': + from behavenet.models import AE as Model + elif hparams['model_class'] == 'vae': + from behavenet.models import VAE as Model + elif hparams['model_class'] == 'cond-ae': + from behavenet.models import ConditionalAE as Model + elif hparams['model_class'] == 'cond-vae': + from behavenet.models import ConditionalVAE as Model + elif hparams['model_class'] == 'cond-ae-msp': + from behavenet.models import AEMSP as Model + elif hparams['model_class'] == 'beta-tcvae': + from behavenet.models import BetaTCVAE as Model + elif hparams['model_class'] == 'sss-vae': + from behavenet.models import SSSVAE as Model + elif hparams['model_class'] == 'labels-images': + from behavenet.models import ConvDecoder as Model + elif hparams['model_class'] == 'neural-ae' or hparams['model_class'] == 'neural-arhmm': + from behavenet.models import Decoder as Model + elif hparams['model_class'] == 'ae-neural' or hparams['model_class'] == 'arhmm-neural': + from behavenet.models import Decoder as Model + elif hparams['model_class'] == 'arhmm': + raise NotImplementedError('Cannot use get_best_model_and_data() for ssm models') + else: + raise NotImplementedError + model = Model(hparams_new) model.version = int(best_version.split('_')[1]) model.load_state_dict(torch.load(model_file, map_location=lambda storage, loc: storage)) diff --git a/behavenet/models/README.md b/behavenet/models/README.md index fa8c46c..cd8a522 100644 --- a/behavenet/models/README.md +++ b/behavenet/models/README.md @@ -12,6 +12,7 @@ Model-related code * `behavenet.data.utils.get_data_generator_inputs` [UPDATE UNIT TEST!] * `behavenet.fitting.utils.get_expt_dir` [UPDATE UNIT TEST!] * `behavenet.fitting.utils.get_model_params` [UPDATE UNIT TEST!] + * `behavenet.fitting.utils.get_best_data_and_model` * `behavenet.fitting.eval.export_xxx` (latents, states, predictions, etc) * potential function updates: * other `behavenet.fitting.eval` methods (like `get_rconstruction`) From 90bd14bd97bc1303fb86e0dda0c0576d07fd0226 Mon Sep 17 00:00:00 2001 From: themattinthehatt Date: Fri, 16 Oct 2020 18:02:31 -0400 Subject: [PATCH 08/50] add flake8 --- .flake8 | 18 ++++++++++++ behavenet/data/data_generator.py | 4 +-- behavenet/data/transforms.py | 4 +-- behavenet/data/utils.py | 21 ++++++++++---- behavenet/fitting/ae_grid_search.py | 2 -- behavenet/fitting/arhmm_grid_search.py | 4 +-- behavenet/fitting/eval.py | 4 +-- behavenet/fitting/hyperparam_utils.py | 2 +- behavenet/fitting/losses.py | 4 +-- behavenet/fitting/utils.py | 2 +- .../models/ae_model_architecture_generator.py | 28 ++++++++++--------- behavenet/models/aes.py | 16 +++++------ behavenet/models/decoders.py | 6 ++-- behavenet/models/vaes.py | 2 +- behavenet/plotting/ae_utils.py | 12 +++++--- behavenet/plotting/arhmm_utils.py | 17 ++++++----- behavenet/plotting/decoder_utils.py | 5 ++-- tests/integration.py | 22 +++++++-------- tests/test_data/test_utils_data.py | 2 +- tests/test_fitting/test_hyperparam_utils.py | 4 +-- .../test_ae_model_architecture_generator.py | 12 ++++---- 21 files changed, 112 insertions(+), 79 deletions(-) create mode 100644 .flake8 diff --git a/.flake8 b/.flake8 new file mode 100644 index 0000000..9045e9e --- /dev/null +++ b/.flake8 @@ -0,0 +1,18 @@ +[flake8] +max-line-length = 99 +ignore = + W504, + W503, + W605, # invalid escape sequence '\ ' + E266, + E402, # module level import not at top of file + E226, # missing whitespace around arithmetic operator +exclude = + .git, + __pycache__, + __init__.py, + build, + dist, + docs/* + example/* + scratch/* diff --git a/behavenet/data/data_generator.py b/behavenet/data/data_generator.py index 83f9bae..dca8f90 100644 --- a/behavenet/data/data_generator.py +++ b/behavenet/data/data_generator.py @@ -199,7 +199,7 @@ def __init__( break elif signal == 'ae_latents': try: - latents = _load_pkl_dict(self.paths[signal], 'latents') #[0] + latents = _load_pkl_dict(self.paths[signal], 'latents') except FileNotFoundError: raise NotImplementedError( ('Could not open %s\nMust create ae latents from model;' + @@ -623,7 +623,7 @@ def next_batch(self, dtype): if self.as_numpy: for i, signal in enumerate(sample): - if signal is not 'batch_idx': + if signal != 'batch_idx': sample[signal] = [ss.cpu().detach().numpy() for ss in sample[signal]] else: if self.device == 'cuda': diff --git a/behavenet/data/transforms.py b/behavenet/data/transforms.py index 6c58bef..cbd913a 100644 --- a/behavenet/data/transforms.py +++ b/behavenet/data/transforms.py @@ -306,8 +306,8 @@ def __call__(self, sample): y_vals[y_vals < 0] = 0 y_vals = np.round(y_vals).astype(np.int) - for l in range(n_labels): - labels_2d[np.arange(time), l, y_vals[:, l], x_vals[:, l]] = 1 + for n in range(n_labels): + labels_2d[np.arange(time), n, y_vals[:, n], x_vals[:, n]] = 1 return labels_2d def __repr__(self): diff --git a/behavenet/data/utils.py b/behavenet/data/utils.py index a58a701..1fedd54 100644 --- a/behavenet/data/utils.py +++ b/behavenet/data/utils.py @@ -76,8 +76,8 @@ def get_data_generator_inputs(hparams, sess_ids, check_splits=True): transforms.append(None) paths.append(os.path.join(data_dir, 'data.hdf5')) if hparams.get('use_label_mask', False) and ( - hparams['model_class'] == 'cond-ae-msp' or hparams['model_class'] == 'sss-vae' - ): + hparams['model_class'] == 'cond-ae-msp' + or hparams['model_class'] == 'sss-vae'): signals.append('labels_masks') transforms.append(None) paths.append(os.path.join(data_dir, 'data.hdf5')) @@ -303,10 +303,19 @@ def get_transforms_paths(data_type, hparams, sess_id, check_splits=True): 'neural_arhmm_predictions' hparams : :obj:`dict` - required keys for :obj:`data_type=neural`: 'neural_type', 'neural_thresh' - - required keys for :obj:`data_type=ae_latents`: 'ae_experiment_name', 'ae_model_type', 'n_ae_latents', 'ae_version' or 'ae_latents_file'; this last option defines either the specific ae version (as 'best' or an int) or a path to a specific ae latents pickle file. - - required keys for :obj:`data_type=arhmm_states`: 'arhmm_experiment_name', 'n_arhmm_states', 'kappa', 'noise_type', 'n_ae_latents', 'arhmm_version' or 'arhmm_states_file'; this last option defines either the specific arhmm version (as 'best' or an int) or a path to a specific ae latents pickle file. - - required keys for :obj:`data_type=neural_ae_predictions`: 'neural_ae_experiment_name', 'neural_ae_model_type', 'neural_ae_version' or 'ae_predictions_file' plus keys for neural and ae_latents data types. - - required keys for :obj:`data_type=neural_arhmm_predictions`: 'neural_arhmm_experiment_name', 'neural_arhmm_model_type', 'neural_arhmm_version' or 'arhmm_predictions_file', plus keys for neural and arhmm_states data types. + - required keys for :obj:`data_type=ae_latents`: 'ae_experiment_name', 'ae_model_type', + 'n_ae_latents', 'ae_version' or 'ae_latents_file'; this last option defines either the + specific ae version (as 'best' or an int) or a path to a specific ae latents pickle file. + - required keys for :obj:`data_type=arhmm_states`: 'arhmm_experiment_name', + 'n_arhmm_states', 'kappa', 'noise_type', 'n_ae_latents', 'arhmm_version' or + 'arhmm_states_file'; this last option defines either the specific arhmm version (as + 'best' or an int) or a path to a specific ae latents pickle file. + - required keys for :obj:`data_type=neural_ae_predictions`: 'neural_ae_experiment_name', + 'neural_ae_model_type', 'neural_ae_version' or 'ae_predictions_file' plus keys for neural + and ae_latents data types. + - required keys for :obj:`data_type=neural_arhmm_predictions`: + 'neural_arhmm_experiment_name', 'neural_arhmm_model_type', 'neural_arhmm_version' or + 'arhmm_predictions_file', plus keys for neural and arhmm_states data types. sess_id : :obj:`dict` each list entry is a session-specific dict with keys 'lab', 'expt', 'animal', 'session' check_splits : :obj:`bool`, optional diff --git a/behavenet/fitting/ae_grid_search.py b/behavenet/fitting/ae_grid_search.py index c08ce5c..09a6921 100644 --- a/behavenet/fitting/ae_grid_search.py +++ b/behavenet/fitting/ae_grid_search.py @@ -165,5 +165,3 @@ def set_n_labels(data_generator, hparams): main, nb_trials=hyperparams.tt_n_cpu_trials, nb_workers=hyperparams.tt_n_cpu_workers) - - diff --git a/behavenet/fitting/arhmm_grid_search.py b/behavenet/fitting/arhmm_grid_search.py index 9ce1334..40c15b8 100644 --- a/behavenet/fitting/arhmm_grid_search.py +++ b/behavenet/fitting/arhmm_grid_search.py @@ -59,7 +59,7 @@ def main(hparams): data_generator, sess_idxs=list(range(n_datasets)), data_key=data_key) obs_dim = latents['train'][0].shape[1] - hparams['total_train_length'] = np.sum([l.shape[0] for l in latents['train']]) + hparams['total_train_length'] = np.sum([z.shape[0] for z in latents['train']]) # get separated by dataset as well latents_sess = {d: None for d in range(n_datasets)} trial_idxs_sess = {d: None for d in range(n_datasets)} @@ -206,7 +206,7 @@ def main(hparams): # save model filepath = os.path.join(hparams['expt_dir'], 'version_%i' % exp.version, 'best_val_model.pt') with open(filepath, 'wb') as f: - pickle.dump(hmm, f) + pickle.dump(hmm, f) # ###################### # ### EVALUATE ARHMM ### diff --git a/behavenet/fitting/eval.py b/behavenet/fitting/eval.py index 2999ea4..0d17162 100644 --- a/behavenet/fitting/eval.py +++ b/behavenet/fitting/eval.py @@ -158,7 +158,7 @@ def export_states(hparams, data_generator, model, filename=None): y = data['labels'][0][0] else: y = data['ae_latents'][0][0] - batch_size = y.shape[0] + # batch_size = y.shape[0] curr_states = model.most_likely_states(y) @@ -315,7 +315,7 @@ def get_reconstruction( import torch model.eval() - + if not isinstance(inputs, torch.Tensor): inputs = torch.Tensor(inputs) diff --git a/behavenet/fitting/hyperparam_utils.py b/behavenet/fitting/hyperparam_utils.py index 7d3e840..8e2817b 100644 --- a/behavenet/fitting/hyperparam_utils.py +++ b/behavenet/fitting/hyperparam_utils.py @@ -130,7 +130,7 @@ def schedule_experiment(self, trial_params, exp_i): self.slurm_files_log_path, '{}_slurm_cmd.sh'.format(timestamp)) run_cmd = self.__get_run_command( trial_params, slurm_cmd_script_path, timestamp, exp_i, self.on_gpu) - sbatch_params = open(self.master_slurm_file,'r').read() + sbatch_params = open(self.master_slurm_file, 'r').read() slurm_cmd = sbatch_params+run_cmd self._SlurmCluster__save_slurm_cmd(slurm_cmd, slurm_cmd_script_path) diff --git a/behavenet/fitting/losses.py b/behavenet/fitting/losses.py index 0e30126..ba24e3c 100644 --- a/behavenet/fitting/losses.py +++ b/behavenet/fitting/losses.py @@ -389,6 +389,6 @@ def subspace_overlap(A, B): """ C = torch.cat([A, B], dim=0) d = C.shape[0] - I = torch.eye(d, device=C.device) - return torch.mean((torch.matmul(C, torch.transpose(C, 1, 0)) - I).pow(2)) + eye = torch.eye(d, device=C.device) + return torch.mean((torch.matmul(C, torch.transpose(C, 1, 0)) - eye).pow(2)) # return torch.mean(torch.matmul(A, torch.transpose(B, 1, 0)).pow(2)) diff --git a/behavenet/fitting/utils.py b/behavenet/fitting/utils.py index e5bc2d5..748a771 100644 --- a/behavenet/fitting/utils.py +++ b/behavenet/fitting/utils.py @@ -516,7 +516,7 @@ def find_session_dirs(hparams): expts = get_subdirs(os.path.join(hparams['save_dir'], lab)) # need to grab all multi-sessions as well as the single session session_dirs = [] # full paths - session_ids = [] # dict of lab/expt/animal/session + session_ids = [] # dict of lab/expt/animal/session for expt in expts: if expt[:5] == 'multi': session_dir = os.path.join(hparams['save_dir'], lab, expt) diff --git a/behavenet/models/ae_model_architecture_generator.py b/behavenet/models/ae_model_architecture_generator.py index fcf5cf6..f975f7f 100644 --- a/behavenet/models/ae_model_architecture_generator.py +++ b/behavenet/models/ae_model_architecture_generator.py @@ -106,19 +106,19 @@ def get_possible_arch(input_dim, n_ae_latents, arch_seed=0): arch = {} arch['ae_input_dim'] = input_dim - arch['model_type'] = 'conv' + arch['model_type'] = 'conv' arch['n_ae_latents'] = n_ae_latents arch['ae_decoding_last_FF_layer'] = 0 # arch['ae_decoding_last_FF_layer'] = np.random.choice( # np.asarray([0, 1]), p=np.asarray([1 - opts['FF_layer_prob'], opts['FF_layer_prob']])) - arch['ae_batch_norm'] = 0 + arch['ae_batch_norm'] = 0 arch['ae_batch_norm_momentum'] = None # First decide if strides only or max pooling # network_types = ['strides_only', 'max_pooling'] # arch['ae_network_type'] = network_types[np.random.randint(2)] arch['ae_network_type'] = 'strides_only' - + # Then decide if padding is 0 (0) or same (1) for all layers padding_types = ['valid', 'same'] arch['ae_padding_type'] = padding_types[np.random.randint(2)] @@ -255,7 +255,7 @@ def get_encoding_conv_block(arch, opts): break last_dims = arch['ae_encoding_n_channels'][-1] * arch['ae_encoding_y_dim'][-1] * \ - arch['ae_encoding_x_dim'][-1] + arch['ae_encoding_x_dim'][-1] smallest_pix = min(arch['ae_encoding_y_dim'][-1], arch['ae_encoding_x_dim'][-1]) p = opts['prob_stopping'][global_layer] stop_this_layer = np.random.choice([0, 1], p=[1 - p, p]) @@ -348,7 +348,8 @@ def calculate_output_dim(input_dim, kernel, stride, padding_type, layer_type): """Calculate output dimension of a layer/dimension based on input size, kernel size, etc. Inspired by: - - https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/common_shape_fns.cc#L21 + - https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/ + common_shape_fns.cc#L21 - https://github.com/pytorch/pytorch/issues/3867 Parameters @@ -506,17 +507,17 @@ def get_handcrafted_dims(arch, symmetric=True): """ arch['model_type'] = 'conv' - + arch['ae_encoding_x_dim'] = [] arch['ae_encoding_y_dim'] = [] arch['ae_encoding_x_padding'] = [] arch['ae_encoding_y_padding'] = [] for i_layer in range(len(arch['ae_encoding_n_channels'])): - + kernel_size = arch['ae_encoding_kernel_size'][i_layer] stride_size = arch['ae_encoding_stride_size'][i_layer] - + if i_layer == 0: # use input dimensions input_dim_y = arch['ae_input_dim'][1] input_dim_x = arch['ae_input_dim'][2] @@ -533,8 +534,8 @@ def get_handcrafted_dims(arch, symmetric=True): arch['ae_encoding_x_dim'].append(output_dim_x) arch['ae_encoding_y_dim'].append(output_dim_y) - arch['ae_encoding_x_padding'].append((x_before_pad,x_after_pad)) - arch['ae_encoding_y_padding'].append((y_before_pad,y_after_pad)) + arch['ae_encoding_x_padding'].append((x_before_pad, x_after_pad)) + arch['ae_encoding_y_padding'].append((y_before_pad, y_after_pad)) if symmetric: arch = get_decoding_conv_block(arch) @@ -552,7 +553,7 @@ def get_handcrafted_dims(arch, symmetric=True): for i_layer in range(len(arch['ae_decoding_n_channels'])): kernel_size = arch['ae_decoding_kernel_size'][i_layer] stride_size = arch['ae_decoding_stride_size'][i_layer] - + if i_layer == 0: # use input dimensions input_dim_y = arch['ae_decoding_starting_dim'][1] input_dim_x = arch['ae_decoding_starting_dim'][2] @@ -562,8 +563,9 @@ def get_handcrafted_dims(arch, symmetric=True): # TODO: not correct if arch['ae_padding_type'] == 'valid': - before_pad = 0 - after_pad = 0 + pass + # before_pad = 0 + # after_pad = 0 elif arch['ae_padding_type'] == 'same': # output_dim_x, x_before_pad, y_before_pad = calculate_output_dim( # input_dim_x, kernel_size, stride_size, 'same', 'conv') diff --git a/behavenet/models/aes.py b/behavenet/models/aes.py index e6b727c..a0901c8 100644 --- a/behavenet/models/aes.py +++ b/behavenet/models/aes.py @@ -115,8 +115,8 @@ def build_model(self): # final ff layer to latents last_conv_size = self.hparams['ae_encoding_n_channels'][-1] \ - * self.hparams['ae_encoding_y_dim'][-1] \ - * self.hparams['ae_encoding_x_dim'][-1] + * self.hparams['ae_encoding_y_dim'][-1] \ + * self.hparams['ae_encoding_x_dim'][-1] self.FF = nn.Linear(last_conv_size, self.hparams['n_ae_latents']) # If VAE model, have additional ff layer to latent variances @@ -260,8 +260,8 @@ def build_model(self): # First ff layer (from latents to size of last encoding layer) first_conv_size = self.hparams['ae_decoding_starting_dim'][0] \ - * self.hparams['ae_decoding_starting_dim'][1] \ - * self.hparams['ae_decoding_starting_dim'][2] + * self.hparams['ae_decoding_starting_dim'][1] \ + * self.hparams['ae_decoding_starting_dim'][2] self.FF = nn.Linear(self.hparams['hidden_layer_size'], first_conv_size) self.decoder = nn.ModuleList() @@ -473,7 +473,7 @@ def forward(self, x, pool_idx=None, target_output_size=None, dataset=None): # (-i does cropping!) x = functional.pad(x, [-i for i in self.conv_t_pads[name]]) elif isinstance(layer, nn.Linear): - x = x.view(x.shape[0],-1) + x = x.view(x.shape[0], -1) x = layer(x) x = x.view( -1, @@ -659,9 +659,9 @@ def __init__(self, hparams): self.hparams = hparams self.model_type = self.hparams['model_type'] self.img_size = ( - self.hparams['n_input_channels'], - self.hparams['y_pixels'], - self.hparams['x_pixels']) + self.hparams['n_input_channels'], + self.hparams['y_pixels'], + self.hparams['x_pixels']) self.encoding = None self.decoding = None self.build_model() diff --git a/behavenet/models/decoders.py b/behavenet/models/decoders.py index 50f992a..af54da2 100644 --- a/behavenet/models/decoders.py +++ b/behavenet/models/decoders.py @@ -384,9 +384,9 @@ def __init__(self, hparams): self.hparams = hparams self.model_type = self.hparams['model_type'] self.img_size = ( - self.hparams['n_input_channels'], - self.hparams['y_pixels'], - self.hparams['x_pixels']) + self.hparams['n_input_channels'], + self.hparams['y_pixels'], + self.hparams['x_pixels']) self.decoding = None self.build_model() diff --git a/behavenet/models/vaes.py b/behavenet/models/vaes.py index 5b93c7b..da20f17 100644 --- a/behavenet/models/vaes.py +++ b/behavenet/models/vaes.py @@ -635,7 +635,7 @@ def loss(self, data, dataset=0, accumulate_grad=True, chunk_size=200): batch_size = x.shape[0] n_chunks = int(np.ceil(batch_size / chunk_size)) n_labels = self.hparams['n_labels'] - n_latents = self.hparams['n_ae_latents'] + # n_latents = self.hparams['n_ae_latents'] # compute hyperparameters alpha = self.hparams['sss_vae.alpha'] diff --git a/behavenet/plotting/ae_utils.py b/behavenet/plotting/ae_utils.py index d6d1cad..3c5b98a 100644 --- a/behavenet/plotting/ae_utils.py +++ b/behavenet/plotting/ae_utils.py @@ -367,10 +367,14 @@ def make_neural_reconstruction_movie( # check that the axes are correct fontsize = 12 idx = 0 - axs[idx].set_title('Original', fontsize=fontsize); idx += 1 - axs[idx].set_title('AE reconstructed', fontsize=fontsize); idx += 1 - axs[idx].set_title('Neural reconstructed', fontsize=fontsize); idx += 1 - axs[idx].set_title('Reconstructions residual', fontsize=fontsize); idx += 1 + axs[idx].set_title('Original', fontsize=fontsize) + idx += 1 + axs[idx].set_title('AE reconstructed', fontsize=fontsize) + idx += 1 + axs[idx].set_title('Neural reconstructed', fontsize=fontsize) + idx += 1 + axs[idx].set_title('Reconstructions residual', fontsize=fontsize) + idx += 1 axs[idx].set_title('AE latent predictions', fontsize=fontsize) axs[idx].set_xlabel('Time (bins)', fontsize=fontsize) diff --git a/behavenet/plotting/arhmm_utils.py b/behavenet/plotting/arhmm_utils.py index f6fb28f..071d91e 100644 --- a/behavenet/plotting/arhmm_utils.py +++ b/behavenet/plotting/arhmm_utils.py @@ -4,7 +4,6 @@ import os import numpy as np import torch -import scipy import matplotlib.pyplot as plt import matplotlib import matplotlib.animation as animation @@ -14,7 +13,8 @@ # to ignore imports for sphix-autoapidoc __all__ = [ - 'get_discrete_chunks', 'get_state_durations', 'get_latent_arrays_by_dtype', 'get_model_latents_states', + 'get_discrete_chunks', 'get_state_durations', 'get_latent_arrays_by_dtype', + 'get_model_latents_states', 'make_syllable_movies_wrapper', 'make_syllable_movies', 'real_vs_sampled_wrapper', 'make_real_vs_sampled_movies', 'plot_real_vs_sampled', 'plot_states_overlaid_with_latents', 'plot_state_transition_matrix', 'plot_dynamics_matrices', @@ -56,9 +56,11 @@ def get_discrete_chunks(states, include_edges=True): which_state = chunk[split_indices[i]+1] if not include_edges: if split_indices[i] != 0 and split_indices[i+1] != (len(chunk)-2): - indexing_list[which_state].append([i_chunk, split_indices[i], split_indices[i+1]]) + indexing_list[which_state].append( + [i_chunk, split_indices[i], split_indices[i+1]]) else: - indexing_list[which_state].append([i_chunk, split_indices[i], split_indices[i + 1]]) + indexing_list[which_state].append( + [i_chunk, split_indices[i], split_indices[i+1]]) # convert lists to numpy arrays indexing_list = [np.asarray(indexing_list[i_state]) for i_state in range(max_state + 1)] @@ -184,7 +186,8 @@ def get_model_latents_states( else: _, version = experiment_exists(hparams, which_version=True) if version is None: - raise FileNotFoundError('Could not find the specified model version in %s' % hparams['expt_dir']) + raise FileNotFoundError( + 'Could not find the specified model version in %s' % hparams['expt_dir']) # load model model_file = os.path.join(hparams['expt_dir'], 'version_%i' % version, 'best_val_model.pt') @@ -916,8 +919,8 @@ def plot_dynamics_matrices(model, deridge=False): for k in range(K): plt.subplot(n_rows, n_cols, k + 1) im = plt.imshow(mats[k], cmap='RdBu_r', clim=[-clim, clim]) - for l in range(n_lags - 1): - plt.axvline((l + 1) * D - 0.5, ymin=0, ymax=K, color=[0, 0, 0]) + for lag in range(n_lags - 1): + plt.axvline((lag + 1) * D - 0.5, ymin=0, ymax=K, color=[0, 0, 0]) plt.xticks([]) plt.yticks([]) plt.title('State %i' % k) diff --git a/behavenet/plotting/decoder_utils.py b/behavenet/plotting/decoder_utils.py index 3c04042..b87f67a 100644 --- a/behavenet/plotting/decoder_utils.py +++ b/behavenet/plotting/decoder_utils.py @@ -63,10 +63,9 @@ def get_r2s_by_trial(hparams, model_types): # read metrics csv file model_dir = os.path.join(expt_dir, version) try: - metric = pd.read_csv( - os.path.join(model_dir, 'metrics.csv')) + metric = pd.read_csv(os.path.join(model_dir, 'metrics.csv')) model_counter += 1 - except: + except FileNotFoundError: continue with open(os.path.join(model_dir, 'meta_tags.pkl'), 'rb') as f: hparams = pickle.load(f) diff --git a/tests/integration.py b/tests/integration.py index efd7082..ad1667c 100644 --- a/tests/integration.py +++ b/tests/integration.py @@ -44,17 +44,17 @@ SESSIONS = ['sess-0', 'sess-1'] MODELS_TO_FIT = [ # ['model_file']_grid_search - {'model_class': 'ae', 'model_file': 'ae', 'sessions': SESSIONS[0]}, - {'model_class': 'arhmm', 'model_file': 'arhmm', 'sessions': SESSIONS[0]}, - {'model_class': 'neural-ae', 'model_file': 'decoder', 'sessions': SESSIONS[0]}, - {'model_class': 'neural-arhmm', 'model_file': 'decoder', 'sessions': SESSIONS[0]}, - {'model_class': 'ae', 'model_file': 'ae', 'sessions': 'all'}, - {'model_class': 'vae', 'model_file': 'ae', 'sessions': SESSIONS[0]}, - {'model_class': 'beta-tcvae', 'model_file': 'ae', 'sessions': SESSIONS[0]}, - {'model_class': 'cond-ae-msp', 'model_file': 'ae', 'sessions': SESSIONS[0]}, - {'model_class': 'cond-vae', 'model_file': 'ae', 'sessions': SESSIONS[0]}, - {'model_class': 'sss-vae', 'model_file': 'ae', 'sessions': SESSIONS[0]}, - {'model_class': 'labels-images', 'model_file': 'label_decoder', 'sessions': SESSIONS[0]}, + {'model_class': 'ae', 'model_file': 'ae', 'sessions': SESSIONS[0]}, + {'model_class': 'arhmm', 'model_file': 'arhmm', 'sessions': SESSIONS[0]}, + {'model_class': 'neural-ae', 'model_file': 'decoder', 'sessions': SESSIONS[0]}, + {'model_class': 'neural-arhmm', 'model_file': 'decoder', 'sessions': SESSIONS[0]}, + {'model_class': 'ae', 'model_file': 'ae', 'sessions': 'all'}, + {'model_class': 'vae', 'model_file': 'ae', 'sessions': SESSIONS[0]}, + {'model_class': 'beta-tcvae', 'model_file': 'ae', 'sessions': SESSIONS[0]}, + {'model_class': 'cond-ae-msp', 'model_file': 'ae', 'sessions': SESSIONS[0]}, + {'model_class': 'cond-vae', 'model_file': 'ae', 'sessions': SESSIONS[0]}, + {'model_class': 'sss-vae', 'model_file': 'ae', 'sessions': SESSIONS[0]}, + {'model_class': 'labels-images', 'model_file': 'label_decoder', 'sessions': SESSIONS[0]}, ] """ diff --git a/tests/test_data/test_utils_data.py b/tests/test_data/test_utils_data.py index e277bbb..2f5fc12 100644 --- a/tests/test_data/test_utils_data.py +++ b/tests/test_data/test_utils_data.py @@ -639,7 +639,7 @@ def test_get_region_list(tmpdir): 'i2': np.array([6, 7, 8])} with h5py.File(path, 'w') as f: group0 = f.create_group('group0') - groupa = f.create_group('groupa') + # groupa = f.create_group('groupa') group1 = group0.create_group('group1') group1.create_dataset('i0', data=idx_data['i0']) group1.create_dataset('i1', data=idx_data['i1']) diff --git a/tests/test_fitting/test_hyperparam_utils.py b/tests/test_fitting/test_hyperparam_utils.py index 0c6ca59..7ae3872 100644 --- a/tests/test_fitting/test_hyperparam_utils.py +++ b/tests/test_fitting/test_hyperparam_utils.py @@ -37,7 +37,7 @@ def test_get_all_params(): training_config = os.path.join( os.getcwd(), 'configs', 'arhmm_jsons', 'arhmm_training.json') compute_config = os.path.join( - os.getcwd(), 'configs', 'arhmm_jsons', 'arhmm_compute.json') + os.getcwd(), 'configs', 'arhmm_jsons', 'arhmm_compute.json') args = [ '--data_config', data_config, '--model_config', model_config, @@ -133,7 +133,7 @@ def test_add_dependent_params(tmpdir): 'i2': np.array([6, 7, 8])} with h5py.File(path, 'w') as f: group0 = f.create_group('regions') - groupa = f.create_group('neural') + # groupa = f.create_group('neural') group1 = group0.create_group('indxs') group1.create_dataset('i0', data=idx_data['i0']) group1.create_dataset('i1', data=idx_data['i1']) diff --git a/tests/test_models/test_ae_model_architecture_generator.py b/tests/test_models/test_ae_model_architecture_generator.py index 7bc7172..f792406 100644 --- a/tests/test_models/test_ae_model_architecture_generator.py +++ b/tests/test_models/test_ae_model_architecture_generator.py @@ -131,9 +131,9 @@ def test_get_decoding_conv_block(): assert arch['ae_decoding_n_channels'][-1] == input_dim[0] for i in range(len(arch['ae_decoding_n_channels']) - 1): assert arch['ae_decoding_layer_type'][i] in ['convtranspose'] - assert arch['ae_decoding_n_channels'][i] == arch['ae_encoding_n_channels'][-2-i] - assert arch['ae_decoding_kernel_size'][i] == arch['ae_encoding_kernel_size'][-1-i] - assert arch['ae_decoding_stride_size'][i] == arch['ae_encoding_stride_size'][-1-i] + assert arch['ae_decoding_n_channels'][i] == arch['ae_encoding_n_channels'][-2 - i] + assert arch['ae_decoding_kernel_size'][i] == arch['ae_encoding_kernel_size'][-1 - i] + assert arch['ae_decoding_stride_size'][i] == arch['ae_encoding_stride_size'][-1 - i] # using correct options (with maxpool) np.random.seed(16) @@ -143,9 +143,9 @@ def test_get_decoding_conv_block(): print(arch) for i in range(len(arch['ae_decoding_n_channels']) - 1): assert arch['ae_decoding_layer_type'][i] in ['convtranspose', 'unpool'] - assert arch['ae_decoding_n_channels'][i] == arch['ae_encoding_n_channels'][-2-i] - assert arch['ae_decoding_kernel_size'][i] == arch['ae_encoding_kernel_size'][-1-i] - assert arch['ae_decoding_stride_size'][i] == arch['ae_encoding_stride_size'][-1-i] + assert arch['ae_decoding_n_channels'][i] == arch['ae_encoding_n_channels'][-2 - i] + assert arch['ae_decoding_kernel_size'][i] == arch['ae_encoding_kernel_size'][-1 - i] + assert arch['ae_decoding_stride_size'][i] == arch['ae_encoding_stride_size'][-1 - i] # using correct options (with final ff layer) arch['ae_decoding_last_FF_layer'] = True From 574415aa8cda12aac5359fe36d1fb7b3d762d8d6 Mon Sep 17 00:00:00 2001 From: themattinthehatt Date: Fri, 16 Oct 2020 18:33:19 -0400 Subject: [PATCH 09/50] contributing file --- CONTRIBUTING.md | 79 +++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 79 insertions(+) create mode 100644 CONTRIBUTING.md diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000..f5a783b --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,79 @@ +# How to contribute + +If you're interested in contributing to the behavenet package, please contact the project +developer Matt Whiteway at m.whiteway ( at ) columbia.edu. + +If you would like to add a new Pytorch model to the package, you can find more detailed information +[here](behavenet/models/README.md). + +Before submitting a pull request, please follow these steps: + +## Style + +The behavenet package follows the PEP8 style guidelines, and allows for line lengths of up to 99 +characters. To ensure that your code matches these guidelines, please flake your code using the +provided configuration file. You will need to first install flake8 in the behavenet conda +environment: + +```bash +(behavenet) $: pip install flake8 +``` + +Once all code, tests, and documentation are in place, you can run the flaker from from the project +directory: + +```bash +(behavenet) $: flake8 +``` + +## Documentation + +Behavenet uses Sphinx and readthedocs to provide documentation to developers and users. + +* complete all docstrings in new functions using google's style (see source code for examples) +* provide inline comments when necessary; the more the merrier +* add a new user guide if necessary (`docs/source/user_guide.[new_model].rst`) +* update data structure docs if adding to hdf5 (`docs/source/data_structure.rst`) +* add new hyperparams to glossary (`docs/source/glossary.rst`) + +To check the documentation, you can compile it on your local machine first. To do so you will need +to first install sphinx in the behavenet conda environment: + +```bash +(behavenet) $: pip install sphinx==3.2.0 sphinx_rtd_theme==0.4.3 sphinx-automodapi==0.12 +``` + +To compile the documentation, from the behavenet project directory cd to `behavenet/docs` and run +the make file: + +```bash +(behavenet) $: cd docs +(behavenet) $: make html +``` + +## Testing + +Behavenet uses pytest to unit test the package; in addition, there is an integration script +provided to ensure the interlocking pieces play nicely. Please write unit tests for all new +(non-plotting) functions, and if you updated any existing functions please update the corresponding +unit tests. + +To run the unit tests, first install pytest in the behavenet conda environment: + +```bash +(behavenet) $: pip install pytest +``` + +Then, from the project directory, run: + +```bash +(behavenet) $: pytest +``` + +To run the integration script: + +```bash +(behavenet) $: python tests/integration.py +``` + +Running the integration test will take approximately 1 minute with a GPU. From 0c44790accf0fe49f1f13e2477e0257f69665d83 Mon Sep 17 00:00:00 2001 From: themattinthehatt Date: Tue, 20 Oct 2020 14:11:24 -0400 Subject: [PATCH 10/50] get_best_model bug fix --- behavenet/fitting/utils.py | 2 +- behavenet/plotting/cond_ae_utils.py | 5 +++-- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/behavenet/fitting/utils.py b/behavenet/fitting/utils.py index 748a771..a686e21 100644 --- a/behavenet/fitting/utils.py +++ b/behavenet/fitting/utils.py @@ -1038,7 +1038,7 @@ def get_best_model_and_data(hparams, Model=None, load_data=True, version='best', signals_list=signals, transforms_list=transforms, paths_list=paths, device=hparams_new['device'], as_numpy=hparams_new['as_numpy'], batch_load=hparams_new['batch_load'], rng_seed=hparams_new['rng_seed_data'], - **data_kwargs) + train_frac=hparams_new['train_frac'], **data_kwargs) else: data_generator = None diff --git a/behavenet/plotting/cond_ae_utils.py b/behavenet/plotting/cond_ae_utils.py index 4d5c30d..02c7d87 100644 --- a/behavenet/plotting/cond_ae_utils.py +++ b/behavenet/plotting/cond_ae_utils.py @@ -724,7 +724,8 @@ def _get_updated_scaled_labels(labels_og, idxs=None, vals=None): def plot_2d_frame_array( - ims_list, markers=None, im_kwargs=None, marker_kwargs=None, figsize=None, save_file=None): + ims_list, markers=None, im_kwargs=None, marker_kwargs=None, figsize=None, save_file=None, + **kwargs): """Plot list of list of interpolated images output by :func:`interpolate_2d()` in a 2d grid. Parameters @@ -776,7 +777,7 @@ def plot_2d_frame_array( def plot_1d_frame_array( ims_list, markers=None, im_kwargs=None, marker_kwargs=None, figsize=None, save_file=None, - plot_ims=True, plot_diffs=True): + plot_ims=True, plot_diffs=True, **kwargs): """Plot list of list of interpolated images output by :func:`interpolate_1d()` in a 2d grid. Parameters From d006de2bb954fd4c2188dd3fe6f0f54e2d568bd7 Mon Sep 17 00:00:00 2001 From: themattinthehatt Date: Wed, 21 Oct 2020 12:50:15 -0400 Subject: [PATCH 11/50] move data loading util --- behavenet/data/utils.py | 49 +++++++++++++++++- behavenet/fitting/ae_grid_search.py | 2 +- behavenet/fitting/arhmm_grid_search.py | 2 +- behavenet/fitting/decoder_grid_search.py | 2 +- .../fitting/label_decoder_grid_search.py | 2 +- behavenet/fitting/utils.py | 51 +------------------ behavenet/plotting/cond_ae_utils.py | 2 +- 7 files changed, 55 insertions(+), 55 deletions(-) diff --git a/behavenet/data/utils.py b/behavenet/data/utils.py index 1fedd54..841f041 100644 --- a/behavenet/data/utils.py +++ b/behavenet/data/utils.py @@ -4,6 +4,8 @@ import numpy as np import pickle +from behavenet.fitting.utils import export_session_info_to_csv + def get_data_generator_inputs(hparams, sess_ids, check_splits=True): """Helper function for generating signals, transforms and paths. @@ -280,6 +282,52 @@ def get_data_generator_inputs(hparams, sess_ids, check_splits=True): return hparams, signals_list, transforms_list, paths_list +def build_data_generator(hparams, sess_ids, export_csv=True): + """Helper function to build data generator from hparams dict. + + Parameters + ---------- + hparams : :obj:`dict` + needs to contain information specifying data inputs to model + sess_ids : :obj:`list` of :obj:`dict` + each entry is a session dict with keys 'lab', 'expt', 'animal', 'session' + export_csv : :obj:`bool`, optional + export csv file containing session info (useful when fitting multi-sessions) + + Returns + ------- + :obj:`ConcatSessionsGenerator` object + data generator + + """ + from behavenet.data.data_generator import ConcatSessionsGenerator + print('using data from following sessions:') + for ids in sess_ids: + print('%s' % os.path.join( + hparams['save_dir'], ids['lab'], ids['expt'], ids['animal'], ids['session'])) + hparams, signals, transforms, paths = get_data_generator_inputs(hparams, sess_ids) + if hparams.get('trial_splits', None) is not None: + # assumes string of form 'train;val;test;gap' + trs = [int(tr) for tr in hparams['trial_splits'].split(';')] + trial_splits = {'train_tr': trs[0], 'val_tr': trs[1], 'test_tr': trs[2], 'gap_tr': trs[3]} + else: + trial_splits = None + print('constructing data generator...', end='') + data_generator = ConcatSessionsGenerator( + hparams['data_dir'], sess_ids, + signals_list=signals, transforms_list=transforms, paths_list=paths, + device=hparams['device'], as_numpy=hparams['as_numpy'], batch_load=hparams['batch_load'], + rng_seed=hparams['rng_seed_data'], trial_splits=trial_splits, + train_frac=hparams['train_frac']) + # csv order will reflect dataset order in data generator + if export_csv: + export_session_info_to_csv(os.path.join( + hparams['expt_dir'], str('version_%i' % hparams['version'])), sess_ids) + print('done') + print(data_generator) + return data_generator + + def check_same_training_split(model_path, hparams): """Ensure data rng seed and trial splits are same for two models.""" @@ -501,7 +549,6 @@ def load_labels_like_latents(hparams, sess_ids, sess_idx, data_key='labels'): """ import copy - from behavenet.fitting.utils import build_data_generator hparams_new = copy.deepcopy(hparams) hparams_new['model_class'] = data_key diff --git a/behavenet/fitting/ae_grid_search.py b/behavenet/fitting/ae_grid_search.py index 09a6921..cd44802 100644 --- a/behavenet/fitting/ae_grid_search.py +++ b/behavenet/fitting/ae_grid_search.py @@ -5,13 +5,13 @@ import torch import math +from behavenet.data.utils import build_data_generator from behavenet.fitting.eval import export_train_plots from behavenet.fitting.hyperparam_utils import get_all_params from behavenet.fitting.hyperparam_utils import get_slurm_params from behavenet.fitting.training import fit from behavenet.fitting.utils import _clean_tt_dir from behavenet.fitting.utils import _print_hparams -from behavenet.fitting.utils import build_data_generator from behavenet.fitting.utils import create_tt_experiment from behavenet.fitting.utils import export_hparams from behavenet.models.aes import load_pretrained_ae diff --git a/behavenet/fitting/arhmm_grid_search.py b/behavenet/fitting/arhmm_grid_search.py index 40c15b8..4345e8a 100644 --- a/behavenet/fitting/arhmm_grid_search.py +++ b/behavenet/fitting/arhmm_grid_search.py @@ -5,13 +5,13 @@ import ssm import pickle +from behavenet.data.utils import build_data_generator from behavenet.fitting.eval import export_states from behavenet.fitting.eval import export_train_plots from behavenet.fitting.hyperparam_utils import get_all_params from behavenet.fitting.hyperparam_utils import get_slurm_params from behavenet.fitting.utils import _clean_tt_dir from behavenet.fitting.utils import _print_hparams -from behavenet.fitting.utils import build_data_generator from behavenet.fitting.utils import create_tt_experiment from behavenet.fitting.utils import export_hparams from behavenet.plotting.arhmm_utils import get_latent_arrays_by_dtype diff --git a/behavenet/fitting/decoder_grid_search.py b/behavenet/fitting/decoder_grid_search.py index 1222935..f8f2cd1 100644 --- a/behavenet/fitting/decoder_grid_search.py +++ b/behavenet/fitting/decoder_grid_search.py @@ -5,12 +5,12 @@ import torch import pickle +from behavenet.data.utils import build_data_generator from behavenet.fitting.hyperparam_utils import get_all_params from behavenet.fitting.hyperparam_utils import get_slurm_params from behavenet.fitting.training import fit from behavenet.fitting.utils import _clean_tt_dir from behavenet.fitting.utils import _print_hparams -from behavenet.fitting.utils import build_data_generator from behavenet.fitting.utils import create_tt_experiment from behavenet.fitting.utils import export_hparams from behavenet.models import Decoder diff --git a/behavenet/fitting/label_decoder_grid_search.py b/behavenet/fitting/label_decoder_grid_search.py index 69ddd4f..1fcec7b 100644 --- a/behavenet/fitting/label_decoder_grid_search.py +++ b/behavenet/fitting/label_decoder_grid_search.py @@ -4,13 +4,13 @@ import random import torch +from behavenet.data.utils import build_data_generator from behavenet.fitting.eval import export_train_plots from behavenet.fitting.hyperparam_utils import get_all_params from behavenet.fitting.hyperparam_utils import get_slurm_params from behavenet.fitting.training import fit from behavenet.fitting.utils import _clean_tt_dir from behavenet.fitting.utils import _print_hparams -from behavenet.fitting.utils import build_data_generator from behavenet.fitting.utils import create_tt_experiment from behavenet.fitting.utils import export_hparams from behavenet.models import ConvDecoder diff --git a/behavenet/fitting/utils.py b/behavenet/fitting/utils.py index a686e21..b431780 100644 --- a/behavenet/fitting/utils.py +++ b/behavenet/fitting/utils.py @@ -3,14 +3,13 @@ import os import pickle import numpy as np -from behavenet.data.utils import get_data_generator_inputs # to ignore imports for sphinx-autoapidoc __all__ = [ 'get_subdirs', 'get_session_dir', 'get_expt_dir', 'read_session_info_from_csv', 'export_session_info_to_csv', 'contains_session', 'find_session_dirs', 'experiment_exists', 'get_model_params', 'export_hparams', 'get_lab_example', 'get_region_dir', - 'create_tt_experiment', 'build_data_generator', 'get_best_model_version', + 'create_tt_experiment', 'get_best_model_version', 'get_best_model_and_data'] @@ -855,53 +854,6 @@ def create_tt_experiment(hparams): return hparams, sess_ids, exp -def build_data_generator(hparams, sess_ids, export_csv=True): - """Helper function to build data generator from hparams dict. - - Parameters - ---------- - hparams : :obj:`dict` - needs to contain information specifying data inputs to model - sess_ids : :obj:`list` of :obj:`dict` - each entry is a session dict with keys 'lab', 'expt', 'animal', 'session' - export_csv : :obj:`bool`, optional - export csv file containing session info (useful when fitting multi-sessions) - - Returns - ------- - :obj:`ConcatSessionsGenerator` object - data generator - - """ - from behavenet.data.data_generator import ConcatSessionsGenerator - from behavenet.data.utils import get_data_generator_inputs - print('using data from following sessions:') - for ids in sess_ids: - print('%s' % os.path.join( - hparams['save_dir'], ids['lab'], ids['expt'], ids['animal'], ids['session'])) - hparams, signals, transforms, paths = get_data_generator_inputs(hparams, sess_ids) - if hparams.get('trial_splits', None) is not None: - # assumes string of form 'train;val;test;gap' - trs = [int(tr) for tr in hparams['trial_splits'].split(';')] - trial_splits = {'train_tr': trs[0], 'val_tr': trs[1], 'test_tr': trs[2], 'gap_tr': trs[3]} - else: - trial_splits = None - print('constructing data generator...', end='') - data_generator = ConcatSessionsGenerator( - hparams['data_dir'], sess_ids, - signals_list=signals, transforms_list=transforms, paths_list=paths, - device=hparams['device'], as_numpy=hparams['as_numpy'], batch_load=hparams['batch_load'], - rng_seed=hparams['rng_seed_data'], trial_splits=trial_splits, - train_frac=hparams['train_frac']) - # csv order will reflect dataset order in data generator - if export_csv: - export_session_info_to_csv(os.path.join( - hparams['expt_dir'], str('version_%i' % hparams['version'])), sess_ids) - print('done') - print(data_generator) - return data_generator - - def get_best_model_version(expt_dir, measure='val_loss', best_def='min', n_best=1): """Get best model version from a test tube experiment. @@ -993,6 +945,7 @@ def get_best_model_and_data(hparams, Model=None, load_data=True, version='best', import torch from behavenet.data.data_generator import ConcatSessionsGenerator + from behavenet.data.utils import get_data_generator_inputs # get session_dir hparams['session_dir'], sess_ids = get_session_dir( diff --git a/behavenet/plotting/cond_ae_utils.py b/behavenet/plotting/cond_ae_utils.py index 02c7d87..32e5a75 100644 --- a/behavenet/plotting/cond_ae_utils.py +++ b/behavenet/plotting/cond_ae_utils.py @@ -6,6 +6,7 @@ import torch from behavenet import make_dir_if_not_exists +from behavenet.data.utils import build_data_generator from behavenet.data.utils import load_labels_like_latents from behavenet.fitting.eval import get_reconstruction from behavenet.fitting.utils import get_session_dir @@ -190,7 +191,6 @@ def get_labels_2d_for_trial( raise ValueError('only one of "trial" or "trial_idx" can be specified') if data_gen is None: - from behavenet.fitting.utils import build_data_generator hparams_new = copy.deepcopy(hparams) hparams_new['conditional_encoder'] = True # ensure scaled labels are returned hparams_new['device'] = 'cpu' From ad0808ff4e89d89dbf6e09c293b1c919f315139d Mon Sep 17 00:00:00 2001 From: themattinthehatt Date: Wed, 21 Oct 2020 14:10:21 -0400 Subject: [PATCH 12/50] allow arhmms/decoders to utilize latents from non-ae models --- behavenet/data/utils.py | 4 +++- behavenet/fitting/decoder_grid_search.py | 1 - behavenet/fitting/utils.py | 3 +++ configs/ae_jsons/ae_arch_2.json | 2 +- configs/ae_jsons/ae_arch_default.json | 2 +- configs/ae_jsons/ae_training.json | 2 +- configs/arhmm_jsons/arhmm_labels_model.json | 2 +- configs/arhmm_jsons/arhmm_model.json | 2 ++ configs/decoding_jsons/decoding_ae_model.json | 6 ++---- configs/decoding_jsons/decoding_arhmm_model.json | 5 ++--- configs/decoding_jsons/decoding_compute.json | 1 - configs/decoding_jsons/decoding_data.json | 2 +- configs/decoding_jsons/decoding_training.json | 3 +-- docs/source/glossary.rst | 3 +++ tests/integration.py | 4 ++++ tests/test_data/test_utils_data.py | 4 +++- tests/test_fitting/test_utils_fitting.py | 4 ++++ 17 files changed, 32 insertions(+), 18 deletions(-) diff --git a/behavenet/data/utils.py b/behavenet/data/utils.py index 841f041..33c121b 100644 --- a/behavenet/data/utils.py +++ b/behavenet/data/utils.py @@ -431,6 +431,8 @@ def get_transforms_paths(data_type, hparams, sess_id, check_splits=True): if hparams['model_type'][-6:] != 'neural': # don't zscore if predicting calcium activity transforms_.append(ZScore()) + elif hparams['neural_type'] == 'ca-zscored': + pass else: raise ValueError('"%s" is an invalid neural type' % hparams['neural_type']) @@ -448,7 +450,7 @@ def get_transforms_paths(data_type, hparams, sess_id, check_splits=True): path = hparams['ae_latents_file'] else: ae_dir = get_expt_dir( - hparams, model_class='ae', + hparams, model_class=hparams['ae_model_class'], expt_name=hparams['ae_experiment_name'], model_type=hparams['ae_model_type']) if 'ae_version' in hparams and isinstance(hparams['ae_version'], int): diff --git a/behavenet/fitting/decoder_grid_search.py b/behavenet/fitting/decoder_grid_search.py index f8f2cd1..9fede1f 100644 --- a/behavenet/fitting/decoder_grid_search.py +++ b/behavenet/fitting/decoder_grid_search.py @@ -72,7 +72,6 @@ def main(hparams, *args): # #################### # ### CREATE MODEL ### # #################### - print(hparams['input_size']) print('constructing model...', end='') torch.manual_seed(hparams['rng_seed_model']) torch_rng_seed = torch.get_rng_state() diff --git a/behavenet/fitting/utils.py b/behavenet/fitting/utils.py index b431780..9296795 100644 --- a/behavenet/fitting/utils.py +++ b/behavenet/fitting/utils.py @@ -679,6 +679,7 @@ def get_model_params(hparams): hparams_less['kappa'] = hparams['kappa'] hparams_less['ae_experiment_name'] = hparams['ae_experiment_name'] hparams_less['ae_version'] = hparams['ae_version'] + hparams_less['ae_model_class'] = hparams['ae_model_class'] hparams_less['ae_model_type'] = hparams['ae_model_type'] hparams_less['n_ae_latents'] = hparams['n_ae_latents'] elif model_class == 'arhmm-labels' or model_class == 'hmm-labels': @@ -690,6 +691,7 @@ def get_model_params(hparams): elif model_class == 'neural-ae' or model_class == 'ae-neural': hparams_less['ae_experiment_name'] = hparams['ae_experiment_name'] hparams_less['ae_version'] = hparams['ae_version'] + hparams_less['ae_model_class'] = hparams['ae_model_class'] hparams_less['ae_model_type'] = hparams['ae_model_type'] hparams_less['n_ae_latents'] = hparams['n_ae_latents'] elif model_class == 'neural-arhmm' or model_class == 'arhmm-neural': @@ -701,6 +703,7 @@ def get_model_params(hparams): hparams_less['transitions'] = hparams['transitions'] if hparams['transitions'] == 'sticky': hparams_less['kappa'] = hparams['kappa'] + hparams_less['ae_model_class'] = hparams['ae_model_class'] hparams_less['ae_model_type'] = hparams['ae_model_type'] hparams_less['n_ae_latents'] = hparams['n_ae_latents'] elif model_class == 'bayesian-decoding': diff --git a/configs/ae_jsons/ae_arch_2.json b/configs/ae_jsons/ae_arch_2.json index 4843491..fbfc7be 100644 --- a/configs/ae_jsons/ae_arch_2.json +++ b/configs/ae_jsons/ae_arch_2.json @@ -59,4 +59,4 @@ "ae_decoding_last_FF_layer": 0 # type: int, help: 0 = False, 1 = True -} \ No newline at end of file +} diff --git a/configs/ae_jsons/ae_arch_default.json b/configs/ae_jsons/ae_arch_default.json index c51173b..db81242 100644 --- a/configs/ae_jsons/ae_arch_default.json +++ b/configs/ae_jsons/ae_arch_default.json @@ -59,4 +59,4 @@ "ae_decoding_last_FF_layer": 0 # type: int, help: 0 = False, 1 = True -} \ No newline at end of file +} diff --git a/configs/ae_jsons/ae_training.json b/configs/ae_jsons/ae_training.json index d80dfb1..bb98c65 100644 --- a/configs/ae_jsons/ae_training.json +++ b/configs/ae_jsons/ae_training.json @@ -44,4 +44,4 @@ "trial_splits": "8;1;1;0" # type: str, help: i;j;k;l correspond to train;val;test;gap' -} \ No newline at end of file +} diff --git a/configs/arhmm_jsons/arhmm_labels_model.json b/configs/arhmm_jsons/arhmm_labels_model.json index de15778..76a59f9 100644 --- a/configs/arhmm_jsons/arhmm_labels_model.json +++ b/configs/arhmm_jsons/arhmm_labels_model.json @@ -32,4 +32,4 @@ "model_type": null -} \ No newline at end of file +} diff --git a/configs/arhmm_jsons/arhmm_model.json b/configs/arhmm_jsons/arhmm_model.json index 1a19d52..80cb22f 100644 --- a/configs/arhmm_jsons/arhmm_model.json +++ b/configs/arhmm_jsons/arhmm_model.json @@ -34,6 +34,8 @@ "ae_version": "best", +"ae_model_class": "ae", # class of AE, ae, vae, etc + "ae_model_type": "conv", # type of AE, linear or conv "n_ae_latents": 9, # type: int diff --git a/configs/decoding_jsons/decoding_ae_model.json b/configs/decoding_jsons/decoding_ae_model.json index c95a1f3..d0e484b 100644 --- a/configs/decoding_jsons/decoding_ae_model.json +++ b/configs/decoding_jsons/decoding_ae_model.json @@ -27,6 +27,8 @@ "ae_version": "best", +"ae_model_class": "ae", # class of AE, ae, vae, etc + "ae_model_type": "conv", # type of AE, linear or conv "n_ae_latents": 9, # type: int @@ -47,7 +49,3 @@ "activation": "relu" # type: str, could be linear, relu, lrelu, sigmoid, tanh } - - - - diff --git a/configs/decoding_jsons/decoding_arhmm_model.json b/configs/decoding_jsons/decoding_arhmm_model.json index 60cfd7e..a752aa5 100644 --- a/configs/decoding_jsons/decoding_arhmm_model.json +++ b/configs/decoding_jsons/decoding_arhmm_model.json @@ -23,6 +23,8 @@ # specify which ARHMM to use (should match how you trained the AE) +"ae_model_class": "ae", # class of AE, ae, vae, etc + "ae_model_type": "conv", # type of AE, linear or conv "n_ae_latents": 9, # type: int @@ -55,6 +57,3 @@ "activation": "relu" # type: str, could be linear, relu, lrelu, sigmoid, tanh } - - - diff --git a/configs/decoding_jsons/decoding_compute.json b/configs/decoding_jsons/decoding_compute.json index 44bbdb2..46f0cb6 100644 --- a/configs/decoding_jsons/decoding_compute.json +++ b/configs/decoding_jsons/decoding_compute.json @@ -30,5 +30,4 @@ "tt_n_cpu_workers": 3 # type: int - } diff --git a/configs/decoding_jsons/decoding_data.json b/configs/decoding_jsons/decoding_data.json index 24a5f2e..6434f06 100644 --- a/configs/decoding_jsons/decoding_data.json +++ b/configs/decoding_jsons/decoding_data.json @@ -57,4 +57,4 @@ "approx_batch_size": 200 # type: int, help: approximate batch size for memory calculation -} \ No newline at end of file +} diff --git a/configs/decoding_jsons/decoding_training.json b/configs/decoding_jsons/decoding_training.json index ffdeb18..907de18 100644 --- a/configs/decoding_jsons/decoding_training.json +++ b/configs/decoding_jsons/decoding_training.json @@ -38,7 +38,6 @@ "train_frac": 1.0, # type: float, help: fraction of data -"trial_splits": "8;1;1;0" # type: str, help: i;j;k;l correspond to train;val;test;gap' - +"trial_splits": "8;1;1;0" # type: str, help: i;j;k;l correspond to train;val;test;gap } diff --git a/docs/source/glossary.rst b/docs/source/glossary.rst index ec35952..5b53d49 100644 --- a/docs/source/glossary.rst +++ b/docs/source/glossary.rst @@ -153,6 +153,7 @@ ARHMM * **ae_experiment_name** (*str*): name of AE test-tube experiment * **ae_version** (*str* or *int*): 'best' to choose best version in AE experiment, otherwise an integer specifying test-tube version number +* **ae_model_class** (*str*): 'ae' | 'vae' | 'beta-tcvae' | ... * **ae_model_type** (*str*): 'conv' | 'linear' * **n_ae_latents** (*int*): number of autoencoder latents; this will be the observation dimension in the ARHMM * **export_train_plots** ('*bool*): ``True`` to automatically export training/validation log probability as a function of epoch upon completion of training @@ -182,6 +183,7 @@ For the continuous decoder: * **ae_experiment_name** (*str*): name of AE test-tube experiment * **ae_version** (*str* or *int*): 'best' to choose best version in AE experiment, otherwise an integer specifying test-tube version number +* **ae_model_class** (*str*): 'ae' | 'vae' | 'beta-tcvae' | ... * **ae_model_type** (*str*): 'conv' | 'linear' * **n_ae_latents** (*int*): number of autoencoder latents; this will be the dimension of the data predicted by the decoder * **ae_multisession** (*int*): use if loading latents from an AE that was trained on multiple datasets @@ -190,6 +192,7 @@ For the continuous decoder: For the discrete decoder: * **n_ae_latents** (*int*): number of autoencoder latents that the ARHMM was trained on +* **ae_model_class** (*str*): 'ae' | 'vae' | 'beta-tcvae' | ... * **ae_model_type** (*str*): 'conv' | 'linear' * **arhmm_experiment_name** (*str*): name of ARHMM test-tube experiment * **n_arhmm_states** (*int*): number of ARHMM discrete states; this will be the number of classes the decoder is trained on diff --git a/tests/integration.py b/tests/integration.py index ad1667c..95b23e8 100644 --- a/tests/integration.py +++ b/tests/integration.py @@ -159,6 +159,7 @@ def define_new_config_values(model, session='sess-0'): # model vals: ae ae_expt_name = 'ae-expt' + ae_model_class = 'ae' ae_model_type = 'conv' n_ae_latents = 6 @@ -238,6 +239,7 @@ def define_new_config_values(model, session='sess-0'): 'transitions': transitions, 'noise_type': noise_type, 'ae_experiment_name': ae_expt_name, + 'ae_model_class': ae_model_class, 'ae_model_type': ae_model_type, 'n_ae_latents': n_ae_latents}, 'training': { @@ -257,6 +259,7 @@ def define_new_config_values(model, session='sess-0'): 'n_max_lags': 8, 'l2_reg': 1e-3, 'ae_experiment_name': ae_expt_name, + 'ae_model_class': ae_model_class, 'ae_model_type': ae_model_type, 'n_ae_latents': n_ae_latents, 'model_type': 'mlp', @@ -280,6 +283,7 @@ def define_new_config_values(model, session='sess-0'): 'n_lags': 2, 'n_max_lags': 8, 'l2_reg': 1e-3, + 'ae_model_class': ae_model_class, 'ae_model_type': ae_model_type, 'n_ae_latents': n_ae_latents, 'arhmm_experiment_name': arhmm_expt_name, diff --git a/tests/test_data/test_utils_data.py b/tests/test_data/test_utils_data.py index 2f5fc12..ca1bced 100644 --- a/tests/test_data/test_utils_data.py +++ b/tests/test_data/test_utils_data.py @@ -178,6 +178,7 @@ def test_get_data_generator_inputs(): # ----------------- hparams['model_class'] = 'ae_latents' hparams['session_dir'] = session_dir + hparams['ae_model_class'] = 'ae' hparams['ae_model_type'] = 'conv' hparams['n_ae_latents'] = 8 hparams['ae_experiment_name'] = 'tt_expt_ae' @@ -483,6 +484,7 @@ def test_get_transforms_paths(): # ae latents # ------------------------ hparams['session_dir'] = session_dir + hparams['ae_model_class'] = 'ae' hparams['ae_model_type'] = 'conv' hparams['n_ae_latents'] = 8 hparams['ae_experiment_name'] = 'tt_expt_ae' @@ -490,7 +492,7 @@ def test_get_transforms_paths(): ae_path = os.path.join( hparams['data_dir'], hparams['lab'], hparams['expt'], hparams['animal'], - hparams['session'], 'ae', hparams['ae_model_type'], + hparams['session'], hparams['ae_model_class'], hparams['ae_model_type'], '%02i_latents' % hparams['n_ae_latents'], hparams['ae_experiment_name']) # user-defined latent path diff --git a/tests/test_fitting/test_utils_fitting.py b/tests/test_fitting/test_utils_fitting.py index 0653093..82244c9 100644 --- a/tests/test_fitting/test_utils_fitting.py +++ b/tests/test_fitting/test_utils_fitting.py @@ -918,6 +918,7 @@ def test_get_model_params(self): 'transitions': 'stationary', 'ae_experiment_name': 'ae_expt', 'ae_version': 4, + 'ae_model_class': 'ae', 'ae_model_type': 'conv', 'n_ae_latents': 5} ret_hparams = utils.get_model_params({**misc_hparams, **base_hparams, **model_hparams}) @@ -932,6 +933,7 @@ def test_get_model_params(self): 'kappa': 100, 'ae_experiment_name': 'ae_expt', 'ae_version': 4, + 'ae_model_class': 'ae', 'ae_model_type': 'conv', 'n_ae_latents': 5} ret_hparams = utils.get_model_params({**misc_hparams, **base_hparams, **model_hparams}) @@ -957,6 +959,7 @@ def test_get_model_params(self): 'model_type': 'mlp', 'ae_experiment_name': 'ae_expt', 'ae_version': 4, + 'ae_model_class': 'ae', 'ae_model_type': 'conv', 'n_ae_latents': 5, 'n_lags': 3, @@ -980,6 +983,7 @@ def test_get_model_params(self): 'noise_type': 'gaussian', 'transitions': 'sticky', 'kappa': 10, + 'ae_model_class': 'ae', 'ae_model_type': 'conv', 'n_ae_latents': 5, 'n_lags': 3, From 9bee2d31bff13739b86b180032c2134f4b625936 Mon Sep 17 00:00:00 2001 From: themattinthehatt Date: Tue, 27 Oct 2020 14:30:11 -0400 Subject: [PATCH 13/50] small plotting updates --- behavenet/data/utils.py | 5 ++++- behavenet/fitting/eval.py | 6 ++++-- behavenet/fitting/utils.py | 3 ++- behavenet/plotting/ae_utils.py | 32 ++++++++++++++++++++------------ 4 files changed, 30 insertions(+), 16 deletions(-) diff --git a/behavenet/data/utils.py b/behavenet/data/utils.py index 33c121b..cf38f36 100644 --- a/behavenet/data/utils.py +++ b/behavenet/data/utils.py @@ -453,7 +453,10 @@ def get_transforms_paths(data_type, hparams, sess_id, check_splits=True): hparams, model_class=hparams['ae_model_class'], expt_name=hparams['ae_experiment_name'], model_type=hparams['ae_model_type']) - if 'ae_version' in hparams and isinstance(hparams['ae_version'], int): + if 'ae_version' in hparams and hparams['ae_version'] != 'best': + # json args read as strings + if isinstance(hparams['ae_version'], str): + hparams['ae_version'] = int(hparams['ae_version']) ae_version = str('version_%i' % hparams['ae_version']) else: ae_version = 'version_%i' % get_best_model_version(ae_dir, 'val_loss')[0] diff --git a/behavenet/fitting/eval.py b/behavenet/fitting/eval.py index 0d17162..63abc71 100644 --- a/behavenet/fitting/eval.py +++ b/behavenet/fitting/eval.py @@ -373,7 +373,7 @@ def get_test_metric(hparams, model_version, metric='r2', sess_idx=0): model_version : :obj:`int` or :obj:`str` version from test tube experiment defined in :obj:`hparams` or the string 'best' metric : :obj:`str`, optional - 'r2' | 'fc' + 'r2' | 'fc' | 'mse' sess_idx : :obj:`int`, optional session index into data generator @@ -418,11 +418,13 @@ def get_test_metric(hparams, model_version, metric='r2', sess_idx=0): metric = r2_score( np.concatenate(true, axis=0), np.concatenate(pred, axis=0), multioutput='variance_weighted') + elif metric == 'mse': + metric = np.mean(np.square(np.concatenate(true, axis=0) - np.concatenate(pred, axis=0))) elif metric == 'fc': metric = accuracy_score( np.concatenate(true, axis=0), np.argmax(np.concatenate(pred, axis=0), axis=1)) - return model.hparams, metric + return model.hparams, metric, true, pred def export_train_plots(hparams, dtype, loss_type='mse', save_file=None, format='png'): diff --git a/behavenet/fitting/utils.py b/behavenet/fitting/utils.py index 9296795..ae23965 100644 --- a/behavenet/fitting/utils.py +++ b/behavenet/fitting/utils.py @@ -28,7 +28,7 @@ def get_subdirs(path): """ if not os.path.exists(path): - raise ValueError('%s is not a path' % path) + raise NotADirectoryError('%s is not a path' % path) try: s = next(os.walk(path))[1] except StopIteration: @@ -718,6 +718,7 @@ def get_model_params(hparams): # decoder arch params if model_class == 'neural-ae' or model_class == 'ae-neural' \ or model_class == 'neural-arhmm' or model_class == 'arhmm-neural': + hparams_less['learning_rate'] = hparams['learning_rate'] hparams_less['n_lags'] = hparams['n_lags'] hparams_less['l2_reg'] = hparams['l2_reg'] hparams_less['model_type'] = hparams['model_type'] diff --git a/behavenet/plotting/ae_utils.py b/behavenet/plotting/ae_utils.py index 3c5b98a..161f82b 100644 --- a/behavenet/plotting/ae_utils.py +++ b/behavenet/plotting/ae_utils.py @@ -99,17 +99,19 @@ def make_reconstruction_movie( plt.tight_layout(pad=0) ani = animation.ArtistAnimation(fig, ims_ani, blit=True, repeat_delay=1000) - writer = FFMpegWriter(fps=frame_rate, bitrate=-1) if save_file is not None: make_dir_if_not_exists(save_file) - if save_file[-3:] != 'mp4': - save_file += '.mp4' + if save_file[-3:] == 'gif': + writer = 'imagemagick' + else: + if save_file[-3:] != 'mp4': + save_file += '.mp4' + writer = FFMpegWriter(fps=frame_rate, bitrate=-1) + print('saving video to %s...' % save_file, end='') - ani.save(save_file, writer=writer) - # if save_file[-3:] != 'gif': - # save_file += '.gif' - # ani.save(save_file, writer='imagemagick', fps=15) + ani.save(save_file, writer=writer, fps=frame_rate) + print('done') @@ -473,7 +475,8 @@ def make_neural_reconstruction_movie( def plot_neural_reconstruction_traces_wrapper( - hparams, save_file=None, trial=None, xtick_locs=None, frame_rate=None, format='png'): + hparams, save_file=None, trial=None, xtick_locs=None, frame_rate=None, format='png', + **kwargs): """Plot ae latents and their neural reconstructions. This is a high-level function that loads the model described in the hparams dictionary and @@ -529,7 +532,7 @@ def plot_neural_reconstruction_traces_wrapper( data_generator = ConcatSessionsGenerator( hparams['data_dir'], [hparams], signals_list=[signals], transforms_list=[transforms], paths_list=[paths], - device='cpu', as_numpy=False, batch_load=False, rng_seed=0) + device='cpu', as_numpy=False, batch_load=True, rng_seed=0) if trial is None: # choose first test trial @@ -539,9 +542,14 @@ def plot_neural_reconstruction_traces_wrapper( traces_ae = batch['ae_latents'].cpu().detach().numpy() traces_neural = batch['ae_predictions'].cpu().detach().numpy() - fig = plot_neural_reconstruction_traces( - traces_ae, traces_neural, save_file, xtick_locs, frame_rate, format) - + n_max_lags = hparams.get('n_max_lags', 0) # only plot valid segment of data + if n_max_lags > 0: + fig = plot_neural_reconstruction_traces( + traces_ae[n_max_lags:-n_max_lags], traces_neural[n_max_lags:-n_max_lags], + save_file, xtick_locs, frame_rate, format, **kwargs) + else: + fig = plot_neural_reconstruction_traces( + traces_ae, traces_neural, save_file, xtick_locs, frame_rate, format, **kwargs) return fig From 99dd4f52a8cfaf5c11b1b72c5d0d1be1b758318f Mon Sep 17 00:00:00 2001 From: themattinthehatt Date: Tue, 27 Oct 2020 19:11:35 -0400 Subject: [PATCH 14/50] neural->labels decoding --- behavenet/data/utils.py | 36 ++++++++ behavenet/fitting/decoder_grid_search.py | 6 ++ behavenet/fitting/utils.py | 15 +++- configs/decoding_jsons/decoding_data.json | 2 + .../decoding_jsons/decoding_labels_model.json | 32 ++++++++ docs/source/adv_user_guide.multisession.rst | 28 +------ docs/source/data_structure.rst | 19 +++-- docs/source/glossary.rst | 1 + .../user_guide.conditional_autoencoders.rst | 78 ++++++++++++++---- docs/source/user_guide.decoders.rst | 65 +++++++++++---- docs/source/user_guide.intro.rst | 82 ++++++++++++++----- tests/integration.py | 27 +++++- tests/test_data/test_utils_data.py | 51 ++++++++++++ tests/test_fitting/test_utils_fitting.py | 43 +++++++++- 14 files changed, 394 insertions(+), 91 deletions(-) create mode 100644 configs/decoding_jsons/decoding_labels_model.json diff --git a/behavenet/data/utils.py b/behavenet/data/utils.py index cf38f36..4c12775 100644 --- a/behavenet/data/utils.py +++ b/behavenet/data/utils.py @@ -6,6 +6,11 @@ from behavenet.fitting.utils import export_session_info_to_csv +# to ignore imports for sphinx-autoapidoc +__all__ = [ + 'get_data_generator_inputs', 'build_data_generator', 'check_same_training_split', + 'get_transforms_paths', 'load_labels_like_latents', 'get_region_list'] + def get_data_generator_inputs(hparams, sess_ids, check_splits=True): """Helper function for generating signals, transforms and paths. @@ -135,6 +140,37 @@ def get_data_generator_inputs(hparams, sess_ids, check_splits=True): transforms = [neural_transform, ae_transform] paths = [neural_path, ae_path] + elif hparams['model_class'] == 'neural-labels': + + hparams['input_signal'] = 'neural' + hparams['output_signal'] = 'labels' + hparams['output_size'] = hparams['n_labels'] + if hparams['model_type'][-2:] == 'mv': + hparams['noise_dist'] = 'gaussian-full' + else: + hparams['noise_dist'] = 'gaussian' + + signals = ['neural', 'labels'] + transforms = [neural_transform, None] + paths = [neural_path, os.path.join(data_dir, 'data.hdf5')] + + elif hparams['model_class'] == 'labels-neural': + + hparams['input_signal'] = 'labels' + hparams['output_signal'] = 'neural' + hparams['output_size'] = None # to fill in after data is loaded + if hparams['neural_type'] == 'ca': + if hparams['model_type'][-2:] == 'mv': + hparams['noise_dist'] = 'gaussian-full' + else: + hparams['noise_dist'] = 'gaussian' + elif hparams['neural_type'] == 'spikes': + hparams['noise_dist'] = 'poisson' + + signals = ['neural', 'labels'] + transforms = [neural_transform, None] + paths = [neural_path, os.path.join(data_dir, 'data.hdf5')] + elif hparams['model_class'] == 'neural-arhmm': hparams['input_signal'] = 'neural' diff --git a/behavenet/fitting/decoder_grid_search.py b/behavenet/fitting/decoder_grid_search.py index 9fede1f..e211cbe 100644 --- a/behavenet/fitting/decoder_grid_search.py +++ b/behavenet/fitting/decoder_grid_search.py @@ -53,6 +53,12 @@ def main(hparams, *args): elif hparams['model_class'] == 'ae-neural': hparams['input_size'] = hparams['n_ae_latents'] hparams['output_size'] = data_generator.datasets[0][ex_trial][o_sig].shape[1] + elif hparams['model_class'] == 'neural-labels': + hparams['input_size'] = data_generator.datasets[0][ex_trial][i_sig].shape[1] + hparams['output_size'] = hparams['n_labels'] + elif hparams['model_class'] == 'labels-neural': + hparams['input_size'] = hparams['n_labels'] + hparams['output_size'] = data_generator.datasets[0][ex_trial][o_sig].shape[1] else: raise ValueError('%s is an invalid model class' % hparams['model_class']) diff --git a/behavenet/fitting/utils.py b/behavenet/fitting/utils.py index ae23965..d5d2ebc 100644 --- a/behavenet/fitting/utils.py +++ b/behavenet/fitting/utils.py @@ -369,6 +369,10 @@ def get_expt_dir(hparams, model_class=None, model_type=None, expt_name=None): model_path = os.path.join( model_class, '%02i_latents' % hparams['n_ae_latents'], model_type, brain_region) session_dir = hparams['session_dir'] + elif model_class == 'neural-labels' or model_class == 'labels-neural': + brain_region = get_region_dir(hparams) + model_path = os.path.join(model_class, model_type, brain_region) + session_dir = hparams['session_dir'] elif model_class == 'neural-arhmm' or model_class == 'arhmm-neural': brain_region = get_region_dir(hparams) model_path = os.path.join( @@ -694,6 +698,8 @@ def get_model_params(hparams): hparams_less['ae_model_class'] = hparams['ae_model_class'] hparams_less['ae_model_type'] = hparams['ae_model_type'] hparams_less['n_ae_latents'] = hparams['n_ae_latents'] + elif model_class == 'neural-labels' or model_class == 'labels-neural': + pass elif model_class == 'neural-arhmm' or model_class == 'arhmm-neural': hparams_less['arhmm_experiment_name'] = hparams['arhmm_experiment_name'] hparams_less['arhmm_version'] = hparams['arhmm_version'] @@ -717,7 +723,8 @@ def get_model_params(hparams): # decoder arch params if model_class == 'neural-ae' or model_class == 'ae-neural' \ - or model_class == 'neural-arhmm' or model_class == 'arhmm-neural': + or model_class == 'neural-arhmm' or model_class == 'arhmm-neural' \ + or model_class == 'neural-labels' or model_class == 'labels-neural': hparams_less['learning_rate'] = hparams['learning_rate'] hparams_less['n_lags'] = hparams['n_lags'] hparams_less['l2_reg'] = hparams['l2_reg'] @@ -1017,9 +1024,11 @@ def get_best_model_and_data(hparams, Model=None, load_data=True, version='best', from behavenet.models import SSSVAE as Model elif hparams['model_class'] == 'labels-images': from behavenet.models import ConvDecoder as Model - elif hparams['model_class'] == 'neural-ae' or hparams['model_class'] == 'neural-arhmm': + elif hparams['model_class'] == 'neural-ae' or hparams['model_class'] == 'neural-arhmm' \ + or hparams['model_class'] == 'neural-labels': from behavenet.models import Decoder as Model - elif hparams['model_class'] == 'ae-neural' or hparams['model_class'] == 'arhmm-neural': + elif hparams['model_class'] == 'ae-neural' or hparams['model_class'] == 'arhmm-neural' \ + or hparams['model_class'] == 'labels-neural': from behavenet.models import Decoder as Model elif hparams['model_class'] == 'arhmm': raise NotImplementedError('Cannot use get_best_model_and_data() for ssm models') diff --git a/configs/decoding_jsons/decoding_data.json b/configs/decoding_jsons/decoding_data.json index 6434f06..7d8a3d3 100644 --- a/configs/decoding_jsons/decoding_data.json +++ b/configs/decoding_jsons/decoding_data.json @@ -31,6 +31,8 @@ "use_output_mask": false, # type: boolean +"n_labels": null, # type: int + ######################## ## Neural data params ## diff --git a/configs/decoding_jsons/decoding_labels_model.json b/configs/decoding_jsons/decoding_labels_model.json new file mode 100644 index 0000000..3fd1bd2 --- /dev/null +++ b/configs/decoding_jsons/decoding_labels_model.json @@ -0,0 +1,32 @@ +{ + +############################# +## Commonly changed params ## +############################# + +"experiment_name": "grid_search", # type: str, name of this experiment + +"n_lags": [4], # type: int + +"n_max_lags": 8, # type: int, should match largest n_lags value (so all lags are evaluated on exact same data) + +"l2_reg": [1e-3], # type: float + +"rng_seed_model": 0, # type: int, help: control model initialization + +"model_class": "neural-labels", # type: str + + +######################## +## Model Architecture ## +######################## + +"model_type": "mlp", # type: str, currently mlp only option (mlp with 0 hidden layers is linear) + +"n_hid_layers": [1], # type: int + +"n_hid_units": [32], # type: int + +"activation": "relu" # type: str, could be linear, relu, lrelu, sigmoid, tanh + +} diff --git a/docs/source/adv_user_guide.multisession.rst b/docs/source/adv_user_guide.multisession.rst index f8f8485..6d6b54e 100644 --- a/docs/source/adv_user_guide.multisession.rst +++ b/docs/source/adv_user_guide.multisession.rst @@ -18,18 +18,13 @@ require modifying the data configuration json before training. We'll use the Mus example; below is the relevant section of the json file located in ``behavenet/configs/data_default.json`` that we will modify below. -.. code-block:: json +.. code-block:: javascript "lab": "musall", # type: str - "expt": "vistrained", # type: str - "animal": "mSM30", # type: str - "session": "10-Oct-2017", # type: str - "sessions_csv": "", # type: str, help: specify multiple sessions - "all_source": "save", # type: str, help: "save" or "data" The Musall dataset provided with the repo (see ``behavenet/example/00_data.ipynb``) contains @@ -45,18 +40,13 @@ This method is appropriate if you want to fit a model on all sessions from a spe experiment, or lab. For example, if we want to fit a model on all sessions from animal ``mSM30``, we would modify the ``session`` parameter value to ``all``: -.. code-block:: json +.. code-block:: javascript "lab": "musall", # type: str - "expt": "vistrained", # type: str - "animal": "mSM30", # type: str - "session": "all", # type: str - "sessions_csv": "", # type: str, help: specify multiple sessions - "all_source": "save", # type: str, help: "save" or "data" In this case the resulting models will be stored in the directory @@ -68,18 +58,13 @@ lists the lab, expt, animal, and session for all sessions in that multisession. If we want to fit a model on all sessions from all animals in the ``vistrained`` experiment, we would modify the ``animal`` parameter value to ``all``: -.. code-block:: json +.. code-block:: javascript "lab": "musall", # type: str - "expt": "vistrained", # type: str - "animal": "all", # type: str - "session": "all", # type: str - "sessions_csv": "", # type: str, help: specify multiple sessions - "all_source": "save", # type: str, help: "save" or "data" In this case the resulting models will be stored in the directory @@ -107,18 +92,13 @@ specify these sessions, you can construct a csv file with the four column header ``expt``, ``animal``, and ``session``. You can then provide this csv file (let's say it's called ``data_dir/example_sessions.csv``) as the value for the ``sessions_csv`` parameter: -.. code-block:: json +.. code-block:: javascript "lab": "musall", # type: str - "expt": "vistrained", # type: str - "animal": "all", # type: str - "session": "all", # type: str - "sessions_csv": "data_dir/example_sessions.csv", # type: str, help: specify multiple sessions - "all_source": "save", # type: str, help: "save" or "data" The ``sessions_csv`` parameter takes precedence over any values supplied for ``lab``, ``expt``, diff --git a/docs/source/data_structure.rst b/docs/source/data_structure.rst index c14d900..c725031 100644 --- a/docs/source/data_structure.rst +++ b/docs/source/data_structure.rst @@ -6,7 +6,6 @@ BehaveNet data structure Introduction ============ - In order to quickly and easily fit many models, BehaveNet uses a standardized data structure. "Raw" experimental data such as behavioral videos and (processed) neural data are stored in the `HDF5 file format `_. This file format can @@ -83,7 +82,6 @@ video or neural data differently than the rate at which it was acquired. Identifying subsets of neurons ============================== - It is possible that the neural data used for encoding and decoding models will have natural partitions - for example, neurons belonging to different brain regions or cell types. In this case you may be interested in, say, decoding behavior from each brain region individually, as well as all together. BehaveNet provides this capability through the addition of another HDF5 group. This group can have any name, but for illustration purposes we will use the name "regions" (this name will be later be provided in the updated data json file). The "regions" group contains a second level of (again user-defined) groups, which will define different index groupings. As a concrete example, let's say we have neural data with 100 neurons: @@ -151,12 +149,18 @@ This HDF5 file will now have the following addtional datasets: * regions/idxs/AUD * regions/idxs/VIS -Just as the top-level group (here named "regions") can have an arbitrary name (later specified in the data json file), the second-level groups (here named "idxs_lr" and "idxs") can also have arbitrary names, and there can be any number of them, as long as the datasets within them contain valid indices into the neural data. The specific set of indices used for any analyses will be specified in the data json file. See the :ref:`decoding documentation` for an example of how to decode behavior using specified subsets of neurons. +Just as the top-level group (here named "regions") can have an arbitrary name (later specified in +the data json file), the second-level groups (here named "idxs_lr" and "idxs") can also have +arbitrary names, and there can be any number of them, as long as the datasets within them contain +valid indices into the neural data. The specific set of indices used for any analyses will be +specified in the data json file. See the :ref:`decoding documentation` for +an example of how to decode behavior using specified subsets of neurons. + +.. _data_structure_labels: Including labels for ARHMMs and conditional autoencoders ======================================================== - In order to fit :ref:`conditional autoencoder models`, you will need to include additional information about labels in the HDF5 file. These labels can be outputs from pose estimation software, or other behavior-related signals such as pupil diameter or lick times. These @@ -177,5 +181,8 @@ data, you simply need to change the ``model_class`` entry of the arhmm model jso .. note:: - The matrix subspace projection model implemented in BehaveNet learns a linear mapping from the original latent space to the predicted labels that **does not contain a bias term**. Therefore you should center each label before adding them to the HDF5 file. Additionally, normalizing each label by its standard deviation can make searching across msp weights less dependent on the size of the input image. - + The matrix subspace projection model implemented in BehaveNet learns a linear mapping from the + original latent space to the predicted labels that **does not contain a bias term**. Therefore + you should center each label before adding them to the HDF5 file. Additionally, normalizing + each label by its standard deviation can make searching across msp weights less dependent on + the size of the input image. diff --git a/docs/source/glossary.rst b/docs/source/glossary.rst index 5b53d49..a109060 100644 --- a/docs/source/glossary.rst +++ b/docs/source/glossary.rst @@ -22,6 +22,7 @@ Data * **x_pixels** (*int*): number of behavioral video pixels in x dimension * **use_output_mask** (*bool*): `True`` to apply frame-wise output masks (must be a key ``masks`` in data HDF5 file) * **use_label_mask** (*bool*): `True`` to apply frame-wise masks to labels in conditional ae models (must be a key ``labels_masks`` in data HDF5 file) +* **n_labels** (*bool*): specify number of labels when model_class is 'neural-labels' or 'labels-neural' * **neural_bin_size** (*float*): bin size of neural/video data (ms) * **neural_type** (*str*): 'spikes' | 'ca' * **approx_batch_size** (*str*): approximate batch size (number of frames) for gpu memory calculation diff --git a/docs/source/user_guide.conditional_autoencoders.rst b/docs/source/user_guide.conditional_autoencoders.rst index cc065f0..b6280a9 100644 --- a/docs/source/user_guide.conditional_autoencoders.rst +++ b/docs/source/user_guide.conditional_autoencoders.rst @@ -3,19 +3,40 @@ Conditional autoencoders ======================== -One drawback to the use of unsupervised dimensionality reduction (performed by the convolutional autoencoder) is that the resulting latents are generally uninterpretable, because any animal movement in a behavioral video will be represented across many (if not all) of the latents. Thus there is no simple way to find an "arm" dimension that is separate from a "pupil" dimension, distinctions that may be important for downstream analyses. - -Semi-supervised approaches to dimensionality reduction offer a partial resolution to this problem. In this framework, the user first collects a set of markers that track body parts of interest over time. These markers can be, for example, the output of standard pose estimation software such as `DeepLabCut `_, `LEAP `_, or `DeepPoseKit `_. These markers can then be used to augment the latent space (using :ref:`conditional autoencoders`) or regularize the latent space (using the :ref:`matrix subspace projection loss`), both of which are described below. - -In order to fit these models, the data HDF5 needs to be augmented to include a new HDF5 group named ``labels``, which contains an hdf5 dataset for each trial. The labels for each trial must match up with the corresponding video frames; for example, if the image data in ``images/trial_0013`` contains 100 frames (a numpy array of shape [100, n_channels, y_pix, x_pix]), the label data in ``labels/trial_0013`` should contain the corresponding labels (a numpy array of shape [100, n_labels]). See the :ref:`data structure documentation` for more information). +One drawback to the use of unsupervised dimensionality reduction (performed by the convolutional +autoencoder) is that the resulting latents are generally uninterpretable, because any animal +movement in a behavioral video will be represented across many (if not all) of the latents. Thus +there is no simple way to find an "arm" dimension that is separate from a "pupil" dimension, +distinctions that may be important for downstream analyses. + +Semi-supervised approaches to dimensionality reduction offer a partial resolution to this problem. +In this framework, the user first collects a set of markers that track body parts of interest over +time. These markers can be, for example, the output of standard pose estimation software such as +`DeepLabCut `_, `LEAP `_, +or `DeepPoseKit `_. These markers can then be used to +augment the latent space (using :ref:`conditional autoencoders`) or regularize the latent +space (using the :ref:`matrix subspace projection loss`), both of which are described +below. + +In order to fit these models, the data HDF5 needs to be augmented to include a new HDF5 group named +``labels``, which contains an HDF5 dataset for each trial. The labels for each trial must match up +with the corresponding video frames; for example, if the image data in ``images/trial_0013`` +contains 100 frames (a numpy array of shape [100, n_channels, y_pix, x_pix]), the label data in +``labels/trial_0013`` should contain the corresponding labels (a numpy array of shape +[100, n_labels]). See the :ref:`data structure documentation` for more +information). .. _cond_ae: Conditional autoencoders ------------------------ -The `conditional autoencoder `_ implemented in BehaveNet is a simple extension of the convolutional autoencoder. Each frame is pushed through the encoder to produce a set of latents, which are concatenated with the corresponding labels; this augmented vector is then used as input to the decoder. +The `conditional autoencoder `_ +implemented in BehaveNet is a simple extension of the convolutional autoencoder. Each frame is +pushed through the encoder to produce a set of latents, which are concatenated with the +corresponding labels; this augmented vector is then used as input to the decoder. -To fit a single conditional autoencoder with the default CAE BehaveNet architecture, edit the ``model_class`` parameter of the ``ae_model.json`` file: +To fit a single conditional autoencoder with the default CAE BehaveNet architecture, edit the +``model_class`` parameter of the ``ae_model.json`` file: .. code-block:: json @@ -31,20 +52,35 @@ To fit a single conditional autoencoder with the default CAE BehaveNet architect "conditional_encoder": false } -Then to fit the model, use the ``ae_grid_search.py`` function using this updated model json. All other input jsons remain unchanged. +Then to fit the model, use the ``ae_grid_search.py`` function using this updated model json. All +other input jsons remain unchanged. -By concatenating the labels to the latents, we are learning a conditional decoder. We can also condition the latents on the labels by learning a conditional encoder. Turning on this feature requires an additional hdf5 group; documentation coming soon. +By concatenating the labels to the latents, we are learning a conditional decoder. We can also +condition the latents on the labels by learning a conditional encoder. Turning on this feature +requires an additional HDF5 group; documentation coming soon. .. _ae_msp: Matrix subspace projection loss ------------------------------- -An alternative way to obtain a more interpretable latent space is to encourage a subspace to predict the labels themselves, rather than appending them to the latents. With appropriate additions to the loss function, we can ensure that the subspace spanned by the label-predicting latents is orthogonal to the subspace spanned by the remaining unconstrained latents. This is the idea of the `matrix subspace projection loss `_. - -For example, imagine we are tracking 4 body parts, each with their own x-y coordinates for each frame. This gives us 8 dimensions of behavior to predict. If we fit a CAE with 10 latent dimensions, we will use 8 of those dimensions to predict the 8 marker dimensions - one latent dimension for each marker dimension. This leaves 2 unconstrained dimensions to predict remaining variability in the images not captured by the labels. The model is trained by minimizing the mean square error between the true and predicted images, as well as the true and predicted labels. Unlike the conditional autoencoder described above, this new loss function has an additional hyperparameter that governs the tradeoff between image reconstruction and label reconstruction. - -To fit a single autoencoder with the matrix subspace projection loss (and the default CAE BehaveNet architecture), edit the ``model_class`` and ``msp.alpha`` parameters of the ``ae_model.json`` file: +An alternative way to obtain a more interpretable latent space is to encourage a subspace to +predict the labels themselves, rather than appending them to the latents. With appropriate +additions to the loss function, we can ensure that the subspace spanned by the label-predicting +latents is orthogonal to the subspace spanned by the remaining unconstrained latents. This is the +idea of the `matrix subspace projection loss `_. + +For example, imagine we are tracking 4 body parts, each with their own x-y coordinates for each +frame. This gives us 8 dimensions of behavior to predict. If we fit a CAE with 10 latent +dimensions, we will use 8 of those dimensions to predict the 8 marker dimensions - one latent +dimension for each marker dimension. This leaves 2 unconstrained dimensions to predict remaining +variability in the images not captured by the labels. The model is trained by minimizing the mean +square error between the true and predicted images, as well as the true and predicted labels. +Unlike the conditional autoencoder described above, this new loss function has an additional +hyperparameter that governs the tradeoff between image reconstruction and label reconstruction. + +To fit a single autoencoder with the matrix subspace projection loss (and the default CAE BehaveNet +architecture), edit the ``model_class`` and ``msp.alpha`` parameters of the ``ae_model.json`` file: .. code-block:: json @@ -61,10 +97,16 @@ To fit a single autoencoder with the matrix subspace projection loss (and the de "conditional_encoder": false } -The ``msp.alpha`` parameter needs to be tuned for each dataset, but ``msp.alpha=1.0`` is a reasonable starting value if the labels have each been z-scored. +The ``msp.alpha`` parameter needs to be tuned for each dataset, but ``msp.alpha=1.0`` is a +reasonable starting value if the labels have each been z-scored. .. note:: - The matrix subspace projection model implemented in BehaveNet learns a linear mapping from the original latent space to the predicted labels that **does not contain a bias term**. Therefore you should center each label before adding them to the HDF5 file. Additionally, normalizing each label by its standard deviation can make searching across msp weights less dependent on the size of the input image. - -Then to fit the model, use the ``ae_grid_search.py`` function using this updated model json. All other input jsons remain unchanged. + The matrix subspace projection model implemented in BehaveNet learns a linear mapping from the + original latent space to the predicted labels that **does not contain a bias term**. Therefore + you should center each label before adding them to the HDF5 file. Additionally, normalizing + each label by its standard deviation can make searching across msp weights less dependent on + the size of the input image. + +Then to fit the model, use the ``ae_grid_search.py`` function using this updated model json. All +other input jsons remain unchanged. diff --git a/docs/source/user_guide.decoders.rst b/docs/source/user_guide.decoders.rst index 2f16f07..c5dd84d 100644 --- a/docs/source/user_guide.decoders.rst +++ b/docs/source/user_guide.decoders.rst @@ -1,12 +1,20 @@ Decoders ======== -The next step of the BehaveNet pipeline uses the neural activity to decode (or reconstruct) aspects of behavior. In particular, you may decode either the AE latents or the ARHMM states on a frame-by-frame basis given the surrounding window of neural activity. +The next step of the BehaveNet pipeline uses the neural activity to decode (or reconstruct) aspects +of behavior. In particular, you may decode either the AE latents or the ARHMM states on a +frame-by-frame basis given the surrounding window of neural activity. -The architecture options consist of a linear model or feedforward neural network: exact architecture parameters such as number of layers in the neural network can be specified in ``decoding_ae_model.json`` or ``decoding_arhmm_model.json``. The size of the window of neural activity used to reconstruct each frame of AE latents or ARHMM states is set by ``n_lags``: the neural activity from ``t-n_lags:t+n_lags`` will be used to predict the latents or states at time ``t``. +The architecture options consist of a linear model or feedforward neural network: exact +architecture parameters such as number of layers in the neural network can be specified in +``decoding_ae_model.json`` or ``decoding_arhmm_model.json``. The size of the window of neural +activity used to reconstruct each frame of AE latents or ARHMM states is set by ``n_lags``: the +neural activity from ``t-n_lags:t+n_lags`` will be used to predict the latents or states at time +``t``. - -To begin fitting decoding models, copy the example json files ``decoding_ae_model.json``, ``decoding_arhmm_model.json``, ``decoding_compute.json``, and ``decoding_training.json`` into your ``.behavenet`` directory. ``cd`` to the ``behavenet`` directory in the terminal, and run: +To begin fitting decoding models, copy the example json files ``decoding_ae_model.json``, +``decoding_arhmm_model.json``, ``decoding_compute.json``, and ``decoding_training.json`` into your +``.behavenet`` directory. ``cd`` to the ``behavenet`` directory in the terminal, and run: Decoding ARHMM states: @@ -23,15 +31,14 @@ Decoding AE states: $: python behavenet/fitting/decoding_grid_search.py --data_config ~/.behavenet/musall_vistrained_params.json --model_config ~/.behavenet/decoding_ae_model.json --training_config ~/.behavenet/decoding_training.json --compute_config ~/.behavenet/decoding_compute.json - - - .. _decoding_with_subsets: Decoding with subsets of neurons -------------------------------- -Continuing with the toy dataset introduced in the :ref:`data structure` documentation, below are some examples for how to modify the decoding data json file to decode from user-specified groups of neurons: +Continuing with the toy dataset introduced in the :ref:`data structure` +documentation, below are some examples for how to modify the decoding data json file to decode from +user-specified groups of neurons: **Example 0**: @@ -72,15 +79,20 @@ Fit separate decoders for each dataset of indices in the HDF5 group ``regions/id "subsample_method": "single" // subsample, use single regions } -In this toy example, these options will fit 4 decoders, each using a different set of indices: ``AUD_R``, ``AUD_L``, ``VIS_L``, and ``VIS_R``. +In this toy example, these options will fit 4 decoders, each using a different set of indices: +``AUD_R``, ``AUD_L``, ``VIS_L``, and ``VIS_R``. .. note:: - At this time the option ``subsample_idxs_dataset`` can only accept a single string as an argument; therefore you can use ``all`` to fit decoders using all datasets in the specified index group, or you can specify a single dataset (e.g. ``AUD_L`` in this example). You cannot, for example, provide a list of strings. + At this time the option ``subsample_idxs_dataset`` can only accept a single string as an + argument; therefore you can use ``all`` to fit decoders using all datasets in the specified + index group, or you can specify a single dataset (e.g. ``AUD_L`` in this example). You cannot, + for example, provide a list of strings. **Example 3**: -Use all indices *except* those in the HDF5 dataset ``regions/idxs_lr/AUD_L`` ("loo" stands for "leave-one-out"): +Use all indices *except* those in the HDF5 dataset ``regions/idxs_lr/AUD_L`` ("loo" stands for +"leave-one-out"): .. code-block:: javascript @@ -91,11 +103,13 @@ Use all indices *except* those in the HDF5 dataset ``regions/idxs_lr/AUD_L`` ("l "subsample_method": "loo" // subsample, use all but specified region } -In this toy example, the combined neurons from ``AUD_R``, ``VIS_L`` and ``VIS_R`` would be used for decoding (i.e. not the neurons in the specified region ``AUD_L``). +In this toy example, the combined neurons from ``AUD_R``, ``VIS_L`` and ``VIS_R`` would be used for +decoding (i.e. not the neurons in the specified region ``AUD_L``). -**Example 3**: +**Example 4**: -For each dataset in ``regions/indxs_lr``, fit a decoder that uses all indices *except* those in the dataset: +For each dataset in ``regions/indxs_lr``, fit a decoder that uses all indices *except* those in the +dataset: .. code-block:: javascript @@ -106,10 +120,31 @@ For each dataset in ``regions/indxs_lr``, fit a decoder that uses all indices *e "subsample_method": "loo" // subsample, use all but specified region } -Again referring to the toy example, these options will fit 4 decoders, each using a different set of indices: +Again referring to the toy example, these options will fit 4 decoders, each using a different set +of indices: 1. ``AUD_L``, ``VIS_L``, and ``VIS_R`` (not ``AUD_R``) 2. ``AUD_R``, ``VIS_L``, and ``VIS_R`` (not ``AUD_L``) 3. ``AUD_R``, ``AUD_L``, and ``VIS_L`` (not ``VIS_R``) 4. ``AUD_R``, ``AUD_L``, and ``VIS_R`` (not ``VIS_L``) + +.. _decoding_labels: + +Decoding arbitrary covariates +----------------------------- +BehaveNet also uses the above decoding infrastructure to allow users to decode an arbitrary set of +labels from neural activity; these could be markers from pose estimation software, stimulus +information, or other task variables. In order to fit these models, the data HDF5 needs to be +augmented to include a new HDF5 group named ``labels``, which contains an HDF5 dataset for each +trial. See the :ref:`data structure documentation ` for more information. + +Once the labels have been added to the data file, you can decode labels as you would CAE latents +above; the only changes that are necessary is the addition of the field ``n_labels`` in the data +json, and changing the model class in the model json from either ``neural-ae`` or ``neural-arhmm`` +to ``neural-labels``. + +.. note:: + + The current BehaveNet implementation only allows for decoding continuous labels using a + Gaussian noise distribution; support for binary and count data forthcoming. diff --git a/docs/source/user_guide.intro.rst b/docs/source/user_guide.intro.rst index 58eba8b..838695d 100644 --- a/docs/source/user_guide.intro.rst +++ b/docs/source/user_guide.intro.rst @@ -1,20 +1,24 @@ Introduction ============ -BehaveNet is a software package that provides tools for analyzing behavioral video and neural activity. Currently BehaveNet supports: +BehaveNet is a software package that provides tools for analyzing behavioral video and neural +activity. Currently BehaveNet supports: * Video compression using convolutional autoencoders * Video segmentation (and generation) using autoregressive hidden Markov models * Neural network decoding of videos from neural activity * Bayesian decoding of videos from neural activity -BehaveNet automatically saves models using a well-defined and flexible directory structure, allowing for easy management of many models and multiple datasets. +BehaveNet automatically saves models using a well-defined and flexible directory structure, +allowing for easy management of many models and multiple datasets. The command line interface -------------------------- -Users interact with BehaveNet using a command line interface, so all model fitting is done from the terminal. To simplify this process all necessary parameters are defined in four configuration files that can be manually updated using a text editor: +Users interact with BehaveNet using a command line interface, so all model fitting is done from the +terminal. To simplify this process all necessary parameters are defined in four configuration files +that can be manually updated using a text editor: * **data_config** - dataset ids, video frames sizes, etc. You can automatically generate this configuration file for a new dataset by following the instructions in the following section. * **model_config** - model hyperparameters @@ -31,7 +35,9 @@ For example, the command line call to fit an autoencoder would be (using the def $: cd behavenet $: python fitting/ae_grid_search.py --data_config ../configs/data_default.json --model_config ../configs/ae_model.json --training_config ../configs/ae_training.json --compute_config ../configs/ae_compute.json -We recommend that you copy the default config files in the behavenet repo into a separate directory on your local machine and make edits there. For more information on the different hyperparameters, see the :ref:`hyperparameters glossary`. +We recommend that you copy the default config files in the behavenet repo into a separate directory +on your local machine and make edits there. For more information on the different hyperparameters, +see the :ref:`hyperparameters glossary`. .. _add_dataset: @@ -39,7 +45,9 @@ We recommend that you copy the default config files in the behavenet repo into a Adding a new dataset -------------------- -When using BehaveNet with a new dataset you will need to make a new data config json file, which can be automatically generated using a BehaveNet helper function. You will be asked to enter the following information (examples shown for Musall dataset): +When using BehaveNet with a new dataset you will need to make a new data config json file, which +can be automatically generated using a BehaveNet helper function. You will be asked to enter the +following information (examples shown for Musall dataset): * lab or experimenter name (:code:`musall`) * experiment name (:code:`vistrained`) @@ -59,19 +67,36 @@ To enter this information, launch python from the behavenet environment and type from behavenet import add_dataset add_dataset() -This function will create a json file named ``[lab_id]_[expt_id].json`` in the ``.behavenet`` directory in your user home directory, which you can manually update at any point using a text editor. +This function will create a json file named ``[lab_id]_[expt_id].json`` in the ``.behavenet`` +directory in your user home directory, which you can manually update at any point using a text +editor. Organizing model fits with test-tube ------------------------------------ -BehaveNet uses the `test-tube package `_ to organize model fits into user-defined experiments, log meta and training data, and perform grid searches over model hyperparameters. Most of this occurs behind the scenes, but there are a couple of important pieces of information that will improve your model fitting experience. - -BehaveNet organizes model fits using a combination of hyperparameters and user-defined experiment names. For example, let's say you want to fit 5 different convolutional autoencoder architectures, all with 12 latents, to find the best one. Let's call this experiment "arch_search", which you will set in the ``model_config`` json in the ``experiment_name`` field. The results will then be stored in the directory ``results_dir/lab_id/expt_id/animal_id/session_id/ae/conv/12_latents/arch_search/``. - -Each model will automatically be assigned it's own "version" by test-tube, so the ``arch_search`` directory will have subdirectories ``version_0``, ..., ``version_4``. If an additional CAE model is later fit with 12 latents (and using the "arch_search" experiment name), test-tube will add it to the ``arch_search`` directory as ``version_5``. Different versions may have different architectures, learning rates, regularization values, etc. Each model class (autoencoder, arhmm, decoders) has a set of hyperparameters that are used for directory names, and another set that are used to distinguish test-tube versions within the user-defined experiment. - -Within the ``version_x`` directory, there are various files saved during training. Here are some of the files automatically output when training an autoencoder: +BehaveNet uses the `test-tube package `_ to organize +model fits into user-defined experiments, log meta and training data, and perform grid searches +over model hyperparameters. Most of this occurs behind the scenes, but there are a couple of +important pieces of information that will improve your model fitting experience. + +BehaveNet organizes model fits using a combination of hyperparameters and user-defined experiment +names. For example, let's say you want to fit 5 different convolutional autoencoder architectures, +all with 12 latents, to find the best one. Let's call this experiment "arch_search", which you will +set in the ``model_config`` json in the ``experiment_name`` field. The results will then be stored +in the directory +``results_dir/lab_id/expt_id/animal_id/session_id/ae/conv/12_latents/arch_search/``. + +Each model will automatically be assigned it's own "version" by test-tube, so the ``arch_search`` +directory will have subdirectories ``version_0``, ..., ``version_4``. If an additional CAE model is +later fit with 12 latents (and using the "arch_search" experiment name), test-tube will add it to +the ``arch_search`` directory as ``version_5``. Different versions may have different +architectures, learning rates, regularization values, etc. Each model class (autoencoder, arhmm, +decoders) has a set of hyperparameters that are used for directory names, and another set that are +used to distinguish test-tube versions within the user-defined experiment. + +Within the ``version_x`` directory, there are various files saved during training. Here are some of +the files automatically output when training an autoencoder: * **best_val_model.pt**: the best model (not necessarily from the final training epoch) as determined by computing the loss on validation data * **meta_tags.csv**: hyperparameters associated with data, computational resources, training, and model @@ -93,11 +118,15 @@ and if you set ``export_train_plots`` to ``True`` in the training config file, y Grid searching with test-tube ----------------------------- -Beyond organizing model fits, test-tube is also useful for performing grid searches over model hyperparameters, using multiple cpus or gpus. All you as the user need to do is enter the relevant hyperparameter choices as a list instead of a single value in the associated configuration file. +Beyond organizing model fits, test-tube is also useful for performing grid searches over model +hyperparameters, using multiple cpus or gpus. All you as the user need to do is enter the relevant +hyperparameter choices as a list instead of a single value in the associated configuration file. -Again using the autoencoder as an example, let's say you want to fit a single AE architecture using 4 different numbers of latents, all with the same regularization value. In the model config file, you will set these values as: +Again using the autoencoder as an example, let's say you want to fit a single AE architecture using +4 different numbers of latents, all with the same regularization value. In the model config file, +you will set these values as: -.. code-block:: json +.. code-block:: javascript { ... @@ -106,9 +135,10 @@ Again using the autoencoder as an example, let's say you want to fit a single AE ... } -To specify the computing resources for this job, you will next edit the compute config file, which looks like this: +To specify the computing resources for this job, you will next edit the compute config file, which +looks like this: -.. code-block:: json +.. code-block:: javascript { ... @@ -120,13 +150,21 @@ To specify the computing resources for this job, you will next edit the compute ... } -With the ``device`` field set to ``cuda``, test-tube will use gpus to run this job. The ``gpus_viz`` field can further specify which subset of gpus to use. The ``tt_n_gpu_trials`` defines the maximum number of jobs to run. If this number is larger than the total number of hyperparameter configurations, all configurations are fit; if this number is smaller than the total number (say if ``"tt_n_gpu_trials": 2`` in this example) then this number of configurations is randomly sampled from all possible choices. +With the ``device`` field set to ``cuda``, test-tube will use gpus to run this job. The +``gpus_viz`` field can further specify which subset of gpus to use. The ``tt_n_gpu_trials`` defines +the maximum number of jobs to run. If this number is larger than the total number of hyperparameter +configurations, all configurations are fit; if this number is smaller than the total number (say if +``"tt_n_gpu_trials": 2`` in this example) then this number of configurations is randomly sampled +from all possible choices. -To fit models using the cpu instead, set the ``device`` field to ``cpu``; then ``tt_n_cpu_workers`` defines the total number of cpus to run the job (total number of models fitting at any one time) and ``tt_n_cpu_trials`` is analogous to ``tt_n_gpu_trials``. +To fit models using the cpu instead, set the ``device`` field to ``cpu``; then ``tt_n_cpu_workers`` +defines the total number of cpus to run the job (total number of models fitting at any one time) +and ``tt_n_cpu_trials`` is analogous to ``tt_n_gpu_trials``. -Finally, multiple hyperparameters can be searched over simultaneously; for example, to search over both AE latents and regularization values, set these parameters in the model config file like so: +Finally, multiple hyperparameters can be searched over simultaneously; for example, to search over +both AE latents and regularization values, set these parameters in the model config file like so: -.. code-block:: json +.. code-block:: javascript { ... diff --git a/tests/integration.py b/tests/integration.py index 95b23e8..7de623a 100644 --- a/tests/integration.py +++ b/tests/integration.py @@ -47,6 +47,7 @@ {'model_class': 'ae', 'model_file': 'ae', 'sessions': SESSIONS[0]}, {'model_class': 'arhmm', 'model_file': 'arhmm', 'sessions': SESSIONS[0]}, {'model_class': 'neural-ae', 'model_file': 'decoder', 'sessions': SESSIONS[0]}, + {'model_class': 'neural-labels', 'model_file': 'decoder', 'sessions': SESSIONS[0]}, {'model_class': 'neural-arhmm', 'model_file': 'decoder', 'sessions': SESSIONS[0]}, {'model_class': 'ae', 'model_file': 'ae', 'sessions': 'all'}, {'model_class': 'vae', 'model_file': 'ae', 'sessions': SESSIONS[0]}, @@ -131,7 +132,7 @@ def get_model_config_files(model, json_dir): 'model': os.path.join(model_json_dir, '%s_model.json' % model), 'training': os.path.join(model_json_dir, '%s_training.json' % model), 'compute': os.path.join(model_json_dir, '%s_compute.json' % model)} - elif model == 'neural-ae' or model == 'neural-arhmm': + elif model == 'neural-ae' or model == 'neural-arhmm' or model == 'neural-labels': m = 'decoding' s = model.split('-')[-1] model_json_dir = os.path.join(json_dir, '%s_jsons' % m) @@ -148,7 +149,8 @@ def get_model_config_files(model, json_dir): def define_new_config_values(model, session='sess-0'): # data vals - data_dict = {'session': session, 'all_source': 'data', **DATA_DICT} + data_dict = { + 'session': session, 'all_source': 'data', 'n_labels': TEMP_DATA['n_labels'], **DATA_DICT} # training vals train_frac = 0.5 @@ -276,6 +278,27 @@ def define_new_config_values(model, session='sess-0'): 'compute': { 'gpus_viz': str(gpu_id), 'tt_n_cpu_workers': 2}} + elif model == 'neural-labels': + new_values = { + 'data': data_dict, + 'model': { + 'n_lags': 3, + 'n_max_lags': 5, + 'l2_reg': 1e-4, + 'model_type': 'mlp', + 'n_hid_layers': 1, + 'n_hid_units': 16, + 'activation': 'relu'}, + 'training': { + 'export_predictions': True, + 'min_n_epochs': 1, + 'max_n_epochs': 1, + 'enable_early_stop': False, + 'train_frac': train_frac, + 'trial_splits': trial_splits}, + 'compute': { + 'gpus_viz': str(gpu_id), + 'tt_n_cpu_workers': 2}} elif model == 'neural-arhmm': new_values = { 'data': data_dict, diff --git a/tests/test_data/test_utils_data.py b/tests/test_data/test_utils_data.py index ca1bced..b919855 100644 --- a/tests/test_data/test_utils_data.py +++ b/tests/test_data/test_utils_data.py @@ -237,6 +237,57 @@ def test_get_data_generator_inputs(): hparams, sess_ids, check_splits=False) assert hparams_['noise_dist'] == 'gaussian-full' + # ----------------- + # neural-labels + # ----------------- + hparams['model_class'] = 'neural-labels' + hparams['model_type'] = 'linear' + hparams['n_labels'] = 4 + hparams['session_dir'] = session_dir + hparams['neural_type'] = 'spikes' + hparams['neural_thresh'] = 0 + hparams_, signals, transforms, paths = utils.get_data_generator_inputs( + hparams, sess_ids, check_splits=False) + assert signals[0] == ['neural', 'labels'] + assert hparams_['input_signal'] == 'neural' + assert hparams_['output_signal'] == 'labels' + assert hparams_['output_size'] == hparams['n_labels'] + assert hparams_['noise_dist'] == 'gaussian' + + hparams['model_type'] = 'linear-mv' + hparams_, signals, transforms, paths = utils.get_data_generator_inputs( + hparams, sess_ids, check_splits=False) + assert hparams_['noise_dist'] == 'gaussian-full' + + # ----------------- + # labels-neural + # ----------------- + hparams['model_class'] = 'labels-neural' + hparams['model_type'] = 'linear' + hparams['n_labels'] = 4 + hparams['session_dir'] = session_dir + hparams['neural_type'] = 'spikes' + hparams['neural_thresh'] = 0 + hparams_, signals, transforms, paths = utils.get_data_generator_inputs( + hparams, sess_ids, check_splits=False) + assert signals[0] == ['neural', 'labels'] + assert hparams_['input_signal'] == 'labels' + assert hparams_['output_signal'] == 'neural' + assert hparams_['output_size'] is None + assert hparams_['noise_dist'] == 'poisson' + + hparams['model_type'] = 'linear' + hparams['neural_type'] = 'ca' + hparams_, signals, transforms, paths = utils.get_data_generator_inputs( + hparams, sess_ids, check_splits=False) + assert hparams_['noise_dist'] == 'gaussian' + + hparams['model_type'] = 'linear-mv' + hparams['neural_type'] = 'ca' + hparams_, signals, transforms, paths = utils.get_data_generator_inputs( + hparams, sess_ids, check_splits=False) + assert hparams_['noise_dist'] == 'gaussian-full' + # ----------------- # arhmm # ----------------- diff --git a/tests/test_fitting/test_utils_fitting.py b/tests/test_fitting/test_utils_fitting.py index 82244c9..7ba4a6a 100644 --- a/tests/test_fitting/test_utils_fitting.py +++ b/tests/test_fitting/test_utils_fitting.py @@ -189,7 +189,7 @@ def test_get_subdirs(self): assert sorted(subdirs) == ['expt0', 'expt1', 'multisession-00'] # raise exception when not a path - with pytest.raises(ValueError): + with pytest.raises(NotADirectoryError): utils.get_subdirs('/ZzZtestingZzZ') def test_get_multisession_paths(self): @@ -571,6 +571,30 @@ def test_get_expt_dir(self): expt_name=hparams['experiment_name']) assert expt_dir == model_path + # ------------------------- + # neural-labels/labels-neural + # ------------------------- + hparams['model_class'] = 'neural-labels' + hparams['model_type'] = 'mlp' + hparams['experiment_name'] = 'tt_expt' + model_path = os.path.join( + session_dir, hparams['model_class'], hparams['model_type'], 'all', + hparams['experiment_name']) + + expt_dir = utils.get_expt_dir( + hparams, model_class=hparams['model_class'], model_type=hparams['model_type'], + expt_name=hparams['experiment_name']) + assert expt_dir == model_path + + hparams['model_class'] = 'labels-neural' + model_path = os.path.join( + session_dir, hparams['model_class'], hparams['model_type'], 'all', + hparams['experiment_name']) + expt_dir = utils.get_expt_dir( + hparams, model_class=hparams['model_class'], model_type=hparams['model_type'], + expt_name=hparams['experiment_name']) + assert expt_dir == model_path + # ------------------------- # neural-arhmm/arhmm-neural # ------------------------- @@ -966,6 +990,22 @@ def test_get_model_params(self): 'l2_reg': 1, 'n_hid_layers': 0, 'activation': 'relu', + 'learning_rate': 1e-3, + 'subsample_method': 'none'} + ret_hparams = utils.get_model_params({**misc_hparams, **base_hparams, **model_hparams}) + assert ret_hparams == {**base_hparams, **model_hparams} + + # ----------------- + # neural-labels/labels-neural + # ----------------- + model_hparams = { + 'model_class': 'neural-labels', + 'model_type': 'mlp', + 'n_lags': 3, + 'l2_reg': 1, + 'n_hid_layers': 0, + 'activation': 'relu', + 'learning_rate': 1e-3, 'subsample_method': 'none'} ret_hparams = utils.get_model_params({**misc_hparams, **base_hparams, **model_hparams}) assert ret_hparams == {**base_hparams, **model_hparams} @@ -991,6 +1031,7 @@ def test_get_model_params(self): 'n_hid_layers': 2, 'n_hid_units': 10, 'activation': 'relu', + 'learning_rate': 1e-3, 'subsample_method': 'single', 'subsample_idxs_name': 'a', 'subsample_idxs_group_0': 'b', From a4165644967d773d717716f9906b359df8023b25 Mon Sep 17 00:00:00 2001 From: themattinthehatt Date: Wed, 28 Oct 2020 15:47:33 -0400 Subject: [PATCH 15/50] generalize get_test_metric --- behavenet/fitting/eval.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/behavenet/fitting/eval.py b/behavenet/fitting/eval.py index 63abc71..84a66fb 100644 --- a/behavenet/fitting/eval.py +++ b/behavenet/fitting/eval.py @@ -363,7 +363,7 @@ def get_reconstruction( return ims_recon -def get_test_metric(hparams, model_version, metric='r2', sess_idx=0): +def get_test_metric(hparams, model_version, metric='r2', dtype='test', sess_idx=0): """Calculate a single R\ :sup:`2` value across all test batches for a decoder. Parameters @@ -374,6 +374,9 @@ def get_test_metric(hparams, model_version, metric='r2', sess_idx=0): version from test tube experiment defined in :obj:`hparams` or the string 'best' metric : :obj:`str`, optional 'r2' | 'fc' | 'mse' + dtype : :obj:`str` + type of trials to use for computing metric + 'train' | 'val' | 'test' sess_idx : :obj:`int`, optional session index into data generator From b6a41c23e382aae623e78f57a336553e8302c12f Mon Sep 17 00:00:00 2001 From: Matt Whiteway Date: Wed, 28 Oct 2020 22:21:52 -0400 Subject: [PATCH 16/50] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 516a523..a1f6452 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # BehaveNet -NOTE: This is a beta version, we will release the first stable version by early February +NOTE: The master branch contains the code version released with the neurips paper in November 2019; for more recent updates, see the develop branch. BehaveNet is a probabilistic framework for the analysis of behavioral video and neural activity. This framework provides tools for compression, segmentation, generation, and decoding of behavioral From 46c2f74c9ca1c9ac484dbde4c05c813206a2859b Mon Sep 17 00:00:00 2001 From: themattinthehatt Date: Tue, 3 Nov 2020 16:29:33 -0500 Subject: [PATCH 17/50] small updates --- behavenet/fitting/eval.py | 25 +++++++++++++++++-------- behavenet/fitting/utils.py | 4 ++++ behavenet/plotting/ae_utils.py | 19 ++++++------------- 3 files changed, 27 insertions(+), 21 deletions(-) diff --git a/behavenet/fitting/eval.py b/behavenet/fitting/eval.py index 84a66fb..1a64adb 100644 --- a/behavenet/fitting/eval.py +++ b/behavenet/fitting/eval.py @@ -317,7 +317,7 @@ def get_reconstruction( model.eval() if not isinstance(inputs, torch.Tensor): - inputs = torch.Tensor(inputs) + inputs = torch.Tensor(inputs).to(model.hparams['device']) # check to see if inputs are images or latents if len(inputs.shape) == 2: @@ -363,7 +363,9 @@ def get_reconstruction( return ims_recon -def get_test_metric(hparams, model_version, metric='r2', dtype='test', sess_idx=0): +def get_test_metric( + hparams, model_version, metric='r2', dtype='test', multioutput='variance_weighted', + sess_idx=0): """Calculate a single R\ :sup:`2` value across all test batches for a decoder. Parameters @@ -377,6 +379,9 @@ def get_test_metric(hparams, model_version, metric='r2', dtype='test', sess_idx= dtype : :obj:`str` type of trials to use for computing metric 'train' | 'val' | 'test' + multioutput : :obj:`str` + defines how to aggregate multiple r2 scores; see r2_score documentation in sklearn + 'raw_values' | 'uniform_average' | 'variance_weighted' sess_idx : :obj:`int`, optional session index into data generator @@ -395,17 +400,22 @@ def get_test_metric(hparams, model_version, metric='r2', dtype='test', sess_idx= model, data_generator = get_best_model_and_data( hparams, Decoder, load_data=True, version=model_version) - n_test_batches = len(data_generator.datasets[sess_idx].batch_idxs['test']) + n_test_batches = len(data_generator.datasets[sess_idx].batch_idxs[dtype]) max_lags = hparams['n_max_lags'] true = [] pred = [] - data_generator.reset_iterators('test') + data_generator.reset_iterators(dtype) for i in range(n_test_batches): - batch, _ = data_generator.next_batch('test') + batch, _ = data_generator.next_batch(dtype) # get true latents/states if metric == 'r2': - curr_true = batch['ae_latents'][0].cpu().detach().numpy() + if 'ae_latents' in batch: + curr_true = batch['ae_latents'][0].cpu().detach().numpy() + elif 'labels' in batch: + curr_true = batch['labels'][0].cpu().detach().numpy() + else: + raise ValueError('no valid key in {}'.format(batch.keys())) elif metric == 'fc': curr_true = batch['arhmm_states'][0].cpu().detach().numpy() else: @@ -419,8 +429,7 @@ def get_test_metric(hparams, model_version, metric='r2', dtype='test', sess_idx= if metric == 'r2': metric = r2_score( - np.concatenate(true, axis=0), np.concatenate(pred, axis=0), - multioutput='variance_weighted') + np.concatenate(true, axis=0), np.concatenate(pred, axis=0), multioutput=multioutput) elif metric == 'mse': metric = np.mean(np.square(np.concatenate(true, axis=0) - np.concatenate(pred, axis=0))) elif metric == 'fc': diff --git a/behavenet/fitting/utils.py b/behavenet/fitting/utils.py index d5d2ebc..2e53269 100644 --- a/behavenet/fitting/utils.py +++ b/behavenet/fitting/utils.py @@ -967,6 +967,10 @@ def get_best_model_and_data(hparams, Model=None, load_data=True, version='best', if version == 'best': best_version_int = get_best_model_version(expt_dir)[0] best_version = str('version_{}'.format(best_version_int)) + elif version is None: + # try to match hparams + _, version_hp = experiment_exists(hparams, which_version=True) + best_version = str('version_{}'.format(version_hp)) else: if isinstance(version, str) and version[0] == 'v': # assume we got a string of the form 'version_{%i}' diff --git a/behavenet/plotting/ae_utils.py b/behavenet/plotting/ae_utils.py index 161f82b..e744b35 100644 --- a/behavenet/plotting/ae_utils.py +++ b/behavenet/plotting/ae_utils.py @@ -243,14 +243,8 @@ def make_neural_reconstruction_movie_wrapper( hparams_ae['experiment_name'] = hparams['ae_experiment_name'] hparams_ae['model_class'] = hparams['ae_model_class'] hparams_ae['model_type'] = hparams['ae_model_type'] - if hparams['model_class'] == 'ae': - from behavenet.models import AE as Model - elif hparams['model_class'] == 'cond-ae': - from behavenet.models import ConditionalAE as Model - else: - raise NotImplementedError('"%s" is an invalid model class' % hparams['model_class']) model_ae, data_generator_ae = get_best_model_and_data( - hparams_ae, Model, version=hparams['ae_version']) + hparams_ae, Model=None, version=hparams['ae_version']) # move model to cpu model_ae.to('cpu') @@ -261,18 +255,17 @@ def make_neural_reconstruction_movie_wrapper( # get images from data generator (move to cpu) batch = data_generator_ae.datasets[sess_idx][trial] ims_orig_pt = batch['images'][:max_frames].cpu() # 400 - if hparams['model_class'] == 'cond-ae': + if hparams_ae['model_class'] == 'cond-ae': labels_pt = batch['labels'][:max_frames] else: labels_pt = None # push images through ae to get reconstruction - ims_recon_ae = get_reconstruction(model_ae, ims_orig_pt, labels=labels_pt) - # push images through ae to get latents - latents_ae_pt, _, _ = model_ae.encoding(ims_orig_pt) + ims_recon_ae, latents_ae = get_reconstruction( + model_ae, ims_orig_pt, labels=labels_pt, return_latents=True) # mask images for plotting - if hparams.get('use_output_mask', False): + if hparams_ae.get('use_output_mask', False): ims_orig_pt *= batch['masks'][:max_frames] ####################################### @@ -302,7 +295,7 @@ def make_neural_reconstruction_movie_wrapper( ims_orig=ims_orig_pt.cpu().detach().numpy(), ims_recon_ae=ims_recon_ae, ims_recon_neural=ims_recon_dec, - latents_ae=latents_ae_pt.cpu().detach().numpy()[:, :max_latents], + latents_ae=latents_ae[:, :max_latents], latents_neural=latents_dec_pt.cpu().detach().numpy()[:, :max_latents], save_file=save_file, frame_rate=frame_rate) From fed2799bcf4914ed23cdf54e67a0739b1f6dd024 Mon Sep 17 00:00:00 2001 From: themattinthehatt Date: Fri, 13 Nov 2020 15:43:56 -0500 Subject: [PATCH 18/50] allow decoding of ae latent motion energy --- behavenet/data/transforms.py | 324 ++++++++++++----------- behavenet/data/utils.py | 30 ++- behavenet/fitting/decoder_grid_search.py | 8 +- behavenet/fitting/utils.py | 9 +- behavenet/models/README.md | 2 +- docs/source/glossary.rst | 16 +- docs/source/user_guide.decoders.rst | 5 +- tests/integration.py | 36 ++- tests/test_data/test_transforms.py | 147 +++++----- tests/test_data/test_utils_data.py | 30 +++ tests/test_fitting/test_utils_fitting.py | 22 +- 11 files changed, 394 insertions(+), 235 deletions(-) diff --git a/behavenet/data/transforms.py b/behavenet/data/transforms.py index cbd913a..5fcb8cc 100644 --- a/behavenet/data/transforms.py +++ b/behavenet/data/transforms.py @@ -55,161 +55,95 @@ def __repr__(self): raise NotImplementedError -class ClipNormalize(Transform): - """Clip upper level of signal and divide by clip value.""" +class BlockShuffle(Transform): + """Shuffle blocks of contiguous discrete states within each trial.""" - def __init__(self, clip_val): + def __init__(self, rng_seed): """ Parameters ---------- - clip_val : :obj:`float` - signal values above this will be set to this value, then divided by this value so that - signal maximum is 1 + rng_seed : :obj:`int` + to control random number generator """ - if clip_val <= 0: - raise ValueError('clip value must be positive') - self.clip_val = clip_val + self.rng_seed = rng_seed - def __call__(self, signal): + def __call__(self, sample): """ Parameters ---------- - signal : :obj:`np.ndarray` + sample : :obj:`np.ndarray` + dense representation of shape (time) Returns ------- :obj:`np.ndarray` + output shape is (time) """ - signal = np.minimum(signal, self.clip_val) - signal = signal / self.clip_val - return signal - - def __repr__(self): - return str('ClipNormalize(clip_val=%f)' % self.clip_val) - - -# class Resize(Transform): -# """Resize the sample images.""" -# -# def __init__(self, size=(128, 128), order=1): -# """ -# -# Parameters -# ---------- -# size : :obj:`int` or :obj:`tuple` -# desired output size for each image; if type is :obj:`int`, the same value is used for -# both height and width -# order : :obj:`int` -# interpolation order -# -# """ -# assert isinstance(size, (tuple, int)) -# self.order = order -# if isinstance(size, tuple): -# self.x = size[0] -# self.y = size[1] -# else: -# self.x = self.y = size -# -# def __call__(self, sample): -# """ -# -# Parameters -# ---------- -# sample: :obj:`np.ndarray` -# input shape is (trial, time, n_channels) -# -# Returns -# ------- -# :obj:`np.ndarray` -# output shape is (trial, time, n_channels) -# -# """ -# sh = sample.shape -# sample = transform.resize(sample, (sh[0], sh[1], self.y, self.x), order=self.order) -# return sample -# -# def __repr__(self): -# return str('Resize(size=(%i, %i))' % (self.y, self.x)) + np.random.seed(self.rng_seed) + n_time = len(sample) + if not any(np.isnan(sample)): + # mark first time point of state change with a nonzero number + state_change = np.where(np.concatenate([[0], np.diff(sample)], axis=0) != 0)[0] + # collect runs + runs = [] + prev_beg = 0 + for curr_beg in state_change: + runs.append(np.arange(prev_beg, curr_beg)) + prev_beg = curr_beg + runs.append(np.arange(prev_beg, n_time)) + # shuffle runs + rand_perm = np.random.permutation(len(runs)) + runs_shuff = [runs[idx] for idx in rand_perm] + # index back into original labels with shuffled indices + sample_shuff = sample[np.concatenate(runs_shuff)] + else: + sample_shuff = np.full(n_time, fill_value=np.nan) + return sample_shuff -class Threshold(Transform): - """Remove channels of neural activity whose mean value is below a threshold.""" + def __repr__(self): + return str('BlockShuffle(rng_seed=%i)' % self.rng_seed) - def __init__(self, threshold, bin_size): - """ - Parameters - ---------- - threshold : :obj:`float` - threshold in Hz - bin_size : :obj:`float` - bin size of neural activity in ms +class ClipNormalize(Transform): + """Clip upper level of signal and divide by clip value.""" + def __init__(self, clip_val): """ - if bin_size <= 0: - raise ValueError('bin size must be positive') - if threshold < 0: - raise ValueError('threshold must be non-negative') - - self.threshold = threshold - self.bin_size = bin_size - - def __call__(self, sample): - """Calculates firing rate over all time points and thresholds. Parameters ---------- - sample: :obj:`np.ndarray` - input shape is (time, n_channels) - - Returns - ------- - :obj:`np.ndarray` - output shape is (time, n_channels) + clip_val : :obj:`float` + signal values above this will be set to this value, then divided by this value so that + signal maximum is 1 """ - # get firing rates - frs = np.squeeze(np.mean(sample, axis=0)) / (self.bin_size * 1e-3) - fr_mask = frs > self.threshold - # get rid of neurons below fr threshold - sample = sample[:, fr_mask] - return sample.astype(np.float) - - def __repr__(self): - return str('Threshold(threshold=%f, bin_size=%f)' % (self.threshold, self.bin_size)) - - -class ZScore(Transform): - """z-score channel activity.""" - - def __init__(self): - pass + if clip_val <= 0: + raise ValueError('clip value must be positive') + self.clip_val = clip_val - def __call__(self, sample): + def __call__(self, signal): """ Parameters ---------- - sample : :obj:`np.ndarray` - input shape is (time, n_channels) + signal : :obj:`np.ndarray` Returns ------- :obj:`np.ndarray` - output shape is (time, n_channels) """ - sample -= np.mean(sample, axis=0) - sample /= np.std(sample, axis=0) - return sample + signal = np.minimum(signal, self.clip_val) + signal = signal / self.clip_val + return signal def __repr__(self): - return 'ZScore()' + return str('ClipNormalize(clip_val=%f)' % self.clip_val) class MakeOneHot(Transform): @@ -314,19 +248,11 @@ def __repr__(self): return str('MakeOneHot2D(y_pixels=%i, x_pixels=%i)' % (self.y_pixels, self.x_pixels)) -class BlockShuffle(Transform): - """Shuffle blocks of contiguous discrete states within each trial.""" - - def __init__(self, rng_seed): - """ - - Parameters - ---------- - rng_seed : :obj:`int` - to control random number generator +class MotionEnergy(Transform): + """Compute motion energy across batch dimension.""" - """ - self.rng_seed = rng_seed + def __init__(self): + pass def __call__(self, sample): """ @@ -334,38 +260,18 @@ def __call__(self, sample): Parameters ---------- sample : :obj:`np.ndarray` - dense representation of shape (time) + input shape is (time, n_channels) Returns ------- :obj:`np.ndarray` - output shape is (time) + output shape is (time, n_channels) """ - - np.random.seed(self.rng_seed) - n_time = len(sample) - if not any(np.isnan(sample)): - # mark first time point of state change with a nonzero number - state_change = np.where(np.concatenate([[0], np.diff(sample)], axis=0) != 0)[0] - # collect runs - runs = [] - prev_beg = 0 - for curr_beg in state_change: - runs.append(np.arange(prev_beg, curr_beg)) - prev_beg = curr_beg - runs.append(np.arange(prev_beg, n_time)) - # shuffle runs - rand_perm = np.random.permutation(len(runs)) - runs_shuff = [runs[idx] for idx in rand_perm] - # index back into original labels with shuffled indices - sample_shuff = sample[np.concatenate(runs_shuff)] - else: - sample_shuff = np.full(n_time, fill_value=np.nan) - return sample_shuff + return np.vstack([np.zeros((1, sample.shape[1])), np.abs(np.diff(sample, axis=0))]) def __repr__(self): - return str('BlockShuffle(rng_seed=%i)' % self.rng_seed) + return 'MotionEnergy()' class SelectIdxs(Transform): @@ -402,3 +308,123 @@ def __call__(self, sample): def __repr__(self): return str('SelectIndxs(idxs=idxs, sample_name=%s)' % self.sample_name) + + +class Threshold(Transform): + """Remove channels of neural activity whose mean value is below a threshold.""" + + def __init__(self, threshold, bin_size): + """ + + Parameters + ---------- + threshold : :obj:`float` + threshold in Hz + bin_size : :obj:`float` + bin size of neural activity in ms + + """ + if bin_size <= 0: + raise ValueError('bin size must be positive') + if threshold < 0: + raise ValueError('threshold must be non-negative') + + self.threshold = threshold + self.bin_size = bin_size + + def __call__(self, sample): + """Calculates firing rate over all time points and thresholds. + + Parameters + ---------- + sample: :obj:`np.ndarray` + input shape is (time, n_channels) + + Returns + ------- + :obj:`np.ndarray` + output shape is (time, n_channels) + + """ + # get firing rates + frs = np.squeeze(np.mean(sample, axis=0)) / (self.bin_size * 1e-3) + fr_mask = frs > self.threshold + # get rid of neurons below fr threshold + sample = sample[:, fr_mask] + return sample.astype(np.float) + + def __repr__(self): + return str('Threshold(threshold=%f, bin_size=%f)' % (self.threshold, self.bin_size)) + + +class ZScore(Transform): + """z-score channel activity.""" + + def __init__(self): + pass + + def __call__(self, sample): + """ + + Parameters + ---------- + sample : :obj:`np.ndarray` + input shape is (time, n_channels) + + Returns + ------- + :obj:`np.ndarray` + output shape is (time, n_channels) + + """ + sample -= np.mean(sample, axis=0) + sample /= np.std(sample, axis=0) + return sample + + def __repr__(self): + return 'ZScore()' + + +# class Resize(Transform): +# """Resize the sample images.""" +# +# def __init__(self, size=(128, 128), order=1): +# """ +# +# Parameters +# ---------- +# size : :obj:`int` or :obj:`tuple` +# desired output size for each image; if type is :obj:`int`, the same value is used for +# both height and width +# order : :obj:`int` +# interpolation order +# +# """ +# assert isinstance(size, (tuple, int)) +# self.order = order +# if isinstance(size, tuple): +# self.x = size[0] +# self.y = size[1] +# else: +# self.x = self.y = size +# +# def __call__(self, sample): +# """ +# +# Parameters +# ---------- +# sample: :obj:`np.ndarray` +# input shape is (trial, time, n_channels) +# +# Returns +# ------- +# :obj:`np.ndarray` +# output shape is (trial, time, n_channels) +# +# """ +# sh = sample.shape +# sample = transform.resize(sample, (sh[0], sh[1], self.y, self.x), order=self.order) +# return sample +# +# def __repr__(self): +# return str('Resize(size=(%i, %i))' % (self.y, self.x)) diff --git a/behavenet/data/utils.py b/behavenet/data/utils.py index 4c12775..a1ece7f 100644 --- a/behavenet/data/utils.py +++ b/behavenet/data/utils.py @@ -120,6 +120,23 @@ def get_data_generator_inputs(hparams, sess_ids, check_splits=True): transforms = [neural_transform, ae_transform] paths = [neural_path, ae_path] + elif hparams['model_class'] == 'neural-ae-me': + + hparams['input_signal'] = 'neural' + hparams['output_signal'] = 'ae_latents' + hparams['output_size'] = hparams['n_ae_latents'] + if hparams['model_type'][-2:] == 'mv': + hparams['noise_dist'] = 'gaussian-full' + else: + hparams['noise_dist'] = 'gaussian' + + ae_transform, ae_path = get_transforms_paths( + 'ae_latents_me', hparams, sess_id=sess_id, check_splits=check_splits) + + signals = ['neural', 'ae_latents'] + transforms = [neural_transform, ae_transform] + paths = [neural_path, ae_path] + elif hparams['model_class'] == 'ae-neural': hparams['input_signal'] = 'ae_latents' @@ -413,11 +430,12 @@ def get_transforms_paths(data_type, hparams, sess_id, check_splits=True): """ + from behavenet.data.transforms import BlockShuffle + from behavenet.data.transforms import Compose + from behavenet.data.transforms import MotionEnergy from behavenet.data.transforms import SelectIdxs from behavenet.data.transforms import Threshold from behavenet.data.transforms import ZScore - from behavenet.data.transforms import BlockShuffle - from behavenet.data.transforms import Compose from behavenet.fitting.utils import get_best_model_version from behavenet.fitting.utils import get_expt_dir @@ -478,9 +496,13 @@ def get_transforms_paths(data_type, hparams, sess_id, check_splits=True): else: transform = Compose(transforms_) - elif data_type == 'ae_latents' or data_type == 'latents': + elif data_type == 'ae_latents' or data_type == 'latents' \ + or data_type == 'ae_latents_me' or data_type == 'latents_me': - transform = None + if data_type == 'ae_latents_me' or data_type == 'latents_me': + transform = MotionEnergy() + else: + transform = None if 'ae_latents_file' in hparams: path = hparams['ae_latents_file'] diff --git a/behavenet/fitting/decoder_grid_search.py b/behavenet/fitting/decoder_grid_search.py index e211cbe..4fb4763 100644 --- a/behavenet/fitting/decoder_grid_search.py +++ b/behavenet/fitting/decoder_grid_search.py @@ -50,6 +50,9 @@ def main(hparams, *args): elif hparams['model_class'] == 'neural-ae': hparams['input_size'] = data_generator.datasets[0][ex_trial][i_sig].shape[1] hparams['output_size'] = hparams['n_ae_latents'] + elif hparams['model_class'] == 'neural-ae-me': + hparams['input_size'] = data_generator.datasets[0][ex_trial][i_sig].shape[1] + hparams['output_size'] = hparams['n_ae_latents'] elif hparams['model_class'] == 'ae-neural': hparams['input_size'] = hparams['n_ae_latents'] hparams['output_size'] = data_generator.datasets[0][ex_trial][o_sig].shape[1] @@ -62,7 +65,8 @@ def main(hparams, *args): else: raise ValueError('%s is an invalid model class' % hparams['model_class']) - if hparams['model_class'] == 'neural-ae' or hparams['model_class'] == 'ae-neural': + if hparams['model_class'] == 'neural-ae' or hparams['model_class'] == 'neural-ae' \ + or hparams['model_class'] == 'ae-neural': hparams['ae_model_path'] = os.path.join( os.path.dirname(data_generator.datasets[0].paths['ae_latents'])) hparams['ae_model_latents_file'] = data_generator.datasets[0].paths['ae_latents'] @@ -72,7 +76,7 @@ def main(hparams, *args): hparams['arhmm_model_states_file'] = data_generator.datasets[0].paths['arhmm_states'] # Store which AE was used for the ARHMM - tags = pickle.load(open(hparams['arhmm_model_path'] + '/meta_tags.pkl', 'rb')) + tags = pickle.load(open(os.path.join(hparams['arhmm_model_path'], 'meta_tags.pkl'), 'rb')) hparams['ae_model_latents_file'] = tags['ae_model_latents_file'] # #################### diff --git a/behavenet/fitting/utils.py b/behavenet/fitting/utils.py index 2e53269..94d8364 100644 --- a/behavenet/fitting/utils.py +++ b/behavenet/fitting/utils.py @@ -364,7 +364,7 @@ def get_expt_dir(hparams, model_class=None, model_type=None, expt_name=None): session_dir, _ = get_session_dir(hparams_) else: session_dir = hparams['session_dir'] - elif model_class == 'neural-ae' or model_class == 'ae-neural': + elif model_class == 'neural-ae' or model_class == 'neural-ae-me' or model_class == 'ae-neural': brain_region = get_region_dir(hparams) model_path = os.path.join( model_class, '%02i_latents' % hparams['n_ae_latents'], model_type, brain_region) @@ -692,7 +692,7 @@ def get_model_params(hparams): hparams_less['transitions'] = hparams['transitions'] if hparams['transitions'] == 'sticky': hparams_less['kappa'] = hparams['kappa'] - elif model_class == 'neural-ae' or model_class == 'ae-neural': + elif model_class == 'neural-ae' or model_class == 'neural-ae-me' or model_class == 'ae-neural': hparams_less['ae_experiment_name'] = hparams['ae_experiment_name'] hparams_less['ae_version'] = hparams['ae_version'] hparams_less['ae_model_class'] = hparams['ae_model_class'] @@ -722,7 +722,7 @@ def get_model_params(hparams): raise NotImplementedError('"%s" is not a valid model class' % model_class) # decoder arch params - if model_class == 'neural-ae' or model_class == 'ae-neural' \ + if model_class == 'neural-ae' or model_class == 'neural-ae-me' or model_class == 'ae-neural' \ or model_class == 'neural-arhmm' or model_class == 'arhmm-neural' \ or model_class == 'neural-labels' or model_class == 'labels-neural': hparams_less['learning_rate'] = hparams['learning_rate'] @@ -1028,7 +1028,8 @@ def get_best_model_and_data(hparams, Model=None, load_data=True, version='best', from behavenet.models import SSSVAE as Model elif hparams['model_class'] == 'labels-images': from behavenet.models import ConvDecoder as Model - elif hparams['model_class'] == 'neural-ae' or hparams['model_class'] == 'neural-arhmm' \ + elif hparams['model_class'] == 'neural-ae' or hparams['model_class'] == 'neural-ae-me' \ + or hparams['model_class'] == 'neural-arhmm' \ or hparams['model_class'] == 'neural-labels': from behavenet.models import Decoder as Model elif hparams['model_class'] == 'ae-neural' or hparams['model_class'] == 'arhmm-neural' \ diff --git a/behavenet/models/README.md b/behavenet/models/README.md index cd8a522..fc99023 100644 --- a/behavenet/models/README.md +++ b/behavenet/models/README.md @@ -15,7 +15,7 @@ Model-related code * `behavenet.fitting.utils.get_best_data_and_model` * `behavenet.fitting.eval.export_xxx` (latents, states, predictions, etc) * potential function updates: - * other `behavenet.fitting.eval` methods (like `get_rconstruction`) + * other `behavenet.fitting.eval` methods (like `get_reconstruction`) * `behavenet.fitting.hyperparam_utils.add_dependent_params` [UPDATE UNIT TEST!] * update relevant jsons (e.g. extra hyperparameters) diff --git a/docs/source/glossary.rst b/docs/source/glossary.rst index a109060..e358203 100644 --- a/docs/source/glossary.rst +++ b/docs/source/glossary.rst @@ -4,7 +4,11 @@ Hyperparameter glossary ####################### -The BehaveNet code requires a diverse array of hyperparameters (hparams) to specify details about the data, computational resources, training algorithms, and the models themselves. This glossary contains a brief description for each of the hparams options. See the `example json files `_ for reasonable hparams defaults. +The BehaveNet code requires a diverse array of hyperparameters (hparams) to specify details about +the data, computational resources, training algorithms, and the models themselves. This glossary +contains a brief description for each of the hparams options. See the +`example json files `_ for reasonable +hparams defaults. Data ==== @@ -27,7 +31,11 @@ Data * **neural_type** (*str*): 'spikes' | 'ca' * **approx_batch_size** (*str*): approximate batch size (number of frames) for gpu memory calculation -For encoders/decoders, additional information can be supplied to control which subsets of neurons are used for encoding/decoding. See the :ref:`data structure documentation` for detailed instructions on how to incorporate this information into your HDF5 data file. The following options must be added to the data json file (an example can be found `here `__): +For encoders/decoders, additional information can be supplied to control which subsets of neurons +are used for encoding/decoding. See the :ref:`data structure documentation` +for detailed instructions on how to incorporate this information into your HDF5 data file. The +following options must be added to the data json file (an example can be found +`here `__): * **subsample_idxs_group_0** (*str*): name of the top-level HDF5 group that contains index groups * **subsample_idxs_group_1** (*str*): name of the second-level HDF5 group that contains index datasets @@ -100,13 +108,17 @@ All models: * 'vae': variational autoencoder * 'beta-tcvae': variational autoencoder with beta tc-vae decomposition of elbo * 'cond-ae': conditional autoencoder + * 'cond-vae': conditional variational autoencoder * 'cond-ae-msp': autoencoder with matrix subspace projection loss + * 'sss-vae': semi-supervised subspace variational autoencoder * 'hmm': hidden Markov model * 'arhmm': autoregressive hidden Markov model * 'neural-ae': decode AE latents from neural activity + * 'neural-ae-me': decode motion energy of AE latents (absolute value of temporal difference) from neural activity * 'neural-arhmm': decode arhmm states from neural activity * 'ae-neural': predict neural activity from AE latents * 'arhmm-neural': predict neural activity from arhmm states + * 'labels-images': decode images from labels with a convolutional decoder * 'bayesian-decoding': baysian decoding of AE latents and arhmm states from neural activity diff --git a/docs/source/user_guide.decoders.rst b/docs/source/user_guide.decoders.rst index c5dd84d..8dfedf0 100644 --- a/docs/source/user_guide.decoders.rst +++ b/docs/source/user_guide.decoders.rst @@ -24,12 +24,15 @@ Decoding ARHMM states: or -Decoding AE states: +Decoding AE latents: .. code-block:: console $: python behavenet/fitting/decoding_grid_search.py --data_config ~/.behavenet/musall_vistrained_params.json --model_config ~/.behavenet/decoding_ae_model.json --training_config ~/.behavenet/decoding_training.json --compute_config ~/.behavenet/decoding_compute.json +It is also possible to decode the motion energy of the AE latents, defined as the absolute value of +the difference between neighboring time points; to do so make the following change in the model +json: ``model_class: 'neural-ae-me'`` .. _decoding_with_subsets: diff --git a/tests/integration.py b/tests/integration.py index 7de623a..746e556 100644 --- a/tests/integration.py +++ b/tests/integration.py @@ -47,6 +47,7 @@ {'model_class': 'ae', 'model_file': 'ae', 'sessions': SESSIONS[0]}, {'model_class': 'arhmm', 'model_file': 'arhmm', 'sessions': SESSIONS[0]}, {'model_class': 'neural-ae', 'model_file': 'decoder', 'sessions': SESSIONS[0]}, + {'model_class': 'neural-ae-me', 'model_file': 'decoder', 'sessions': SESSIONS[0]}, {'model_class': 'neural-labels', 'model_file': 'decoder', 'sessions': SESSIONS[0]}, {'model_class': 'neural-arhmm', 'model_file': 'decoder', 'sessions': SESSIONS[0]}, {'model_class': 'ae', 'model_file': 'ae', 'sessions': 'all'}, @@ -132,9 +133,10 @@ def get_model_config_files(model, json_dir): 'model': os.path.join(model_json_dir, '%s_model.json' % model), 'training': os.path.join(model_json_dir, '%s_training.json' % model), 'compute': os.path.join(model_json_dir, '%s_compute.json' % model)} - elif model == 'neural-ae' or model == 'neural-arhmm' or model == 'neural-labels': + elif model == 'neural-ae' or model == 'neural-ae-me' or model == 'neural-arhmm' \ + or model == 'neural-labels': m = 'decoding' - s = model.split('-')[-1] + s = model.split('-')[1] # take string after "neural" model_json_dir = os.path.join(json_dir, '%s_jsons' % m) base_config_files = { 'data': os.path.join(model_json_dir, '%s_data.json' % m), @@ -196,7 +198,7 @@ def define_new_config_values(model, session='sess-0'): 'data': data_dict, 'model': { 'experiment_name': ae_expt_name, - 'model_class': 'cond-ae-msp', + 'model_class': model, 'model_type': ae_model_type, 'n_ae_latents': n_ae_latents + TEMP_DATA['n_labels'], 'l2_reg': 0.0, @@ -257,6 +259,33 @@ def define_new_config_values(model, session='sess-0'): new_values = { 'data': data_dict, 'model': { + 'model_class': model, + 'n_lags': 4, + 'n_max_lags': 8, + 'l2_reg': 1e-3, + 'ae_experiment_name': ae_expt_name, + 'ae_model_class': ae_model_class, + 'ae_model_type': ae_model_type, + 'n_ae_latents': n_ae_latents, + 'model_type': 'mlp', + 'n_hid_layers': 1, + 'n_hid_units': 16, + 'activation': 'relu'}, + 'training': { + 'export_predictions': True, + 'min_n_epochs': 1, + 'max_n_epochs': 1, + 'enable_early_stop': False, + 'train_frac': train_frac, + 'trial_splits': trial_splits}, + 'compute': { + 'gpus_viz': str(gpu_id), + 'tt_n_cpu_workers': 2}} + elif model == 'neural-ae-me': + new_values = { + 'data': data_dict, + 'model': { + 'model_class': model, 'n_lags': 4, 'n_max_lags': 8, 'l2_reg': 1e-3, @@ -282,6 +311,7 @@ def define_new_config_values(model, session='sess-0'): new_values = { 'data': data_dict, 'model': { + 'model_class': model, 'n_lags': 3, 'n_max_lags': 5, 'l2_reg': 1e-4, diff --git a/tests/test_data/test_transforms.py b/tests/test_data/test_transforms.py index 018fde1..c13d712 100644 --- a/tests/test_data/test_transforms.py +++ b/tests/test_data/test_transforms.py @@ -16,6 +16,43 @@ def test_compose(): assert np.allclose(np.std(s, axis=0), [1, 1], atol=1e-3) +def test_blockshuffle(): + + def get_runs(sample): + + vals = np.unique(sample) + n_time = len(sample) + + # mark first time point of state change with a nonzero number + change = np.where(np.concatenate([[0], np.diff(sample)], axis=0) != 0)[0] + # collect runs + runs = {val: [] for val in vals} + prev_beg = 0 + for curr_beg in change: + runs[sample[prev_beg]].append(curr_beg - prev_beg) + prev_beg = curr_beg + runs[sample[-1]].append(n_time - prev_beg) + return runs + + t = transforms.BlockShuffle(0) + + # signal has changed + signal = np.array([0, 0, 0, 1, 1, 1, 2, 2, 0, 0, 1, 1]) + s = t(signal) + assert not np.all(signal == s) + + # frequency of values unchanged + n_ex_og = np.array([len(np.argwhere(signal == i)) for i in range(3)]) + n_ex_sh = np.array([len(np.argwhere(s == i)) for i in range(3)]) + assert np.all(n_ex_og == n_ex_sh) + + # distribution of runs unchanged + runs_og = get_runs(signal) + runs_sh = get_runs(s) + for key in runs_og.keys(): + assert np.all(np.sort(np.array(runs_og[key])) == np.sort(np.array(runs_sh[key]))) + + def test_clipnormalize(): # raise exception when clip value <= 0 @@ -35,40 +72,6 @@ def test_clipnormalize(): assert np.max(s) == 1 -def test_threshold(): - - # raise exception when bin size <= 0 - with pytest.raises(ValueError): - transforms.Threshold(1, 0) - - # raise exception when threshold < 0 - with pytest.raises(ValueError): - transforms.Threshold(-1, 1) - - # no thresholding with 0 threshold - t = transforms.Threshold(0, 1) - signal = np.random.uniform(0, 4, (5, 4)) - s = t(signal) - assert s.shape == (5, 4) - - # correct thresholding - t = transforms.Threshold(1, 1e3) - signal = np.random.uniform(2, 4, (5, 4)) - signal[:, 0] = 0 - s = t(signal) - assert s.shape == (5, 3) - - -def test_zscore(): - - t = transforms.ZScore() - signal = 10 + 0.3 * np.random.randn(100, 3) - s = t(signal) - assert s.shape == (100, 3) - assert np.allclose(np.mean(s, axis=0), [0, 0, 0], atol=1e-3) - assert np.allclose(np.std(s, axis=0), [1, 1, 1], atol=1e-3) - - def test_makeonehot(): t = transforms.MakeOneHot() @@ -133,41 +136,17 @@ def test_makeonehot2d(): assert np.all(s == sp) -def test_blockshuffle(): - - def get_runs(sample): +def test_motionenergy(): - vals = np.unique(sample) - n_time = len(sample) - - # mark first time point of state change with a nonzero number - change = np.where(np.concatenate([[0], np.diff(sample)], axis=0) != 0)[0] - # collect runs - runs = {val: [] for val in vals} - prev_beg = 0 - for curr_beg in change: - runs[sample[prev_beg]].append(curr_beg - prev_beg) - prev_beg = curr_beg - runs[sample[-1]].append(n_time - prev_beg) - return runs - - t = transforms.BlockShuffle(0) - - # signal has changed - signal = np.array([0, 0, 0, 1, 1, 1, 2, 2, 0, 0, 1, 1]) + T = 100 + D = 4 + t = transforms.MotionEnergy() + signal = np.random.randn(T, D) s = t(signal) - assert not np.all(signal == s) - - # frequency of values unchanged - n_ex_og = np.array([len(np.argwhere(signal == i)) for i in range(3)]) - n_ex_sh = np.array([len(np.argwhere(s == i)) for i in range(3)]) - assert np.all(n_ex_og == n_ex_sh) - - # distribution of runs unchanged - runs_og = get_runs(signal) - runs_sh = get_runs(s) - for key in runs_og.keys(): - assert np.all(np.sort(np.array(runs_og[key])) == np.sort(np.array(runs_sh[key]))) + me = np.vstack([np.zeros((1, signal.shape[1])), np.abs(np.diff(signal, axis=0))]) + assert s.shape == (T, D) + assert np.allclose(s, me, atol=1e-3) + assert np.all(me >= 0) def test_selectindxs(): @@ -179,3 +158,37 @@ def test_selectindxs(): s = t(signal) assert s.shape == (5, 2) assert np.all(signal[:, idxs] == s) + + +def test_threshold(): + + # raise exception when bin size <= 0 + with pytest.raises(ValueError): + transforms.Threshold(1, 0) + + # raise exception when threshold < 0 + with pytest.raises(ValueError): + transforms.Threshold(-1, 1) + + # no thresholding with 0 threshold + t = transforms.Threshold(0, 1) + signal = np.random.uniform(0, 4, (5, 4)) + s = t(signal) + assert s.shape == (5, 4) + + # correct thresholding + t = transforms.Threshold(1, 1e3) + signal = np.random.uniform(2, 4, (5, 4)) + signal[:, 0] = 0 + s = t(signal) + assert s.shape == (5, 3) + + +def test_zscore(): + + t = transforms.ZScore() + signal = 10 + 0.3 * np.random.randn(100, 3) + s = t(signal) + assert s.shape == (100, 3) + assert np.allclose(np.mean(s, axis=0), [0, 0, 0], atol=1e-3) + assert np.allclose(np.std(s, axis=0), [1, 1, 1], atol=1e-3) diff --git a/tests/test_data/test_utils_data.py b/tests/test_data/test_utils_data.py index b919855..3107111 100644 --- a/tests/test_data/test_utils_data.py +++ b/tests/test_data/test_utils_data.py @@ -209,6 +209,29 @@ def test_get_data_generator_inputs(): hparams, sess_ids, check_splits=False) assert hparams_['noise_dist'] == 'gaussian-full' + # ----------------- + # neural-ae-me + # ----------------- + hparams['model_class'] = 'neural-ae-me' + hparams['model_type'] = 'linear' + hparams['session_dir'] = session_dir + hparams['neural_type'] = 'spikes' + hparams['neural_thresh'] = 0 + hparams_, signals, transforms, paths = utils.get_data_generator_inputs( + hparams, sess_ids, check_splits=False) + assert signals[0] == ['neural', 'ae_latents'] + assert transforms[0][0] is None + assert transforms[0][1].__repr__().find('MotionEnergy') > -1 + assert hparams_['input_signal'] == 'neural' + assert hparams_['output_signal'] == 'ae_latents' + assert hparams_['output_size'] == hparams['n_ae_latents'] + assert hparams_['noise_dist'] == 'gaussian' + + hparams['model_type'] = 'linear-mv' + hparams_, signals, transforms, paths = utils.get_data_generator_inputs( + hparams, sess_ids, check_splits=False) + assert hparams_['noise_dist'] == 'gaussian-full' + # ----------------- # ae-neural # ----------------- @@ -561,6 +584,13 @@ def test_get_transforms_paths(): ae_path, 'version_%i' % hparams['ae_version'], '%slatents.pkl' % sess_id_str) assert transform is None + # get correct transform + transform, path = utils.get_transforms_paths( + 'ae_latents_me', hparams, sess_id=None, check_splits=False) + assert path == os.path.join( + ae_path, 'version_%i' % hparams['ae_version'], '%slatents.pkl' % sess_id_str) + assert transform.__repr__().find('MotionEnergy') > -1 + # TODO: use get_best_model_version() # ------------------------ diff --git a/tests/test_fitting/test_utils_fitting.py b/tests/test_fitting/test_utils_fitting.py index 7ba4a6a..a7f032c 100644 --- a/tests/test_fitting/test_utils_fitting.py +++ b/tests/test_fitting/test_utils_fitting.py @@ -547,7 +547,7 @@ def test_get_expt_dir(self): assert expt_dir == model_path # ------------------------- - # neural-ae/ae-neural + # neural-ae/neural-ae-me/ae-neural # ------------------------- hparams['model_class'] = 'neural-ae' hparams['model_type'] = 'mlp' @@ -562,6 +562,16 @@ def test_get_expt_dir(self): expt_name=hparams['experiment_name']) assert expt_dir == model_path + hparams['model_class'] = 'neural-ae-me' + model_path = os.path.join( + session_dir, hparams['model_class'], '%02i_latents' % hparams['n_ae_latents'], + hparams['model_type'], 'all', hparams['experiment_name']) + + expt_dir = utils.get_expt_dir( + hparams, model_class=hparams['model_class'], model_type=hparams['model_type'], + expt_name=hparams['experiment_name']) + assert expt_dir == model_path + hparams['model_class'] = 'ae-neural' model_path = os.path.join( session_dir, hparams['model_class'], '%02i_latents' % hparams['n_ae_latents'], @@ -976,7 +986,7 @@ def test_get_model_params(self): assert ret_hparams == {**base_hparams, **model_hparams} # ----------------- - # neural-ae/ae-neural + # neural-ae/neural-ae-me/ae-neural # ----------------- model_hparams = { 'model_class': 'neural-ae', @@ -995,6 +1005,14 @@ def test_get_model_params(self): ret_hparams = utils.get_model_params({**misc_hparams, **base_hparams, **model_hparams}) assert ret_hparams == {**base_hparams, **model_hparams} + model_hparams['model_class'] = 'neural-ae-me' + ret_hparams = utils.get_model_params({**misc_hparams, **base_hparams, **model_hparams}) + assert ret_hparams == {**base_hparams, **model_hparams} + + model_hparams['model_class'] = 'ae-neural' + ret_hparams = utils.get_model_params({**misc_hparams, **base_hparams, **model_hparams}) + assert ret_hparams == {**base_hparams, **model_hparams} + # ----------------- # neural-labels/labels-neural # ----------------- From 08c881f729c5d89f74b0ee40156715e74d6c1ed5 Mon Sep 17 00:00:00 2001 From: themattinthehatt Date: Fri, 13 Nov 2020 15:59:39 -0500 Subject: [PATCH 19/50] cleaning up integration test --- tests/integration.py | 115 +++++++++++++------------------------------ 1 file changed, 35 insertions(+), 80 deletions(-) diff --git a/tests/integration.py b/tests/integration.py index 746e556..d7684f7 100644 --- a/tests/integration.py +++ b/tests/integration.py @@ -158,14 +158,28 @@ def define_new_config_values(model, session='sess-0'): train_frac = 0.5 trial_splits = '8;1;1;1' + training_dict = { + 'export_train_plots': False, + 'export_latents': True, + 'export_predictions': True, + 'min_n_epochs': 1, + 'max_n_epochs': 1, + 'enable_early_stop': False, + 'train_frac': train_frac, + 'trial_splits': trial_splits + } + # compute vals gpu_id = 0 + compute_dict = {'gpus_viz': str(gpu_id), 'tt_n_cpu_workers': 2} + # model vals: ae ae_expt_name = 'ae-expt' ae_model_class = 'ae' ae_model_type = 'conv' n_ae_latents = 6 + l2_reg = 0.0 # model vals: arhmm arhmm_expt_name = 'arhmm-expt' @@ -182,17 +196,9 @@ def define_new_config_values(model, session='sess-0'): 'model_class': model, 'model_type': ae_model_type, 'n_ae_latents': n_ae_latents, - 'l2_reg': 0.0}, - 'training': { - 'export_train_plots': False, - 'export_latents': True, - 'min_n_epochs': 1, - 'max_n_epochs': 1, - 'enable_early_stop': False, - 'train_frac': train_frac, - 'trial_splits': trial_splits}, - 'compute': { - 'gpus_viz': str(gpu_id)}} + 'l2_reg': l2_reg}, + 'training': training_dict, + 'compute': compute_dict} elif model == 'cond-ae-msp': new_values = { 'data': data_dict, @@ -201,18 +207,10 @@ def define_new_config_values(model, session='sess-0'): 'model_class': model, 'model_type': ae_model_type, 'n_ae_latents': n_ae_latents + TEMP_DATA['n_labels'], - 'l2_reg': 0.0, + 'l2_reg': l2_reg, 'msp.alpha': 1e-5}, - 'training': { - 'export_train_plots': False, - 'export_latents': True, - 'min_n_epochs': 1, - 'max_n_epochs': 1, - 'enable_early_stop': False, - 'train_frac': train_frac, - 'trial_splits': trial_splits}, - 'compute': { - 'gpus_viz': str(gpu_id)}} + 'training': training_dict, + 'compute': compute_dict} elif model == 'cond-vae': new_values = { 'data': data_dict, @@ -221,18 +219,10 @@ def define_new_config_values(model, session='sess-0'): 'model_class': model, 'model_type': ae_model_type, 'n_ae_latents': n_ae_latents, - 'l2_reg': 0.0, + 'l2_reg': l2_reg, 'conditional_encoder': False}, - 'training': { - 'export_train_plots': False, - 'export_latents': True, - 'min_n_epochs': 1, - 'max_n_epochs': 1, - 'enable_early_stop': False, - 'train_frac': train_frac, - 'trial_splits': trial_splits}, - 'compute': { - 'gpus_viz': str(gpu_id)}} + 'training': training_dict, + 'compute': compute_dict} elif model == 'arhmm': new_values = { 'data': data_dict, @@ -252,9 +242,7 @@ def define_new_config_values(model, session='sess-0'): 'n_iters': 2, 'train_frac': train_frac, 'trial_splits': trial_splits}, - 'compute': { - 'gpus_viz': str(gpu_id), - 'tt_n_cpu_workers': 2}} + 'compute': compute_dict} elif model == 'neural-ae': new_values = { 'data': data_dict, @@ -271,16 +259,8 @@ def define_new_config_values(model, session='sess-0'): 'n_hid_layers': 1, 'n_hid_units': 16, 'activation': 'relu'}, - 'training': { - 'export_predictions': True, - 'min_n_epochs': 1, - 'max_n_epochs': 1, - 'enable_early_stop': False, - 'train_frac': train_frac, - 'trial_splits': trial_splits}, - 'compute': { - 'gpus_viz': str(gpu_id), - 'tt_n_cpu_workers': 2}} + 'training': training_dict, + 'compute': compute_dict} elif model == 'neural-ae-me': new_values = { 'data': data_dict, @@ -297,16 +277,8 @@ def define_new_config_values(model, session='sess-0'): 'n_hid_layers': 1, 'n_hid_units': 16, 'activation': 'relu'}, - 'training': { - 'export_predictions': True, - 'min_n_epochs': 1, - 'max_n_epochs': 1, - 'enable_early_stop': False, - 'train_frac': train_frac, - 'trial_splits': trial_splits}, - 'compute': { - 'gpus_viz': str(gpu_id), - 'tt_n_cpu_workers': 2}} + 'training': training_dict, + 'compute': compute_dict} elif model == 'neural-labels': new_values = { 'data': data_dict, @@ -319,16 +291,8 @@ def define_new_config_values(model, session='sess-0'): 'n_hid_layers': 1, 'n_hid_units': 16, 'activation': 'relu'}, - 'training': { - 'export_predictions': True, - 'min_n_epochs': 1, - 'max_n_epochs': 1, - 'enable_early_stop': False, - 'train_frac': train_frac, - 'trial_splits': trial_splits}, - 'compute': { - 'gpus_viz': str(gpu_id), - 'tt_n_cpu_workers': 2}} + 'training': training_dict, + 'compute': compute_dict} elif model == 'neural-arhmm': new_values = { 'data': data_dict, @@ -348,16 +312,8 @@ def define_new_config_values(model, session='sess-0'): 'n_hid_layers': 1, 'n_hid_units': [8, 16], 'activation': 'relu'}, - 'training': { - 'export_predictions': True, - 'min_n_epochs': 1, - 'max_n_epochs': 1, - 'enable_early_stop': False, - 'train_frac': train_frac, - 'trial_splits': trial_splits}, - 'compute': { - 'gpus_viz': str(gpu_id), - 'tt_n_cpu_workers': 2}} + 'training': training_dict, + 'compute': compute_dict} elif model == 'labels-images': new_values = { 'data': data_dict, @@ -366,17 +322,16 @@ def define_new_config_values(model, session='sess-0'): 'model_class': 'labels-images', 'model_type': ae_model_type, 'n_ae_latents': 0, - 'l2_reg': 0.0}, + 'l2_reg': l2_reg}, 'training': { 'export_train_plots': False, - 'export_latents': False, + 'export_predictions': False, 'min_n_epochs': 1, 'max_n_epochs': 1, 'enable_early_stop': False, 'train_frac': train_frac, 'trial_splits': trial_splits}, - 'compute': { - 'gpus_viz': str(gpu_id)}} + 'compute': compute_dict} else: raise NotImplementedError From b909b28466c695a8c698caab73f73e951425f2b9 Mon Sep 17 00:00:00 2001 From: themattinthehatt Date: Mon, 16 Nov 2020 18:27:01 -0500 Subject: [PATCH 20/50] multisession doc update --- behavenet/data/data_generator.py | 2 +- behavenet/plotting/arhmm_utils.py | 8 +++--- docs/source/adv_user_guide.multisession.rst | 30 ++++++++++++++++----- 3 files changed, 29 insertions(+), 11 deletions(-) diff --git a/behavenet/data/data_generator.py b/behavenet/data/data_generator.py index dca8f90..0cf3e00 100644 --- a/behavenet/data/data_generator.py +++ b/behavenet/data/data_generator.py @@ -287,7 +287,7 @@ def __getitem__(self, idx): else: sample[signal] = [f[signal][str('trial_%04i' % idx)][()].astype(dtype)] - elif signal == 'ae_latents': + elif signal == 'ae_latents' or signal == 'latents': dtype = 'float32' sample[signal] = self._try_to_load(signal, key='latents', idx=idx, dtype=dtype) diff --git a/behavenet/plotting/arhmm_utils.py b/behavenet/plotting/arhmm_utils.py index 071d91e..073d8f2 100644 --- a/behavenet/plotting/arhmm_utils.py +++ b/behavenet/plotting/arhmm_utils.py @@ -377,13 +377,13 @@ def make_syllable_movies( maximum number of frames to animate frame_rate : :obj:`float`, optional frame rate of saved movie - n_buffer : :obj:`int` + n_buffer : :obj:`int`, optional number of blank frames between syllable instances - n_pre_frames : :obj:`int` + n_pre_frames : :obj:`int`, optional number of behavioral frames to precede each syllable instance - n_rows : :obj:`int` or :obj:`NoneType` + n_rows : :obj:`int` or :obj:`NoneType`, optional number of rows in output movie - single_syllable : :obj:`int` or :obj:`NoneType` + single_syllable : :obj:`int` or :obj:`NoneType`, optional choose only a single state for movie """ diff --git a/docs/source/adv_user_guide.multisession.rst b/docs/source/adv_user_guide.multisession.rst index 6d6b54e..9095d24 100644 --- a/docs/source/adv_user_guide.multisession.rst +++ b/docs/source/adv_user_guide.multisession.rst @@ -18,7 +18,7 @@ require modifying the data configuration json before training. We'll use the Mus example; below is the relevant section of the json file located in ``behavenet/configs/data_default.json`` that we will modify below. -.. code-block:: javascript +.. code-block:: JSON "lab": "musall", # type: str "expt": "vistrained", # type: str @@ -40,7 +40,7 @@ This method is appropriate if you want to fit a model on all sessions from a spe experiment, or lab. For example, if we want to fit a model on all sessions from animal ``mSM30``, we would modify the ``session`` parameter value to ``all``: -.. code-block:: javascript +.. code-block:: JSON "lab": "musall", # type: str "expt": "vistrained", # type: str @@ -58,7 +58,7 @@ lists the lab, expt, animal, and session for all sessions in that multisession. If we want to fit a model on all sessions from all animals in the ``vistrained`` experiment, we would modify the ``animal`` parameter value to ``all``: -.. code-block:: javascript +.. code-block:: JSON "lab": "musall", # type: str "expt": "vistrained", # type: str @@ -89,10 +89,11 @@ Method 2: specify sessions in a csv file This method is appropriate if you want finer control over which sessions are included; for example, if you want all sessions from one animal, as well as all but one session from another animal. To specify these sessions, you can construct a csv file with the four column headers ``lab``, -``expt``, ``animal``, and ``session``. You can then provide this csv file (let's say it's called -``data_dir/example_sessions.csv``) as the value for the ``sessions_csv`` parameter: +``expt``, ``animal``, and ``session`` (see below). You can then provide this csv file +(let's say it's called ``data_dir/example_sessions.csv``) as the value for the ``sessions_csv`` +parameter: -.. code-block:: javascript +.. code-block:: JSON "lab": "musall", # type: str "expt": "vistrained", # type: str @@ -104,6 +105,23 @@ specify these sessions, you can construct a csv file with the four column header The ``sessions_csv`` parameter takes precedence over any values supplied for ``lab``, ``expt``, ``animal``, ``session``, and ``all_source``. +Below is an example csv file that includes two sessions from one animal: + +.. code-block:: text + + lab,expt,animal,session + musall,vistrained,mSM36,05-Dec-2017 + musall,vistrained,mSM36,07-Dec-2017 + +Here is another example that include the previous two sessions, as well as a third from a different +animal: + +.. code-block:: text + + lab,expt,animal,session + musall,vistrained,mSM30,12-Oct-2017 + musall,vistrained,mSM36,05-Dec-2017 + musall,vistrained,mSM36,07-Dec-2017 Loading a trained multisession model ------------------------------------ From 682f1660803b4ffa4ffc30cee7b1f74c86102497 Mon Sep 17 00:00:00 2001 From: themattinthehatt Date: Tue, 17 Nov 2020 11:50:01 -0500 Subject: [PATCH 21/50] small bug fix in ae video writer --- behavenet/models/vaes.py | 8 +++++++- behavenet/plotting/ae_utils.py | 12 ++++++------ 2 files changed, 13 insertions(+), 7 deletions(-) diff --git a/behavenet/models/vaes.py b/behavenet/models/vaes.py index da20f17..b55394d 100644 --- a/behavenet/models/vaes.py +++ b/behavenet/models/vaes.py @@ -722,7 +722,13 @@ def loss(self, data, dataset=0, accumulate_grad=True, chunk_size=200): y_all = y.cpu().detach().numpy() if n is not None: n_np = n.cpu().detach().numpy() - r2 = r2_score(y_all[n_np == 1], y_hat_all[n_np == 1], multioutput='variance_weighted') + try: + r2 = r2_score(y_all[n_np == 1], y_hat_all[n_np == 1], multioutput='variance_weighted') + print(np.sum(np.isnan(y_hat_all[n_np == 1]))) + except: + print(y_all[n_np == 1]) + print() + print(y_hat_all[n_np == 1]) else: r2 = r2_score(y_all, y_hat_all, multioutput='variance_weighted') diff --git a/behavenet/plotting/ae_utils.py b/behavenet/plotting/ae_utils.py index e744b35..54150f9 100644 --- a/behavenet/plotting/ae_utils.py +++ b/behavenet/plotting/ae_utils.py @@ -103,16 +103,16 @@ def make_reconstruction_movie( if save_file is not None: make_dir_if_not_exists(save_file) if save_file[-3:] == 'gif': - writer = 'imagemagick' + print('saving video to %s...' % save_file, end='') + ani.save(save_file, writer='imagemagick', fps=frame_rate) + print('done') else: if save_file[-3:] != 'mp4': save_file += '.mp4' writer = FFMpegWriter(fps=frame_rate, bitrate=-1) - - print('saving video to %s...' % save_file, end='') - ani.save(save_file, writer=writer, fps=frame_rate) - - print('done') + print('saving video to %s...' % save_file, end='') + ani.save(save_file, writer=writer) + print('done') def make_ae_reconstruction_movie_wrapper( From 2f9006e36b30a5e9b339bd697f8d2a6c338ec602 Mon Sep 17 00:00:00 2001 From: themattinthehatt Date: Wed, 18 Nov 2020 12:16:35 -0500 Subject: [PATCH 22/50] removing debugging printouts from sssvae --- behavenet/models/vaes.py | 9 ++------- 1 file changed, 2 insertions(+), 7 deletions(-) diff --git a/behavenet/models/vaes.py b/behavenet/models/vaes.py index b55394d..324361c 100644 --- a/behavenet/models/vaes.py +++ b/behavenet/models/vaes.py @@ -722,19 +722,14 @@ def loss(self, data, dataset=0, accumulate_grad=True, chunk_size=200): y_all = y.cpu().detach().numpy() if n is not None: n_np = n.cpu().detach().numpy() - try: - r2 = r2_score(y_all[n_np == 1], y_hat_all[n_np == 1], multioutput='variance_weighted') - print(np.sum(np.isnan(y_hat_all[n_np == 1]))) - except: - print(y_all[n_np == 1]) - print() - print(y_hat_all[n_np == 1]) + r2 = r2_score(y_all[n_np == 1], y_hat_all[n_np == 1], multioutput='variance_weighted') else: r2 = r2_score(y_all, y_hat_all, multioutput='variance_weighted') # compile (properly weighted) loss terms for key in loss_dict_vals.keys(): loss_dict_vals[key] /= batch_size + # store hyperparams loss_dict_vals['alpha'] = alpha loss_dict_vals['beta'] = beta From 65c09e782ec84333193ba49f2d288c4853d51399 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 18 Nov 2020 22:27:52 +0000 Subject: [PATCH 23/50] Bump notebook from 6.0.3 to 6.1.5 Bumps [notebook](https://github.com/jupyter/jupyterhub) from 6.0.3 to 6.1.5. - [Release notes](https://github.com/jupyter/jupyterhub/releases) - [Changelog](https://github.com/jupyterhub/jupyterhub/blob/master/CHECKLIST-Release.md) - [Commits](https://github.com/jupyter/jupyterhub/commits) Signed-off-by: dependabot[bot] --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index c35d451..4ae695a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,7 +2,7 @@ commentjson==0.8.2 h5py==2.9.0 ipykernel==5.1.0 matplotlib==3.0.3 -notebook==6.0.3 +notebook==6.1.5 numpy==1.17.4 requests==2.22.0 scikit-image==0.15.0 From 9f12a7bda8a85abd39261040ca432db1d65da84a Mon Sep 17 00:00:00 2001 From: themattinthehatt Date: Wed, 2 Dec 2020 13:20:18 -0500 Subject: [PATCH 24/50] small plotting updates --- behavenet/plotting/ae_utils.py | 38 +++++++++++++++++++------------ behavenet/plotting/arhmm_utils.py | 9 +++++--- 2 files changed, 30 insertions(+), 17 deletions(-) diff --git a/behavenet/plotting/ae_utils.py b/behavenet/plotting/ae_utils.py index 54150f9..6e64cde 100644 --- a/behavenet/plotting/ae_utils.py +++ b/behavenet/plotting/ae_utils.py @@ -548,7 +548,7 @@ def plot_neural_reconstruction_traces_wrapper( def plot_neural_reconstruction_traces( traces_ae, traces_neural, save_file=None, xtick_locs=None, frame_rate=None, format='png', - scale=0.5, max_traces=8, add_r2=True): + scale=0.5, max_traces=8, add_r2=True, add_legend=True, colored_predictions=True): """Plot ae latents and their neural reconstructions. Parameters @@ -571,6 +571,11 @@ def plot_neural_reconstruction_traces( maximum number of traces to plot, for easier visualization add_r2 : :obj:`bool`, optional print R2 value on plot + add_legend : :obj:`bool`, optional + print legend on plot + colored_predictions : :obj:`bool`, optional + color predictions using default seaborn colormap; else predictions are black + Returns ------- @@ -596,23 +601,28 @@ def plot_neural_reconstruction_traces( traces_neural_sc = traces_neural_sc[:, :max_traces] fig = plt.figure(figsize=(12, 8)) - plt.plot(traces_neural_sc + np.arange(traces_neural_sc.shape[1]), linewidth=3) + if colored_predictions: + plt.plot(traces_neural_sc + np.arange(traces_neural_sc.shape[1]), linewidth=3) + else: + plt.plot(traces_neural_sc + np.arange(traces_neural_sc.shape[1]), linewidth=3, color='k') plt.plot( traces_ae_sc + np.arange(traces_ae_sc.shape[1]), color=[0.2, 0.2, 0.2], linewidth=3, alpha=0.7) - # add legend - # original latents - gray - orig_line = mlines.Line2D([], [], color=[0.2, 0.2, 0.2], linewidth=3, alpha=0.7) - # predicted latents - cycle through some colors - colors = plt.rcParams['axes.prop_cycle'].by_key()['color'] - dls = [] - for c in range(5): - dls.append(mlines.Line2D( - [], [], linewidth=3, linestyle='--', dashes=(0, 3 * c, 20, 1), color='%s' % colors[c])) - plt.legend( - [orig_line, tuple(dls)], ['Original latents', 'Predicted latents'], - loc='lower right', frameon=True, framealpha=0.7, edgecolor=[1, 1, 1]) + # add legend if desired + if add_legend: + # original latents - gray + orig_line = mlines.Line2D([], [], color=[0.2, 0.2, 0.2], linewidth=3, alpha=0.7) + # predicted latents - cycle through some colors + colors = plt.rcParams['axes.prop_cycle'].by_key()['color'] + dls = [] + for c in range(5): + dls.append(mlines.Line2D( + [], [], linewidth=3, linestyle='--', dashes=(0, 3 * c, 20, 1), + color='%s' % colors[c])) + plt.legend( + [orig_line, tuple(dls)], ['Original latents', 'Predicted latents'], + loc='lower right', frameon=True, framealpha=0.7, edgecolor=[1, 1, 1]) # add r2 info if desired if add_r2: diff --git a/behavenet/plotting/arhmm_utils.py b/behavenet/plotting/arhmm_utils.py index 073d8f2..77652aa 100644 --- a/behavenet/plotting/arhmm_utils.py +++ b/behavenet/plotting/arhmm_utils.py @@ -771,7 +771,8 @@ def plot_real_vs_sampled( def plot_states_overlaid_with_latents( - latents, states, save_file=None, ax=None, xtick_locs=None, frame_rate=None, format='png'): + latents, states, save_file=None, ax=None, xtick_locs=None, frame_rate=None, cmap='tab20b', + format='png'): """Plot states for a single trial overlaid with latents. Parameters @@ -788,6 +789,8 @@ def plot_states_overlaid_with_latents( tick locations in bin values for plot frame_rate : :obj:`float`, optional behavioral video framerate; to properly relabel xticks + cmap : :obj:`str`, optional + matplotlib colormap format : :obj:`str`, optional any accepted matplotlib save format, e.g. 'png' | 'pdf' | 'jpeg' @@ -805,10 +808,10 @@ def plot_states_overlaid_with_latents( spc = 1.1 * abs(latents.max()) n_latents = latents.shape[1] plotting_latents = latents + spc * np.arange(n_latents) - ymin = min(-spc - 1, np.min(plotting_latents)) + ymin = min(-spc, np.min(plotting_latents)) ymax = max(spc * n_latents, np.max(plotting_latents)) ax.imshow( - states[None, :], aspect='auto', extent=(0, len(latents), ymin, ymax), cmap='tab20b', + states[None, :], aspect='auto', extent=(0, len(latents), ymin, ymax), cmap=cmap, alpha=1.0) ax.plot(plotting_latents, '-k', lw=3) ax.set_ylim([ymin, ymax]) From ca24683d7ad87ac97d2953d1655746a413ced0a3 Mon Sep 17 00:00:00 2001 From: themattinthehatt Date: Mon, 7 Dec 2020 11:03:31 -0500 Subject: [PATCH 25/50] ae plotting updates --- behavenet/plotting/ae_utils.py | 200 ++++++++++++++++++++++-------- behavenet/plotting/arhmm_utils.py | 3 +- 2 files changed, 147 insertions(+), 56 deletions(-) diff --git a/behavenet/plotting/ae_utils.py b/behavenet/plotting/ae_utils.py index 6e64cde..e60716d 100644 --- a/behavenet/plotting/ae_utils.py +++ b/behavenet/plotting/ae_utils.py @@ -2,6 +2,7 @@ import copy import matplotlib.animation as animation +import matplotlib.lines as mlines import matplotlib.pyplot as plt from matplotlib.gridspec import GridSpec from matplotlib.animation import FFMpegWriter @@ -207,7 +208,8 @@ def make_ae_reconstruction_movie_wrapper( def make_neural_reconstruction_movie_wrapper( - hparams, save_file, trial=None, sess_idx=0, max_frames=400, max_latents=8, frame_rate=15): + hparams, save_file, trials=None, sess_idx=0, max_frames=400, max_latents=8, + zscore_by_dim=False, colored_predictions=False, xtick_locs=None, frame_rate=15): """Produce movie with original video, ae reconstructed video, and neural reconstructed video. This is a high-level function that loads the model described in the hparams dictionary and @@ -221,7 +223,7 @@ def make_neural_reconstruction_movie_wrapper( needs to contain enough information to specify an autoencoder save_file : :obj:`str` full save file (path and filename) - trial : :obj:`int`, optional + trials : :obj:`int` or :obj:`list`, optional if :obj:`NoneType`, use first test trial sess_idx : :obj:`int`, optional session index into data generator @@ -229,6 +231,12 @@ def make_neural_reconstruction_movie_wrapper( maximum number of frames to animate from a trial max_latents : :obj:`int`, optional maximum number of ae latents to plot + zscore_by_dim : :obj:`bool`, optional + True to z-score each dim, False to leave relative scales + colored_predictions : :obj:`bool`, optional + False to plot reconstructions in black, True to plot in different colors + xtick_locs : :obj:`array-like`, optional + tick locations in units of bins frame_rate : :obj:`float`, optional frame rate of saved movie @@ -236,6 +244,9 @@ def make_neural_reconstruction_movie_wrapper( from behavenet.models import Decoder + # define number of frames that separate trials + n_buffer = 5 + ############################### # build ae model/data generator ############################### @@ -248,26 +259,6 @@ def make_neural_reconstruction_movie_wrapper( # move model to cpu model_ae.to('cpu') - if trial is None: - # choose first test trial - trial = data_generator_ae.batch_idxs[sess_idx]['test'][0] - - # get images from data generator (move to cpu) - batch = data_generator_ae.datasets[sess_idx][trial] - ims_orig_pt = batch['images'][:max_frames].cpu() # 400 - if hparams_ae['model_class'] == 'cond-ae': - labels_pt = batch['labels'][:max_frames] - else: - labels_pt = None - - # push images through ae to get reconstruction - ims_recon_ae, latents_ae = get_reconstruction( - model_ae, ims_orig_pt, labels=labels_pt, return_latents=True) - - # mask images for plotting - if hparams_ae.get('use_output_mask', False): - ims_orig_pt *= batch['masks'][:max_frames] - ####################################### # build decoder model/no data generator ####################################### @@ -281,28 +272,90 @@ def make_neural_reconstruction_movie_wrapper( # move model to cpu model_dec.to('cpu') - # get neural activity from data generator (move to cpu) - batch = data_generator_dec.datasets[0][trial] # 0 not sess_idx since decoders only have 1 sess - neural_activity_pt = batch['neural'][:max_frames].cpu() - - # push neural activity through decoder to get prediction - latents_dec_pt, _ = model_dec(neural_activity_pt) - # push prediction through ae to get reconstruction - ims_recon_dec = get_reconstruction(model_ae, latents_dec_pt, labels=labels_pt) + if trials is None: + # choose first test trial, put in list + trials = data_generator_ae.batch_idxs[sess_idx]['test'][0] + + if isinstance(trials, int): + trials = [trials] + + # loop over trials, putting black frames/nans in between + ims_orig = [] + ims_recon_ae = [] + ims_recon_neural = [] + latents_ae = [] + latents_neural = [] + for i, trial in enumerate(trials): + + # get images from data generator (move to cpu) + batch = data_generator_ae.datasets[sess_idx][trial] + ims_orig_pt = batch['images'][:max_frames].cpu() # 400 + if hparams_ae['model_class'] == 'cond-ae': + labels_pt = batch['labels'][:max_frames] + else: + labels_pt = None + + # push images through ae to get reconstruction + ims_recon_ae_curr, latents_ae_curr = get_reconstruction( + model_ae, ims_orig_pt, labels=labels_pt, return_latents=True) + + # mask images for plotting + if hparams_ae.get('use_output_mask', False): + ims_orig_pt *= batch['masks'][:max_frames] + + # get neural activity from data generator (move to cpu) + # 0, not sess_idx, since decoders only have 1 sess + batch = data_generator_dec.datasets[0][trial] + neural_activity_pt = batch['neural'][:max_frames].cpu() + + # push neural activity through decoder to get prediction + latents_dec_pt, _ = model_dec(neural_activity_pt) + # push prediction through ae to get reconstruction + ims_recon_dec_curr = get_reconstruction(model_ae, latents_dec_pt, labels=labels_pt) + + # store all relevant quantities + ims_orig.append(ims_orig_pt.cpu().detach().numpy()) + ims_recon_ae.append(ims_recon_ae_curr) + ims_recon_neural.append(ims_recon_dec_curr) + latents_ae.append(latents_ae_curr[:, :max_latents]) + latents_neural.append(latents_dec_pt.cpu().detach().numpy()[:, :max_latents]) + + # add blank frames + if i < len(trials) - 1: + n_channels, y_pix, x_pix = ims_orig[-1].shape[1:] + n = latents_ae[-1].shape[1] + ims_orig.append(np.zeros((n_buffer, n_channels, y_pix, x_pix))) + ims_recon_ae.append(np.zeros((n_buffer, n_channels, y_pix, x_pix))) + ims_recon_neural.append(np.zeros((n_buffer, n_channels, y_pix, x_pix))) + latents_ae.append(np.nan * np.zeros((n_buffer, n))) + latents_neural.append(np.nan * np.zeros((n_buffer, n))) + + latents_ae = np.vstack(latents_ae) + latents_neural = np.vstack(latents_neural) + if zscore_by_dim: + means = np.nanmean(latents_ae, axis=0) + std = np.nanstd(latents_ae, axis=0) + latents_ae = (latents_ae - means) / std + latents_neural = (latents_neural - means) / std # away make_neural_reconstruction_movie( - ims_orig=ims_orig_pt.cpu().detach().numpy(), - ims_recon_ae=ims_recon_ae, - ims_recon_neural=ims_recon_dec, - latents_ae=latents_ae[:, :max_latents], - latents_neural=latents_dec_pt.cpu().detach().numpy()[:, :max_latents], + ims_orig=np.vstack(ims_orig), + ims_recon_ae=np.vstack(ims_recon_ae), + ims_recon_neural=np.vstack(ims_recon_neural), + latents_ae=latents_ae, + latents_neural=latents_neural, + ae_model_class=hparams_ae['model_class'].upper(), + colored_predictions=colored_predictions, + xtick_locs=xtick_locs, + frame_rate_beh=hparams['frame_rate'], save_file=save_file, frame_rate=frame_rate) def make_neural_reconstruction_movie( - ims_orig, ims_recon_ae, ims_recon_neural, latents_ae, latents_neural, save_file=None, + ims_orig, ims_recon_ae, ims_recon_neural, latents_ae, latents_neural, ae_model_class='AE', + colored_predictions=False, scale=0.5, xtick_locs=None, frame_rate_beh=None, save_file=None, frame_rate=15): """Produce movie with original video, ae reconstructed video, and neural reconstructed video. @@ -312,13 +365,25 @@ def make_neural_reconstruction_movie( Parameters ---------- ims_orig : :obj:`np.ndarray` - shape (n_frames, n_channels, y_pix, x_pix) + original images; shape (n_frames, n_channels, y_pix, x_pix) ims_recon_ae : :obj:`np.ndarray` - shape (n_frames, n_channels, y_pix, x_pix) - ims_recon_neural : :obj:`np.ndarray`, optional - shape (n_frames, n_channels, y_pix, x_pix) - latents_ae : :obj:`np.ndarray`, optional - shape (n_frames, n_latents) + images reconstructed by AE; shape (n_frames, n_channels, y_pix, x_pix) + ims_recon_neural : :obj:`np.ndarray` + images reconstructed by neural activity; shape (n_frames, n_channels, y_pix, x_pix) + latents_ae : :obj:`np.ndarray` + original AE latents; shape (n_frames, n_latents) + latents_neural : :obj:`np.ndarray` + latents reconstruted by neural activity; shape (n_frames, n_latents) + ae_model_class : :obj:`str`, optional + 'AE', 'VAE', etc. for plot titles + colored_predictions : :obj:`bool`, optional + False to plot reconstructions in black, True to plot in different colors + scale : :obj:`int`, optional + scale magnitude of traces + xtick_locs : :obj:`array-like`, optional + tick locations in units of bins + frame_rate_beh : :obj:`float`, optional + frame rate of behavorial video; to properly relabel xticks save_file : :obj:`str`, optional full save file (path and filename) frame_rate : :obj:`float`, optional @@ -326,8 +391,8 @@ def make_neural_reconstruction_movie( """ - means = np.mean(latents_ae, axis=0) - std = np.std(latents_ae) * 2 + means = np.nanmean(latents_ae, axis=0) + std = np.nanstd(latents_ae) / scale latents_ae_sc = (latents_ae - means) / std latents_dec_sc = (latents_neural - means) / std @@ -364,14 +429,19 @@ def make_neural_reconstruction_movie( idx = 0 axs[idx].set_title('Original', fontsize=fontsize) idx += 1 - axs[idx].set_title('AE reconstructed', fontsize=fontsize) + axs[idx].set_title('%s reconstructed' % ae_model_class, fontsize=fontsize) idx += 1 axs[idx].set_title('Neural reconstructed', fontsize=fontsize) idx += 1 axs[idx].set_title('Reconstructions residual', fontsize=fontsize) idx += 1 - axs[idx].set_title('AE latent predictions', fontsize=fontsize) - axs[idx].set_xlabel('Time (bins)', fontsize=fontsize) + axs[idx].set_title('%s latent predictions' % ae_model_class, fontsize=fontsize) + if xtick_locs is not None and frame_rate_beh is not None: + axs[idx].set_xticks(xtick_locs) + axs[idx].set_xticklabels((np.asarray(xtick_locs) / frame_rate_beh).astype('int')) + axs[idx].set_xlabel('Time (s)', fontsize=fontsize) + else: + axs[idx].set_xlabel('Time (bins)', fontsize=fontsize) time = np.arange(n_time) @@ -380,7 +450,9 @@ def make_neural_reconstruction_movie( im_kwargs = {'animated': True, 'cmap': 'gray', 'vmin': 0, 'vmax': 1} tr_kwargs = {'animated': True, 'linewidth': 2} latents_ae_color = [0.2, 0.2, 0.2] - latents_dec_color = [0, 0, 0] + + label_ae_base = '%s latents' % ae_model_class + label_dec_base = 'Predicted %s latents' % ae_model_class # ims is a list of lists, each row is a list of artists to draw in the # current frame; here we are just animating one artist, the image, in @@ -425,11 +497,16 @@ def make_neural_reconstruction_movie( # traces ######## # latents over time + axs[idx].set_prop_cycle(None) # reset colors for latent in range(n_ae_latents): + if colored_predictions: + latents_dec_color = axs[idx]._get_lines.get_next_color() + else: + latents_dec_color = [0, 0, 0] # just put labels on last lvs if latent == n_ae_latents - 1 and i == 0: - label_ae = 'AE latents' - label_dec = 'Predicted AE latents' + label_ae = label_ae_base + label_dec = label_dec_base else: label_ae = None label_dec = None @@ -447,9 +524,24 @@ def make_neural_reconstruction_movie( axs[idx].spines['top'].set_visible(False) axs[idx].spines['right'].set_visible(False) axs[idx].spines['left'].set_visible(False) - plt.legend( - loc='lower right', fontsize=fontsize, frameon=True, - framealpha=0.7, edgecolor=[1, 1, 1]) + if colored_predictions: + # original latents - gray + orig_line = mlines.Line2D([], [], color=[0.2, 0.2, 0.2], linewidth=3, alpha=0.7) + # predicted latents - cycle through some colors + colors = plt.rcParams['axes.prop_cycle'].by_key()['color'] + dls = [] + for c in range(5): + dls.append(mlines.Line2D( + [], [], linewidth=3, linestyle='--', dashes=(0, 3 * c, 20, 1), + color='%s' % colors[c])) + plt.legend( + [orig_line, tuple(dls)], [label_ae_base, label_dec_base], + loc='lower right', fontsize=fontsize, frameon=True, framealpha=0.7, + edgecolor=[1, 1, 1]) + else: + plt.legend( + loc='lower right', fontsize=fontsize, frameon=True, + framealpha=0.7, edgecolor=[1, 1, 1]) ims_curr.append(im) ims.append(ims_curr) @@ -584,8 +676,6 @@ def plot_neural_reconstruction_traces( """ - import matplotlib.pyplot as plt - import matplotlib.lines as mlines import seaborn as sns sns.set_style('white') diff --git a/behavenet/plotting/arhmm_utils.py b/behavenet/plotting/arhmm_utils.py index 77652aa..e23dda0 100644 --- a/behavenet/plotting/arhmm_utils.py +++ b/behavenet/plotting/arhmm_utils.py @@ -639,7 +639,8 @@ def real_vs_sampled_wrapper( fig = plot_real_vs_sampled( latents, latents_samp, states, states_samp, save_file=save_file, xtick_locs=xtick_locs, - frame_rate=frame_rate_beh, format=format) + frame_rate=hparams['frame_rate'] if frame_rate_beh is None else frame_rate_beh, + format=format) if output_type == 'movie': return None From 0448122367b3594ccc623800d2ae56eecf4e9cef Mon Sep 17 00:00:00 2001 From: themattinthehatt Date: Mon, 7 Dec 2020 11:08:12 -0500 Subject: [PATCH 26/50] plotting function refactor --- behavenet/plotting/ae_utils.py | 544 +-------------------------- behavenet/plotting/decoder_utils.py | 548 +++++++++++++++++++++++++++- 2 files changed, 550 insertions(+), 542 deletions(-) diff --git a/behavenet/plotting/ae_utils.py b/behavenet/plotting/ae_utils.py index e60716d..a38b21b 100644 --- a/behavenet/plotting/ae_utils.py +++ b/behavenet/plotting/ae_utils.py @@ -1,22 +1,16 @@ """Plotting and video making functions for autoencoders.""" -import copy import matplotlib.animation as animation -import matplotlib.lines as mlines import matplotlib.pyplot as plt from matplotlib.gridspec import GridSpec from matplotlib.animation import FFMpegWriter -import numpy as np -from behavenet.plotting import concat from behavenet import make_dir_if_not_exists -from behavenet.fitting.utils import get_best_model_and_data from behavenet.fitting.eval import get_reconstruction +from behavenet.fitting.utils import get_best_model_and_data +from behavenet.plotting import concat # to ignore imports for sphix-autoapidoc -__all__ = [ - 'make_ae_reconstruction_movie_wrapper', 'make_reconstruction_movie', - 'make_neural_reconstruction_movie_wrapper', 'make_neural_reconstruction_movie', - 'plot_neural_reconstruction_traces_wrapper', 'plot_neural_reconstruction_traces'] +__all__ = ['make_ae_reconstruction_movie_wrapper', 'make_reconstruction_movie'] def make_reconstruction_movie( @@ -205,535 +199,3 @@ def make_ae_reconstruction_movie_wrapper( make_reconstruction_movie( ims=ims, titles=titles, n_rows=n_rows, n_cols=n_cols, save_file=save_file, frame_rate=frame_rate) - - -def make_neural_reconstruction_movie_wrapper( - hparams, save_file, trials=None, sess_idx=0, max_frames=400, max_latents=8, - zscore_by_dim=False, colored_predictions=False, xtick_locs=None, frame_rate=15): - """Produce movie with original video, ae reconstructed video, and neural reconstructed video. - - This is a high-level function that loads the model described in the hparams dictionary and - produces the necessary predicted video frames. Latent traces are additionally plotted, as well - as the residual between the ae reconstruction and the neural reconstruction. Currently produces - ae latents and decoder predictions from scratch (rather than saved pickle files). - - Parameters - ---------- - hparams : :obj:`dict` - needs to contain enough information to specify an autoencoder - save_file : :obj:`str` - full save file (path and filename) - trials : :obj:`int` or :obj:`list`, optional - if :obj:`NoneType`, use first test trial - sess_idx : :obj:`int`, optional - session index into data generator - max_frames : :obj:`int`, optional - maximum number of frames to animate from a trial - max_latents : :obj:`int`, optional - maximum number of ae latents to plot - zscore_by_dim : :obj:`bool`, optional - True to z-score each dim, False to leave relative scales - colored_predictions : :obj:`bool`, optional - False to plot reconstructions in black, True to plot in different colors - xtick_locs : :obj:`array-like`, optional - tick locations in units of bins - frame_rate : :obj:`float`, optional - frame rate of saved movie - - """ - - from behavenet.models import Decoder - - # define number of frames that separate trials - n_buffer = 5 - - ############################### - # build ae model/data generator - ############################### - hparams_ae = copy.copy(hparams) - hparams_ae['experiment_name'] = hparams['ae_experiment_name'] - hparams_ae['model_class'] = hparams['ae_model_class'] - hparams_ae['model_type'] = hparams['ae_model_type'] - model_ae, data_generator_ae = get_best_model_and_data( - hparams_ae, Model=None, version=hparams['ae_version']) - # move model to cpu - model_ae.to('cpu') - - ####################################### - # build decoder model/no data generator - ####################################### - hparams_dec = copy.copy(hparams) - hparams_dec['experiment_name'] = hparams['decoder_experiment_name'] - hparams_dec['model_class'] = hparams['decoder_model_class'] - hparams_dec['model_type'] = hparams['decoder_model_type'] - - model_dec, data_generator_dec = get_best_model_and_data( - hparams_dec, Decoder, version=hparams['decoder_version']) - # move model to cpu - model_dec.to('cpu') - - if trials is None: - # choose first test trial, put in list - trials = data_generator_ae.batch_idxs[sess_idx]['test'][0] - - if isinstance(trials, int): - trials = [trials] - - # loop over trials, putting black frames/nans in between - ims_orig = [] - ims_recon_ae = [] - ims_recon_neural = [] - latents_ae = [] - latents_neural = [] - for i, trial in enumerate(trials): - - # get images from data generator (move to cpu) - batch = data_generator_ae.datasets[sess_idx][trial] - ims_orig_pt = batch['images'][:max_frames].cpu() # 400 - if hparams_ae['model_class'] == 'cond-ae': - labels_pt = batch['labels'][:max_frames] - else: - labels_pt = None - - # push images through ae to get reconstruction - ims_recon_ae_curr, latents_ae_curr = get_reconstruction( - model_ae, ims_orig_pt, labels=labels_pt, return_latents=True) - - # mask images for plotting - if hparams_ae.get('use_output_mask', False): - ims_orig_pt *= batch['masks'][:max_frames] - - # get neural activity from data generator (move to cpu) - # 0, not sess_idx, since decoders only have 1 sess - batch = data_generator_dec.datasets[0][trial] - neural_activity_pt = batch['neural'][:max_frames].cpu() - - # push neural activity through decoder to get prediction - latents_dec_pt, _ = model_dec(neural_activity_pt) - # push prediction through ae to get reconstruction - ims_recon_dec_curr = get_reconstruction(model_ae, latents_dec_pt, labels=labels_pt) - - # store all relevant quantities - ims_orig.append(ims_orig_pt.cpu().detach().numpy()) - ims_recon_ae.append(ims_recon_ae_curr) - ims_recon_neural.append(ims_recon_dec_curr) - latents_ae.append(latents_ae_curr[:, :max_latents]) - latents_neural.append(latents_dec_pt.cpu().detach().numpy()[:, :max_latents]) - - # add blank frames - if i < len(trials) - 1: - n_channels, y_pix, x_pix = ims_orig[-1].shape[1:] - n = latents_ae[-1].shape[1] - ims_orig.append(np.zeros((n_buffer, n_channels, y_pix, x_pix))) - ims_recon_ae.append(np.zeros((n_buffer, n_channels, y_pix, x_pix))) - ims_recon_neural.append(np.zeros((n_buffer, n_channels, y_pix, x_pix))) - latents_ae.append(np.nan * np.zeros((n_buffer, n))) - latents_neural.append(np.nan * np.zeros((n_buffer, n))) - - latents_ae = np.vstack(latents_ae) - latents_neural = np.vstack(latents_neural) - if zscore_by_dim: - means = np.nanmean(latents_ae, axis=0) - std = np.nanstd(latents_ae, axis=0) - latents_ae = (latents_ae - means) / std - latents_neural = (latents_neural - means) / std - - # away - make_neural_reconstruction_movie( - ims_orig=np.vstack(ims_orig), - ims_recon_ae=np.vstack(ims_recon_ae), - ims_recon_neural=np.vstack(ims_recon_neural), - latents_ae=latents_ae, - latents_neural=latents_neural, - ae_model_class=hparams_ae['model_class'].upper(), - colored_predictions=colored_predictions, - xtick_locs=xtick_locs, - frame_rate_beh=hparams['frame_rate'], - save_file=save_file, - frame_rate=frame_rate) - - -def make_neural_reconstruction_movie( - ims_orig, ims_recon_ae, ims_recon_neural, latents_ae, latents_neural, ae_model_class='AE', - colored_predictions=False, scale=0.5, xtick_locs=None, frame_rate_beh=None, save_file=None, - frame_rate=15): - """Produce movie with original video, ae reconstructed video, and neural reconstructed video. - - Latent traces are additionally plotted, as well as the residual between the ae reconstruction - and the neural reconstruction. - - Parameters - ---------- - ims_orig : :obj:`np.ndarray` - original images; shape (n_frames, n_channels, y_pix, x_pix) - ims_recon_ae : :obj:`np.ndarray` - images reconstructed by AE; shape (n_frames, n_channels, y_pix, x_pix) - ims_recon_neural : :obj:`np.ndarray` - images reconstructed by neural activity; shape (n_frames, n_channels, y_pix, x_pix) - latents_ae : :obj:`np.ndarray` - original AE latents; shape (n_frames, n_latents) - latents_neural : :obj:`np.ndarray` - latents reconstruted by neural activity; shape (n_frames, n_latents) - ae_model_class : :obj:`str`, optional - 'AE', 'VAE', etc. for plot titles - colored_predictions : :obj:`bool`, optional - False to plot reconstructions in black, True to plot in different colors - scale : :obj:`int`, optional - scale magnitude of traces - xtick_locs : :obj:`array-like`, optional - tick locations in units of bins - frame_rate_beh : :obj:`float`, optional - frame rate of behavorial video; to properly relabel xticks - save_file : :obj:`str`, optional - full save file (path and filename) - frame_rate : :obj:`float`, optional - frame rate of saved movie - - """ - - means = np.nanmean(latents_ae, axis=0) - std = np.nanstd(latents_ae) / scale - - latents_ae_sc = (latents_ae - means) / std - latents_dec_sc = (latents_neural - means) / std - - n_channels, y_pix, x_pix = ims_orig.shape[1:] - n_time, n_ae_latents = latents_ae.shape - - n_cols = 3 - n_rows = 2 - offset = 2 # 0 if ims_recon_lin is None else 1 - scale_ = 5 - fig_width = scale_ * n_cols * n_channels / 2 - fig_height = y_pix / x_pix * scale_ * n_rows / 2 - fig = plt.figure(figsize=(fig_width, fig_height + offset)) - - gs = GridSpec(n_rows, n_cols, figure=fig) - axs = [] - axs.append(fig.add_subplot(gs[0, 0])) # 0: original frames - axs.append(fig.add_subplot(gs[0, 1])) # 1: ae reconstructed frames - axs.append(fig.add_subplot(gs[0, 2])) # 2: neural reconstructed frames - axs.append(fig.add_subplot(gs[1, 0])) # 3: residual - axs.append(fig.add_subplot(gs[1, 1:3])) # 4: ae and predicted ae latents - for i, ax in enumerate(fig.axes): - ax.set_yticks([]) - if i > 2: - ax.get_xaxis().set_tick_params(labelsize=12, direction='in') - axs[0].set_xticks([]) - axs[1].set_xticks([]) - axs[2].set_xticks([]) - axs[3].set_xticks([]) - - # check that the axes are correct - fontsize = 12 - idx = 0 - axs[idx].set_title('Original', fontsize=fontsize) - idx += 1 - axs[idx].set_title('%s reconstructed' % ae_model_class, fontsize=fontsize) - idx += 1 - axs[idx].set_title('Neural reconstructed', fontsize=fontsize) - idx += 1 - axs[idx].set_title('Reconstructions residual', fontsize=fontsize) - idx += 1 - axs[idx].set_title('%s latent predictions' % ae_model_class, fontsize=fontsize) - if xtick_locs is not None and frame_rate_beh is not None: - axs[idx].set_xticks(xtick_locs) - axs[idx].set_xticklabels((np.asarray(xtick_locs) / frame_rate_beh).astype('int')) - axs[idx].set_xlabel('Time (s)', fontsize=fontsize) - else: - axs[idx].set_xlabel('Time (bins)', fontsize=fontsize) - - time = np.arange(n_time) - - ims_res = ims_recon_ae - ims_recon_neural - - im_kwargs = {'animated': True, 'cmap': 'gray', 'vmin': 0, 'vmax': 1} - tr_kwargs = {'animated': True, 'linewidth': 2} - latents_ae_color = [0.2, 0.2, 0.2] - - label_ae_base = '%s latents' % ae_model_class - label_dec_base = 'Predicted %s latents' % ae_model_class - - # ims is a list of lists, each row is a list of artists to draw in the - # current frame; here we are just animating one artist, the image, in - # each frame - ims = [] - for i in range(n_time): - - ims_curr = [] - idx = 0 - - if i % 100 == 0: - print('processing frame %03i/%03i' % (i, n_time)) - - ################### - # behavioral videos - ################### - # original video - ims_tmp = ims_orig[i, 0] if n_channels == 1 else concat(ims_orig[i]) - im = axs[idx].imshow(ims_tmp, **im_kwargs) - ims_curr.append(im) - idx += 1 - - # ae reconstruction - ims_tmp = ims_recon_ae[i, 0] if n_channels == 1 else concat(ims_recon_ae[i]) - im = axs[idx].imshow(ims_tmp, **im_kwargs) - ims_curr.append(im) - idx += 1 - - # neural reconstruction - ims_tmp = ims_recon_neural[i, 0] if n_channels == 1 else concat(ims_recon_neural[i]) - im = axs[idx].imshow(ims_tmp, **im_kwargs) - ims_curr.append(im) - idx += 1 - - # residual - ims_tmp = ims_res[i, 0] if n_channels == 1 else concat(ims_res[i]) - im = axs[idx].imshow(0.5 + ims_tmp, **im_kwargs) - ims_curr.append(im) - idx += 1 - - ######## - # traces - ######## - # latents over time - axs[idx].set_prop_cycle(None) # reset colors - for latent in range(n_ae_latents): - if colored_predictions: - latents_dec_color = axs[idx]._get_lines.get_next_color() - else: - latents_dec_color = [0, 0, 0] - # just put labels on last lvs - if latent == n_ae_latents - 1 and i == 0: - label_ae = label_ae_base - label_dec = label_dec_base - else: - label_ae = None - label_dec = None - im = axs[idx].plot( - time[0:i + 1], latent + latents_ae_sc[0:i + 1, latent], - color=latents_ae_color, alpha=0.7, label=label_ae, - **tr_kwargs)[0] - axs[idx].spines['top'].set_visible(False) - axs[idx].spines['right'].set_visible(False) - axs[idx].spines['left'].set_visible(False) - ims_curr.append(im) - im = axs[idx].plot( - time[0:i + 1], latent + latents_dec_sc[0:i + 1, latent], - color=latents_dec_color, label=label_dec, **tr_kwargs)[0] - axs[idx].spines['top'].set_visible(False) - axs[idx].spines['right'].set_visible(False) - axs[idx].spines['left'].set_visible(False) - if colored_predictions: - # original latents - gray - orig_line = mlines.Line2D([], [], color=[0.2, 0.2, 0.2], linewidth=3, alpha=0.7) - # predicted latents - cycle through some colors - colors = plt.rcParams['axes.prop_cycle'].by_key()['color'] - dls = [] - for c in range(5): - dls.append(mlines.Line2D( - [], [], linewidth=3, linestyle='--', dashes=(0, 3 * c, 20, 1), - color='%s' % colors[c])) - plt.legend( - [orig_line, tuple(dls)], [label_ae_base, label_dec_base], - loc='lower right', fontsize=fontsize, frameon=True, framealpha=0.7, - edgecolor=[1, 1, 1]) - else: - plt.legend( - loc='lower right', fontsize=fontsize, frameon=True, - framealpha=0.7, edgecolor=[1, 1, 1]) - ims_curr.append(im) - ims.append(ims_curr) - - plt.tight_layout(pad=0) - - ani = animation.ArtistAnimation(fig, ims, blit=True, repeat_delay=1000) - writer = FFMpegWriter(fps=frame_rate, bitrate=-1) - - if save_file is not None: - make_dir_if_not_exists(save_file) - if save_file[-3:] != 'mp4': - save_file += '.mp4' - print('saving video to %s...' % save_file, end='') - ani.save(save_file, writer=writer) - print('done') - - -def plot_neural_reconstruction_traces_wrapper( - hparams, save_file=None, trial=None, xtick_locs=None, frame_rate=None, format='png', - **kwargs): - """Plot ae latents and their neural reconstructions. - - This is a high-level function that loads the model described in the hparams dictionary and - produces the necessary predicted latents. - - Parameters - ---------- - hparams : :obj:`dict` - needs to contain enough information to specify an ae latent decoder - save_file : :obj:`str` - full save file (path and filename) - trial : :obj:`int`, optional - if :obj:`NoneType`, use first test trial - xtick_locs : :obj:`array-like`, optional - tick locations in units of bins - frame_rate : :obj:`float`, optional - frame rate of behavorial video; to properly relabel xticks - format : :obj:`str`, optional - any accepted matplotlib save format, e.g. 'png' | 'pdf' | 'jpeg' - - Returns - ------- - :obj:`matplotlib.figure.Figure` - matplotlib figure handle of plot - - """ - - # find good trials - import copy - from behavenet.data.utils import get_transforms_paths - from behavenet.data.data_generator import ConcatSessionsGenerator - - # ae data - hparams_ae = copy.copy(hparams) - hparams_ae['experiment_name'] = hparams['ae_experiment_name'] - hparams_ae['model_class'] = hparams['ae_model_class'] - hparams_ae['model_type'] = hparams['ae_model_type'] - - ae_transform, ae_path = get_transforms_paths('ae_latents', hparams_ae, None) - - # ae predictions data - hparams_dec = copy.copy(hparams) - hparams_dec['neural_ae_experiment_name'] = hparams['decoder_experiment_name'] - hparams_dec['neural_ae_model_class'] = hparams['decoder_model_class'] - hparams_dec['neural_ae_model_type'] = hparams['decoder_model_type'] - ae_pred_transform, ae_pred_path = get_transforms_paths( - 'neural_ae_predictions', hparams_dec, None) - - signals = ['ae_latents', 'ae_predictions'] - transforms = [ae_transform, ae_pred_transform] - paths = [ae_path, ae_pred_path] - - data_generator = ConcatSessionsGenerator( - hparams['data_dir'], [hparams], - signals_list=[signals], transforms_list=[transforms], paths_list=[paths], - device='cpu', as_numpy=False, batch_load=True, rng_seed=0) - - if trial is None: - # choose first test trial - trial = data_generator.datasets[0].batch_idxs['test'][0] - - batch = data_generator.datasets[0][trial] - traces_ae = batch['ae_latents'].cpu().detach().numpy() - traces_neural = batch['ae_predictions'].cpu().detach().numpy() - - n_max_lags = hparams.get('n_max_lags', 0) # only plot valid segment of data - if n_max_lags > 0: - fig = plot_neural_reconstruction_traces( - traces_ae[n_max_lags:-n_max_lags], traces_neural[n_max_lags:-n_max_lags], - save_file, xtick_locs, frame_rate, format, **kwargs) - else: - fig = plot_neural_reconstruction_traces( - traces_ae, traces_neural, save_file, xtick_locs, frame_rate, format, **kwargs) - return fig - - -def plot_neural_reconstruction_traces( - traces_ae, traces_neural, save_file=None, xtick_locs=None, frame_rate=None, format='png', - scale=0.5, max_traces=8, add_r2=True, add_legend=True, colored_predictions=True): - """Plot ae latents and their neural reconstructions. - - Parameters - ---------- - traces_ae : :obj:`np.ndarray` - shape (n_frames, n_latents) - traces_neural : :obj:`np.ndarray` - shape (n_frames, n_latents) - save_file : :obj:`str`, optional - full save file (path and filename) - xtick_locs : :obj:`array-like`, optional - tick locations in units of bins - frame_rate : :obj:`float`, optional - frame rate of behavorial video; to properly relabel xticks - format : :obj:`str`, optional - any accepted matplotlib save format, e.g. 'png' | 'pdf' | 'jpeg' - scale : :obj:`int`, optional - scale magnitude of traces - max_traces : :obj:`int`, optional - maximum number of traces to plot, for easier visualization - add_r2 : :obj:`bool`, optional - print R2 value on plot - add_legend : :obj:`bool`, optional - print legend on plot - colored_predictions : :obj:`bool`, optional - color predictions using default seaborn colormap; else predictions are black - - - Returns - ------- - :obj:`matplotlib.figure.Figure` - matplotlib figure handle - - """ - - import seaborn as sns - - sns.set_style('white') - sns.set_context('poster') - - means = np.nanmean(traces_ae, axis=0) - std = np.nanstd(traces_ae) / scale # scale for better visualization - - traces_ae_sc = (traces_ae - means) / std - traces_neural_sc = (traces_neural - means) / std - - traces_ae_sc = traces_ae_sc[:, :max_traces] - traces_neural_sc = traces_neural_sc[:, :max_traces] - - fig = plt.figure(figsize=(12, 8)) - if colored_predictions: - plt.plot(traces_neural_sc + np.arange(traces_neural_sc.shape[1]), linewidth=3) - else: - plt.plot(traces_neural_sc + np.arange(traces_neural_sc.shape[1]), linewidth=3, color='k') - plt.plot( - traces_ae_sc + np.arange(traces_ae_sc.shape[1]), color=[0.2, 0.2, 0.2], linewidth=3, - alpha=0.7) - - # add legend if desired - if add_legend: - # original latents - gray - orig_line = mlines.Line2D([], [], color=[0.2, 0.2, 0.2], linewidth=3, alpha=0.7) - # predicted latents - cycle through some colors - colors = plt.rcParams['axes.prop_cycle'].by_key()['color'] - dls = [] - for c in range(5): - dls.append(mlines.Line2D( - [], [], linewidth=3, linestyle='--', dashes=(0, 3 * c, 20, 1), - color='%s' % colors[c])) - plt.legend( - [orig_line, tuple(dls)], ['Original latents', 'Predicted latents'], - loc='lower right', frameon=True, framealpha=0.7, edgecolor=[1, 1, 1]) - - # add r2 info if desired - if add_r2: - from sklearn.metrics import r2_score - r2 = r2_score(traces_ae, traces_neural, multioutput='variance_weighted') - plt.text( - 0.05, 0.06, '$R^2$=%1.3f' % r2, horizontalalignment='left', verticalalignment='bottom', - transform=plt.gca().transAxes, - bbox=dict(facecolor='white', alpha=0.7, edgecolor=[1, 1, 1])) - - if xtick_locs is not None and frame_rate is not None: - plt.xticks(xtick_locs, (np.asarray(xtick_locs) / frame_rate).astype('int')) - plt.xlabel('Time (s)') - else: - plt.xlabel('Time (bins)') - plt.ylabel('Latent state') - plt.yticks([]) - - if save_file is not None: - make_dir_if_not_exists(save_file) - plt.savefig(save_file + '.' + format, dpi=300, format=format) - - plt.show() - return fig diff --git a/behavenet/plotting/decoder_utils.py b/behavenet/plotting/decoder_utils.py index b87f67a..2cce1f2 100644 --- a/behavenet/plotting/decoder_utils.py +++ b/behavenet/plotting/decoder_utils.py @@ -1,15 +1,29 @@ """Plotting functions for decoders.""" +import copy +import matplotlib.animation as animation +import matplotlib.lines as mlines +import matplotlib.pyplot as plt +from matplotlib.gridspec import GridSpec +from matplotlib.animation import FFMpegWriter +import numpy as np import os import pandas as pd import pickle +from behavenet import make_dir_if_not_exists +from behavenet.fitting.eval import get_reconstruction +from behavenet.fitting.utils import get_best_model_and_data from behavenet.data.utils import get_region_list from behavenet.fitting.utils import get_expt_dir from behavenet.fitting.utils import get_session_dir from behavenet.fitting.utils import get_subdirs +from behavenet.plotting import concat # to ignore imports for sphix-autoapidoc -__all__ = ['get_r2s_by_trial', 'get_best_models', 'get_r2s_across_trials'] +__all__ = [ + 'get_r2s_by_trial', 'get_best_models', 'get_r2s_across_trials', + 'make_neural_reconstruction_movie_wrapper', 'make_neural_reconstruction_movie', + 'plot_neural_reconstruction_traces_wrapper', 'plot_neural_reconstruction_traces'] def _get_dataset_str(hparams): @@ -176,3 +190,535 @@ def get_r2s_across_trials(hparams, best_models_df): 'model_type': hparams['model_type'], 'r2': r2}, index=[0])) return pd.concat(all_test_r2s) + + +def make_neural_reconstruction_movie_wrapper( + hparams, save_file, trials=None, sess_idx=0, max_frames=400, max_latents=8, + zscore_by_dim=False, colored_predictions=False, xtick_locs=None, frame_rate=15): + """Produce movie with original video, ae reconstructed video, and neural reconstructed video. + + This is a high-level function that loads the model described in the hparams dictionary and + produces the necessary predicted video frames. Latent traces are additionally plotted, as well + as the residual between the ae reconstruction and the neural reconstruction. Currently produces + ae latents and decoder predictions from scratch (rather than saved pickle files). + + Parameters + ---------- + hparams : :obj:`dict` + needs to contain enough information to specify an autoencoder + save_file : :obj:`str` + full save file (path and filename) + trials : :obj:`int` or :obj:`list`, optional + if :obj:`NoneType`, use first test trial + sess_idx : :obj:`int`, optional + session index into data generator + max_frames : :obj:`int`, optional + maximum number of frames to animate from a trial + max_latents : :obj:`int`, optional + maximum number of ae latents to plot + zscore_by_dim : :obj:`bool`, optional + True to z-score each dim, False to leave relative scales + colored_predictions : :obj:`bool`, optional + False to plot reconstructions in black, True to plot in different colors + xtick_locs : :obj:`array-like`, optional + tick locations in units of bins + frame_rate : :obj:`float`, optional + frame rate of saved movie + + """ + + from behavenet.models import Decoder + + # define number of frames that separate trials + n_buffer = 5 + + ############################### + # build ae model/data generator + ############################### + hparams_ae = copy.copy(hparams) + hparams_ae['experiment_name'] = hparams['ae_experiment_name'] + hparams_ae['model_class'] = hparams['ae_model_class'] + hparams_ae['model_type'] = hparams['ae_model_type'] + model_ae, data_generator_ae = get_best_model_and_data( + hparams_ae, Model=None, version=hparams['ae_version']) + # move model to cpu + model_ae.to('cpu') + + ####################################### + # build decoder model/no data generator + ####################################### + hparams_dec = copy.copy(hparams) + hparams_dec['experiment_name'] = hparams['decoder_experiment_name'] + hparams_dec['model_class'] = hparams['decoder_model_class'] + hparams_dec['model_type'] = hparams['decoder_model_type'] + + model_dec, data_generator_dec = get_best_model_and_data( + hparams_dec, Decoder, version=hparams['decoder_version']) + # move model to cpu + model_dec.to('cpu') + + if trials is None: + # choose first test trial, put in list + trials = data_generator_ae.batch_idxs[sess_idx]['test'][0] + + if isinstance(trials, int): + trials = [trials] + + # loop over trials, putting black frames/nans in between + ims_orig = [] + ims_recon_ae = [] + ims_recon_neural = [] + latents_ae = [] + latents_neural = [] + for i, trial in enumerate(trials): + + # get images from data generator (move to cpu) + batch = data_generator_ae.datasets[sess_idx][trial] + ims_orig_pt = batch['images'][:max_frames].cpu() # 400 + if hparams_ae['model_class'] == 'cond-ae': + labels_pt = batch['labels'][:max_frames] + else: + labels_pt = None + + # push images through ae to get reconstruction + ims_recon_ae_curr, latents_ae_curr = get_reconstruction( + model_ae, ims_orig_pt, labels=labels_pt, return_latents=True) + + # mask images for plotting + if hparams_ae.get('use_output_mask', False): + ims_orig_pt *= batch['masks'][:max_frames] + + # get neural activity from data generator (move to cpu) + # 0, not sess_idx, since decoders only have 1 sess + batch = data_generator_dec.datasets[0][trial] + neural_activity_pt = batch['neural'][:max_frames].cpu() + + # push neural activity through decoder to get prediction + latents_dec_pt, _ = model_dec(neural_activity_pt) + # push prediction through ae to get reconstruction + ims_recon_dec_curr = get_reconstruction(model_ae, latents_dec_pt, labels=labels_pt) + + # store all relevant quantities + ims_orig.append(ims_orig_pt.cpu().detach().numpy()) + ims_recon_ae.append(ims_recon_ae_curr) + ims_recon_neural.append(ims_recon_dec_curr) + latents_ae.append(latents_ae_curr[:, :max_latents]) + latents_neural.append(latents_dec_pt.cpu().detach().numpy()[:, :max_latents]) + + # add blank frames + if i < len(trials) - 1: + n_channels, y_pix, x_pix = ims_orig[-1].shape[1:] + n = latents_ae[-1].shape[1] + ims_orig.append(np.zeros((n_buffer, n_channels, y_pix, x_pix))) + ims_recon_ae.append(np.zeros((n_buffer, n_channels, y_pix, x_pix))) + ims_recon_neural.append(np.zeros((n_buffer, n_channels, y_pix, x_pix))) + latents_ae.append(np.nan * np.zeros((n_buffer, n))) + latents_neural.append(np.nan * np.zeros((n_buffer, n))) + + latents_ae = np.vstack(latents_ae) + latents_neural = np.vstack(latents_neural) + if zscore_by_dim: + means = np.nanmean(latents_ae, axis=0) + std = np.nanstd(latents_ae, axis=0) + latents_ae = (latents_ae - means) / std + latents_neural = (latents_neural - means) / std + + # away + make_neural_reconstruction_movie( + ims_orig=np.vstack(ims_orig), + ims_recon_ae=np.vstack(ims_recon_ae), + ims_recon_neural=np.vstack(ims_recon_neural), + latents_ae=latents_ae, + latents_neural=latents_neural, + ae_model_class=hparams_ae['model_class'].upper(), + colored_predictions=colored_predictions, + xtick_locs=xtick_locs, + frame_rate_beh=hparams['frame_rate'], + save_file=save_file, + frame_rate=frame_rate) + + +def make_neural_reconstruction_movie( + ims_orig, ims_recon_ae, ims_recon_neural, latents_ae, latents_neural, ae_model_class='AE', + colored_predictions=False, scale=0.5, xtick_locs=None, frame_rate_beh=None, save_file=None, + frame_rate=15): + """Produce movie with original video, ae reconstructed video, and neural reconstructed video. + + Latent traces are additionally plotted, as well as the residual between the ae reconstruction + and the neural reconstruction. + + Parameters + ---------- + ims_orig : :obj:`np.ndarray` + original images; shape (n_frames, n_channels, y_pix, x_pix) + ims_recon_ae : :obj:`np.ndarray` + images reconstructed by AE; shape (n_frames, n_channels, y_pix, x_pix) + ims_recon_neural : :obj:`np.ndarray` + images reconstructed by neural activity; shape (n_frames, n_channels, y_pix, x_pix) + latents_ae : :obj:`np.ndarray` + original AE latents; shape (n_frames, n_latents) + latents_neural : :obj:`np.ndarray` + latents reconstruted by neural activity; shape (n_frames, n_latents) + ae_model_class : :obj:`str`, optional + 'AE', 'VAE', etc. for plot titles + colored_predictions : :obj:`bool`, optional + False to plot reconstructions in black, True to plot in different colors + scale : :obj:`int`, optional + scale magnitude of traces + xtick_locs : :obj:`array-like`, optional + tick locations in units of bins + frame_rate_beh : :obj:`float`, optional + frame rate of behavorial video; to properly relabel xticks + save_file : :obj:`str`, optional + full save file (path and filename) + frame_rate : :obj:`float`, optional + frame rate of saved movie + + """ + + means = np.nanmean(latents_ae, axis=0) + std = np.nanstd(latents_ae) / scale + + latents_ae_sc = (latents_ae - means) / std + latents_dec_sc = (latents_neural - means) / std + + n_channels, y_pix, x_pix = ims_orig.shape[1:] + n_time, n_ae_latents = latents_ae.shape + + n_cols = 3 + n_rows = 2 + offset = 2 # 0 if ims_recon_lin is None else 1 + scale_ = 5 + fig_width = scale_ * n_cols * n_channels / 2 + fig_height = y_pix / x_pix * scale_ * n_rows / 2 + fig = plt.figure(figsize=(fig_width, fig_height + offset)) + + gs = GridSpec(n_rows, n_cols, figure=fig) + axs = [] + axs.append(fig.add_subplot(gs[0, 0])) # 0: original frames + axs.append(fig.add_subplot(gs[0, 1])) # 1: ae reconstructed frames + axs.append(fig.add_subplot(gs[0, 2])) # 2: neural reconstructed frames + axs.append(fig.add_subplot(gs[1, 0])) # 3: residual + axs.append(fig.add_subplot(gs[1, 1:3])) # 4: ae and predicted ae latents + for i, ax in enumerate(fig.axes): + ax.set_yticks([]) + if i > 2: + ax.get_xaxis().set_tick_params(labelsize=12, direction='in') + axs[0].set_xticks([]) + axs[1].set_xticks([]) + axs[2].set_xticks([]) + axs[3].set_xticks([]) + + # check that the axes are correct + fontsize = 12 + idx = 0 + axs[idx].set_title('Original', fontsize=fontsize) + idx += 1 + axs[idx].set_title('%s reconstructed' % ae_model_class, fontsize=fontsize) + idx += 1 + axs[idx].set_title('Neural reconstructed', fontsize=fontsize) + idx += 1 + axs[idx].set_title('Reconstructions residual', fontsize=fontsize) + idx += 1 + axs[idx].set_title('%s latent predictions' % ae_model_class, fontsize=fontsize) + if xtick_locs is not None and frame_rate_beh is not None: + axs[idx].set_xticks(xtick_locs) + axs[idx].set_xticklabels((np.asarray(xtick_locs) / frame_rate_beh).astype('int')) + axs[idx].set_xlabel('Time (s)', fontsize=fontsize) + else: + axs[idx].set_xlabel('Time (bins)', fontsize=fontsize) + + time = np.arange(n_time) + + ims_res = ims_recon_ae - ims_recon_neural + + im_kwargs = {'animated': True, 'cmap': 'gray', 'vmin': 0, 'vmax': 1} + tr_kwargs = {'animated': True, 'linewidth': 2} + latents_ae_color = [0.2, 0.2, 0.2] + + label_ae_base = '%s latents' % ae_model_class + label_dec_base = 'Predicted %s latents' % ae_model_class + + # ims is a list of lists, each row is a list of artists to draw in the + # current frame; here we are just animating one artist, the image, in + # each frame + ims = [] + for i in range(n_time): + + ims_curr = [] + idx = 0 + + if i % 100 == 0: + print('processing frame %03i/%03i' % (i, n_time)) + + ################### + # behavioral videos + ################### + # original video + ims_tmp = ims_orig[i, 0] if n_channels == 1 else concat(ims_orig[i]) + im = axs[idx].imshow(ims_tmp, **im_kwargs) + ims_curr.append(im) + idx += 1 + + # ae reconstruction + ims_tmp = ims_recon_ae[i, 0] if n_channels == 1 else concat(ims_recon_ae[i]) + im = axs[idx].imshow(ims_tmp, **im_kwargs) + ims_curr.append(im) + idx += 1 + + # neural reconstruction + ims_tmp = ims_recon_neural[i, 0] if n_channels == 1 else concat(ims_recon_neural[i]) + im = axs[idx].imshow(ims_tmp, **im_kwargs) + ims_curr.append(im) + idx += 1 + + # residual + ims_tmp = ims_res[i, 0] if n_channels == 1 else concat(ims_res[i]) + im = axs[idx].imshow(0.5 + ims_tmp, **im_kwargs) + ims_curr.append(im) + idx += 1 + + ######## + # traces + ######## + # latents over time + axs[idx].set_prop_cycle(None) # reset colors + for latent in range(n_ae_latents): + if colored_predictions: + latents_dec_color = axs[idx]._get_lines.get_next_color() + else: + latents_dec_color = [0, 0, 0] + # just put labels on last lvs + if latent == n_ae_latents - 1 and i == 0: + label_ae = label_ae_base + label_dec = label_dec_base + else: + label_ae = None + label_dec = None + im = axs[idx].plot( + time[0:i + 1], latent + latents_ae_sc[0:i + 1, latent], + color=latents_ae_color, alpha=0.7, label=label_ae, + **tr_kwargs)[0] + axs[idx].spines['top'].set_visible(False) + axs[idx].spines['right'].set_visible(False) + axs[idx].spines['left'].set_visible(False) + ims_curr.append(im) + im = axs[idx].plot( + time[0:i + 1], latent + latents_dec_sc[0:i + 1, latent], + color=latents_dec_color, label=label_dec, **tr_kwargs)[0] + axs[idx].spines['top'].set_visible(False) + axs[idx].spines['right'].set_visible(False) + axs[idx].spines['left'].set_visible(False) + if colored_predictions: + # original latents - gray + orig_line = mlines.Line2D([], [], color=[0.2, 0.2, 0.2], linewidth=3, alpha=0.7) + # predicted latents - cycle through some colors + colors = plt.rcParams['axes.prop_cycle'].by_key()['color'] + dls = [] + for c in range(5): + dls.append(mlines.Line2D( + [], [], linewidth=3, linestyle='--', dashes=(0, 3 * c, 20, 1), + color='%s' % colors[c])) + plt.legend( + [orig_line, tuple(dls)], [label_ae_base, label_dec_base], + loc='lower right', fontsize=fontsize, frameon=True, framealpha=0.7, + edgecolor=[1, 1, 1]) + else: + plt.legend( + loc='lower right', fontsize=fontsize, frameon=True, + framealpha=0.7, edgecolor=[1, 1, 1]) + ims_curr.append(im) + ims.append(ims_curr) + + plt.tight_layout(pad=0) + + ani = animation.ArtistAnimation(fig, ims, blit=True, repeat_delay=1000) + writer = FFMpegWriter(fps=frame_rate, bitrate=-1) + + if save_file is not None: + make_dir_if_not_exists(save_file) + if save_file[-3:] != 'mp4': + save_file += '.mp4' + print('saving video to %s...' % save_file, end='') + ani.save(save_file, writer=writer) + print('done') + + +def plot_neural_reconstruction_traces_wrapper( + hparams, save_file=None, trial=None, xtick_locs=None, frame_rate=None, format='png', + **kwargs): + """Plot ae latents and their neural reconstructions. + + This is a high-level function that loads the model described in the hparams dictionary and + produces the necessary predicted latents. + + Parameters + ---------- + hparams : :obj:`dict` + needs to contain enough information to specify an ae latent decoder + save_file : :obj:`str` + full save file (path and filename) + trial : :obj:`int`, optional + if :obj:`NoneType`, use first test trial + xtick_locs : :obj:`array-like`, optional + tick locations in units of bins + frame_rate : :obj:`float`, optional + frame rate of behavorial video; to properly relabel xticks + format : :obj:`str`, optional + any accepted matplotlib save format, e.g. 'png' | 'pdf' | 'jpeg' + + Returns + ------- + :obj:`matplotlib.figure.Figure` + matplotlib figure handle of plot + + """ + + # find good trials + import copy + from behavenet.data.utils import get_transforms_paths + from behavenet.data.data_generator import ConcatSessionsGenerator + + # ae data + hparams_ae = copy.copy(hparams) + hparams_ae['experiment_name'] = hparams['ae_experiment_name'] + hparams_ae['model_class'] = hparams['ae_model_class'] + hparams_ae['model_type'] = hparams['ae_model_type'] + + ae_transform, ae_path = get_transforms_paths('ae_latents', hparams_ae, None) + + # ae predictions data + hparams_dec = copy.copy(hparams) + hparams_dec['neural_ae_experiment_name'] = hparams['decoder_experiment_name'] + hparams_dec['neural_ae_model_class'] = hparams['decoder_model_class'] + hparams_dec['neural_ae_model_type'] = hparams['decoder_model_type'] + ae_pred_transform, ae_pred_path = get_transforms_paths( + 'neural_ae_predictions', hparams_dec, None) + + signals = ['ae_latents', 'ae_predictions'] + transforms = [ae_transform, ae_pred_transform] + paths = [ae_path, ae_pred_path] + + data_generator = ConcatSessionsGenerator( + hparams['data_dir'], [hparams], + signals_list=[signals], transforms_list=[transforms], paths_list=[paths], + device='cpu', as_numpy=False, batch_load=True, rng_seed=0) + + if trial is None: + # choose first test trial + trial = data_generator.datasets[0].batch_idxs['test'][0] + + batch = data_generator.datasets[0][trial] + traces_ae = batch['ae_latents'].cpu().detach().numpy() + traces_neural = batch['ae_predictions'].cpu().detach().numpy() + + n_max_lags = hparams.get('n_max_lags', 0) # only plot valid segment of data + if n_max_lags > 0: + fig = plot_neural_reconstruction_traces( + traces_ae[n_max_lags:-n_max_lags], traces_neural[n_max_lags:-n_max_lags], + save_file, xtick_locs, frame_rate, format, **kwargs) + else: + fig = plot_neural_reconstruction_traces( + traces_ae, traces_neural, save_file, xtick_locs, frame_rate, format, **kwargs) + return fig + + +def plot_neural_reconstruction_traces( + traces_ae, traces_neural, save_file=None, xtick_locs=None, frame_rate=None, format='png', + scale=0.5, max_traces=8, add_r2=True, add_legend=True, colored_predictions=True): + """Plot ae latents and their neural reconstructions. + + Parameters + ---------- + traces_ae : :obj:`np.ndarray` + shape (n_frames, n_latents) + traces_neural : :obj:`np.ndarray` + shape (n_frames, n_latents) + save_file : :obj:`str`, optional + full save file (path and filename) + xtick_locs : :obj:`array-like`, optional + tick locations in units of bins + frame_rate : :obj:`float`, optional + frame rate of behavorial video; to properly relabel xticks + format : :obj:`str`, optional + any accepted matplotlib save format, e.g. 'png' | 'pdf' | 'jpeg' + scale : :obj:`int`, optional + scale magnitude of traces + max_traces : :obj:`int`, optional + maximum number of traces to plot, for easier visualization + add_r2 : :obj:`bool`, optional + print R2 value on plot + add_legend : :obj:`bool`, optional + print legend on plot + colored_predictions : :obj:`bool`, optional + color predictions using default seaborn colormap; else predictions are black + + + Returns + ------- + :obj:`matplotlib.figure.Figure` + matplotlib figure handle + + """ + + import seaborn as sns + + sns.set_style('white') + sns.set_context('poster') + + means = np.nanmean(traces_ae, axis=0) + std = np.nanstd(traces_ae) / scale # scale for better visualization + + traces_ae_sc = (traces_ae - means) / std + traces_neural_sc = (traces_neural - means) / std + + traces_ae_sc = traces_ae_sc[:, :max_traces] + traces_neural_sc = traces_neural_sc[:, :max_traces] + + fig = plt.figure(figsize=(12, 8)) + if colored_predictions: + plt.plot(traces_neural_sc + np.arange(traces_neural_sc.shape[1]), linewidth=3) + else: + plt.plot(traces_neural_sc + np.arange(traces_neural_sc.shape[1]), linewidth=3, color='k') + plt.plot( + traces_ae_sc + np.arange(traces_ae_sc.shape[1]), color=[0.2, 0.2, 0.2], linewidth=3, + alpha=0.7) + + # add legend if desired + if add_legend: + # original latents - gray + orig_line = mlines.Line2D([], [], color=[0.2, 0.2, 0.2], linewidth=3, alpha=0.7) + # predicted latents - cycle through some colors + colors = plt.rcParams['axes.prop_cycle'].by_key()['color'] + dls = [] + for c in range(5): + dls.append(mlines.Line2D( + [], [], linewidth=3, linestyle='--', dashes=(0, 3 * c, 20, 1), + color='%s' % colors[c])) + plt.legend( + [orig_line, tuple(dls)], ['Original latents', 'Predicted latents'], + loc='lower right', frameon=True, framealpha=0.7, edgecolor=[1, 1, 1]) + + # add r2 info if desired + if add_r2: + from sklearn.metrics import r2_score + r2 = r2_score(traces_ae, traces_neural, multioutput='variance_weighted') + plt.text( + 0.05, 0.06, '$R^2$=%1.3f' % r2, horizontalalignment='left', verticalalignment='bottom', + transform=plt.gca().transAxes, + bbox=dict(facecolor='white', alpha=0.7, edgecolor=[1, 1, 1])) + + if xtick_locs is not None and frame_rate is not None: + plt.xticks(xtick_locs, (np.asarray(xtick_locs) / frame_rate).astype('int')) + plt.xlabel('Time (s)') + else: + plt.xlabel('Time (bins)') + plt.ylabel('Latent state') + plt.yticks([]) + + if save_file is not None: + make_dir_if_not_exists(save_file) + plt.savefig(save_file + '.' + format, dpi=300, format=format) + + plt.show() + return fig From f9273c510cd8d9f272f8b74b7e59e44d8a719c8d Mon Sep 17 00:00:00 2001 From: themattinthehatt Date: Mon, 7 Dec 2020 11:24:36 -0500 Subject: [PATCH 27/50] save movie helper function --- behavenet/plotting/__init__.py | 33 ++++++++++++++++++++++++++++- behavenet/plotting/ae_utils.py | 19 ++--------------- behavenet/plotting/arhmm_utils.py | 18 +++------------- behavenet/plotting/decoder_utils.py | 13 ++---------- 4 files changed, 39 insertions(+), 44 deletions(-) diff --git a/behavenet/plotting/__init__.py b/behavenet/plotting/__init__.py index 451709a..6914506 100644 --- a/behavenet/plotting/__init__.py +++ b/behavenet/plotting/__init__.py @@ -1,9 +1,11 @@ """Utility functions shared across multiple plotting modules.""" +from matplotlib.animation import FFMpegWriter import numpy as np import os import pandas as pd +from behavenet import make_dir_if_not_exists from behavenet.fitting.utils import experiment_exists from behavenet.fitting.utils import get_expt_dir from behavenet.fitting.utils import get_session_dir @@ -12,7 +14,7 @@ from behavenet.fitting.utils import read_session_info_from_csv # to ignore imports for sphix-autoapidoc -__all__ = ['load_metrics_csv_as_df'] +__all__ = ['concat', 'load_metrics_csv_as_df', 'save_movie'] # TODO: use load_metrics_csv_as_df in ae example notebook @@ -118,3 +120,32 @@ def load_metrics_csv_as_df(hparams, lab, expt, metrics_list, test=False, version # tr_dict[metric] = row['tr_%s' % metric] # metrics_df.append(pd.DataFrame(tr_dict, index=[0])) return pd.concat(metrics_df, sort=True) + + +def save_movie(save_file, ani, frame_rate=15): + """Save out matplotlib ArtistAnimation + + Parameters + ---------- + save_file : :obj:`str` + full save file (path and filename) + ani : :obj:`matplotlib.animation.ArtistAnimation` object + animation to save + frame_rate : :obj:`int`, optional + frame rate of saved movie + + """ + + if save_file is not None: + make_dir_if_not_exists(save_file) + if save_file[-3:] == 'gif': + print('saving video to %s...' % save_file, end='') + ani.save(save_file, writer='imagemagick', fps=frame_rate) + print('done') + else: + if save_file[-3:] != 'mp4': + save_file += '.mp4' + writer = FFMpegWriter(fps=frame_rate, bitrate=-1) + print('saving video to %s...' % save_file, end='') + ani.save(save_file, writer=writer) + print('done') diff --git a/behavenet/plotting/ae_utils.py b/behavenet/plotting/ae_utils.py index a38b21b..196fc6c 100644 --- a/behavenet/plotting/ae_utils.py +++ b/behavenet/plotting/ae_utils.py @@ -3,11 +3,9 @@ import matplotlib.animation as animation import matplotlib.pyplot as plt from matplotlib.gridspec import GridSpec -from matplotlib.animation import FFMpegWriter -from behavenet import make_dir_if_not_exists from behavenet.fitting.eval import get_reconstruction from behavenet.fitting.utils import get_best_model_and_data -from behavenet.plotting import concat +from behavenet.plotting import concat, save_movie # to ignore imports for sphix-autoapidoc __all__ = ['make_ae_reconstruction_movie_wrapper', 'make_reconstruction_movie'] @@ -94,20 +92,7 @@ def make_reconstruction_movie( plt.tight_layout(pad=0) ani = animation.ArtistAnimation(fig, ims_ani, blit=True, repeat_delay=1000) - - if save_file is not None: - make_dir_if_not_exists(save_file) - if save_file[-3:] == 'gif': - print('saving video to %s...' % save_file, end='') - ani.save(save_file, writer='imagemagick', fps=frame_rate) - print('done') - else: - if save_file[-3:] != 'mp4': - save_file += '.mp4' - writer = FFMpegWriter(fps=frame_rate, bitrate=-1) - print('saving video to %s...' % save_file, end='') - ani.save(save_file, writer=writer) - print('done') + save_movie(save_file, ani, frame_rate=frame_rate) def make_ae_reconstruction_movie_wrapper( diff --git a/behavenet/plotting/arhmm_utils.py b/behavenet/plotting/arhmm_utils.py index e23dda0..9fbfd68 100644 --- a/behavenet/plotting/arhmm_utils.py +++ b/behavenet/plotting/arhmm_utils.py @@ -7,9 +7,9 @@ import matplotlib.pyplot as plt import matplotlib import matplotlib.animation as animation -from matplotlib.animation import FFMpegWriter from behavenet import make_dir_if_not_exists from behavenet.models import AE as AE +from behavenet.plotting import save_movie # to ignore imports for sphix-autoapidoc __all__ = [ @@ -495,7 +495,6 @@ def make_syllable_movies( ani = animation.ArtistAnimation( fig, [ims[i] for i in range(len(ims)) if ims[i] != []], interval=20, blit=True, repeat=False) - writer = FFMpegWriter(fps=max(frame_rate, 10), bitrate=-1) print('done') if save_file is not None: @@ -508,10 +507,7 @@ def make_syllable_movies( state_str = '' save_file += state_str save_file += '.mp4' - make_dir_if_not_exists(save_file) - print('saving video to %s...' % save_file, end='') - ani.save(save_file, writer=writer) - print('done') + save_movie(save_file, ani, frame_rate=frame_rate) def real_vs_sampled_wrapper( @@ -701,15 +697,7 @@ def make_real_vs_sampled_movies( ims.append(ims_curr) ani = animation.ArtistAnimation(fig, ims, blit=True, repeat_delay=1000) - writer = FFMpegWriter(fps=frame_rate, bitrate=-1) - - if save_file is not None: - make_dir_if_not_exists(save_file) - if save_file[-3:] != 'mp4': - save_file += '.mp4' - print('saving video to %s...' % save_file, end='') - ani.save(save_file, writer=writer) - print('done') + save_movie(save_file, ani, frame_rate=frame_rate) def plot_real_vs_sampled( diff --git a/behavenet/plotting/decoder_utils.py b/behavenet/plotting/decoder_utils.py index 2cce1f2..50dd112 100644 --- a/behavenet/plotting/decoder_utils.py +++ b/behavenet/plotting/decoder_utils.py @@ -5,7 +5,6 @@ import matplotlib.lines as mlines import matplotlib.pyplot as plt from matplotlib.gridspec import GridSpec -from matplotlib.animation import FFMpegWriter import numpy as np import os import pandas as pd @@ -17,7 +16,7 @@ from behavenet.fitting.utils import get_expt_dir from behavenet.fitting.utils import get_session_dir from behavenet.fitting.utils import get_subdirs -from behavenet.plotting import concat +from behavenet.plotting import concat, save_movie # to ignore imports for sphix-autoapidoc __all__ = [ @@ -533,15 +532,7 @@ def make_neural_reconstruction_movie( plt.tight_layout(pad=0) ani = animation.ArtistAnimation(fig, ims, blit=True, repeat_delay=1000) - writer = FFMpegWriter(fps=frame_rate, bitrate=-1) - - if save_file is not None: - make_dir_if_not_exists(save_file) - if save_file[-3:] != 'mp4': - save_file += '.mp4' - print('saving video to %s...' % save_file, end='') - ani.save(save_file, writer=writer) - print('done') + save_movie(save_file, ani, frame_rate=frame_rate) def plot_neural_reconstruction_traces_wrapper( From 75552bfc3709420597eb57646903fa6b67775a34 Mon Sep 17 00:00:00 2001 From: themattinthehatt Date: Fri, 8 Jan 2021 18:55:12 -0500 Subject: [PATCH 28/50] streamlined cond ae plotting functions round 1 --- behavenet/plotting/__init__.py | 74 ++- behavenet/plotting/cond_ae_utils.py | 987 ++++++++++++++++++++++++++-- 2 files changed, 1013 insertions(+), 48 deletions(-) diff --git a/behavenet/plotting/__init__.py b/behavenet/plotting/__init__.py index 6914506..e5b1ed5 100644 --- a/behavenet/plotting/__init__.py +++ b/behavenet/plotting/__init__.py @@ -3,6 +3,7 @@ from matplotlib.animation import FFMpegWriter import numpy as np import os +import pickle import pandas as pd from behavenet import make_dir_if_not_exists @@ -14,7 +15,7 @@ from behavenet.fitting.utils import read_session_info_from_csv # to ignore imports for sphix-autoapidoc -__all__ = ['concat', 'load_metrics_csv_as_df', 'save_movie'] +__all__ = ['concat', 'get_crop', 'load_metrics_csv_as_df', 'save_movie'] # TODO: use load_metrics_csv_as_df in ae example notebook @@ -37,6 +38,75 @@ def concat(ims, axis=1): return np.concatenate([ims[0, :, :], ims[1, :, :]], axis=axis) +def get_crop(im, y_0, y_ext, x_0, x_ext): + """Get crop of image, filling in borders with zeros. + + Parameters + ---------- + im : :obj:`np.ndarray` + input image + y_0 : :obj:`int` + y-pixel center value + y_ext : :obj:`int` + y-pixel extent; crop in y-direction will be [y_0 - y_ext, y_0 + y_ext] + x_0 : :obj:`int` + y-pixel center value + x_ext : :obj:`int` + x-pixel extent; crop in x-direction will be [x_0 - x_ext, x_0 + x_ext] + + Returns + ------- + :obj:`np.ndarray` + cropped image + + """ + y_min = y_0 - y_ext + y_max = y_0 + y_ext + y_pix = y_max - y_min + x_min = x_0 - x_ext + x_max = x_0 + x_ext + x_pix = x_max - x_min + im_crop = np.copy(im[y_min:y_max, x_min:x_max]) + y_pix_, x_pix_ = im_crop.shape + im_tmp = np.zeros((y_pix, x_pix)) + im_tmp[:y_pix_, :x_pix_] = im_crop + return im_tmp + + +def load_latents(hparams, version, dtype='val'): + """Load all latents as a single array. + + Parameters + ---------- + hparams : :obj:`dict` + needs to contain enough information to specify both a model and the associated data + version : :obj:`int` + version from test tube experiment defined in :obj:`hparams` + dtype : :obj:`str` + 'train' | 'val' | 'test' + + Returns + ------- + :obj:`np.ndarray` + shape (time, n_latents) + + """ + sess_id = str('%s_%s_%s_%s_latents.pkl' % ( + hparams['lab'], hparams['expt'], hparams['animal'], hparams['session'])) + filename = os.path.join( + hparams['expt_dir'], 'version_%i' % version, sess_id) + if not os.path.exists(filename): + raise FileNotFoundError('latents located at %s do not exist' % filename) + latent_dict = pickle.load(open(filename, 'rb')) + print('loaded latents from %s' % filename) + # get all test latents + latents = [] + for trial in latent_dict['trials'][dtype]: + ls = latent_dict['latents'][trial] + latents.append(ls) + return np.concatenate(latents) + + def load_metrics_csv_as_df(hparams, lab, expt, metrics_list, test=False, version='best'): """Load metrics csv file and return as a pandas dataframe for easy plotting. @@ -149,3 +219,5 @@ def save_movie(save_file, ani, frame_rate=15): print('saving video to %s...' % save_file, end='') ani.save(save_file, writer=writer) print('done') + + diff --git a/behavenet/plotting/cond_ae_utils.py b/behavenet/plotting/cond_ae_utils.py index 32e5a75..27357f2 100644 --- a/behavenet/plotting/cond_ae_utils.py +++ b/behavenet/plotting/cond_ae_utils.py @@ -3,54 +3,37 @@ import pickle import numpy as np import matplotlib.pyplot as plt +import pandas as pd +import seaborn as sns import torch +from tqdm import tqdm +from behavenet import get_user_dir from behavenet import make_dir_if_not_exists from behavenet.data.utils import build_data_generator from behavenet.data.utils import load_labels_like_latents from behavenet.fitting.eval import get_reconstruction +from behavenet.fitting.utils import experiment_exists +from behavenet.fitting.utils import get_best_model_and_data +from behavenet.fitting.utils import get_expt_dir +from behavenet.fitting.utils import get_lab_example from behavenet.fitting.utils import get_session_dir +from behavenet.plotting import get_crop +from behavenet.plotting import load_latents +from behavenet.plotting import load_metrics_csv_as_df # to ignore imports for sphix-autoapidoc __all__ = [ - 'get_crop', 'get_input_range', 'compute_range', 'get_labels_2d_for_trial', 'get_model_input', - 'interpolate_2d', 'interpolate_1d', 'plot_2d_frame_array', 'plot_1d_frame_array'] + 'get_input_range', 'compute_range', 'get_labels_2d_for_trial', 'get_model_input', + 'interpolate_2d', 'interpolate_1d', 'plot_2d_frame_array', 'plot_1d_frame_array', + 'plot_hyperparameter_search_results', 'plot_label_reconstructions', + 'plot_label_latent_regression_barplots', 'plot_latent_traversals', + 'make_latent_traversal_movie'] -def get_crop(im, y_0, y_ext, x_0, x_ext): - """Get crop of image, filling in borders with zeros. - - Parameters - ---------- - im : :obj:`np.ndarray` - input image - y_0 : :obj:`int` - y-pixel center value - y_ext : :obj:`int` - y-pixel extent; crop in y-direction will be [y_0 - y_ext, y_0 + y_ext] - x_0 : :obj:`int` - y-pixel center value - x_ext : :obj:`int` - x-pixel extent; crop in x-direction will be [x_0 - x_ext, x_0 + x_ext] - - Returns - ------- - :obj:`np.ndarray` - cropped image - - """ - y_min = y_0 - y_ext - y_max = y_0 + y_ext - y_pix = y_max - y_min - x_min = x_0 - x_ext - x_max = x_0 + x_ext - x_pix = x_max - x_min - im_crop = np.copy(im[y_min:y_max, x_min:x_max]) - y_pix_, x_pix_ = im_crop.shape - im_tmp = np.zeros((y_pix, x_pix)) - im_tmp[:y_pix_, :x_pix_] = im_crop - return im_tmp - +# ---------------------------------------- +# low-level util functions +# ---------------------------------------- def get_input_range( input_type, hparams, sess_ids=None, sess_idx=0, model=None, data_gen=None, version=0, @@ -723,9 +706,13 @@ def _get_updated_scaled_labels(labels_og, idxs=None, vals=None): return labels_sc +# ---------------------------------------- +# mid-level plotting functions +# ---------------------------------------- + def plot_2d_frame_array( ims_list, markers=None, im_kwargs=None, marker_kwargs=None, figsize=None, save_file=None, - **kwargs): + format='pdf'): """Plot list of list of interpolated images output by :func:`interpolate_2d()` in a 2d grid. Parameters @@ -739,10 +726,12 @@ def plot_2d_frame_array( kwargs for `matplotlib.pyplot.imshow()` function (vmin, vmax, cmap, etc) marker_kwargs : :obj:`dict` or NoneType, optional kwargs for `matplotlib.pyplot.plot()` function (markersize, markeredgewidth, etc) - figsize : :obj:`tuple` + figsize : :obj:`tuple`, optional (width, height) in inches save_file : :obj:`str` or NoneType, optional figure saved if not None + format : :obj:`str`, optional + format of saved image; 'pdf' | 'png' | 'jpeg' | ... """ @@ -771,13 +760,13 @@ def plot_2d_frame_array( plt.subplots_adjust(wspace=0, hspace=0, bottom=0, left=0, top=1, right=1) if save_file is not None: make_dir_if_not_exists(save_file) - plt.savefig(save_file, dpi=300, bbox_inches='tight') + plt.savefig(save_file + '.' + format, dpi=300, bbox_inches='tight') plt.show() def plot_1d_frame_array( - ims_list, markers=None, im_kwargs=None, marker_kwargs=None, figsize=None, save_file=None, - plot_ims=True, plot_diffs=True, **kwargs): + ims_list, markers=None, im_kwargs=None, marker_kwargs=None, plot_ims=True, plot_diffs=True, + figsize=None, save_file=None, format='pdf'): """Plot list of list of interpolated images output by :func:`interpolate_1d()` in a 2d grid. Parameters @@ -791,14 +780,16 @@ def plot_1d_frame_array( kwargs for `matplotlib.pyplot.imshow()` function (vmin, vmax, cmap, etc) marker_kwargs : :obj:`dict` or NoneType, optional kwargs for `matplotlib.pyplot.plot()` function (markersize, markeredgewidth, etc) - figsize : :obj:`tuple` + plot_ims : :obj:`bool`, optional + plot images + plot_diffs : :obj:`bool`, optional + plot differences + figsize : :obj:`tuple`, optional (width, height) in inches save_file : :obj:`str` or NoneType, optional figure saved if not None - plot_ims : :obj:`bool` - plot images - plot_diffs : :obj:`bool` - plot differences + format : :obj:`str`, optional + format of saved image; 'pdf' | 'png' | 'jpeg' | ... """ @@ -848,5 +839,907 @@ def plot_1d_frame_array( plt.subplots_adjust(wspace=0, hspace=0, bottom=0, left=0, top=1, right=1) if save_file is not None: make_dir_if_not_exists(save_file) - plt.savefig(save_file, dpi=300, bbox_inches='tight') + plt.savefig(save_file + '.' + format, dpi=300, bbox_inches='tight') plt.show() + + +# ---------------------------------------- +# high-level plotting functions +# ---------------------------------------- + +def _get_sssvae_hparams(**kwargs): + hparams = { + 'data_dir': get_user_dir('data'), + 'save_dir': get_user_dir('save'), + 'model_class': 'sss-vae', + 'model_type': 'conv', + 'rng_seed_data': 0, + 'trial_splits': '8;1;1;0', + 'train_frac': 1.0, + 'rng_seed_model': 0, + 'fit_sess_io_layers': False, + 'learning_rate': 1e-4, + 'l2_reg': 0, + 'conditional_encoder': False, + 'vae.beta': 1} + # update hparams + for key, val in kwargs.items(): + if key == 'alpha' or key == 'beta' or key == 'gamma': + hparams['sss_vae.%s' % key] = val + else: + hparams[key] = val + return hparams + + +def plot_sssvae_training_curves( + lab, expt, animal, session, alphas, betas, gammas, n_ae_latents, rng_seeds_model, + experiment_name, n_labels, dtype='val', save_file=None, format='pdf', **kwargs): + """Create training plots for each term in the sss-vae objective function. + + The `dtype` argument controls which type of trials are plotted ('train' or 'val'). + Additionally, multiple models can be plotted simultaneously by varying one (and only one) of + the following parameters: + + - alpha + - beta + - gamma + - number of unsupervised latents + - random seed used to initialize model weights + + Each of these entries must be an array of length 1 except for one option, which can be an array + of arbitrary length (corresponding to already trained models). This function generates a single + plot with panels for each of the following terms: + + - total loss + - pixel mse + - label R^2 (note the objective function contains the label MSE, but R^2 is easier to parse) + - KL divergence of supervised latents + - index-code mutual information of unsupervised latents + - total correlation of unsupervised latents + - dimension-wise KL of unsupervised latents + - subspace overlap + + Parameters + ---------- + lab : :obj:`str` + lab id + expt : :obj:`str` + expt id + animal : :obj:`str` + animal id + session : :obj:`str` + session id + alphas : :obj:`array-like` + alpha values to plot + betas : :obj:`array-like` + beta values to plot + gammas : :obj:`array-like` + gamma values to plot + n_ae_latents : :obj:`array-like` + unsupervised dimensionalities to plot + rng_seeds_model : :obj:`array-like` + model seeds to plot + experiment_name : :obj:`str` + test-tube experiment name + n_labels : :obj:`str` + dimensionality of supervised latent space + dtype : :obj:`str` + 'train' | 'val' + save_file : :obj:`str`, optional + absolute path of save file; does not need file extension + format : :obj:`str`, optional + format of saved image; 'pdf' | 'png' | 'jpeg' | ... + kwargs + keys are keys of `hparams`, for example to set `train_frac`, `rng_seed_model`, etc. + + """ + # check for arrays, turn ints into lists + n_arrays = 0 + if len(alphas) > 1: + n_arrays += 1 + hue = 'alpha' + if len(betas) > 1: + n_arrays += 1 + hue = 'beta' + if len(gammas) > 1: + n_arrays += 1 + hue = 'gamma' + if len(n_ae_latents) > 1: + n_arrays += 1 + hue = 'n latents' + if len(rng_seeds_model) > 1: + n_arrays += 1 + hue = 'rng seed' + if n_arrays > 1: + raise ValueError( + 'Can only set one of "alphas", "betas", "gammas", "n_ae_latents", or ' + + '"rng_seeds_model" as an array') + + # set model info + hparams = _get_sssvae_hparams(experiment_name=experiment_name, **kwargs) + + metrics_list = [ + 'loss', 'loss_data_mse', 'label_r2', + 'loss_zs_kl', 'loss_zu_mi', 'loss_zu_tc', 'loss_zu_dwkl', 'loss_AB_orth'] + + metrics_dfs = [] + i = 0 + for alpha in alphas: + for beta in betas: + for gamma in gammas: + for n_latents in n_ae_latents: + for rng in rng_seeds_model: + + # update hparams + hparams['sss_vae.alpha'] = alpha + hparams['sss_vae.beta'] = beta + hparams['sss_vae.gamma'] = gamma + hparams['n_ae_latents'] = n_latents + n_labels + hparams['rng_seed_model'] = rng + + try: + + get_lab_example(hparams, lab, expt) + hparams['animal'] = animal + hparams['session'] = session + hparams['session_dir'], sess_ids = get_session_dir(hparams) + hparams['expt_dir'] = get_expt_dir(hparams) + _, version = experiment_exists(hparams, which_version=True) + + print( + 'loading results with alpha=%i, beta=%i, gamma=%i (version %i)' % + (alpha, beta, gamma, version)) + + metrics_dfs.append(load_metrics_csv_as_df( + hparams, lab, expt, metrics_list, version=None)) + + metrics_dfs[i]['alpha'] = alpha + metrics_dfs[i]['beta'] = beta + metrics_dfs[i]['gamma'] = gamma + metrics_dfs[i]['n latents'] = hparams['n_ae_latents'] + metrics_dfs[i]['rng seed'] = rng + i += 1 + + except TypeError: + print( + 'could not find model for alpha=%i, beta=%i, gamma=%i' % + (alpha, beta, gamma)) + continue + + metrics_df = pd.concat(metrics_dfs, sort=False) + + sns.set_style('white') + sns.set_context('talk') + data_queried = metrics_df[ + (metrics_df.epoch > 10) & ~pd.isna(metrics_df.val) & (metrics_df.dtype == dtype)] + g = sns.FacetGrid( + data_queried, col='loss', col_wrap=3, hue=hue, sharey=False, height=4) + g = g.map(plt.plot, 'epoch', 'val').add_legend() # , color=".3", fit_reg=False, x_jitter=.1); + + if save_file is not None: + make_dir_if_not_exists(save_file) + g.savefig(save_file + '.' + format, dpi=300, format=format) + + +def plot_hyperparameter_search_results( + lab, expt, animal, session, n_labels, label_names, alpha_weights, alpha_n_ae_latents, + alpha_expt_name, beta_weights, gamma_weights, beta_gamma_n_ae_latents, + beta_gamma_expt_name, alpha, beta, gamma, save_file, format='pdf', **kwargs): + """Create a variety of diagnostic plots to assess the sss-vae hyperparameters. + + These diagnostic plots are based on the recommended way to perform a hyperparameter search in + the sss-vae models; first, fix beta=1 and gamma=0, and do a sweep over alpha values and number + of latents (for example alpha=[50, 100, 500, 1000] and n_ae_latents=[2, 4, 8, 16]). The best + alpha value is subjective because it involves a tradeoff between pixel mse and label mse. After + choosing a suitable value, fix alpha and the number of latents and vary beta and gamma. This + function will then plot the following panels: + + - pixel mse as a function of alpha/num latents (for fixed beta/gamma) + - label mse as a function of alpha/num_latents (for fixed beta/gamma) + - pixel mse as a function of beta/gamma (for fixed alpha/n_ae_latents) + - label mse as a function of beta/gamma (for fixed alpha/n_ae_latents) + - index-code mutual information (part of the KL decomposition) as a function of beta/gamma (for + fixed alpha/n_ae_latents) + - total correlation(part of the KL decomposition) as a function of beta/gamma (for fixed + alpha/n_ae_latents) + - dimension-wise KL (part of the KL decomposition) as a function of beta/gamma (for fixed + alpha/n_ae_latents) + - average correlation coefficient across all pairs of unsupervised latent dims as a function of + beta/gamma (for fixed alpha/n_ae_latents) + - subspace overlap computed as ||[A; B] - I||_2^2 for A, B the projections to the supervised + and unsupervised subspaces, respectively, and I the identity - as a function of beta/gamma + (for fixed alpha/n_ae_latents) + - example subspace overlap matrix for gamma=0 and beta=1, with fixed alpha/n_ae_latents + - example subspace overlap matrix for gamma=1000 and beta=1, with fixed alpha/n_ae_latents + + Parameters + ---------- + lab : :obj:`str` + lab id + expt : :obj:`str` + expt id + animal : :obj:`str` + animal id + session : :obj:`str` + session id + n_labels : :obj:`str` + number of label dims + label_names : :obj:`array-like` + names of label dims + alpha_weights : :obj:`array-like` + array of alpha weights for fixed values of beta, gamma + alpha_n_ae_latents : :obj:`array-like` + array of latent dimensionalities for fixed values of beta, gamma using alpha_weights + alpha_expt_name : :obj:`str` + test-tube experiment name of alpha-based hyperparam search + beta_weights : :obj:`array-like` + array of beta weights for a fixed value of alpha + gamma_weights : :obj:`array-like` + array of beta weights for a fixed value of alpha + beta_gamma_n_ae_latents : :obj:`int` + latent dimensionality used for beta-gamma hyperparam search + beta_gamma_expt_name : :obj:`str` + test-tube experiment name of beta-gamma hyperparam search + alpha : :obj:`float` + fixed value of alpha for beta-gamma search + beta : :obj:`float` + fixed value of beta for alpha search + gamma : :obj:`float` + fixed value of gamma for alpha search + save_file : :obj:`str` + absolute path of save file; does not need file extension + format : :obj:`str`, optional + format of saved image; 'pdf' | 'png' | 'jpeg' | ... + kwargs + keys are keys of `hparams`, preceded by either `alpha_` or `beta_gamma_`. For example, to + set the train frac of the alpha models, use `alpha_train_frac`; to set the rng_data_seed of + the beta-gamma models, use `beta_gamma_rng_data_seed`. + + """ + + def apply_masks(data, masks): + return data[masks == 1] + + def get_label_r2(hparams, model, data_generator, version, dtype='val', overwrite=False): + from sklearn.metrics import r2_score + save_file = os.path.join( + hparams['expt_dir'], 'version_%i' % version, 'r2_supervised.csv') + if not os.path.exists(save_file) or overwrite: + if not os.path.exists(save_file): + print('R^2 metrics do not exist; computing from scratch') + else: + print('overwriting metrics at %s' % save_file) + metrics_df = [] + data_generator.reset_iterators(dtype) + for i_test in tqdm(range(data_generator.n_tot_batches[dtype])): + # get next minibatch and put it on the device + data, sess = data_generator.next_batch(dtype) + x = data['images'][0] + y = data['labels'][0].cpu().detach().numpy() + if 'labels_masks' in data: + n = data['labels_masks'][0].cpu().detach().numpy() + else: + n = np.ones_like(y) + z = model.get_transformed_latents(x, dataset=sess) + for i in range(n_labels): + y_true = apply_masks(y[:, i], n[:, i]) + y_pred = apply_masks(z[:, i], n[:, i]) + if len(y_true) > 10: + r2 = r2_score(y_true, y_pred, multioutput='variance_weighted') + mse = np.mean(np.square(y_true - y_pred)) + else: + r2 = np.nan + mse = np.nan + metrics_df.append(pd.DataFrame({ + 'Trial': data['batch_idx'].item(), + 'Label': label_names[i], + 'R2': r2, + 'MSE': mse, + 'Model': 'SSS-VAE'}, index=[0])) + + metrics_df = pd.concat(metrics_df) + print('saving results to %s' % save_file) + metrics_df.to_csv(save_file, index=False, header=True) + else: + print('loading results from %s' % save_file) + metrics_df = pd.read_csv(save_file) + return metrics_df + + # ----------------------------------------------------- + # load pixel/label MSE as a function of n_latents/alpha + # ----------------------------------------------------- + + # set model info + hparams = _get_sssvae_hparams(experiment_name=alpha_expt_name) + # update hparams + for key, val in kwargs.items(): + # hparam vals should be named 'alpha_[property]', for example 'alpha_train_frac' + if key.split('_')[0] == 'alpha': + prop = key[6:] + hparams[prop] = val + + metrics_list = ['loss_data_mse'] + + metrics_dfs_frame = [] + metrics_dfs_marker = [] + for n_latent in alpha_n_ae_latents: + hparams['n_ae_latents'] = n_latent + n_labels + for alpha_ in alpha_weights: + hparams['sss_vae.alpha'] = alpha_ + hparams['sss_vae.beta'] = beta + hparams['sss_vae.gamma'] = gamma + try: + get_lab_example(hparams, lab, expt) + hparams['animal'] = animal + hparams['session'] = session + hparams['session_dir'], sess_ids = get_session_dir(hparams) + hparams['expt_dir'] = get_expt_dir(hparams) + _, version = experiment_exists(hparams, which_version=True) + print('loading results with alpha=%i, beta=%i, gamma=%i (version %i)' % ( + hparams['sss_vae.alpha'], hparams['sss_vae.beta'], hparams['sss_vae.gamma'], + version)) + # get frame mse + metrics_dfs_frame.append(load_metrics_csv_as_df( + hparams, lab, expt, metrics_list, version=None, test=True)) + metrics_dfs_frame[-1]['alpha'] = alpha_ + metrics_dfs_frame[-1]['n_latents'] = hparams['n_ae_latents'] + # get marker mse + model, data_gen = get_best_model_and_data( + hparams, Model=None, load_data=True, version=version) + metrics_df_ = get_label_r2(hparams, model, data_gen, version, dtype='val') + metrics_df_['alpha'] = alpha_ + metrics_df_['n_latents'] = hparams['n_ae_latents'] + metrics_dfs_marker.append(metrics_df_[metrics_df_.Model == 'SSS-VAE']) + except TypeError: + print('could not find model for alpha=%i, beta=%i, gamma=%i' % ( + hparams['sss_vae.alpha'], hparams['sss_vae.beta'], hparams['sss_vae.gamma'])) + continue + metrics_df_frame = pd.concat(metrics_dfs_frame, sort=False) + metrics_df_marker = pd.concat(metrics_dfs_marker, sort=False) + print('done') + + # ----------------------------------------------------- + # load pixel/label MSE as a function of beta/gamma + # ----------------------------------------------------- + # update hparams + hparams['experiment_name'] = beta_gamma_expt_name + for key, val in kwargs.items(): + # hparam vals should be named 'beta_gamma_[property]', for example 'alpha_train_frac' + if key.split('_')[0] == 'beta' and key.split('_')[1] == 'gamma': + prop = key[11:] + hparams[prop] = val + + metrics_list = ['loss_data_mse', 'loss_zu_mi', 'loss_zu_tc', 'loss_zu_dwkl', 'loss_AB_orth'] + + metrics_dfs_frame_bg = [] + metrics_dfs_marker_bg = [] + metrics_dfs_corr_bg = [] + overlaps = {} + for beta in beta_weights: + for gamma in gamma_weights: + hparams['n_ae_latents'] = beta_gamma_n_ae_latents + n_labels + hparams['sss_vae.alpha'] = alpha + hparams['sss_vae.beta'] = beta + hparams['sss_vae.gamma'] = gamma + try: + get_lab_example(hparams, lab, expt) + hparams['animal'] = animal + hparams['session'] = session + hparams['session_dir'], sess_ids = get_session_dir(hparams) + hparams['expt_dir'] = get_expt_dir(hparams) + _, version = experiment_exists(hparams, which_version=True) + print('loading results with alpha=%i, beta=%i, gamma=%i (version %i)' % ( + hparams['sss_vae.alpha'], hparams['sss_vae.beta'], hparams['sss_vae.gamma'], + version)) + # get frame mse + metrics_dfs_frame_bg.append(load_metrics_csv_as_df( + hparams, lab, expt, metrics_list, version=None, test=True)) + metrics_dfs_frame_bg[-1]['beta'] = beta + metrics_dfs_frame_bg[-1]['gamma'] = gamma + # get marker mse + model, data_gen = get_best_model_and_data( + hparams, Model=None, load_data=True, version=version) + metrics_df_ = get_label_r2(hparams, model, data_gen, version, dtype='val') + metrics_df_['beta'] = beta + metrics_df_['gamma'] = gamma + metrics_dfs_marker_bg.append(metrics_df_[metrics_df_.Model == 'SSS-VAE']) + # get subspace overlap + A = model.encoding.A.weight.data.cpu().detach().numpy() + B = model.encoding.B.weight.data.cpu().detach().numpy() + C = np.concatenate([A, B], axis=0) + overlap = np.matmul(C, C.T) + overlaps['beta=%i_gamma=%i' % (beta, gamma)] = overlap + # get corr + latents = load_latents(hparams, version, dtype='test') + corr = np.corrcoef(latents[:, n_labels + np.array([0, 1])].T) + metrics_dfs_corr_bg.append(pd.DataFrame({ + 'loss': 'corr', + 'dtype': 'test', + 'val': np.abs(corr[0, 1]), + 'beta': beta, + 'gamma': gamma}, index=[0])) + except TypeError: + print('could not find model for alpha=%i, beta=%i, gamma=%i' % ( + hparams['sss_vae.alpha'], hparams['sss_vae.beta'], hparams['sss_vae.gamma'])) + continue + print() + metrics_df_frame_bg = pd.concat(metrics_dfs_frame_bg, sort=False) + metrics_df_marker_bg = pd.concat(metrics_dfs_marker_bg, sort=False) + metrics_df_corr_bg = pd.concat(metrics_dfs_corr_bg, sort=False) + print('done') + + # ----------------------------------------------------- + # ----------------- PLOT DATA ------------------------- + # ----------------------------------------------------- + sns.set_style('white') + sns.set_context('paper', font_scale=1.2) + + alpha_palette = sns.color_palette('Greens') + beta_palette = sns.color_palette('Reds', len(metrics_df_corr_bg.beta.unique())) + gamma_palette = sns.color_palette('Blues', len(metrics_df_corr_bg.gamma.unique())) + + from matplotlib.gridspec import GridSpec + + fig = plt.figure(figsize=(12, 10), dpi=300) + + n_rows = 3 + n_cols = 12 + gs = GridSpec(n_rows, n_cols, figure=fig) + + def despine(ax): + ax.spines['top'].set_visible(False) + ax.spines['right'].set_visible(False) + + sns.set_palette(alpha_palette) + + # -------------------------------------------------- + # MSE per pixel + # -------------------------------------------------- + ax_pixel_mse_alpha = fig.add_subplot(gs[0, 0:3]) + data_queried = metrics_df_frame[(metrics_df_frame.dtype == 'test')] + splt = sns.barplot( + x='n_latents', y='val', hue='alpha', data=data_queried, ax=ax_pixel_mse_alpha) + ax_pixel_mse_alpha.legend().set_visible(False) + ax_pixel_mse_alpha.set_xlabel('Latent dimension') + ax_pixel_mse_alpha.set_ylabel('MSE per pixel') + ax_pixel_mse_alpha.ticklabel_format(axis='y', style='sci', scilimits=(-3, 3)) + ax_pixel_mse_alpha.set_title('Beta=1, Gamma=0') + despine(ax_pixel_mse_alpha) + + # -------------------------------------------------- + # MSE per marker + # -------------------------------------------------- + ax_marker_mse_alpha = fig.add_subplot(gs[0, 3:6]) + data_queried = metrics_df_marker + splt = sns.barplot( + x='n_latents', y='MSE', hue='alpha', data=data_queried, ax=ax_marker_mse_alpha) + ax_marker_mse_alpha.set_xlabel('Latent dimension') + ax_marker_mse_alpha.set_ylabel('MSE per marker') + ax_marker_mse_alpha.set_title('Beta=1, Gamma=0') + ax_marker_mse_alpha.legend(frameon=True, title='Alpha') + despine(ax_marker_mse_alpha) + + sns.set_palette(gamma_palette) + + # -------------------------------------------------- + # MSE per pixel (beta/gamma) + # -------------------------------------------------- + ax_pixel_mse_bg = fig.add_subplot(gs[0, 6:9]) + data_queried = metrics_df_frame_bg[ + (metrics_df_frame_bg.dtype == 'test') & + (metrics_df_frame_bg.loss == 'loss_data_mse') & + (metrics_df_frame_bg.epoch == 200)] + splt = sns.barplot( + x='beta', y='val', hue='gamma', data=data_queried, ax=ax_pixel_mse_bg) + ax_pixel_mse_bg.legend().set_visible(False) + ax_pixel_mse_bg.set_xlabel('Beta') + ax_pixel_mse_bg.set_ylabel('MSE per pixel') + ax_pixel_mse_bg.ticklabel_format(axis='y', style='sci', scilimits=(-3, 3)) + ax_pixel_mse_bg.set_title('Latents=%i, Alpha=1000' % hparams['n_ae_latents']) + despine(ax_pixel_mse_bg) + + # -------------------------------------------------- + # MSE per marker (beta/gamma) + # -------------------------------------------------- + ax_marker_mse_bg = fig.add_subplot(gs[0, 9:12]) + data_queried = metrics_df_marker_bg + splt = sns.barplot( + x='beta', y='MSE', hue='gamma', data=data_queried, ax=ax_marker_mse_bg) + ax_marker_mse_bg.set_xlabel('Beta') + ax_marker_mse_bg.set_ylabel('MSE per marker') + ax_marker_mse_bg.set_title('Latents=%i, Alpha=1000' % hparams['n_ae_latents']) + ax_marker_mse_bg.legend(frameon=True, title='Gamma', loc='lower left') + despine(ax_marker_mse_bg) + + # -------------------------------------------------- + # ICMI + # -------------------------------------------------- + ax_icmi = fig.add_subplot(gs[1, 0:4]) + data_queried = metrics_df_frame_bg[ + (metrics_df_frame_bg.dtype == 'test') & + (metrics_df_frame_bg.loss == 'loss_zu_mi') & + (metrics_df_frame_bg.epoch == 200)] + splt = sns.lineplot( + x='beta', y='val', hue='gamma', data=data_queried, ax=ax_icmi, ci=None, + palette=gamma_palette) + ax_icmi.legend().set_visible(False) + ax_icmi.set_xlabel('Beta') + ax_icmi.set_ylabel('Index-code Mutual Information') + ax_icmi.set_title('Latents=%i, Alpha=1000' % hparams['n_ae_latents']) + despine(ax_icmi) + + # -------------------------------------------------- + # TC + # -------------------------------------------------- + ax_tc = fig.add_subplot(gs[1, 4:8]) + data_queried = metrics_df_frame_bg[ + (metrics_df_frame_bg.dtype == 'test') & + (metrics_df_frame_bg.loss == 'loss_zu_tc') & + (metrics_df_frame_bg.epoch == 200)] + splt = sns.lineplot( + x='beta', y='val', hue='gamma', data=data_queried, ax=ax_tc, ci=None, + palette=gamma_palette) + ax_tc.legend().set_visible(False) + ax_tc.set_xlabel('Beta') + ax_tc.set_ylabel('Total Correlation') + ax_tc.set_title('Latents=%i, Alpha=1000' % hparams['n_ae_latents']) + despine(ax_tc) + + # -------------------------------------------------- + # DWKL + # -------------------------------------------------- + ax_dwkl = fig.add_subplot(gs[1, 8:12]) + data_queried = metrics_df_frame_bg[ + (metrics_df_frame_bg.dtype == 'test') & + (metrics_df_frame_bg.loss == 'loss_zu_dwkl') & + (metrics_df_frame_bg.epoch == 200)] + splt = sns.lineplot( + x='beta', y='val', hue='gamma', data=data_queried, ax=ax_dwkl, ci=None, + palette=gamma_palette) + ax_dwkl.legend().set_visible(False) + ax_dwkl.set_xlabel('Beta') + ax_dwkl.set_ylabel('Dimension-wise KL') + ax_dwkl.set_title('Latents=%i, Alpha=1000' % hparams['n_ae_latents']) + despine(ax_dwkl) + + # -------------------------------------------------- + # CC + # -------------------------------------------------- + ax_cc = fig.add_subplot(gs[2, 0:3]) + data_queried = metrics_df_corr_bg + splt = sns.lineplot( + x='beta', y='val', hue='gamma', data=data_queried, ax=ax_cc, ci=None, + palette=gamma_palette) + ax_cc.legend().set_visible(False) + ax_cc.set_xlabel('Beta') + ax_cc.set_ylabel('Correlation Coefficient') + ax_cc.set_title('Latents=%i, Alpha=1000' % hparams['n_ae_latents']) + despine(ax_cc) + + # -------------------------------------------------- + # AB orth + # -------------------------------------------------- + ax_orth = fig.add_subplot(gs[2, 3:6]) + data_queried = metrics_df_frame_bg[ + (metrics_df_frame_bg.dtype == 'test') & + (metrics_df_frame_bg.loss == 'loss_AB_orth') & + (metrics_df_frame_bg.epoch == 200) & + ~metrics_df_frame_bg.val.isna()] + splt = sns.lineplot( + x='gamma', y='val', hue='beta', data=data_queried, ax=ax_orth, ci=None, + palette=beta_palette) + ax_orth.legend(frameon=False, title='Beta') + ax_orth.set_xlabel('Gamma') + ax_orth.set_ylabel('Subspace overlap') + ax_orth.set_title('Latents=%i, Alpha=1000' % hparams['n_ae_latents']) + despine(ax_orth) + + # -------------------------------------------------- + # Gamma = 0 overlap + # -------------------------------------------------- + ax_gamma0 = fig.add_subplot(gs[2, 6:9]) + overlap = overlaps['beta=%i_gamma=%i' % (1, 0)] + im = ax_gamma0.imshow(overlap, cmap='PuOr', vmin=-1, vmax=1) + ax_gamma0.set_xticks(np.arange(overlap.shape[1])) + ax_gamma0.set_yticks(np.arange(overlap.shape[0])) + ax_gamma0.set_title('Subspace overlap\nGamma=0') + fig.colorbar(im, ax=ax_gamma0, orientation='vertical', shrink=0.75) + + # -------------------------------------------------- + # Gamma = 1000 overlap + # -------------------------------------------------- + ax_gamma1 = fig.add_subplot(gs[2, 9:12]) + overlap = overlaps['beta=%i_gamma=%i' % (1, 1000)] + im = ax_gamma1.imshow(overlap, cmap='PuOr', vmin=-1, vmax=1) + ax_gamma1.set_xticks(np.arange(overlap.shape[1])) + ax_gamma1.set_yticks(np.arange(overlap.shape[0])) + ax_gamma1.set_title('Subspace overlap\nGamma=1000') + fig.colorbar(im, ax=ax_gamma1, orientation='vertical', shrink=0.75) + + plt.tight_layout(h_pad=3) # h_pad is fraction of font size + + if save_file is not None: + make_dir_if_not_exists(save_file) + plt.savefig(save_file + '.' + format, dpi=300, format=format) + + +def plot_label_reconstructions( + lab, expt, animal, session, n_ae_latents, experiment_name, n_labels, trials, version=None, + plot_scale=0.5, sess_idx=0, save_file=None, format='pdf', **kwargs): + """Plot labels and their reconstructions from an sss-vae. + + Parameters + ---------- + lab : :obj:`str` + lab id + expt : :obj:`str` + expt id + animal : :obj:`str` + animal id + session : :obj:`str` + session id + n_ae_latents : :obj:`str` + dimensionality of unsupervised latent space; n_labels will be added to this + experiment_name : :obj:`str` + test-tube experiment name + n_labels : :obj:`str` + dimensionality of supervised latent space + trials : :obj:`array-like` + array of trials to reconstruct + version : :obj:`str` or :obj:`int`, optional + can be 'best' to load best model, and integer to load a specific model, or NoneType to use + the values in hparams to load a specific model + plot_scale : :obj:`float` + scale the magnitude of reconstructions + sess_idx : :obj:`int`, optional + session index into data generator + save_file : :obj:`str`, optional + absolute path of save file; does not need file extension + format : :obj:`str`, optional + format of saved image; 'pdf' | 'png' | 'jpeg' | ... + kwargs + keys are keys of `hparams`, for example to set `train_frac`, `rng_seed_model`, etc. + + """ + + from behavenet.plotting.decoder_utils import plot_neural_reconstruction_traces + + # set model info + hparams = _get_sssvae_hparams( + experiment_name=experiment_name, n_ae_latents=n_ae_latents + n_labels, **kwargs) + + # programmatically fill out other hparams options + get_lab_example(hparams, lab, expt) + hparams['animal'] = animal + hparams['session'] = session + + model, data_generator = get_best_model_and_data( + hparams, Model=None, load_data=True, version=version, data_kwargs=None) + print(data_generator) + print('alpha: %i' % model.hparams['sss_vae.alpha']) + print('beta: %i' % model.hparams['sss_vae.beta']) + print('gamma: %i' % model.hparams['sss_vae.gamma']) + print('model seed: %i' % model.hparams['rng_seed_model']) + + for trial in trials: + batch = data_generator.datasets[sess_idx][trial] + labels_og = batch['labels'].detach().cpu().numpy() + labels_pred = model.get_predicted_labels(batch['images']).detach().cpu().numpy() + if save_file is not None: + save_file_trial = save_file + '_trial-%i' % trial + else: + save_file_trial = None + fig = plot_neural_reconstruction_traces( + labels_og, labels_pred, scale=plot_scale, save_file=save_file_trial, format=format) + + +def plot_label_latent_regression_barplots(): + pass + + +def plot_latent_traversals( + lab, expt, animal, session, model_class, alpha, beta, gamma, n_ae_latents, rng_seed_model, + experiment_name, n_labels, label_idxs, label_min_p=5, label_max_p=95, + channel=0, n_frames_zs=4, n_frames_zu=4, trial_idx=1, batch_idx=1, crop_type=None, + crop_kwargs=None, sess_idx=0, save_file=None, format='pdf', **kwargs): + """Plot video frames representing the traversal of individual dimensions of the latent space. + + Parameters + ---------- + lab : :obj:`str` + lab id + expt : :obj:`str` + expt id + animal : :obj:`str` + animal id + session : :obj:`str` + session id + model_class : :obj:`str` + model class in which to perform traversal; currently supported models are: + 'ae' | 'vae' | 'cond-ae' | 'cond-vae' | 'beta-tcvae' | 'cond-ae-msp' | 'sss-vae' + note that models with conditional encoders are not currently supported + alpha : :obj:`float` + sss-vae alpha value + beta : :obj:`float` + sss-vae beta value + gamma : :obj:`array-like` + sss-vae gamma value + n_ae_latents : :obj:`int` + dimensionality of unsupervised latents + rng_seed_model : :obj:`int` + model seed + experiment_name : :obj:`str` + test-tube experiment name + n_labels : :obj:`str` + dimensionality of supervised latent space (ignored when using fully unsupervised models) + label_idxs : :obj:`array-like`, optional + set of label indices (dimensions) to individually traverse + label_min_p : :obj:`float`, optional + lower percentile of training data used to compute range of traversal + label_max_p : :obj:`float`, optional + upper percentile of training data used to compute range of traversal + channel : :obj:`int`, optional + image channel to plot + n_frames_zs : :obj:`int`, optional + number of frames (points) to display for traversal through supervised dimensions + n_frames_zu : :obj:`int`, optional + number of frames (points) to display for traversal through unsupervised dimensions + trial_idx : :obj:`int`, optional + trial index of base frame used for interpolation + batch_idx : :obj:`int`, optional + batch index of base frame used for interpolation + crop_type : :obj:`str`, optional + cropping method used on interpolated frames + 'fixed' | None + crop_kwargs : :obj:`dict`, optional + if crop_type is not None, provides information about the crop + keys for 'fixed' type: 'y_0', 'x_0', 'y_ext', 'x_ext'; window is + (y_0 - y_ext, y_0 + y_ext) in vertical direction and + (x_0 - x_ext, x_0 + x_ext) in horizontal direction + sess_idx : :obj:`int`, optional + session index into data generator + save_file : :obj:`str`, optional + absolute path of save file; does not need file extension + format : :obj:`str`, optional + format of saved image; 'pdf' | 'png' | 'jpeg' | ... + kwargs + keys are keys of `hparams`, for example to set `train_frac`, `rng_seed_model`, etc. + + """ + + hparams = _get_sssvae_hparams( + model_class=model_class, alpha=alpha, beta=beta, gamma=gamma, n_ae_latents=n_ae_latents, + experiment_name=experiment_name, rng_seed_model=rng_seed_model, **kwargs) + + if model_class == 'cond-ae-msp' or model_class == 'sss-vae': + hparams['n_ae_latents'] += n_labels + + # programmatically fill out other hparams options + get_lab_example(hparams, lab, expt) + hparams['animal'] = animal + hparams['session'] = session + hparams['session_dir'], sess_ids = get_session_dir(hparams) + hparams['expt_dir'] = get_expt_dir(hparams) + _, version = experiment_exists(hparams, which_version=True) + model_ae, data_generator = get_best_model_and_data(hparams, Model=None, version=version) + + # get latent/label info + latent_range = get_input_range( + 'latents', hparams, model=model_ae, data_gen=data_generator, min_p=15, max_p=85, + version=version) + label_range = get_input_range( + 'labels', hparams, sess_ids=sess_ids, sess_idx=sess_idx, + min_p=label_min_p, max_p=label_max_p) + try: + label_sc_range = get_input_range( + 'labels_sc', hparams, sess_ids=sess_ids, sess_idx=sess_idx, + min_p=label_min_p, max_p=label_max_p) + except KeyError: + import copy + label_sc_range = copy.deepcopy(label_range) + + # ---------------------------------------- + # label traversals + # ---------------------------------------- + interp_func_label = interpolate_1d + plot_func_label = plot_1d_frame_array + save_file_new = save_file + '_label-traversals' + + if model_class == 'cond-ae' or model_class == 'cond-ae-msp' or model_class == 'sss-vae' or \ + model_class == 'cond-vae': + + # get model input for this trial + ims_pt, ims_np, latents_np, labels_pt, labels_np, labels_2d_pt, labels_2d_np = \ + get_model_input( + data_generator, hparams, model_ae, trial_idx=trial_idx, + compute_latents=True, compute_scaled_labels=False, compute_2d_labels=False) + + if labels_2d_np is None: + labels_2d_np = np.copy(labels_np) + if crop_type == 'fixed': + crop_kwargs_ = crop_kwargs + else: + crop_kwargs_ = None + + # perform interpolation + ims_label, markers_loc_label, ims_crop_label = interp_func_label( + 'labels', model_ae, ims_pt[None, batch_idx, :], latents_np[None, batch_idx, :], + labels_np[None, batch_idx, :], labels_2d_np[None, batch_idx, :], + mins=label_range['min'], maxes=label_range['max'], + n_frames=n_frames_zs, input_idxs=label_idxs, crop_type=crop_type, + mins_sc=label_sc_range['min'], maxes_sc=label_sc_range['max'], + crop_kwargs=crop_kwargs_, ch=channel) + + # plot interpolation + if crop_type: + marker_kwargs = { + 'markersize': 30, 'markeredgewidth': 8, 'markeredgecolor': [1, 1, 0], + 'fillstyle': 'none'} + plot_func_label( + ims_crop_label, markers=None, marker_kwargs=marker_kwargs, save_file=save_file_new, + format=format) + else: + marker_kwargs = { + 'markersize': 20, 'markeredgewidth': 5, 'markeredgecolor': [1, 1, 0], + 'fillstyle': 'none'} + plot_func_label( + ims_label, markers=None, marker_kwargs=marker_kwargs, save_file=save_file_new, + format=format) + + # ---------------------------------------- + # latent traversals + # ---------------------------------------- + interp_func_latent = interpolate_1d + plot_func_latent = plot_1d_frame_array + save_file_new = save_file + '_latent-traversals' + + if hparams['model_class'] == 'cond-ae-msp' or hparams['model_class'] == 'sss-vae': + latent_idxs = n_labels + np.arange(n_ae_latents) + elif hparams['model_class'] == 'ae' \ + or hparams['model_class'] == 'vae' \ + or hparams['model_class'] == 'cond-vae' \ + or hparams['model_class'] == 'beta-tcvae': + latent_idxs = np.arange(n_ae_latents) + else: + raise NotImplementedError + + # simplify options here + scaled_labels = False + twod_labels = False + crop_type = None + crop_kwargs = None + labels_2d_np_sel = None + + # get model input for this trial + ims_pt, ims_np, latents_np, labels_pt, labels_np, labels_2d_pt, labels_2d_np = \ + get_model_input( + data_generator, hparams, model_ae, trial=None, trial_idx=trial_idx, + compute_latents=True, compute_scaled_labels=scaled_labels, + compute_2d_labels=twod_labels) + + latents_np[:, n_labels:] = 0 + + if hparams['model_class'] == 'ae' or hparams['model_class'] == 'beta-tcvae': + labels_np_sel = labels_np + else: + labels_np_sel = labels_np[None, batch_idx, :] + + # perform interpolation + ims_latent, markers_loc_latent_, ims_crop_latent = interp_func_latent( + 'latents', model_ae, ims_pt[None, batch_idx, :], latents_np[None, batch_idx, :], + labels_np_sel, labels_2d_np_sel, + mins=latent_range['min'], maxes=latent_range['max'], + n_frames=n_frames_zu, input_idxs=latent_idxs, crop_type=crop_type, + mins_sc=None, maxes_sc=None, crop_kwargs=crop_kwargs, ch=channel) + + # plot interpolation + marker_kwargs = { + 'markersize': 20, 'markeredgewidth': 5, 'markeredgecolor': [1, 1, 0], + 'fillstyle': 'none'} + plot_func_latent( + ims_latent, markers=None, marker_kwargs=marker_kwargs, save_file=save_file_new, + format=format) + + +def make_latent_traversal_movie(): + pass From 5743570e747526e3836aa150f27f0894d35a933a Mon Sep 17 00:00:00 2001 From: themattinthehatt Date: Sat, 9 Jan 2021 15:11:34 -0500 Subject: [PATCH 29/50] streamlined cond ae plotting functions round 2 --- behavenet/fitting/eval.py | 4 +- behavenet/plotting/__init__.py | 2 +- behavenet/plotting/cond_ae_utils.py | 585 +++++++++++++++++++++++++++- 3 files changed, 572 insertions(+), 19 deletions(-) diff --git a/behavenet/fitting/eval.py b/behavenet/fitting/eval.py index 1a64adb..8d36959 100644 --- a/behavenet/fitting/eval.py +++ b/behavenet/fitting/eval.py @@ -300,8 +300,8 @@ def get_reconstruction( labels_2d : :obj:`torch.Tensor` object or :obj:`NoneType`, optional label tensor of shape (batch, n_labels, y_pix, x_pix) apply_inverse_transform : :obj:`bool` - if inputs are latents (and model class is 'cond-ae-msp'), apply inverse transform to put in - original latent space + if inputs are latents (and model class is 'cond-ae-msp' or 'sss-vae'), apply inverse + transform to put in original latent space use_mean : :obj:`bool` if inputs are images (and model class is variational), use mean of approximate posterior without sampling diff --git a/behavenet/plotting/__init__.py b/behavenet/plotting/__init__.py index e5b1ed5..c2dc34a 100644 --- a/behavenet/plotting/__init__.py +++ b/behavenet/plotting/__init__.py @@ -15,7 +15,7 @@ from behavenet.fitting.utils import read_session_info_from_csv # to ignore imports for sphix-autoapidoc -__all__ = ['concat', 'get_crop', 'load_metrics_csv_as_df', 'save_movie'] +__all__ = ['concat', 'get_crop', 'load_latents', 'load_metrics_csv_as_df', 'save_movie'] # TODO: use load_metrics_csv_as_df in ae example notebook diff --git a/behavenet/plotting/cond_ae_utils.py b/behavenet/plotting/cond_ae_utils.py index 27357f2..faae95f 100644 --- a/behavenet/plotting/cond_ae_utils.py +++ b/behavenet/plotting/cond_ae_utils.py @@ -2,6 +2,7 @@ import copy import pickle import numpy as np +import matplotlib.animation as animation import matplotlib.pyplot as plt import pandas as pd import seaborn as sns @@ -18,17 +19,19 @@ from behavenet.fitting.utils import get_expt_dir from behavenet.fitting.utils import get_lab_example from behavenet.fitting.utils import get_session_dir +from behavenet.plotting import concat from behavenet.plotting import get_crop from behavenet.plotting import load_latents from behavenet.plotting import load_metrics_csv_as_df +from behavenet.plotting import save_movie # to ignore imports for sphix-autoapidoc __all__ = [ 'get_input_range', 'compute_range', 'get_labels_2d_for_trial', 'get_model_input', - 'interpolate_2d', 'interpolate_1d', 'plot_2d_frame_array', 'plot_1d_frame_array', + 'interpolate_2d', 'interpolate_1d', 'interpolate_point_path', 'plot_2d_frame_array', + 'plot_1d_frame_array', 'make_interpolated', 'make_interpolated_multipanel', 'plot_hyperparameter_search_results', 'plot_label_reconstructions', - 'plot_label_latent_regression_barplots', 'plot_latent_traversals', - 'make_latent_traversal_movie'] + 'plot_latent_traversals', 'make_latent_traversal_movie'] # ---------------------------------------- @@ -676,6 +679,118 @@ def interpolate_1d( return ims_list, labels_list, ims_crop_list +def interpolate_point_path( + interp_type, model, ims_0, labels_0, points, n_frames=10, ch=0, crop_kwargs=None, + apply_inverse_transform=True): + """Return reconstructed images created by interpolating through multiple points. + + This function is a simplified version of :func:`interpolate_1d()`; this function computes a + traversal for a single dimension instead of all dimensions; also, this function does not + support conditional encoders, nor does it attempt to compute the interpolated, scaled values + of the labels as :func:`interpolate_1d()` does. This function should supercede + :func:`interpolate_1d()` in a future refactor. Also note that this function is utilized by + the code to make traversal movies, whereas :func:`interpolate_1d()` is utilized by the code to + make traversal plots. + + Parameters + ---------- + interp_type : :obj:`str` + 'latents' | 'labels' + model : :obj:`behavenet.models` object + autoencoder model + ims_0 : :obj:`np.ndarray` + base images for interpolating labels, of shape (1, n_channels, y_pix, x_pix) + labels_0 : :obj:`np.ndarray` + base labels of shape (1, n_labels); these values will be used if + `interp_type='latents'`, and they will be ignored if `inter_type='labels'` + (since `points` will be used) + points : :obj:`list` + one entry for each point in path; each entry is an np.ndarray of shape (n_latents,) + n_frames : :obj:`int` or :obj:`array-like` + number of interpolation points between each point; can be an integer that is used + for all paths, or an array/list of length one less than number of points + ch : :obj:`int`, optional + specify which channel of input images to return (can only be a single value) + crop_kwargs : :obj:`dict`, optional + if crop_type is not None, provides information about the crop (for a fixed crop window) + keys : 'y_0', 'x_0', 'y_ext', 'x_ext'; window is + (y_0 - y_ext, y_0 + y_ext) in vertical direction and + (x_0 - x_ext, x_0 + x_ext) in horizontal direction + apply_inverse_transform : :obj:`bool` + if inputs are latents (and model class is 'cond-ae-msp' or 'sss-vae'), apply inverse + transform to put in original latent space + + Returns + ------- + :obj:`tuple` + - ims_list (:obj:`list` of :obj:`np.ndarray`) interpolated images + - inputs_list (:obj:`list` of :obj:`np.ndarray`) interpolated values + + """ + + if model.hparams.get('conditional_encoder', False): + raise NotImplementedError + + n_points = len(points) + if isinstance(n_frames, int): + n_frames = [n_frames] * (n_points - 1) + assert len(n_frames) == (n_points - 1) + + ims_list = [] + inputs_list = [] + + for p in range(n_points - 1): + + p0 = points[None, p] + p1 = points[None, p + 1] + p_vec = (p1 - p0) / n_frames[p] + + for pn in range(n_frames[p]): + + vec = p0 + pn * p_vec + + if interp_type == 'latents': + + if model.hparams['model_class'] == 'cond-ae' \ + or model.hparams['model_class'] == 'cond-vae': + im_tmp = get_reconstruction( + model, vec, apply_inverse_transform=apply_inverse_transform, + labels=torch.from_numpy(labels_0).float().to(model.hparams['device'])) + else: + im_tmp = get_reconstruction( + model, vec, apply_inverse_transform=apply_inverse_transform) + + elif interp_type == 'labels': + + if model.hparams['model_class'] == 'cond-ae-msp' \ + or model.hparams['model_class'] == 'sss-vae': + im_tmp = get_reconstruction( + model, vec, apply_inverse_transform=True) + else: # cond-ae + im_tmp = get_reconstruction( + model, ims_0, + labels=torch.from_numpy(vec).float().to(model.hparams['device'])) + else: + raise NotImplementedError + + if crop_kwargs is not None: + if not isinstance(ch, int): + raise ValueError('"ch" must be an integer to use crop_kwargs') + ims_list.append(get_crop( + im_tmp[0, ch], + crop_kwargs['y_0'], crop_kwargs['y_ext'], + crop_kwargs['x_0'], crop_kwargs['x_ext'])) + else: + if isinstance(ch, int): + ims_list.append(np.copy(im_tmp[0, ch])) + else: + ims_list.append(np.copy(concat(im_tmp[0]))) + + inputs_list.append(vec) + + return ims_list, inputs_list + + def _get_updated_scaled_labels(labels_og, idxs=None, vals=None): """Helper function for interpolate_xd functions.""" @@ -843,6 +958,184 @@ def plot_1d_frame_array( plt.show() +def make_interpolated( + ims, save_file, markers=None, text=None, text_title=None, text_color=[1, 1, 1], + frame_rate=20, scale=3, markersize=10, markeredgecolor='w', markeredgewidth=1, ax=None): + """Make a latent space interpolation movie. + + Parameters + ---------- + ims : :obj:`list` of :obj:`np.ndarray` + each list element is an array of shape (y_pix, x_pix) + save_file : :obj:`str` + absolute path of save file; does not need file extension, will automatically be saved as + mp4. To save as a gif, include the '.gif' file extension in `save_file`. The movie will + only be saved if `ax` is `NoneType`; else the list of animated frames is returned + markers : :obj:`array-like`, optional + array of size (n_frames, 2) which specifies the (x, y) coordinates of a marker on each + frame + text : :obj:`array-like`, optional + array of size (n_frames) which specifies text printed in the lower left corner of each + frame + text_title : :obj:`array-like`, optional + array of size (n_frames) which specifies text printed in the upper left corner of each + frame + text_color : :obj:`array-like`, optional + rgb array specifying color of `text` and `text_title`, if applicable + frame_rate : :obj:`float`, optional + frame rate of saved movie + scale : :obj:`float`, optional + width of panel is (scale / 2) inches + markersize : :obj:`float`, optional + size of marker if `markers` is not `NoneType` + markeredgecolor : :obj:`float`, optional + color of marker edge if `markers` is not `NoneType` + markeredgewidth : :obj:`float`, optional + width of marker edge if `markers` is not `NoneType` + ax : :obj:`matplotlib.axes.Axes` object + optional axis in which to plot the frames; if this argument is not `NoneType` the list of + animated frames is returned and the movie is not saved + + Returns + ------- + :obj:`list` + list of list of animated frames if `ax` is True; else save movie + + """ + + y_pix, x_pix = ims[0].shape + + if ax is None: + fig_width = scale / 2 + fig_height = y_pix / x_pix * scale / 2 + fig = plt.figure(figsize=(fig_width, fig_height), dpi=300) + ax = plt.gca() + return_ims = False + else: + return_ims = True + + ax.set_xticks([]) + ax.set_yticks([]) + + default_kwargs = {'animated': True, 'cmap': 'gray', 'vmin': 0, 'vmax': 1} + txt_kwargs = { + 'fontsize': 4, 'color': text_color, 'fontname': 'monospace', + 'horizontalalignment': 'left', 'verticalalignment': 'center', + 'transform': ax.transAxes} + + # ims is a list of lists, each row is a list of artists to draw in the current frame; here we + # are just animating one artist, the image, in each frame + ims_ani = [] + for i, im in enumerate(ims): + im_tmp = [] + im_tmp.append(ax.imshow(im, **default_kwargs)) + # [s.set_visible(False) for s in ax.spines.values()] + if markers is not None: + im_tmp.append(ax.plot( + markers[i, 0], markers[i, 1], '.r', markersize=markersize, + markeredgecolor=markeredgecolor, markeredgewidth=markeredgewidth)[0]) + if text is not None: + im_tmp.append(ax.text(0.02, 0.06, text[i], **txt_kwargs)) + if text_title is not None: + im_tmp.append(ax.text(0.02, 0.92, text_title[i], **txt_kwargs)) + ims_ani.append(im_tmp) + + if return_ims: + return ims_ani + else: + plt.tight_layout(pad=0) + ani = animation.ArtistAnimation(fig, ims_ani, blit=True, repeat_delay=1000) + save_movie(save_file, ani, frame_rate=frame_rate) + + +def make_interpolated_multipanel( + ims, save_file, markers=None, text=None, text_title=None, frame_rate=20, n_cols=3, scale=1, + **kwargs): + """Make a multi-panel latent space interpolation movie. + + Parameters + ---------- + ims : :obj:`list` of :obj:`list` of :obj:`np.ndarray` + each list element is used to for a single panel, and is another list that contains arrays + of shape (y_pix, x_pix) + save_file : :obj:`str` + absolute path of save file; does not need file extension, will automatically be saved as + mp4. To save as a gif, include the '.gif' file extension in `save_file`. + markers : :obj:`list` of :obj:`array-like`, optional + each list element is used for a single panel, and is an array of size (n_frames, 2) + which specifies the (x, y) coordinates of a marker on each frame for that panel + text : :obj:`list` of :obj:`array-like`, optional + each list element is used for a single panel, and is an array of size (n_frames) which + specifies text printed in the lower left corner of each frame for that panel + text_title : :obj:`list` of :obj:`array-like`, optional + each list element is used for a single panel, and is an array of size (n_frames) which + specifies text printed in the upper left corner of each frame for that panel + frame_rate : :obj:`float`, optional + frame rate of saved movie + n_cols : :obj:`int`, optional + movie is `n_cols` panels wide + scale : :obj:`float`, optional + width of panel is (scale / 2) inches + kwargs + arguments are additional arguments to :func:`make_interpolated`, like 'markersize', + 'markeredgewidth', 'markeredgecolor', etc. + + """ + + n_panels = len(ims) + + markers = [None] * n_panels if markers is None else markers + text = [None] * n_panels if text is None else text + + y_pix, x_pix = ims[0][0].shape + n_rows = int(np.ceil(n_panels / n_cols)) + fig_width = scale / 2 * n_cols + fig_height = y_pix / x_pix * scale / 2 * n_rows + fig, axes = plt.subplots(n_rows, n_cols, figsize=(fig_width, fig_height), dpi=300) + plt.subplots_adjust(wspace=0, hspace=0, left=0, bottom=0, right=1, top=1) + + # fill out empty panels with black frames + while len(ims) < n_rows * n_cols: + ims.append(np.zeros(ims[0].shape)) + markers.append(None) + text.append(None) + + # ims is a list of lists, each row is a list of artists to draw in the current frame; here we + # are just animating one artist, the image, in each frame + ims_ani = [] + for i, (ims_curr, markers_curr, text_curr) in enumerate(zip(ims, markers, text)): + col = i % n_cols + row = int(np.floor(i / n_cols)) + if i == 0: + text_title_str = text_title + else: + text_title_str = None + ims_ani_curr = make_interpolated( + ims=ims_curr, markers=markers_curr, text=text_curr, text_title=text_title_str, + ax=axes[row, col], save_file=None, **kwargs) + ims_ani.append(ims_ani_curr) + + # turn off other axes + i += 1 + while i < n_rows * n_cols: + col = i % n_cols + row = int(np.floor(i / n_cols)) + axes[row, col].set_axis_off() + i += 1 + + # rearrange ims: + # currently a list of length n_panels, each element of which is a list of length n_t + # we need a list of length n_t, each element of which is a list of length n_panels + n_frames = len(ims_ani[0]) + ims_final = [[] for _ in range(n_frames)] + for i in range(n_frames): + for j in range(n_panels): + ims_final[i] += ims_ani[j][i] + + ani = animation.ArtistAnimation(fig, ims_final, blit=True, repeat_delay=1000) + save_movie(save_file, ani, frame_rate=frame_rate) + + # ---------------------------------------- # high-level plotting functions # ---------------------------------------- @@ -921,7 +1214,7 @@ def plot_sssvae_training_curves( model seeds to plot experiment_name : :obj:`str` test-tube experiment name - n_labels : :obj:`str` + n_labels : :obj:`int` dimensionality of supervised latent space dtype : :obj:`str` 'train' | 'val' @@ -930,7 +1223,7 @@ def plot_sssvae_training_curves( format : :obj:`str`, optional format of saved image; 'pdf' | 'png' | 'jpeg' | ... kwargs - keys are keys of `hparams`, for example to set `train_frac`, `rng_seed_model`, etc. + arguments are keys of `hparams`, for example to set `train_frac`, `rng_seed_model`, etc. """ # check for arrays, turn ints into lists @@ -1091,9 +1384,9 @@ def plot_hyperparameter_search_results( format : :obj:`str`, optional format of saved image; 'pdf' | 'png' | 'jpeg' | ... kwargs - keys are keys of `hparams`, preceded by either `alpha_` or `beta_gamma_`. For example, to - set the train frac of the alpha models, use `alpha_train_frac`; to set the rng_data_seed of - the beta-gamma models, use `beta_gamma_rng_data_seed`. + arguments are keys of `hparams`, preceded by either `alpha_` or `beta_gamma_`. For example, + to set the train frac of the alpha models, use `alpha_train_frac`; to set the rng_data_seed + of the beta-gamma models, use `beta_gamma_rng_data_seed`. """ @@ -1458,6 +1751,10 @@ def despine(ax): plt.tight_layout(h_pad=3) # h_pad is fraction of font size + # reset to default color palette + # sns.set_palette(sns.color_palette(None, 10)) + sns.reset_orig() + if save_file is not None: make_dir_if_not_exists(save_file) plt.savefig(save_file + '.' + format, dpi=300, format=format) @@ -1498,7 +1795,7 @@ def plot_label_reconstructions( format : :obj:`str`, optional format of saved image; 'pdf' | 'png' | 'jpeg' | ... kwargs - keys are keys of `hparams`, for example to set `train_frac`, `rng_seed_model`, etc. + arguments are keys of `hparams`, for example to set `train_frac`, `rng_seed_model`, etc. """ @@ -1533,10 +1830,6 @@ def plot_label_reconstructions( labels_og, labels_pred, scale=plot_scale, save_file=save_file_trial, format=format) -def plot_label_latent_regression_barplots(): - pass - - def plot_latent_traversals( lab, expt, animal, session, model_class, alpha, beta, gamma, n_ae_latents, rng_seed_model, experiment_name, n_labels, label_idxs, label_min_p=5, label_max_p=95, @@ -1603,7 +1896,7 @@ def plot_latent_traversals( format : :obj:`str`, optional format of saved image; 'pdf' | 'png' | 'jpeg' | ... kwargs - keys are keys of `hparams`, for example to set `train_frac`, `rng_seed_model`, etc. + arguments are keys of `hparams`, for example to set `train_frac`, `rng_seed_model`, etc. """ @@ -1741,5 +2034,265 @@ def plot_latent_traversals( format=format) -def make_latent_traversal_movie(): - pass +def make_latent_traversal_movie( + lab, expt, animal, session, model_class, alpha, beta, gamma, n_ae_latents, + rng_seed_model, experiment_name, n_labels, trial_idxs, batch_idxs, trials, + label_min_p=5, label_max_p=95, channel=0, sess_idx=0, n_frames=10, n_buffer_frames=5, + crop_kwargs=None, n_cols=3, movie_kwargs={}, panel_titles=None, order_idxs=None, + save_file=None, **kwargs): + """Create a multi-panel movie with each panel showing traversals of an individual latent dim. + + The traversals will start at a lower bound, increase to an upper bound, then return to a lower + bound; the traversal of each dimension occurs simultaneously. It is also possible to specify + multiple base frames for the traversals; the traversal of each base frame is separated by + several blank frames. Note that support for plotting markers on top of the corresponding + supervised dimensions is not supported by this function. + + Parameters + ---------- + lab : :obj:`str` + lab id + expt : :obj:`str` + expt id + animal : :obj:`str` + animal id + session : :obj:`str` + session id + model_class : :obj:`str` + model class in which to perform traversal; currently supported models are: + 'ae' | 'vae' | 'cond-ae' | 'cond-vae' | 'sss-vae' + note that models with conditional encoders are not currently supported + alpha : :obj:`float` + sss-vae alpha value + beta : :obj:`float` + sss-vae beta value + gamma : :obj:`array-like` + sss-vae gamma value + n_ae_latents : :obj:`int` + dimensionality of unsupervised latents + rng_seed_model : :obj:`int` + model seed + experiment_name : :obj:`str` + test-tube experiment name + n_labels : :obj:`str` + dimensionality of supervised latent space (ignored when using fully unsupervised models) + trial_idxs : :obj:`array-like` of :obj:`int` + trial indices of base frames used for interpolation; if an entry is an integer, the + corresponding entry in `trials` must be `None`. This value is a trial index into all + *test* trials, and is not affected by how the test trials are shuffled. The `trials` + argument (see below) takes precedence over `trial_idxs`. + batch_idxs : :obj:`array-like` of :obj:`int` + batch indices of base frames used for interpolation; correspond to entries in `trial_idxs` + and `trials` + trials : :obj:`array-like` of :obj:`int` + trials of base frame used for interpolation; if an entry is an integer, the + corresponding entry in `trial_idxs` must be `None`. This value is a trial index into all + possible trials (train, val, test), whereas `trial_idxs` is an index only into test trials + label_min_p : :obj:`float`, optional + lower percentile of training data used to compute range of traversal + label_max_p : :obj:`float`, optional + upper percentile of training data used to compute range of traversal + channel : :obj:`int`, optional + image channel to plot + sess_idx : :obj:`int`, optional + session index into data generator + n_frames : :obj:`int`, optional + number of frames (points) to display for traversal across latent dimensions; the movie + will display a traversal of `n_frames` across each dim, then another traversal of + `n_frames` in the opposite direction + n_buffer_frames : :obj:`int`, optional + number of blank frames to insert between base frames + crop_kwargs : :obj:`dict`, optional + if crop_type is not None, provides information about the crop (for a fixed crop window) + keys : 'y_0', 'x_0', 'y_ext', 'x_ext'; window is + (y_0 - y_ext, y_0 + y_ext) in vertical direction and + (x_0 - x_ext, x_0 + x_ext) in horizontal direction + n_cols : :obj:`int`, optional + movie is `n_cols` panels wide + movie_kwargs : :obj:`dict`, optional + additional kwargs for individual panels; possible keys are 'markersize', 'markeredgecolor', + 'markeredgewidth', and 'text_color' + panel_titles : :obj:`list` of :obj:`str`, optional + optional titles for each panel + order_idxs : :obj:`array-like`, optional + used to reorder panels (which are plotted in row-major order) if desired + save_file : :obj:`str`, optional + absolute path of save file; does not need file extension, will automatically be saved as + mp4. To save as a gif, include the '.gif' file extension in `save_file` + kwargs + arguments are keys of `hparams`, for example to set `train_frac`, `rng_seed_model`, etc. + + """ + + panel_titles = [''] * (n_labels + n_ae_latents) if panel_titles is None else panel_titles + + hparams = _get_sssvae_hparams( + model_class=model_class, alpha=alpha, beta=beta, gamma=gamma, n_ae_latents=n_ae_latents, + experiment_name=experiment_name, rng_seed_model=rng_seed_model, **kwargs) + + if model_class == 'cond-ae-msp' or model_class == 'sss-vae': + hparams['n_ae_latents'] += n_labels + + # programmatically fill out other hparams options + get_lab_example(hparams, lab, expt) + hparams['animal'] = animal + hparams['session'] = session + hparams['session_dir'], sess_ids = get_session_dir(hparams) + hparams['expt_dir'] = get_expt_dir(hparams) + _, version = experiment_exists(hparams, which_version=True) + model_ae, data_generator = get_best_model_and_data(hparams, Model=None, version=version) + + # get latent/label info + latent_range = get_input_range( + 'latents', hparams, model=model_ae, data_gen=data_generator, min_p=15, max_p=85, + version=version) + label_range = get_input_range( + 'labels', hparams, sess_ids=sess_ids, sess_idx=sess_idx, + min_p=label_min_p, max_p=label_max_p) + + # ---------------------------------------- + # collect frames/latents/labels + # ---------------------------------------- + if hparams['model_class'] == 'vae': + csl = False + c2dl = False + else: + csl = True + c2dl = False + + ims_pt = [] + ims_np = [] + latents_np = [] + labels_pt = [] + labels_np = [] + labels_2d_pt = [] + labels_2d_np = [] + for trial, trial_idx in zip(trials, trial_idxs): + ims_pt_, ims_np_, latents_np_, labels_pt_, labels_np_, labels_2d_pt_, labels_2d_np_ = \ + get_model_input( + data_generator, hparams, model_ae, trial_idx=trial_idx, trial=trial, + compute_latents=True, compute_scaled_labels=csl, compute_2d_labels=c2dl, + max_frames=200) + ims_pt.append(ims_pt_) + ims_np.append(ims_np_) + latents_np.append(latents_np_) + labels_pt.append(labels_pt_) + labels_np.append(labels_np_) + labels_2d_pt.append(labels_2d_pt_) + labels_2d_np.append(labels_2d_np_) + + if hparams['model_class'] == 'sss-vae': + label_idxs = np.arange(n_labels) + latent_idxs = n_labels + np.arange(n_ae_latents) + elif hparams['model_class'] == 'vae': + label_idxs = [] + latent_idxs = np.arange(hparams['n_ae_latents']) + elif hparams['model_class'] == 'cond-vae': + label_idxs = np.arange(n_labels) + latent_idxs = np.arange(hparams['n_ae_latents']) + else: + raise Exception + + # ---------------------------------------- + # label traversals + # ---------------------------------------- + ims_all = [] + txt_strs_all = [] + txt_strs_titles = [] + + for label_idx in label_idxs: + + ims = [] + txt_strs = [] + + for b, batch_idx in enumerate(batch_idxs): + if hparams['model_class'] == 'sss-vae': + points = np.array([latents_np[b][batch_idx, :]] * 3) + elif hparams['model_class'] == 'cond-vae': + points = np.array([labels_np[b][batch_idx, :]] * 3) + else: + raise Exception + points[0, label_idx] = label_range['min'][label_idx] + points[1, label_idx] = label_range['max'][label_idx] + points[2, label_idx] = label_range['min'][label_idx] + ims_curr, inputs = interpolate_point_path( + 'labels', model_ae, ims_pt[b][None, batch_idx, :], + labels_np[b][None, batch_idx, :], points=points, n_frames=n_frames, ch=channel, + crop_kwargs=crop_kwargs) + ims.append(ims_curr) + txt_strs += [panel_titles[label_idx] for _ in range(len(ims_curr))] + + if label_idx == 0: + tmp = trial_idxs[b] if trial_idxs[b] is not None else trials[b] + txt_strs_titles += [ + 'base frame %02i-%02i' % (tmp, batch_idx) for _ in range(len(ims_curr))] + + # add blank frames + y_pix, x_pix = ims_curr[0].shape + ims.append([np.zeros((y_pix, x_pix)) for _ in range(n_buffer_frames)]) + txt_strs += ['' for _ in range(n_buffer_frames)] + if label_idx == 0: + txt_strs_titles += ['' for _ in range(n_buffer_frames)] + + ims_all.append(np.vstack(ims)) + txt_strs_all.append(txt_strs) + + # ---------------------------------------- + # latent traversals + # ---------------------------------------- + crop_kwargs_ = None + for latent_idx in latent_idxs: + + ims = [] + txt_strs = [] + + for b, batch_idx in enumerate(batch_idxs): + + points = np.array([latents_np[b][batch_idx, :]] * 3) + + # points[:, latent_idxs] = 0 + points[0, latent_idx] = latent_range['min'][latent_idx] + points[1, latent_idx] = latent_range['max'][latent_idx] + points[2, latent_idx] = latent_range['min'][latent_idx] + if hparams['model_class'] == 'vae': + labels_curr = None + else: + labels_curr = labels_np[b][None, batch_idx, :] + ims_curr, inputs = interpolate_point_path( + 'latents', model_ae, ims_pt[b][None, batch_idx, :], + labels_curr, points=points, n_frames=n_frames, ch=channel, + crop_kwargs=crop_kwargs_) + ims.append(ims_curr) + if hparams['model_class'] == 'cond-vae': + txt_strs += [panel_titles[latent_idx + n_labels] for _ in range(len(ims_curr))] + else: + txt_strs += [panel_titles[latent_idx] for _ in range(len(ims_curr))] + + if latent_idx == 0 and len(label_idxs) == 0: + # add frame ids here if skipping labels + tmp = trial_idxs[b] if trial_idxs[b] is not None else trials[b] + txt_strs_titles += [ + 'base frame %02i-%02i' % (tmp, batch_idx) for _ in range(len(ims_curr))] + + # add blank frames + y_pix, x_pix = ims_curr[0].shape + ims.append([np.zeros((y_pix, x_pix)) for _ in range(n_buffer_frames)]) + txt_strs += ['' for _ in range(n_buffer_frames)] + if latent_idx == 0 and len(label_idxs) == 0: + txt_strs_titles += ['' for _ in range(n_buffer_frames)] + + ims_all.append(np.vstack(ims)) + txt_strs_all.append(txt_strs) + + # ---------------------------------------- + # make video + # ---------------------------------------- + if order_idxs is None: + # don't change order of latents + order_idxs = np.arange(len(ims_all)) + + make_interpolated_multipanel( + ims=[ims_all[i] for i in order_idxs], + text=[txt_strs_all[i] for i in order_idxs], + text_title=txt_strs_titles, + save_file=save_file, scale=2, n_cols=n_cols, **movie_kwargs) From ff1e748437c04579afb0f66e30980d1a1ab1870f Mon Sep 17 00:00:00 2001 From: themattinthehatt Date: Mon, 11 Jan 2021 15:31:54 -0500 Subject: [PATCH 30/50] sss-vae docs --- docs/source/adv_user_guide.rst | 1 + .../adv_user_guide.sss_vae_hparam_search.rst | 157 ++++++++++++++++++ .../user_guide.conditional_autoencoders.rst | 37 +++++ 3 files changed, 195 insertions(+) create mode 100644 docs/source/adv_user_guide.sss_vae_hparam_search.rst diff --git a/docs/source/adv_user_guide.rst b/docs/source/adv_user_guide.rst index 9d3c643..568428d 100644 --- a/docs/source/adv_user_guide.rst +++ b/docs/source/adv_user_guide.rst @@ -9,3 +9,4 @@ Advanced user guide adv_user_guide.slurm adv_user_guide.load_model adv_user_guide.multisession + adv_user_guide.sss_vae_hparam_search diff --git a/docs/source/adv_user_guide.sss_vae_hparam_search.rst b/docs/source/adv_user_guide.sss_vae_hparam_search.rst new file mode 100644 index 0000000..72478b9 --- /dev/null +++ b/docs/source/adv_user_guide.sss_vae_hparam_search.rst @@ -0,0 +1,157 @@ +.. _sssvae_hparams: + +SSS-VAE hyperparameter search guide +=================================== + +The SSS-VAE objective function :math:`\mathscr{L}_{\text{SSS-VAE}}` is comprised of several +different terms: + +.. math:: + + \mathscr{L}_{\text{SSS-VAE}} = + \mathscr{L}_{\text{frames}} + + \alpha \mathscr{L}_{\text{labels}} + + \mathscr{L}_{\text{KL-s}} + + \mathscr{L}_{\text{ICMI}} + + \beta \mathscr{L}_{\text{TC}} + + \mathscr{L}_{\text{DWKL}} + + \gamma \mathscr{L}_{\text{orth}} + +where + + * :math:`\mathscr{L}_{\text{frames}}`: log-likelihood of the video frames + * :math:`\mathscr{L}_{\text{labels}}`: log-likelihood of the labels + * :math:`\mathscr{L}_{\text{KL-s}}`: KL divergence of the supervised latents + * :math:`\mathscr{L}_{\text{ICMI}}`: index-code mutual information of the unsupervised latents + * :math:`\mathscr{L}_{\text{TC}}`: total correlation of the unsupervised latents + * :math:`\mathscr{L}_{\text{DWKL}}`: dimension-wise KL of the unsupervised latents + * :math:`\mathscr{L}_{\text{orth}}`: orthogonality of the full latent space (supervised + unsupervised) + +There are three important hyperparameters of the model that we address below: :math:`\alpha`, which +weights the reconstruction of the labels; :math:`\beta`, which weights the factorization of the +unsupervised latent space; and :math:`\gamma`, which weights the orthogonality of the entire latent +space. The purpose of this guide is to propose a series of model fits that efficiently explores +this space of hyperparameters, as well as point out several BehaveNet plotting utilities to assist +in this exploration. + + +How to select :math:`\alpha` +---------------------------- +The hyperparameter :math:`\alpha` controls the strength of the label log-likelihood term, which +needs to be balanced against the frame log-likelihood term. We first recommend z-scoring each +individual label, which removes the scale of the labels as a confound. We then recommend fitting +models with a range of :math:`\alpha` values, while setting the defaults :math:`\beta=1` (no extra +weight on the total correlation term) and :math:`\gamma=0` (no constraint on orthogonality). In our +experience the range :math:`\alpha=[50, 100, 500, 1000]` is a reasonable range to start with. The +"best" value for :math:`\alpha` is subjective because it involves a tradeoff between pixel +log-likelihood (or the related mean square error, MSE) and label log-likelihood (or MSE). +After choosing a suitable value, we will fix :math:`\alpha` and vary :math:`\beta` and +:math:`\gamma`. + + +How to select :math:`\beta` and :math:`\gamma` +---------------------------------------------- +The choice of :math:`\beta` and :math:`\gamma` is more difficult because there does not yet exist +a single robust measure of "disentanglement" that can tell us which models learn a suitable +unsupervised representation. Instead we will fit models with a range of hypeparameters, then use +a quantitative metric to guide a qualitative analysis. + +A reasonable range to start with is :math:`\beta=[1, 5, 10, 20]` and :math:`\gamma=1000`. While it +is possible to extend the range for :math:`\gamma`, we have found :math:`\gamma=1000` to work for +many datasets. How, then, do we choose a good value for :math:`\beta`? Currently our best advice is +to compute the correlation of the training data across all pairs of unsupervised dimensions. The +value of :math:`\beta` that minimizes the average of the pairwise correlations is a good place to +start more qualitative evaluations. + +Ultimately, the choice of the "best" model comes down to a qualitative evaluation, the *latent +traversal*. A latent traversal is the result of changing the value of a latent dimension while +keeping the value of all other latent dimensions fixed. If the model has learned an interpretable +representation then the resulting generated frames should show one single behavioral feature +changing per dimension - an arm, or a jaw, or the chest (see :ref:`below` +for more information on tools +for constructing and visualizing these traversals). In order to choose the "best" model, we perform +these latent traversals for all values of :math:`\beta` and look at the resulting latent traversal +outputs. The model with the (subjectively) most interpretable dimensions is then chosen. + + +A note on model robustness +-------------------------- +We have found the SSS-VAE to be somewhat sensitive to initialization of the neural network +parameters. We also recommend choosing the set of hyperparamters with the lowest pairwise +correlations and refitting the model with several random seeds (by changing the ``rng_seed_model`` +parameter of the ``ae_model.json`` file), which may lead to even better results. + +.. _sss_vae_plotting: + +Tools for investigating SSS-VAE model fits +------------------------------------------ +The functions listed below are provided in the BehaveNet plotting module ( +:mod:`behavenet.plotting`) to facilitate model checking and comparison at different stages. + +Hyperparameter search visualization +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +The function :func:`behavenet.plotting.cond_ae_utils.plot_hyperparameter_search_results` creates +a variety of diagnostic plots after the user has performed the :math:`\alpha` search and the +:math:`\beta/\gamma` search detailed above: + +- pixel mse as a function of :math:`\alpha`, num latents (for fixed :math:`\beta, \gamma`) +- label mse as a function of :math:`\alpha`, num_latents (for fixed :math:`\beta, \gamma`) +- pixel mse as a function of :math:`\beta, \gamma` (for fixed :math:`\alpha`, n_ae_latents) +- label mse as a function of :math:`\beta, \gamma` (for fixed :math:`\alpha`, n_ae_latents) +- index-code mutual information (part of the KL decomposition) as a function of + :math:`\beta, \gamma` (for fixed :math:`\alpha`, n_ae_latents) +- total correlation(part of the KL decomposition) as a function of :math:`\beta, \gamma` + (for fixed :math:`\alpha`, n_ae_latents) +- dimension-wise KL (part of the KL decomposition) as a function of :math:`\beta, \gamma` + (for fixed :math:`\alpha`, n_ae_latents) +- average correlation coefficient across all pairs of unsupervised latent dims as a function of + :math:`\beta, \gamma` (for fixed :math:`\alpha`, n_ae_latents) +- subspace overlap computed as :math:`||[A; B] - I||_2^2` for :math:`A, B` the projections to the + supervised and unsupervised subspaces, respectively, and :math:`I` the identity - as a function + of :math:`\beta, \gamma` (for fixed :math:`\alpha`, n_ae_latents) +- example subspace overlap matrix for :math:`\gamma=0` and :math:`\beta=1`, with fixed + :math:`\alpha`, n_ae_latents +- example subspace overlap matrix for :math:`\gamma=1000` and :math:`\beta=1`, with fixed + :math:`\alpha`, n_ae_latents + +These plots help with the selection of hyperparameter settings. + +Model training curves +^^^^^^^^^^^^^^^^^^^^^ +The function :func:`behavenet.plotting.cond_ae_utils.plot_sssvae_training_curves` creates training +plots for each term in the SSS-VAE objective function for a *single* model: + +- total loss +- pixel mse +- label R^2 (note the objective function contains the label MSE, but R^2 is easier to parse) +- KL divergence of supervised latents +- index-code mutual information of unsupervised latents +- total correlation of unsupervised latents +- dimension-wise KL of unsupervised latents +- subspace overlap + +A function argument allows the user to plot either training or validation curves. These plots allow +the user to check whether or not models have trained to completion. + +Label reconstruction +^^^^^^^^^^^^^^^^^^^^ +The function :func:`behavenet.plotting.cond_ae_utils.plot_label_reconstructions` creates a series +of plots that show the true labels and their SSS-VAE reconstructions for a given list of batches. +These plots are useful for qualitatively evaluating the supervised subspace of the SSS-VAE; +a quantitative evaluation (the label MSE) can be found in the ``metrics.csv`` file created in the +model folder during training. + +Latent traversals: plots +^^^^^^^^^^^^^^^^^^^^^^^^ +The function :func:`behavenet.plotting.cond_ae_utils.plot_latent_traversals` displays video frames +representing the traversal of chosen dimensions in the latent space. This function uses a +single base frame to create all traversals. + +Latent traversals: movies +^^^^^^^^^^^^^^^^^^^^^^^^^ +The function :func:`behavenet.plotting.cond_ae_utils.make_latent_traversal_movies` creates a +multi-panel movie with each panel showing traversals of an individual latent dimension. +The traversals will start at a lower bound, increase to an upper bound, then return to a lower +bound; the traversal of each dimension occurs simultaneously. It is also possible to specify +multiple base frames for the traversals; the traversal of each base frame is separated by +several blank frames. diff --git a/docs/source/user_guide.conditional_autoencoders.rst b/docs/source/user_guide.conditional_autoencoders.rst index b6280a9..8c1019b 100644 --- a/docs/source/user_guide.conditional_autoencoders.rst +++ b/docs/source/user_guide.conditional_autoencoders.rst @@ -110,3 +110,40 @@ reasonable starting value if the labels have each been z-scored. Then to fit the model, use the ``ae_grid_search.py`` function using this updated model json. All other input jsons remain unchanged. + + +.. _sss_vae: + +Semi-supervised subspace variational autoencoder +------------------------------------------------ +One downside to the MSP model introduced in the previous section is that the representation in the +unsupervised latent space may be difficult to interpret. The semi-supervised subspace VAE (SSS-VAE) +attempts to remedy this situation by encouraging the unsupervised representation to be factorized, +which has shown to help with interpretability (see paper `here `_). + +To fit a single SSS-VAE (and the default CAE BehaveNet +architecture), edit the ``model_class``, ``sss_vae.alpha``, ``sss_vae.beta`` and ``sss_vae.gamma`` +parameters of the ``ae_model.json`` file: + +.. code-block:: json + + { + "experiment_name": "ae-example", + "model_type": "conv", + "n_ae_latents": 12, + "l2_reg": 0.0, + "rng_seed_model": 0, + "fit_sess_io_layers": false, + "ae_arch_json": null, + "model_class": "sss-vae", + "sss_vae.alpha": 1000, + "sss_vae.beta": 10, + "sss_vae.gamma": 1000, + "conditional_encoder": false + } + +The ``sss_vae.alpha``, ``sss_vae.beta`` and ``sss_vae.gamma`` parameters need to be tuned for +each dataset. See the guidelines for setting these parameters :ref:`here`. + +Then to fit the model, use the ``ae_grid_search.py`` function using this updated model json. All +other input jsons remain unchanged. From bd0cc6f7f34b169f33d54d3e091cceba8d5b677c Mon Sep 17 00:00:00 2001 From: themattinthehatt Date: Mon, 11 Jan 2021 15:35:01 -0500 Subject: [PATCH 31/50] doc updates --- behavenet/plotting/cond_ae_utils.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/behavenet/plotting/cond_ae_utils.py b/behavenet/plotting/cond_ae_utils.py index faae95f..6a7d064 100644 --- a/behavenet/plotting/cond_ae_utils.py +++ b/behavenet/plotting/cond_ae_utils.py @@ -30,8 +30,8 @@ 'get_input_range', 'compute_range', 'get_labels_2d_for_trial', 'get_model_input', 'interpolate_2d', 'interpolate_1d', 'interpolate_point_path', 'plot_2d_frame_array', 'plot_1d_frame_array', 'make_interpolated', 'make_interpolated_multipanel', - 'plot_hyperparameter_search_results', 'plot_label_reconstructions', - 'plot_latent_traversals', 'make_latent_traversal_movie'] + 'plot_sssvae_training_curves', 'plot_hyperparameter_search_results', + 'plot_label_reconstructions', 'plot_latent_traversals', 'make_latent_traversal_movie'] # ---------------------------------------- @@ -1754,7 +1754,7 @@ def despine(ax): # reset to default color palette # sns.set_palette(sns.color_palette(None, 10)) sns.reset_orig() - + if save_file is not None: make_dir_if_not_exists(save_file) plt.savefig(save_file + '.' + format, dpi=300, format=format) From 2d9dccdbc37d8b07c10c0c158cbafd532e4bbcb8 Mon Sep 17 00:00:00 2001 From: themattinthehatt Date: Mon, 11 Jan 2021 15:39:01 -0500 Subject: [PATCH 32/50] doc typo --- docs/source/adv_user_guide.sss_vae_hparam_search.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/adv_user_guide.sss_vae_hparam_search.rst b/docs/source/adv_user_guide.sss_vae_hparam_search.rst index 72478b9..f70853a 100644 --- a/docs/source/adv_user_guide.sss_vae_hparam_search.rst +++ b/docs/source/adv_user_guide.sss_vae_hparam_search.rst @@ -149,7 +149,7 @@ single base frame to create all traversals. Latent traversals: movies ^^^^^^^^^^^^^^^^^^^^^^^^^ -The function :func:`behavenet.plotting.cond_ae_utils.make_latent_traversal_movies` creates a +The function :func:`behavenet.plotting.cond_ae_utils.make_latent_traversal_movie` creates a multi-panel movie with each panel showing traversals of an individual latent dimension. The traversals will start at a lower bound, increase to an upper bound, then return to a lower bound; the traversal of each dimension occurs simultaneously. It is also possible to specify From c3d277d700d364df5ad8ec9e71d7945190eb9ca3 Mon Sep 17 00:00:00 2001 From: themattinthehatt Date: Fri, 22 Jan 2021 09:08:07 -0500 Subject: [PATCH 33/50] small bug fixes --- behavenet/fitting/eval.py | 2 +- behavenet/plotting/cond_ae_utils.py | 52 +++++++++++++++++++++-------- 2 files changed, 40 insertions(+), 14 deletions(-) diff --git a/behavenet/fitting/eval.py b/behavenet/fitting/eval.py index 8d36959..966160e 100644 --- a/behavenet/fitting/eval.py +++ b/behavenet/fitting/eval.py @@ -409,7 +409,7 @@ def get_test_metric( batch, _ = data_generator.next_batch(dtype) # get true latents/states - if metric == 'r2': + if metric == 'r2' or metric == 'mse': if 'ae_latents' in batch: curr_true = batch['ae_latents'][0].cpu().detach().numpy() elif 'labels' in batch: diff --git a/behavenet/plotting/cond_ae_utils.py b/behavenet/plotting/cond_ae_utils.py index 6a7d064..c32b62b 100644 --- a/behavenet/plotting/cond_ae_utils.py +++ b/behavenet/plotting/cond_ae_utils.py @@ -195,7 +195,7 @@ def get_labels_2d_for_trial( def get_model_input( - data_generator, hparams, model, trial=None, trial_idx=None, sess_idx=0, max_frames=100, + data_generator, hparams, model, trial=None, trial_idx=None, sess_idx=0, max_frames=200, compute_latents=False, compute_2d_labels=True, compute_scaled_labels=False, dtype='test'): """Return images, latents, and labels for a given trial. @@ -242,6 +242,8 @@ def get_model_input( if (trial_idx is not None) and (trial is not None): raise ValueError('only one of "trial" or "trial_idx" can be specified') + if (trial_idx is None) and (trial is None): + raise ValueError('one of "trial" or "trial_idx" must be specified') # get trial if trial is None: @@ -1317,7 +1319,8 @@ def plot_sssvae_training_curves( def plot_hyperparameter_search_results( lab, expt, animal, session, n_labels, label_names, alpha_weights, alpha_n_ae_latents, alpha_expt_name, beta_weights, gamma_weights, beta_gamma_n_ae_latents, - beta_gamma_expt_name, alpha, beta, gamma, save_file, format='pdf', **kwargs): + beta_gamma_expt_name, alpha, beta, gamma, save_file, batch_size=None, format='pdf', + **kwargs): """Create a variety of diagnostic plots to assess the sss-vae hyperparameters. These diagnostic plots are based on the recommended way to perform a hyperparameter search in @@ -1381,6 +1384,9 @@ def plot_hyperparameter_search_results( fixed value of gamma for alpha search save_file : :obj:`str` absolute path of save file; does not need file extension + batch_size : :obj:`int`, optional + size of batches, used to compute correlation coefficient per batch; if NoneType, the + correlation coefficient is computed across all time points format : :obj:`str`, optional format of saved image; 'pdf' | 'png' | 'jpeg' | ... kwargs @@ -1544,13 +1550,30 @@ def get_label_r2(hparams, model, data_generator, version, dtype='val', overwrite overlaps['beta=%i_gamma=%i' % (beta, gamma)] = overlap # get corr latents = load_latents(hparams, version, dtype='test') - corr = np.corrcoef(latents[:, n_labels + np.array([0, 1])].T) - metrics_dfs_corr_bg.append(pd.DataFrame({ - 'loss': 'corr', - 'dtype': 'test', - 'val': np.abs(corr[0, 1]), - 'beta': beta, - 'gamma': gamma}, index=[0])) + if batch_size is None: + corr = np.corrcoef(latents[:, n_labels + np.array([0, 1])].T) + metrics_dfs_corr_bg.append(pd.DataFrame({ + 'loss': 'corr', + 'dtype': 'test', + 'val': np.abs(corr[0, 1]), + 'beta': beta, + 'gamma': gamma}, index=[0])) + else: + n_batches = int(np.ceil(latents.shape[0] / batch_size)) + for i in range(n_batches): + try: + corr = np.corrcoef( + latents[i * batch_size:(i + 1) * batch_size, + n_labels + np.array([0, 1])].T) + metrics_dfs_corr_bg.append(pd.DataFrame({ + 'loss': 'corr', + 'dtype': 'test', + 'val': np.abs(corr[0, 1]), + 'beta': beta, + 'gamma': gamma}, index=[0])) + except: + print(i) + break except TypeError: print('could not find model for alpha=%i, beta=%i, gamma=%i' % ( hparams['sss_vae.alpha'], hparams['sss_vae.beta'], hparams['sss_vae.gamma'])) @@ -1833,8 +1856,8 @@ def plot_label_reconstructions( def plot_latent_traversals( lab, expt, animal, session, model_class, alpha, beta, gamma, n_ae_latents, rng_seed_model, experiment_name, n_labels, label_idxs, label_min_p=5, label_max_p=95, - channel=0, n_frames_zs=4, n_frames_zu=4, trial_idx=1, batch_idx=1, crop_type=None, - crop_kwargs=None, sess_idx=0, save_file=None, format='pdf', **kwargs): + channel=0, n_frames_zs=4, n_frames_zu=4, trial=None, trial_idx=1, batch_idx=1, + crop_type=None, crop_kwargs=None, sess_idx=0, save_file=None, format='pdf', **kwargs): """Plot video frames representing the traversal of individual dimensions of the latent space. Parameters @@ -1877,6 +1900,9 @@ def plot_latent_traversals( number of frames (points) to display for traversal through supervised dimensions n_frames_zu : :obj:`int`, optional number of frames (points) to display for traversal through unsupervised dimensions + trial : :obj:`int`, optional + trial index into all possible trials (train, val, test); one of `trial` or `trial_idx` + must be specified; `trial` takes precedence over `trial_idx` trial_idx : :obj:`int`, optional trial index of base frame used for interpolation batch_idx : :obj:`int`, optional @@ -1944,7 +1970,7 @@ def plot_latent_traversals( # get model input for this trial ims_pt, ims_np, latents_np, labels_pt, labels_np, labels_2d_pt, labels_2d_np = \ get_model_input( - data_generator, hparams, model_ae, trial_idx=trial_idx, + data_generator, hparams, model_ae, trial_idx=trial_idx, trial=trial, compute_latents=True, compute_scaled_labels=False, compute_2d_labels=False) if labels_2d_np is None: @@ -2006,7 +2032,7 @@ def plot_latent_traversals( # get model input for this trial ims_pt, ims_np, latents_np, labels_pt, labels_np, labels_2d_pt, labels_2d_np = \ get_model_input( - data_generator, hparams, model_ae, trial=None, trial_idx=trial_idx, + data_generator, hparams, model_ae, trial=trial, trial_idx=trial_idx, compute_latents=True, compute_scaled_labels=scaled_labels, compute_2d_labels=twod_labels) From bfffdabf2dea4550aa7393256182fa917ea6539f Mon Sep 17 00:00:00 2001 From: themattinthehatt Date: Fri, 22 Jan 2021 09:22:09 -0500 Subject: [PATCH 34/50] add flexibility to checking training splits --- behavenet/data/utils.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/behavenet/data/utils.py b/behavenet/data/utils.py index a1ece7f..19d8523 100644 --- a/behavenet/data/utils.py +++ b/behavenet/data/utils.py @@ -387,10 +387,12 @@ def check_same_training_split(model_path, hparams): import_params_file = os.path.join(os.path.dirname(model_path), 'meta_tags.pkl') import_params = pickle.load(open(import_params_file, 'rb')) - if import_params['rng_seed_data'] != hparams['rng_seed_data']: + if import_params['rng_seed_data'] != hparams['rng_seed_data'] and \ + hparams.get('check_rng_seed_data', True): raise ValueError('Different data random seed from existing models') - if import_params['trial_splits'] != hparams['trial_splits']: + if import_params['trial_splits'] != hparams['trial_splits'] and \ + hparams.get('check_trial_splits', True): raise ValueError('Different trial split from existing models') From c4408871c7531704370c33b4116e0bf5f240bcae Mon Sep 17 00:00:00 2001 From: themattinthehatt Date: Tue, 26 Jan 2021 09:45:15 -0500 Subject: [PATCH 35/50] sss-vae -> ps-vae --- behavenet/data/__init__.py | 2 +- behavenet/data/utils.py | 4 +- behavenet/fitting/ae_grid_search.py | 4 +- behavenet/fitting/eval.py | 10 +- behavenet/fitting/hyperparam_utils.py | 2 +- behavenet/fitting/utils.py | 16 +- behavenet/models/__init__.py | 2 +- behavenet/models/vaes.py | 28 +- behavenet/plotting/cond_ae_utils.py | 156 +++---- behavenet/plotting/decoder_utils.py | 2 +- configs/ae_jsons/ae_model.json | 8 +- ...> adv_user_guide.ps_vae_hparam_search.rst} | 24 +- docs/source/glossary.rst | 2 +- .../user_guide.conditional_autoencoders.rst | 24 +- example/05_conditional_ae.ipynb | 427 ------------------ tests/integration.py | 6 +- tests/test_data/test_utils_data.py | 8 +- tests/test_fitting/test_utils_fitting.py | 14 +- 18 files changed, 152 insertions(+), 587 deletions(-) rename docs/source/{adv_user_guide.sss_vae_hparam_search.rst => adv_user_guide.ps_vae_hparam_search.rst} (92%) delete mode 100644 example/05_conditional_ae.ipynb diff --git a/behavenet/data/__init__.py b/behavenet/data/__init__.py index 6aedd20..fcc2edc 100644 --- a/behavenet/data/__init__.py +++ b/behavenet/data/__init__.py @@ -1 +1 @@ -"""Test string""" +"""Data module""" diff --git a/behavenet/data/utils.py b/behavenet/data/utils.py index 19d8523..ef90732 100644 --- a/behavenet/data/utils.py +++ b/behavenet/data/utils.py @@ -73,7 +73,7 @@ def get_data_generator_inputs(hparams, sess_ids, check_splits=True): elif hparams['model_class'] == 'cond-ae' \ or hparams['model_class'] == 'cond-ae-msp' \ or hparams['model_class'] == 'cond-vae' \ - or hparams['model_class'] == 'sss-vae': + or hparams['model_class'] == 'ps-vae': signals = ['images', 'labels'] transforms = [None, None] @@ -84,7 +84,7 @@ def get_data_generator_inputs(hparams, sess_ids, check_splits=True): paths.append(os.path.join(data_dir, 'data.hdf5')) if hparams.get('use_label_mask', False) and ( hparams['model_class'] == 'cond-ae-msp' - or hparams['model_class'] == 'sss-vae'): + or hparams['model_class'] == 'ps-vae'): signals.append('labels_masks') transforms.append(None) paths.append(os.path.join(data_dir, 'data.hdf5')) diff --git a/behavenet/fitting/ae_grid_search.py b/behavenet/fitting/ae_grid_search.py index cd44802..1d321b2 100644 --- a/behavenet/fitting/ae_grid_search.py +++ b/behavenet/fitting/ae_grid_search.py @@ -65,8 +65,8 @@ def set_n_labels(data_generator, hparams): from behavenet.models import VAE as Model elif hparams['model_class'] == 'beta-tcvae': from behavenet.models import BetaTCVAE as Model - elif hparams['model_class'] == 'sss-vae': - from behavenet.models import SSSVAE as Model + elif hparams['model_class'] == 'ps-vae': + from behavenet.models import PSVAE as Model set_n_labels(data_generator, hparams) elif hparams['model_class'] == 'cond-vae': from behavenet.models import ConditionalVAE as Model diff --git a/behavenet/fitting/eval.py b/behavenet/fitting/eval.py index 966160e..7ed0c8b 100644 --- a/behavenet/fitting/eval.py +++ b/behavenet/fitting/eval.py @@ -68,7 +68,7 @@ def export_latents(data_generator, model, filename=None): else: y_in = y[idx_beg:idx_end] output = model.encoding(y_in, dataset=sess) - if model.hparams['model_class'] == 'sss-vae': + if model.hparams['model_class'] == 'ps-vae': curr_latents = torch.cat([output[0], output[1]], axis=1) else: curr_latents = output[0] @@ -84,7 +84,7 @@ def export_latents(data_generator, model, filename=None): else: y_in = y output = model.encoding(y_in, dataset=sess) - if model.hparams['model_class'] == 'sss-vae': + if model.hparams['model_class'] == 'ps-vae': curr_latents = torch.cat([output[0], output[1]], axis=1) else: curr_latents = output[0] @@ -300,7 +300,7 @@ def get_reconstruction( labels_2d : :obj:`torch.Tensor` object or :obj:`NoneType`, optional label tensor of shape (batch, n_labels, y_pix, x_pix) apply_inverse_transform : :obj:`bool` - if inputs are latents (and model class is 'cond-ae-msp' or 'sss-vae'), apply inverse + if inputs are latents (and model class is 'cond-ae-msp' or 'ps-vae'), apply inverse transform to put in original latent space use_mean : :obj:`bool` if inputs are images (and model class is variational), use mean of approximate posterior @@ -331,7 +331,7 @@ def get_reconstruction( elif model.hparams['model_class'] == 'vae' \ or model.hparams['model_class'] == 'beta-tcvae': ims_recon, latents, _, _ = model(inputs, dataset=dataset, use_mean=use_mean) - elif model.hparams['model_class'] == 'sss-vae': + elif model.hparams['model_class'] == 'ps-vae': ims_recon, _, latents, _, _ = model(inputs, dataset=dataset, use_mean=use_mean) elif model.hparams['model_class'] == 'cond-ae': ims_recon, latents = model(inputs, dataset=dataset, labels=labels, labels_2d=labels_2d) @@ -346,7 +346,7 @@ def get_reconstruction( inputs = torch.cat((inputs, labels), dim=1) elif model.hparams['model_class'] == 'cond-ae-msp' and apply_inverse_transform: inputs = model.get_inverse_transformed_latents(inputs, as_numpy=False) - elif model.hparams['model_class'] == 'sss-vae' and apply_inverse_transform: + elif model.hparams['model_class'] == 'ps-vae' and apply_inverse_transform: # assume "inputs" are [labels, unsupervised latents] where "labels" need to be # transformed into N(0, 1) latent space inputs = model.get_inverse_transformed_latents(inputs, as_numpy=False) diff --git a/behavenet/fitting/hyperparam_utils.py b/behavenet/fitting/hyperparam_utils.py index 8e2817b..7e92ce2 100644 --- a/behavenet/fitting/hyperparam_utils.py +++ b/behavenet/fitting/hyperparam_utils.py @@ -68,7 +68,7 @@ def add_dependent_params(parser, namespace): or namespace.model_class == 'cond-vae' \ or namespace.model_class == 'cond-ae' \ or namespace.model_class == 'cond-ae-msp' \ - or namespace.model_class == 'sss-vae' \ + or namespace.model_class == 'ps-vae' \ or namespace.model_class == 'labels-images': max_latents = 64 diff --git a/behavenet/fitting/utils.py b/behavenet/fitting/utils.py index 94d8364..a7cb2b9 100644 --- a/behavenet/fitting/utils.py +++ b/behavenet/fitting/utils.py @@ -350,7 +350,7 @@ def get_expt_dir(hparams, model_class=None, model_type=None, expt_name=None): or model_class == 'cond-vae' \ or model_class == 'cond-ae' \ or model_class == 'cond-ae-msp' \ - or model_class == 'sss-vae': + or model_class == 'ps-vae': model_path = os.path.join( model_class, model_type, '%02i_latents' % hparams['n_ae_latents']) if hparams.get('ae_multisession', None) is not None: @@ -657,7 +657,7 @@ def get_model_params(hparams): or model_class == 'cond-vae' \ or model_class == 'cond-ae' \ or model_class == 'cond-ae-msp' \ - or model_class == 'sss-vae': + or model_class == 'ps-vae': hparams_less['n_ae_latents'] = hparams['n_ae_latents'] hparams_less['fit_sess_io_layers'] = hparams['fit_sess_io_layers'] hparams_less['learning_rate'] = hparams['learning_rate'] @@ -671,10 +671,10 @@ def get_model_params(hparams): # hparams_less['vae.beta_anneal_epochs'] = hparams['vae.beta_anneal_epochs'] if model_class == 'beta-tcvae': hparams_less['beta_tcvae.beta'] = hparams['beta_tcvae.beta'] - if model_class == 'sss-vae': - hparams_less['sss_vae.alpha'] = hparams['sss_vae.alpha'] - hparams_less['sss_vae.beta'] = hparams['sss_vae.beta'] - hparams_less['sss_vae.gamma'] = hparams['sss_vae.gamma'] + if model_class == 'ps-vae': + hparams_less['ps_vae.alpha'] = hparams['ps_vae.alpha'] + hparams_less['ps_vae.beta'] = hparams['ps_vae.beta'] + hparams_less['ps_vae.gamma'] = hparams['ps_vae.gamma'] elif model_class == 'arhmm' or model_class == 'hmm': hparams_less['n_arhmm_lags'] = hparams['n_arhmm_lags'] hparams_less['noise_type'] = hparams['noise_type'] @@ -1024,8 +1024,8 @@ def get_best_model_and_data(hparams, Model=None, load_data=True, version='best', from behavenet.models import AEMSP as Model elif hparams['model_class'] == 'beta-tcvae': from behavenet.models import BetaTCVAE as Model - elif hparams['model_class'] == 'sss-vae': - from behavenet.models import SSSVAE as Model + elif hparams['model_class'] == 'ps-vae': + from behavenet.models import PSVAE as Model elif hparams['model_class'] == 'labels-images': from behavenet.models import ConvDecoder as Model elif hparams['model_class'] == 'neural-ae' or hparams['model_class'] == 'neural-ae-me' \ diff --git a/behavenet/models/__init__.py b/behavenet/models/__init__.py index 8db1cde..72429b7 100644 --- a/behavenet/models/__init__.py +++ b/behavenet/models/__init__.py @@ -1,4 +1,4 @@ from behavenet.models.aes import AE, ConditionalAE, AEMSP from behavenet.models.base import CustomDataParallel from behavenet.models.decoders import Decoder, ConvDecoder -from behavenet.models.vaes import VAE, ConditionalVAE, BetaTCVAE, SSSVAE +from behavenet.models.vaes import VAE, ConditionalVAE, BetaTCVAE, PSVAE diff --git a/behavenet/models/vaes.py b/behavenet/models/vaes.py index 324361c..893f33c 100644 --- a/behavenet/models/vaes.py +++ b/behavenet/models/vaes.py @@ -9,7 +9,7 @@ from behavenet.models.aes import AE, ConvAEDecoder, ConvAEEncoder # to ignore imports for sphix-autoapidoc -__all__ = ['reparameterize', 'VAE', 'ConditionalVAE', 'BetaTCVAE', 'SSSVAE', 'ConvAESSSEncoder'] +__all__ = ['reparameterize', 'VAE', 'ConditionalVAE', 'BetaTCVAE', 'PSVAE', 'ConvAEPSEncoder'] def reparameterize(mu, logvar): @@ -501,8 +501,8 @@ def loss(self, data, dataset=0, accumulate_grad=True, chunk_size=200): return loss_dict_vals -class SSSVAE(AE): - """Semi-supervised subspace variational autoencoder class. +class PSVAE(AE): + """Partitioned subspace variational autoencoder class. This class constructs a VAE that... @@ -516,16 +516,16 @@ def __init__(self, hparams): hparams : :obj:`dict` in addition to the standard keys, must also contain: - 'n_labels' (:obj:`n_labels`) - - 'sss.alpha' (:obj:`float`) - - 'sss.beta' (:obj:`float`) - - 'sss.gamma' (:obj:`float`) + - 'ps_vae.alpha' (:obj:`float`) + - 'ps_vae.beta' (:obj:`float`) + - 'ps_vae.gamma' (:obj:`float`) """ if hparams['model_type'] == 'linear': raise NotImplementedError if hparams['n_ae_latents'] < hparams['n_labels']: - raise ValueError('SSS-VAE model must contain at least as many latents as labels') + raise ValueError('PS-VAE model must contain at least as many latents as labels') self.n_latents = hparams['n_ae_latents'] self.n_labels = hparams['n_labels'] @@ -534,9 +534,9 @@ def __init__(self, hparams): super().__init__(hparams) # set up beta annealing - anneal_epochs = self.hparams.get('sss_vae.anneal_epochs', 0) + anneal_epochs = self.hparams.get('ps_vae.anneal_epochs', 0) self.curr_epoch = 0 # must be modified by training script - beta = hparams['sss_vae.beta'] + beta = hparams['ps_vae.beta'] # TODO: these values should not be precomputed if anneal_epochs > 0: # annealing for total correlation term @@ -555,7 +555,7 @@ def build_model(self): """Construct the model using hparams.""" self.hparams['hidden_layer_size'] = self.hparams['n_ae_latents'] if self.model_type == 'conv': - self.encoding = ConvAESSSEncoder(self.hparams) + self.encoding = ConvAEPSEncoder(self.hparams) self.decoding = ConvAEDecoder(self.hparams) elif self.model_type == 'linear': raise NotImplementedError @@ -600,7 +600,7 @@ def forward(self, x, dataset=None, use_mean=False, **kwargs): return x_hat, z, mu, logvar, y_hat def loss(self, data, dataset=0, accumulate_grad=True, chunk_size=200): - """Calculate modified ELBO loss for SSSVAE. + """Calculate modified ELBO loss for PSVAE. The batch is split into chunks if larger than a hard-coded `chunk_size` to keep memory requirements low; gradients are accumulated across all chunks before a gradient step is @@ -638,9 +638,9 @@ def loss(self, data, dataset=0, accumulate_grad=True, chunk_size=200): # n_latents = self.hparams['n_ae_latents'] # compute hyperparameters - alpha = self.hparams['sss_vae.alpha'] + alpha = self.hparams['ps_vae.alpha'] beta = self.beta_vals[self.curr_epoch] - gamma = self.hparams['sss_vae.gamma'] + gamma = self.hparams['ps_vae.gamma'] kl = self.kl_anneal_vals[self.curr_epoch] loss_strs = [ @@ -860,7 +860,7 @@ def get_inverse_transformed_latents(self, inputs, dataset=None, as_numpy=True): return latents_tr -class ConvAESSSEncoder(ConvAEEncoder): +class ConvAEPSEncoder(ConvAEEncoder): """Convolutional encoder that separates label-related subspace.""" def __init__(self, hparams): diff --git a/behavenet/plotting/cond_ae_utils.py b/behavenet/plotting/cond_ae_utils.py index c32b62b..91339b3 100644 --- a/behavenet/plotting/cond_ae_utils.py +++ b/behavenet/plotting/cond_ae_utils.py @@ -30,7 +30,7 @@ 'get_input_range', 'compute_range', 'get_labels_2d_for_trial', 'get_model_input', 'interpolate_2d', 'interpolate_1d', 'interpolate_point_path', 'plot_2d_frame_array', 'plot_1d_frame_array', 'make_interpolated', 'make_interpolated_multipanel', - 'plot_sssvae_training_curves', 'plot_hyperparameter_search_results', + 'plot_psvae_training_curves', 'plot_hyperparameter_search_results', 'plot_label_reconstructions', 'plot_latent_traversals', 'make_latent_traversal_movie'] @@ -261,7 +261,7 @@ def get_model_input( elif hparams['model_class'] == 'cond-ae' \ or hparams['model_class'] == 'cond-vae' \ or hparams['model_class'] == 'cond-ae-msp' \ - or hparams['model_class'] == 'sss-vae' \ + or hparams['model_class'] == 'ps-vae' \ or hparams['model_class'] == 'labels-images': labels_pt = batch['labels'][:max_frames] labels_np = labels_pt.cpu().detach().numpy() @@ -287,7 +287,7 @@ def get_model_input( # latents if compute_latents: - if hparams['model_class'] == 'cond-ae-msp' or hparams['model_class'] == 'sss-vae': + if hparams['model_class'] == 'cond-ae-msp' or hparams['model_class'] == 'ps-vae': latents_np = model.get_transformed_latents(ims_pt, dataset=sess_idx, as_numpy=True) else: _, latents_np = get_reconstruction( @@ -412,7 +412,7 @@ def interpolate_2d( if model.hparams['model_class'] == 'ae' \ or model.hparams['model_class'] == 'vae' \ or model.hparams['model_class'] == 'beta-tcvae' \ - or model.hparams['model_class'] == 'sss-vae': + or model.hparams['model_class'] == 'ps-vae': labels = None elif model.hparams['model_class'] == 'cond-ae' \ or model.hparams['model_class'] == 'cond-vae': @@ -438,7 +438,7 @@ def interpolate_2d( labels_2d = None if model.hparams['model_class'] == 'cond-ae-msp' \ - or model.hparams['model_class'] == 'sss-vae': + or model.hparams['model_class'] == 'ps-vae': # change latents that correspond to desired labels latents = np.copy(latents_0) latents[0, input_idxs[0]] = inputs[0][i0] @@ -602,7 +602,7 @@ def interpolate_1d( if model.hparams['model_class'] == 'ae' \ or model.hparams['model_class'] == 'vae' \ or model.hparams['model_class'] == 'beta-tcvae' \ - or model.hparams['model_class'] == 'sss-vae': + or model.hparams['model_class'] == 'ps-vae': labels = None elif model.hparams['model_class'] == 'cond-ae' \ or model.hparams['model_class'] == 'cond-vae': @@ -628,7 +628,7 @@ def interpolate_1d( labels_2d = None if model.hparams['model_class'] == 'cond-ae-msp' \ - or model.hparams['model_class'] == 'sss-vae': + or model.hparams['model_class'] == 'ps-vae': # change latents that correspond to desired labels latents = np.copy(latents_0) latents[0, input_idxs[i0]] = inputs[i0][i1] @@ -719,7 +719,7 @@ def interpolate_point_path( (y_0 - y_ext, y_0 + y_ext) in vertical direction and (x_0 - x_ext, x_0 + x_ext) in horizontal direction apply_inverse_transform : :obj:`bool` - if inputs are latents (and model class is 'cond-ae-msp' or 'sss-vae'), apply inverse + if inputs are latents (and model class is 'cond-ae-msp' or 'ps-vae'), apply inverse transform to put in original latent space Returns @@ -765,7 +765,7 @@ def interpolate_point_path( elif interp_type == 'labels': if model.hparams['model_class'] == 'cond-ae-msp' \ - or model.hparams['model_class'] == 'sss-vae': + or model.hparams['model_class'] == 'ps-vae': im_tmp = get_reconstruction( model, vec, apply_inverse_transform=True) else: # cond-ae @@ -1142,11 +1142,11 @@ def make_interpolated_multipanel( # high-level plotting functions # ---------------------------------------- -def _get_sssvae_hparams(**kwargs): +def _get_psvae_hparams(**kwargs): hparams = { 'data_dir': get_user_dir('data'), 'save_dir': get_user_dir('save'), - 'model_class': 'sss-vae', + 'model_class': 'ps-vae', 'model_type': 'conv', 'rng_seed_data': 0, 'trial_splits': '8;1;1;0', @@ -1160,16 +1160,16 @@ def _get_sssvae_hparams(**kwargs): # update hparams for key, val in kwargs.items(): if key == 'alpha' or key == 'beta' or key == 'gamma': - hparams['sss_vae.%s' % key] = val + hparams['ps_vae.%s' % key] = val else: hparams[key] = val return hparams -def plot_sssvae_training_curves( +def plot_psvae_training_curves( lab, expt, animal, session, alphas, betas, gammas, n_ae_latents, rng_seeds_model, experiment_name, n_labels, dtype='val', save_file=None, format='pdf', **kwargs): - """Create training plots for each term in the sss-vae objective function. + """Create training plots for each term in the ps-vae objective function. The `dtype` argument controls which type of trials are plotted ('train' or 'val'). Additionally, multiple models can be plotted simultaneously by varying one (and only one) of @@ -1251,7 +1251,7 @@ def plot_sssvae_training_curves( '"rng_seeds_model" as an array') # set model info - hparams = _get_sssvae_hparams(experiment_name=experiment_name, **kwargs) + hparams = _get_psvae_hparams(experiment_name=experiment_name, **kwargs) metrics_list = [ 'loss', 'loss_data_mse', 'label_r2', @@ -1266,9 +1266,9 @@ def plot_sssvae_training_curves( for rng in rng_seeds_model: # update hparams - hparams['sss_vae.alpha'] = alpha - hparams['sss_vae.beta'] = beta - hparams['sss_vae.gamma'] = gamma + hparams['ps_vae.alpha'] = alpha + hparams['ps_vae.beta'] = beta + hparams['ps_vae.gamma'] = gamma hparams['n_ae_latents'] = n_latents + n_labels hparams['rng_seed_model'] = rng @@ -1321,10 +1321,10 @@ def plot_hyperparameter_search_results( alpha_expt_name, beta_weights, gamma_weights, beta_gamma_n_ae_latents, beta_gamma_expt_name, alpha, beta, gamma, save_file, batch_size=None, format='pdf', **kwargs): - """Create a variety of diagnostic plots to assess the sss-vae hyperparameters. + """Create a variety of diagnostic plots to assess the ps-vae hyperparameters. These diagnostic plots are based on the recommended way to perform a hyperparameter search in - the sss-vae models; first, fix beta=1 and gamma=0, and do a sweep over alpha values and number + the ps-vae models; first, fix beta=1 and gamma=0, and do a sweep over alpha values and number of latents (for example alpha=[50, 100, 500, 1000] and n_ae_latents=[2, 4, 8, 16]). The best alpha value is subjective because it involves a tradeoff between pixel mse and label mse. After choosing a suitable value, fix alpha and the number of latents and vary beta and gamma. This @@ -1434,7 +1434,7 @@ def get_label_r2(hparams, model, data_generator, version, dtype='val', overwrite 'Label': label_names[i], 'R2': r2, 'MSE': mse, - 'Model': 'SSS-VAE'}, index=[0])) + 'Model': 'PS-VAE'}, index=[0])) metrics_df = pd.concat(metrics_df) print('saving results to %s' % save_file) @@ -1449,7 +1449,7 @@ def get_label_r2(hparams, model, data_generator, version, dtype='val', overwrite # ----------------------------------------------------- # set model info - hparams = _get_sssvae_hparams(experiment_name=alpha_expt_name) + hparams = _get_psvae_hparams(experiment_name=alpha_expt_name) # update hparams for key, val in kwargs.items(): # hparam vals should be named 'alpha_[property]', for example 'alpha_train_frac' @@ -1464,9 +1464,9 @@ def get_label_r2(hparams, model, data_generator, version, dtype='val', overwrite for n_latent in alpha_n_ae_latents: hparams['n_ae_latents'] = n_latent + n_labels for alpha_ in alpha_weights: - hparams['sss_vae.alpha'] = alpha_ - hparams['sss_vae.beta'] = beta - hparams['sss_vae.gamma'] = gamma + hparams['ps_vae.alpha'] = alpha_ + hparams['ps_vae.beta'] = beta + hparams['ps_vae.gamma'] = gamma try: get_lab_example(hparams, lab, expt) hparams['animal'] = animal @@ -1475,7 +1475,7 @@ def get_label_r2(hparams, model, data_generator, version, dtype='val', overwrite hparams['expt_dir'] = get_expt_dir(hparams) _, version = experiment_exists(hparams, which_version=True) print('loading results with alpha=%i, beta=%i, gamma=%i (version %i)' % ( - hparams['sss_vae.alpha'], hparams['sss_vae.beta'], hparams['sss_vae.gamma'], + hparams['ps_vae.alpha'], hparams['ps_vae.beta'], hparams['ps_vae.gamma'], version)) # get frame mse metrics_dfs_frame.append(load_metrics_csv_as_df( @@ -1488,10 +1488,10 @@ def get_label_r2(hparams, model, data_generator, version, dtype='val', overwrite metrics_df_ = get_label_r2(hparams, model, data_gen, version, dtype='val') metrics_df_['alpha'] = alpha_ metrics_df_['n_latents'] = hparams['n_ae_latents'] - metrics_dfs_marker.append(metrics_df_[metrics_df_.Model == 'SSS-VAE']) + metrics_dfs_marker.append(metrics_df_[metrics_df_.Model == 'PS-VAE']) except TypeError: print('could not find model for alpha=%i, beta=%i, gamma=%i' % ( - hparams['sss_vae.alpha'], hparams['sss_vae.beta'], hparams['sss_vae.gamma'])) + hparams['ps_vae.alpha'], hparams['ps_vae.beta'], hparams['ps_vae.gamma'])) continue metrics_df_frame = pd.concat(metrics_dfs_frame, sort=False) metrics_df_marker = pd.concat(metrics_dfs_marker, sort=False) @@ -1517,9 +1517,9 @@ def get_label_r2(hparams, model, data_generator, version, dtype='val', overwrite for beta in beta_weights: for gamma in gamma_weights: hparams['n_ae_latents'] = beta_gamma_n_ae_latents + n_labels - hparams['sss_vae.alpha'] = alpha - hparams['sss_vae.beta'] = beta - hparams['sss_vae.gamma'] = gamma + hparams['ps_vae.alpha'] = alpha + hparams['ps_vae.beta'] = beta + hparams['ps_vae.gamma'] = gamma try: get_lab_example(hparams, lab, expt) hparams['animal'] = animal @@ -1528,7 +1528,7 @@ def get_label_r2(hparams, model, data_generator, version, dtype='val', overwrite hparams['expt_dir'] = get_expt_dir(hparams) _, version = experiment_exists(hparams, which_version=True) print('loading results with alpha=%i, beta=%i, gamma=%i (version %i)' % ( - hparams['sss_vae.alpha'], hparams['sss_vae.beta'], hparams['sss_vae.gamma'], + hparams['ps_vae.alpha'], hparams['ps_vae.beta'], hparams['ps_vae.gamma'], version)) # get frame mse metrics_dfs_frame_bg.append(load_metrics_csv_as_df( @@ -1541,7 +1541,7 @@ def get_label_r2(hparams, model, data_generator, version, dtype='val', overwrite metrics_df_ = get_label_r2(hparams, model, data_gen, version, dtype='val') metrics_df_['beta'] = beta metrics_df_['gamma'] = gamma - metrics_dfs_marker_bg.append(metrics_df_[metrics_df_.Model == 'SSS-VAE']) + metrics_dfs_marker_bg.append(metrics_df_[metrics_df_.Model == 'PS-VAE']) # get subspace overlap A = model.encoding.A.weight.data.cpu().detach().numpy() B = model.encoding.B.weight.data.cpu().detach().numpy() @@ -1561,22 +1561,18 @@ def get_label_r2(hparams, model, data_generator, version, dtype='val', overwrite else: n_batches = int(np.ceil(latents.shape[0] / batch_size)) for i in range(n_batches): - try: - corr = np.corrcoef( - latents[i * batch_size:(i + 1) * batch_size, - n_labels + np.array([0, 1])].T) - metrics_dfs_corr_bg.append(pd.DataFrame({ - 'loss': 'corr', - 'dtype': 'test', - 'val': np.abs(corr[0, 1]), - 'beta': beta, - 'gamma': gamma}, index=[0])) - except: - print(i) - break + corr = np.corrcoef( + latents[i * batch_size:(i + 1) * batch_size, + n_labels + np.array([0, 1])].T) + metrics_dfs_corr_bg.append(pd.DataFrame({ + 'loss': 'corr', + 'dtype': 'test', + 'val': np.abs(corr[0, 1]), + 'beta': beta, + 'gamma': gamma}, index=[0])) except TypeError: print('could not find model for alpha=%i, beta=%i, gamma=%i' % ( - hparams['sss_vae.alpha'], hparams['sss_vae.beta'], hparams['sss_vae.gamma'])) + hparams['ps_vae.alpha'], hparams['ps_vae.beta'], hparams['ps_vae.gamma'])) continue print() metrics_df_frame_bg = pd.concat(metrics_dfs_frame_bg, sort=False) @@ -1613,8 +1609,7 @@ def despine(ax): # -------------------------------------------------- ax_pixel_mse_alpha = fig.add_subplot(gs[0, 0:3]) data_queried = metrics_df_frame[(metrics_df_frame.dtype == 'test')] - splt = sns.barplot( - x='n_latents', y='val', hue='alpha', data=data_queried, ax=ax_pixel_mse_alpha) + sns.barplot(x='n_latents', y='val', hue='alpha', data=data_queried, ax=ax_pixel_mse_alpha) ax_pixel_mse_alpha.legend().set_visible(False) ax_pixel_mse_alpha.set_xlabel('Latent dimension') ax_pixel_mse_alpha.set_ylabel('MSE per pixel') @@ -1627,8 +1622,7 @@ def despine(ax): # -------------------------------------------------- ax_marker_mse_alpha = fig.add_subplot(gs[0, 3:6]) data_queried = metrics_df_marker - splt = sns.barplot( - x='n_latents', y='MSE', hue='alpha', data=data_queried, ax=ax_marker_mse_alpha) + sns.barplot(x='n_latents', y='MSE', hue='alpha', data=data_queried, ax=ax_marker_mse_alpha) ax_marker_mse_alpha.set_xlabel('Latent dimension') ax_marker_mse_alpha.set_ylabel('MSE per marker') ax_marker_mse_alpha.set_title('Beta=1, Gamma=0') @@ -1645,8 +1639,7 @@ def despine(ax): (metrics_df_frame_bg.dtype == 'test') & (metrics_df_frame_bg.loss == 'loss_data_mse') & (metrics_df_frame_bg.epoch == 200)] - splt = sns.barplot( - x='beta', y='val', hue='gamma', data=data_queried, ax=ax_pixel_mse_bg) + sns.barplot(x='beta', y='val', hue='gamma', data=data_queried, ax=ax_pixel_mse_bg) ax_pixel_mse_bg.legend().set_visible(False) ax_pixel_mse_bg.set_xlabel('Beta') ax_pixel_mse_bg.set_ylabel('MSE per pixel') @@ -1659,8 +1652,7 @@ def despine(ax): # -------------------------------------------------- ax_marker_mse_bg = fig.add_subplot(gs[0, 9:12]) data_queried = metrics_df_marker_bg - splt = sns.barplot( - x='beta', y='MSE', hue='gamma', data=data_queried, ax=ax_marker_mse_bg) + sns.barplot(x='beta', y='MSE', hue='gamma', data=data_queried, ax=ax_marker_mse_bg) ax_marker_mse_bg.set_xlabel('Beta') ax_marker_mse_bg.set_ylabel('MSE per marker') ax_marker_mse_bg.set_title('Latents=%i, Alpha=1000' % hparams['n_ae_latents']) @@ -1675,7 +1667,7 @@ def despine(ax): (metrics_df_frame_bg.dtype == 'test') & (metrics_df_frame_bg.loss == 'loss_zu_mi') & (metrics_df_frame_bg.epoch == 200)] - splt = sns.lineplot( + sns.lineplot( x='beta', y='val', hue='gamma', data=data_queried, ax=ax_icmi, ci=None, palette=gamma_palette) ax_icmi.legend().set_visible(False) @@ -1692,7 +1684,7 @@ def despine(ax): (metrics_df_frame_bg.dtype == 'test') & (metrics_df_frame_bg.loss == 'loss_zu_tc') & (metrics_df_frame_bg.epoch == 200)] - splt = sns.lineplot( + sns.lineplot( x='beta', y='val', hue='gamma', data=data_queried, ax=ax_tc, ci=None, palette=gamma_palette) ax_tc.legend().set_visible(False) @@ -1709,7 +1701,7 @@ def despine(ax): (metrics_df_frame_bg.dtype == 'test') & (metrics_df_frame_bg.loss == 'loss_zu_dwkl') & (metrics_df_frame_bg.epoch == 200)] - splt = sns.lineplot( + sns.lineplot( x='beta', y='val', hue='gamma', data=data_queried, ax=ax_dwkl, ci=None, palette=gamma_palette) ax_dwkl.legend().set_visible(False) @@ -1723,7 +1715,7 @@ def despine(ax): # -------------------------------------------------- ax_cc = fig.add_subplot(gs[2, 0:3]) data_queried = metrics_df_corr_bg - splt = sns.lineplot( + sns.lineplot( x='beta', y='val', hue='gamma', data=data_queried, ax=ax_cc, ci=None, palette=gamma_palette) ax_cc.legend().set_visible(False) @@ -1741,7 +1733,7 @@ def despine(ax): (metrics_df_frame_bg.loss == 'loss_AB_orth') & (metrics_df_frame_bg.epoch == 200) & ~metrics_df_frame_bg.val.isna()] - splt = sns.lineplot( + sns.lineplot( x='gamma', y='val', hue='beta', data=data_queried, ax=ax_orth, ci=None, palette=beta_palette) ax_orth.legend(frameon=False, title='Beta') @@ -1786,7 +1778,7 @@ def despine(ax): def plot_label_reconstructions( lab, expt, animal, session, n_ae_latents, experiment_name, n_labels, trials, version=None, plot_scale=0.5, sess_idx=0, save_file=None, format='pdf', **kwargs): - """Plot labels and their reconstructions from an sss-vae. + """Plot labels and their reconstructions from an ps-vae. Parameters ---------- @@ -1825,7 +1817,7 @@ def plot_label_reconstructions( from behavenet.plotting.decoder_utils import plot_neural_reconstruction_traces # set model info - hparams = _get_sssvae_hparams( + hparams = _get_psvae_hparams( experiment_name=experiment_name, n_ae_latents=n_ae_latents + n_labels, **kwargs) # programmatically fill out other hparams options @@ -1836,9 +1828,9 @@ def plot_label_reconstructions( model, data_generator = get_best_model_and_data( hparams, Model=None, load_data=True, version=version, data_kwargs=None) print(data_generator) - print('alpha: %i' % model.hparams['sss_vae.alpha']) - print('beta: %i' % model.hparams['sss_vae.beta']) - print('gamma: %i' % model.hparams['sss_vae.gamma']) + print('alpha: %i' % model.hparams['ps_vae.alpha']) + print('beta: %i' % model.hparams['ps_vae.beta']) + print('gamma: %i' % model.hparams['ps_vae.gamma']) print('model seed: %i' % model.hparams['rng_seed_model']) for trial in trials: @@ -1849,7 +1841,7 @@ def plot_label_reconstructions( save_file_trial = save_file + '_trial-%i' % trial else: save_file_trial = None - fig = plot_neural_reconstruction_traces( + plot_neural_reconstruction_traces( labels_og, labels_pred, scale=plot_scale, save_file=save_file_trial, format=format) @@ -1872,14 +1864,14 @@ def plot_latent_traversals( session id model_class : :obj:`str` model class in which to perform traversal; currently supported models are: - 'ae' | 'vae' | 'cond-ae' | 'cond-vae' | 'beta-tcvae' | 'cond-ae-msp' | 'sss-vae' + 'ae' | 'vae' | 'cond-ae' | 'cond-vae' | 'beta-tcvae' | 'cond-ae-msp' | 'ps-vae' note that models with conditional encoders are not currently supported alpha : :obj:`float` - sss-vae alpha value + ps-vae alpha value beta : :obj:`float` - sss-vae beta value + ps-vae beta value gamma : :obj:`array-like` - sss-vae gamma value + ps-vae gamma value n_ae_latents : :obj:`int` dimensionality of unsupervised latents rng_seed_model : :obj:`int` @@ -1926,11 +1918,11 @@ def plot_latent_traversals( """ - hparams = _get_sssvae_hparams( + hparams = _get_psvae_hparams( model_class=model_class, alpha=alpha, beta=beta, gamma=gamma, n_ae_latents=n_ae_latents, experiment_name=experiment_name, rng_seed_model=rng_seed_model, **kwargs) - if model_class == 'cond-ae-msp' or model_class == 'sss-vae': + if model_class == 'cond-ae-msp' or model_class == 'ps-vae': hparams['n_ae_latents'] += n_labels # programmatically fill out other hparams options @@ -1964,7 +1956,7 @@ def plot_latent_traversals( plot_func_label = plot_1d_frame_array save_file_new = save_file + '_label-traversals' - if model_class == 'cond-ae' or model_class == 'cond-ae-msp' or model_class == 'sss-vae' or \ + if model_class == 'cond-ae' or model_class == 'cond-ae-msp' or model_class == 'ps-vae' or \ model_class == 'cond-vae': # get model input for this trial @@ -2012,7 +2004,7 @@ def plot_latent_traversals( plot_func_latent = plot_1d_frame_array save_file_new = save_file + '_latent-traversals' - if hparams['model_class'] == 'cond-ae-msp' or hparams['model_class'] == 'sss-vae': + if hparams['model_class'] == 'cond-ae-msp' or hparams['model_class'] == 'ps-vae': latent_idxs = n_labels + np.arange(n_ae_latents) elif hparams['model_class'] == 'ae' \ or hparams['model_class'] == 'vae' \ @@ -2086,14 +2078,14 @@ def make_latent_traversal_movie( session id model_class : :obj:`str` model class in which to perform traversal; currently supported models are: - 'ae' | 'vae' | 'cond-ae' | 'cond-vae' | 'sss-vae' + 'ae' | 'vae' | 'cond-ae' | 'cond-vae' | 'ps-vae' note that models with conditional encoders are not currently supported alpha : :obj:`float` - sss-vae alpha value + ps-vae alpha value beta : :obj:`float` - sss-vae beta value + ps-vae beta value gamma : :obj:`array-like` - sss-vae gamma value + ps-vae gamma value n_ae_latents : :obj:`int` dimensionality of unsupervised latents rng_seed_model : :obj:`int` @@ -2152,11 +2144,11 @@ def make_latent_traversal_movie( panel_titles = [''] * (n_labels + n_ae_latents) if panel_titles is None else panel_titles - hparams = _get_sssvae_hparams( + hparams = _get_psvae_hparams( model_class=model_class, alpha=alpha, beta=beta, gamma=gamma, n_ae_latents=n_ae_latents, experiment_name=experiment_name, rng_seed_model=rng_seed_model, **kwargs) - if model_class == 'cond-ae-msp' or model_class == 'sss-vae': + if model_class == 'cond-ae-msp' or model_class == 'ps-vae': hparams['n_ae_latents'] += n_labels # programmatically fill out other hparams options @@ -2207,7 +2199,7 @@ def make_latent_traversal_movie( labels_2d_pt.append(labels_2d_pt_) labels_2d_np.append(labels_2d_np_) - if hparams['model_class'] == 'sss-vae': + if hparams['model_class'] == 'ps-vae': label_idxs = np.arange(n_labels) latent_idxs = n_labels + np.arange(n_ae_latents) elif hparams['model_class'] == 'vae': @@ -2232,7 +2224,7 @@ def make_latent_traversal_movie( txt_strs = [] for b, batch_idx in enumerate(batch_idxs): - if hparams['model_class'] == 'sss-vae': + if hparams['model_class'] == 'ps-vae': points = np.array([latents_np[b][batch_idx, :]] * 3) elif hparams['model_class'] == 'cond-vae': points = np.array([labels_np[b][batch_idx, :]] * 3) diff --git a/behavenet/plotting/decoder_utils.py b/behavenet/plotting/decoder_utils.py index 50dd112..d776183 100644 --- a/behavenet/plotting/decoder_utils.py +++ b/behavenet/plotting/decoder_utils.py @@ -338,7 +338,7 @@ def make_neural_reconstruction_movie_wrapper( def make_neural_reconstruction_movie( - ims_orig, ims_recon_ae, ims_recon_neural, latents_ae, latents_neural, ae_model_class='AE', + ims_orig, ims_recon_ae, ims_recon_neural, latents_ae, latents_neural, ae_model_class='AE', colored_predictions=False, scale=0.5, xtick_locs=None, frame_rate_beh=None, save_file=None, frame_rate=15): """Produce movie with original video, ae reconstructed video, and neural reconstructed video. diff --git a/configs/ae_jsons/ae_model.json b/configs/ae_jsons/ae_model.json index 6fff2f0..baaf763 100644 --- a/configs/ae_jsons/ae_model.json +++ b/configs/ae_jsons/ae_model.json @@ -42,12 +42,12 @@ "beta_tcvae.beta_anneal_epochs": 100, # type: int, help: number of epochs to linearly increase betatcvae beta -"sss_vae.alpha": 1, # type: int, help: weight on label reconstruction term +"ps_vae.alpha": 1, # type: int, help: weight on label reconstruction term -"sss_vae.beta": 1, # type: int, help: weight on total correlation term +"ps_vae.beta": 1, # type: int, help: weight on total correlation term -"sss_vae.gamma": 1, # type: int, help: weight on subspace overlap term +"ps_vae.gamma": 1, # type: int, help: weight on subspace overlap term -"sss_vae.anneal_epochs": 100 # type: int, help: number of epochs to linearly increase sss beta value +"ps_vae.anneal_epochs": 100 # type: int, help: number of epochs to linearly increase sss beta value } diff --git a/docs/source/adv_user_guide.sss_vae_hparam_search.rst b/docs/source/adv_user_guide.ps_vae_hparam_search.rst similarity index 92% rename from docs/source/adv_user_guide.sss_vae_hparam_search.rst rename to docs/source/adv_user_guide.ps_vae_hparam_search.rst index f70853a..c69af5c 100644 --- a/docs/source/adv_user_guide.sss_vae_hparam_search.rst +++ b/docs/source/adv_user_guide.ps_vae_hparam_search.rst @@ -1,14 +1,14 @@ -.. _sssvae_hparams: +.. _psvae_hparams: -SSS-VAE hyperparameter search guide +PS-VAE hyperparameter search guide =================================== -The SSS-VAE objective function :math:`\mathscr{L}_{\text{SSS-VAE}}` is comprised of several +The PS-VAE objective function :math:`\mathscr{L}_{\text{PS-VAE}}` is comprised of several different terms: .. math:: - \mathscr{L}_{\text{SSS-VAE}} = + \mathscr{L}_{\text{PS-VAE}} = \mathscr{L}_{\text{frames}} + \alpha \mathscr{L}_{\text{labels}} + \mathscr{L}_{\text{KL-s}} + @@ -67,7 +67,7 @@ Ultimately, the choice of the "best" model comes down to a qualitative evaluatio traversal*. A latent traversal is the result of changing the value of a latent dimension while keeping the value of all other latent dimensions fixed. If the model has learned an interpretable representation then the resulting generated frames should show one single behavioral feature -changing per dimension - an arm, or a jaw, or the chest (see :ref:`below` +changing per dimension - an arm, or a jaw, or the chest (see :ref:`below` for more information on tools for constructing and visualizing these traversals). In order to choose the "best" model, we perform these latent traversals for all values of :math:`\beta` and look at the resulting latent traversal @@ -76,14 +76,14 @@ outputs. The model with the (subjectively) most interpretable dimensions is then A note on model robustness -------------------------- -We have found the SSS-VAE to be somewhat sensitive to initialization of the neural network +We have found the PS-VAE to be somewhat sensitive to initialization of the neural network parameters. We also recommend choosing the set of hyperparamters with the lowest pairwise correlations and refitting the model with several random seeds (by changing the ``rng_seed_model`` parameter of the ``ae_model.json`` file), which may lead to even better results. -.. _sss_vae_plotting: +.. _ps_vae_plotting: -Tools for investigating SSS-VAE model fits +Tools for investigating PS-VAE model fits ------------------------------------------ The functions listed below are provided in the BehaveNet plotting module ( :mod:`behavenet.plotting`) to facilitate model checking and comparison at different stages. @@ -118,8 +118,8 @@ These plots help with the selection of hyperparameter settings. Model training curves ^^^^^^^^^^^^^^^^^^^^^ -The function :func:`behavenet.plotting.cond_ae_utils.plot_sssvae_training_curves` creates training -plots for each term in the SSS-VAE objective function for a *single* model: +The function :func:`behavenet.plotting.cond_ae_utils.plot_psvae_training_curves` creates training +plots for each term in the PS-VAE objective function for a *single* model: - total loss - pixel mse @@ -136,8 +136,8 @@ the user to check whether or not models have trained to completion. Label reconstruction ^^^^^^^^^^^^^^^^^^^^ The function :func:`behavenet.plotting.cond_ae_utils.plot_label_reconstructions` creates a series -of plots that show the true labels and their SSS-VAE reconstructions for a given list of batches. -These plots are useful for qualitatively evaluating the supervised subspace of the SSS-VAE; +of plots that show the true labels and their PS-VAE reconstructions for a given list of batches. +These plots are useful for qualitatively evaluating the supervised subspace of the PS-VAE; a quantitative evaluation (the label MSE) can be found in the ``metrics.csv`` file created in the model folder during training. diff --git a/docs/source/glossary.rst b/docs/source/glossary.rst index e358203..e81e7a2 100644 --- a/docs/source/glossary.rst +++ b/docs/source/glossary.rst @@ -110,7 +110,7 @@ All models: * 'cond-ae': conditional autoencoder * 'cond-vae': conditional variational autoencoder * 'cond-ae-msp': autoencoder with matrix subspace projection loss - * 'sss-vae': semi-supervised subspace variational autoencoder + * 'ps-vae': partitioned subspace variational autoencoder * 'hmm': hidden Markov model * 'arhmm': autoregressive hidden Markov model * 'neural-ae': decode AE latents from neural activity diff --git a/docs/source/user_guide.conditional_autoencoders.rst b/docs/source/user_guide.conditional_autoencoders.rst index 8c1019b..9469289 100644 --- a/docs/source/user_guide.conditional_autoencoders.rst +++ b/docs/source/user_guide.conditional_autoencoders.rst @@ -112,17 +112,17 @@ Then to fit the model, use the ``ae_grid_search.py`` function using this updated other input jsons remain unchanged. -.. _sss_vae: +.. _ps_vae: -Semi-supervised subspace variational autoencoder ------------------------------------------------- +Partitioned subspace variational autoencoder +-------------------------------------------- One downside to the MSP model introduced in the previous section is that the representation in the -unsupervised latent space may be difficult to interpret. The semi-supervised subspace VAE (SSS-VAE) +unsupervised latent space may be difficult to interpret. The partitioned subspace VAE (PS-VAE) attempts to remedy this situation by encouraging the unsupervised representation to be factorized, which has shown to help with interpretability (see paper `here `_). -To fit a single SSS-VAE (and the default CAE BehaveNet -architecture), edit the ``model_class``, ``sss_vae.alpha``, ``sss_vae.beta`` and ``sss_vae.gamma`` +To fit a single PS-VAE (and the default CAE BehaveNet +architecture), edit the ``model_class``, ``ps_vae.alpha``, ``ps_vae.beta`` and ``ps_vae.gamma`` parameters of the ``ae_model.json`` file: .. code-block:: json @@ -135,15 +135,15 @@ parameters of the ``ae_model.json`` file: "rng_seed_model": 0, "fit_sess_io_layers": false, "ae_arch_json": null, - "model_class": "sss-vae", - "sss_vae.alpha": 1000, - "sss_vae.beta": 10, - "sss_vae.gamma": 1000, + "model_class": "ps-vae", + "ps_vae.alpha": 1000, + "ps_vae.beta": 10, + "ps_vae.gamma": 1000, "conditional_encoder": false } -The ``sss_vae.alpha``, ``sss_vae.beta`` and ``sss_vae.gamma`` parameters need to be tuned for -each dataset. See the guidelines for setting these parameters :ref:`here`. +The ``ps_vae.alpha``, ``ps_vae.beta`` and ``ps_vae.gamma`` parameters need to be tuned for +each dataset. See the guidelines for setting these parameters :ref:`here`. Then to fit the model, use the ``ae_grid_search.py`` function using this updated model json. All other input jsons remain unchanged. diff --git a/example/05_conditional_ae.ipynb b/example/05_conditional_ae.ipynb deleted file mode 100644 index e4c12a5..0000000 --- a/example/05_conditional_ae.ipynb +++ /dev/null @@ -1,427 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Analyze AEs with matrix subspace projection loss\n", - "This notebook is a template showcasing some ways to analyze autoencoders that have been fit with the matrix subspace projection (MSP) loss.\n", - "\n", - "
\n", - " \n", - "### Contents\n", - "* [Plot loss metrics as a function of epochs](#Plot-loss-metrics-as-a-function-of-epoch)\n", - "* [Plot true vs predicted labels](#Plot-true-vs-predicted-labels)\n", - "* [Evaluate orthogonality of projection matrix](#Evaluate-orthogonality-of-projection-matrix)\n", - "* [Explore label/latent space](#Explore-label/latent-space)\n", - " * [explore label space](#Explore-2D-label-space)\n", - " * [explore latent space](#Explore-2D-latent-space)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import copy\n", - "import os\n", - "import pandas as pd\n", - "import seaborn as sns\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from behavenet import get_user_dir\n", - "from behavenet import make_dir_if_not_exists\n", - "from behavenet.fitting.utils import get_expt_dir\n", - "from behavenet.fitting.utils import get_session_dir\n", - "from behavenet.fitting.utils import get_best_model_version\n", - "from behavenet.fitting.utils import get_lab_example\n", - "\n", - "save_outputs = False # true to save figures/movies to user's figure directory\n", - "format = 'png' # figure format ('png' | 'jpeg' | 'pdf'); movies saved as mp4" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plot loss metrics as a function of epoch\n", - "\n", - "[Back to contents](#Contents)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from behavenet.plotting import load_metrics_csv_as_df\n", - "\n", - "# set data info\n", - "lab = ?\n", - "expt = ?\n", - "n_labels = ?\n", - "\n", - "# set model info\n", - "n_ae_latents = ? # n_labels will be added to this\n", - "tt_expt_name = ?\n", - "\n", - "hparams = {\n", - " 'data_dir': get_user_dir('data'),\n", - " 'save_dir': get_user_dir('save'),\n", - " 'experiment_name': tt_expt_name,\n", - " 'model_class': 'cond-ae-msp',\n", - " 'model_type': 'conv',\n", - " 'n_ae_latents': n_ae_latents + n_labels}\n", - "\n", - "metrics_list = ['loss', 'loss_mse', 'loss_msp', 'r2']\n", - "metrics_df = load_metrics_csv_as_df(hparams, lab, expt, metrics_list)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# plot data\n", - "sns.set_style('white')\n", - "sns.set_context('talk')\n", - "\n", - "for y in metrics_list:\n", - " \n", - " data_queried = metrics_df[(metrics_df.epoch > 10) & ~pd.isna(metrics_df.loss)]\n", - " splt = sns.relplot(x='epoch', y=y, hue='dtype', kind='line', data=data_queried)\n", - " splt.ax.set_xlabel('Epoch')\n", - " if y == 'loss':\n", - " splt.ax.set_ylabel('Total loss')\n", - " splt.ax.set_yscale('log')\n", - " elif y == 'loss_mse':\n", - " splt.ax.set_ylabel('MSE per pixel')\n", - " splt.ax.set_yscale('log')\n", - " elif y == 'loss_msp':\n", - " splt.ax.set_ylabel('MSE per label')\n", - " splt.ax.set_yscale('log')\n", - " elif y == 'r2':\n", - " splt.ax.set_ylabel('Label $R^2$')\n", - "\n", - " if save_outputs:\n", - " save_file = os.path.join(get_user_dir('fig'), 'ae', 'loss_vs_epoch')\n", - " make_dir_if_not_exists(save_file)\n", - " plt.savefig(save_file + '.' + format, dpi=300, format=format)\n", - "\n", - " plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plot true vs predicted labels\n", - "\n", - "[Back to contents](#Contents)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from behavenet.fitting.utils import get_best_model_and_data\n", - "from behavenet.models import AEMSP\n", - "\n", - "# set model info\n", - "version = 0 # 'best' # test-tube version; 'best' finds the version with the lowest mse\n", - "sess_idx = 0 # when using a multisession, this determines which session is used\n", - "hparams = {\n", - " 'data_dir': get_user_dir('data'),\n", - " 'save_dir': get_user_dir('save'),\n", - " 'experiment_name': tt_expt_name,\n", - " 'model_class': 'cond-ae-msp',\n", - " 'model_type': 'conv',\n", - " 'n_ae_latents': n_ae_latents + n_labels}\n", - "\n", - "trial_idxs = [1, 2, 3] # test trials to plot\n", - "\n", - "# programmatically fill out other hparams options\n", - "get_lab_example(hparams, lab, expt) \n", - "\n", - "model, data_generator = get_best_model_and_data(\n", - " hparams, AEMSP, load_data=True, version=version, data_kwargs=None)\n", - "n_labels = model.n_labels\n", - "print(data_generator)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from behavenet.plotting.ae_utils import plot_neural_reconstruction_traces\n", - "\n", - "for trial_idx in trial_idxs:\n", - " trial = data_generator.datasets[sess_idx].batch_idxs['test'][trial_idx]\n", - " batch = data_generator.datasets[sess_idx][trial]\n", - " labels_og = batch['labels'].detach().cpu().numpy()\n", - " labels_pred = model.get_transformed_latents(batch['images'])[:, :n_labels]\n", - " plot = plot_neural_reconstruction_traces(labels_og, labels_pred, scale=2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Evaluate orthogonality of projection matrix\n", - "\n", - "[Back to contents](#Contents)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "U = model.U.weight.data.cpu().detach().numpy()\n", - "\n", - "plt.figure(figsize=(6, 6))\n", - "overlap = np.matmul(U, U.T)\n", - "m = np.max(np.abs(overlap))\n", - "plt.imshow(overlap, cmap='RdBu', vmin=-m, vmax=m)\n", - "plt.colorbar()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Explore label/latent space\n", - "\n", - "[Back to contents](#Contents)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "\n", - "from behavenet.data.utils import get_data_generator_inputs\n", - "\n", - "from behavenet.fitting.utils import get_best_model_and_data\n", - "from behavenet.fitting.eval import get_reconstruction\n", - "\n", - "from behavenet.plotting.cond_ae_utils import get_crop\n", - "from behavenet.plotting.cond_ae_utils import get_input_range\n", - "from behavenet.plotting.cond_ae_utils import get_labels_2d_for_trial\n", - "from behavenet.plotting.cond_ae_utils import get_model_input\n", - "from behavenet.plotting.cond_ae_utils import interpolate_2d\n", - "from behavenet.plotting.cond_ae_utils import plot_2d_frame_array" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### setup - define model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from behavenet.models import AEMSP as Model\n", - "\n", - "# dataset info\n", - "n_ae_latents = 2 # not including label-related latents\n", - "label_min_p = 15 # minimum percentile for latent/label space interpolation\n", - "label_max_p = 85 # maximum percentile for latent/label space interpolation\n", - "n_frames = 3 # number of frames to plot along each manipulated dim\n", - "trial_idx = 0 # index into trials for base frame\n", - "batch_idx = 0 # index into batch for base frame\n", - "label_idxs = [5, 1] # indices of labels to manipulate; y label first, then x\n", - "latent_idxs = np.array([0, 1]) # indices of latents to manipulate\n", - " \n", - "show_markers = True\n", - " \n", - "# set model info\n", - "version = 0 # test-tube version; 'best' finds the version with the lowest mse\n", - "sess_idx = 0 # when using a multisession, this determines which session is used\n", - "hparams = {\n", - " 'data_dir': get_user_dir('data'),\n", - " 'save_dir': get_user_dir('save'),\n", - " 'experiment_name': tt_expt_name,\n", - " 'model_class': 'cond-ae-msp',\n", - " 'model_type': 'conv',\n", - " 'n_ae_latents': n_ae_latents + n_labels,\n", - " 'rng_seed_data': 0,\n", - " 'trial_splits': '8;1;1;0',\n", - " 'train_frac': 1.0,\n", - " 'rng_seed_model': 0,\n", - " 'conditional_encoder': False,\n", - "}\n", - "\n", - "# programmatically fill out other hparams options\n", - "get_lab_example(hparams, lab, expt)\n", - "hparams['session_dir'], sess_ids = get_session_dir(hparams)\n", - "hparams['expt_dir'] = get_expt_dir(hparams)\n", - "\n", - "# build model\n", - "model_ae, data_generator = get_best_model_and_data(hparams, Model, version=version)\n", - "\n", - "latent_range = get_input_range(\n", - " 'latents', hparams, model=model_ae, data_gen=data_generator)\n", - "label_range = get_input_range(\n", - " 'labels', hparams, sess_ids=sess_ids, sess_idx=sess_idx, \n", - " min_p=label_min_p, max_p=label_max_p)\n", - "label_sc_range = get_input_range(\n", - " 'labels_sc', hparams, sess_ids=sess_ids, sess_idx=sess_idx,\n", - " min_p=label_min_p, max_p=label_max_p)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Explore 2D label space\n", - "\n", - "[Back to contents](#Contents)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ims_pt, ims_np, latents_np, labels_pt, labels_np, labels_2d_pt, labels_2d_np = \\\n", - " get_model_input(\n", - " data_generator, hparams, model_ae, trial_idx=trial_idx, compute_latents=True, \n", - " compute_scaled_labels=False, compute_2d_labels=True)\n", - "\n", - "ims_label, markers_loc_label, ims_crop_label = interpolate_2d(\n", - " 'labels', model_ae, ims_pt[None, batch_idx, :], latents_np[None, batch_idx, :], \n", - " labels_np[None, batch_idx, :], labels_2d_np[None, batch_idx, :], \n", - " mins=[label_range['min'][label_idxs[0]], label_range['min'][label_idxs[1]]], \n", - " maxes=[label_range['max'][label_idxs[0]], label_range['max'][label_idxs[1]]], \n", - " n_frames=n_frames, input_idxs=label_idxs, crop_type=None, \n", - " mins_sc=[label_sc_range['min'][label_idxs[0]], label_sc_range['min'][label_idxs[1]]], \n", - " maxes_sc=[label_sc_range['max'][label_idxs[0]], label_sc_range['max'][label_idxs[1]]], \n", - " crop_kwargs=None, ch=0)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "marker_kwargs = {\n", - " 'markersize': 20, 'markeredgewidth': 3, 'markeredgecolor': [1, 1, 0],\n", - " 'fillstyle': 'none'}\n", - "\n", - "if save_outputs:\n", - " save_file = os.path.join(\n", - " get_user_dir('fig'), \n", - " 'ae', 'D=%02i_label-manipulation_%s_%s-crop.png' % \n", - " (hparams['n_ae_latents'], hparams['session'], crop_type))\n", - "else:\n", - " save_file = None\n", - "\n", - "plot_2d_frame_array(\n", - " ims_label, markers=markers_loc_label, marker_kwargs=marker_kwargs, save_file=None,\n", - " figsize=(15, 15))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Explore 2D latent space\n", - "\n", - "[Back to contents](#Contents)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ims_pt, ims_np, latents_np, labels_pt, labels_np, labels_2d_pt, labels_2d_np = \\\n", - " get_model_input(data_generator, hparams, model_ae, trial=None, trial_idx=trial_idx,\n", - " compute_latents=True, compute_scaled_labels=False, compute_2d_labels=True)\n", - "\n", - "latent_idxs += n_labels # first `n_labels` dims are used to reconstruct labels\n", - "\n", - "ims_latent, markers_loc_latent_, ims_crop_latent = interpolate_2d(\n", - " 'latents', model_ae, ims_pt[None, batch_idx, :], latents_np[None, batch_idx, :], \n", - " labels_np[None, batch_idx, :], labels_2d_np[None, batch_idx, :], \n", - " mins=[latent_range['min'][latent_idxs[0]], latent_range['min'][latent_idxs[1]]], \n", - " maxes=[latent_range['max'][latent_idxs[0]], latent_range['max'][latent_idxs[1]]], \n", - " n_frames=n_frames, input_idxs=latent_idxs, crop_type=None, \n", - " mins_sc=None, maxes_sc=None, crop_kwargs=None, marker_idxs=label_idxs, ch=0)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "marker_kwargs = {\n", - " 'markersize': 20, 'markeredgewidth': 5, 'markeredgecolor': [1, 1, 0],\n", - " 'fillstyle': 'none'}\n", - "\n", - "if save_outputs:\n", - " save_file = os.path.join(\n", - " get_user_dir('fig'), \n", - " 'ae', 'D=%02i_latent-manipulation_%s_%s-crop.png' % \n", - " (hparams['n_ae_latents'], hparams['session'], crop_type))\n", - "else:\n", - " save_file = None\n", - "\n", - "plot_2d_frame_array(\n", - " ims_latent, markers=markers_loc_latent_, marker_kwargs=marker_kwargs, \n", - " save_file=None, figsize=(15, 15))" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "behavenet", - "language": "python", - "name": "behavenet" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tests/integration.py b/tests/integration.py index d7684f7..edf6ca5 100644 --- a/tests/integration.py +++ b/tests/integration.py @@ -55,7 +55,7 @@ {'model_class': 'beta-tcvae', 'model_file': 'ae', 'sessions': SESSIONS[0]}, {'model_class': 'cond-ae-msp', 'model_file': 'ae', 'sessions': SESSIONS[0]}, {'model_class': 'cond-vae', 'model_file': 'ae', 'sessions': SESSIONS[0]}, - {'model_class': 'sss-vae', 'model_file': 'ae', 'sessions': SESSIONS[0]}, + {'model_class': 'ps-vae', 'model_file': 'ae', 'sessions': SESSIONS[0]}, {'model_class': 'labels-images', 'model_file': 'label_decoder', 'sessions': SESSIONS[0]}, ] @@ -122,7 +122,7 @@ def get_model_config_files(model, json_dir): or model == 'cond-vae' \ or model == 'beta-tcvae' \ or model == 'cond-ae-msp' \ - or model == 'sss-vae' \ + or model == 'ps-vae' \ or model == 'labels-images' \ or model == 'arhmm': if model != 'arhmm': @@ -188,7 +188,7 @@ def define_new_config_values(model, session='sess-0'): transitions = 'stationary' noise_type = 'gaussian' - if model == 'ae' or model == 'vae' or model == 'beta-tcvae' or model == 'sss-vae': + if model == 'ae' or model == 'vae' or model == 'beta-tcvae' or model == 'ps-vae': new_values = { 'data': data_dict, 'model': { diff --git a/tests/test_data/test_utils_data.py b/tests/test_data/test_utils_data.py index 3107111..a2947d4 100644 --- a/tests/test_data/test_utils_data.py +++ b/tests/test_data/test_utils_data.py @@ -76,16 +76,16 @@ def test_get_data_generator_inputs(): hparams['use_output_mask'] = False # ----------------- - # sss-vae + # ps-vae # ----------------- - hparams['model_class'] = 'sss-vae' + hparams['model_class'] = 'ps-vae' hparams_, signals, transforms, paths = utils.get_data_generator_inputs( hparams, sess_ids, check_splits=False) assert signals[0] == ['images', 'labels'] assert transforms[0] == [None, None] assert paths[0] == [hdf5_path, hdf5_path] - hparams['model_class'] = 'sss-vae' + hparams['model_class'] = 'ps-vae' hparams['use_output_mask'] = True hparams_, signals, transforms, paths = utils.get_data_generator_inputs( hparams, sess_ids, check_splits=False) @@ -94,7 +94,7 @@ def test_get_data_generator_inputs(): assert paths[0] == [hdf5_path, hdf5_path, hdf5_path] hparams['use_output_mask'] = False - hparams['model_class'] = 'sss-vae' + hparams['model_class'] = 'ps-vae' hparams['use_label_mask'] = True hparams_, signals, transforms, paths = utils.get_data_generator_inputs( hparams, sess_ids, check_splits=False) diff --git a/tests/test_fitting/test_utils_fitting.py b/tests/test_fitting/test_utils_fitting.py index a7f032c..4cbbd8e 100644 --- a/tests/test_fitting/test_utils_fitting.py +++ b/tests/test_fitting/test_utils_fitting.py @@ -528,9 +528,9 @@ def test_get_expt_dir(self): assert expt_dir == model_path # ------------------------- - # sss-vae + # ps-vae # ------------------------- - hparams['model_class'] = 'sss-vae' + hparams['model_class'] = 'ps-vae' hparams['model_type'] = 'conv' hparams['n_ae_latents'] = 10 hparams['experiment_name'] = 'tt_expt' @@ -925,17 +925,17 @@ def test_get_model_params(self): ret_hparams = utils.get_model_params({**misc_hparams, **base_hparams, **model_hparams}) assert ret_hparams == {**base_hparams, **model_hparams} - # sss-vae + # ps-vae model_hparams = { - 'model_class': 'sss-vae', + 'model_class': 'ps-vae', 'model_type': 'conv', 'n_ae_latents': 6, 'fit_sess_io_layers': False, 'learning_rate': 1e-4, 'l2_reg': 1e-2, - 'sss_vae.alpha': 1, - 'sss_vae.beta': 2, - 'sss_vae.gamma': 3, + 'ps_vae.alpha': 1, + 'ps_vae.beta': 2, + 'ps_vae.gamma': 3, # 'beta_tcvae.beta_anneal_epochs': 100 } ret_hparams = utils.get_model_params({**misc_hparams, **base_hparams, **model_hparams}) From c31e21e01dfd368ad13505aff583c5708ccc32e1 Mon Sep 17 00:00:00 2001 From: themattinthehatt Date: Tue, 26 Jan 2021 09:53:44 -0500 Subject: [PATCH 36/50] ps-vae doc update --- ...hparam_search.rst => adv_user_guide.psvae_hparam_search.rst} | 0 docs/source/adv_user_guide.rst | 2 +- 2 files changed, 1 insertion(+), 1 deletion(-) rename docs/source/{adv_user_guide.ps_vae_hparam_search.rst => adv_user_guide.psvae_hparam_search.rst} (100%) diff --git a/docs/source/adv_user_guide.ps_vae_hparam_search.rst b/docs/source/adv_user_guide.psvae_hparam_search.rst similarity index 100% rename from docs/source/adv_user_guide.ps_vae_hparam_search.rst rename to docs/source/adv_user_guide.psvae_hparam_search.rst diff --git a/docs/source/adv_user_guide.rst b/docs/source/adv_user_guide.rst index 568428d..d90a958 100644 --- a/docs/source/adv_user_guide.rst +++ b/docs/source/adv_user_guide.rst @@ -9,4 +9,4 @@ Advanced user guide adv_user_guide.slurm adv_user_guide.load_model adv_user_guide.multisession - adv_user_guide.sss_vae_hparam_search + adv_user_guide.psvae_hparam_search From 13c2348d7b3a4282eefce3218c4deb210d4005bc Mon Sep 17 00:00:00 2001 From: John Zhou Date: Thu, 28 Jan 2021 12:46:56 -0500 Subject: [PATCH 37/50] one-line fix for matplotlib display error --- behavenet/fitting/eval.py | 1 + 1 file changed, 1 insertion(+) diff --git a/behavenet/fitting/eval.py b/behavenet/fitting/eval.py index 7ed0c8b..ee1f625 100644 --- a/behavenet/fitting/eval.py +++ b/behavenet/fitting/eval.py @@ -461,6 +461,7 @@ def export_train_plots(hparams, dtype, loss_type='mse', save_file=None, format=' import pandas as pd import seaborn as sns import matplotlib.pyplot as plt + mpl.use('Agg') #deal with display-less machines from behavenet.fitting.utils import read_session_info_from_csv sns.set_style('white') From 4064e6a3dc59c6e7c561ef654ddc249389c70114 Mon Sep 17 00:00:00 2001 From: John Zhou Date: Thu, 28 Jan 2021 13:52:52 -0500 Subject: [PATCH 38/50] added import statement --- behavenet/fitting/eval.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/behavenet/fitting/eval.py b/behavenet/fitting/eval.py index ee1f625..4a8dd2c 100644 --- a/behavenet/fitting/eval.py +++ b/behavenet/fitting/eval.py @@ -460,8 +460,9 @@ def export_train_plots(hparams, dtype, loss_type='mse', save_file=None, format=' import os import pandas as pd import seaborn as sns - import matplotlib.pyplot as plt + import matplotlib as mpl mpl.use('Agg') #deal with display-less machines + import matplotlib.pyplot as plt from behavenet.fitting.utils import read_session_info_from_csv sns.set_style('white') From aeae5bc3caa5b54a5e4f32abfac3e5e4b5bf272b Mon Sep 17 00:00:00 2001 From: themattinthehatt Date: Fri, 29 Jan 2021 09:13:45 -0500 Subject: [PATCH 39/50] fix directory error with new project --- behavenet/fitting/utils.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/behavenet/fitting/utils.py b/behavenet/fitting/utils.py index a7cb2b9..f0eac1a 100644 --- a/behavenet/fitting/utils.py +++ b/behavenet/fitting/utils.py @@ -68,6 +68,8 @@ def _get_multisession_paths(base_dir, lab='', expt='', animal=''): multi_paths.append(os.path.join(base_dir, lab, expt, animal, sub_dir)) except ValueError: print('warning: did not find requested multisession(s)') + except StopIteration: + print('warning: did not find any sessions') return multi_paths From 2cf3eede399ab332b787fa920d9796e7c479b7ad Mon Sep 17 00:00:00 2001 From: themattinthehatt Date: Fri, 29 Jan 2021 09:15:50 -0500 Subject: [PATCH 40/50] update default ae model arch --- behavenet/models/ae_model_architecture_generator.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/behavenet/models/ae_model_architecture_generator.py b/behavenet/models/ae_model_architecture_generator.py index f975f7f..9ceafb1 100644 --- a/behavenet/models/ae_model_architecture_generator.py +++ b/behavenet/models/ae_model_architecture_generator.py @@ -705,16 +705,16 @@ def load_handcrafted_arches( def load_default_arch(): - """Load default convolutional AE architecture used in Batty et al 2019""" + """Load default convolutional AE architecture used in Whiteway et al 2021.""" arch = { 'ae_network_type': 'strides_only', 'ae_padding_type': 'same', 'ae_batch_norm': 0, 'ae_batch_norm_momentum': None, 'symmetric_arch': 1, - 'ae_encoding_n_channels': [32, 64, 256, 512], - 'ae_encoding_kernel_size': [5, 5, 5, 5], - 'ae_encoding_stride_size': [2, 2, 2, 2], - 'ae_encoding_layer_type': ['conv', 'conv', 'conv', 'conv'], + 'ae_encoding_n_channels': [32, 64, 128, 256, 512], + 'ae_encoding_kernel_size': [5, 5, 5, 5, 5], + 'ae_encoding_stride_size': [2, 2, 2, 2, 5], + 'ae_encoding_layer_type': ['conv', 'conv', 'conv', 'conv', 'conv'], 'ae_decoding_last_FF_layer': 0} return arch From 3bd12d756e386c049fdfef3ae33e999bacbcd097 Mon Sep 17 00:00:00 2001 From: themattinthehatt Date: Fri, 29 Jan 2021 14:31:25 -0500 Subject: [PATCH 41/50] add batch norm params to hparams --- behavenet/models/aes.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/behavenet/models/aes.py b/behavenet/models/aes.py index a0901c8..c10edfc 100644 --- a/behavenet/models/aes.py +++ b/behavenet/models/aes.py @@ -91,7 +91,8 @@ def build_model(self): if self.hparams['ae_batch_norm']: module = nn.BatchNorm2d( self.hparams['ae_encoding_n_channels'][i_layer], - momentum=self.hparams['ae_batch_norm_momentum']) + momentum=self.hparams.get('ae_batch_norm_momentum', 0.1), + track_running_stats=self.hparams.get('track_running_stats', True)) self.encoder.add_module( str('batchnorm%i' % global_layer_num), module) @@ -331,7 +332,8 @@ def build_model(self): if self.hparams['ae_batch_norm']: module = nn.BatchNorm2d( self.hparams['ae_decoding_n_channels'][i_layer], - momentum=self.hparams['ae_batch_norm_momentum']) + momentum=self.hparams.get('ae_batch_norm_momentum', 0.1), + track_running_stats=self.hparams.get('track_running_stats', True)) self.decoder.add_module( str('batchnorm%i' % global_layer_num), module) From 226c499f63f132b1f7732cc31fb07151e79147e5 Mon Sep 17 00:00:00 2001 From: themattinthehatt Date: Sun, 31 Jan 2021 15:28:18 -0500 Subject: [PATCH 42/50] ps-vae example notebook --- behavenet/plotting/cond_ae_utils.py | 1 + {example => examples}/00_data.ipynb | 0 {example => examples}/01_ae.ipynb | 0 {example => examples}/02_arhmm.ipynb | 0 {example => examples}/03_decoder.ipynb | 0 .../04_bayesian_decoder.ipynb | 0 examples/ps-vae/01_ps-vae.ipynb | 681 ++++++++++++++++++ 7 files changed, 682 insertions(+) rename {example => examples}/00_data.ipynb (100%) rename {example => examples}/01_ae.ipynb (100%) rename {example => examples}/02_arhmm.ipynb (100%) rename {example => examples}/03_decoder.ipynb (100%) rename {example => examples}/04_bayesian_decoder.ipynb (100%) create mode 100644 examples/ps-vae/01_ps-vae.ipynb diff --git a/behavenet/plotting/cond_ae_utils.py b/behavenet/plotting/cond_ae_utils.py index 91339b3..8d22ef1 100644 --- a/behavenet/plotting/cond_ae_utils.py +++ b/behavenet/plotting/cond_ae_utils.py @@ -1230,6 +1230,7 @@ def plot_psvae_training_curves( """ # check for arrays, turn ints into lists n_arrays = 0 + hue = None if len(alphas) > 1: n_arrays += 1 hue = 'alpha' diff --git a/example/00_data.ipynb b/examples/00_data.ipynb similarity index 100% rename from example/00_data.ipynb rename to examples/00_data.ipynb diff --git a/example/01_ae.ipynb b/examples/01_ae.ipynb similarity index 100% rename from example/01_ae.ipynb rename to examples/01_ae.ipynb diff --git a/example/02_arhmm.ipynb b/examples/02_arhmm.ipynb similarity index 100% rename from example/02_arhmm.ipynb rename to examples/02_arhmm.ipynb diff --git a/example/03_decoder.ipynb b/examples/03_decoder.ipynb similarity index 100% rename from example/03_decoder.ipynb rename to examples/03_decoder.ipynb diff --git a/example/04_bayesian_decoder.ipynb b/examples/04_bayesian_decoder.ipynb similarity index 100% rename from example/04_bayesian_decoder.ipynb rename to examples/04_bayesian_decoder.ipynb diff --git a/examples/ps-vae/01_ps-vae.ipynb b/examples/ps-vae/01_ps-vae.ipynb new file mode 100644 index 0000000..4d1b9ce --- /dev/null +++ b/examples/ps-vae/01_ps-vae.ipynb @@ -0,0 +1,681 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyze a PS-VAE model\n", + "Because the PS-VAEs currently require significant computation time (generally ~5 hours on a GPU) the data downloaded in the previous notebook also contains already trained PS-VAEs, which we will analyze here.\n", + "\n", + "There are a variety of files that are automatically saved during the fitting of a PS-VAE, which can be used for later analyses such as those below. Some of these files (many of which are common to all BehaveNet models, not just the PS-VAE):\n", + "* `best_val_model.pt`: the best PS-VAE (not necessarily from the final training epoch) as determined by computing the loss on validation data\n", + "* `meta_tags.csv`: hyperparameters associated with data, computational resources, and model\n", + "* `metrics.csv`: metrics computed on dataset as a function of epochs; the default is that metrics are computed on training and validation data every epoch (and reported as a mean over all batches) while metrics are computed on test data only at the end of training using the best model (and reported per batch).\n", + "* `[lab_id]_[expt_id]_[animal_id]_[session_id]_latents.pkl`: list of np.ndarrays of PS-VAE latents (both supervised and unsupervised) computed using the best model\n", + "* `session_info.csv`: sessions used to fit the model\n", + "\n", + "To fit your own PS-VAEs, see additional documentation [here](https://behavenet.readthedocs.io/en/latest/source/user_guide.html).\n", + "\n", + "
\n", + "\n", + "### Contents\n", + "* [Plot validation losses as a function of epochs](#Plot-losses-as-a-function-of-epochs)\n", + "* [Plot label reconstructions](#Plot-label-reconstructions)\n", + "* [Plot latent traversals](#Plot-latent-traversals)\n", + "* [Make latent traversal movie](#Make-latent-traversal-movie)\n", + "* [Make frame reconstruction movie](#Make-reconstruction-movies)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "from behavenet import get_user_dir\n", + "from behavenet.plotting.cond_ae_utils import plot_psvae_training_curves\n", + "from behavenet.plotting.cond_ae_utils import plot_label_reconstructions\n", + "from behavenet.plotting.cond_ae_utils import plot_latent_traversals\n", + "from behavenet.plotting.cond_ae_utils import make_latent_traversal_movie\n", + "\n", + "dataset = 'ibl'\n", + "save_outputs = True # true to save figures/movies to user's figure directory\n", + "file_ext = 'pdf' # figure format ('png' | 'jpeg' | 'pdf'); movies saved as mp4" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# parameters common to all datasets\n", + "n_latents = 2 # number of unsupervised latents\n", + "train_frac = 0.5 # all models trained with 50% of training data to speed up fitting\n", + "experiment_name = 'demo-run' # test-tube exp name\n", + "\n", + "# set dataset-specific parameters\n", + "if dataset == 'ibl':\n", + " \n", + " lab = 'ibl'\n", + " expt = 'angelakilab'\n", + " animal = 'IBL-T4'\n", + " session = '2019-04-23-001'\n", + " n_labels = 4\n", + " label_names = ['L paw (x)', 'R paw (x)', 'L paw (y)', 'R paw (y)']\n", + "\n", + " # define \"best\" model\n", + " best_alpha = 1000\n", + " best_beta = 5\n", + " best_gamma = 500\n", + " best_rng = 0\n", + "\n", + " # label reconstructions\n", + " label_recon_trials= [229, 289, 419] # good validation trials; also used for frame recon\n", + " xtick_locs= [0, 30, 60, 90]\n", + " frame_rate= 60\n", + " scale= 0.4\n", + " \n", + " # latent traversal params\n", + " label_min_p = 35 # lower bound of label traversals\n", + " label_max_p = 85 # upper bound of label traversals\n", + " ch = 0 # video channel to display\n", + " n_frames_zs = 4 # n frames for supervised static traversals\n", + " n_frames_zu = 4 # n frames for unsupervised static traversals\n", + " label_idxs = [1, 0] # horizontally move left/right paws\n", + " crop_type = None # no image cropping\n", + " crop_kwargs = None # no image cropping\n", + " # select base frames for traversals\n", + " trial_idxs = [11, 4, 0, None, None, None, None] # trial index wrt to all test trials\n", + " trials = [None, None, None, 169, 129, 429, 339] # trial index wrt to *all* trials\n", + " batch_idxs = [99, 99, 99, 16, 46, 11, 79] # batch index within trial\n", + " n_cols = 3 # width of traversal movie\n", + " text_color = [1, 1, 1] # text color for labels" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot losses as a function of epochs\n", + "The PS-VAE loss function contains many individual terms; this function plots each term separately (as well as the overall loss) to better understand model performance. Note that this function can also be used to plot training curves for multiple models simultaneously; see function documentation. \n", + "\n", + "Panel info (see paper for mathematical descriptions):\n", + "* loss=loss: total PS-VAE loss\n", + "* loss=loss_data_mse: mean square error on frames (actual loss function uses log-likelihood, a scaled version of the MSE)\n", + "* loss=label_r2: $R^2$ (per trial) of the label reconstructions (actual loss function uses log-likelihood)\n", + "* loss=loss_zs_kl: Kullback-Leibler (KL) divergence of supervised latents\n", + "* loss=loss_zu_mi: index-code mutual information of unuspervised latents\n", + "* loss=loss_zu_tc: total correlation of unuspervised latents\n", + "* loss=loss_zu_dwkl: dimension-wise KL of unuspervised latents\n", + "* loss=loss_AB_orth: orthogonality between supervised/unsupervised subspaces\n", + "\n", + "[Back to contents](#Contents)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading results with alpha=1000, beta=5, gamma=500 (version 0)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "save_file = os.path.join(\n", + " get_user_dir('fig'), lab, expt, animal, session, 'ps-vae', 'training_curves')\n", + "\n", + "save_file_new = save_file + '_alpha={}_beta={}_gamma={}_rng={}_latents={}'.format(\n", + " best_alpha, best_beta, best_gamma, best_rng, n_latents)\n", + "plot_psvae_training_curves(\n", + " lab=lab, expt=expt, animal=animal, session=session, alphas=[best_alpha], \n", + " betas=[best_beta], gammas=[best_gamma], n_ae_latents=[n_latents], \n", + " rng_seeds_model=[0], experiment_name=experiment_name,\n", + " n_labels=n_labels, train_frac=train_frac,\n", + " save_file=save_file_new, format=file_ext)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot label reconstructions\n", + "Plot the original labels and their reconstructions from the supervised subspace of the PS-VAE.\n", + "\n", + "[Back to contents](#Contents)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading model defined in /media/mattw/data/ps-vae_demo_head-fixed/results/ibl/angelakilab/IBL-T4/2019-04-23-001/ps-vae/conv/06_latents/demo-run/version_0/meta_tags.pkl\n", + "Generator contains 1 SingleSessionDatasetBatchedLoad objects:\n", + "ibl_angelakilab_IBL-T4_2019-04-23-001\n", + " signals: ['images', 'labels']\n", + " transforms: OrderedDict([('images', None), ('labels', None)])\n", + " paths: OrderedDict([('images', '/media/mattw/data/ps-vae_demo_head-fixed/data/ibl/angelakilab/IBL-T4/2019-04-23-001/data.hdf5'), ('labels', '/media/mattw/data/ps-vae_demo_head-fixed/data/ibl/angelakilab/IBL-T4/2019-04-23-001/data.hdf5')])\n", + "\n", + "alpha: 1000\n", + "beta: 5\n", + "gamma: 500\n", + "model seed: 0\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "save_file = os.path.join(\n", + " get_user_dir('fig'), lab, expt, animal, session, 'ps-vae', 'label_recon')\n", + "\n", + "plot_label_reconstructions(\n", + " lab=lab, expt=expt, animal=animal, session=session, n_ae_latents=n_latents, \n", + " experiment_name=experiment_name,\n", + " n_labels=n_labels, trials=label_recon_trials, version=None,\n", + " alpha=best_alpha, beta=best_beta, gamma=best_gamma, rng_seed_model=best_rng, \n", + " train_frac=train_frac, save_file=save_file, format=file_ext)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot latent traversals\n", + "Latent traversals provide a qualitative way to assess the quality of the learned PS-VAE representation. We generate these traversals by changing the latent representation one dimension at a time and visually compare the outputs. If the representation is sufficiently interpretable we should be able to easily assign semantic meaning to each latent dimension.\n", + "\n", + "[Back to contents](#Contents)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading model defined in /media/mattw/data/ps-vae_demo_head-fixed/results/ibl/angelakilab/IBL-T4/2019-04-23-001/ps-vae/conv/06_latents/demo-run/version_0/meta_tags.pkl\n", + "using data from following sessions:\n", + "/media/mattw/data/ps-vae_demo_head-fixed/results/ibl/angelakilab/IBL-T4/2019-04-23-001\n", + "constructing data generator...done\n", + "Generator contains 1 SingleSessionDataset objects:\n", + "ibl_angelakilab_IBL-T4_2019-04-23-001\n", + " signals: ['labels']\n", + " transforms: OrderedDict([('labels', None)])\n", + " paths: OrderedDict([('labels', '/media/mattw/data/ps-vae_demo_head-fixed/data/ibl/angelakilab/IBL-T4/2019-04-23-001/data.hdf5')])\n", + "\n", + "using data from following sessions:\n", + "/media/mattw/data/ps-vae_demo_head-fixed/results/ibl/angelakilab/IBL-T4/2019-04-23-001\n", + "constructing data generator...done\n", + "Generator contains 1 SingleSessionDataset objects:\n", + "ibl_angelakilab_IBL-T4_2019-04-23-001\n", + " signals: ['labels_sc']\n", + " transforms: OrderedDict([('labels_sc', None)])\n", + " paths: OrderedDict([('labels_sc', '/media/mattw/data/ps-vae_demo_head-fixed/data/ibl/angelakilab/IBL-T4/2019-04-23-001/data.hdf5')])\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "n_latents = 2\n", + "\n", + "# for trial, trial_idx, batch_idx in zip(trials, trial_idxs, batch_idxs):\n", + "# just plot traversals for single base frame\n", + "trial = trials[0]\n", + "trial_idx = trial_idxs[0]\n", + "batch_idx = batch_idxs[0]\n", + "\n", + "if trial is not None:\n", + " trial_str = 'trial-%i-%i' % (trial, batch_idx)\n", + "else:\n", + " trial_str = 'trial-idx-%i-%i' % (trial_idx, batch_idx)\n", + "\n", + "save_file = os.path.join(\n", + " get_user_dir('fig'), lab, expt, animal, session, 'ps-vae', \n", + " 'traversals_alpha={}_beta={}_gamma={}_rng={}_latents={}_{}'.format(\n", + " best_alpha, best_beta, best_gamma, best_rng, n_latents, trial_str))\n", + "\n", + "plot_latent_traversals(\n", + " lab=lab, expt=expt, animal=animal, session=session, model_class='ps-vae', \n", + " alpha=best_alpha, beta=best_beta, gamma=best_gamma, n_ae_latents=2, \n", + " rng_seed_model=best_rng, experiment_name=experiment_name, \n", + " n_labels=n_labels, label_idxs=label_idxs,\n", + " label_min_p=label_min_p, label_max_p=label_max_p, channel=ch, \n", + " n_frames_zs=n_frames_zs, n_frames_zu=n_frames_zu, trial_idx=trial_idx, \n", + " trial=trial, batch_idx=batch_idx, crop_type=crop_type, crop_kwargs=crop_kwargs,\n", + " train_frac=train_frac, save_file=save_file, format='png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Make latent traversal movie\n", + "A dynamic version of the traversals above; these typically provide a richer look at the traversal results.\n", + "\n", + "[Back to contents](#Contents)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading model defined in /media/mattw/data/ps-vae_demo_head-fixed/results/ibl/angelakilab/IBL-T4/2019-04-23-001/ps-vae/conv/06_latents/demo-run/version_0/meta_tags.pkl\n", + "using data from following sessions:\n", + "/media/mattw/data/ps-vae_demo_head-fixed/results/ibl/angelakilab/IBL-T4/2019-04-23-001\n", + "constructing data generator...done\n", + "Generator contains 1 SingleSessionDataset objects:\n", + "ibl_angelakilab_IBL-T4_2019-04-23-001\n", + " signals: ['labels']\n", + " transforms: OrderedDict([('labels', None)])\n", + " paths: OrderedDict([('labels', '/media/mattw/data/ps-vae_demo_head-fixed/data/ibl/angelakilab/IBL-T4/2019-04-23-001/data.hdf5')])\n", + "\n", + "saving video to /media/mattw/data/ps-vae_demo_head-fixed/figs/ibl/angelakilab/IBL-T4/2019-04-23-001/ps-vae/traversals_alpha=1000_beta=5_gamma=500_rng=0_latents=2.mp4...done\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "n_frames = 10 # number of sample frames per dimension\n", + "model_class = 'ps-vae' # 'sss-vae' | 'vae'\n", + "\n", + "# NOTE: below I hand label each dimension; semantic labels for unsupervised dims are chosen\n", + "# by looking at the latent traversals above, and are indicated with quotes to distinguish\n", + "# them from the supervised dims\n", + "\n", + "if dataset == 'ibl':\n", + " if model_class == 'ps-vae':\n", + " panel_titles = [\n", + " 'L paw (x)', 'R paw (x)', 'L paw (y)', 'R paw (y)', '\"Jaw\"', '\"L paw config\"']\n", + " order_idxs = [0, 1, 4, 2, 3, 5] # reorder nicely\n", + " elif model_class == 'vae':\n", + " panel_titles = [\n", + " 'Latent 0', 'Latent 1', 'Latent 2', 'Latent 3', 'Latent 4', 'Latent 5']\n", + " order_idxs = [0, 1, 2, 3, 4, 5]\n", + " else:\n", + " raise NotImplementedError\n", + "\n", + "elif dataset == 'dipoppa':\n", + " crop_kwargs = None\n", + " if model_class == 'ps-vae':\n", + " panel_titles = [\n", + " 'Pupil area', 'Pupil (y)', 'Pupil (x)', '\"Whisker pad\"', '\"Eyelid\"']\n", + " order_idxs = [2, 1, 0, 3, 4]\n", + " elif model_class == 'vae':\n", + " panel_titles = [\n", + " 'Latent 0', 'Latent 1', 'Latent 2', 'Latent 3', 'Latent 4']\n", + " order_idxs = [0, 1, 2, 3, 4]\n", + " else:\n", + " raise NotImplementedError\n", + "\n", + "elif dataset == 'musall-wpaw':\n", + "# crop_kwargs_ = None\n", + "# show_markers = True \n", + " if model_class == 'sss-vae':\n", + " panel_titles = [\n", + " 'Lever', 'R spout', 'L spout', 'R paw (y)', 'R paw (x)', '\"Chest\"', \n", + " '\"Jaw\"']\n", + " order_idxs = [1, 2, 3, 4, 0, 5, 6, 7]\n", + " elif model_class == 'vae':\n", + " panel_titles = [\n", + " 'Latent 0', 'Latent 1', 'Latent 2', 'Latent 3', 'Latent 4', 'Latent 5', \n", + " 'Latent 6']\n", + " order_idxs = [0, 1, 2, 3, 4, 5, 6, 7]\n", + " else:\n", + " raise NotImplementedError\n", + "\n", + "else:\n", + " raise NotImplementedError\n", + "\n", + "save_file = os.path.join(\n", + " get_user_dir('fig'), lab, expt, animal, session, model_class, \n", + " 'traversals_alpha={}_beta={}_gamma={}_rng={}_latents={}'.format(\n", + " best_alpha, best_beta, best_gamma, best_rng, n_latents))\n", + "\n", + "make_latent_traversal_movie(\n", + " lab=lab, expt=expt, animal=animal, session=session, model_class=model_class, \n", + " alpha=best_alpha, beta=best_beta, gamma=best_gamma, n_ae_latents=n_latents, \n", + " rng_seed_model=best_rng, experiment_name=experiment_name, \n", + " n_labels=n_labels, trial_idxs=trial_idxs, batch_idxs=batch_idxs, trials=trials, \n", + " panel_titles=panel_titles, label_min_p=label_min_p, \n", + " label_max_p=label_max_p, channel=ch, n_frames=n_frames, crop_kwargs=crop_kwargs, \n", + " n_cols=n_cols, movie_kwargs={'text_color': text_color}, order_idxs=order_idxs,\n", + " train_frac=train_frac, save_file=save_file)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Make reconstruction movies\n", + "Compare original frames to VAE and PS-VAE reconstructions.\n", + "\n", + "[Back to contents](#Contents)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### helper function" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "from behavenet.plotting.ae_utils import make_reconstruction_movie\n", + "from behavenet.plotting.cond_ae_utils import get_model_input\n", + "from behavenet.fitting.eval import get_reconstruction\n", + "from behavenet.fitting.utils import get_best_model_and_data, get_lab_example\n", + "from behavenet.plotting import concat, save_movie\n", + "\n", + "def make_reconstruction_movie_wrapper(\n", + " hparams, save_file, model_info, trial_idxs=None, trials=None, sess_idx=0, \n", + " max_frames=400, frame_rate=15, layout_pattern=None):\n", + " \"\"\"Produce movie with original video and reconstructed videos.\n", + "\n", + " This is a high-level function that loads the model described in the hparams dictionary \n", + " and produces the necessary predicted video frames.\n", + "\n", + " Parameters\n", + " ----------\n", + " hparams : :obj:`dict`\n", + " needs to contain enough information to specify an autoencoder\n", + " save_file : :obj:`str`\n", + " full save file (path and filename)\n", + " model_info : :obj:`list`\n", + " each entry is a dict that contains model-specific parameters; must include\n", + " 'title', 'model_class'\n", + " trial_idxs : :obj:`list`, optional\n", + " list of test trials to construct videos from; each element is index into \n", + " test trials only; one of `trial_idxs` or `trials` must be \n", + " specified; `trials` takes precedence over `trial_idxs`\n", + " trials : :obj:`list`, optional\n", + " list of test trials to construct videos from; each element is index into all \n", + " possible trials (train, val, test); one of `trials` or `trial_idxs` must be \n", + " specified; `trials` takes precedence over `trial_idxs`\n", + " sess_idx : :obj:`int`, optional\n", + " session index into data generator\n", + " max_frames : :obj:`int`, optional\n", + " maximum number of frames to animate from a trial\n", + " frame_rate : :obj:`float`, optional\n", + " frame rate of saved movie\n", + " layout_pattern : :obj:`array-like`, optional\n", + " boolean entries specify which panels are used to display frames\n", + " \n", + " \"\"\"\n", + "\n", + " n_labels = hparams['n_labels']\n", + " n_latents = hparams['n_ae_latents']\n", + " expt_name = hparams['experiment_name']\n", + "\n", + " # set up models to fit\n", + " titles = ['Original']\n", + " for model in model_info:\n", + " titles.append(model['title'])\n", + " \n", + " # insert original video at front\n", + " model_info.insert(0, {'model_class': None})\n", + "\n", + " ims_recon = [[] for _ in titles]\n", + " latents = [[] for _ in titles]\n", + " \n", + " if trial_idxs is None:\n", + " trial_idxs = [None] * len(trials)\n", + " if trials is None:\n", + " trials = [None] * len(trial_idxs)\n", + "\n", + " for i, model in enumerate(model_info):\n", + "\n", + " if i == 0:\n", + " continue\n", + " \n", + " # further specify model\n", + " version = model.get('version', 'best')\n", + " hparams['experiment_name'] = model.get('experiment_name', expt_name)\n", + " hparams['model_class'] = model['model_class']\n", + " model_ae, data_generator = get_best_model_and_data(hparams, None, version=version)\n", + "\n", + " # get images\n", + " for trial_idx, trial in zip(trial_idxs, trials):\n", + "\n", + " # get model inputs\n", + " ims_orig_pt, ims_orig_np, _, labels_pt, _, labels_2d_pt, _ = get_model_input(\n", + " data_generator, hparams, model_ae, trial_idx=trial_idx, trial=trial,\n", + " sess_idx=sess_idx, max_frames=max_frames, compute_latents=False, \n", + " compute_2d_labels=False)\n", + " \n", + " # get model outputs\n", + " ims_recon_tmp, latents_tmp = get_reconstruction(\n", + " model_ae, ims_orig_pt, labels=labels_pt, labels_2d=labels_2d_pt,\n", + " return_latents=True)\n", + " ims_recon[i].append(ims_recon_tmp)\n", + " latents[i].append(latents_tmp)\n", + " \n", + " # add a couple black frames to separate trials\n", + " final_trial = True\n", + " if (trial_idx is not None and (trial_idx != trial_idxs[-1])) or \\\n", + " (trial is not None and (trial != trials[-1])):\n", + " final_trial = False\n", + "\n", + " n_buffer = 5\n", + " if not final_trial:\n", + " _, n, y_p, x_p = ims_recon[i][-1].shape\n", + " ims_recon[i].append(np.zeros((n_buffer, n, y_p, x_p)))\n", + " latents[i].append(np.nan * np.zeros((n_buffer, n_latents)))\n", + "\n", + " if i == 1: # deal with original frames only once\n", + " ims_recon[0].append(ims_orig_np)\n", + " latents[0].append([])\n", + " # add a couple black frames to separate trials\n", + " if not final_trial:\n", + " _, n, y_p, x_p = ims_recon[0][-1].shape\n", + " ims_recon[0].append(np.zeros((n_buffer, n, y_p, x_p)))\n", + " \n", + " for i, (ims, zs) in enumerate(zip(ims_recon, latents)):\n", + " ims_recon[i] = np.concatenate(ims, axis=0)\n", + " latents[i] = np.concatenate(zs, axis=0)\n", + " \n", + " if layout_pattern is None:\n", + " if len(titles) < 4:\n", + " n_rows, n_cols = 1, len(titles)\n", + " elif len(titles) == 4:\n", + " n_rows, n_cols = 2, 2\n", + " elif len(titles) > 4:\n", + " n_rows, n_cols = 2, 3\n", + " else:\n", + " raise ValueError('too many models')\n", + " else:\n", + " assert np.sum(layout_pattern) == len(ims_recon)\n", + " n_rows, n_cols = layout_pattern.shape\n", + " count = 0\n", + " for pos_r in layout_pattern:\n", + " for pos_c in pos_r:\n", + " if not pos_c:\n", + " ims_recon.insert(count, [])\n", + " titles.insert(count, [])\n", + " count += 1\n", + "\n", + " make_reconstruction_movie(\n", + " ims=ims_recon, titles=titles, n_rows=n_rows, n_cols=n_cols, \n", + " save_file=save_file, frame_rate=frame_rate)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading model defined in /media/mattw/data/ps-vae_demo_head-fixed/results/ibl/angelakilab/IBL-T4/2019-04-23-001/ps-vae/conv/06_latents/demo-run/version_0/meta_tags.pkl\n", + "Loading model defined in /media/mattw/data/ps-vae_demo_head-fixed/results/ibl/angelakilab/IBL-T4/2019-04-23-001/vae/conv/06_latents/demo-run/version_0/meta_tags.pkl\n", + "saving video to /media/mattw/data/ps-vae_demo_head-fixed/figs/ibl/angelakilab/IBL-T4/2019-04-23-001/ps-vae/reconstructions_alpha=1000_beta=5_gamma=500_rng=0_latents=2.mp4...done\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# set model info\n", + "hparams = {\n", + " 'data_dir': get_user_dir('data'),\n", + " 'save_dir': get_user_dir('save'),\n", + " 'n_labels': n_labels,\n", + " 'n_ae_latents': n_latents + n_labels,\n", + " 'experiment_name': None,\n", + " 'model_type': 'conv',\n", + " 'conditional_encoder': False,\n", + "}\n", + "\n", + "# programmatically fill out other hparams options\n", + "get_lab_example(hparams, lab, expt)\n", + "\n", + "# compare vae/ps-vae reconstructions\n", + "model_info = [\n", + " {\n", + " 'model_class': 'ps-vae',\n", + " 'experiment_name': 'demo-run',\n", + " 'title': 'PS-VAE (%i latents)' % n_latents,\n", + " 'version': 0},\n", + " {\n", + " 'model_class': 'vae',\n", + " 'experiment_name': 'demo-run',\n", + " 'title': 'VAE (%i latents)' % n_latents,\n", + " 'version': 0},\n", + "]\n", + "\n", + "save_file = os.path.join(\n", + " get_user_dir('fig'), lab, expt, animal, session, model_class, \n", + " 'reconstructions_alpha={}_beta={}_gamma={}_rng={}_latents={}'.format(\n", + " best_alpha, best_beta, best_gamma, best_rng, n_latents))\n", + "\n", + "make_reconstruction_movie_wrapper(\n", + " hparams, save_file=save_file, trial_idxs=None, trials=label_recon_trials, \n", + " model_info=model_info, frame_rate=15)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "behavenet", + "language": "python", + "name": "behavenet" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From aa7f43e2cc694731b99277efb0bef12d25b477f7 Mon Sep 17 00:00:00 2001 From: themattinthehatt Date: Sun, 31 Jan 2021 15:29:53 -0500 Subject: [PATCH 43/50] README update --- README.md | 2 +- examples/ps-vae/01_ps-vae.ipynb | 186 +++----------------------------- 2 files changed, 14 insertions(+), 174 deletions(-) diff --git a/README.md b/README.md index 26f0d33..1bd43de 100644 --- a/README.md +++ b/README.md @@ -12,7 +12,7 @@ for more information about how to install the software and begin fitting models Additionally, we provide an example dataset and several jupyter notebooks that walk you through how to download the dataset, fit models, and analyze the results. The jupyter notebooks can be found -[here](example). +[here](examples). ## Bibtex diff --git a/examples/ps-vae/01_ps-vae.ipynb b/examples/ps-vae/01_ps-vae.ipynb index 4d1b9ce..3a8e527 100644 --- a/examples/ps-vae/01_ps-vae.ipynb +++ b/examples/ps-vae/01_ps-vae.ipynb @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -47,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -117,27 +117,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loading results with alpha=1000, beta=5, gamma=500 (version 0)\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "save_file = os.path.join(\n", " get_user_dir('fig'), lab, expt, animal, session, 'ps-vae', 'training_curves')\n", @@ -164,57 +146,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading model defined in /media/mattw/data/ps-vae_demo_head-fixed/results/ibl/angelakilab/IBL-T4/2019-04-23-001/ps-vae/conv/06_latents/demo-run/version_0/meta_tags.pkl\n", - "Generator contains 1 SingleSessionDatasetBatchedLoad objects:\n", - "ibl_angelakilab_IBL-T4_2019-04-23-001\n", - " signals: ['images', 'labels']\n", - " transforms: OrderedDict([('images', None), ('labels', None)])\n", - " paths: OrderedDict([('images', '/media/mattw/data/ps-vae_demo_head-fixed/data/ibl/angelakilab/IBL-T4/2019-04-23-001/data.hdf5'), ('labels', '/media/mattw/data/ps-vae_demo_head-fixed/data/ibl/angelakilab/IBL-T4/2019-04-23-001/data.hdf5')])\n", - "\n", - "alpha: 1000\n", - "beta: 5\n", - "gamma: 500\n", - "model seed: 0\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", 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qMhYpdT90eVwzHudyuSrWTMez8Yr7K1qJN83eSryofLPj+OT4LEcSXV4M00RERFSTiWip+6DX653xOCEEnM7SrHUkGam4PxYv1YrubJy9+2FRe2u7PQ6GgvN6DNHlwDBNRERENQnGSiHW5529nJ3TVQrTsWQpPOeMHDJJq5GLgJizlXjRqtZV9pil8Wg5MUwTERFRTULx0lpnv88/67EuV2kZSDRVCr/BdBAoNEV0qk7U19dPfWhV3c3ddopJJpPIZqe3KSd6PzBMExERUU3C8dJa54Bv9pJ2brfbHsdSpZnpYDoIkSu0ElfnbiVe1OZrsxu35IwcZ6dp2TBMExERUU1iiVIorvfPPqPscXvscSJVKo03kZ6omJmed5j2tkG6rEoiWTOLZDI5xyOILg+GaSIiIqpJ+drn5sDsJe28ntIGxXiqVM1jPD4OFLqLuxyuWTcylmv1tAJW3xbkjBxSqdTsDyC6TBimiYiIqCaJZGmGuSnQNOux5WE6mS7NIg9NDtljv98PIcS8ntvr8NqbGk1pVmyGJHo/MUwTERFRTcpng1vrq7cSL/J7ShsUU+nS48ZCpVrVDYEGLITfWzrnWHRsliOJLh+GaSIiIqpJOp22x22B2Zut1HlLa6HTmdLjJkKlWtWN9XO3Ei9XXo4vkojMciTR5cMwTURERDXJpkvl6DoaO2Y9tnwWuTxMl7cSb2loWdDzezylTY3lmyGJ3k8M00RERLRgOT0HPWftHBQQaKuffWa63leq9pHJZOxxJFaaUW5vasdClM9Ml29qJHo/MUwTERHRgk3EJwDTGjscDricrlmPD3hLdahz2RwAa+NgIl7axNjZNL/uh0XlYbq83B7R+4lhmoiIiBZsJDJij13u2YM0ADT4S5sLi90Ko9kozIyVyDVFQ0vjwpZ5lHddZGk8Wi4M00RERLRg49Fxe+z2uGc50lLnqYOAVfbOyBnIG3mrlXhh2bVTdcLvn70l+VTlXRfLK4QQvZ+05b4AIiIi+vCZiJWqcJTXkJ6Jx+OBqqjQTR3IAEfeO4J8cx4iW2glrjgRCMzeknyq8nXY5ZVFiN5PDNNERES0YJOxSXvs8/lmOdLicrmgCQ26oUMZUPDd73wX3Tu6K2amFxym68o2NaYzsxxJdPlwmQcREREtWChWKmlX56ub5UiL2+2GqqhADhCGgGEauHD2AmAU7ne64XLNvfa6XKOvVJc6m8lCSrmgxxMtBYZpIiIiWrDyJin1/vpZjrS4XC6oQoXIC0AChjQQCZfOUVdXN+9W4vZjPHWAao11Q0cul1vQ44mWAsM0ERERLVh5k5SGurnbgLtcLmiqBuQBdxbA8TPIjIft+xfa/RAA/A4/4LDGuqlz3TQtC4ZpIiIiWrBYshSmm+qa5jxeCAFN1aDlBFx5CcQScI4l7FrVTQ1zn2Mqn8Nnh2lDGgzTtCy4AZGIiIgWLJlM2uPmQPO8HqMKFWphjbQJQOQNoLDMua1h9g6K1fidfkiHhIC1BnuuMC11HeHHnwBUBY2f+QyEwjlFWjyGaSIiIlqw8rrOrfWt83qMYihQCzPRJgSkIa1UrQIdTR0Lvga/w28nGV3Ovcwj8tRTGPvLvwQAqHV1qH/wwQU/J9FUfEtGRERECyKlrChF11Y/v1llqUuopjUVLQFIE1AMQBEK2pvaF3wdXoe3tMzDNObsgph8/fWy8RsLfj6iahimiYiIaEFSegpmzppiVoWKxrr5bR40MyaUwrIOUwAGBNQ84FAcC64xDViP05zW1LSERCQemfX4zMmT9jh7+vSCn4+oGoZpIiIiWpBwJgzkrbFDccDrnbsDYjqdhpnOQRTDNABDAKou4FJdNYVpAHB6nPY4kpw5TBuJBPJ9/fbt7LlzkLpe03MSlWOYJiIiogUJZ8JAoaSzQ51fmB4YGIBmlJqqmLD6taiG1f2wrm7uxi/VeNweexxNRGc8LnvqVMVtmcshd+lSTc9JVI5hmoiIiBZkLDZmV+FwOpzQtLnrGfT390PVrVIeCkphWllsmPaUwnQ8GZ/xuMzJ3unfO8WlHrR4DNNERES0IBOxCXvs9rjn9Zj+/n5oeStMe6SEDiuPC8OaXXY6nbM+fiY+n88ex1OzhOne6WE6e/pUlSOJFoZhmoiIiBZkIloK0+VhdjYDAwNQ8tZCay+APAqtww2gPjB3O/KZ+LxlYXq2mekqYTrDTYi0BBimiYiIaEEmY5P22O/1z3l8NBpFOBSCks1BwJqZzheytDCBhsDc7chnUv78M5XGM3M5ZM+dm/b9LJd50BJgmCYiIqIFKS9BV+ebe63zwMAAzFQKCgAXABVAsY6GlEBz/fw6KFYT8JWqgCTTyarHZM+cBQqVOxyrVkG4XAAAfXwcejhc83MTAQzTREREtEDlYbq+bu4lGn19fTCTSStMS4mUuxRAFAk0+GqfmS4P0+lU9Q6Imd5SfWn3tm1wbdhg32a9aVoshmkiIiJakGiyVIKuwT93EB4YGICZSEDACtNxj7ADiJBAwDG/ddfV1PtKYT6TyUBKOe2YbNl6afeWLXBt2lh6zCluQqTFmbuWDREREVGZ8o1+zXWzL9GQUqK/v9+amZZW8JhwAY5ifTwAWiZX87XUueusk+qAburIZrNwuysrjJSXxXN/ZAsUrxfFtwPZ02dqfm4igGGaiIiIFqh8o19zYPYwPTk5aXU/TCbhgYQBIO0EHEIChYoeZnzmKhxz8Tv9dpg2TAPpdLoiTEvDqKja4d6yBcJVuj/D8ni0SFzmQURERPNWDKxFLfUtsx7f398PqeswMxl0mCYggIwDkKoonTOeqPl6fJoPcBTOIyuvDQByfX2Qhe9pra3QWlvh3rypdP/Zc5CFkn1EtWCYJiIionmL5qKlVuKKY85qHsUlHgDQZZrIO1VIAUi1dIy+iJlpn9MH6bDWSeumPq08XvkSD9dHtgAA1Pp6aJ2dAACZz7OtOC0KwzQRERHNWyQTAQoTuQ7FAa/XO+vxfX19MBNWmF5lSuQ91gpTUy1tFMxEI1UfOx9+h79iZjqTyVTcX1HJY8uW0nhj+SbE6hU9pJSY+PrX0fdvPo/0u+/WfI20sjFMExER0byFs2GIQscVh+KAx+OZ8VjDMDA4OAgjaS3j6DRNwGe1DTcKu7ZUAKlorObr8TnKlnmYRpWZ6fIw/RF77Nq82R7P1FY89eabGPj7b+HSW29h7L//bc3XSCsbwzQRERHNWzgTLs1Mq7PPTI+OjkLXdZjJJOqkhA+ACFjh2ygs81ABxGO1h2m/w19a5iH1ijXTUkpkp1TysMdl66YzM1T0GPr/foLvu5z4gcuJl86wHjVVx2oeRCtY9vx5GNEYPDu2Qwgx9wOIiOYQyoQqlnnMNjPd398PKSXMRBIdphV4RaMfGJ+A7rBCiAogkazeuXA+fA6fnWambo7UR0ZgRK0ieEpdHbINDXjy+9+Hoij4xM6d9nHZKrWmzXQaL7z8MhKF187eVApmOg1llp+XfjMxTBOtUJnTZ3DxoYcAKdH11/8N9Q8+uNyXREQrQDAWBArLnd0uNzRt5ijR398PmctB6nl0mCYUnw+OhgAwDuSdgBuAKoFEJg0pZU1v+j2aB8JpPc6QBpKpUjDPlDVrcWzahO9973u4cOECAECaJq5zuSCzWegTE9BDIWhNTfbx55/6Gd4zDft2RAikLl2Cv2zdNRHAZR5EK1bi5ZeAQiew2AsvLvPVENFKMRGbsMdzbT4cGBiwK3l0SBOuDRvgcReWeWhWlWkVEno+j3SNSz2EEBV1paOJUnfG8koeB31eO0gDwJGjRzG0Zo19e2pb8Z//6MliTxkA1vuH4bL110RFDNNEK1T2wgUcVVUc0FQk2C6XiJZIKBayx36ff8bjcrkchoeH7UoebaaEa+NGeD1WAJcAhGIt8wCASH9/zddUvtSkvNV5cWb6nKLgjSph/V8UUazyV1HR4/Tbb6N3YGDa8cPslkhVMEwTrVAnek/hJYeGNzQNr42NwUjU3hSBiKgonAjb44AvMONxQ0ND1nrpZAJNUsINwLVhA3wen32MIkQpTFcJr/NVPkNe3uo809uLiBB43uGA4reC/4YNG+zj404n3igsUynOTEsp8dN/+Ef7k72yctgY6e+r+Rpp5WKYJlqBpJQ4OzJs3z6nKsie4YwKES1eNF6a+a3318943NDQEADATCatzocAXBs3wu8tzWYLtSxMDw9PPcW8+bylgF5cM62Hw8iMjOBZhwM5VYHi9aKhoQGf//znsWfPHgCA4vPhsKZiXAi75fg777yDvkKpPA3AHVdcYZ97ZGS05muklYthmmgF0icmMJQrtcedFAJjR48u4xUR0UoRS5aWSzTWNc54XDAYLMxMJ9FUmOV1bdyAK5uvhKZYs8GawwGtsJkxNlp7UK3zlrowJgo1rTMnT+Jlh4YxRUDx+aE5HPjCF74An8+H6667DuvXr4fq98ME8KLDgcy5c8in03jmiSdgFDoy7pTArs9/wT73eDgEoqkYpolWoPjpM5hQKnfFnzx8eJmuhohWkmJYBYCmQNOMx01OTkKm05CmiXpTQm1tgdbYiHpfPa7vuB472nbA5/ZDLZQGiU1MzHiuuZTPTKfSVtOWN/ftw7uqNe+t1Pnx0EMPYe3atQCsTYuf+tSn4PB4oLjdGFMEjpgmXvnZzzBemKH2SOCjt9yC1TfdiOKr6WQyiVwuh6WWTqcRjUYhpZz7YPrAYWk8ohXo4jtHMfUl+fT5C9i9LFdDRCtFRs8gl8lBgQJFKGj0zzwzPTk5CaNQyaNeSrg3WO273W43nKoTTtWJrGvcXuYRC9U+61vvKy03SWfSGBgYwNOvv25/b/vmzbjlllsqHtPa2op7770XPz1yBGYmgwMODf4XXoA+NgYAuFHX0f6vfguetjY0qBrChg5pGBg5exZrt26t+VrLjY+P4/nnn8fRwieHmqahtbUVLS0t9ldbWxu6u7srKpbQBwvDNNEKdKGstmrR+WAQ+VwODqdzGa6IiFaCSDZS0bDF5/NVPU5KicnJSbuSR4OUcG3YAAAVodBfV4diJed4JDr1NPPmd/mtRKMDyUQSX//615EtVO9okhKf/sxnqtawvuuuu/Dad7+LoclJ5AHELl6CmcmgQUrs8Pngu/12CCHQGgggXFjiMXT8+KLDdDgcxgsvvIBDhw5VzEbruo6RkRGMjIxMe0xrayu6u7uxevVqdHd3o7OzEz6fb161uaWUME0TqqrOeSwtHMM00Qp0qcqu+Kyh4/xbb2HzlNkZIqL5CmfCEHkrvGmKNmP3w2RhOYSZTMApreYsro2lmemihsZGTBbGiXh8+okKent78dprr2HdunW47bbb4JwyKeB3+gEHgBgQngwjFYjDTKXhlMAndQOBbduqnlfTNPzr++/H3508CQnAiFmB/ta8jsbfegBK4Xna21pxphCmh8+fn+0/0axisRj27duHgwcPwjCMivvcbjcymcyMj52YmMDExIQ9iw0Aqqqirq4O9fX19p9utxuJRALxeNz+Mx6PQ9d1NDY2oqurC52dnVi1ahU6OzvR2toKReGq38VgmCZaYaSUGAgG7ds9bjcuFV6gTxw4wDBNRDULZ8P2zLRTcc7YtCVYeA0yk0k0SgmBUphuaWmxj1vV04PJgwcBAIlU9ZbiUko89v3vIzo4iOOHD2P//v249957ceONN9rdF32qDyImIOICUpPIxxOokyZ+K5fHqvVXztoCfMOtt+Hqv/4bHNOsWdtOU2KjaVZ0je3sWgUU1lIP9y28PJ6UEvv378cvf/lL5PP5ivs2btyIBx54AD09PUgmkwgGg/bXxNgYhoeHMTo+XnU9tWEYiEQiiEQi87qOcDiMcDiMEydO2N/TNA0+nw8ulwtOTYNL0+BUVbg1DVfdeCN2lLVdXwgzm0V83z5En3kGRiSK9v/tP8J77bU1neuDjmGaaIUZHRhAKm2FZy+Am2+4AZf27wcAnDx+HA8v47UR0YdbOFMK0w7VMePMdCgUgjQMmOk06qUEhIBr/ZUAgI6ODuzZsweDg4O49eab8e7jTwCQSGRzkLkcxJRZ51AohImDr8OIWoEx7ffj8ffew682bMDuT30K27Ztw7FfHgOK+yINE/WP3Me7AAAgAElEQVSpJD6VzcEPwDVH+2/n2jW4Q9OQNQwkBPCxvA7n6tXw7NhhH7Nq/ZXAyy8BAMbGxxf03ywajeLxxx/HmSnlSXt6evDxj38cXeEwIl//Oi4NDcNMJWEmU6hLpeBLJrEmn8cuISDr6xFqbMCEz4dxzYExKREydOSnzG4DVnCX+TyQz0Pm8zDzeUDXIXW90Cln+jVGTAlpmsCU3TavPfYY/vw730HP5s3z/nkzp04h8pOfIvqLX8CMlpbuDHz597Hm+9+HZ9vSrDf/IGGYJlphzh16C8UXxG5/HbbcdDOU/fthAhgaGUE8HkddXd2s5yAiqiaSjVQs85hpZnpychJmKgVIiXop4VizumJ2+M477wRgBT/V6YCRyyErgMzYGDyrV1eca+DUKTtIA4CZSCCXSGDg4kV8+7UD8LS3IZqOoSUO5A0Jv5nBzcEzKFazdm/5yKw/k1BV1G3YgN3vvWd/r/6Tn6xYi9y5aRMErFfWyUgE+XweDodjjv9awHvvvYcnn3wSqVSqdK7OTnz84x9HTzSKyf/yf6H/7bdnP4mUEJEImiMRNAMoj7U5ACkhkBBAEgJJIZAXViUSj5TwSgkfrLEGICwEJoRAUFEwoVjjxCxrrs1kEge+98/o+epfzXGJEtGnn0H4hz9EpmzWu+JcqRQGvvxlrH3sh3CV1e5eCRimiVaY8++9a4/XdHWi4apt6DJNDCoKzEQCp0+fxrUr9KM2Irq8wpkwiv23Z1vmMTk5CTNZ2nzoLizxmEoIAZ/bjVih3Fykr29amO47dMgeX20YaDAlDmkaMgIwU0kkL16EBsAtBHywNhzqwn4C+G+7dc6fy715EzLlYfrBT1bc77viCtRLiYgQMNMZjI2Nobu7e8bzxc+fx7Mvvog3T5wANM0O5h/96Edxm8+HyF/8VwwcOzbndUFVAdO0uzFO5QTglBINEpg6q1xNi5RokdI6Z0EW1l9pTgjkHBryDifGhMCruvURxDuvv47fkXLWjY7x55/HyJ/92bTv611deHXdOgwdewf3JJLoDIUw8G+/iLVPPAFHe9uc1/thwTBN9D6ShgFpGNbHb4WxcDihOB2Aw1HxYmUkEshdvIjs+fPIXbiI3MULyA8Nw3PtLrT/2Z/N+MJ2qWxzzBXrroRr/Xr0SGAQgJnOoPfddxmmiagm4UwY0K2xQ3HMWK7NnpmGVRbPeeWVM57T5/MhVqi8ER0cROeU+wdPnrTH62+9Fbd84hO4+ZfP4ZXXD+KwaSIPq2nGNsPAuKLAUAXEho+g+cabUHfvx+Bav37On8u1qTTf677majh7eirud6xahWZTIqIKmNkMRoeGZgzT7/35f8IP9j6LUPE1WggEHA58sqERa3pPYeTSpcoHaBrqH9qDhj17oATqofi8ULxeKD6fteTFMKCHQjAmJ6EHg9CDkzAmgzASiWnPDQBCUaE2NEBtbLD+LH4FAoCilIK5lPZYOJ0QLhdEodpHNhTCobvvQUaaCEcjOPfqq9hwxx0z/vcLfe+fS8/vdKLuYx9D/p678cPDh60SiVddhb3vvIPPp9LA8DAGvvhFrP3hD6DWz9xB88OEYZroMtLDYYz91/8b8ZdegsxkZpxdAAAIYb2gOZ0QigIjWr1MVObkSfhuvhl1hY9Jy6VSKYwVGh8oAHquugqKy4UNXV14bWwUgMTJw4chf+/35lVOiYioXDAWtCdAPW6PvQFwqqlheraP9QOBgF0KLlKlJNxQ2Ya/nptvRv3u3ajfvRvr0ml8/MUXceyll+BvdOO/9z+HkZCAw+OD79/8T2h74IF5/1yBjz+AyW9/G3oohNZ//++n3a+43Wj1+3A+nQakxNCZM7j2hhumHWdks3jsuV+WgjSATbqBe9IZuGNxlLd7EQ4H6h95GC1f/CIcq1bNfHGaBkdbGxxt799MrqupCVs3bMDhM9amy0NPPDljmE4fP4F0YZZdOBy48oXncTEex3e/+127OolaX4/ctm049M47uCWXR/bsWQz84R9hzXf+HygroH42a6EQXSbJQ4dwcc9DiO3dC1l4AZ6VlJDZLMx4fMYgXZR6482q3+/r67N/gbWZEv4N1ozM2o98BN7C08eDQQxUKZ1HRDSXUKLUWKXOV33vha7rCIfDMFNpCAABKafN9Jara2iwx7FCw5SiXDaLYKGZiwCw5tbb7PsUjwdde/bggb/7O6z7yv+Ci6sFchpgmAbS6fSCfi6tqQlX7vsXbHrzDfhvu63qMR1t7fZ4+Ny5qsf0HTiAYOG1XhMKHlA17M7nUR4XhcuFxt/9XVz5Ly+i8z//59mD9DK6bs8ee/zOkSMwZ+j8GH78cXtc98D9ePvSJfzjP/7jtDJ/WnMzjl91FSKFNxrpw4cx9Cf/wdoY+SHHME20xKRhYOJ/fAP9X/g96NV2fTscEB4PlLo6KPX1ULxeoMrsjnA44NqwHnX33Yfmf/cHaP7iv7XvS82wYeXixYswU9YvkU7ThGvdOgCAe/Nm9JjWrm8jkcCpU6cW+2Pa2P6W6DdHJFbaCBjwB6ofE4lYFSXSKfgLG99mC9OBslJ5U1uKD7z1FsxC2Gp0ueDrWVv1HH6HH1KzXot0qS84TAOA4nRCmaEJDQB0lq3lHhkcrHrMsVdescdb16/Hw4ffxuZ3j2H9q/txxTPPYO1jP8T6V15Gx3/6czg6OhZ8je+nax55GC6X9TYgnMvi7NNPTztGD4cR27sXAGACOLh6NX784x/DLKzJDgQC+JM/+ROsWbMGACDa2nDozjvt1d2Jl19G8Fv/cNl/lsuNyzyIllB+dBTDX/mPdthNAHivPoDU3Xej5aqr0NLaiubmZvvL6/Xayy2kaULmcpDZLGQ+D7WhAaIsZBuxGCa/8/8CUiJz8iSMRBKqv/KF/8LJk5CG9YtnldsNtbkZAODatBE9homTqgozkURvby/uvffeRf2siUQCzz//PA4dOoTu7m488sgj6OrqWtQ5ieiDLZIohekGf0PVYyYnJ63XMdNEvZRQm5pmXRtbHqbj4XDFfX1vlj6F6+rsnHF5mtfhtZq2oLaZ6fnovHIdxGu/tip6BINVK3ocP37cHl+1YzuAQkh/n5dpLAWHy4VtV1+Fw2+9BQA49NOfYtOnPlVxTPSpn0Fms9ABPL+qC0Pnz9t/R6tWrcIXv/hFCI/AdR+7Dv3f6QcAXHK7EPyth9D6Myucp958E/ifpy+t+TBhmCZaAno4jMT+/Rj/q6/CiEYxIgSOaBrOtzTDuWULlHQa58t2pBc5nU44nU4oigJFUSCEgKqqUFW1olNVZ2cn2tvb4dq8GdneXsA0kT56tGKXummauFhoKgAAPWvX2i9q7k2bsNY0IQCYyQT6+vqQTqdnrBE768+q6zh48CCee+45+2O8ixcv4m//9m9xzz334J577plxHSURfXhJKRFPxiEKhYobA41Vj5u2+XCWWWkAqC97Ex6Pxiru63+vFE5XbaheEQSwNkM63U4YMCBhXedS865ZW1bRIz2tokc0GsXA6CiAwobIKvtaPmyuf/hhO0y/d/oM8sEgHIU3P9IwEH7iCQDAIU1FX2srHIXfOdu2bcPnPvc5xM04HvofDyE+Gcfdq+6GHLLmpF/xePDl//AnyB58HS1/+O+W4SdbWr/Rv/ESrx3A8b/+a0STCRguF3SnC7rTCcPpgK5pkEJAMSUACaVQSkaR1rotVQCqEFCEgAJrLEwJmAakYUKaBmCYVhF0KSEVBVJVrZ20qgKpFMaQUGCds/h+W4GAqggIRYWiKlCKf6oqYEqYug7DMGAaOqRuwDQMSNOwPlaTEtK0duia0oQqBBxCwFn406WocAgBTVgvh4qUUAoha86P6wUghAIoCkwBmBAwhYAoBkFVhVAEFKFAFIJh+W5hicJYwi71I6UJmHJa6R9ZVuJHCMXaZexyQXE5IVxuCJcTQnNA6nlrvVXeKkgvdR0wDUBRIRwOCE2DcGhWaSKHA1pLKxydHXB0dsLR2QmtrQ1iysxCsdGATKetTYFuNxSXq+I4IxZD6u23kXrzTSTfPITsqVPQAZxVFRx1OjGiKnD29MBVFmiryeVyyM2wDm1sbKxiOYaiKPBrGlZrKm7WDaTefrsiTI+OjiJbWGvtlxKtZbvntY4O+AMBtGcyGNV1GOk0zpw5g2uuuWaWv/DpTp06haeffhpjU9Y1AlYnrhdeeAHvvvsufvu3f9v+WI+IVoZ4Pg4zZ0KFCk3RZlwzPS1MX9Ez63krwnSiMgQP912yx2sKM70z8Xg8SBQ6t8SSsVmPrYVjdXepokcmg9HR0Yow/d7bb9vL7Lol0FTW9OXD6uq774a7oQGZSAQhSJx/8klsLmzQTPz618gPDiIO4G23B67CzPsdd9yBPXv2QAiBR599FMk3klCg4JRyCju8O5BKpRAOh3H0uuvwwPe+vIw/3dL5jQ7TP/lvX8Vr3IhlU1BaRC8ACFkZ8g1h5WAD86lmaT2u+EZBkaXzy7IvU5TG1R4PAKoE/JDwSQm/lPBJwCclPIXIbUDAKFyXWTinKq1/3NaXtG97pIQHgFtKuAAIRYHW0gKoKmQqZYXoKuFWAsiqKhIuF+JOJ8LJBOISiAuBmBCIuZxIFkKz4nLB+5GPQC1sqlm3bh2uv/565PN5u0VsKBTC5OTkjEG6GtM0Mel0YkjT4JHALVPWTV+8eBFG4RdYlynt9dKAVcvVvXEjet45ilFFg5lIoLe3d95hOhKJ4Cc/+UlFC1rAagt89913480338SlQrmnkZERPProo7jrrrvwwAMPzKuxwQeRlBLj4+PIZrPo7Oz80P4cROPj4xgeHsbmzZtnLGU3H+FMuKJhy0yfbFlh2gqV85mZbli1Cii0REmkM5D5PITDATOZxKi97EOg55bZ60X7vX47TF+OmWlndzdapMR5WGVGRwuz0EXHXn0Vxd9mmzs6Zm1h/mHhcDiwddcuHP7VrwAAh575OTb90R9BCGFvPHzNoUF0dkKoKlatWoUHH3wQQghIKfHCcy/Y54oGo/jEFz6BH//4xwCAl156Cdddd11Fe/kPq9/oMD25ejUwOARIc+6DfwOYhS/bIiunFYP3Ys+lCyALgcklLuWmohCuIxE4YG2SUQEoDgc0AAokMkIgBoGYIqwOuqYBZNJWIf1yQkCtq4Pa1ARHdzccbjd27tyJ22+/fcZapFJKpNNpmKZpfdJQ9mc+n7d/AY6MjGBkZAShUAhqg7Xu8DWHhvXvvos1mYxdVujixYuQdpg24VxXWYrKtXkzeg4fxhsaYCaSOH36NOQchfgBK8R/+9vftktXAYDL5cK9996L22+/HZqm4frrr8evf/1r7N27F/m8Vej/5ZdfxpEjR7B9+3Zcc8016Onpqfpc4+PjOHHiBE6ePIl8Po8tW7Zg165dS/YCG4lEcOHCBVy6dAnDw8Ooq6vDpk2bsHnzZjQ0TF/zOTk5iSNHjuDw4cP2DLyqqujq6sKaNWvsr/b2dpYXpA+0cDiM5557Dm8VPqZvaGjAZz/7Wayfo+5yfmQE0Wefhe+GG+C5+urS+cpaic/VsEWmCzPT5uxl8QAg0NQE4XRA5nJIC0CfnISjowPjh95CsW+g2+9D25rVs57H5y3tIUmmkrMeWwutvR3NijXlJPM5jJRNxmWzWZwum2zYtsBP/T7Irn/4YRx5+RVI00Dv2Biyvb1QfD4kX/01RoXASVWDb5X16cKePXugFP4bvfDGC4iMldbYp2Np7Lp+F9544w309/dD13U89dRT+NKXvvShfy39jQ7Tn/7zP8e+vXuRjkSgScABCYeU0AwDWrHfvVAghRUypRDWbKoEDNOEIU3rT9OEKSVM07QKnivWMgcIBYpq/aOyZnqtZQ6isKRBFJdlADCnjg0ThllcwmHCKC4dKSypUFS14st6PmH9IxbC/odpSolcPo9sPo9c4Subz0PXDZiwntMsX94x0z/o8mMKY0VRoCoKYJr2eWRhPOuSkbJC9rM9p70xr7DERpqmtRykfFxYZgIhrGUzQlhfxeUlxeUkprWcROayMDMZa3NMJoNELo+EkKhM+wJCVQClEJhNw16uU/4zqH4/1MZGqxh+fT2EpqGlpQXXXXcdbr75Zvj9fsxGCDHjLyPA2ryxo+xjwnQ6ja9//es4f/Qo8qkUXhTA1mPH4C/UOi0vi9dlmtN+gbk2bUSnlHBJa910JBLB2NgYOubYUX7y5MmKIH399ddj9+7dCARKO/kVRcEdd9yBrVu34kc/+hHOFcpGRaNR7N+/H/v370cgEMBVV12F7du3Q1EUHD9+HMePH8fElN37fX19eP7557F27Vrs2rULO3bssP9bpgvrFMfHxzE+Po5QKARN0+y1506nEy6XCwDQ39+PS5cuIRKJYKp33nkHANDR0YFNmzZh06ZNCAaDOHLkiD3DXs4wDAwMDGBgYAAHDhwAAHi9Xtx000247bbbUL9CGg/QypBMJrFv3z689tpr0MvKjkUiEXzzm9/EHXfcgd27d1f9tCX97rsY+P0/gBEOY7KuDutffglq4f9/kWzEDtOzzUwHg0H7tahhHjPTvkJzEpnLISUE8uPjcHR0oO/11+1jOmbZfFhU56srTnAjnUnDMAyoUyc+FkGoKtrbWoFgEAAwfOGCfd/p06eRKyyzazElOq9bOY2xtu3aBXd7G9IjI5hQBM499jha6+ogAbzi0KA1N0HxeLB161Zs2LABgPV7+7GnH6s4j8xL9E324eGHH8ajjz4KAOjt7cWJEyewbdu29/vHWlK/0WG6u7sbX/j931/uy/hAkFLCMMrWXZd9FUvcFDfGqapqv/Oc7XzFxxZnW4tjUQj75Zvuin+WP74om80iFoshGo0iFovZ41QqBU3T7GvSNA1aoW2rruvI5/P2n8WvZDKJRCKBRCKBfD5vhf9srrAQvvCmZMq1FDk0DY2BABr8fjS1tqKpvR2NjY32VyAQuKwb7zweDz796U/jr/fvRy6VQp+i4ODPf457b7gBiUQCE2NjMDNZaABahYBjbWUJKfemTVAArDVNnEtYsza9vb1zhulXyko93XXXXXjwwQdnPLalpQV/+Id/iDfeeAN79+5FMlmaHYrFYjhw4IAdRufS19eHvr4+/OxnP0N3dzcikQji8aX96HZ0dBSjo6PYv39/1fudTicCgQCChV+e5VKpFH71q1/hlVdewa5du3DXXXfN+d/ycip+yuGbpbQXrWz5fB6vvvoq9u3bN73Gr6bZwXr//v04ffo0PvvZz1Z8chZ/5RWr7m+hEoYZjyN37hw82621yuXLPJxq9ZnpVCqFdCplvxZ5FQWOOfZPOBwOeDweJBMJmADig4PwXn01Bo6X2nuvmqWDYpHf4bdSTR4wpIFMJrPk/3/oWL0GIhi0KnqMjiKXy8HpdOL48eMwC10crzRNeK66akmfdzm5XC5svfFGvP2znwEAjrz4Im4QAmcVBUOKAs+qVVBVFXvK6lIfO3YM5/vOTzvXmcEz2L1zN2666Sa8XnizdODAAYZpWhmEEEsaBMsD82LP63A44Pf7l7zsWi6Xs8N1Pp+HYRjQC5s7dV2HrutwOp1oampCY2NjRRm75dLT04PbbrgBvyq8qO194w3cEI1iYGAAZjoNQKLNNOHp7obidFY81rV+PSAEekwDZ9IpSNPE22+/jTvvvHPGn2twcBDnC+3JhRC4/fbbpx1jptMQbrd9DiEEbrrpJlx33XU4d+4cjh07hvfee68iWJdzOBzYuHEjtm3bBqfTiSNHjqC3t9d+EyelXFSTGafTiTVr1mDdunVYvXo1xsfHcerUKVy4cAGGYUw7XlEUbN68Gbt27cLWrVvhcrmQTCYxMDCAvr4+9Pf3o7+/H4lCK1/DMHDo0CEcOnQIW7Zswe23345AIFDxhrP4/4PGxsYl/TeUyWRw/PhxHD58GGfOnIGUEjfccAPuv/9+zpb/BonH4zh48CAOHDgw7Q3nmjVr8MlPfhKtra148skn7U3No6Oj+NrXvob7778fd911F2JPPYXR//O/AFP+P5EfHUVx/jmcDaPYws+hOKqG6VAoVJiVlqg3JZyrVk17LarG7/MhWfiUKjI4iDYpMXTxon3/6nksm/A5fVZ5vDygm1at6aUO0941a9Bw5DDCQsBIpzE+Po6uri4cP3IEZjYLAFivafNqYf5hcu3u3Tjy3HMwMxmcymaxI5fDr11OKB4P1KYm3HrrrWhtbQVgvWY/8cwTSOvTyxNeGLFm83fv3o3Tp08jFAqhuVDC9cNsUSlncnISFy9eRDwex1133QXTNJFKpeb8eJvog6C4LKCxsXp5pw+qB3/v93D4qacQEQKpSAQ/+fGP0d7ZWbbEo/oaRcXrhXPNGlzZ1wdNSpiJJIaHh3H48GFce231jyTLZ2y3b99escY4NzCAiUe/hthzz8HR2YmGT38aDY88DK3wwqhpGjZv3ozNmzfjkUcewYULF3Ds2DGcPHkSALB582Zs3boVGzdurPi4eefOnUgkEnjnnXdw+PDhimUXmqahtbUVbW1taG9vR3NzM0zTtKuiFL90XUdbWxvWrVuHzs7OaR/1fvSjH0U2m8WFCxfsYO12u3HNNddg+/bt017DfD6f/bMA1jry48eP4+WXX664vt7eXvT29s74d9fU1IRrr70W1157rf2LZ6F0XUdvby8OHz6MEydOVHyMDwBvvPGG/Sbp7rvvXtSGs98kxU/Dyj+NK46rfWIHwL6v/PFF5Z/kaZo27dO3pTA4OIhXX30VR44cmfbGsKWlBbt378Y111xjP++Xv/xlHDx4EM8884w9gfDss8/iF9/6FurPnkWHEOhQFbSbEo1SQgDIj5Q22UUypWUeDsVRdZlHMBgsvLGf3+bDorr6ehRrBEVHRqCPjGA8mQIUAaFqWLtr15znKDZuERAwTGPa7PxScK7uRpMpEVaFvQkxn88jXthf4ZMSPVu3VvQIWAm2bdsGV2cn0hcvYlwReEXTEBECrq5V8Pl8FX0Ljh49it5LhddBDZBtEmLY+jc4MGZNjPh8PnzlK1/B6OgoVq+efS38h0FNf9u/+tWv8M1vftP+pSGEwMmTJzE4OIiHHnoIn/70p/Gnf/qnrDVLdBn41qzBA41NeCIShjQMHHvjDfjb2+0w3WmacM6w4ce1eTO8fX3YpRs4lkxADdRh7969uOaaa6atn4zFYjh69Kh9uzgrrYfDmPyHf0Do8SeAwmbD/PAwJh59FMFvfAN199+Pxs98Bp4d2+1f4qqqYsOGDfZ6urn4/X7ceuutuPXWWxEKhRAMBtHU1ISmpqY5lxjNl8vlwpYtW7Bly5YFP1ZRFFx99dW4+uqrcfHiRbz88st477335nxcKBTCiy++iBdffBFr167Ftddeix07dsDtdiORSFQsY4rFYvaSpPLlScUZ8dnouo59+/bh9ddfx7333oubb755yV+PdV1HNptFJpNBJpOxw1n5V/GTnuJSr+Lt4ibbbDZrnyOXyyGbzc5Z4aY8vJaH2mKwLR8Xl66VLzUrfhXPUR6ILzdN0+ByueB2u6d9FZfPlS99UxTF/n55OBdC4Pjx47hQtma3KBAI4GMf+xhuvPHGaX/nQgjccsst2LBhAx577DH0XbyI7NlzyI8MI6koGFYAxV8PraEBYmAQuwwdu0dL+yXC2dIyj5lmpicnJ+2N0PMpi1dUVzapERsfR+LoUUwqhQpJgTp0zaPlts/hsxu31NoFcS6OsooeMpPG6OgohoeHYZQt8fCuoCUeRW63G1tvuQVvFz4tOKZZyyIdnR2477777H8Lpmniueefw3jK6v5rXmHi6s6rcXzYqhc+OlF6c+bxeHDFHJtTPywW/Or6jW98A9/85jftKgCqqtrviIeGhpBKpfC9730PZ86cwbe//e0lXfxPRJYtN9yAq3+5F++qKoxIBOlAoLKSx0xhetNGxF94AdfpOk5nszBgbUp67bXXcNddd1Uce+DAAfv/2z09PVjT0YHgP/0TJr/9TzBnWLss83nEfvELxH7xC7i2bEHj73wG9Q8+CKWwKXA22fPnYUSjcG/dWnF8MURPfZ7kwYPIXrwIz7Zt8Ozcaa13n4GZTiP23PNIvv46tJYWNDz8r+b1MWxuYABGJAqtrQ1ac1PV2aYrrrgCV1xxBcbHx/Hqq69aLd2nzGYC1kfx5TNlxTXhTz311KLCXGdnJ3bu3IkdO3ZgYmICzz77LIaGhgBYG9Geeuop/OrFF3HFlVeita0NbW1taG1tRWtra8VH4OWhM5fLIRQK2W9kiqUcQ6GQHZ6nzojT3IrLx2Za8rQYa9euxe23346rr77aWh8dDiN59iyy584he/Ys8gODMOIxmLE4jHgc98ViOGQaOKaqyBfe9GpNTXBv3Qp9chIZARzUNFzf14/2wnOEMqGKmemZwvRCNh8W1ZdV74kGgxh+8xCK/8Iap/xbnYnf4a/ogphKpWZ/QA0c3avRbJoAVLvW9Pj4uP2aeKVhwn31ygvTALDz9ttx9NlnYRQ2dWvt7Wjr6sLNN99sH3PkyBGcGziHrJEFNCCwJYB7uu7B8VetMB2cnL7/ZCVYUJh+/fXX8Y1vfAN+vx9f+cpX8PGPfxx/8Ad/YM9e3XDDDfjqV7+Kv/iLv8DBgwfxxBNP4HOf+9xluXCi32Te667Fbc88gwuKgkwkAqxZAzOVQkBK+AG41lUP0+5NmwAALgA3CQWvFb7/4osv4vrrr7d/YeXz+YqNgtc3N+P8/Q9An1JX1bNzJ1r/+H+FPjKC0OOPI3PsXfu+bG8vRv/3/wMTj34Njb/zO2j8nc9AmxKKzWwWseeeQ/iJJ+zHKj4f/Hfeibp774X/9tvsWq1SSqTfeQexXzyL2HPPwShrO+zo7kb9g59E/YMPVvzyzp49i/CPfozoM89UvAEIffe78OzahcZP/WvU3XefXV5QSonMiZOI7/sXxPftQ1gTAlUAACAASURBVO5c2QYaRYHW3GwF67Y2OLq64Nl+Dbw7d0Lr6kJbWxseeeSRGf7GrCB14sQJvPXWWzh16pT9RqWWIN3S0oLt27dj165dFZse66XEH9x0E9586WX8y1tvIRwKwUwlkTBNDEFAaCqE5oBwaBCFKihQNZiKgKko1ibcwkbcYsUcaZpAoaqQvZ62eM3S/p/Sz1FszFRs0lR23Lx+UvuxpXPbipWSCk2iUKiaZD/XlC85taqPfV+Vc5c/DQDAasgFUai5X/YnJKCI0rWKsp+3ODYAGBIwCmP7r7lipYeovD3jz144Tgh7XJy93uzz4fqGRnSn0hDnz2MonkD23DkYVTbNTnlm3ADgBt1AAkDyzjuR+sRuDAwN4ezhwyi+7RsZHkZxa9hQfAjFhOvSXFWXeUytMT1XWbyiQNmyp3gkgv53S68lq9bNvfkQeH9mpp3dq9Bc/HQkk8HZs2eRzWZhxOJwAFizwjYfltu2bRs869YhcfQdCFWFc80a7Nmzx/4EpNi4azxdmJW+0sR9G+/DpvpN9jmi4eiyXPvltqAw/c///M8QQuBv/uZvps1iAdZHnw899BCam5vxpS99CT//+c8ZpokuA++118IN4J68jmeiUevj6lQKXYWPr2eemS69qH1kaAindu1EMBhEJpPBvn377N3Yhw8ftmfPAm43Gh79GvSy2TRnTw/avvKn8N99t72Uo37PHqSPn0D4yScQe3YvZGEW1giFEPzGNzD5T/+E+j170PSFz0M4HIj86EeI/OSn9ixHkZlMIrZ3L2J790K43fDfdhuca9cg9sKLyM+wETE/OIjg338Lwb//Fjzbt8N/5x1IvPprpI8cmfG/YfrwYaQPH4byl3+F+j1WhZL4vn3Qh0eqP8A0oU9MQJ+YAAr1ZMOPWaWftPZ2eHfthGfHTrg2rIc+MYHcwADyg0PIDw4iNzgAIxxB/ebN2HPLLXjo4Yfx/7N35nFylVXe/95be9fS3dX7kk7SnaWTELYkkBiQVWRTQHgdRRhQEdSXYcCPjDgojq/4jtuIqHyGQV8ZQVRmBIcAIktYZDchZCFm7SVJ72tV177c+7x/3O5bXb0kXZ1eks7zzed+6rm3nvv0qUp31bnnnvM7u1MpNm/ZwoEDBwAjtcXr9ZKfn4/X68Xn8+Hz+fB4PLjdbjwejzke+vIaitCHNr5M6JWXTdsrgc8A2ywW3rVaiSkAItMldNBTOprYsgrYBdgR2IThw6iDeu0qRuMklUHt9sFOr8OPWwGbENjJrGEHLAjUIzdjzfJBh3eRVbMehdkwavRz2Rsj1pxqdIz3OwUkFYUkkFAgiUJiSH4VxdT7NxtbYTTMMo4bz2sYXU6XaRpGD8Imjpz8MwY2G9biYhZe+2mKbrrJ/Fv+3UO/YONf/wpAd49RFCiEoKWvxfTxC9wFY6Zc9fX1occm3kp8iKwuiP39pDu7zP15p5w81imjcNvcCFsmZ3o6nGk1P59itxslnUJoGvFwGNJpRDrFfE3HkZ+PbQ7kAI9FXl4ey848k11uN6gqS+vrWb58ufn8pk2b6OruojvaDTYjxeOShZdQm5dpIBYbiJHSUtgsc6sJVk7O9NatWykvLx/TkR7O2WefTWVlpak1K5FIphbb/PlYSoqp6+5haSLJgb4+hKaxQNdRfT4s41RH2yorUd1u9EgEgkEuXr+e3zz1FACvv/46Z599NoWFhVmFhys7OmDQkbYUFlJy2z9QcM01o9qwA7hOWoHr3nspu/NOAk88Sd+jj5Ie1KgWiQSB//ovAv/1Xxkt8GEoNhvWkhJSbW3mMRGPE3rxxTFfi7WsDNfppxF58y1TkgogtnUrsUEd6azXXlND/pVXkNizl9DGjTCYpqAHg/Q/8uiYP0NxOrHX1JDu6UHr6xtzDkC6s5OBPz3HwJ+eG3cOQOz994kN3skr8Xq5Zu2Z2M44A7u/CIsCQtNB6MZjIokSHEBNa6jJlLElkohIhIHt2wltfJnwa69lvfas9wdYpWmcoml0uVz0JxP0Kyr9imJsqjLKmR7u8HqFIH/E5hOCvEEH2Mr0Op9zDRUGLxyMIjXgiBHyo0FxOnHU1eFYtAjHksXYF9ZiKSzA4vOher1YfD4Uh2PMosiKRXUMCTZ3h8KIZJJebYBEPIEVK1bVis/jG3Weruv0dnYiBmspCpxOrGVlo+aNxXBnOhyLGxcHFhXV6aJ6orUWw9M8xPQ404qi4Jo3j4LGBvoVBRGLoQ8GDup0DefJJ8+66tN08tGPfpR9+/bh9Xr5xCc+Yb7WdDrNCy+8QH+8n5SeQl+iU5Zfxmmlp6EqKg6ng0Q8gdAETV1NLKlYMsuvZGrJyZmORCITlifz+/1jarNKJJKjR1EU8lavJvTcn7kolWJbIoGWTrNc07EvHLvTIBjt0x1Ll5oR2yWqyvz58zlw4ACapvGnP/2JNWvWmG1yLZEIdW+/Y55f9eN/w71u3RHts+TnU/S5z+K//joGnn+BvocfJj68FfkwR9pWWUnBpz5FwTVXYyksJLFnD6EXXmDg+RdINmTrlKpeL96PXkT+5R8jb81qFIsFPZEg/OprBJ96ivBf/mI6yQBYrXgvuIDCv/skeWvXmnnV6Z4eAk/+kcB///eoaLfq8+E971w8F16I56yzMmkmySTp3l7SXV2kOjtJ7NtH7L0txLZuNXNEc0EPhQi9+BK8+FLO546H4nLhWLIY55IlOJYsxbFkCY4li1lZWIhIp9FCIfRgEG1ggHQgSLS3ByWZhGgMJZFAxGOGcxCLozgcqE4HitOJ6nSaj6gWI81gqEmSkXOQaXSkKkYKhjL0OBi9VDLpCeM2hzJfiJqZP/RzhEBPJBCJJCIRR48nEAmjAROqBcVqGUxTsQ6OjVQWxWoBq9VIbxnaH2rIZPaNGmbPeLaNajI1KAVpUc10E0VVhqWejMXw1JfhqShkv1ZFzbxXgNBFdvMoXTfuMqTSiFRqcEuabbgddXXYqqqM938SlFVUoNpt6Mkk/QqkurppsXebxYcuq2vMfOlAIEB6sEDWLQTuBfMPW8swHG9BAYrNjkgZXRAjQ8XLPu+E/Q633W16NUPSeNOBvbqaov37DUWPeBx9YAAFqNV0XCuPb73kI7FgwQK+973vIYTIKlrfvn07/f39RuGh3YhKX7zgYtTBvwVPgYdEhyEduKdlz4ntTJeWltLU1EQ6nT5sZXgymaSpqWnS0k8SieTIDDnTNmD1BzvNfFbHwtrDnudYusR0ppN79/Lxj3+cn/3sZ4CR3jFUvCZ0ncUNjQwJq3kvvnhCjvRwFJuN/Msvw3fZpcQ2b6b34f8k/MorALjPPovCT38az4c/nPWl76yvx1lfT8ltt5FoaCD04ouku3vIO/MMPOecM6qYUXU48H30InwfvYh0Xx8Df3qO+AcfYK+tpeCqK7GO8TlkLS6m+OYvUHTT54m8/TahF19EsdnxnncueWvWjBl1V+x2bBUV2CoqDN3dQSkokU6T2LuX6HtbiG55j1RbG7aycmzV1djnVRvt5aurUd1uYps3E37zTSJvvjUq/3yyWCsr8J5/Ad4LLyBv1aoxbQdQrFashYUwTDXBOyUWSOYapaWlKE4nJJP0KSrpjnYOFbabxYdOi3NC+dITTfEA8Hq9KA7DmQ4oKtHBaxZrQQGlpaUTWsNj8yBsxsXKdEWmwajRKBKC/YCIxdBCISp0nTzAOUfzpYczlv/X3NyMpmv0xHrQF+lghUtqLzGfLywspLejF4CmjqZR5x/v5ORMr1+/nv/+7//mwQcf5NZbbx133gMPPEAkEuHSSy89agMlEsnY5K1ek9kZpjE7Xr70EM5hedPh1/7Cwhtu4KSTTuKDDwaliwadvHRbGysHUy4Ul4uyr/3TpG1VFIW8NWvIW7OGVGcnoGArO/IXpKOuDscEOp8NYfX78V/3mYnbpap41q/Hs379hM8ZtYbVinP5cpzLl+O//vA1IrZLL8V36aUIIUg2NhJ58y1iW99HpNKDRXVGdFexGNFNkUyiRyLGFo2aj9aSEjznnYf3gvNxLFs2p28rS2aewsJCbE4n2sAAUQUGDhygxdaVcaatziMqeeQiiweGM63a7ehgOtIAFfPnT1jWcXgB4nTlTAPY5lVTPKToEYuhh8Is0ox6lblafHgk2tra6Iv3oQkNUSio8daw3J/Jpy4tLmU/RurvkNb0XCInZ/rmm2/m6aef5oEHHqC1tZXLLrvMlHoKBoM0NDTw+9//nqeffhqHw8HnPve5aTFaIpGAY/EiLPn5aMHs6mj7OEoeQwy1BgaIbtpEyz/ezqVfv4udO3dmdHuTSaqbmigc3C/+4hexVVRMid22CeZQzmUURclcKPz99bNtjkSShaqqFBcW0tJlFAG2NzbSUpZp2OKyusaPTA8rPnTkEJl2Op1Ync6sPH5FUalesWLCawzPmZ4uNQ8w0jz8g5+N6d5ehK5Rp+tYKyuwDpP4O1EQQtDa2mpqSwuf4JKFl2Rd5FeVZnTCh2tNzxVy6n5QXV3NT3/6U9xuN3/84x/5whe+YDZuWbt2LZ/5zGfYsGEDdrud73//+3NGjFsiORZRVBXXGJ0LjyRF5ayvx3/D35v74ZdfJvWtf2HtsA5jyYYGThuMMNnnz8f/2RunxmiJRHJcUFqSuXPUefAQLaEWlKThHE0kMl0gxBHvkg1HUZRRnUdVr4fK6uoJrzGk5gHTHJmunkeREHiEQKRSlOsCvxC4Vk5MdWSu0d/fTzgapjfea1zMOOGShZdkzVlQscAc9/b2zqyBM0DOrcTOPvtsnnrqKa699lrKy8uzGhMUFhZy1VVX8eSTT3LxxRdPh70SiWQYeSOdaYsFW03NEc8rvesuir5wk7kfeestlj/9DC67HS0YpKStjXm68aVU9o1voNrtU2q3RCI5timtyhT9dXV20BJuyUSmLWMXIPb09CAGHVhfjjnTYLQUH47q8024+BCMiLliMxx+TWhEolPfHAfAVlWJBfhfyRTnpdJ8fLBr51wvPhyPtrY2emO96EJH+ARL/EuoK8hOz1tUkWmSNdA/tgLR8cyk+stWVVVxzz33cM8995htbvPy8vB6ZTmLRDKTjHSmbdVVE3J8FUWh5CtfQc3Lo/v+nwJg2bqVq+NxDsTjLEimUADPhRfgOfus6TBdIpEcw1QMiyp39fbSFe1CTakoKOM3bGltNRRHAH9BIRbfaPm8wzG8pTiAJUdnWlEU3C438UEh9XB0UurbR0R1OLCWleHv7MQ/rF7FeYJGpltbW+lPGE20RL7govkXjZqzpDqj3hELxUilU9isc0drOqfI9M9//nOefPLJrGNut5uysrJRjvRDDz3EnXfeefQWSiSScXEuq0cdFiFyLMjttmrxl75E6V1fM4/l7d7NsuZmXIDicFB219en0lyJRHKcULEk4/x0D2qZKynDkVYVdVR773g8zsCgHK4FKMoxKg2QP0J5x11aSv6IaPWRcOe5TcnDWDw2bW3vbSPTTxQFZw753XOJ1tZW+uODzrRPsK5ytOpTgbsAu8sI9Oi6TkNHw6g5xzM5O9NPPPHEhOY+99xzvDhOswWJRDI1KFYrrtNPN/fttYeXxRuLohtvpPzb3x6lr1t0y83Yq6vGOUsikcxlKpYuNT8TAqkUtqSAmJHiAYzKb+7r68tS8nAsXJDzzxzeuEWx26muq8tZqcZr92a0pkXaFEkYiRBH1yzHPsKZttfVYvG4x5k9t9nTvIeEZmhI5/nzWF60fMx53oJM0HVv694ZsW2mGDfNo6WlJasL2hDd3d08NthCdzxaW1vZu3fvqD82iUQy9Xgv+giRN94AwP2h3HSghyj8u0+iupy0ff2fQdOw1dRQ9PnPT6WZEonkOMLlduN1OgnFYmhAQRf0hxWcDkN5vry8PGv+SFm8IxVCj0V+ZSX2+fNJd3Zhr12YU4rHEKY8XsooQozH41m+SEekgy+99CUsioV/v/DfKcmbXD+MkZHpE7X4MB6P09zebOwosGrRKqzq2K6l3++nt31Qa7ptbmlNj+tMl5aW8sgjj3Dw4EHzmKIoHDp0iHvvvfeICwshOP/886fGSolEMi4F11yD6spDdTlxnzX5/Ob8j38cW/U8Im+8TsHVV49qjiKRSE4sij1eQoMFhe426E+Dy20UH45M8+jt7c0UH+q5Fx+CoTXtqK3FMXiHrWIScpxDih4KCmk9TXREd9LH9zzO/oChd/xM4zN89qTP5vwzwNCaHo7zBC0+bG9vN1M88MK66vEDOqUlpexjHwAtXS0zYd6MMa4zbbfbueeee3jwwQfNY5s2bcLr9VJfXz/ugqqqkpeXx9KlS7n55pun1lqJRDIKRVXJ/9jlU7JW3umnkXf6aVOylkQiOb4pKfLT1G1oB9u6jaxQp9VJRUXFqPSLnp6eScviDTHybvZRRaYZuwvivv595rgn1pPz+kOMTPM4USPTh1oOEUgEACNf+syKM8edO1xrurO7c9ptm0kOq+axfv161g/rDFZfX8+SJUt49NFHp90wiUQikUgks0dpeTns3g2ACCrgNVqJl43ReKmnsxM9buTN5isKtnnzcv55I4UMRqaSTISsxi366MYtDYFM4VsoGcp5/SFs8zISpIrNhnPpksPMnrts3beVlG5oJrqL3CwqWDTu3AXlC8xxT9/kL2SORXKSxnvkkUek/J1EIpFIJCcA5dUZhzgVVcFraDmP6UwfPAgMdkwtK5uUNr3f78dqtZJOp6moqMDpdOa8xuEi0/F0nNZwKwweGkhOXu/YVlaK96MfJfT88/hvvAHlBNXi396w3RyfXHfyYQtGF1XOXa3pnJzpM844I6fFW1tbqaqSagASiUQikRxvlNdmUjWSSQWrasGqWkflMgsh6GlvN/dLFi6Y1M9zuVx85jOfYfv27Zx77rmTWsNj9yCsRs70yC6IzQPN0A3Wd62gQI/3yNFRIQTfePMbvNf5Ht9c+03WV2Xu1lff/xO0YBBLjvJ9cwVd12luaTb3z1p++JqdpVVLDdlCAbFwjGQqid02Ny5Ccm7aEgwG+cMf/sD+/fuJx+PogwLtQ2iaRiKRoKuri/3797Nz584pM1YikUgkEsnMULJoMRZAA1IaOBQHiqKMikwHg0FSYaNBikuAdxISnUOceuqpnHrqqZM+PyvNQ2SneTT0N6DuVo0AuoDe5iO3td7dt5sNDRsAeHDbg1nONHDCOtIA7R3tBKJGvjROOKfunMPO97l82PPsJCNJdKGzv20/y+ePLaN3vJGTM93T08M111xDZ2enqdGoKEqWXuNQiF8IgdU6qQaLEolEIpFIZhlHVSUFuqBTVUCAHTsul2tUuudUyOJNFXm2vEyax4jI9Oadm1ECmTSEaCQ68vRRdMe6zfHB0MHDzDzxeGPXG2jC6ADpKfZQ7a0+whngLfTSGzEuYva17pszznROTVt++ctf0tHRgcvl4pprruH6669HCMHq1au55ZZbuOKKK/D5fAghWLt2LX/961+ny26JRCKRSCTTiMXvp1CFpKKgCLCnLZSXl4/KizWcacNpzReTk8WbKjw2D8JmBPhGFiBue2tb1tx4ZOyGLsMJJoLmuC/eRywdO8zsE4t397xrjpfMn1gBpt/vN8dN7XNHazqn0PFf/vIXFEXhoYceYvXq1QA888wzKIrCHXfcARh/VJ///Od599132blzJ2vWrJl6qyUSiUQikUwriqKQl2cjFTOij2pybIWN7u7uo5bFmyrcNrfp2QwvQGxpaaHrQFfW3GTMSDdQlfHjiiOLFNsj7dTmTz6NZS6xq2mXOV61eNWEziktnpta0zlFptvb2ykvLzcdaYDly5ezY8cOM3e6qKiIf/3Xf0UIISX0JBKJRCI5jrG6VZKDYyWpj+lMf/D++4i0IY9WYrNhLS2dQQuzGU8a78WXXiSWGhFVTkA4FT7segOJEc50uH2cmScW0VSUzo6MVvR5K8+b0HlzVWs6J2da0zSKi4uzji1YsIBEIpHVKXHZsmVUV1ezbdu2kUtIJBKJRCI5Tkh6NVKDaR16UhtVfNjR0UFrYyNgBISX1tSgqDm5FlPKUAdEyESme3p6eOe9d9DJFkwgcWSt6YHkAEq/gtqkQhJDWk/C281vI2LG++xxelg6b+mEzltYkblr0dt75ALQ44WcfuP9fv+oFz9vUJh93759Wcfz8/Pp6+s7SvMkEolEIpHMBiktRcATNyPT6VR6lDP9/vvvmykeCzUdb+3spXiAIY03sgDx1VdfJZKMACBKRMbzSUNf+PB+Sm+wF8vbFtQPVCy7LLRHZGQa4NWdr5rj6spq1AleQC2qGqY1HZg7WtM5OdMrV66kvb2dTZs2mcfq6uoQQmQVGyaTSVpaWvD5fFNnqUQikUgkkhmjLdJGr8eQBgZQhEDTNPN5IQRbtmwxnel6TZtVJQ8At9WdJY0XiUR49913iaYNG/U6HYZJG3cFusZYJUN3e7ehDQgo3Qpt4bbpMPu4Y8veLeZ4Rd2KCZ+3tGKp6XnGo3Hi8SMXgR4P5ORMf+ITn0AIwS233MJ9991HOp1m9erV5Ofn87vf/Y6nnnqKvXv3cs899xAMBqk9Cq1JiUQikUgks0dLqIVOmzLkm2ITgq6ujPPZ0tJCT08PWiCAXUCtruOor58dYwdx29xgARQjMp1Op0mn00RTUUSBQBQLhCMj59vTf/jGLcGBjJoHMWjpnztFc5MlmAjS2makuygonLFk4g39vA4vNo/xG6UJjf1t+6fFxpkmJ2f6vPPO4+qrryYajfKrX/0Ki8WCy+XixhtvJJ1Oc9ddd3HFFVfw1FNPoSgKN91003TZLZFIJBKJZBppCbXQq6jYBntJ2DQty5nesmULIpVCC4VYpGtYFQV3jp2SpxqbxYbD6gAbCAS6MPKkI+mIEZVWIN+babTSEzy8Mx0eyC5QbO2UOdObOjahDBj3K7x2L4vmLzrCGdn48jNZC/ta9x1m5vFDzl1Vvvvd73LBBRfw9ttvm1qTX/ziF4nH4zzyyCPEYjF8Ph9f/vKXOeecw3fDkUgkEolEcmzSEm4hpCkUDu7b0mnTmRZCsHXrVrRgEISgXtNxrliBpaBg9gwexG1zM2AbgKSR6qEKlZg9hqgwLgqqiqoYaDHydfsGDp8zHQ1nN3bp7+knpaWwWWzjnDH3eavlLRis2yx0FlJZWZnT+f4iP72HjPq75vbmqTVulphUi8Lzzz+f888/39wf0pn+h3/4B/r6+igqKsJisUyZkRKJRCKRSGaWllALybiCFQEo2HWdzkOHAGhoaCAQCJDu68MloEbXca9bO7sGD+KxeQjagigoaLpGXMRJ1iZBgSJnEWWFZezC0EgODAQOu1YsMkJOLwQdkQ7m+eZNl/nHPG/vedtoyQ7UlNfgdDpzOr+suGzOaU3nlObx85//nCeffHLc561WK6WlpVgsFh566CHuvPPOozZQIpFIJBLJzHModAglrCAGw242IUxneuvWrQBo/QGWaBoWwL1u3SxZms3Ixi3CLhDVhvdXW1CbleYxMDC+okQ8HUeLaVnHlLBCW+TELULsjHSa+dKqonJS3Uk5r1Fdlmk7Ple0pnN2pp944okJzX3uued48cUXJ2WURCKRSCSS2UMIQUt3C6RAtyhYMPzTQE8P0WiUrVu3oicS6NEIS3UNxW7Hdfrps202MOhMDwZL03qa4hXFRlEiUJtfS4Evk4oSDo/ftGUgOYCSyG6droRPbEWPdzveNfOl8+351FTV5LzG/Ir55niuSCiPm+bR0tLCa6+9Nup4d3c3jz322GEXbW1tZe/evXg8nqO3UCKRSCQSyYwSTASJ9kexYCFtV/AmjMiuSCR48803iUQiaP39eISgWhe4TjsNNcfb/dOFx+ZBW6hhDVqpqKtA1Ao4YDxXm1+bFW2OhCPjrjOQGIDEiIMRI/3lROXVQ6+aznSBs4Dq6uojnDGaJVVLzHEoEEIIYdbgHa+M60yXlpbyyCOPZHU2VBSFQ4cOce+99x5xYSFEVl61RCKRSCSS44OWcAtK2HBwhN2KXxitW/R4gldffRUArb+fFZqOwrGT4gHgtruhANLnpll31joe3/24+VxtQS398X5zPx4dX+e4J9wD6REHdTjQfmCqTT4uCCaCvHow40wXu4pzLj4EqCutM7zPNMTiMSKRyHEffB3Xmbbb7dxzzz08+OCD5rFNmzbh9XqpP4yOpKqq5OXlsXTpUm6++eaptVYikUgkEsm00xJqMRUbFIeDUj1Ot6oiEnGi0ShCCLT+fuoHm7gcK8WHYESmhwglQzQGG839uvw6DsQzznA8Mr4z3dHbYY4tigVNGK/1UPuhqTT3uOH55udJRVNYk1a8di9F3iIKCwuPfOII8h352Nw2UsEUmtBoam9i5eKV02DxzHFYNY/169ezfv16c7++vp4lS5bw6KOPTrthEolEIpFIZoeWcAtKyIhAWpxOKvUA3SroCSPvQURj+OJxyoRA9Xpxrph4F7zpxm1zm+PmYDPhlJEX7bV5KXYVk/BncjeSseS4aQbd/d1Zaw4kjWLF4VrbJxJPNzxtRqXL8sqoqqqadHqGt9BLX9DIl97Xum9uO9MjeeSRR/B6vdNli0QikUgkkmOAQwOHzDQPqyuPGk1jm9WCGHSm0/19rNQ0FCDvjDNQrJNS2p0Whkemd/TsMMe1BbUoikJhXqHRcjwFaS1NNBrF7XaPWqc30GuOhzvTwZ4gaT2NVT12XvN0c3DgIFu7t6L2q6iolOaVTirFY4gifxF9zYYz3dTRNFVmzho5/SaccZjORl1dXbz00kvous5ZZ53FggULjtY2iUQikUgks8CB7gNgpEnjzi+kerALoognjBSPQIB6zegu6F577KR4AOTZ8szxnr495rg2v9Z43pqH4lAQKYEmNPqD/WM6032BjNJEYXEhvfFekloSPazTHe2mwlMxja/iHFhm3wAAIABJREFU2OLpxqdhANQGFb/Lj91ip6YmdyWPIcpLyk2t6bnQVTLny6rt27dz3333sWTJEr7+9a8DRkvRm266iVjMEDdXVZXbb7+dL3zhC1NrrUQikUgkkmmnpT2jWDFv3gI8+btxxePE0BHJJP7+AEWDDrb7Q8dO8SFkR6bTIlNBWFdQBxhiCnaXnUTYiLJ39HVQXTlalSI4EDTHlfMraWlvIaklUUIKreHWE8aZ1oXOhr0bsG6xgp5J8Tj11FMnvWZVaZU57uo5/tNmctKZbmxs5IYbbuCdd96hoaHBPP6tb32LaDRKUVERZ5xxBoqi8OMf/5i//vWvU26wRCKRSCSS6SOlp+jp6jH36+bVYSsvp1AYkWitp4elg8Eza0kJ9traWbFzPIY708NZmL/QHDvcDnPcHegea3pWQ5fKqkpcTpexk4Km7uM/NWGivN/1Ph2bOiAEVtVKubec6667DutRpPYsrMz8X/QP9B9m5vFBTs70ww8/TCwW44ILLuDb3/42AB988AH79u3D6XTy5JNP8utf/5of/vCHCCH4zW9+My1GSyQSiUQimR46wh2IkBF1dlgcVFdWYysvZ8VgWoetq4sVQyoeH1p3zGkEu+2jUzYgE5kGyMvLpIKM50wP16AuLiymqLjI3N9/cP/Rmnnc8Ogrj6I2Ge5iaV4pV111FeXl5Ue15oqFKxCFxu+YXqoftY2zTU7O9DvvvIPb7eYHP/gBVVVGiP6VV14B4Oyzz6a0tBSASy65hNLSUt5///0pNlcikUgkEsl0cih8yFTycFqdlJWVYa0o52RN4/OJJDd0dDLkruatPbZSPGDsyLTL6qLCnUnLyPNknOn+4NiR0Wg4ao5L/aVZDuSJIo/X3d/Nm8+8ae6vO20dH/rQh4563SWFS6i6pIr0BWnWnn9s5dxPhpxi9F1dXdTV1WVd0b3xxhsoipIloQdG05fdu3dPjZUSiUQikUhmhJZQRhbPZXVRXl6OXm44ogWDedJDHEv60kMMl8YbYoFvAaqSiR8OVyYbL81guAZ1ub+cmspMwV1HZ8dYp8wphBD88Bc/JB038s7dXjf/+Ll/nJI7ETaLjccufYzd/btZU7bmqNebbXKKTKuqSjKZNPcDgQA7dhiyM+tGdD/q6enB5XJNgYkSiUQikUhmisbuxoySh9NNYWEhtorRt/XtCxdiO8rb/dPBWM50bUF2XrfP6zPHwwsNh0in06RiKWNHMZzpuupMmkhvT++oc+Yar7/+Olu2bzH3L/7ExVMqj1zgLGBtxVosqmXK1pwtcnKma2traW5uNgXLX3zxRXRdp6amhvnz55vz3nzzTTo6OqirqxtvKYlEIpFIJMcgTYcyxXXl5eUoioK1fLRyxbEYlYax0zzq8rP9kQJvgTkeCA+MnE4oFCKtDSqB2KHQVcjy+cvN58O9YXRx/Of6jkd7ezt/+J8/0Bc35AH1Op0bzr1hlq06dskpzePyyy9n586d3HjjjZx99tk88cQTKIrCVVddBUBfXx9PPPEEDz74IIqicPnll0+L0RKJRCKRSKaH1vaM7u/8KiNQNlZkOm/dsZcvDUZqioKCIJOSMqQxPYS/0G+Ow6HwqDW6+7vRMZxli8ti6CpX1GCz2EhpKfSoTsdAB5X5k29ccizz3HPP0TbQhkAg8gWnnH0K87zzZtusY5acItPXXXcdH/7wh2lsbOTXv/414XCYk08+mc997nMANDc382//9m9EIhEuuugiPv3pT0+L0RKJRCKRSKYeXeh0dnSa+4vnLQbAOjKdQ1VxH6aR22yiKMqo6PTINI8iX0aZIxqJMpL2vnZz7MgzZPSsVivugkwKyd8O/G1K7D3WEEKwb98+OqPG74F2isYVS66YZauObXKKTNtsNh566CFee+019uzZQ01NDRdccAE2mw0w0kDOOussLr/8cq688sppMVgikUgkEsn00BpqJTWQQkHBbrGzuMZwplW7HUtREVqvkSvsXLECS37+bJp6WNx2N6FUCDC0kUdGVYt8RaAAApKJJKlUyvRlALr6Mo1EXJ5M/VdhcSGB3gAA+w7t48KTL5zGVzE7tLe30xPqIZwMgwPsBXYuWnDRbJt1TDMpxe1zzjmHc845Z9TxgoICfvnLXx61URKJRCKRSGaePf17UAYMtQa31Z0lB2crLzed6WOthfhIhkemF/gWYFWz3R2fwwcOIG40qQmHwxQWFprPd/dntKfdnkw0uqy0jKY9Rk75gdYD02T97NLY2EhnxIhKC7/g/Pnn47VPXeHhXCSnNA+JRCKRSCRzlx3NO0wlj3xvPn5/JrfYsazeHHvOO3eGLcuNPFtGwnd458MhvHYvwmHkVGu6RigUynq+L9Bnjj2+jGNeXZFpO97W2TZl9h5LNDQ0EEgY0XdRJLis9rJZtujYZ/K9ICUSiUQikcwpdjbsNMfza+ZnaQqX3Horis2Gc8kS8k4/fTbMmzDDI9Mjiw8BfHYf2I1xSk+NcqaHN3Ip8GWUP4bL4w1vuT5XEEKwv2E/kZTR/VH365xedmz/Xx8LSGdaIpFIJBIJYAgJDLF80fKs52zl5VR861szbNHkKHYVm+Ol/qWjnvfavUaaB5DW04TD2Yoew7WnC/IzzvTS+Zm1Qn0hdF1HVefOTf7e3l7ae9sN2T8rVFVWGRceksMinWmJRCKRSCQktSR9HZn0hlX1q2bRmqPj75f/PfsD+5nnncd5884b9bzP7kM4BArKmGkew+Xyigoyyh8LixYaTngCYskYvb29lJSUTNvrmGkaGxuNwkOMfOnlxcuPcIYEpDMtkUgkEokE2N+3HxEw8oidVidLa0dHdI8XlvqX8vjlj4/7vN1ix+ayoaGho9MXzFxECCGIhCLmfklhxlnOd+Rj9VlJd6fRhEZDS8Occ6aHVFCEX7C8SDrTE2Hu3JuQSCQSiUQyaTbv28xgnxIKCwvxeEZ3EpxL5LkzRYo9wUz+cywWI5kerMK0gt+TKcJUFIX84owk4N5De6ff0BlkZGR6mX/ZLFt0fJCTM71p0yZ27949oblvvfUWv/3tbydllEQikUgkkpll+97t5rh6XvVhZs4Nhl8sDI9MDwwMkNYHW4k7GJUzPDwS3dTaxFwhFArR2dVpFB+qIAoEy4qkMz0RcnKmr7/+eu69994Jzf3xj3/MfffdNymjJBKJRCKRzCyNzY3meGnd8ZviMVG8nox28vCCw1AoREpPASAcwtCkHkZVRZU5Ht56/XinsbGRaDqKJjREgaDcV47f6T/yiZLxc6ZDoRCdnZ2jjkejUfbv3z/ugkII2traaGhomBoLJRKJRCKRTDudrZnv/NOXzn05NJ8v4yQPhAbMcTAYzESmnZBvz+70uLAqo1vd3dXNXEGmeEyecZ3pZDLJpz71KSKRTBK+oijs2rWLj33sYxNa/Mwzzzx6CyUSiUQikUwrPQM9RANRAFRVZc3SNbNs0fRTmJ/peBgKhxBCoCgKoVDIdKbHikzXVtQa3lMawpEw4XB4TuSXZznTRTLFIxfGTfMoKiri1ltvRQhhbkDW/lgbQF5eHqeddhrf/va3Z+ZVSCQSiUQimTRv73objK9wvMVePK7j3zk8EvnOfLAZ41Q6RTRqXEwEB7Ij0yNbaVd5qhBu482Ka/Ex7+Ifb8TjcVpaWgwlDwVEoWC5Xyp5TJTDSuPdeOON3HjjjeZ+fX09q1at4rHHHptuuyQSiUQikcwQW/dsNceVVZWzaMnM4XMMdkFMQVqkCYVCuN1uuvu7EYNXFg63A5tqyzqv0lMJHiAIiXSCrq4u6urqRv+A44jm5maEEISTYYRPgA0pi5cDOelM33rrrVRUVEyXLRKJRCKRSGaB/c2ZWqjFdYtn0ZKZw2zcElFIa5kuiL39veacsdI3ipxFWH1WtFaNlJ7iYNtB1rFuxuyeDhoaGjLFh35BsauYkry5o5893eTsTEskEolEIplbtB1qM8enLj51Fi2ZObJaiouMMz1cJs/r8446T1EUikqK6NrVBUBDy/EvuNDU1CSLD4+CSXVA3LFjB9u2bSMcDqNpmpkrPRbSAZdIZgdd6Px6568JJAJ88ZQv4rK6ZtskiURyDBIIBAgGB6XhrLB68erZNWiG8Nl9CLvRUjytp82W4oFgwJxTkF8w5rnlZeV0YTjTBw4dIJ1OY7Uen02l0+k0Bw4cIJwa5kzL4sOcyOl/PplMcscdd/Dyyy8fce5QVax0piWS2eGN1je4b+N9kIS0lubOM+6cbZMkEskxyI79O0xdZWuhlWrv3G/YAiMi04POdCqVIhIdVDFToNBbOOa58yvns92+HZLQH+jnL3/5C+eff/4MWT61HDp0iHQ6bUSm3YATWXyYIzk50w8//DAbN24EoKamhoULF+JwOKbFMIlEcnRs2rMJy+sWAJ7SnuKO1XdgVac2chIIBvjjK3/kjOVnsGLJiildWyKRzAzv7XrPHJdVlaEoyixaM3P47L4sZzocDmfJ4uEYVPwYg+r8avTFOupOlbgW5/nnn2fVqlXk5489/1imoaEBIQShVAhRamQayOLD3Mjpm3XDhg0oisLdd9/NddddN102SSSSKeBg00FzHG4Is7lzM2sr1k7Z+h/s+4Dbv387vYFe7FY7991zHx9a/qEpW18ikcwMuxt3m+Pa+bWzaMnM4rV7EXbDeRyKTA9vJS6cYlQr8SEq3BXoC3TUgyrxdJxkMsnTTz99XPpGjY2NxNNx0noa3a9T4Cig3F0+22YdV+TUTvzQoUNUVFQcl78sEsmJRiCcyftTAgob/rZhytZ+/q3nueVfbqE3YFS9J9NJ/umBf6I7One6gUkkJwJCCFoOtZj7KxevnEVrZpaxChAHBgbMlBcckO8YO9Jc5akCFbQVGgktAcB7771HU1PTTJg+Zei6TlNTk6EvzWCzFv+yE+buxFSRkzPtdrvnRJcfieREYHh7XAS8/N7LpLTUUa0phOA//vAffPMn3ySaiGY9F+mM8Plffp5oKjrO2RKJ5Fijq6uLgejgZ4UDTq45eXYNmkFG5kwPhAayI9OO8SPTlR5Di1uUCCLFEfNz78knn0TX9ek3fopob28nHo8b+dIOIA9ZfDgJcnKmV69eTVNTE319fUeeLJFIZpVQJJS1H2+L81bbW5NeL51O842ffYOHHn/IjNxYPBbWrFmDghHFOPTuIb668atoujZ5wyUSyYzR3NxsOoKiQLDEv2SWLZo5VEXF7Xab+4GBQHbOtJNxnemyvDLq/fUAJJYl2B3cjS50WlpaePfdd6fMRk3X0MX0OeeNjY0AhFNhRJEARTrTkyGnnOn//b//N6+++ip33303999/P3a7fbrskkgkR8lQa9whlG6FZxuf5Zx55+S8VigU4h+//4+8v/t985iz3MlP7vwJp5afyi133cK2Q9sgAW9ufJMf+n/IXWfcddSvQSKRTC/b921HE8bFr6fUQ6FzbPWKuYrP4yOuxEFAJBaht7c3qwBxPGdaURS+s/47fPrZT5POSxOoCXCg/QAL8xfy7LPPcsopp5CXlzchG8LJMBsPbKS5r5m+dB898R66o930xHrojfdiU23UFdRR56ljnnUepUopBXoBFs2CLnSSWpK4FjcfE+kEiXiCRCxBIjH4GE+gJTUK7AUgjLuMuq4TiUSM4sNkCOEfLD6USh45k5MzffDgQa655hp+97vf8eEPf5gzzjiDsrIybDbbmPMVReHOO6Ucl0Qy0+hCJx6LZx9MwCs7XiG2Ppaz5vRXfvKVLEfav8TPw//0MDUFNQDcceMdfPMn3+RQ6BBqs8pj7zxGlaeK65dff9SvRSKRTB879+80x/Nr5s+iJbODz+Gjy9EFcSPVo62tjbTIpHmMlzMNUO+v5/bTb+dHm3+EXqdz4NABCuPGxcif//xnPvGJT4x7rhCCbd3b+MPeP/D8xudJ707DUBaedXCzgWJV0DSNvbG97E3tzVrDoljMC6GJUuAo4OSSk1GVTGJCQkuQ0lPofh2vzcs877yc1pTk6EzfdtttZlJ6IBDghRdeGDdJfUhnWjrTEsnMM5AYQCSNKINVteKwOIikIiTbk7zW8hoXL7h4wmslU0ne35FxpBetX8Qvv/zLrC+Zk08+mYvXXMz/vP0/dMe6sXxg4Ye+H1LhruDC+RdO3QuTSCRTRjqd5lDrIXN/+aITLyLptXuNluJxo3FLZ2dnpgDxMGkeQ1y//Hpeb32dd9vfRVuusfv93awqX8Ubb7zB2rVrqayszJrfH+/n6YaneXLfkzR0NmDZakHpHuFHpQe3OGYK3Vjk6kgDBBIBDgwYEfQhwqkwokKAD+qL6mXx4STIyZm+8sor5ZsskRwH9MZ7zSiHXbVTkldCJBhB6VL4c9Ofc3Kmm9qbzBxom9vGb//xt9jU7LtRiqJwzTXXsK9hH1vatzDQNwAtcMerd7DMv4xz553LufPOlVXiEskxRGtrK6H4YG2FG5aXn3jOtM/ug8GM1bSeRtf17AJEx+GdaVVR+e7673L101cTLA8SK4yxr38fy/zL+M///E/OPfdcTjvtNLpT3fzqg1+xoWEDKT2F0qFg3WaFpLGO2+amxF2CTbHhUB3YLXZz03SNSCpCVI+SsCUIW8MElAApm/Ehb1ftOFQHDotxns1iw+KwoNpVLHbjMaEm2B/ZDwo0Ko186dwvsaZiDaqq8qudv2Lr/q0Aso34JMnJmf7e9743XXZIJJIppDeWcaZtFhtleWU0B5tRAgqvN75OaH3IqGSfAPva95ljb753lCM9RGlpKRecdwHJF5K83/U+sV0x0uVpdvXtYlffLv59279TllfGufPO5eIFF7O6/MRoWSyRHKscPHiQSNro9icKBIsLF8+yRTPPSEUPIYTpTCsOBY/tyApmZe4yvrXuW3zl1a+gnaTR9VoXRdEilG6Fhx97mO/84jt05HegzdMQBQJ1l4rarGJRLJS6S6n0VHLFxVdwySWXoKqqkeecSBCPx4nFYlgsFgoKCvB6vWYwQtM1YmkjZc+iWo5ooy50bnnxFt5pfweB4Hs7v8cfav9AgauAhniDOU8WH06OnNQ8JBLJ8UFHsAOMLA+cDif1dfXGl4aAVGeKlw++POG1mtozuql+v/+wcz/ykY9QWlTKKSWnUKwWY9tpQ+lRUPoVCEJnVyePb32czz73We59594JS/VJdRCJZOppbG4klooBhjNdm3/iNGwZwmf3IRyZxi1m8aENPC7PhBxVgI/M/whXLboKvKCt1NgX3MeO7h1s7txMR6gDWsDytgXrC1by2/JZUriEdZXrOGPhGXzt9q/xsY99DKvViqqquFwuCgoKKC8vZ+HChdTU1ODz+bLu6llUCx77xO1TFZX/e9b/xe80PsN7Yj18481voAudv/X+zZwniw8nx6Sd6T179vCLX/yCf/7nf+a2224DIBKJ8MQTT5BMJqfMQIlEkjud/Z3mOC8vj/r6ekpdpQCoXSrPNT034bUOdWVyKkuLSw871+FwcOWVV+K0Ojmp+CTW6+tZuXcllVsqcb3hwvqKFetLVqwvWnn8vce56YWb6In1jLveu+3v8smnP8m6363jwW0PIoSYsN1zFfkeSKaCdDrN9j3bEYNX3WVVZeTZJqY+MZfw2r2ZNA+RzilfeiR3nXEXNd4axHxB/MI43Yu6YdgSfqefU/2ncnrp6VR6KjntlNO48847Wbx4Zu4IlOSV8N2zvmvuv9H6Bve9d5/5GeyyupjvO/GKUKeCnNI8AILBIHfffTcbN24EMoWGYHRIvPvuu/npT3/KQw89xNKlS6fWWolEMiG6gl3m2O12s2zZMkrySmgINqB0K7zT9g598T4zSnE4OrszjnlVadUR55988sksXbqUPXv2YFWtlOaVUppXii50BhIDtIRb6In1YNlnYYtnC3/3zN/xk3N/wsqSTOe1tnAbP9r8I1488KJ57IGtD9AYbOQ767+Dw+IY82entBS/2PELnm18ljMrzuT2VbdP6Asxlo6R1JKHrdwf77y9/XvZ1buL3X272dW3i/ZwO4qioCoqqmLcyh161IRmbLqWNbaoFqyKFZvFhlWxYlWNTRMaSS1JQkuQ0BLmuMRVwrrKdayvXM+6ynUnnJyZ5OjQdZ3HHnuM9q5244AKSxecmN/XQwWIACk9NaGGLeORZ8vje2d/j+ufux7NrqEv1BELBOs961kRXUHb7jZisRhWq5WrrrqKdevWzXgNyVlVZ3Hjihv5z53/CWA+gqFOMtFItySbnJzpZDLJ5z73OXbu3EleXh7r1q1jx44ddHcbLYSFEPh8Pjo7O7n++uv5n//5n1GVrBKJZPrpHeg1x/mefGpqaijKLyK/L59gIogW0HjpwEt8cuknj7xWX2atmrKaI85XFIXrrruOZ555hmAwSCqVMrfSVCn53fkcCh0yChtP0uiKdnHDn2/gm2u/ySULL+HhnQ/zqx2/Iq7FR639XNNztIfbuf/8+0ddCPyt9298481vsK/fyPE+GDrIay2v8S/r/oWzq88e09aOSAcPbH2ADQ0b0IVOubuc+sJ66ovqqS+sZ6l/KV67l7ZwG22RNtrD7eZjY7CR5oHmaW2oMB7dsW42NGxgQ8MGFBTjLkDVes6tPpcVxStm3B7Jsc2QFrHT6kQIwe9//3u2bt1KOBU2nl+ss6TkxGnWMhyf3WfmTGu6ZsriHa6V+OFYWbKSfz37X/nVB79imX8ZN550o5k+k0qlaG5uprS0lPz83NeeKm477TY2d2zmg94Pso7L4sPJk5Mz/Zvf/IadO3eyZs0a7r//fvx+P9dee63pTC9btoyXX36ZL33pS2zevJlf/OIXfOtb35oWwyUSyfj0DWS6lOZ78lEUhfr6evZ27CWYCKJ2qfyp6U8TcqaDgaA5XlS5aEI/3+Px8KlPfWrUcSEEP/jBD1AUBY/dw57uPUQqI6T0FPe8dQ8/fu/HBBKBrHMuq70Mp8XJE/ueAGBr91auffZaHrjgAeoK6khqSf5j+3/w/3b8v1FSUV3RLr688ct8YvEn+Orqr5pFl6FkiIc/eJhH//ZoltPeEemgI9LBqy2vTuh1HgsIBDt6drCjZwcPbnuQC2ou4J/W/JPZ7lhyYtAZ6WRb9zaaB5rpinbRHe2mO9ZNV7SL3lgvaZGm0FGIb7cPrUnDZXURSATQF+roi/UTsvgQBnOm7WNEpp25R6aHuGThJVyy8JJRx20224yldBwOm8XGDz78A/7XM/+LSCpiHpfFh5MnJ2f6mWeewWq18qMf/WjcQiSPx8OPfvQjLrzwQl5//fUpMVIikeRGMJxxgAu9RgpAfX09b//1bfYH9qN0K2zp3EJHpINyd/m466RSKWKhmLm/tOrobgUrisKZZ57JU089hd/p53Lr5Wwp3GJGk4c70vX+er5+xteh1Wh5W7G0ggf2PIBA0Bpu5fo/Xc/tq27nd7t/x/7AfvM8h+LgsqLLeKX/Ffq1fgCe3Pckb7W9xT1r7+Fg6CD/se0/6E/0ZwxLgzVlJU0aFDKbCmhAEpSEYjwmjUc0UCwKJb4S5hfMZ4F/AbXFtSwqWUR1dTVWuxVd6GhCQ9d1dHRURcWqWLGoFnOsqqoRERssfhr6Qk/pKayqFbvFjsOSkb2yqlZ29+7mjbY3eLP1TXb07MiKjm88uJE3W9/kppU3ceNJN46bEnM8k9JThJNhwsmwqUahkEmtURQFFRWrasWm2rBZbNhVQzLMptqyGlaMhxAiKxUnLQZl00Ta+H/VNXR0LIoFBcX8Px1K69GFbnSZQzfHmtAyvxPDfjc0oZm5y0IIhv4BqKjYVJuZ+jM0bgu3sa17G1u7t7KtexsdkY4jvCAIvh8k1BDKHKoR6Ct0UGBJwQkamXb4stQ8zJxpB0eUxTuemeebxzfXfpO7Xs90qpWR6cmTkzPd1NTEokWLKCsrO+y8srIyamtrzZ7vEolkZhnuTPt9xoVvfX09doudAmcB/f39iKTg+ebnuWHFDeOu09zVnNGYzrNRmHf0ubmrV6/m6aefRtd1ett6ue/a+/jZ/p/xfPPzgHFr9bbTbuPqxVfT1NjEA48+AMDChQu5/8r7+drrXyOWjhFKhfjOO9/JWvv00tNZ1bKKppeauKjsInpX9/JS60uAEXX+8sYvj7KnNl5L8Y5iPFYPsXTMcNJSYfNRFzpOqxOHxYHT6sRpMcYuqwu3zY0laoEO6B38t4lNWCwWFixYwOLFi1myZAnz5s3Das25RGVcVpasZGXJSr50ypcIJoK80/4OLx540XwP41qcn2/9ORsaNnDXGXeNSnNJaAm6o930xfsIJUNZrzeUDBFNR01nNGtTjNcw3EHUhXGhEE/HiaaiRNNRoqmooYubjmZkxgabTyiKgoKCwJAgG55DntYNR3VozpBjPDQ/nAwTSUXGTAHKheGO9/DNdKCH3+4/HtCBCCgDitHswwkiT0AeMJgCa9tnQzRkildFlUA7WQPFyBue5zsxu955bdkFiMNzpvPts5eKMRNcVnsZe/v38vAHD3NO9TksKTwxL6imgpw+3RVFIRaLHXkiRoGD3W6flFESieToCEfC5rg031Dg8Hg81NTU0B5ppz/ej9Kt8JeWvxzWmd7bmmlf6y2YmC71kfB4PCxbtoydO402xjve38EPL/shH5n/ETojnXy87uMUOAsQQvDss8+a5zU1NfFJ+yf59cW/5taXb6UrmimydFqc3L7qdi7wX8D3X/g+AL2dvVztvJqPnvNRvvvOd0elj1R5qrj1lFvZ+vut9NqMvHC3zY3b5qaMwwcMjoSmaTQ0NNDQ0MCf//xn7HY7dXV1+P1+3G43eXl5uN1u3G43LpcLRVEMR04bjGIPbsFgkEAgQH9/v7kFAgFUVcXv9+P3+yksLKSoqIjr/ddzxYeu4Ke7f8ruvt2AkTf+5Y1fZlXZKqyKle6Yces/lAwd4RVMMwJTunHc5/TBTQx7dGEoGRtaAAAgAElEQVQ6h5P6mTqQGow6qxqaqhl3H4bfjZhpBKPfj+FjDbMjnpJWDP34NCgxBSWkGA50BCzCgtfuxWv3mncxHBYHhb5CSgpL6Ih0kKhIEEvHKFhQQPk55bREWoilY1y77Npx9ePnOl671/idso2QxnPO7cj0EHesuoObT74Zt80926Yc1+TkTC9cuJDdu3fT0tJCdXX1uPMOHjzI/v37Oemkk47awLmGEIJIJEIoFCISiZBMJrMKtFKpFJqmYbFYsNvtOBwObDab+eh0OnE4HOZmscjK27EQQpBMJgmHw+aWSqVYtGgRHs+RRfiPZ+LpOMl40oi+oVKcX2w+t2zZMvY07AFA7VZpDB7+7lFzR7M5LiycOsWItWvXms705s2bufTSS/nogo9mzdm9ezfNzc1Zx/7617/y8Y9/nN9e+lu+8tpX2N69ndVlq/n2h75Nja+GDRs2ZM3fuHEjd999N6uvWM133/kuLx18CZ/dxy0n38Kn6j/F1ve20ttrONIWiwWv12s6tEOPFosFj8eD2+3OenQ4HCSTSZLJJIlEwhz39vbS0ZF9yz2ZTLJr164pe/8AOjo6Rv0cRVH48se/TMfiDn72/s9Mp/m9zvem9GdnITDaHscUiA4+xkCJKsY4zdiO8WRQACfgAbvPjsvnIi8/D0VX0BIaekJHSxqPQ2MtoaEndWPT9cMXjCrZm6IoxqYqKEIxo+QKmf2RaRlDY0VRMmuoiqnaoKCg6Ir5nih65vhwO4b2h9YfuhMw9M+iWPDZffgcPnwlPtw295jpK3pCp7OjE0VRcFqdnHrSqXz+85+f0jslxzOmw2wHLaqZaR6TUfM4XpGO9NGT01/Txz72MXbu3MnXvvY1HnjgAQoKCkbNCQQCfPWrXwXgkktGJ+Afa6TTabq7u+ns7KSzs5Ouri46Ozvp7u42o+sjNyGE6fgOOcND2tp2ux2n02nOdTqdAIRCIXObSp1Yq9WK0+nM2vLy8nA6nbhcrlHO99BmtVrRNG3MbXiEbHikDDJfLkMM7auqiqqq5lhRFBKJBLFYjHg8TjQaJRqNEo/HEUJgtVpHbRaLJfPlNWxdRVGw2+3YbDasVqs5VhSFgYGBrPd2aAuHw6TTo2/T2u12rrnmGlavXj1n21r3x/uzuh/m5WW0Y+vr63H+2WnkdHbp9ER7GEgOjPulcagzozFdUlQyZTbW19fj8XgIh8MEg0H27NnDsmWZfD0hBH/6059Gnbdp0yYuvfRSytxl/OaS39AX76PIVQQYf8ubNm3Kmh8IBHjnnXc466yzuO+8+2gPt+N3+XFYHKTTaV544QVz7kUXXcRFF100Ja9vYGCAffv2sW/fPvbs2UMgEDjySVOAEIINT23giiuu4Okrn+b+Lffzx/1/HDXPqljxu/wUu4qNaKbNi8fuwWPz4LV7cdvc6MJoq5xMJ4mEIkRDUaIDURKhBIlQgngoTmLAeBS6MPOFh2T+LKoFS57xNz2e82w6p8OcVNOnFGQ5qICZaqIqqpG73oOxjbk4Ri7siJTxoc/f4TnKw/ez7DmOPiMKCgqoqKjA4/Fk3ckY+uwGWLRoEZ/97GelIz0Mp8WJVbUiHAItYkhRAkbO9AniTEuOnpz+oq699lqeffZZ3nvvPS699FLWrVtHS0sLAA8//DCNjY288MILBINBFi9ezGc+85lpMXqq2LJlC48//vhhm8yk02mi0eiE1xyKhs4U6XTajLxKjkwymeS3v/0tu3fv5pprrsHlcs22SVNOb7zXKJBjtDNdU1NjpBZYXUQSERiA5mAzJ5ecPOZaHT2ZyGdl6dSpQ1itVlatWsVrr70GwLvvvpvlTG/fvt38bBm6YBz6Pd+9ezcnnXQSiqKYjjTAzp07x/w72LhxI2vXrsVqtVLhqTCPb9682YxK5+Xl8eEPf3jKXp/P52PVqlWsWrUKIQS9vb0cPHiQcDhMJBIhEokQjUaJRCJm6tzQBenQZrFYcLvdFBQUmOkchYWFFBQUoGkafX19Wdv+/fvNSPVTTz0FwP859/9ww4ob2NW3iwJHASWuEopdxRQ6C8eMYiYSCd5//312vreTQCBAIBAY9Z6qqLgG/wEwyewfVc38/JEBhqGL6+EX2pqmHfVFicViweVyoaoq6fRgQWE6bQYRZovhgYPhxxRFMX//HQ5HVtDE4/FQUVFBRUUFZWVlY36W6brOwMAAvb29JJNJFi9eLB3pESiKgs/uI+gw6kzi6biR+mObnDSe5MQkp78qu93OL3/5S772/9k77/g46jvvv2e2r1a9WV0usuWCG7jgAsY2mBASSIWEhJKQRpLj8pB7LvVyl+e5XHqeJJAj9SA+AhdCCIReDBgM7tjG3bIkq3etpO27M/P8Mbuzu2qWjOWVrN+b17zmN/07K7Pz2e98yz//M6+++mpSPOMPf/hD48toxYoV/OQnP8Fmm9xZ5G+88UZKujU6HA7S09NxuVyGlzVxMplMRCIR47VxzPMdDAaTpkDg3SXhXOyYzWZcLpcxdXZ2GuJp//791NXVceutt1JZWZlaQ88zPYEepLD+ULbK1iQxLcsy1dXV7GnYgzfsRe6QqeurG1FMd3XH3X4VM85vZ6yVK1caYvrw4cP4fD6cTieqqvLss/EOjevXr0eWZaNR1K5du4YNIdu5c6cx3rBhA3v37sXj8eB2u9m1axdr1641tkciEV588cWk/WNvkc43kiSRl5dHXl7e2XceB2lpaZSVxZPGAoEAv/nNb6ir09u/xwT1hg0bmJ01e9RztbW18eabb7Jnz55z+l5JS0tLEvyxeO6srCxcLpchiGPTYOE4ViKRCN3d3XR1ddHZ2UlXVxe9vb1YrVYjFj0Wjx4bOxwOY4q90RqO2Bs4TdOS4tY1TTOak8WOjf0QiC0PFuKJx8XGMcGe+CMh9rkk/rA4n8iyTFZW1rBvkQVxMqwZuK1uJCQ9udUBSMIzLRg74/6JmpmZyf3338/hw4d5+eWXOX36NB6PB4fDQUVFBRs2bGDlypUTYet554orrqC3txdJkigsLCQ7LxtrphUpXUJNU8EEkiKhqRpaRJ/UsIosy5gtZj3swGI2xpqmEQgGCIaCBANRwRsKoCgKZocZs8OMbJfRZI2wGkbV1KQKAYmVAjTiZZQSyzDJyPrr02g5JhSIhCJEQhGCgSDhYJhgIEgoGCIUCBEORkNRgtGY7FCYUDCEoijIpqgXzBT3hkmyhGSKx/oho2e5S1rSA8OI34s+LCJqBE3V4p3dIgpmqxmbQ/em2Ow27A7du2I2mdEUTf9cE+eapr/ajZ5XU6OxgoqKSTPpfwtF02MMFf11rM1pw5HmwOK0YHPaMDvMWBwWLA4LmOLZ2WE1TGGwkBPbT9BwuAGLbKG3t5df/OIXbNmyhauvvtp4oIWVMA0DDTT0N+Cyulicv3hM5cUGQgMElSB5jvMrmsZLt787HuYhJ3umQY+bdr7i1ONauyTq+upGPFd/b78xHmuN6bFSXFxMaWkpTU1NKIrC/v37WbduHfv376e9Xe+6aLPZ2LhxIz6fzxDTR48exePxJMW+u91ujh8/biyvX7+ejIwMI4b6pZdeYtWqVYZXbu/evfT06LW4nU4n69cP39RlKmG32/nsZz87oqAeTCgU4ujRo+zYsYOampoh2xPJyMggMzOTjIwMcnNzycnJSZpfKMeJ2WymsLDwrBWlzoXYd6Bg+pFuTTfCgVRNNToiTocERMH54Zzf9yxatOisCYY9PT0j1qOeDNjL7XSt66K+v55Ofydejxcu1mgJS3Rynm3HCUABvNHpfCAR/5cbik69I++ehA2kIgn7YTsOzYHT4qT+z/X851/+E5+il/PyK349flICLUvDvMLMsuJlrCpaxeqi1czPmY8sybR6W9nfsZ8DHQfY37Gfmt4aNDTmZs/l2sprubby2nGXmwoqQdq97XT4OvBH/ITVMCE1RFgJE1JCKJrCZYWXMStr1ojn6PbHwzysJusQMT1r1iycZn2d1C9R6x4+CTESieAbiIc4vdsa08OxatUqI5xj9+7drF69mueee87YfuWVVxpvFiorK6mvr0dVVfbu3ZskEHft2mWM586dS05ODmvWrGHbtm2Gd3r37t2sWbNmSKz0VVddNWFe6QvNSII6HA6Tn59Pa2srLS0ttLW10dU1fLBxXl4ea9asYebMmUmeZYHgYiXDmmGUxwMMYX2xl8YTnD/G9Q25adMmFi9ezM9+9rOz7vvxj3+cpqYmtm/ffs7GTTQ/3vtjdrXuOvuOgosKrVjDn+Un9HaI/p7+UfeV2iXCJ8PslHays3UnP+fnpFvTcZgdSaXZEjnZe5KTvSf5xdu/YGHuQq6tvJYryq5AURXcQTc9gR56/b0cPXSUuqN1BK1BfC4ffY4+3Ba3Hq83ClbZytMffHrEZiudnk69UgBgs+pVYBLJzs4mMy0TeoAQ1HUM75k+03nGqDFtdpjJTcsddr93w7Jly/jb3/6Goig0Njby5JNPGqE4DocjSTCvWrXKqO6xa9currzySiRJQlXVpBCP1atXA7pXe8OGDTz11FOA7p1euXIle/bsobdX//WVlpZ2UXilExlOUA+XzJmIJEksWrSItWvXMnfu3CmVeCcQvFvSremoefqbXwAtT6+WIqpcCMbKuMR0c3MzM2aM3C0tRiQSoa2t7YJlsJ8ri3IXJYlpq2wl35lPgbOAPEceVpNVbyoQLeAfq0Gp6WnmBlrCglk2J2W0y5IelmGRLUbnqtgYCUJKiEAkQEAJ6PPoONZEINZNyySZ4rVoY92zYiEVmpJcnimaBT84ji/RTg0tXuJJimfRS0jGtWRJRkY2xrF9Yuti/w3u5maS9DCUWDmnxC5fQ7p/JcxVTU0qDxV7oCuqQkAJEIwE45+TEiCshPXOcOZ4dzi7yR7vdpb4eZv0MJxmTzP1/fV4nV6UyxXkGhn5tKyX74pdF0lvymFxEIwE8Z3xoc5Rjfq2A6GBYev0xj6DkBqPwz/SfYQj3Uf4yb6fxHcMgemQCal1qGAxm8xoGRpapoZWpqFlDU2KCqkhdrXu4oY5NwzZBtDZ32mMXU7XEGEkSRJzyuews1EXoE3NTYTV8JA6s6eaTxnj9MzzU2N6MGlpaSxatIiDBw8CJHVN3bhxY1JS1dKlS/nrX/9KOBymra2NxsZGysvLOXnypPFd43Q6ueSSS4xj1q1bxyuvvILX66W3t5edO3ca4SKge6Une27HuTCcoB6OwsJCli5dyurVq0VcrWDakm5Nh3RQ1isQAK1AI8uaJX5UCsbMiGL69OnTfO1rXxsiyGJVEEZC0zQ6Ozvp7OykvLz8/Fk6Ady9/G5umHMDETVCgbOADGuG+J9nGqBpGl3+Lur66qjvr6e+tx6n7KQ8o5yK9ArKXGWYJTM//elP6evrIxAJMC93Hu057exq3UWnXxerTrOTJflLWFawjGWFy1ictxgNjVcaX+H5uud5o+WNeAOAKFKnhOmACUbI8ZIUCXu/HavXiqXLwtyPzsXutGM1Wal113KsR69V3DjQOPwJgO6+bmOckTZ8zF9FWQU2k42gEkTtU2keaKYyszJpn7rWuAjLypk4obVq1SpDTMdwuVxDPMZ2u52lS5ca5e927dpFeXl5kld6xYoVSSEJMe90LFn68ccfN0qFuVwu1q1bNyH3NBmICepHHnmE5uZm8vLyKCoqori4mBkzZlBYWDjkrYVAMB2JJRomOi9E8qFgPIwopmfPnk1JSUlS/KIkSXi9Xg4fPnzWE0uSxOc+97nzY+UEIUkSMzNnptoMwQVGkiTynfnkO/NZWTRysuzatWt55plnsJvtqKdVvvf+7wFwpv8MYTXMzMyZmOWh/wtdP+t6rp91PX3BPrY1bOP5+uc50XUC+biMUqPonvI03Vs++5LZFBUUEewO4unw4Pf4k0qW3VZ8G0uXLgXgr6f+ynfe/A4ATZ6mEe3uGegxxpnpw8f8FRcX47Q4CSpBpH49CXGwmE6sMV2QWzDi9d4t8+bNIyMjg/7+eMjN1VdfPazHOBamAXpFls2bNyd9H61atWrIMevXr+eVV17B5/Ml1dzduHHjRemVTsRut3P77ben2gyBYFKTbh365k2UxROMh1HDPL71rW9x5ZVXAro37xvf+AaVlZWjimRJkkhLS6OqquqiKzkmmF5cfvnlvPDCC0QiERobGzlz5gyVlZVDROdIZNoy+UDVB1iVtoqtW7fS1t8GUU3qcrm46aabhiTxejwenn76acPbWlNTY4jpUle86+honum+gT5jnJMxfAJwcXExTrOTXnp1Md1fx1VclbRPa2erMS7KLxp8ivOGLMusWLHCCL/IyspizZo1w+47e/Zs8vLy6OrqIhAI8OCDD6Ioelx3RUUFRUVD7bTZbFx11VVJpTxdLteI1xAIBNOL4ap2CM+0YDyMKqbz8vL4wAc+YCzfe++9VFdXJ60TCC5WXC4Xl156qVEpYvv27eP+gXjq1Cl+97vfJdUzX7BgATfffDPp6UO9IS6Xi2XLliWJ6Rhl6fHKIE0Dw3umVU1lwBuP585NHz5pcMaMGTgt0SofHjjdc3rIPt098XCR811jejBXXHEF+/fvZ2BggA9/+MMjVo+QJImVK1caCXVnzpwxtsUSD4cjFjsda8C0adOmi94rLRAIxsZwnmkhpgXjYVwJiNu2bZsoOwSCScm6desMMX3w4EHcbveYE7WOHDnCAw88YLQ1N5vN3HjjjaxZs2bU2PzKykqj41t7ezsDAwOkp6dT4CzAIlsIq2F6Aj14w94h2eb9wX7UkJ6VbpbNpLuGTxy02+0U5hdyqvcUaHCq4dSQffrccQ/37KLRm368WzIyMvjmN79JJBI5q8hdsWLFkOoUVquVZcuWjXiM3W7nox/9KA8//DAVFRVJDVwEAsH0JsMyjGda1JgWjIN3VaE+GAzi9/uTJo/HQ3d3N8eOHeO+++47X3YKBCmhtLSU2bN1IamqKjt27BjTcQcOHOAPf/iDIaQzMzO55557WLt27VmTXK1WKxUVcU9wzDttkk2UuEqM9cN5pxNbiQ/ufjiYqvIqY9zY3JiUbKwoCt7+eGHwiagxPRiTyTQmb3FWVhbV1dVJ65YtW3bWY5csWcL3v/99vvCFL4jEO4EgxRw/fpxt27YZb4tSiQjzELxbxl2J/5lnnuFXv/oVdXV1Sck8I/HFL37xnAwTCCYL69ev5/RpPQzirbfe4pprrhlVjO3evZuHH37YWM7JyeGuu+4iN3fsdZrnzJlDba3eTKWmpsbwupaml1LfXw/oYnpeTrLI7Qn0xLsfmoZ2P0ykqqIKk2RC0RR8PT56Aj3kOnQbmzqbjFhks91Mfnr+mG2/EKxatSqp4+FoIR4CgWBy8eabb/Loo48C0NHRwc0335xSe0QCouDdMi7P9K5du7jnnnuoqalBUZR42+dhpszMTK677rqJslsguGAsWrTICO3wer28/fbbI+77xhtvJAnpgoICvvzlL49LSIMupmOcOhUPwThbEmJ3oBsprHu+z+aZjlX0AIyKHjGON8eFalpW2qQrGblo0SKys7MBPSwm0ZMvEAgmL4cPHzaENOjhcINL8F5ohvNCC8+0YDyMS0xv3boVTdNYs2YNDz30EI899hiSJHHDDTfw3HPP8cADD/De974XgKKiIr7//e9PiNECwYXEZDIl1SPevn37kC//SCTC888/z2OPPWasKykp4ctf/vI5NcOIxU0DdHZ20tenxy8nJiEOJ6Z7/D1GmMfZPNMlJSVJbcXr+uNiOrHGdEy0TibMZjP/8A//wC233MKdd9456cS+QCAYSn19PQ8++CBoGvQ1QvtRPL1dRtfTVOGyuoasEzHTgvEwrjCPAwcOYLVa+fGPf0xOjl5ya+bMmRw6dEgvGVZZyerVq8nNzWXr1q08/PDD3HrrrRNiuEBwIVm9ejXPPfcckUiE5uZmamtrmT17NqFQiJ07d/LKK68kdfysqKjgs5/97KhidjQsFguVlZVGeMnp06dZvnx5ckWPYWpNdwe6jTCPs3mmc3NzSXek0+5rhyCcaD0Bc/VtjR1xoZ6fM7lCPGJkZWVx2WWXpdoMgUAwBjo6Ovjtb39LJByGrhPQE/3BroSoq6sjLy8vZbZZZAsOswN/xG+sE55pwXgYl2fa7XZTVlZmCGmAuXPncubMGbzeeLLSl770JSwWS1JdV4FgKpOWlpYk3LZt28aLL77Id7/7XR5//PEkIT1nzhw+//nPn7OQTjxPjFgSYmn66GEePYEeI8zjbJ5pSZIoKY4nNJ46Ew8naetoM8bFBcXnYL1AIBDo9Pf38+tf/1pPNuyuIa3/NCvyosLV20Vd7dDSnBeaweJZiGnBeBiXmLbZbEMy5svLy9E0zfCggV7mqrKykrq6usGnEAimLIntrY8ePcozzzyT9CPS5XJx/fXX87nPfQ673f6urzdc3HSimG71tA5pV54Y5nE2zzToSYgxGpoajHFXT5cxLi8sH7/xAoFAAAQCAX7729/S09MD3acx99Zw59weVuRFq3ioYeqO7EutkQxNQhQJiILxMK4wj+LiYpqamgiHw0Y1g7Iy/bXziRMnWLx4sbGvpmn4/f5hzyMQTEWKi4uZM2dOUiMV0MMNNm3axKpVq85rybWKigrMZjORSISuri6jxnWeI48ufxcRLUKbty1JYHd7uyGqr60mKw6HY9RrLJi5AAkJDY3ejl4CkQB2sz3Z0148Z5QzCAQCwfAoisKDDz5IU1MT9NZD90lur+qh0hUmqFmQJD18uq2hBr/ff9bvq4lEeKYF74ZxeaZXr17NwMAAP/3pT40ErIULF6JpGk8++aSxrqGhgbq6umFb+woEU5nNmzcb4/z8fD72sY/xzW9+k3Xr1p332sWxuOkYMRE/Wtx010Dco5zuSj9rYl55aTl2c9SL3g9n+s+gqiq+vnjt17klc8/1FgQCwTRm9+7deglLdyN0HOMjFX0szApC5Xps136XEmc0wcPXQ319fUptTRTPZtmMw5w6YS+YeoxLTN92223YbDYeeOABNm7cSCgUYsGCBSxYsIC9e/dy55138oMf/IBPfvKTKIrCpZdeOlF2CwQpYd68eXzlK1/hrrvu4mtf+xorV64csfX1+WC4uOnRKnr0DvQa4yzX2auIJJXH80ic7j1NU1cTEUV3b5vsJgozCs/9BgQCwbRE0zS2b98O/c3QfoSNRR7WFPigbDV87BGYs5mZrmhMmr+X+hSHhSaGeWRYM0SFIMG4GJeYLi0t5d577yUvLw+Px4PVagXg61//OhaLhR07dvDAAw/Q3t5ORkYGd99994QYLRCkkvLycqqqqpDld9VAdEwkiulYXsJItaYDkYARWiUjk+k6e8yfw+GIl75T4Uj9EU42nzS2uzJd4qEiEAjGTU1NDW31J6DtHawmlc1FHiheBrf8GWwuyKuiMi+a06GEqD28O6X2JpbCE/HSgvEybpfaunXrePnllzly5IixbsWKFTz22GNs3bqV5uZmKisruf3225kxY8Z5NVYgmG4MFzedGCOd2FK8N9CbVGM6LS1tTNcoLS6lpkX3ep86c4rMQPxBkpklHioCgWD8vP766+BuAE1jRa4fR8ki+MRfwR79TpEkZi28FI7sB6Dh1BEURTHq619oBnumBYLxcE7vp61Wq9HeOEZVVRXf/e53z4tRAoFAx2w2M3PmTKOaR01NDWXlCTHTCWI6sca0xWQZczLPnIo5vLr3VQAamxspoMDYVpBXMMJRAoFAMDw9PT28c+ggDLQDsK7QC9f/DJw5Sftlzd9AlnUv7pBMqL+T1tZWSktLhzvlhJNuEWJacO5MyHvqUCjEvffey7333jsRpxcIphWD46YH15qOJf4m1pgeS1m8GItnxavwdLZ30trZaiwX5YskYoFAoKNpGq2trXg8nlH3e/PNN8HTAWqYuZlBZhSXQukwDZYq11KZEDddV1s7AVaPjVxH7rBjgWAsTIiYDgaD3Hvvvdx3330TcXqBYFpRVRWvBX3q1Cly7blGprkn7KEvqLca7/Z3x8M85NEbtiQyb+Y8LLJeiURxK5xpO2NsEzWmBQJBjNdff50f/vCHfO9736O5uXnYfcLhMG+99RYM6D/K1xd44ZKPwHC5F/nzmZmr514RCVJ3ZM9EmX5Wriq7ioW5CylwFHDTvJtSZodgajLxGVQCgeBdUVZWZpTd6+npobe3NzluOloeryfQE28lbhq7ZzovL480ezS+OgDd7d3GtllFs87DHQgEgqmOpmls27YNAL/fzwMPPEAgEBiy3759+/AN9IGnk2ybwoKsIFzy0eFPKsvMrF5iLNYdfXtCbB8LTouTR65/hBc/8iKL8halzA7B1ESIaYFgkhOLm45RU1NDmWtoebzuQHe8lfg4PNOyLJNXkBdfEYoPRY1pgUAAUFdXR5/bDf5eCPvp6urikUceMcLMQBfcr7/+OnjaQVNYX+BFLl4C+SN/jxRdciUWWT+Hu6M5qWFUKpAlIYsE40f8qxEIpgCD46aHqzXdE0hoJT4OzzRAacnQpB+TzURJVsk5WiwQCC4mDhw4AD210LAT6ndAcICDBw/q4jlKbW0tLS0t0N+CWYaV+T49xGMUzLPWUeGKvlKbBPWmBYJzQYhpgWAKkBg3PTgJMVbRo8cfD/OwyGMvjQcwp3xoy/C0jDRRY1ogEKCqKgfe3q+3BAcqnT7oOAaaxhNPPGF0L3z99dchHABfD5fl+kgzA4s+NPrJZ1xCZVa0sFgkkPJ60wLBuSDEtEAwBSgtLTWaJPX29pKlxLsbJnqmjWoeJuuYS+MBXDL7kiHrsrLP3kFRIBBc/NTW1jLQVgdKCJdF5QvzuimT2mCgDVVVefDBB2lqauLQoUPRxEON9YVemLkeMopHP7lsYtbc+cZifQqTEAWCc0WIaYFgCjA4blrrjccpxhIQk9vFPvEAACAASURBVOpMjyNmGmDJ7CVDYgXzcvNG2FsgEEwnDhw4AP0tACzJDmA1adw+pxeH+zioEdxuN7/85S/1+OmBFuZkhCh2RkZOPBxExdL1xrjpTB3BYHBC7kMgmCiEmBYIpgjFxXEPj9KvGOK33dtOIBKg1997zjHTmemZpKUnh4UUFYga0wLBdEdRFA7u36PXjQaW5vjBlkGOTeGWshY9jhq9vwRBDwT6WVfgBZMV5r9vTNdwzr2KGY4IAJqvh8bGxom5GYFgghixA+L8+fNH2iQQCFJAYWGhMe7q6KIovYhmTzMaGsd6jqGEFcyaGbNsxm6zYzaPr8FpXmEeA/0DxnJZQdkoewsEgunA6dOn8bSdBk0h3aIyq2ourL4LnriLhVlBNnmP8HKoBKxpMNBKllVlUXYA5r4PHGMMFStaTGUmtPmBsJ+6w7uTkq7RNCIv/ztN2/+bkituw7L56xNyrwLBuTKiZ1rTtHc1CQSC80uimG5vb6fUFU9CPNhxMF5jehzdDxMpK0kWz6LGtEAgePvtt40Qj6U5fuQlN8OSj0HpCgDeU9zLbLUWNA36W1hb4MUkMeYQDwBMFmbOjidZ1x16K75N02j5n3v44a8e5Od74Ve/+S1af+swJxFMSvxu+MO18B/lcPKFVFszYYzouvrjH/94Ie0QCARnIVFMd3Z2UpIWL1t3sDMupi2mMcRLaxoEB8CeYayqqqhiO9uN5eqS6vNjuEAgmJJEIhEO7d0Jvh4AluUG9VJ3sgzv+SH8diMmSeP2nH084yjH1t/JhhkesGVC1TXjutbMxWtgh14Wr67mhOGU2/Xru3nsqReIqCYA6j1W2vc+yYyNnzuPdyqYKALPfJMHXzxBi9/OJ7x3U/Uv+8A6fmfPZGdEMb1y5coLaYdAIDgLDoeDjIwM+vv7URSFHDXH2Haw8yBSaIwNW8J+mn66ibbmMyz52L9guVx/KCVW9JCtMiU5osa0QDCdqampwdd+GoBMq0rl4jWQPkPfWLIclt8K+x/EZVH5aOQxKI8euOB9YLGP61p5izfjsmzFE5YJ9HXR0NDA6w/+H/btfGOoXXteEmJ6KlD/Bk/9/e8c79OfRw8fifDNnfdjuuJ/pdiw849IQBQIphCJ3um0QDxhsNPfOeZW4j17HuXnr/fwUE06v/zZj/HV6nVdV1atxF6oPwArFlWITmACwTTn7f37k0I8pCUfS95h07+APXPogeMJ8YgilVxKRUY0RDTs41ffuStJSFvT4m/RTh0/Aqo67msILiDhALVb72ZHR/xZ1Bs08fbffqWHflxkiKelQDCFSBTTZt+gF0sJMdOj1Zg+uOMlItHnUKPHxH/+25fx9vXisDj44//5I3d99S5+85XfnG/TBQLBFCISiXBoz+sQ8gCwtACYf33yTml5cNW3ktelF0HluvFf0GxlZmWFsRjqOG2MV1WX8MXvb9UrhAA13Qpa64HxX0NwwQi/8kMeebtfX5DNYNGfSS+dAe3NX6bQsolBiGmBYAqRKKaVfiVpmxHmYRq9++HRY0eTlps6+/jVtz+Lx+OhKqeKz6z+DHkOUWNaIJjOnDhxgkCHHsOcbVOoWPEevWLHYC77FBQuii8v+hDIpnO65qxFyeGlZhk+trqEm7//JGWz5uLK0b//fBGJ5t1/P6drCC4A7Ud54dHf0xnQ/x1YixZgK1qgb/KbOfT074xSixcLQkwLBFOIGTNmGOO+rj4ybQmvWBMatozkmQ50NVDX7omviHYLbzl9mPt+8B0GBgaGPU4gEEwvDry9P9rNEJbmBJCW3jz8jiYzfOB+yCqHggWw9u5zvmbZpdeQb9edBIWOCPdsLGLlP/8NrGlIkkTV/HheR83+7SOdRpBKVJWWh77EtpboM8iRzftu/TLrrrsJbOkAvNRoRtv+kxQaef4RYlogmEIMLo+XWNHDaCU+Smm8kzueQImGJZYU5vHx1WW6oNY02t55jXt/8XP6+vomzH6BQDD5CYfDHN65DSJ6J8KlZS6YeeXIB8y4BP7xHbjrLXAVnPN1zZWX85W16Xyxupuvbi5ixhceNwQYQNWKTcb4VG29XpFIMKlQdv+WR946g6oBkszMFVtYu24dGzZuxDxD71/S5LVw/OX/BndDao09jwgxLRBMIVwulyGUQ6EQM+S4pzrW/XC00nhH9+4wxvMXLGDFF3/DJ+YGkCQg5KHj2A7uvfde3O6LL0FEIBCMjRMnThDorAcg16ZQdvkHzzl0Y1yYbTg+/wJz7noE82e3DWn6UrVkFdj0RMSafjOR069NvE2CsdPXzPatP6TRawHAlDebm+74ApIk4XK5uHzzDcbf9MUmG7z2g1Rae14RYlogmEJIkkRBQdzzkxVOeNgkVPMYLmZa0zSOn4on9SxYtQlyZnLpx7/NrbN7kSWgp56uxlPcf//9+P3+iboNgUAwidm/+814+/DcYap4TCRpeTBnE5gsQzbl5uaSVVAEQEiRaNrzzIWzSzA6mkbXn/+RZ85E/25WF1tu/nzS29SNmzZhKtD7F9QNWKnZ/hfoPJkKa887QkwLBFOMxC8nhz8eGy2FJWRkzJJ52Jjp1poj9EXbhTssULHyvfqGyz7F0ssu57Y5vZgkDdreob21ha1bt6KK8lMCwbSira2NAzteAk2PXV5aVQ4zFp3lqAuDJElULbrUWD51cGcKrREkotW8xJ9f3mdUiiq65AquunpL0j5ZWVlcduW14NQT3F9uccIr/36hTZ0QhJgWCKYYiUmIJm/Cq9eQHuIhSdKwnumjO+LZ79VlBch2l74gSXDDvSyeYeXmmW4IeaHtIMcOH+Kpp56asPsQCASTj7//5U9o3fobrKqMEKXrRkg8TBFzV2wGSf/eO9Xihp66FFskADjw1O841a+XLiSzjJs/84+YzUP7Am7evBny5wJwvM9Gw+6noWXqlzkUYlogmGIkeqYjfZH4hrAupoFhPdPHDuwxxgsWL03emFkKW77HZXl+NhV7YKAd6t/glb//D7t37z6/NyAQCCYlJ3e/zNEXt+o/qCW4odKvtw+fRMypXgBOvftrncdK+MSLKbZIoIX8vPDWIWP5yi03UF5ePuy+eXl5LLt8A6Trz7GXWl3w5i8uhJkTihDTAsEUI1FMD3QPYJEtoACKXslDlmVsNlvSMT6fj7qGRn1BgurLk1+/AbDsE3DpHby3ZICF2QGIBKB5H3/+f1+n7ujU9xwIBIKRUVsO8cTP/hHCeq7EyvwgJbf8EjKKUmxZMllZWeQX681dIiqc2ftCii0SHHnxj7R59DJRVkcaV3/49lH337x5M+TOBUninV47bZ3dF8DKiUWIaYFgipGdnY3Fonug/X4/ReaipBrTTqcTSZKSjjl5cA9aQI+XLk9TcM3bMPTEkgTv+39INz/EJxZZKHLqXm/F3cwfvn0rvbsegZ5aqHkJdv8Wnv0aPPRR+NUa+NNN8NZ90HZYtPkVCKYaDTvZ88MP0NKnf5FYzDLv+fLP4JIPp9iw4alaerkxPnXkACjhFFozvdE0jZeefsJYXnPZEtJcrlGPKS4uZuHyVVCyAnJm8artmok2c8IZGtAiEAgmNZIkUVhYSFNTEwB5Sh4NYb1ep9U0fI3po289b4znzywevpOZscP12CvWcOdj9/DTR3fgjch4/CF+96Nv8dm53SiaRECRCCoSQVUmpErk204w48Rzeok9Zy5UrodZV8KcqyGr7Lzev0AgOI+ceongnz7BM/V6yTlkC1d95DNkXXpjau0ahaqla3jz8d9BxM+pXpX3NO2FisvPfqDgvFNz/ChnGvTnj0mCDR/81JiOu/rqqzly5Aik5dJ9ERSOEmJaIJiCJIrpnHCO0Up8ODGtaRrHDh80lhcsS27ZOyzOHHI++V/cXvQA9//yZyjhIC0+M/96oHDEQzKsKlUZQaozfFS5nyTz6N8ACWZfBctvg3nXgdk6/psVCATnHyUM+x6A577Gq012+kMymG2kz7uCjR/9XKqtG5U5VVV6Cb2+Rs54rASPv4BNiOmU8OJf/gCq/hZzZYWDzLlrx3RcRUUFN910E4cPH+aaa4RnWiAQpIDEuOn54Xz2BzXCSBRa0oeI6cbGRjy9nQC4LCpllw4TLz0CczbfzoetpfzPf35fT0qUTWB1giU6WdPAbIdgP/2+Hvb1drOvS+8eM8MRYVF2gMuDr5FzepteDmnpx3RhnVcFmgYBN3g6wRudNBWyKyFn1pCGDcOiaXp4ylj28/fq7ZGtaZBeLIS9YHqihOHgw7D9R+BuoC8ks63NBRYHlK7gug/dMiTnYrLhcrkoqphN66FGVA3q9r1M9ZbvpNqsaUd9fb0eZgMgwcbN14zt+zjK6tWrWb169QRZd2ERYlogmIIklsdTDr3KD+jkEV8WUucJHPaNSfseO7gHgv0AzMsMI5WP78tr9RWb6fMrvPrKKyBJ2O12bDabMZdlmTNnzuhNXjRNb/Hr76HN00lbSzcvtbpYmBlkbcEA1d5fIr35S0grAH+P4dEYFkeOLqpzZuktiv294O0CXxf4usHbDaEBsGdBWn50ytPnjixdnPc1Q1+TPoW9CSeX9HNmlEBmCWSU6k0iQt7o5EELeujt99DtCTOgWPBEZAbCJjxhiYGwBJKJynwXs4syKcvPwmyzg8mmi3TZDLIlOjfp55bNYLbpPz5ic5NNH8smvdyXbAJJjk+aqn9GakQXQbGxGgFV0WsBa6oep64pkD4DZiwe1wNNME0YJKJjPNucTsicDiUrKCqfycqVY3hzNQmoWrae1ndeA03j1Ok6qr3dkJabarOmFS+98LzR3OfSHD95q25KsUWpQ4hpgWAKYnimw346musJFMpIAL4e0roOAp809j22+1VjvGBOBdhGTw4Zji1btrBly8gebVVVaWpq4sSJE5w8eZK6ujqU7Eq9MkBfE0f6mjnitpFnV1iT72VBVg+KBiHVQkSVCKsSIVXCLGsU2iPk2hQkfw8090Dz3iHX0zToDZnoD1vIC/fjCrih+9SI9mkadAdNtPotmCSNDItKeqiTtP52zC37AVA0aPFZqPNYqR2wUuex6q++R+HIcX1ulqHSFWJ2epDytDCypJ9P0SRUDSKqLm5dFpUsq0KWVcFu0s7yqY8NTQN3yESzz0xEk8ibuYj8a76KbcG170pUa5pGJBIhEAjg9/sJBAIEAgEURUFVVTRNQ9M0YxwKhZL2DQaD+P1+wuGwcYyqqkQiEeOYwUiSZFw3cYodH/sRl/iDzm63Y7FYMJvNmEympLks638/WZaTxsCQe4jd82B7YusT73W4+1cUxVinaRqyLBs2mEwmw6aYzQ6HA4fDgdPpxG63YzKZjGMH25J4vdhks9mGrSefSDAY5Mzxg5ze/ijd77yIM9xNukUl3eIk3aKgWTPZxQIoLwfZzPvf/37j85nsVC1cwnZ7Jvjden3juldh0YdSbda0obW1lSN7toOqJ39umpcBxctSbFXqEGJaIJiC5ObmYjKZULqacAdleoLx5i3OU0/AwFchfQYej4cztVGRKcG85WOLZxsvsixTXl5OeXk5V199NcFgkOPHj/PWW29x4oQDcueAt4uuvkaebOrgycaoUIh5a01WfQII+7AqXmbYgxQ5wsxwREi3qHQFTLQHzHT4zbQHLEanLYB0i0qJM0yRM0KxI0yBPUJX0ESj10qTz0KTz0JAtejeYDUCkSCg25BmVnFZVHpDJkLKuYnPiAo1/VZq+sceOmIzaWRZFTKtCk6ThsOsYjdpOEwqDpM+NssaZklDlkCWwBQd9wZNNPksNPsstPgs+CIJdtc0w4tfITPr38ifv5aCeavIyc3FZrNhs9mwWq3GXJIk+vr66O3tpbe3l56eHtxuN319ffj9fhRFOafPY6IIBAL09fWl2oxJg9PppLCwkIKCAgoLCyksLESSJE6fOMbpvS/TcOodVE+X/vYCgKj4Nln1cKrsCsjRZUB1dTXV1dUpuY9zYfbs2fpbKL+bRp8F39EXcQoxfcF4+eWXYaANgEXZAYpWfmRavxETYlogmIKYTCbyc3NpO6knIdZ7rHqIgKrg1LzwzD/BTVs5fvw4+HoAqEwLkTbvqgtin81mY8mSJSxZsoTOzk527NjBrl27CLjy4+EKpqjNwxDSNBoiQRrCXgj6wB/WwyZsVkizxsW3bAIlwoAS5LgS4ngkBJ4Q9IX10AqLA7LtUODQhXvsy15TdUEdDuCN+PFGAvp6yRQPzZBN2OwOigvzyXBacdktuBxW0u1mXDYZv89HbVM7tc1ddPd59HMakwZoURGj6ctJ21WCmkq7qtIeSNjHmENM7OshHxIQm0v6PDa2AjZJ393fYwinPrebvreepmb/65BTGf+xkoimghKCSChhHtT/RomhJ8ZnkvjI0BJmWvx8aPF1sXs6K4MewkkPZSlhNsJnkPT5JV57mHPFFpPMGmTj4L9B4vmS/k6J20e7v8SNUvxvmvi3NfYb7nMdfB09V8AnmairN1GX+LcKDuj5Deow5eISRbSc/Pi/4YYbRruBSYfD4aBs9nwau06BBrVvb2fRR8aYQyF4V3R1dbFv717w6iEem4s8sHDyVn+5EAgxLRBMUQpNfbRFRWBT0AXFi6FpDw6TCseehONPc+xgk/5wBRZkh6F81QW3Mz8/nxtvvJHrrruO/fv3s3v3bvr7+7FYLFgsFqxWq/GaPhAI0NraitfrBYtdn5zDx0G6XC7S09Pp7OwkEjm7R9jpdFJSUoIkSQwMDDAwMIDH4wGyjX2ysrKYOXMms2bNorKykuLi4lFfe8eiz91uN7W1tdTU1NDZ2Wm82k981Q8wMDCA2+3G7XYTiYwSLz5O7HY7JSUl2LQgnUdeo7upxghdINgPrYdGP8EImCSwm1QcZg17zFsuaboOBCRJQ0bXLxZZMzzq+qR72C0yyJKGKcGzbooeB0ly1MAsaZjl2FzDLOnnCKkSAUUmoCTPIypENImIKqFo8bEGqFH9qWkSanRZjtmPfl6Js2swY9/osXLs/iWMz0BGi/9ei4b5xOyJhfsEFBm/IuFXZPzh6FyRdb2c8LshZo4sadF5fL0kaXjCMmH17MJxhiPC7PJiSpddRaB4NQP+CP39/cb/A+FwmCuuuCIpD2OqMHfZWhr3PgtKmJNtAyzqOAqFC1Nt1kXPtm3b9NwVJczczCAVJUVQvDzVZqUUIaYFgilKYf/bxljLKNWT7zJLSTPrnmj1qa9yvO5SYjJl/tzZYEtPhakAWK3WMWdvezweWltbjcnr9ZKbm2u8yi4oKDDiRVVVpauri5aWFmPq6uoiOzub0tJSSktLKSsrIzs7e0gzG0VR8Hg8DAwM4HK5yMoaQwWRYcjKymL58uUsXz62B4qmafh8PtxuN/39/fh8vqS4ZL/fb4RZxKbEmOO0tDRKSkooKSmhtLR00L3dQ8TdTO8LP6Fj16N0ehXcIROhaFx6UNHnIUVG0SDDqpBjVci2KmTbFCOmO82sYpYml6PPqcvjVJsxKYjFyrcHzLT7zXRE5xFNojwtxOySPGav/RCuVR/Xk3gvQubMncfLzlwYaONUvw1OvSDE9ATjdrvZvXu3EeKxucgDCz4+ub4oUoAQ0wLBVKS3nsK+g0AWIEFWqb4+fx6O9AagndqWLnxn9LJFGVaVkkXrUmXtuHG5XFRVVVFVVXXWfWVZpqCggIKCApYuXTqu65hMJjIzM8nMzDxXU88JSZJIS0szRPH5xpxVQv5Hf0r+dV+H3b+GlrcTQgUSkE16yUJXfkJFlHxwZOthHyEPBD1GhRNCnmg5woQwBSPkIhYOIkdDakzxUJERGRQuMTiEYnDIjKomVzTRFL2qSaz6Sex6sXHSOdV4uI3x4JeSx5BQTSV2fySfe0jlFWnoeaQE33LS+bXkyixKWA/HUMIJn6MUt4GEcdIkIakRssM+skM+qsN+vVpN2K+HNlVfDyWXXvQCZ+bMmZgyClEG2mjzmxl45znS130l1WZd1Lz22msokTB42qlwhZmTHoKFH0i1WSlHiGmBYCqy70EKHdGYyLQ8veYzgMlK2jXfIPjCP/LomUyj9Nz8zADSzCtSZKwgZbjyYeO3Um2FQDAh2Gw2KqqXU9usN6U6cfQdLvP36j8GBeedQCDAzp079dKkSpjNRQNImaX6D7dpztSogSMQCOJEQvD2f1Ngj+jOrszkdt3OZR/mb4FVdPj138oWWWNjkQ/GWV9aIBAIJjvVi5eDXX+zdLzPAjUvp9iii5fdu3cTCARgoI08u8LCrCAsuOGifwMyFoSYFgimGieeBm8HFhlyXTbd+5i4+eRJdvpnGtn6H6zop2DWIrBnpMJagUAgmDDmz5+vN4ECTvTZ0E4+n2KLLk5UVeW1117Tw6Q8HVxZ6NE19DSv4hFDiGmBYKqx97+MYeHsxfG4TvT44UcffVSPm8yby9KcAKvyfHoMpUAgEFxklJSU4CooB8ATlmk6sE2Pox+NQJ8+CcbM4cOH6enpAV8PDinIijy/3kG25LJUmzYpEGJaIJhKdJ+Gutf0sSRTuGSzsUnTNJqbm/XXcED2rKV89O7/i/Se78Pau1NhrUAgEEwokiQxb+lqvfkTcKw9AM37Rj6gcTf8qAp+PBf2/mH4xNyxsu8B+MO1cPTJcz/HFOHVV1/Vc3A6T7CmwIvNpOkhHlOkY+ZEIxIQBYKRUCIQCejNPSL+aJOP6FxTEhpaxLL7zXqr7vSiiYsh2xf3SjP3Wgorq2GnXrGjo6MDn88H6A+YW2+9FUdl5cTYIRAIBJOE+QsWsC8tH/qaOOa2cc3J56Fs5fA7v/SvemMigKe+As374bof6zXtx0NfM51/+Son3BaW1t6B61OPwawN7+IuJi8NDQ3U1dZC2zvIoX7WFXj1JlrLb021aZOG6S2mjz0Fz39Dz0wFhpY3GuE4ozxRQgkmyaT/QjNZ9X9kJnN8LJuSSyDFSiJpyjDnii4nlnFSlXjnNBi+BNOgsklJ4yH7RstYmcxRWy26EIzNY9dN7NyGpotLNWZ/wjipfNWgzmODu6gZJZ8G3YfBoBJZse0mq26fyRK1ObF7npY00z+z6GethPUSX4mfvTEllNlSgnpiXyQQ7QQXSGjBO06cuVC0BIqW6vPipZBV8e4FdjgAbz8UX770DgqthQB4vV66urqM2svXXXcdlUJICwSCacC8efP03JG+Juq9VnxHn8O56dtDd2zaB2d2JK97eyu0H4GbtkJm6Ziv6d3/F35xNAdPWObVNoV7Hr4Dx12v6B0mLzJeffVV6KmFgTaW5fnJsqpw/c+hYH6qTZs0TG8x/da94D6TaisEFwmaBgFFwtvjxtfxOt59O/ApMt6ITNjkJDMjg5ysDHJycsjIzkV25upZ6DHxHvYTCfkJ+Dz4fH5CoRCRcJhwOEQoHCYS8BF2+wmqTvy2AoIng/QPvEVTU1O0k59eN3nOnDls3LgxxZ+GQCAQXBhcLhdlc5fQ2HIQNJWTp2pY2t8CGcXJO7758/g4LR+8nfq4ZT/8+kr4yH/BGEuIvvjUY3jCumOoO2ji4aNh7nj440h3vgjWtPNxW5MCt9vNwR0vQNdJADYUemHl54RXehDTW0wv+yThlsOEAh5k9Hatia1lYy1iBRcXWrS9sGrMk//IiUsRs5OQZCMkOQhiJSjbCWNhICTT64/Q61Pp9Sv0BjR6/SqRUNQLPiy+6NSGLEGmVSHTohBSZXyKhC8iE1LO9g8u2qEvrwJe3gboXmlF0RNuHA4Hn/jEJ0ZtgS0QCAQXG/MXLaFxVzb4ujneZ2PpqRfg0tvjO/TUwrG/x5c/+TfdS/38N/S3k74u+OONcPV34fIvjvrw7647zBvHO/SF6Bvfd3rtvHaong1/uws+8sDkEA+qCgMteq5Nz2nob9Vjy60uXfBb0/SxLV3vHDlMxafXn/4f1GY9lHB2eojShathy79f6DuZ9ExrMf1WYBZ/dd9MJBwcNQlBkiVkSUaSpajQlpAlvZuVLEmG+JbQkKNzU2wuA5qGioSiaqiaLt5iY5MsIUlSdI4x1yXdoNAMQJYlZFlGlvS5SZYxRdeZTfrYZNLHZlk/VlNVVE1FVaJzVdXXqdHuYaqCFu0kpqlq/JqQHIohy0hG1y8ZKdrtTBenmhHhoQGqqi9o0fAUTVWNsRT7zGKfZ/QzJXoOFdBUDVXT9MgNTY0q31i4i6J/SWiqcS0NPQEvtqxoEhEVIqpmzGOf+dDPdVCYTCzUJinsZwQs0Sn2HaRpEPZBoB+C/XrGeLA/2uEsjqpBb9BEb3C07nAjXdMJWfHa0jabjUhEb86yfv36C97NTyAQCFJNdXU1L6TlR8W0He3E80iJYvqt++Khe7M3wYxF+lS4CB69Hbwd+rPlhW/qgnPlZ0a81jMP3YcSlQxmVy4R5wxoP8zfGzOo3P00lUU/hfX3jGyspkF/C3TXRKeo2A159e2Dn8GSSbfJZI3PTdZoWGb0OW48FxUIDug/Hnrq9HyfsWCywqyrYMH7Yd514Mwh6G7nzcd/bTT/2lDlgo88qIdbCpKY1mJ6z549RBTFqMc7EhqggK7yktYmzhMZLhZ4BIaE5Y523olGAkYSd7FP4SwlhyYUU3Qax//IMhe0Zo3VZiMtJwen00laWhoOhwNXWhpmNUBvZxu93R309nTjGRiIx3AntkKWTTgddpwOJ1abFYtFn8xWKxarDbPFii27GLvDid1ux263s2/fPo4cOYLNZmPBggUX7mYFAoFgklBeXo4jrwx/53H6QjJtR96gKBLUxae3OznfZO0/xMeVa+Fzr8Gfb4WmPfq6138Cy28Ds3XIdRobG9n/9gFj+bMfv5Gnak007OlHdTfw4Olsvvr8/yWt8BKYe42+U6APzrwJddt1b3jXKd3pMplQQnDqeX2STDBzPbtP9RGIJrXnOSUWfvEhSMtNsaGTk2ktpjdt2sQTTzzBwMAAmqbpli/rzQAAIABJREFUHltNQ9M047W54OJFlmUjHEKKeqC1QW8ozGYzVqsVq9WKzWbDYrFgs9lwOp1kZ2cPmRwOx5iuHQqFcLvd9Pf3G+dzOnWBLI3z9eAll1zC73//eywWC8uWLRvXsQKBQHAxYDKZmHvJpRys2QFhH8e7FIrq34A5m2DPb+Me2hmLYeaVyQdnFMNtT8HPF4OnHQZa4ejfYPFHk3bTNI0nH30I/L0AXJITomrzHdwWlPhRezuBkAe3r4c/1WZx51/uRLrsVl1Et7x97gnt7xZHDuTORsmahdtSSDAYIhzwEA54o5OfiKeTtIFaMq0qGRYFh0lBO/0q248UEHOwXfnBTyMVXZKae5gCTGsxvXDhQhYuXDji9piwShTZiePBU2ybqqpJk6ZpmEwmPSwjYS5JkiHcY3NVVVEUJUlQJQq92PkVRUk6TlEUIpEIkUgkaVnTNEM0Jk6SJCVNg691ts8kcTl2jsTzAkOEamwcO8fgzzN2TOxcoH9BjkbiPcSOiX2+ZrMZs9mcNB5sY6qwWq0UFBRQUFDwrs+Vk5PDP/3TP50HqwQCgWDqMn/+fA6+WAC99Rzrs3PVqReg/HLY/Zv4TmvvRgP63G46Ozvp7Oykq6tLnzcux9yyh/eX9VP11n1wyUeSQv2OHz9OzaFdgL76+jULIC2XnDS45ROf4Pe/7oczb3HUDdvODLAp9MuRjXXkQO6c6DRbnxw5GFWxAOMNtaroJVljVaeUYMKbzWglMSlepjWomWgNpdPkM9HS1U9TUxOtJ1qJRDzR81qjU3Z0uQpCS6GnDQbaMYfcuMwq7pD+TLUXzWPFh7787v44FznTWkyfjZjgOpugEwgEAoFAkFqqq6v1Kh299dQOWAkefwFbXlW8/G1mOU2ZK/jjf/wHnZ2dQ0+g5oLPzq9OWLh2oIZrtryJVLlW36Sq/P3vf4eBNgAuz/dRsPJDxqGLFi1iw+YtvPpcABp28XRTOjIaFlkjosmEMyuJ5FYTyaki7CohLFkJh8OE3WEiXRHC4VagFZPJNGTSNE3fN2EKhUJJzrOYA01VVUKh0Pg/PKsTcmZBziwi4QBuTzv4u8GWydobPo3NZhv/OacRQkwLBAKBQCCY8mRmZlI0awGtLW+jqBFqzjSzMPwfxva+xZ/md3/4L/r6RmglbrbqIR99jTzXnE79T77LLf/xGC6Xi71799LaUAf+XqwmjS0lPpj/vqTD3/ve91JfX0+9EkFz1/OkpxKcObrHOWSBVqC1HWifsM9gNDIyMnA4HFgslqTJbDbj9Xrp6+ujr6+PEEB2BWRX4HK5WH/llWc79bRHiGmBQCAQCAQXBfMXLqJ1fy542jnWZ2dhVhcAYWsW/3UgZAhps9lMaWkpubm55Ofnk5+fT0ZGBs899t+cfu0RAI7XNvDjf/9XPnb7Z3j22WfBo3ulr5rhJWPeWkjLS7q22Wzmtttu40c/6sCX4kS9wsJCSkpKKC0tpbi4mJKSElwu15iODQQC9Pf34/V6KSgoMJqBCUZGiGmBQCAQCAQXBdXV1WxLywdPO8fdNrRyff3/BDdwprPV2O/Tn/60HhYyiC989ds82/cmLx9oAE2jr+Ew999/v75xoA2XRWXDDA8suHHY62dlZfGFL3yBHTt2oKqq4fkdPFmt1iTPsMWiV6mK5U4lhnAAxv6xeeyYWE5QLB8rMU/oXIlVihKMHSGmBQKBQCAQXBTMnDkTa3YxofbDdAdNdAVNHOpLZ585zVA8N95447BCGvQcqevv+N/MvO/jPFSbjb+vSU8SVBXw97KlYgC7WR4S4pFIaWkpN91000TcnmCSItqkCQQCgUAguCgwm81ULVgMNr2T1hONGTzVN1evNw2sWrWKK644S8vwOZtYOKeSry7spMLph/5m8LSRb1dYne+DmeuHhHgIpjdCTAsEAoFAILhoqK6uhmw9vuNIfzpkzwRg1qxZfPjDHz57aVRJgtVfIMem8KXqLq7NqmWho5M75vRglhkxxEMwfRFhHgKBQCAQCC4aqqurIbNMr6IhW8BsJTs7mzvuuGPsscRLboaXv4vZ38OWrPr4esk0aoiHYHoiPNMCgUAgEAguGvLy8sjPzwdrGpj1DrZ33nnnmKtZAGBxwGWfGrq+cp0I8RAMQYhpgUAgEAgEFxUrV640xrfccgvFxcXncJLP6J7tRBZ+4F1aJrgYEWEeAoFAIBAILio2btxIUVER2dnZ5yakAdJnwKIPwSG97rQI8RCMhPBMCwQCgUAguKiQZZmFCxeeu5COcfldIEWl0pzNIsRDMCzCMy0QCAQCgWBCUFWNkKJit5hSbcq5UbQEbnoImnbDys+l2hrBJEWIaYFgiuN2u/nTn/6ExWLhk5/8pOhcJRAIJgVtfQE++KsdeIIRHvjUSpaXZ6fapHOj+jp9EghGQIR5CARTnMcff5xTp05x9OhRdu7cmWpzBAKBAIDfv1FLS1+A/kCEv+xrSrU5AsGEITzTAsEUpqmpiUOHDhnLDQ0NKbRGIBAIdBRV44kDzVi6TyNFgrT15qTaJIFgwhBiWiCYwjz33HNJy83NzSmyRCAQCOLsqu2mt/4Yjkb9bVnTMSdweWqNEggmCBHmIRBMURobGzly5EjSuo6ODoLBYIosEggEAp2/7m/A1v6Osdzf3Z5CawSCiUWIaYFgijLYKx2jpaXlAlsiEAgEcQJhhRdefRM55DXW+T2eFFokEEwsQkwLBFOQM2fOcPToUWO5rKzMGItQD4FAkEpeOtKK2ngwaV0k6MMXiqTIIoFgYhFiWiCYgiR6pZctW8by5cuN5aYmkTUvEAhSx3///WXksDdpnRzx0+0JpcgigWBiEWJaIJhi1NfXc/z4cWN5y5YtlJSUGMtCTAsEglTR0+/n6N4dxrLFpMsMSQnT2juQKrMEgglFiGmBYIrx7LPPGuNLL72UwsJCSktLjXVtbW1EIuJ1qkAguPD8+rHnIajHR2dkpJOVmW5sa27vSZVZAsGEIsS0QDCFqK2t5eTJkwBIksSWLVsAcDgc5OTodVwVRaGtrS1lNgoEgulJJBLhhRdfNJavvPIqXBlZxnJrV28qzBIIJhwhpgWCKUSiV/qyyy4jPz/fWE70TotQD4FAcKF5dtvrdPfoglkz2/n8Te8hMzPD2N7WLcS04OJEiGmBYIpQU1NDTU0NoHulr7nmmqTtiWJaVPQQCAQXkkgkwsOPP2Usz1yyirK8TLIzM411XT3uVJgmEEw4QkwLBFMATdOSKnisXLmSvLy8pH1EEqJAIEgVO3fupL6lEwDVbOfm668GIC87LqZ73H0psU2QWgKBAJ2dnak2Y0IRYlogmAKcPHmS06dPAyDLMldfffWQfRI90y0tLaiqesHsEwgE05dIJMJfn3oOT1BPfFaLFnL9Mr32fX5utrFfX19/SuwTpA6v18v/+sZ3+NL//javbn8j1eZMGEJMCwSTHE3TkmKlV61aRW5u7pD9MjIySE/XM+dDoRBdXV0XzEaBQDB92b17N6ca9XbhqtnOFevWkmG3AP+fvfOMj6M6+/Y1ZfuqV1fJkm25dwwGjOmhmIRmSGISHAJPgCQk5A156EmeFAgJkGBSSKghQAgdExOabQIG3C13W1axrGbVlbaXmXk/jHYkWatmy02e6+f17sycmXN2Vzvzn/vcBYZnpRvtfF4zNd7Jxger17F653521rbxtzc+PNbDOWKYYtrE5Dhn586d7Nu3DwBJkrr5SnfGdPUwMTE52nz22WccaAsBEMmexBVz8o1tIzqJ6aDfFNMnGx9v2IGm6a+rDgxdA48ppk1MjmMOtkqffvrppKam9tjezOhhYmJyNKmqqmJ7STmhqIImSNiHF3F2UUeWoVG5HbNo4aAfRdWOxTBNjhF7S8uN1wGfF00bmt+/KaZNTI5jtm3bZohiWZY577zzem1vimkTE5Ojydq1a6n3hgGIpY7i0ll52GTJ2J6emoLcXgWRaIgmX+hYDNPkGBAMBmloOGAsRyLRIes3b4ppE5PjlIOt0meeeSYpndJMJaKzm0d1dfWQtQKYmJgce2KxGBs2bKDZHwEgkl7IlybndmkjyzI2mx0AAY3qBjM93snCjpIyAuGOarwaUFE7NLN6mGLaxOQ4pbi4mNraWgCsVmufVmmAjIwM7Hb9whUIBPB4zAuXiYnJkWHbtm20tPnwh2OoVhdiSi5zx6R3a+dwuY3XVQeajuYQTY4hqzft7LauvLr+GIzkyGOKaROT4xBVVbvklZ4/fz5ut7uXPXQEQTBdPUxMTI4Ka9asoaWTVXrm6HScVrlbO1d7liGAGrOk+EnDll0l3dbtrzMt0yYmJkeJjRs3cuCA7mtms9k455xz+r2vmdHDxMTkSOPxeNi1axct/ggaAtH0MZw+tnvKToCU5I6S4gdMMX1SoGmakYWqM7UNQ3NmwhTTJibHGYqi8N577xnLCxYswOVy9Xt/s6y4iYnJkWbdunUANAciKO4cNKubM8ZmJmzbpaR4i+l6djJQX1+Pp83ffX1j8zEYzZGn+3yMiYnJUUNVVdra2mhubqa5uZmWlhaqqqqMgit2u52zzz57QMc03TxMTEyOJJqmsWbNGoIRhVBUITK8EKdVYvrIxGk7M9I71jebYvqkYNOOPYRjCgCaZEVQdHeg5pahOTNhimkTk2NARUUFK1euZPv27SiK0mO7c889F4fD0efx6lpDWCSBDLeNrKwsZFkmFovR2tqKz+frl7+1iYmJSX8IBAJcccUVhKIKXw2rxJJyyXTbsMqJJ7uXXHYO50wfC4DDPBedFIzJG83Sn/0YQA9OjehWaqvVciyHdcQwxbSJyVFCVVW2bdvGypUrqaio6LN9eno68+fP77PdJyUNfPPptYiCwLfPHMMPzhvHiBEjDH+1qqoqJkyYcLjDNzExMQFAFEVGjx5NVFFRBQlNtOCyST22z81MJclhBUAQTe/Sk4GczHQy0/QZCcliQ4nqucgFQZ/ZEAThWA5v0DHFtInJESYajbJmzRo+/vhjw32jM263m7S0NNLS0khPTzeex44da6S5640/rtyLpoGiafz1v2UsK67hImeSccIyxbSJiclgoSgK5eXlqKpKsz9C1JWFJlk5NUFKvDihcJRtu0sBEC0W5s+eerSGa3IMUBSFHXtKicZUNKBg7HjKysoRVBVBgHmzpmKxDC0LtSmmTUyOIH6/n6VLlxqZOeJIksSsWbM4++yzGT58+CEff1+Tny/KugZ01LaG+Eepl5GeVopykky/aRMTk0GjtbUVTdNQVA1VtKCJVqySiNvWs5yw2zqEk9qLW5vJ0CAYDBJTVH1BspCZbKdMkAAVTYNwOGKKaRMTk/4Ri8V45plnughpu93OGWecwfz58/usZtgfXt3QIZSLcpJo9IVp8kdQnOk0VYX5wh+hjR1cf/3Qm1YzMTE5+sQLQUUVFdXiBgHSnNZezy82ixyf3wdVJRKLYZVN+TFU8bT5iBffFS02HBYJSbagKlEAAqEwbnf/M1SdCJh/zSYmRwBN03jllVcoLS011i1cuJAzzzwTm802KH0oqtZFTP/owvGcNiaD37y3i5e+iKEhoGoa20qrePaTEr511vhB6dfExOTkJBQKEQwGAYiqGprDCUCaq3croyAIiJKEGtNLSwdDUaxuU34MVVq9HSnxnE4ngiAgW2Qiuts0wXDkGI3syGFGApiYHAFWrFjB2rVrjeVLL72U8847b9CENOiBh7WtIQAy3VbOnZBNitPCr6+YyuvfPYvUjCyj7WufbBm0fk1MTE5O4lZpTdOICDY0QZcQ6S5rn/tKUod4DkWGnpgy0dE0DX8wYCyntFugrZ3cOobi92+KaROTQWbLli288847xvIpp5zCeeedN+j9vLK+wyp9xcwRWKSOn/PM0WksuWC2sVxSto/WYHTQx2BiYnLy4PP5AIipGoqsW6XtFgmHpedMHnEkuaNNOGKei4YqkUiEaFSfgdAEkdQkPbWrzdpxwzUUv39TTJuYDCJVVVX84x//MJYLCwu55pprBt1fudkf4f0ddcbyojmjurUZV5BHkl23Boj+Bj4v7buM6/7mAK9vqDSFt4mJSRcURSEc1ufpo4qKKuuzbOlOS7/Ob7LcYZkcimLKRMcfCBBTdYdpTbaS4tC/985iOjoEv39TTJuYDBIej4e//e1vRKP6iSIzM5MlS5YgH4FAm7c2VxNV9BPWjFGpjM9J6tZm7NixZLRPv8reWj7eXdetTWf84ShX/b/f8NN77+GGB54b9DGbmJicuAQCHVP3McGiBxTSPxcPAKul4zwYiQ49MXU00eLRfcdh3542H7Q3sVjtWNtnJBz2TmI6NvS+f1NMm5gMAtFolKeeeoq2tjZAz9px4403HpHKg5qm8fK6/cbyNQms0gDDhg1j9DDdb1pQony6cUevx339v1sI1ZYgaAolGz8l1D5VZ2JiYhIIBFAUhZUrV/Lwg7/i7m9dwW1Xn8vC8+bz1a9+lSeffBKv19vj/p1ToUUOOrcUFRUxadKkwxrfN77xDYqKili/fv1hHWcgLF26lKKiIv70pz8NatveWL16NTfeeONhHeNQ8Hq9/PKXv+Ttt9/uvZ2vI/jQ7XIar502a1xjo8QUVFU9EsM8Zphi2sRkEFi+fLmRz1kQBJYsWUJOTs4R6Wt7TRu76vSLlt0isnD6sITtBEHgzFNmILZbkOorS9nfHEjYFuCdVZ93LMQirNtZOXiDNjExOaEpLS3lxz/+MUuXLmV78UbSs3OZedp8ioqK2L17N7/97W+5+OKL2bhxY8L9bZ3EdMy0TB8SBw4c4IYbbqC8vPyo9/3QQw/x/PPPo/SSJ1xRFIIhPSheA1KTOtLf2a0StFe/VDWNWGxoGWvM3DQmJodJeXk5q1atMpYvv/xyioqKjlh//1rfYZW+ZMowku09p6WaNnUKaa7lNPkiyG3VfFLSyNdPHd2tXTASY/eObV3WrdlWwvxpBYM3cBMTkxOS2tpabr/9djweD6fOO52v3PBDUrJyGZXmpCg3Ca/Xy5NPPskTTzzBN7/5TZ566ilOPfXULsewdSrcEjtIkC1fvrxPv+toNEpbWxtutzthVqTf/OY3BINBRowYcRjv9PjmWLp39MeSrBdraR+jZCHF2fE92WQJ2gu3qJpGJBLFau2fi9CJgGmZNjE5DMLhMC+++KKxPH78eObPn3/E+gtFFd7cVG0sJwo87My4cePITNatA1K4jY82703Y7s3V21GDbV3W7dhbdpijNTExGQr85Cc/wePx8KUvfYnvfP9HpGTlApDenl86KSmJ22+/nfvuu49oNMpdd91lZP6I47B1CCflIKtkYWEhBQU937grikJZWRl1dXXs27cvobAbPnw4hYWF2O32Q36fJoeHzx9AiQcfSlaS7B32WkkUEOLpEbWhlx7PFNMJ0DSNcDg85Hx6TAaff//73zQ2NgK6n/TXvva1I1pp8L3tdbSF9AvR6HQnp45J77W9xWJh9tSJxvKGzVuNk11n3l75Wbd1Ffv2d1tnYmJyclFcXMzatWtJTk7m+iVLiLRPaAtAqrOrZXHx4sXMnDmT6upq3nrrLWP90qVLOW32DDasW8Ozf/sLt3zrOubOncuf//xnoGef6T179vCDH/yAM888k6uvvpo777qLz9es4bHHHqOoqIg1a9YYbRP5TMfXxS3nF198MVOnTmX+/Pn8/Oc/p7m5uVufHo+H3//+91x++eXMmjWLKVOmcNZZZ3HHHXdQVjb4Bob+9rd06VIWLFgAQHV1NUVFRXzjG9/ocqzi4mK++93vctpppzF16lS+9KUv8eijj3a7samqqqKoqIjbbruN2tpa7rjjDubNm8e0adO4/PLLeeWVV7q0Lyoq4tVXXwXgrrvu6vLZq6rKs88+y9VXX80lF1/E9/9nCf933528v+x1ovEqLe3InVx9AqGu2050Tmo3j6qqKt577z08Hg/hcJhwOEwoFCLS6Y4pOTmZ5ORkUlJSjNdJSUm4XC7cbjculwuXy4XT6USWZTRNQ1EUYrGY8VAU3dk+/hx/aJqGxWLBarV2eYiieY9zIlBSUsInn3xiLF9++eWkpqYe0T4755ZeNHskoti3cD/zlBm8+tEXRGIqkcZKtla3MmNUxzhDUYWd27Z226+5vpaYoiJL5t+jicnJyocffgjA6aefjiDKaII+dZ9kt3TJbR/nqquuYtOmTSxbtozFixd32fbmqy/T0tzMxClT8bW2MHbs2B77Xb9+PTfddBOBQICCgkLyC8dSVlLCA7/6FePHD8yN7s4772TFihXMmDGDgoICvvjiC1588UU2bdrEa6+9hiTpGScaGxu59tprqaqqIi8vj9NPP51gMMi2bdt4++23WblyJcuWLWPYsMRxKgNlIP0VFRVxwQUX8MEHH+B0OjnvvPMoLCw0jvX6669z7733omkaU6ZMYdiwYWzZsoW//OUvrFixgueff77b9amuro5FixYRjUaZMWMGXq+XjRs3cu+99+Lz+fjWt74FwGWXXUZxcTGVlZXMnDmTkSNHkpmZCcCDDz7Ic889R1paGuOLJqBqsHfPbl5/8Vmq9u7g73//u2FgslosxOckwkOsCuJJLabfeust9u5NPO0dp62tjba2NiO4rDckSerVOb+/yLJMamoqmZmZZGRkdHmkpKTgcDiMH//xRNyiHwwG8fv9BAIB/H4/fr8fn89nvI5/RgdbcO12O9nZ2cYjPT29W1q5WCyGz+fD6/Xi9/sJhULGI34zFI1GyczMZPjw4QwfPrzHjBqKotDW1kZrayuZmZkDyrwRCoV46aWXjOVJkyYxd+7cfu9/KOxvDrC6VLeCCwJcNXtkv/abPHky6S4rda0hJN8BVu2o7iKm/71mF2pAr2zmsFtRNJFIOIQaDVO8dz+zi/IG/82YmJicEBQXFwO6K0ZUUdHa3TV6SokXPw/u3Lmz27aG+gPc/bNfMjpvDDOnFOF2OhIeIxKJcNdddxEIBPjud7/L7HnziSka0UiEp574IxvXr024X0+sWbOGf/7zn0yfPh3QA/muuOIKdu7cyerVqznrrLMA+OMf/0hVVRVLlizhzjvvNK5RPp+Pm266iY0bN/Lmm29yyy23DKj/nhhIfxdeeCHTpk3jgw8+IC0tjd/97nfGcUpLS7n//vtxOp088cQTzJ6tF+yKRqP84he/4OWXX+YXv/gFDz/8cJf+i4uLmT9/Po888gjJyckAvPLKK9x777089dRThpj+3e9+xz333ENlZSXXXHMNV155JQA1NTU899xzjBkzhpdeeoldZfuIRFV8gQCPPPB/rF27lrVr1xr+81arhXgI/FDLNX5Si+m8vLwexbQsywOONh0MIQ26YGxsbDTcBxLhdDoNq7jL5TJEZzxAQdM0NE1DkiTD+n2wFdxut2Oz2bDb7cZDEATa2trwer20trYaNxN+v7+LRV3TNON1IBAgGAwSCAQG1TVGEAQyMjJwu934fD58Ph+h9kjhgeB2uxk2bBg5OTkEg0E8Hg/Nzc14PB7j8xIEgSlTpjBv3jwmTJjQp6vG22+/TUtLCwAOh+OIFGaJs7fexztbanhrcw3x+JP547IYnpr4QnQwaWlpFIweRd3WEgRNZeWaYn74pcnG9jdXdLh4TJwwkboWHzX7SgH4YmuJKaZNTnpWrlzJf/7zny6zlscj8XOzKIpYrVYuuugizjnnnMM6ZlOTXuwpNTWVqCqgifpUfbozsZiOWyxDoRCtra2kpKQY28ZNmMTovDH69kiUZLcr4TE+/vhjKisrOeOMMzj1zAWEIgqaKCLb7Cy58WZ27dhOIOBPuG8iFi9ebAhpgJycHM4//3xefvll9u7da4jptLQ05s+fz/e///0u53O3283ChQvZuHEjtbW1/e63Lwarv+eee45oNMpPfvITQ0iD7uZ37733smrVKpYvX85PfvKTblmm7rvvPkNIA1x55ZX86le/oqGhgZaWFtLS0nrsN65RUlNTEQTBCD50pqRz7/0/o7m+llGjOuJ67J0CDodarvGTWkwvXLiQU045hXA4jM1m6/IQRZFYLGaIydbWVkNgxq2iBz/iSJKEJEnIsowsy8ayKIqIomi8Bv3OMRKJdHn0h0AgQCAQoKGh4Yh8NscDmqb1eVPRH3w+HyUlJZSUlPTa19atW9m6dStpaWnMmzePuXPndrkQxNm1axeff96RRu6qq65K2C5Oiz/CrjovqU4Lw1LspDh6rxgWiansbwnwn211LCuuMdLgdeaaOf2zSsdZcOpMPtuqv/+9e3bhD8dw2WQiMZWtW7YY7S5dcBorNu02xPTWPWYQoonJqlWr+nVu7mzMEAThiMZPxPtRVdVwJ4wbdOJZElatWnXYYjpuVBJFiahgAQFEQSDFmTiLUOfZxIOzT+SN6QgyDIV7FlOffabf4E+dPoNQRH9Pqj0VIRbC7lCZMm06a7/oHufRE52FdJysLD0Hf+diNLfddlu3ds3NzezevdvwxY4OoggcrP7i/ssHZ1AB/W9h7ty5LFu2jPXr13PppZca21JTU8nL62oskSSJ9PR0qqurCQaDvYrpcePGkZqayqZNm7jxxhuZOnMOU6bNIH3UWBacMa+bG6K9UxDqYH6OxwMntZgGes0FLMsy6enppKf3HuQFGD7Rsiwf1gk07irR3NxMc3OzISabmppobm7G5/N1+fEfb8iyjMvlwuFwGD7lna3obre7x4qAbW1t1NfX09DQQH19PR6Pp1sbQRBISkoyju1wOBJa1w8cOEBtbS21tbW9/mjdbjdOp5P6+npjXUtLC8uXL+fdd98lNze3mw973FIDMG3aNGbNmpXw2HvrfTz1aRmvbawmEuuw2NtkkWEpdnKS7WS6bfjCMTyBCM2BCB5/FG+45xkRh0Vi0ZyRXDJlYD57p82ejuv51/GHYwit1XxR1sh5E3N5d8MeVJ/+fuxdjziKAAAgAElEQVQ2C5cuOIV6f5TV/10FQPk+M9e0yeCiKIqeQisWw2azYbFYBqVKaDgcprGxkYaGBhoaGmhubsbpdJKVlUVWVhbZ2dm43e5u5+f4bFo86LzzzFt8++zZs1mxYgWRSMTYDh2W4M7LBxMX1fFHZ+NKT9eKeP/x8Ry8LT4uRVES9hmJRLBarZx99tkD+AQTk5GRQUVFBS2trQyXdTGUYpeReojXiBt4HA5HNx/dpOQOo0O4l/NyTU0NAK5kXcipFjvpqSl42gSIBEjPyBzQe+hseY0Td5U8+POrrKzkH//4Bxs3bqS8vNwI3ot/V4Odnm4w+qur06vbfvnLX+613cFW7qSk7pVzoeOz6Wum2eFw8Pvf/54f/ehHhjEKYNiIUXx54SV8/etfJzc3t6O9zYomgKBhxJANlRixk15MDxZxq/PhIggCdrvd8PdNRPxiFHd9iLtgdD4xd/4hxi3ecSt4NBrt4mPc+aGqKklJSaSkpBjPycnJhgjufFGIXwwcDgcOhwOn09mlytXhEg6HaWhoIBQK4Xa7SUpKwul0DuhmRdM0mpqaqKmpobGxEafTSVpamvGIj7euro4vvviCdevWGTcrmqb1OsXmdrtZtGhRl/FomsYXZc387ZMyVuyqT7hfOKZS0RSgoql/N0U2WeScomwWTh/GuROycVoH/rPNy8sjJz2ZstpmxGiQ99bu4LyJubzxYYeFfcL4IpxOJ/OmjeeJ9nWNdTVD6oRncuTQNA2/328YAOJGgPhMXnw2LZGrliRJ2Gw2rFYrDofDCPru/HC5XEb8RTxuIv5oaGigtbW1zzHa7XaysrIQBME4zje/+U1Gjx5NZWXPN46FhYVdgr0Gg/i53ul04nA4UBSly7l4METbYBSOys/PZ8OGDezZvYdJZy0EILWXEuJb2me6xo8f322bLHfE+kR68ZmNz/SqqoYmiFiSMpg8IoXtgMffkYFjsK2by5Yt43//939RFIX8/HzOOussCgsLmTp1KnV1ddx///3HZX/xGYmFCxf2en082Ao9GDMn8+bN48MPP+TpZ//O+vUb2LVjG7XV+3niiSd4/vnnee6555g2bRoAdkt7rmlNQVX1wi1DJde0KaZPQCRJwu12H5FS1ccTNpuNkSMH5s5wMIIgkJmZafjx9URubi6XX345l156KVu2bOGLL77oNThVFEW++tWvdvkO/rOtjsdXlrCtuq1b+7HZbjRNo641hD/Su2+9JAqkOS3MGJXKwmnDOX9SDm7b4f1URVFk1rSplNV+DMCaDVuIXne2EVwEcNECfYpweuFIRIsNNRomGg6xp7KOCfmJb+xMTg5aW1spLS1l3759BIPBbq5pkUiElpaWQ/YpVhTFENsej2dQ/VI7EwqF2L//yKZ8jBsb+rLqaZpGMBgkGAweVn+yLHeZBayurjYMAgcOHDBiTw6FWCzG6aefzmuvvcbnn6/m4m/9EKsMaT34SwNGCrVEVlJJ7BDT0VhiIezz+XC6dYtpc1MjqiOVqaPSsEgiWUl2WhpsNDfrs2md3SsPF7/fz09/+lNEUeTPf/6zkYYuzvPPPz9ofQ12f9nZ2VRXV3PHHXd0sQQfLURRZPap85g2+1Q0UUIWNJ5/8s+sWrWKP/zhDzz11FMA2CwimighqEp74ZaIKaZNTIYiFouF2bNnM3v2bDweT7f8nHFSU1MNIR2KKvz0re28vL7rRVoQ4LwJOdw0fwxzx6QbVgBvKMqBthC1rSGa/RGS7DKpTivpTitpTj3RfX9S3g2UC06fzWvv/1f3Ra8q5YX/7kRp063nNovMl8/WxbQkiWRkD6OhugKAz7bsNsX0SUZbWxulpaXs3buXvXv3dnGDGgziqUQjkcghBRUnQhRF0tPTDZeO9PR0I64k7joWDnfPbRsXv3a73ZhtO/hxcLzLwTEwnZfjsziJ/Jmj0agRrN3XjYcsy9jtdmP2rLMPdtxdJF4NsLOFMW5ljwvquro6BEHol7viwQQCAcaMGcPcuXNZu3Yt//rbH/jmbXeR4kg8A/n666+zevVqsrOzE4rpzmk2Y9Hu7myqqlK2bz9jx0/k45Ur2LKlmMXf/g4pDl1wZbitRFTJSOXpH0SXx9LSUvx+P1OnTjUCEjuzevVqY4yD2d+MGTO6Ceme+uvJkjxnzhyqq6v5+OOPufbaa7tt//a3v43P5+Oee+4xrMQDJVHf7777Lo888ggXX3wxs884G9CLtcyeOp6cH/+YVatWdbkxtkoighB3r+l9duJEwxTTJiY9kJqa2mfe6PJGP7e+sJGdtR3WaJssctXskXz7zDEUZnWfPUiyW0iyWxibndhf7Ugxfeok0lxWmn1hpEATS196x9g2btzYLlb2vLxRhpgu3lUKXz68ICaTEwOPx8Mbb7xhTNUPFJvNZqT0jD+npaUZcRNxl4aDXaNisRjhcJhIJEIgEKC1tbXbIxAI4HQ6jZiJpKQk43W8n958rzVNw+fz0dDQgCAIxuxePM5isGf6OvtHJyIWixmZkILBIJIkYbfbcTgc2O32Q/YjlySJ0aNHG7MI0OErO1BBHRfkN9z4P5TsLePzj5ajxSJM/dXPjAA+0F3ynn76aZYuXYokSfzmN7/p1U8ZSJgty+cP4PEFmTlnLmmv/JNN675g43/fJ++KKwCQBY1Xn3sCr1c/3wZCYaLR6KC4F8aDyEtKSli/fj1z5sxBEAQUReGvf/0rK1euNN7rYBC3IO/Zs4f9+/cbWS966y9uxfX7/UaQK+jFaZYtW8YjjzxCYWEhc+bMAfS/+T/+8Y98+umnZGZmMmHChEMeb7yEu9fbERBfWFhIZWUlL7/8MqMKJ5Cdk4tksWG3iLzzjn59mTp1qtFeEAQkiwU1qv9dBgfpszweMMW0ickh8u7WWu54dQu+TgGDX5kxnPsXTiLDbTuGI0uM0+mkoKCA5i16/tfI/q3EJc2X5nfNkT1lXAHrP9ML0pRVmEGIQx1FUfjkk0949913E1pMJUkiPz+fwsJC0tPTu6TYjKfcTE5OxuVyDdgPUxAELBaLIYjS09MP272rp37iAvx4QJZloxDYYCNJEnl5ed0EtSiKAyosFRfTTncS//vgYzzx8K/44uMPOfvsVUybNo3s7Gy8Xi+bN2/G7/eTlZXF7373O0477bSEx7N08plOlEq2vqkFTdNF4w3fvZ3HHvw5d955Jy+88AIjRoxg69at1NXVkZ6RSXNTIxoCXq/3kKzuByNJEmeccQarV6/m29/+NrNmzcLpdLJlyxYaGhoYO3Yse/fuPezsUnGys7O55JJLWL58OZdddhlz585FluVe+4sbeDweD1/72teYOnUq99xzD1OnTuV///d/efDBB7nuuuuYNGkSI0aMYM+ePVRUVGC32/nDH/5wWC4VcX/rP/7xj2zYsIHrr7+e2bNns2TJEp599ll+dvcdjB1fRHJaJg/WVVFaWkpmZibf//73uxzHIluIS+jgECrcYorpI8yWLVt48803WbNmDdXV1aSmpjJ9+nR++MMfMmbMmGM9PJMEKKrGrro2NlV6iCkqOcl2clLs5CbbyUqyoWnwwLs7eWZ1hbGPVRK5/7JJLD519BFPh3U4nDV3JuvbxbSg6dOHVlniK+fM69Ju7pRxPNv+ur6uposVZCijqio1NTWUlZVRVlZGRUUFqqqSl5dHQUEBY8aMYeTIkYOSfeJ4obKykn/9619UV1d3WV9QUMDYsWMZN24cWcNGUNMWpaolSF0kplfT9KlEYipRRSES8xOKeglFFYLtj1BUIRRViSoqqqahqqBqGpqmP0uiQJJdbp+pkUmyy7htHa9dVhmXrf21TcZlk3DbZBwW6Yj+LWqahoY+Da1pGmr7s6JpqKr+rLS/F1XV29LenvalLqGDnRcEPSbCIopYJBFZErBIYo+ZMQ6HuKCuqKgwXGlqamqwWCy4XInzO3dGVVVjv6iikZw5jB/9+nGadn7Byg/+w7Zt29iyZQvp6elMnjyZiy66iC9/+cu93qzIkogmCAjtLjCKonSxVntaO2b4zpi/gNNn/JPHH39cD4Dcs4cpU6Zwz89+wV//+iTNTY1YbA7a2toOW0zHC3h973vfY8SIEaxevZr169djtVopKCjgGzfcxJcuu4JFl5zP5s2baW5uHhQB/+tf/5qCggL+/e9/8/nnn2OxWCgoKOCWW25h0aJFnHnmmV36EwSBhx56iAceeIBt27bR2NjIPffcA8CSJUuYNGkSzzzzDJs2baKkpITc3FyuuOIKvvOd7xy23rjmmmvYunUrH330EZ988gmnn346s2fP5o477kAVZP778UrKy0pRlD3k5uRw3XXXcfPNN3eZwQC9cEtcTA+lwi2CNth5Xg6Bb3zjG6xdu5a5c+cOupP/sea2225j48aNXHTRRRQVFdHQ0MALL7xAIBDgn//8J0VFAyuLajK4qKpGIKqwtaqV9RXNrNvXwsZ9LV2szZ0RBHBZ5S7bR6U7+PPi2UwZ0XOu6eOF2tpaFt54B1Glww9v4vhxvPzYz7u0C0VjzL3iRojploNlzzxK3rDDzwxwtAiHw0aO977aVVZWUlFRYYjnvnx4ZVk2xPX48ePJz88/5uK6sbGR9957j9raWsNHN/7c5A3RHIhid7lxulNwJafgStIfzXX72bt1fXt6N100OlMzKDz1AryWDCqb/VQ0BWjwHj/TsYIATouE0ybjsko4rHK76NWIqbrAjT/rfsZ6XmSx/VkQQNX0fO4xVeW3V09n8vBktlS3GuL5aBMfH9A+WyTE/+laPC7w0f/TAFkUSHZYSLbL7c8WrHL3rDuxWIyKigrDXUCSJAoKCvq0UgYCAcrLy1FVjaagSiwpB1EQWDA+65DFf1RRWb1hK4KqgADzZk4xxhEKhVi/ZSeKqtHa1sqw3Bymji/Abrd3OYaqaVxw0aVU7yvjsSeeJjc9mcmTJh5WVWCPx9PlZlJr/1uKKBohewaRdrvj8BQ7k4Yf/+f5o0kgEGDjtt0oqoYmyUybNKHH6pgAOyobaKjR44uS3U5mTjl015PDYbB159AxrwwCiqrRForSFoziDcWME5YsCsiSgCzqlgQpHpQSP0mL7Sfp+NkP44mvLv4GP//1g4iSTEzRUFSNOWeeyw2LF/HIY3/knl88iAjtQS4goB9Xo6sVp4uVhPgyqHS00a0mukBUNb0vARBFoX2cncYcH78o6P3H27QPXkC/6MTfiwbEFJWIohFV1E6PrlceodOL9k8EQaD9WJ0uGELnbYKxn24Ran9/EDf3IIr65975WRAgJ9lOdUuQZ1aXG5awcEwhHNOtZhFFJRzVnyMxlXCsY+z6xXRgV05No4uQvnBSDr9dNL3HgJzjjdzcXHKyMqmq6wgou+DM7mXQ7RaZ1MxcPHW6i8fqzbtPCDHd2NjIG2+8wY4dO5AkiczMTHJycsjOziYnJ4fMzEyampooLy+noqKCmpqaAacgi8VilJaWUlpaygcffIDNZmPcuHFMmDCBCRMmHHLmhEMhGo3y4YcfsmLFim4+qKqqsbfBx/7meJDWgV6PpQkS4dwpROwT+WRrEKg6MoM+TDQN/BEFf0RhMEpWRRQVVdPP/8cKtZuI73ssEUWj0Rem0ddxo+OwSKQ5LRRkufU0ZOg3f6NHj6asrMwIhKysrGTMmDG9CtB4poyIoqLF80s7es4v3R9kUQBRAlUBDcKdsjk0e1qN76CyqoY7vn8zc+bM4Zlnnuki/F979VWqKkqZPG0GNpudcEzB5/P1WjirL+I1DVRVQ3Ak4WltQ2lPu6dFGxBc2WiiRJN/6LglDBY+f8D43jTJSrK9d1lpt3e4QA6lwi0ntZjWNI1wTKW80U9bMIovHOvHKWyAJI9mw/6DKthZ0sgdmU/J3r3sOdC9up1J/0m2W9jfEuCf6wY33VVOso1T8tNJcVg40Bairi1EXWuYJn8YTQO7ReT/XVDEjfPHnFDuD4IgMGPaFKrqVgC6e8oV55+esO2oUSMNMb15Zxlfv7h7hPvxQiwWY+XKlbz//vuGqFQUhQMHDnDgQO8i8mCSk5MpKCigsLCQgoICRFGkvLycsrIyysvLuxTtAd26vW3bNrZt2wbofo2yLBtFCeLPkiQxceJEzjnnnEFJX7V9+3Zef/11mpubu23zh2Nsq27tcYblYGJJwwiOPAXNlniKXhYFRqY5GJ3hItkuY5VFbLKIVRKxyrrLgt0i4bBI2K0SdlnEYZWwyxIWWdRvgNtvnOM38VFFxRuKtT+ixrMvHMMXVvCFovjDSvtyDH84hj8SIxQdnGwKvaHf6LePud2wIIkCUrvRQRI7jBJC3IYSz7TR6RgHo6GL9rghIqro1vHB0vHBqEKwVaElEGVOXhq2dkFttVoZPXo0FRUVRmGwqqoqRo/u2S0t7msdVVQ0WRdAvaXE6w96UKaM2j7jFQxHSWqP+2xq6SjSNevUM5g8eTLr169nwYIFTJ8+HYvFYtzEZmRmce2N3wMgEtNoa2s7ZDEdjUbx+/1EFZXWYJSYaAFbOlKsAUFTEVQVKdBEzJVlGGkSzQCcrHi8HRmv7HZ7l4wtiXDYLB2FW1Slm6vPicpJLaZ31XnJdNuo9hxens+Bomka3tYWsoeP6ruxyRHHIgnkZbg4JT+dU/LTOCU/nZFpjoQXmaii0uANk+6yGpafE40rzzuN9z9aRURRmTKhkIz0xOViJ40dw9Z1esneveX7juYQB0RJSQmvvPKKUXltoOTm5jJmzBjy8/MpKCggIyOj23efm5vLvHm6X3lrayvl5eXs2bOHXbt20dLS0qVtosqdcdauXcvatWsNUT127NgB34w1Njby5ptvsn379i7rR40axSWXXMLH5T5+v6KUUCEgiCBInJafwkiXRtDXRsjXRjigP2saZOZPIH30eCySiNg+E2eRRIanOsjLcJKf4WJYSt8XyaOFomoEIjECEQV/WH8WBJBFEUkESWwX7+3DNWbuOs3gSe3vUZYEMlxWZEnk7PFZHQL6KN4gx11UOgvqjskSDQ6aJYyPLRRV2mdSY/psajiG2r5jMKqwab+HWaPTDOHndDoZPny44c7g8/k4cOBAwhs7TdOM4MOYoqG1l4E+XDENuqU80m5MD7cHu0ajUfwB/TqsCZCTmc7f//53XnrpJZYvX87GjRsJh8Pk5uZyww038M1v3cCO+jD46okpKm1e7yEXl2ptbUVVNbyhGKpkQ4vnwnalYwu3YJNF/JEYWrAFxZGOLxwjXR4auZEHA5+/Iz1hkrtvX3yb3LVwSzQaNcX0iU442jWaWACcVpkUh+6DJosCXn+AL593ZrfckqIo4U5OprBoEhd85RomzzzloBNgxysB/QQvt1s2Vn/0HzxNDVx3w/8wKs2Jhh6co2kaKvpUU9zi0dmaE7eQGH6AdHotCAR8Xv7+1F/4ZNVKmhobSEtL57TTz2DJTd8hJ3dYR9BM/MKiaobrSOeLDegn833le3n1H8+wbdN6AgE/OcNGMP/sc1n0tevISNeT6FskocOvL/6etY73rmkaWzZv5jvfvp7snFxef+c/XYJ7Fn35Yupqa/r1fT36p78xbdYc3Z1F1T8rl01ifE4Sv7piCnZZwm6RsMkiNouITZawtlvPbJb253YrmqV9vUUSBnThjIuME5nZ0ybz429dwe6Scr61+Joe282ZPI6X218fqK065kGI8SIX8eIewWCQdevWsWHDhi7tRo4cyaJFi8jOzqa+vp76+nrq6uqor6+nsbGRpKQkQzzn5eXhcAzs+0xJSWHGjBnMmDEDTdOor69n165d7Nq1i7179yZM+XUwO3fuZOfOnYwcOZKpp5xOYd4orKKeIi4SiRCLxYhGo3g8HlpaWmhqaqKlpYXm5uZuPt1Op5NLL72UidNmc/eb23h32wGQ3CCBVRa599KJfOO0vBNqBqU39MBFPb3kYCJKx+bzEQTddW2gckIPypQZ1m6QVVWNem+YHTWtqOjuaJv3e5g1OtW4EUpNTTXKrgM0NTVhs9lIS+t6Qx0Oh9tnVDRimoAmyoiC7qN9uMiyTNxZIhzRfyter9dwGdQkGxlJdtxOKzfddBM33XRTwuPs9zcRCMgIaoxwJIbf7z+kTC0ej0e/EVE1NJsTWRQoyk0iO8lGq8dNbW0t4ZiAEg2iST58IXevPsEnE4qiEG7PyKEJkJbk7HMfm9y5cEviFIknIie1mC7KTUbVNAqz3KQ49Chyy0HWl9qyXYaQ7lxFz+v10uZpYdOa1Wxas5q7776b66+/vs8+S0tLeWrpb5kxYwa3fmvxoN2Rtba28j/fWUJZWRkul4uioiKqqqpY9tYbfLzyI55//vkB5Zj88MMP+cnttxOJRHA4HIwbO5a6ujpefPZJVr2/nKeeeoqCgoI+jxMOh3ngFz/Vp7lFoVvKuOnTpjJ8WM9T3lVVVdTX12O1Wpk8No+Rad1/rKPTZRafmpdgb5NECILAV6++qs928ybno0kWBCWKz+fnQEMjudlZfe53uMRiMerq6qiqqjIeDQ0NhqWsJ2w2G5dccglzTp1HZUuQsKrn2x09enSffdZ7Q4iCQOYAUxoKgkBOTg45OTksWLCAQChMRXU9KU4rdqseABnPN1xfX8+qVatYt3EzTb4ILYEIn+xp4LkP9JsBWRJxWCQcFhG7VXeZsEhil7iMuLtERNEIRRWSRk/CP3oWv1ynUP7uSoKdDATjc9w89rWZTMgd/NRrJscfoiiQm2IHNLbXtKEBbaEoxVUeZoxKM3yds7OzCYfDRr7g2tpaFEXBZrMhyzIWi8X4rRn+0sLh+0vHsVg6ZEckqguxZo9uHQYQrY5+ifYMtw2fx44U9hFRVNra2gYspoPBIC1eP9GYiiYIqBYHU4enkJWknwfS09MJh8ME6xoIoyJG/Hj76Tp1MhAMBjuC2UULqc6+z582WQRBBiKomjZk/KZPajHtsOpCdkwvpZp37NgB6NaoeEUi0K1kGzdu5I477qC6upqHH36Yyy67rNd0OQ0NDXznO98hKSmJxx57bFCnNu677z7KyspYsGABjzzyCG63m3A4zM9+9jNef/11fvSjH7Fs2bJ+9bl//37uuOMOIpEI559/Pg888ADJyckoisLjjz/On/70J2666SaWL19uJHLviccff5yysrIetz/22GM9bmtubjaqaP30pz818lyaHB2SHVbc6Tn4G6rQgM+3lHDF+UdGTNfV1bFmzRr27t1rXNyhw60mHFPbK0VakBNM5c6YMYPxc89l+e5WvvfACtpCMSySnnngsunDuWBSDk5r1995RaOff2+tZfnWWrbX6Cm5hqXYmTYyhWkjU5k2MoWpI1JI7WFqOxhR2FnXxvbqVrbXtLG9po3ddV4i7ReXVKeFTLeNLLeNrCQbsiSwrjGfatmBNboLi7fMSE8IeoCvV1Hx9lEQUBMkFFcm4WEzUMKZUNLarc11p43m3ksnnbCuSCaHTm6KA0WFnXX633RLIMrWKg/TRqa2B24L5A4fTltJKYFgCEXVCFfXJvQDjioqmjR4Lh4A1k4FVqJRPeNMa1uH321KcjJiP2ZRMt1WKmQHhH1EYiper3fAs2c19U0Ewvq5RpPt5GW6DCEdJycnhwMNuiVfUGN4g2YQYpxWr9+YiRYsVpzWvs83siQiSjJEAU3PNZ3Y0fDE4qQW0/1h165dAEycOLHLekEQmD17NnfffTff/e53CYfDrF+/ngsvvDDhcbxeLzfddBNer5cXXniBnJzBy4xQWlrK+++/j9Pp5KGHHjIqedlsNn75y19SXFxsZB646KKL+jzes88+SyAQYOzYsTz66KNGJLUkSfzgBz9g/fr1rF27lr///e89TsGBHiD19NNPY7fbD6lk8P33309DQwMXXnghV1999YD3Nzl8Ro4cye4GPavDpl17ewxWPBSi0SibN2/m888/p7y8vGN9TKXBF+ZAW4gWf6RrULBkJSPFzYjMVMYMS2NEVjqt7jxeqpPZ9VzXqn1RRePDnfV8uLMeh0Xi/Ek5XDwll/JGfxcB3ZnaVr3M+3vbO4IWUxwWBKGT+1L7C1841mvgmCcQxROIsrf+oJL09mRCo+YSHjYNa+MenN5qFFVFQZ9OR9CnQREkNIsd1eJGtblQLS40qwtNthvBbgczItXB/ZdN4kuTDz/A0eTEZUSag5iqUtL+t9foj7CtphWXTcbjj9AaiqKpbqRYEEHVsx2lOCzdZmajioZmHZzgwzg2aycxHYvh8/mIxNoFrWQhI7lvVwHQg88tNhtqUARVJRTRAwn7W8kyGIlRXd8RTOxKSk5YsVYURVxOB60B3YIaCAR1V8wjkBv8RKPV6zdeuxzOft/IWCwy0XZJEBoiVRBNMd0Hccv0pEmTEm7v7DrR1tb94gy6q8PNN99MRUUFzzzzDGPHjh3UMb799ttomsa5557brbqVJElceeWV/Pa3v2X58uX9EtOffvopAIsXL06Yi3Tx4sWsXbuWZcuW9Simo9Eod911F4IgcOutt/LII48M6D199NFHfPDBB7jdbu67774B7WsyeBQV5rN70xcA7Ck9/CDEkgNennh3A3u3bSRQsweUSJccwL5wjBZ/BMXqQnHkoAxLR3GkozpS20WkSBtQDnzaCrQCdA8gTnVa8AQ6pg+DUYVlxTUsK07sn2+VRESRhJkiWoMDm4ZMdVrwhmI9plpzWCTmjknnjLEZnF54PpOG6W4Y9d4w+1sC7G8OsL85SFVLAH8kRjiqEoophKN6asdQVMFtl8nPcJGX4Wx/uMjPcJHmtAwZ32iTwyMvw0VM0Shv0gVPvTcMnfOFizKKOwch4tctrjGVbLsFJRbVi9SoGjHRgiZZBs1fGrqK6VgsRpu3YzZHle399kcWBIEMl40DPgdSxE8kptLS0tIvMa1qGlsrDqC1z4AJksS0/JweLeIupxNR9OquKEoEfyQ26D77JyK+Tq53yUl9B6ZON7QAACAASURBVB/GsVotxM+q4bDp5jHkicVilJSUAN0t03Hq6uqM14miohVF4Yc//CGbN2/mT3/6EzNnzuyxvzvvvJM33nijX2PrnGh8yxbdItfTsWfMmAHQLVCrJ2prawGYPHlywu35+fmAnkUhGAwmDOB64okn2L17N7fccgvjx4/vV79xFEXh4YcfBuCWW24hOzt7QPubDB5zJo3l7fbXNTWHHoS4s7aNx97fwcoP/oO1cU+37ZogEkseQTS7AMWVZaTiApg+MoUpI1LYsK+FXXU9p5K0W0QumTKMq+eM5LQxGexrDrCsuIa3i2u6W4fRBfRZ47O4dFou503MwWmRKKn3sbWqleIqD1urW9lZ29Ytl3ocQYDCLDeThyczeXgyU4anMGl4MqlOK4qq0RKI0OAN0+DVcwF7QzEmDU9m+sjUhFPquSl2clPsnJJ/+JXVTEwACrJcxFSN/S3d4w3cNpk0p4W6VpmoqqIAfruVGaNS0VSVOk+A+gMBECDVYRm0Ko32TmJaVRRaPG1GBLvV4eqXq0CcDLeVuhYXRPTUdl6vl1gs1mcRpb31PvzeNuK/whHZGditPe/jcDiQRYGIqiEoEXxhU0zHYjEi8VzcgkB6P2cUAGxWK3Gbdtj0mR76lJWVGVWjEglLTdN48sknAT1Kes6cOd3aPPjgg6xYsYJzzjkHj8fDW2+91WX7V77yFeN1fn4+s2bN6tfYOgvUfft0i+HIkSMTth0+fDigp9Ty+/39KiULPUfZxgMGVFWlrq6uW5nSPXv28Je//IWCggJuvfXWLr7m/eH111+ntLSUnJwcvvnNbw5oX5PBZd7kMWiiBUGN4vX6aG5uISOjQ+xtqmzhyU/LcVtlxuW4GZeTxPgcN7nJdgRBYGtVK4+tKGHFF5tw7F+LNervcnzVlkQkvZBoegGapeOmbMaoVC6dOoyLp+Z2CTpt8Ib5rLSR1XsbWb23iWpPkFmjU1k0ZxSXThtGcqcL3JhMF7edN47vnzuWXXVe3i6u4YuyJjLdNi6Zqgvo5IMuiBOHJTNxWDLXnKKnrYzEVHzhmFFgCDCKM9lksUefZEnUgxkz3TYmDjukj97E5LARBIHxOW5kSaDJFyHJLpPmtJLmsugpyoAMd5ji/R40oNkfoazBz9hsN20RzUiWneYcPOFos0hoooigqqiqZogpTZRIT3YN6GY93WUDSbeex5QoiqLi8Xi6JAs4mCZfmMomP3JM9zNw2iSGZ/deaMnhcBg3E4ISwRuKGVlUTlb8/gCKkYHFQoqj/25AdltH22g0eswzRQ0GppjuhbiLh8Ph6CIYY7EYu3fv5pFHHuHTTz9FEATuuuuubmVPocPneuXKlaxcubLb9s5i+uabb+bmm28e8DjjeW4PdvGI0zmZfUtLS59ieuTIkZSWlrJnzx5mz57dbXtpaanx+mDXFkVRuPvuu4nFYvzyl7/ss2TtwWiaxtNPPw3A9ddfP+D9TQaXnBQHtpQsIi01KKrGuu0lXHTWqaiqxl8/KeO37+1O6M7gtskMT7VTUt2ErXoDrpYOn+hMt41Z0yYzcdbppA4bZWSmCMdUXDaZcydkM6KH9INZSTa+MmMEX5kxwqiU2ZfvoiAIhkgeKFZZNHPKmpzQCIJAYZabwh5ihzPdNvIzXZQ36je6FU1+UhwyLZ2q/aUOYio4q6TnPgcVVdOIxNoF2QBcPIxjySIpDgttERdS0EM4povpRLni4+xvDiBGgwiahlUWSUtyJbx2d8Zms2GRJYIRBUFV8QZCwMDT8A0lmjsFjVpt9m7+9r1ht1rQBAFB07oUtTqRMcV0L+zcuROASCTC/PnzjfWtra2GdTY1NZWf/vSnXHLJJQmPMRg13/siHtzX0wmh8/pwP5z9zz77bEpLS3n66ae56qqrughaVVV56qmnjOWD09o888wzbN26lcWLFycU4n3x2WefUVZWRlJSEtdee+2A9zcZfIaNGMG+Ft3XeP32vcyZNYPbX97M6q1lSP4GLEE9iEcTrWiSBU2yEpIslFdHcdVtQWy3AGUl2Zg4KotvLb6WWbNmHbYlIp5j3cTE5PAoyHTRFowa5bK317QRa79JlgSh2wzO4WCRRTRB1OsTaBBrTz2rWhykHYJoz02x4wk4EUOtemxBOEwwGMTp7O52EI4pNPkjiFHd7cVlk0lJSenzXCQIAm6Xk7aA/vn4/YEhYU09HNp8Ha5DbufAcvXbLe03VJoexD0UCreYYroX4mJaURQjyX1nxowZw3PPPTeomTkOBUmSuhWV6Uxv2xKxZMkSXnvtNSorK7nxxhu58847GTduHPv37+fhhx9m3759RoaOzr5pFRUVLF26lGHDhvGjH/3okN7LCy+8AMCiRYv6HZVtcmQpKsxn37Z1AHz8xXre+nwHweZa3O0iOdlhISfJhr+9Ip0/HDMuxADZyXbGZDiZP28uV155pfm9mpgcZwiCwOThKaytaCYUVbr8flOcg+cvDe1l2CUZYu2GHU33uXU5nYbryUDISbaz54APzeJAiQSIqXogYiIxfaA1BKqCEAsjSyKSKPQ4o3swSS4nNHpAg1g0TCSmGuXaTzbiBbTipCYN7Jxuk6X2wi0xowpiX7MDxzummO6FuIvGr3/9a666Si9y4fP52LRpE//3f/9HeXk5t99+Oy+++OKg9PeXv/yFjz/+uF9tJ02aZGS5cDgcRKPRHq3OkUjHdF1//mCzs7P585//zC233MKaNWu44oorjG1Op5NHHnmEe+65h1AoZAgjTdO4++67CYVC/PznPz8kwRQIBPjkk08AjPzSJseeWZPG8n67q/+BA3rKuPiE3ugMJ4VZ7i5R8JqmEVFUAhEFh0UiJzOdq6++milTphzlkZuYmPQXqywybUQK6/e1GJVwAdIGKYtHZ2RZMrQ06C4eBxf06i8WSSTTbaUh6kKMBAhHVVpbW8nNze1m7axpDSGGvQjo1lGXy4XF0vf70zQNp1OvjhhT9CBEbzh2Qovpw7GsR6NRIrF48KE4oOBDAKskQHvZ9qFSuMUU0z1QVVVFa6teDKGoqMhY73a7mT9/Pg8//DCLFi1iw4YNrFu3jlNOOeWw+6yoqGDjxo39atvZIpyamkpbWxsejydh287reysq05lZs2bx7rvv8uKLL1JcXAzoAv7aa68lMzPTqJ6VlaU74r3wwgts2LCBhQsXsmDBgn71cTCrV68mEomQn5/fY/YUk6PPaZPGoEo2RKXj6meRRGYW5HDa9Ink5+djtVoJhUIEg0HjORwOM3LkSM4999wBl+w2MTE5+iQ7LBTlJBkFXwDKdhZz0Zk3JGxvsVhwu92MGzeOhQsXcvXVV/drul6WLXQOb9cs9kNy8YgjhVq59eoLycjM4jePLsWp6oJ63rx5SJLEjh078IaiBHxepIgfBP3m4eAy6gdTX1/Pb37zG6699lpmzpyJZIjpKN5QdMAVUxNxzz338Oqrr/LAAw9w5ZVX9tp26dKlPP744/zgBz/g1ltvPeQ+V69ezdNPP93FZXMgtHr9xOtNCbIFdy+F7zrj9Xr5wx/+wNSpU0nO09MNaxpEIqaYHrLErdKSJDFu3Lhu26dNm8bUqVPZunUrb7/99qCI6QcffJAHH3xwwPsVFBRQWVlJdXV1wu01Nbq/a1ZW1oBETXp6Ot/73ve6rd+2bRuKopCdnW0EN7733nsAvPPOO7zzzjsJj1ddXW3cmHz00Ufdso/EAzT7kwvb5OiRn+HCOv4MQpVb0awuxhYW8Ovrz2fK2LyT2mfQxGQoMjzVjj8So7I5QJrTQrQ9VZ3T6eS8887r0jYWi9Hc3MyGDRtYu3Ytq1ev7rWqbRyrRSZexksTANlxWBlD4gVlNEHQ3QYUzQjMj1Pd5EMM6oYlqyySkpxMcnLvQcl33nknq1ev5pprrsFisWCzWglHQwiaRpsvCJknnsvagQMHuOGGGxgxYgSgZyyqaw2S5rL2O91fS1tHVia7w9Hv68BDDz3Ev/71Lx544AFk2ULcATUUOfGrSppiugfimTzy8vJ6LJl9/vnns3XrVj788EN+/vOfIyYoc3w0mDJlCqtWraK4uJivf/3r3bZv3rwZgOnTp/freOvXr2fLli3Mnj074T6rVq0C9FzXccaPH99jKr22tjb27t2L1Wo1pvoTfabxcZ566qn9GqfJ0UEUBR68cSFPfTqZcydkc+P8gkH1oTQxMTl+0NPpJZGf4cIiCayt03/raWlp/O53v0u4z86dO7nuuut47733+OCDD7jgggt67aPz+V+TbKS4rEiHcf3syOgjoAkC4ZhCKBTijTfewG63oygq9XU1CO3mVLfDxogRI/oUgQfHG7lcDtr8+m2AP9A9d/eJgNbJhUfVNDbuaybg9yNabMwbm9Uv1xWvv0NMJ7n67+LR+fO0WizGDVUobIrpIUtcTHd28TiY+fPn8+ijj9Lc3ExxcXGvBVmOJBdccAGPP/44H374IR6Pp0tAhaIoRiGY/vohFxcX89BDD3HJJZfw6KOPdtnm8/n45z//CcDXvvY1Y31vVQpXrlzJzTffTFZWFi+99FLCNsFg0Cgp3VO1SZNjx4WTc7nQLFFtYnLSkKiwUE9MnDiRa665hqeffpr333+/TzFtt9tR7MkIShTVnqznix4UBDTZTjgWxK1ppKWlMXz4cPbuq0KL6m5qgihQkJd3SNkjUtxuaut1i3c4FEJRtRPasLC/OUCw5QBSLIwmipTV25g4onfXF03TjAxiAOnJh2adt1mthpiORhMb4k4kjo0p9QQg7ubRm5ieNGmSkRw+UQ7po8WECRM4++yz8fl83Hbbbcb0Vjgc5t5776W0tJQxY8YkPMFVVlZSWlpKc3Ozse7888/HYrHw7rvvdnHZaGho4NZbb6WhoYEFCxYkLFJzqJSUlKCqKllZWf2OrjYxMTExOT6Iu+39//buPL6ma338+OeczJNERIgxERJFQgxtKCHGb5FqaShuiN5WB5fvrxd1tdpqY7haU5HyVWlr7NVqiqiqKmq4JJSahQwIksgkMsh0zv79cXoORwbJUZHwvF+v89Ksvc7ea5+VJk/WXutZd/8u6d27N8888wznzp1jyJAhtGvXjj59+pCUGIfWygGNrTOn//idDydPoEuXLvj6+hIUFERERITRwvm7RUVFERwcjJ+fH927d2fu3Lnk/TlSqlKB1tIOFCgs0RIYGEibNm1IS7uTjauuswu5uTnMmTOHfv364evrS58+fZg+fTpXr14FdGumvL29OXToEABjxozB29ubW9k3DaPg+bcy+fe8T+jfvz8+Pj74+/szadIkQ+xwr5SUFN5//3169uxJ+/btefnllw3nf1A3b95k8eLFvPDCC3Ts2JF27doREBDA1KlTSUhIMNRbunSpYU3TtWvX6N+tIwvD3tN9dlotqcnXiTl6jAkTJuDv74+Pjw8DBgxg0aJF5Obq8koXFBZSotGQnpbG+LEjmfX+dJKTk5k6dSpdu3bF19eXF154ge+++86ojd7e3mzatAmA6dOnM+J/unPugm43XLWVDV9//TUvvfQSnTt3xs/PjyFDhrB8+XKjrCE1mQTTZcjKyjJsqV1RMK1SqejevTvwaINpgI8++ojGjRsTHR1NYGAgQ4cOpUePHkRGRuLg4EB4eHiZ01BCQ0MZOHCgISUd6Ka2TJs2DUVRmDx5Mr179+aFF14gMDCQ6Oho2rZty8KFC//S9t+4cQPgvnPYhBBC1DxxcXEAuLkZb/lZVFTE+PHjKSgoICAgAHNzc1q19ATgp42r+fzjqRz7/SitWrUiICCA9PR0PvnkE1599dVSAfWCBQuYMmUKsbGxdOnSBW9vb9avX8+kSZMA3c6jipkVitqcwhLdlAJ9diEArbkVxXk3GTp0KKtXr0atVtOrVy/s7OyIjIxk2LBhXL58GVtbW4KCggwL7Lt160ZQUBB169bFXK0iIyOdOTPeYc3XX1JSUkJAQADu7u7s3LmT4ODgUvFAUlISw4cP59tvv8XW1paePXuSmZnJ3//+d37//fcH+tzT09MZNmwYy5cvJz8/n27duvHMM89QWFjI1q1bGT58uFE8ox9Us7a24emuz/JUWx/DuQ7t2cnYkNHs3r2bpk2bEhgYSGFhIStWrGDkyJHcvHmTzOxcw/bvADdupBIcHMy+ffvw9fWlXbt2nD9/nhkzZvDVV18Z6gUFBdGsWTMA/Pz86N3/OewbeFDi0JCvv/qSuXPncvXqVTp37oy/vz8pKSksXryY8ePHG01NqalkmkcZ9PmloeJgGiAgIIDNmzdz4cIFkpKSaNq06cNuXpkaNmzI999/T3h4OLt37+bChQs4ODgwePBgJk6ciLu7e5XOFxISQsOGDVm9ejVnzpzhxo0buLu7M3jwYEJDQ//ynJD6jCMODk/2rlJCCFHbxMTEGEYiBw0aZHQsPz+fVq1asX79eiwsLNBqtRSWKMTu3Me2byKo36AhX0WsMiz0z8/PZ/LkyezevZtly5YZ9iw4efIkX3zxBS4uLqxdu5YWLVoAcOHCBcaOHQvogmnd6LQtxQV37c6rgKJWY+PowkdTxpOens7EiROZMGGCYd70smXLWLp0KWFhYaxatYr58+cTGhpKWloab7zxhmEtz5XkNCJWLCM97QYvjhjN7A/fM0wZOXjwIG+++SZTp07l559/pl493Tbls2bNIjU1lXHjxjFt2jRUKhUajYbZs2cbDWSZIjw8nKtXrxIaGsq//vUvw/3k5uby2muvcezYMTZv3sybb75J//798fX15ZdffsHO3p5X39AlGLCxtiAh4RLrvl6FlbU1ixYvIbCnbqO64uJiwsLC2LhxIzNnziTkldeMrn/ixAl69OjBwoULDYNh3333HTNmzCAiIoJx48YBMH/+fN577z2uXLnC8OHD6drnOc5cv0VGWipbv/sGDw8Pvv/+e8MOzdnZ2QwfPpyYmBhiYmJq/FoqCabL0K1bN2JjYytVd9CgQaV+eDwqdevWZcaMGcyYMaPS79m9e3e5x/r163ffuW+VERgYeN/P86WXXuKll1564GsJIcTDsOiXC3z268UK6/w/8038P/PICut87uTI8rqOFdYJ3q8h+EDFo3EJ7gO55F7+7x4bzR5e+SKswnNURVZWFlOmTDEqKyoq4tKlS4af73/729/o2rVrqfeOHDnSkM9ZrVZjYwnRO3SP/D94/32jjFm2trbMnj2bwMBA1q9fzz/+8Q8sLS3ZuHEjiqIwadIkQyANusXvkyZNYubMmahVKuraWpKltUW5K5hWAI2NM1lX4zl//jzt2rUrlanqzTff5JdffqGkpISioiKjnX/vlnTlMnEXYmnW3IMXXw4xmnv97LPPMnr0aL788ks2bdrE66+/TkpKCnv37sXNzY0pU6YYgl0zMzOmT5/O7t27DSPHpqhbty49evRg4sSJRgsq7e3tGTx4MMeOHTM6/51RXt2/lhbmdGjjTcQXX1BSUsKwEaOxc3SiuLgYCwsLLCwsmDFjBnv27OHnn3+m14DB1K3rrMvC8qf333/f6Kny0KFDmT17NmlpaWRlZZWZglC/Qc+tLN0Ouk5OToZAGsDR0ZGwsLBHOkhZFTLNQwghhBAVys/PJyoqyui1Z88esrOzCQwMZOnSpeUuRG/durXR1xqNhhPHdXsqdOvqX6q+s7Mzbdq0ITc315AM4MgR3S6sAQEBperfnbKvoaM1itoMxeLO01OtlQMqCyviz+oyRgUGBpY6h5mZGVu2bOHrr78uN5AGuHj+z+QETz1FQUFBqSkIPXroRnRjYmKM/u3WrZvR/hCgy9Nd1v1UxaRJk1i1apXRRmmZmZkcOnSIo0ePAhhtinIlNePOm1Xg2bwJlpaWxF/4c53YU224XVjM5StJhnsrLCykdevWaLVaLsaeR1GpsHDQjbo7OTnRvHlzozaZmZkZ9rQob86ztYUu/GzUvAX2DnU4fvw4o0ePZv369SQlJQG6jGHDhg2jUaNGJn8+1UVGpoUQQghRocaNG1f4JLMi+v0I9G7evGnICNGpU6cK35ucnEyHDh0M62oaNGhQqo6rq6th5NvVwYrYFBVamzujoVorB+rbWZGZoVuI2LCh6ZmJbmbpFlj+smM7v+zYXm69lJQUgArbDRjyPT+IK1eusG7dOo4dO0ZiYqJhsaB+pFofFOcXFJKckmp4n5OTE64uuqA37Yau/OMZ0yq8VmZWJop9fVq76DJwlDc1Uz9if296QT1bS3MaOVpzI0fFzLmfMueD6Rw9etTwB4CHhwf9+/dn1KhRD9Rf1UWCaSGEEOI+3u7nxdv9vO5TaxDwVYU13vrzVaGx92/P/feI7X3/k1STexe/azQaAGxsbOjbt2+F79UvArw3MLyXftTX3EyNq4MVybf0iddUoFLh5mRd7l4IVaFvRwvPVtR3daWOYx2sLUqHUvqR2fvlsjYlRd/doqKimDZtGhqNBnd3dwICAvD09MTHx4eUlBQ++OADQPe5nYu7BIbgVsVTns0M59H3SZdne6LWluh2iTQzQ6WCEq0WjUZBUalwaebFU03qUZKTUan7q0ibRo48pSiovHvRt/tu9uzZw969ezl06BCJiYn83//9H2vXrmX16tX4+vqafJ3qIMG0EEIIIaqNk5MTFhYWlJSUMG/evEoFlK6urly6dInr16+XmkObk5NjNJ2goaP1XcE0WJipcbGzMgTm+lHje+3du9eQEaO8FK2urq4A+LTvwKAhQ2nYqDHezYxHnTVahYy8QgqLNYYRaf1OxPdKS0ur6LYrlJeXx4cffoharWb58uWGtHd6a9euBXSBdNL1FHJz72y2Ym5uhuVd005cXV25du0af3vzn1ipNKhLCrG2UGNpbsat28UoZuZobF1oUs+Bho42XM0xudlG9MG4jY0NAwcOZODAgYAuPfGiRYvYu3cvn332mclbn1cXmTMthBBCiGpjaWlJ+/btKS4uLjPXclFREUOHDmXUqFGG3M/dunUD4Ndffy1Vf9++fUZfO9tZYnXXpjMN61ijVqvo2LEjAAcOHCh1DkVRmDVrFpMnTzaMYJc16qrfX+H0yRMoikJunvFOiOs2bKDf/zzHvAVL+G98Bk1bt0etVnPgwAEKCwtLXXP//v2lrlFZ8fHx5OXl0bZt21KBNOiyiwBkZudw+Zr+D4g/F0De87RAf1/Xzv2O1qYuilpNQYmWnIJitGaWLJ7/CQvfm8jtlARMVdbn+dNPP9GvXz9WrFhhVN66dWvDgtcHWaBZXSSYFkIIIUS10qez+/DDD7nw5+YdACUlJYSFhXHmzBny8/MNm8GMGjUKCwsLwsPDOXXqlKF+UlISn376qdG5VSoVTZ3vbHPduK4NAF27dsXDw4Pjx4/z5ZdfGr3n888/JykpiW7duhk2Y9Nve56Tc2cY1t/fH8+WrYiPu8DmTRvJzcs1HDt56jQLFy7i2uVEGrm3QKMopBVb4t+zL+np6cycOdOwGFBRFBYvXkx8fLyJn+Cdud/61Lx6twuL+PfCJYZ813m5uWi1uukx5ja6hYr5+flGU2ZCQkJQq9X837LPSE44j8amHoraghJzG6K2bePcH0fIupFMmzb3n2BUnrI+T09PT65cucKaNWu4fPmyUX39pnE+Pj7UdDLNQwghhBDVqn///owdO5bVq1czdOhQ2rVrh4uLC6dPnyY5ORlnZ2ejzcFatWrFtGnTmD17Ni+//DL+/v5YWFhw6NAhWrVqRWpqqtH5m/8ZTKtUYG+lC3XUajULFy4kNDSUefPmERkZSYsWLYiPjycuLg4XFxfmzJlz5xx/Zqn46KOP2Lp1K5MnT6Z58+YsWDCfsWPH8tO2LRz+7346tO9Abm4OR3//HUWrJfB/gujUphXK7Sy0VnV4fuwEEi7GEhkZSXR0ND4+PsTFxREXF0f79u05ceJEmZ9RSUkJuXl5FBZrUNTm5NzWBeLpuYWcSLpJscacZ3r2Jfq3XQwcNJjWPn6ozc1JjD1LdlYGbo2bkHztKreyswFQrOxo38oLJycnbt68yciRI/Hx8eG9997Dx8eHadOm8e9//5tZU9+iaQsvnOs34PqVBG5cv4qVtTVLliypMNPJ/eg/z/DwcH7//XfGjh1Lp06dCA0N5euvv2bQoEF06tQJR0dH4uLiiI+Px8XFhYkTJ5p8zeoiI9NCCCGEqHbvvvsu4eHhdOnShfj4ePbt24e1tTUhISFs3rzZKJ806EZPV65cSceOHTl+/DjHjx9n4MCBrFq1qtQUgvIWxrVp04bIyEiCg4PJyclh9+7dZGdnM3ToUDZt2mSUdeP111+nV69e5OTkcPDgQS5dugRAa29vPpzzCX0HDMTCwpKDBw8QezGOlq3b8vqkf/LyyyNxsFRhRyHmuak42pgzeW44fYaMoLBYw+49e1BUaj7693w6+et2Ub6ZX0RiWg6nL6Vy5GwCB4+f4b/HTnHiXDzn4y4ReyGOjD+zkeTcukVG+g1uZd9k1GsTGRgcQt169Tl74ijnT/yOk7MzI0PG8f7Hc7G1syMh/iIODnZ0921F03r2fPLJJ3h4eHD69Gmj3RpDQ0NZs2YNgYGBZKWncvr3w2i1Cv0GBrFl82bDVBBTDR8+nOeff56SkhL279/PxYu6vO3Tpk3jww8/5KmnnuLkyZPs3r2bwsJC/va3v7F582bD04maTKXUgH0aQ0JCiImJ4emnnzZMmBdCCCGEqImOnL5Avj4FnU0dNMWFqEt0c6LtrM2xsdAtqizRaMkpLKFEUaO1dkRrbqOftoxK0aIqKQJNISpNke71F0Vk5mZqrCzU2NtY0bxZM2xsbKr0/sJiDQnpeThYm9PYyeaBsnbURH913CnTPIQQQgghqsDO1tYQTCu3bxke89tZmWFvbUm9evXIzs6GwkKcbCy4Xawh/3YmKjNLUJujKilCpa04VZ8CYG6JmZkZZooGlVKCWqXCR5dfegAAGqNJREFUTAVqlQq1SqXL/Ad//qsLeFV/HndwcKBx48Ympd+zsjDjKbc6968oAAmmhRBCCCGqpI69LWk3jMtsrcxp4uZK/fr1MTMzw8XFhaysLG7cuIGtSoWluZqCIg1apQTMADM1d4/3qlBhZW2Fja0ddeztcKzjgLWluS5oRrcBSlFREYWFhRQWFlJcXIxGo0Gj0aDVag3/rVarcXZ2xsXF5bEbUa6pJJgWQgghhKgCZ0d74lQqVH/OlHVwsOOpFsbTKVQqFc7Ozjg6OpKWlkZGRgb21mqj49bW1tja2hpe9245fje1Wo21tTXW1tbl1hGPhgTTQgghhBBVYGtlSf0GbqRnZlHf2YmnmjcsdxTYzMyMhg0bUrduXW7duqV7v60tNjY2pXaHFLWTBNNCCCGEEFXU1r0hSvMGlZ5KYWV1ZxdG8XiRP4mEEEIIIUwgc5IFSDAthBBCCCGEySSYFkIIIYQQwkQSTAshhBBCCGEiCaaFEEIIIYQwkQTTQgghhBBCmEiCaSGEEEIIIUwkwbQQQgghhBAmkmBaCCGEEEIIE0kwLYQQQgghhIkkmBZCCCGEEMJEEkwLIYQQQghhIgmmhRBCCCGEMJEE00IIIYQQQphIgmkhhBBCCCFMJMG0EEIIIYQQJjJ/1A0AuHz5MgDnzp0jJCTkEbdGCCGEEEI8rs6dOwfciT8fVI0IpvPz8wHIyckhJibmEbdGCCGEEEI87vTx54OqEcF0kyZNuHr1Kra2tjRv3vxRN0cIIYQQQjymLl++TH5+Pk2aNPlLzqdSFEX5S84khBBCCCHEE0YWIAohhBBCCGEiCaaFEEIIIYQwkQTTQgghhBBCmEiCaSGEEEIIIUwkwbQQQgghhBAmkmBaCCGEEEIIE0kwLYQQQgghhIkkmBZCCCGEEMJEEkwLIYQQQghhIgmmhRBCCCGEMJEE00IIIYQQQphIgmkhhBBCCCFMJMG0EEIIIYQQJpJgWgghhBBCCBM9scH0f//7X8aMGcMzzzxDx44dCQkJYd++fY+6WcJEGo2GdevWMWzYMPz8/PD19WXQoEGEh4dTWFhYqv6pU6d44403ePbZZ/Hz8yM4OJioqKhH0HLxoG7evEmPHj3w9vYu83hiYiL//Oc/6dmzJ+3btycoKIi1a9ei1WqruaXCFNeuXePdd98lICCAdu3a0aNHD95//33S0tJK1ZW+rt22bNnC8OHD6dChA76+vgwZMoTVq1ej0WhK1ZW+rn0iIyPx9vbm6NGjZR6vap+mpqbywQcf0KdPH3x9fRkwYADh4eEUFRU9zNsok0pRFKXar/qIRUZGMn36dCwtLfH390er1RIdHU1xcTEff/wxI0aMeNRNFFWg0Wh466232Lt3L7a2trRv3x5zc3NOnDjBrVu3aN++PatXr8bGxgaAgwcP8vrrr6PVaunSpQs2NjYcOnSIgoIC3njjDd5+++1HfEeiKt5++222b98OQGxsrNGx8+fPM3r0aHJzc+nYsSP16tUjOjqaW7duERQUxPz58x9Fk0UlnTp1inHjxpGTk4OXlxfNmjXj9OnTpKSk0KxZMzZt2oSjoyMgfV3bffLJJ0RERGBpaUmXLl0wMzPj6NGj5Ofn07dvX5YtW4ZKpQKkr2uj48eP88orr5Cfn8/69evp3Lmz0fGq9mlKSgojRowgJSWFNm3a0LRpU44dO0ZaWhpPP/00X375JRYWFtV3g8oTJiUlRWnXrp3SqVMnJTY21lB+4sQJpWPHjoqPj4+SkpLyCFsoquqbb75RvLy8lKCgIKO+y8jIUEaMGKF4eXkp8+fPVxRFUW7fvq107dpVadu2rXLo0CFD3cuXLysBAQGKl5eXcurUqWq/B2GaqKgoxcvLy/C6m1arVYKCghQvLy9l8+bNhvKMjAxD+Y4dO6q7yaKSCgsLlf79+yteXl7KmjVrDOUFBQXKxIkTFS8vLyUsLExRFOnr2u7cuXOKt7e34u/vryQkJBjKU1JSlMDAQKP+k76ufXbs2KH4+fkZfk4fOXLE6Lgpffr6668rXl5eSnh4uKEsLy9PCQ0NVby8vJSIiIiHe1P3eOKmeaxfv56ioiJCQ0Px8vIylPv6+vLaa69RWFjIxo0bH2ELRVX98MMPALz77rs0aNDAUO7s7MzMmTMB+PHHHwHdY8SMjAyCgoLw9/c31G3WrBlTpkwBYO3atdXUcvEgUlNT+fjjj/Hz88PMzKzU8YMHDxIbG8vTTz/NkCFDDOV3f19IX9dc27dv59KlSwQFBRESEmIot7KyYvr06bi4uJCYmAhIX9d2hw4dQlEUnn/+eTw8PAzlDRo0YNSoUQAcOXIEkL6uTVJSUnjnnXeYNGkSWq0WFxeXMutVtU8TEhLYu3cvzZo144033jCU29raMnv2bMzMzFi3bt3DualyPHHB9P79+wHo27dvqWP6Mpk7XbvUrVuXFi1a4OvrW+qYu7s7ADdu3ADu9H+fPn1K1Q0MDMTMzEz6v5Z47733KCoqYt68eWUer+j/df1jxN9//53c3NyH2k5hmp07dwIwbty4Usfc3Nw4ePAgERERgPR1baefvpGamlrqWFZWFgBOTk6A9HVtsnjxYrZs2UK7du3YuHEjLVq0KLNeVfv0wIEDKIpCYGAgarVxGNuoUSPatGnDtWvXiIuL+4vvqHxPVDCtKApxcXGo1eoyO9Xd3R21Wk1cXBzKkzeVvNZasWIFP/30E7a2tqWOnTp1CoCGDRsCcPHiRQCjpxJ69vb2uLq6kpmZSXp6+kNssXhQGzZsYP/+/UyZMoXmzZuXWUf/g7Ssvgbw8PBAq9USHx//0NopTHf27FksLCxo3bo1ycnJrFy5khkzZjB//nxOnjxpVFf6unbr0aMHKpWKHTt2sHLlSjIzM7l16xabNm1izZo1ODo6MmzYMED6ujZp0aIF8+bN47vvvit3gThUvU/19Vu1alXudQEuXLhgcturyrzarlQDZGdnU1RUhLOzM5aWlqWOm5ubU7duXTIyMsjLy8Pe3v4RtFL8VRRF4bPPPgOgf//+AIYMAPXr1y/zPfXr1yc5OZn09PRyH0mJR+vy5ct8+umndO3aldGjR5dbT/80oqK+BuQPpxqoqKiI5ORkGjZsyI4dO3jvvfe4ffu24fgXX3zB3//+d9555x1A+rq28/T0JCwsjNmzZ7NgwQIWLFhgOObn58fcuXNxc3MDpK9rk/Hjx1eqXlX7VF/f1dW1UvWrwxM1Mq3/YazP6lAWa2trAPLy8qqlTeLhWbhwIUeOHMHFxYVXX30VuPM9oO/ne+nL8/Pzq6eRoko0Gg3Tpk1DrVYzZ84cw+Phskhf1176x7nZ2dlMmzaNvn37smPHDo4cOcKiRYtwcnIiIiLCsL5F+rr269ixI127dsXW1hZ/f3+6deuGnZ0dp06dYsOGDYanxdLXj5+q9mlN/B54okam751bUxGZ5lG7ffbZZ6xcuRJLS0sWL16Ms7MzAGZmZiiKUmEQBkiu0hpq1apVHD9+nFmzZtGoUaMK6+oXJUpf1z76PLG3b9+me/fuRmmxBg4ciK2tLa+//jrh4eEMHz5c+rqW++OPP3jllVdo3Lgx27Zto3HjxoBuDvU//vEP1qxZg729Pf/7v/8rff0Yqmqf1sTvgSdqZFo/p7asTTz0CgoKjOqK2qWkpIQPPviAzz//HCsrK5YtW0aXLl0Mx21sbFAUpdzvAX3/29nZVUt7ReWdP3+epUuX0qtXL4KDg+9bX/8ESt+n95K+rrnuHnEaOXJkqeO9evWiQYMGpKamcunSJenrWm7OnDnk5eUxe/ZsQyANumweCxcuxNzcnK+//prbt29LXz+Gqtqnla1fnXHcExVM29vbY2trS1ZWFiUlJaWOl5SUkJWVhZWVFXXq1HkELRQPIi8vjzfeeIONGzdSp04dIiIi6Nmzp1Ed/RyrsnZPu7u8vLlb4tFZtGgRxcXFFBcXM2XKFKOXfgRC/3VmZqahr8ubNyd9XXM5ODgYNlxo0qRJmXX0TyaysrKkr2uxgoICTp48SZ06dcrMyNS0aVM8PDzIz8/n8uXL0tePoar2aWXrlzen+mF4ooJplUpFy5Yt0Wg0XLp0qdTxxMREtFptuStKRc2VnZ1NSEgI+/fvx83NjfXr1xuNSOvpV/+WtdI7NzeXGzdu4OzsLIsPayD9/LeDBw8SFRVl9NJPy9J/nZ+fb+jrstIjKYpCQkICZmZmeHp6Vt9NiEq5u1/KSpcGd36R1qtXT/q6FsvJyUFRlAqnYeof6xcXF0tfP4aq2qcV1Yc7v9+rM5Z7ooJp0KXgAdi1a1epY/qye0czRc1WVFTE+PHjOXPmDC1btuQ///lPuf8TVdT/u3fvRqPRSP/XUGvXriU2NrbMl/6Xrf7rJk2aGPr6119/LXWuY8eOkZmZSadOnSRrTw0VEBAAwI4dO0odS0hI4Nq1a7i6utK0aVPp61qsXr16ODk5cfPmzVIpD0H3x1R8fDwWFha0aNFC+voxVNU+1dffs2dPqXnR169f59y5czRu3JiWLVs+5Jbf8cQF00OHDsXKyoovvviC06dPG8pPnTrFqlWrsLa2Nuy4JGqHJUuW8Mcff+Dm5sbatWsNOaXLMmDAAOrVq8cPP/zAb7/9ZihPSkpiwYIFqFQqQkNDq6HV4mF7+umnadWqFQcPHuTbb781lGdmZvLRRx8BZW8IImqGl19+GVtbWzZv3kxUVJShPDs7mxkzZqDVahk9ejRqtVr6uhZTq9W89NJLgG4jprufRGRmZjJlyhSKi4sZNmwYdnZ20tePoar2qf4P6ISEBEP6W9A9vZwxYwYajabavwdUyhOYtmL9+vV8/PHHWFhY8MwzzwAQHR1NSUkJ8+bNM9rOUtRsWVlZ9OrVi4KCAtq2bVvuDkuAISPAr7/+yqRJk9BoNHTp0gU7OzsOHz7M7du3efvtt422JxW1Q5s2bdBoNMTGxhqVnzx5krFjx5Kfn0/79u1xdXUlJiaG7Oxshg8fTlhY2CNqsaiM7du3M3XqVEpKSmjbti2urq788ccfZGVl4e/vz6pVqwxzq6Wva6/CwkJeffVVYmJisLKyokuXLqhUKk6cOMGtW7fo0KEDX331lWFBmfR17RQSEkJMTAzr16+nc+fORseq2qdJSUmMHDmStLQ0vLy88PDw4NixY6SlpREQEMDy5csxN6++hHVPZDANuscDq1at4uzZs1haWuLt7c2bb75J165dH3XTRBXs3LmTiRMnVqru3YHWsWPHCA8P58SJEyiKQsuWLQkNDeW55557WE0VD1F5wTTo5tUtWbKE6OhoioqKaN68OS+//DLBwcGG6SGi5jp37hzLly/nyJEj5OXl0bRpU4YMGcK4ceMMgbSe9HXtVVxczIYNG9iyZQsJCQlotVrc3d0ZPHgwoaGhpTZak76ufSoKpqHqfZqcnMySJUvYt28fOTk5hp8NY8eOxcrKqjpuyeCJDaaFEEIIIYR4UE/cnGkhhBBCCCH+KhJMCyGEEEIIYSIJpoUQQgghhDCRBNNCCCGEEEKYSIJpIYQQQgghTCTBtBBCCCGEECaSYFoIIYQQQggTSTAthHisLF26FG9v7yq/rl69Cug2FvD29mbdunWP+E5MN3v2bDp37kxmZiag2+HV29vbsONrZfXu3Rtvb2/27NnzMJpZrtzcXLp27cqMGTOq9bpCCGGK6ttrUQghqoGbmxsdO3YsVX769GmKiopwd3fH2dm51PHq3jHrYTl69Chr165l8uTJZd5nbWBvb8+ECRMICwtjwIAB9OjR41E3SQghyiU7IAohngi9e/fm2rVrzJ07l6FDh5Zb7/r169y+fZv69etTp06damzhg9NoNLz44otkZmaya9curK2tAd3I9JgxY3ByciI6OrrS57ty5QrFxcW4ublha2v7sJpdpuLiYgYMGICFhQVRUVGltpMWQoiaQqZ5CCHEXRo1aoSnp2etC6QBtm7dSmxsLKGhoYZA+kE0a9YMT0/Pag+kASwsLHj11Ve5dOkS3333XbVfXwghKkuCaSGEeAwoisKKFSuwsLCocOS9NgkKCsLKyopVq1ah0WgedXOEEKJMEkwLIcRdylqAGBkZibe3N7NnzyY9PZ0PPviA7t274+vry8CBA1m7di2gC2j/85//8Pzzz+Pr64u/vz9Tpkzhxo0bZV4rIyODefPmMWDAAHx9fenSpQtjx45lx44dVW734cOHuXTpEl27dq1wrnROTg6zZs2iR48e+Pj4MGjQIJYvX05BQUGpumUtQNQv8IyIiODq1au88847dO/enXbt2tG3b18+/fRTcnJySp0rNzeXJUuWEBQUhI+PDx06dOC5555j9uzZpKamltlWBwcHevbsyfXr1/ntt9+q/JkIIUR1kAWIQghRSdevX+eFF14gKysLT09PVCoV8fHxzJo1i9u3b5OYmEhkZCT169fHw8ODCxcuEBUVxdmzZ9myZQsWFhaGc505c4bXXnuNjIwMLC0t8fDwID8/n8OHD3P48GGGDh3KnDlzUKlUlWrbzz//DED37t3LrVNcXMzo0aOJjY2ladOmeHh4cPHiRRYvXswvv/zC6tWrcXBwqNT1Ll68yPLly8nPz6d58+bY2dlx6dIlVq1axaFDh/j2228xN9f9iikoKCAkJISzZ89iZWWFu7s7arWaxMRE1qxZw7Zt2/j2229p2rRpqes8++yz7Ny5kx07dtC7d+9KtU0IIaqTjEwLIUQl7dq1izp16vDTTz+xdetW9u3bR3BwMACLFi1i27ZtLFq0iAMHDrBlyxY2bNiAhYUF8fHx7Nu3z3CenJwcJkyYQEZGBsHBwRw6dIitW7eya9cuNmzYgKurK5GRkaxevbrSbTt8+DAAfn5+5dbJy8vjypUrhIeHs2vXLrZu3crWrVtp3LgxZ86c4dNPP6309X744Qc8PT3ZsWMHP/30Ez///DPLli1DpVJx5swZdu7caaj7/fffc/bsWTp16sS+ffuIiopiy5Yt7Nmzhw4dOpCZmcny5cvLvI4+M0tVFk4KIUR1kmBaCCGqICwsjGbNmgGgUql49dVXAdBqtYwdO5aBAwca6vr5+dG5c2cAzp07Zyj/9ttvSU5O5umnnyYsLAx7e3vDsU6dOjFr1iwAVq5cSXFx8X3blJ6eTmJiIiqVCk9PzwrrTp06lb59+xq+btWqFZ988gmgm86iz019P+bm5ixZssTwWQD069cPf39/AE6cOGEoj42NBaB///44OTkZyuvVq8e//vUvevbsSePGjcu8jru7O+bm5qSkpJCUlFSptgkhRHWSYFoIISrJwcGhVA7rRo0aGf772WefLfWeevXqAbpRYb1ff/0VgIEDB5Y5jSMgIABHR0cyMjI4c+bMfdt17do1AJydnbGzsyu3nqWlZZmLEzt37kzz5s0pLi6u9Aiwt7c3DRo0KFXu4eEB6OZI6+kD7lWrVrF9+3ajY35+fqxcuZIJEyaU22Y3NzcAw8Y6QghRk8icaSGEqKT69euXCn7vzn9c1sK/u+dJ68XHxwOwdu1atm7dWua19CPSiYmJdOjQocJ26UeT7zff2d3dHRsbmzKPtWzZksuXL5OYmFjhOfRcXV3LLNen5NNqtYay4OBgNm7cyJUrV3j77bexsLDAz8+PHj160Lt3b1q2bFnhtfQj91lZWZVqmxBCVCcJpoUQopLKC0T1KrtYUD8yqw+qK1JWZox73bp1C+C+uaUrGrXW55IuK6tHWe63icrd+4E5OjqyadMmVqxYwY8//khqaioxMTHExMSwYMEC/Pz8mDVrVrlBtf5z19+nEELUJBJMCyFENbOxsSEnJ4dNmzbh4+PzwOfTb4V+v8A7Pz+/3GP6aSiVzeZRVY6OjkybNo1p06Zx/vx5Dh8+zP79+zl8+DDHjx9n3Lhx7Ny5s8w/WPT39bhs+S6EeLzInGkhhKhmzZs3ByAhIaHcOtHR0cTHx1NUVHTf8+nnZd+8ebPCeklJSeVufnL+/HlAtyDxr5aWlkZ0dLQhmG/dujWhoaFERESwefNmrK2tuXHjRrnztfX3pb9PIYSoSSSYFkKIatarVy9Al9Xj7ukQekeOHGHMmDEMGjSI69ev3/d8+kV/eXl5Rov77pWfn2+Usk7vt99+4/r169jZ2Rmyj/yVQkJCGDNmjNHmL3qtWrXCxcUFoMxAv6ioyDBXWn+fQghRk0gwLYQQ1WzUqFHUrVuXo0eP8u677xpNzzh16hT//Oc/AejTpw/u7u73PZ+Li4shtdzdKenKMnPmTI4dO2b4+sSJE0yfPh2AcePGGaXp+6sMHjwYgLlz53Lq1ClDuVarZfXq1Vy9ehVbW1s6depU6r2nT5+mpKQEFxeXMjd1EUKIR03mTAshRDWrV68eS5cu5a233iIyMpIff/yRli1bkpuby+XLlwFd6rm5c+dW+pzdu3dn48aNHD9+vMwUfQBeXl4UFRUxcuRIPD09UavVXLx4EYC+ffvy5ptvPvjNlWH8+PEcOHCA48eP89JLL9GkSRMcHR1JTk4mMzMTtVrNRx99ZJSDWu/48eOG+xNCiJpIRqaFEOIR6NKlC1FRUYwZMwY3Nzfi4uJISUnBy8uLSZMm8c0331CnTp1Kn08/+nvgwIFy69jb2/PNN98wdOhQMjMzuXLlCm3atGHmzJksXbrUsP33X83S0pKIiAgmTZpEmzZtyMzM5MKFC1haWhIUFMSmTZt4/vnny3zvwYMHgTv3J4QQNY1KKWvCnhBCiFrnxRdf5OzZs/z888+Vmh5S06WkpBAYGEiLFi3Ytm1bpVMPCiFEdZKRaSGEeEyMHz8e0G0L/jj44Ycf0Gq1vPbaaxJICyFqLBmZFkKIx4RWq2XIkCGkp6eza9euCjdpqekKCwvp378/VlZWbN++/aFNQRFCiAclI9NCCPGYUKvVzJo1i5s3b/LVV1896uY8kHXr1pGamkpYWJgE0kKIGk2CaSGEeIy0b9+e0NBQvvzySzIzMx91c0ySk5PDypUrGTFiBM8888yjbo4QQlRIpnkIIYQQQghhIhmZFkIIIYQQwkQSTAshhBBCCGEiCaaFEEIIIYQwkQTTQgghhBBCmEiCaSGEEEIIIUz0/wEoERimIOrgkAAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "save_file = os.path.join(\n", " get_user_dir('fig'), lab, expt, animal, session, 'ps-vae', 'label_recon')\n", @@ -239,55 +173,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading model defined in /media/mattw/data/ps-vae_demo_head-fixed/results/ibl/angelakilab/IBL-T4/2019-04-23-001/ps-vae/conv/06_latents/demo-run/version_0/meta_tags.pkl\n", - "using data from following sessions:\n", - "/media/mattw/data/ps-vae_demo_head-fixed/results/ibl/angelakilab/IBL-T4/2019-04-23-001\n", - "constructing data generator...done\n", - "Generator contains 1 SingleSessionDataset objects:\n", - "ibl_angelakilab_IBL-T4_2019-04-23-001\n", - " signals: ['labels']\n", - " transforms: OrderedDict([('labels', None)])\n", - " paths: OrderedDict([('labels', '/media/mattw/data/ps-vae_demo_head-fixed/data/ibl/angelakilab/IBL-T4/2019-04-23-001/data.hdf5')])\n", - "\n", - "using data from following sessions:\n", - "/media/mattw/data/ps-vae_demo_head-fixed/results/ibl/angelakilab/IBL-T4/2019-04-23-001\n", - "constructing data generator...done\n", - "Generator contains 1 SingleSessionDataset objects:\n", - "ibl_angelakilab_IBL-T4_2019-04-23-001\n", - " signals: ['labels_sc']\n", - " transforms: OrderedDict([('labels_sc', None)])\n", - " paths: OrderedDict([('labels_sc', '/media/mattw/data/ps-vae_demo_head-fixed/data/ibl/angelakilab/IBL-T4/2019-04-23-001/data.hdf5')])\n", - "\n" - ] - }, - { - "data": { - "image/png": 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\n", 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "n_latents = 2\n", "\n", @@ -330,37 +218,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading model defined in /media/mattw/data/ps-vae_demo_head-fixed/results/ibl/angelakilab/IBL-T4/2019-04-23-001/ps-vae/conv/06_latents/demo-run/version_0/meta_tags.pkl\n", - "using data from following sessions:\n", - "/media/mattw/data/ps-vae_demo_head-fixed/results/ibl/angelakilab/IBL-T4/2019-04-23-001\n", - "constructing data generator...done\n", - "Generator contains 1 SingleSessionDataset objects:\n", - "ibl_angelakilab_IBL-T4_2019-04-23-001\n", - " signals: ['labels']\n", - " transforms: OrderedDict([('labels', None)])\n", - " paths: OrderedDict([('labels', '/media/mattw/data/ps-vae_demo_head-fixed/data/ibl/angelakilab/IBL-T4/2019-04-23-001/data.hdf5')])\n", - "\n", - "saving video to /media/mattw/data/ps-vae_demo_head-fixed/figs/ibl/angelakilab/IBL-T4/2019-04-23-001/ps-vae/traversals_alpha=1000_beta=5_gamma=500_rng=0_latents=2.mp4...done\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "n_frames = 10 # number of sample frames per dimension\n", "model_class = 'ps-vae' # 'sss-vae' | 'vae'\n", @@ -448,7 +308,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -594,29 +454,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading model defined in /media/mattw/data/ps-vae_demo_head-fixed/results/ibl/angelakilab/IBL-T4/2019-04-23-001/ps-vae/conv/06_latents/demo-run/version_0/meta_tags.pkl\n", - "Loading model defined in /media/mattw/data/ps-vae_demo_head-fixed/results/ibl/angelakilab/IBL-T4/2019-04-23-001/vae/conv/06_latents/demo-run/version_0/meta_tags.pkl\n", - "saving video to /media/mattw/data/ps-vae_demo_head-fixed/figs/ibl/angelakilab/IBL-T4/2019-04-23-001/ps-vae/reconstructions_alpha=1000_beta=5_gamma=500_rng=0_latents=2.mp4...done\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# set model info\n", "hparams = {\n", From 87ec44dacb000797a2eac79a57c3db62682de455 Mon Sep 17 00:00:00 2001 From: themattinthehatt Date: Sun, 31 Jan 2021 16:50:24 -0500 Subject: [PATCH 44/50] more updates to ps-vae example notebook --- examples/ps-vae/01_ps-vae.ipynb | 117 +++++++++++++++++++++++++++++--- 1 file changed, 106 insertions(+), 11 deletions(-) diff --git a/examples/ps-vae/01_ps-vae.ipynb b/examples/ps-vae/01_ps-vae.ipynb index 3a8e527..f98bbf8 100644 --- a/examples/ps-vae/01_ps-vae.ipynb +++ b/examples/ps-vae/01_ps-vae.ipynb @@ -26,6 +26,13 @@ "* [Make frame reconstruction movie](#Make-reconstruction-movies)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## imports" + ] + }, { "cell_type": "code", "execution_count": null, @@ -40,11 +47,22 @@ "from behavenet.plotting.cond_ae_utils import plot_latent_traversals\n", "from behavenet.plotting.cond_ae_utils import make_latent_traversal_movie\n", "\n", - "dataset = 'ibl'\n", + "dataset = 'head-fixed'\n", + "# 'head-fixed': IBL data\n", + "# 'face': dipoppa data\n", + "# 'two-view': musall data\n", + "\n", "save_outputs = True # true to save figures/movies to user's figure directory\n", "file_ext = 'pdf' # figure format ('png' | 'jpeg' | 'pdf'); movies saved as mp4" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### define dataset parameters" + ] + }, { "cell_type": "code", "execution_count": null, @@ -57,7 +75,7 @@ "experiment_name = 'demo-run' # test-tube exp name\n", "\n", "# set dataset-specific parameters\n", - "if dataset == 'ibl':\n", + "if dataset == 'head-fixed':\n", " \n", " lab = 'ibl'\n", " expt = 'angelakilab'\n", @@ -92,7 +110,84 @@ " trials = [None, None, None, 169, 129, 429, 339] # trial index wrt to *all* trials\n", " batch_idxs = [99, 99, 99, 16, 46, 11, 79] # batch index within trial\n", " n_cols = 3 # width of traversal movie\n", - " text_color = [1, 1, 1] # text color for labels" + " text_color = [1, 1, 1] # text color for labels\n", + " \n", + "elif dataset == 'face':\n", + " \n", + " lab = 'dipoppa'\n", + " expt = 'pupil'\n", + " animal = 'MD0ST5'\n", + " session = 'session-3'\n", + " n_labels = 3\n", + " label_names = ['Pupil area', 'Pupil (y)', 'Pupil (x)']\n", + "\n", + " # define \"best\" model\n", + " best_alpha = 1000\n", + " best_beta = 20\n", + " best_gamma = 1000\n", + " best_rng = 0\n", + "\n", + " # label reconstructions\n", + " label_recon_trials= [43, 83, 73] # good validation trials; also used for frame recon\n", + " xtick_locs= [0, 30, 60, 90, 120, 150]\n", + " frame_rate= 30\n", + " scale= 0.45\n", + " \n", + " # latent traversal params\n", + " label_min_p = 5 # lower bound of label traversals\n", + " label_max_p = 95 # upper bound of label traversals\n", + " ch = 0 # video channel to display\n", + " n_frames_zs = 4 # n frames for supervised static traversals\n", + " n_frames_zu = 4 # n frames for unsupervised static traversals\n", + " label_idxs = [1, 2] # pupil location\n", + " crop_type = 'fixed' # crop around eye\n", + " crop_kwargs = {'y_0': 48, 'y_ext': 48, 'x_0': 192, 'x_ext': 64}\n", + " # select base frames for traversals\n", + " trial_idxs = [11, None, 21] # trial index wrt to all test trials\n", + " trials = [None, 393, None] # trial index wrt to *all* trials\n", + " batch_idxs = [60, 27, 99] # batch index within trial\n", + " n_cols = 3 # width of traversal movie\n", + " text_color = [0, 0, 0] # text color for labels\n", + " \n", + "elif dataset == 'two-view':\n", + " \n", + " lab = 'musall'\n", + " expt = 'vistrained'\n", + " animal = 'mSM36'\n", + " session = '05-Dec-2017-wpaw'\n", + " n_labels = 5\n", + " label_names = ['Levers', 'L Spout', 'R Spout', 'R paw (x)', 'R paw (y)']\n", + "\n", + " # define \"best\" model\n", + " best_alpha = 1000\n", + " best_beta = 1\n", + " best_gamma = 1000\n", + " best_rng = 1\n", + "\n", + " # label reconstructions\n", + " label_recon_trials= [9, 19, 29] # good validation trials; also used for frame recon\n", + " xtick_locs= [0, 60, 120, 180]\n", + " frame_rate= 30\n", + " scale= 0.25\n", + "\n", + " # latent traversal params\n", + " label_min_p = 5 # lower bound of label traversals\n", + " label_max_p = 95 # upper bound of label traversals\n", + " ch = 1 # video channel to display\n", + " n_frames_zs = 3 # n frames for supervised static traversals\n", + " n_frames_zu = 3 # n frames for unsupervised static traversals\n", + " label_idxs = [3, 4] # move right paw\n", + " crop_type = None # no image cropping\n", + " crop_kwargs = None # no image cropping\n", + " # select base frames for traversals\n", + " trial_idxs = [11, 11, 11, 5] # trial index wrt to all test trials\n", + " trials = [None, None, None, None] # trial index wrt to *all* trials\n", + " batch_idxs = [99, 0, 50, 180] # batch index within trial\n", + " n_cols = 2 # width of traversal movie\n", + " text_color = [1, 1, 1] # text color for labels\n", + "\n", + "else:\n", + " raise ValueError('Invalid dataset; must choose \"head-fixed\", \"face\", or \"two-view\"')\n" ] }, { @@ -129,7 +224,7 @@ "plot_psvae_training_curves(\n", " lab=lab, expt=expt, animal=animal, session=session, alphas=[best_alpha], \n", " betas=[best_beta], gammas=[best_gamma], n_ae_latents=[n_latents], \n", - " rng_seeds_model=[0], experiment_name=experiment_name,\n", + " rng_seeds_model=[best_rng], experiment_name=experiment_name,\n", " n_labels=n_labels, train_frac=train_frac,\n", " save_file=save_file_new, format=file_ext)" ] @@ -229,7 +324,7 @@ "# by looking at the latent traversals above, and are indicated with quotes to distinguish\n", "# them from the supervised dims\n", "\n", - "if dataset == 'ibl':\n", + "if dataset == 'head-fixed':\n", " if model_class == 'ps-vae':\n", " panel_titles = [\n", " 'L paw (x)', 'R paw (x)', 'L paw (y)', 'R paw (y)', '\"Jaw\"', '\"L paw config\"']\n", @@ -241,7 +336,7 @@ " else:\n", " raise NotImplementedError\n", "\n", - "elif dataset == 'dipoppa':\n", + "elif dataset == 'face':\n", " crop_kwargs = None\n", " if model_class == 'ps-vae':\n", " panel_titles = [\n", @@ -254,19 +349,19 @@ " else:\n", " raise NotImplementedError\n", "\n", - "elif dataset == 'musall-wpaw':\n", + "elif dataset == 'two-view':\n", "# crop_kwargs_ = None\n", "# show_markers = True \n", - " if model_class == 'sss-vae':\n", + " if model_class == 'ps-vae':\n", " panel_titles = [\n", - " 'Lever', 'R spout', 'L spout', 'R paw (y)', 'R paw (x)', '\"Chest\"', \n", + " 'Lever', 'R spout', 'L spout', 'R paw (x)', 'R paw (y)', '\"Chest\"', \n", " '\"Jaw\"']\n", - " order_idxs = [1, 2, 3, 4, 0, 5, 6, 7]\n", + " order_idxs = [1, 2, 3, 4, 0, 5, 6]\n", " elif model_class == 'vae':\n", " panel_titles = [\n", " 'Latent 0', 'Latent 1', 'Latent 2', 'Latent 3', 'Latent 4', 'Latent 5', \n", " 'Latent 6']\n", - " order_idxs = [0, 1, 2, 3, 4, 5, 6, 7]\n", + " order_idxs = [0, 1, 2, 3, 4, 5, 6]\n", " else:\n", " raise NotImplementedError\n", "\n", From b6debbc730140b27cda1dd602b0bedd9e5a12d2d Mon Sep 17 00:00:00 2001 From: themattinthehatt Date: Mon, 1 Feb 2021 13:28:54 -0500 Subject: [PATCH 45/50] data download instructions --- examples/ps-vae/00_data.ipynb | 163 ++++++++++++++++++++++++++++++++++ 1 file changed, 163 insertions(+) create mode 100644 examples/ps-vae/00_data.ipynb diff --git a/examples/ps-vae/00_data.ipynb b/examples/ps-vae/00_data.ipynb new file mode 100644 index 0000000..fb4f3d2 --- /dev/null +++ b/examples/ps-vae/00_data.ipynb @@ -0,0 +1,163 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fitting the PS-VAE to an example dataset\n", + "\n", + "This notebook will walk you through how to download an example dataset, including some already trained models; the next notebook shows how to evaluate those models.\n", + "\n", + "Before beginning, first make sure that you have properly installed the BehaveNet package and environment by following the instructions [here](https://behavenet.readthedocs.io/en/latest/source/installation.html). Specifically, (1) set up the Anaconda virtual environment; and (2) install the `BehaveNet` package. You do not need to set user paths at this time (this will be covered below).\n", + "\n", + "To illustrate the use of BehaveNet we will use an example dataset from the [International Brain Lab](https://www.biorxiv.org/content/10.1101/2020.01.17.909838v5).\n", + "\n", + "Briefly, a head-fixed mouse performed a visual decision-making task. Behavioral data was recorded using a single camera at 60 Hz frame rate. Grayscale video frames were downsampled to 192x192 pixels. We labeled the forepaw positions using [Deep Graph Pose](https://papers.nips.cc/paper/2020/file/4379cf00e1a95a97a33dac10ce454ca4-Paper.pdf). Data consists of batches of 100 contiguous frames and their accompanying labels.\n", + "\n", + "The data are stored on the IBL data repository; you will download this data after setting some user paths.\n", + "\n", + "**Note**: make sure that you are running the `behavenet` ipython kernel - you should see the current ipython kernel name in the upper right hand corner of this notebook. If it is not `behavenet` (for example it might be `Python 3`) then change it using the dropdown menus above: `Kernel > Change kernel > behavenet`. If you do not see `behavenet` as an option see [here](https://behavenet.readthedocs.io/en/latest/source/installation.html#environment-setup).\n", + "\n", + "
\n", + "\n", + "### Contents\n", + "* [Set user paths](#0.-Set-user-paths)\n", + "* [Download the data](#1.-Download-the-data)\n", + "* [Add dataset hyperparameters](#2.-Add-dataset-hyperparameters)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 0. Set user paths\n", + "First set the paths to the directories where data, results, and figures will be stored on your local machine. Note that the data is ~3GB, so make sure that your data directory has enough space.\n", + "\n", + "A note about the BehaveNet path structure: every dataset is uniquely identified by a lab id, experiment id, animal id, and session id. Paths to data and results contain directories for each of these id types. For example, a sample data path will look like `/home/user/data/lab_id/expt_id/animal_id/session_id/data.hdf5`. In this case the base data directory is `/home/user/data/`.\n", + "\n", + "The downloaded zip file will automatically be saved as `data_dir/ibl/angelakilab/IBL-T4/2019-04-23-001/data.hdf5`\n", + "\n", + "Additionally, the zip file contains already trained VAE and PS-VAE models, which will automatically be saved in the directories:\n", + "* `results_dir/ibl/angelakilab/IBL-T4/2019-04-23-001/vae/conv/06_latents/demo-run/`\n", + "* `results_dir/ibl/angelakilab/IBL-T4/2019-04-23-001/ps-vae/conv/06_latents/demo-run/`\n", + "\n", + "To set the user paths, run the cell below.\n", + "\n", + "[Back to contents](#Contents)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from behavenet import setup\n", + "setup()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The directory file is stored in your user home directory; this is a json file that can be updated in a text editor at any time." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. Download the data\n", + "Run the cell below; data and results will be stored in the directories provided in the previous step.\n", + "\n", + "[Back to contents](#Contents)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import io\n", + "import shutil\n", + "import requests\n", + "import zipfile as zf\n", + "from behavenet import get_user_dir\n", + "\n", + "url = 'https://ibl.flatironinstitute.org/public/ps-vae_demo_head-fixed.zip'\n", + "\n", + "print('Downloading data - this may take several minutes')\n", + "\n", + "# fetch data from IBL data repository\n", + "print('fetching data from url...', end='')\n", + "r = requests.get(url, stream=True)\n", + "z = zf.ZipFile(io.BytesIO(r.content))\n", + "print('done')\n", + "\n", + "# extract data\n", + "data_dir = get_user_dir('data')\n", + "if not os.path.exists(data_dir):\n", + " os.makedirs(data_dir)\n", + "print('extracting data to %s...' % data_dir, end='')\n", + "for file in z.namelist():\n", + " if file.startswith('ps-vae_demo_head-fixed/data/'):\n", + " z.extract(file, data_dir)\n", + "# clean up paths\n", + "shutil.move(os.path.join(data_dir, 'ps-vae_demo_head-fixed', 'data', 'ibl'), data_dir)\n", + "shutil.rmtree(os.path.join(data_dir, 'ps-vae_demo_head-fixed'))\n", + "print('done')\n", + "\n", + "# extract results\n", + "results_dir = get_user_dir('save')\n", + "if not os.path.exists(results_dir):\n", + " os.makedirs(results_dir)\n", + "print('extracting results to %s...' % results_dir, end='')\n", + "for file in z.namelist():\n", + " if file.startswith('ps-vae_demo_head-fixed/results/'):\n", + " z.extract(file, results_dir)\n", + "# clean up paths\n", + "shutil.move(os.path.join(results_dir, 'ps-vae_demo_head-fixed', 'results', 'ibl'), results_dir)\n", + "shutil.rmtree(os.path.join(results_dir, 'ps-vae_demo_head-fixed'))\n", + "print('done')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. Add dataset hyperparameters\n", + "The last step is to save some of the dataset hyperparameters in their own json file. This is used to simplify command line arguments to model fitting functions. This json file has already been provided in the data directory, where the `data.hdf5` file is stored - you should see a file named `ibl_angelakilab_params.json`. Copy and paste this file into the `.behavenet` directory in your home directory:\n", + "\n", + "* In Linux, `~/.behavenet`\n", + "* In MacOS, `/Users/CurrentUser/.behavenet`\n", + "\n", + "The next notebook will now walk you through how to evaluate the downloaded models/data.\n", + "\n", + "[Back to contents](#Contents)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "behavenet", + "language": "python", + "name": "behavenet" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 3fce70166eee6d64d8c8396415104f293a80fb04 Mon Sep 17 00:00:00 2001 From: themattinthehatt Date: Mon, 1 Feb 2021 14:08:04 -0500 Subject: [PATCH 46/50] updating tests for new ae default arch --- behavenet/fitting/eval.py | 2 +- .../test_ae_model_architecture_generator.py | 30 +++++++++---------- 2 files changed, 16 insertions(+), 16 deletions(-) diff --git a/behavenet/fitting/eval.py b/behavenet/fitting/eval.py index 4a8dd2c..eae9cbf 100644 --- a/behavenet/fitting/eval.py +++ b/behavenet/fitting/eval.py @@ -461,10 +461,10 @@ def export_train_plots(hparams, dtype, loss_type='mse', save_file=None, format=' import pandas as pd import seaborn as sns import matplotlib as mpl - mpl.use('Agg') #deal with display-less machines import matplotlib.pyplot as plt from behavenet.fitting.utils import read_session_info_from_csv + mpl.use('Agg') # deal with display-less machines sns.set_style('white') sns.set_context('talk') diff --git a/tests/test_models/test_ae_model_architecture_generator.py b/tests/test_models/test_ae_model_architecture_generator.py index f792406..7119549 100644 --- a/tests/test_models/test_ae_model_architecture_generator.py +++ b/tests/test_models/test_ae_model_architecture_generator.py @@ -377,14 +377,14 @@ def test_get_handcrafted_dims(): arch0 = utils.load_default_arch() arch0['ae_input_dim'] = [2, 128, 128] arch0 = utils.get_handcrafted_dims(arch0, symmetric=True) - assert arch0['ae_encoding_x_dim'] == [64, 32, 16, 8] - assert arch0['ae_encoding_y_dim'] == [64, 32, 16, 8] - assert arch0['ae_encoding_x_padding'] == [(1, 2), (1, 2), (1, 2), (1, 2)] - assert arch0['ae_encoding_y_padding'] == [(1, 2), (1, 2), (1, 2), (1, 2)] - assert arch0['ae_decoding_x_dim'] == [16, 32, 64, 128] - assert arch0['ae_decoding_y_dim'] == [16, 32, 64, 128] - assert arch0['ae_decoding_x_padding'] == [(1, 2), (1, 2), (1, 2), (1, 2)] - assert arch0['ae_decoding_y_padding'] == [(1, 2), (1, 2), (1, 2), (1, 2)] + assert arch0['ae_encoding_x_dim'] == [64, 32, 16, 8, 2] + assert arch0['ae_encoding_y_dim'] == [64, 32, 16, 8, 2] + assert arch0['ae_encoding_x_padding'] == [(1, 2), (1, 2), (1, 2), (1, 2), (1, 1)] + assert arch0['ae_encoding_y_padding'] == [(1, 2), (1, 2), (1, 2), (1, 2), (1, 1)] + assert arch0['ae_decoding_x_dim'] == [8, 16, 32, 64, 128] + assert arch0['ae_decoding_y_dim'] == [8, 16, 32, 64, 128] + assert arch0['ae_decoding_x_padding'] == [(1, 1), (1, 2), (1, 2), (1, 2), (1, 2)] + assert arch0['ae_decoding_y_padding'] == [(1, 1), (1, 2), (1, 2), (1, 2), (1, 2)] # asymmetric arch (TODO: source code not updated) arch1 = utils.load_default_arch() @@ -395,10 +395,10 @@ def test_get_handcrafted_dims(): arch1['ae_decoding_layer_type'] = ['conv', 'conv', 'conv'] arch1['ae_decoding_starting_dim'] = [1, 8, 8] arch1 = utils.get_handcrafted_dims(arch1, symmetric=False) - assert arch1['ae_encoding_x_dim'] == [64, 32, 16, 8] - assert arch1['ae_encoding_y_dim'] == [64, 32, 16, 8] - assert arch1['ae_encoding_x_padding'] == [(1, 2), (1, 2), (1, 2), (1, 2)] - assert arch1['ae_encoding_y_padding'] == [(1, 2), (1, 2), (1, 2), (1, 2)] + assert arch1['ae_encoding_x_dim'] == [64, 32, 16, 8, 2] + assert arch1['ae_encoding_y_dim'] == [64, 32, 16, 8, 2] + assert arch1['ae_encoding_x_padding'] == [(1, 2), (1, 2), (1, 2), (1, 2), (1, 1)] + assert arch1['ae_encoding_y_padding'] == [(1, 2), (1, 2), (1, 2), (1, 2), (1, 1)] assert arch1['ae_decoding_x_dim'] == [15, 29, 57] assert arch1['ae_decoding_y_dim'] == [15, 29, 57] assert arch1['ae_decoding_x_padding'] == [(2, 2), (2, 2), (2, 2)] @@ -425,7 +425,7 @@ def test_load_handcrafted_arch(): assert arch['x_pixels'] == input_dim[2] assert arch['ae_input_dim'] == input_dim assert arch['n_ae_latents'] == n_ae_latents - assert arch['ae_encoding_n_channels'] == [32, 64, 256, 512] + assert arch['ae_encoding_n_channels'] == [32, 64, 128, 256, 512] # load arch from json ae_arch_json = os.path.join( @@ -447,7 +447,7 @@ def test_load_handcrafted_arch(): assert arch['x_pixels'] == input_dim[2] assert arch['ae_input_dim'] == input_dim assert arch['n_ae_latents'] == n_ae_latents - assert arch['ae_encoding_n_channels'] == [32, 64, 256, 512] + assert arch['ae_encoding_n_channels'] == [32, 64, 128, 256, 512] # check memory runs ae_arch_json = None @@ -458,7 +458,7 @@ def test_load_handcrafted_arch(): assert arch['x_pixels'] == input_dim[2] assert arch['ae_input_dim'] == input_dim assert arch['n_ae_latents'] == n_ae_latents - assert arch['ae_encoding_n_channels'] == [32, 64, 256, 512] + assert arch['ae_encoding_n_channels'] == [32, 64, 128, 256, 512] # raise exception when not enough gpu memory ae_arch_json = None From d33fa5d36b0d009c4c2be48fd1eb4406e1e9097f Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 1 Feb 2021 19:17:15 +0000 Subject: [PATCH 47/50] Bump notebook from 6.0.3 to 6.1.5 in /docs Bumps [notebook](https://github.com/jupyter/jupyterhub) from 6.0.3 to 6.1.5. - [Release notes](https://github.com/jupyter/jupyterhub/releases) - [Changelog](https://github.com/jupyterhub/jupyterhub/blob/master/CHECKLIST-Release.md) - [Commits](https://github.com/jupyter/jupyterhub/commits) Signed-off-by: dependabot[bot] --- docs/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/requirements.txt b/docs/requirements.txt index 0daa7a2..ec5ba6f 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -4,7 +4,7 @@ sphinx-automodapi==0.12 commentjson==0.8.2 h5py==2.9.0 matplotlib==3.0.3 -notebook==6.0.3 +notebook==6.1.5 numpy==1.17.4 requests==2.22.0 scikit-image==0.15.0 From 35bf5360e136075ca5ec30b3f98a2112a53e992c Mon Sep 17 00:00:00 2001 From: Matt Whiteway Date: Mon, 1 Feb 2021 14:47:21 -0500 Subject: [PATCH 48/50] Update README.md --- README.md | 2 -- 1 file changed, 2 deletions(-) diff --git a/README.md b/README.md index c6b6af2..41e886d 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,5 @@ # BehaveNet -NOTE: The master branch contains the code version released with the neurips paper in November 2019; for more recent updates, see the develop branch. - BehaveNet is a probabilistic framework for the analysis of behavioral video and neural activity. This framework provides tools for compression, segmentation, generation, and decoding of behavioral videos. Please see the From d7c0676e475285b7196c6077d08e94f3c3f21cdd Mon Sep 17 00:00:00 2001 From: nihaarshah Date: Fri, 5 Feb 2021 13:33:52 +0530 Subject: [PATCH 49/50] conv and lstm hierarchical model file --- behavenet/models/hierarchical_decoders.py | 461 ++++++++++++++++++++++ 1 file changed, 461 insertions(+) create mode 100644 behavenet/models/hierarchical_decoders.py diff --git a/behavenet/models/hierarchical_decoders.py b/behavenet/models/hierarchical_decoders.py new file mode 100644 index 0000000..268017b --- /dev/null +++ b/behavenet/models/hierarchical_decoders.py @@ -0,0 +1,461 @@ +"""Hierarchical encoding/decoding models implemented in PyTorch.""" + +import numpy as np +from sklearn.metrics import r2_score, accuracy_score +import torch +from torch import nn +import behavenet.fitting.losses as losses +from behavenet.models.base import BaseModule +from behavenet.models.decoders import Decoder + + +class HierarchicalDecoder(Decoder): + """General wrapper class for hierarchical encoding/decoding models.""" + + def __init__(self, hparams): + """ + + Parameters + ---------- + hparams : :obj:`dict` + - model_type (:obj:`str`): 'mlp' | 'mlp-mv' | 'lstm' + - input_size (:obj:`int`) + - output_size (:obj:`int`) + - n_hid_layers (:obj:`int`) + - n_hid_units (:obj:`int`) + - n_lags (:obj:`int`): number of lags in input data to use for temporal convolution + - noise_dist (:obj:`str`): 'gaussian' | 'gaussian-full' | 'poisson' | 'categorical' + - activation (:obj:`str`): 'linear' | 'relu' | 'lrelu' | 'sigmoid' | 'tanh' + + """ + super().__init__(hparams) + self.hparams = hparams + self.model = None + self.build_model() + # choose loss based on noise distribution of the model + if self.hparams['noise_dist'] == 'gaussian': + self._loss = nn.MSELoss() + elif self.hparams['noise_dist'] == 'gaussian-full': + from behavenet.fitting.losses import GaussianNegLogProb + self._loss = GaussianNegLogProb() # model holds precision mat + elif self.hparams['noise_dist'] == 'poisson': + self._loss = nn.PoissonNLLLoss(log_input=False) + elif self.hparams['noise_dist'] == 'categorical': + self._loss = nn.CrossEntropyLoss() + else: + raise ValueError('"%s" is not a valid noise dist' % self.model['noise_dist']) + + def __str__(self): + """Pretty print model architecture.""" + return self.model.__str__() + + def build_model(self): + """Construct the model using hparams.""" + + # TODO + if self.hparams['model_type'] == 'mlp' or self.hparams['model_type'] == 'mlp-mv': + self.model = HierarchicalMLP(self.hparams) + elif self.hparams['model_type'] == 'lstm': + self.model = HierarchicalLSTM(self.hparams) + else: + raise ValueError('"%s" is not a valid model type' % self.hparams['model_type']) + + def forward(self, x,dataset): + """Process input data.""" + return self.model(x,dataset) + + def loss(self, data,dataset, accumulate_grad=True, chunk_size=200, **kwargs): + # TODO + """Calculate negative log-likelihood loss for supervised models. + + The batch is split into chunks if larger than a hard-coded `chunk_size` to keep memory + requirements low; gradients are accumulated across all chunks before a gradient step is + taken. + + Parameters + ---------- + data : :obj:`dict` + signals are of shape (1, time, n_channels) + accumulate_grad : :obj:`bool`, optional + accumulate gradient for training step + chunk_size : :obj:`int`, optional + batch is split into chunks of this size to keep memory requirements low + + Returns + ------- + :obj:`dict` + - 'loss' (:obj:`float`): total loss (negative log-like under specified noise dist) + - 'r2' (:obj:`float`): variance-weighted $R^2$ when noise dist is Gaussian + - 'fc' (:obj:`float`): fraction correct when noise dist is Categorical + + """ + # self.dataset = dataset # it is passed as a kwarg, not sure how else to access this and pass it into forward() + predictors = data[self.hparams['input_signal']][0] + targets = data[self.hparams['output_signal']][0] + + max_lags = self.hparams['n_max_lags'] + + batch_size = targets.shape[0] + n_chunks = int(np.ceil(batch_size / chunk_size)) + + outputs_all = [] + loss_val = 0 + for chunk in range(n_chunks): + + # take chunks of size chunk_size, plus overlap due to max_lags + idx_beg = np.max([chunk * chunk_size - max_lags, 0]) + idx_end = np.min([(chunk + 1) * chunk_size + max_lags, batch_size]) + + outputs, precision = self.forward(predictors[idx_beg:idx_end],dataset) + + # define loss on allowed window of data + if self.hparams['noise_dist'] == 'gaussian-full': + loss = self._loss( + outputs[max_lags:-max_lags], + targets[idx_beg:idx_end][max_lags:-max_lags], + precision[max_lags:-max_lags]) + else: + loss = self._loss( + outputs[max_lags:-max_lags], + targets[idx_beg:idx_end][max_lags:-max_lags]) + + if accumulate_grad: + loss.backward() + + # get loss value (weighted by batch size) + loss_val += loss.item() * outputs[max_lags:-max_lags].shape[0] + + outputs_all.append(outputs[max_lags:-max_lags].cpu().detach().numpy()) + + loss_val /= batch_size + outputs_all = np.concatenate(outputs_all, axis=0) + + if self.hparams['noise_dist'] == 'gaussian' or \ + self.hparams['noise_dist'] == 'gaussian-full': + # use variance-weighted r2s to ignore small-variance latents + r2 = r2_score( + targets[max_lags:-max_lags].cpu().detach().numpy(), + outputs_all, + multioutput='variance_weighted') + fc = 0 + elif self.hparams['noise_dist'] == 'poisson': + raise NotImplementedError + elif self.hparams['noise_dist'] == 'categorical': + r2 = 0 + fc = accuracy_score( + targets[max_lags:-max_lags].cpu().detach().numpy(), + np.argmax(outputs_all, axis=1)) + else: + raise ValueError('"%s" is not a valid noise_dist' % self.hparams['noise_dist']) + + return {'loss': loss_val, 'r2': r2, 'fc': fc} + + +class HierarchicalMLP(BaseModule): + """Feedforward neural network model.""" + + def __init__(self, hparams): + super().__init__() + self.hparams = hparams + self.decoder = None + self.build_model() + + def __str__(self): + """Pretty print model architecture.""" + # TODO + pass + + def build_model(self): + """Construct the model.""" + # TODO + pass + self.decoder = nn.ModuleList() + + global_layer_num = 0 + # Ask if the input size field of the hparams should be populated according to the multiple datasets that are + # present in the datagenerator.datasets somewhere else? Because at the moment hparams just has one single inp dim + out_size = self.hparams['n_hid_units']# fix it to the input size of the global backbone network + # for i,i_layer in enumerate(range(len(sess_ids))): + # in_size = self.hparams['input_size'][i] + # + # # first layer is 1d conv for incorporating past/future neural activity + # # Separate 1d conv for each dataset + # layer = nn.Conv1D(in_channels=in_size, out_channels=out_size, + # kernel_size=self.hparams['n_lags']*2+1, #window around t + # padding=self.hparams['n_lags'])# same output + # name = str('conv1d_layer_%02i'% global_layer_num) + # self.decoder.add_module(name,layer) + # self.final_layer = name + + layer = nn.ModuleList([ + nn.Conv1d(in_channels=in_size, out_channels=out_size, + kernel_size=self.hparams['n_lags']*2+1, + padding=self.hparams['n_lags']) + for in_size in self.hparams['input_size'] + ]) + + name = str('conv1d_layer_%02i' % global_layer_num) + self.decoder.add_module(name, layer) + self.final_layer = name + + # add activation + if self.hparams['n_hid_layers'] == 0: + if self.hparams['noise_dist'] == 'gaussian': + activation = None + elif self.hparams['noise_dist'] == 'gaussian-full': + activation = None + elif self.hparams['noise_dist'] == 'poisson': + activation = nn.Softplus() + elif self.hparams['noise_dist'] == 'categorical': + activation = None + else: + raise ValueError('"%s" is an invalid noise dist'% self.hparams['noise_dist']) + + else: + if self.hparams['activation'] == 'linear': + activation = None + elif self.hparams['activation'] == 'relu': + activation = nn.ReLU() + elif self.hparams['activation'] == 'lrelu': + activation = nn.LeakyReLU(0.05) + elif self.hparams['activation'] == 'sigmoid': + activation = nn.Sigmoid() + elif self.hparams['activation'] == 'tanh': + activation = nn.Tanh() + else: + raise ValueError( + '"%s" is an invalid activation function' % self.hparams['activation']) + + if activation: + name = '%s_%02i' % (self.hparams['activation'], global_layer_num) + self.decoder.add_module(name, activation) + + # add layer for data dependent precision matrix if requires + if self.hparams['n_hid_layers'] == 0 and self.hparams['noise_dist'] == 'gaussian-full': + # build sqrt of precision matrix + self.precision_sqrt = nn.Linear(in_features=in_size, out_features=out_size**2) + else: + self.precision_sqrt = None + + # update layer info + global_layer_num += 1 + in_size = out_size + + # loop over hidden layers + for i_layer in range(self.hparams['n_hid_layers']): + + if i_layer == self.hparams['n_hid_layers'] - 1: + out_size = self.hparams['output_size'] + else: + out_size = self.hparams['n_hid_units'] + + # add layer + layer = nn.Linear(in_features=in_size, out_features=out_size) + name = str('dense_layer_%02i'%global_layer_num) + self.decoder.add_module(name,layer) + self.final_layer = name + + # add activation + if i_layer == self.hparams['n_hid_layers'] - 1: + if self.hparams['noise_dist'] == 'gaussian': + activation = None + elif self.hparams['noise_dist'] == 'gaussian-full': + activation = None + elif self.hparams['noise_dist'] == 'poisson': + activation = nn.Softplus() + elif self.hparams['noise_dist'] == 'categorical': + activation = None + else: + raise ValueError('"%s" is an invalid noise dist' % self.hparams['noise_dist']) + else: + if self.hparams['activation'] == 'linear': + activation = None + elif self.hparams['activation'] == 'relu': + activation = nn.ReLU() + elif self.hparams['activation'] == 'lrelu': + activation = nn.LeakyReLU(0.05) + elif self.hparams['activation'] == 'sigmoid': + activation = nn.Sigmoid() + elif self.hparams['activation'] == 'tanh': + activation = nn.Tanh() + else: + raise ValueError( + '"%s" is an invalid activation function' % self.hparams['activation']) + + if activation: + self.decoder.add_module( + '%s_%02i' % (self.hparams['activation'], global_layer_num), activation) + + # add layer for data-dependent precision matrix if required + if i_layer == self.hparams['n_hid_layers'] - 1 \ + and self.hparams['noise_dist'] == 'gaussian-full': + # build sqrt of precision matrix + self.precision_sqrt = nn.Linear(in_features=in_size, out_features=out_size ** 2) + else: + self.precision_sqrt = None + + # update layer info + global_layer_num += 1 + in_size = out_size + + in_size_list = self.hparams['input_size'] + + # + # if self.hparams['n_hid_layers'] == 0: + # out_size + + def forward(self, x,dataset): + """Process input data. + + Parameters + ---------- + x : :obj:`torch.Tensor` + shape of (time, neurons) + + Returns + ------- + :obj:`tuple` + - x (:obj:`torch.Tensor`): mean prediction of model + - y (:obj:`torch.Tensor`): precision matrix prediction of model (when using 'mlp-mv') + + """ + # sess_id = [s for s in self.hparams['input_size']] + y = None + for name, layer in self.decoder.named_children(): + + if name == 'conv1d_layer_00': + # input is batch x in_channels x time + # output is batch x out_channels x time + x = layer[dataset](x.transpose(1,0).unsqueeze(0)).squeeze().transpose(1,0) + # x = layer(x.transpose(1,0).unsqueeze(0)).squeeze().transpose(1,0) + else: + x = layer(x) + + return x, y + # pass + +class HierarchicalLSTM(BaseModule): + """Feedforward neural network model.""" + + def __init__(self, hparams): + super().__init__() + self.hparams = hparams + self.decoder = None + self.build_model() + self.hidden_cell = (torch.zeros(hparams["stack"], hparams["batch"], hparams["hidden_layer_size"]), + torch.zeros(hparams["stack"], hparams["batch"], hparams["hidden_layer_size"])) + + def __str__(self): + """Pretty print model architecture.""" + # TODO + pass + + def build_model(self): + """Construct the model.""" + # TODO + self.decoder = nn.ModuleList() + + global_layer_num = 0 + + out_size = self.hparams['n_hid_units']# fix it to the input size of the global backbone network + + in_size_1 = self.hparams['input_size'][0] + in_size_2 = self.hparams['input_size'][1] + + + layer = nn.ModuleList( + [ + nn.Linear(in_size_1, self.hparams['lstm_in_size']) + ]) + name = str('InputMLP_layer_%02i' % global_layer_num) + self.decoder.add_module(name, layer) + + # # Add activation + # global_layer_num += 1 + # name = '%s_%02i' % (self.hparams['activation'], global_layer_num) + # activation = nn.ReLU() + # self.decoder.add_module(name, activation) + + # Add a second head of linear and activations + global_layer_num += 1 + layer = nn.ModuleList( + [ + nn.Linear(in_size_2, self.hparams['lstm_in_size']) + ]) + name = str('InputMLP_layer_%02i' % global_layer_num) + self.decoder.add_module(name, layer) + + # # Add activation + # global_layer_num += 1 + # name = '%s_%02i' % (self.hparams['activation'], global_layer_num) + # activation = nn.ReLU() + # self.decoder.add_module(name, activation) + + # update layer info # add lstm layer + global_layer_num += 1 + layer = nn.LSTM(input_size=self.hparams["lstm_in_size"], hidden_size=self.hparams["hidden_layer_size"], num_layers=self.hparams["stack"]) + name = str('lstm_layer_%02i'%global_layer_num) + self.decoder.add_module(name,layer) + + # update layer info + global_layer_num += 1 + in_size = out_size + + # add linear layer + layer = nn.Linear(in_features=self.hparams["hidden_layer_size"],out_features=self.hparams["output_size"]) + name = str('dense_layer_%02i'%global_layer_num) + self.decoder.add_module(name,layer) + self.final_layer = name + + + + + def forward(self, x,dataset): + """Process input data. + + Parameters + ---------- + x : :obj:`torch.Tensor` + shape of (time, neurons) + + Returns + ------- + :obj:`tuple` + - x (:obj:`torch.Tensor`): mean prediction of model + - y (:obj:`torch.Tensor`): precision matrix prediction of model (when using 'mlp-mv') + + """ + # sess_id = [s for s in self.hparams['input_size']] + + + + y = None + for name, layer in self.decoder.named_children(): + + if name == 'InputMLP_layer_00' and dataset==0: + # input is batch x in_channels x time + # output is batch x out_channels x time + x = layer[0](x.unsqueeze(0)).squeeze().transpose(1,0) + + # if name=='relu_01' and dataset==0: + # x = layer(x) + + if name == 'InputMLP_layer_01' and dataset==1: + # input is batch x in_channels x time + # output is batch x out_channels x time + x = layer[0](x.unsqueeze(0)).squeeze().transpose(1,0) + + # if name=='relu_03' and dataset==1: + # x = layer(x) + + if name == 'lstm_layer_02': + x = x.reshape(189,1,-1) + x, _ = layer(x,self.hidden_cell) + + elif name == 'dense_layer_03': + x = layer(x) + + return x.reshape(189,10), y + + + From 77c97af664d6e393fe49717602b8280175ab82b9 Mon Sep 17 00:00:00 2001 From: nihaarshah Date: Tue, 9 Feb 2021 18:30:39 +0530 Subject: [PATCH 50/50] Adding a test file to new_branch --- test | 1 + 1 file changed, 1 insertion(+) create mode 100644 test diff --git a/test b/test new file mode 100644 index 0000000..d25c715 --- /dev/null +++ b/test @@ -0,0 +1 @@ +“some test file”