start a config for using models in a deterministic setup

rather than score-sde/diffusion setting

src/ml_downscaling_emulator/score_sde_pytorch/configs/default_ukcp_local_pr_1em_configs.py

@@ -74,5 +74,6 @@ def get_default_configs():
 
   config.seed = 42
   config.device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
+  config.deterministic = False
 
   return config

src/ml_downscaling_emulator/score_sde_pytorch/configs/determinisitic/ukcp_local_pr_12em_cncsnpp.py

@@ -0,0 +1,109 @@
+# coding=utf-8
+# Copyright 2020 The Google Research Authors.
+# Modifications copyright 2024 Henry Addison
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Lint as: python3
+"""Training NCSN++ on precip data in a deterministic fashion."""
+
+import ml_collections
+
+def get_config():
+  config = ml_collections.ConfigDict()
+
+  # training
+  config.training = training = ml_collections.ConfigDict()
+  config.training.batch_size = 16#128
+  training.n_epochs = 100
+  training.snapshot_freq = 25
+  training.log_freq = 50
+  training.eval_freq = 1000
+  ## store additional checkpoints for preemption in cloud computing environments
+  training.snapshot_freq_for_preemption = 1000
+  ## produce samples at each snapshot.
+  training.snapshot_sampling = False
+  training.likelihood_weighting = False
+  training.continuous = True
+  training.reduce_mean = False
+  training.random_crop_size = 0
+  training.continuous = True
+  training.reduce_mean = True
+  training.n_epochs = 20
+  training.snapshot_freq = 5
+  training.eval_freq = 5000
+
+  # sampling
+  config.sampling = sampling = ml_collections.ConfigDict()
+
+  # evaluation
+  config.eval = evaluate = ml_collections.ConfigDict()
+
+  # data
+  config.data = data = ml_collections.ConfigDict()
+  data.dataset = 'UKCP18'
+  data.dataset_name = 'bham_gcmx-4x_1em_psl-sphum4th-temp4th-vort4th_eqvt_random-season'
+  data.image_size = 64
+  data.random_flip = False
+  data.centered = False
+  data.uniform_dequantization = False
+  data.time_inputs = False
+  data.centered = True
+  data.dataset_name = 'bham_gcmx-4x_12em_psl-sphum4th-temp4th-vort4th_eqvt_random-season'
+  data.input_transform_key = "stan"
+  data.target_transform_key = "sqrturrecen"
+
+  # model
+  config.model = model = ml_collections.ConfigDict()
+  model.dropout = 0.1
+  model.embedding_type = 'fourier'
+  model.loc_spec_channels = 0
+  model.name = 'cncsnpp'
+  model.scale_by_sigma = False
+  model.ema_rate = 0.9999
+  model.normalization = 'GroupNorm'
+  model.nonlinearity = 'swish'
+  model.nf = 128
+  model.ch_mult = (1, 2, 2, 2)
+  model.num_res_blocks = 4
+  model.attn_resolutions = (16,)
+  model.resamp_with_conv = True
+  model.conditional = True
+  model.fir = True
+  model.fir_kernel = [1, 3, 3, 1]
+  model.skip_rescale = True
+  model.resblock_type = 'biggan'
+  model.progressive = 'none'
+  model.progressive_input = 'residual'
+  model.progressive_combine = 'sum'
+  model.attention_type = 'ddpm'
+  model.embedding_type = 'positional'
+  model.init_scale = 0.
+  model.fourier_scale = 16
+  model.conv_size = 3
+
+  # optimization
+  config.optim = optim = ml_collections.ConfigDict()
+  optim.weight_decay = 0
+  optim.optimizer = 'Adam'
+  optim.lr = 2e-4
+  optim.beta1 = 0.9
+  optim.eps = 1e-8
+  optim.warmup = 5000
+  optim.grad_clip = 1.
+
+  config.seed = 42
+  config.device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
+  config.deterministic = True
+
+  return config