From b27a21d848f6c0d7d56c28bf5d062758feb4f96f Mon Sep 17 00:00:00 2001 From: matteobeltrami Date: Tue, 16 Jan 2024 10:36:17 +0000 Subject: [PATCH 01/31] fix input shape parsing --- recipes/object_detection/inference.py | 2 +- recipes/object_detection/train.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/recipes/object_detection/inference.py b/recipes/object_detection/inference.py index b37380f..48cbd38 100644 --- a/recipes/object_detection/inference.py +++ b/recipes/object_detection/inference.py @@ -62,7 +62,7 @@ def forward(self, img): ) hparams = parse_configuration(sys.argv[1]) - if isinstance(hparams.input_shape, str): + if len(hparams.input_shape) != 3: hparams.input_shape = [ int(x) for x in "".join(hparams.input_shape).split(",") ] # temp solution diff --git a/recipes/object_detection/train.py b/recipes/object_detection/train.py index 87f2497..dfc6f91 100644 --- a/recipes/object_detection/train.py +++ b/recipes/object_detection/train.py @@ -231,7 +231,7 @@ def replace_datafolder(hparams, data_cfg): if __name__ == "__main__": assert len(sys.argv) > 1, "Please pass the configuration file to the script." hparams = parse_configuration(sys.argv[1]) - if isinstance(hparams.input_shape, str): + if len(hparams.input_shape) != 3: hparams.input_shape = [ int(x) for x in "".join(hparams.input_shape).split(",") ] # temp solution From c1dc2d6643faeee02c61bcdbf652987e489bad98 Mon Sep 17 00:00:00 2001 From: matteobeltrami Date: Fri, 9 Feb 2024 11:20:35 +0100 Subject: [PATCH 02/31] modified --- .../object_detection/modified_load_params.py | 34 +++++++++++++++++++ 1 file changed, 34 insertions(+) create mode 100644 recipes/object_detection/modified_load_params.py diff --git a/recipes/object_detection/modified_load_params.py b/recipes/object_detection/modified_load_params.py new file mode 100644 index 0000000..ebdcc91 --- /dev/null +++ b/recipes/object_detection/modified_load_params.py @@ -0,0 +1,34 @@ +from micromind.networks.yolo import YOLOv8 +from micromind.utils.yolo import get_variant_multiples +import torch + +if __name__ == "__main__": + + conf = "n" + w, r, d = get_variant_multiples(conf) + model = YOLOv8(w, r, d, num_classes=80) + model_dict_keys = list(model.state_dict().keys()) + model_dict = model.state_dict() + + # downloaded_dict = torch.load(download_filename)["model"].state_dict() + # downloaded_dict_keys = downloaded_dict.keys() + + + downloaded_dict = torch.load(f"../../../ultralytics/modelll.pt") # scaricato + downloaded_dict_keys = downloaded_dict.keys() + + for i, key in enumerate(downloaded_dict_keys): + assert model_dict[model_dict_keys[i]].shape == downloaded_dict[key].shape + if downloaded_dict[key].size != 1: + tmp = torch.tensor(downloaded_dict[key], dtype=downloaded_dict[key].dtype) + else: + tmp = torch.tensor([downloaded_dict[key]], dtype=downloaded_dict[key].dtype) + model_dict[model_dict_keys[i]] = tmp + assert ( + model_dict[model_dict_keys[i]] == downloaded_dict[key] + ).all(), f"Failed at: {model_dict_keys[i]}." + torch.save(model_dict, "test.pt") + + breakpoint() + model.load_state_dict(dict) + breakpoint() \ No newline at end of file From 5bdc66f15014a0d7b161fc1d0336a7db00ce38bc Mon Sep 17 00:00:00 2001 From: matteobeltrami Date: Fri, 9 Feb 2024 11:29:50 +0100 Subject: [PATCH 03/31] train yolov8n --- .../object_detection/train_yolov8n_test.py | 279 ++++++++++++++++++ 1 file changed, 279 insertions(+) create mode 100644 recipes/object_detection/train_yolov8n_test.py diff --git a/recipes/object_detection/train_yolov8n_test.py b/recipes/object_detection/train_yolov8n_test.py new file mode 100644 index 0000000..4907541 --- /dev/null +++ b/recipes/object_detection/train_yolov8n_test.py @@ -0,0 +1,279 @@ +""" +YOLO training. + +This code allows you to train an object detection model with the YOLOv8 neck and loss. + +To run this script, you can start it with: + python train.py cfg/yolo_phinet.py + +Authors: + - Matteo Beltrami, 2023 + - Francesco Paissan, 2023 +""" + +import torch +from prepare_data import create_loaders +from ultralytics.utils.ops import scale_boxes, xywh2xyxy +from yolo_loss import Loss + +import micromind as mm +from micromind.networks import PhiNet +from micromind.networks.yolo import SPPF, DetectionHead, Yolov8Neck, Yolov8NeckOpt +from micromind.utils import parse_configuration +from micromind.utils.yolo import ( + load_config, + mean_average_precision, + postprocess, +) +from micromind.networks.yolo import YOLOv8 +import sys +import os +from micromind.utils.yolo import get_variant_multiples + + +class YOLO(mm.MicroMind): + def __init__(self, m_cfg, hparams, *args, **kwargs): + """Initializes the YOLO model.""" + super().__init__(*args, **kwargs) + self.m_cfg = m_cfg + w, r, d = get_variant_multiples("n") + self.modules["yolov8"] = YOLOv8(w,r,d, 80) + self.modules["yolov8"].load_state_dict(torch.load("usable_yolov8n.pt")) + + # self.modules["backbone"] = PhiNet( + # input_shape=hparams.input_shape, + # alpha=hparams.alpha, + # num_layers=hparams.num_layers, + # beta=hparams.beta, + # t_zero=hparams.t_zero, + # include_top=False, + # compatibility=False, + # divisor=hparams.divisor, + # downsampling_layers=hparams.downsampling_layers, + # return_layers=hparams.return_layers, + # ) + + # sppf_ch, neck_filters, up, head_filters = self.get_parameters( + # heads=hparams.heads + # ) + + # self.modules["sppf"] = SPPF(*sppf_ch) + # self.modules["neck"] = Yolov8NeckOpt( + # filters=neck_filters, up=up, heads=hparams.heads + # ) + # self.modules["head"] = DetectionHead(filters=head_filters, heads=hparams.heads) + + self.criterion = Loss(self.m_cfg, self.modules["yolov8"].head, self.device) + + print("Number of parameters for each module:") + print(self.compute_params()) + + # def get_parameters(self, heads=[True, True, True]): + # """ + # Gets the parameters with which to initialize the network detection part + # (SPPF block, Yolov8Neck, DetectionHead). + + # Arguments + # --------- + # heads : Optional[List] + # List indicating whether each detection head is active. + # Default: [True, True, True]. + + # Returns + # ------- + # Tuple containing the parameters for initializing the network detection part. + # Contains + # - Tuple (c1, c2): Tuple of input channel sizes for the SPPF block. + # - List neck_filters: List of filter sizes for Yolov8Neck. + # - List up: List of upsampling factors for Yolov8Neck. + # - List head_filters: List of filter sizes for DetectionHead. : Tuple + # """ + # in_shape = self.modules["backbone"].input_shape + # x = torch.randn(1, *in_shape) + # y = self.modules["backbone"](x) + + # c1 = c2 = y[0].shape[1] + # sppf = SPPF(c1, c2) + # out_sppf = sppf(y[0]) + + # neck_filters = [y[1][0].shape[1], y[1][1].shape[1], out_sppf.shape[1]] + # up = [2, 2] + # up[0] = y[1][1].shape[2] / out_sppf.shape[2] + # up[1] = y[1][0].shape[2] / (up[0] * out_sppf.shape[2]) + # temp = """The layers you selected are not valid. \ + # Please choose only layers between which the spatial resolution \ + # doubles every time. Eventually, you can achieve this by \ + # changing the downsampling layers. If you are trying to change \ + # the input resolution, make sure you also change it in the \ + # dataset configuration file and that it is a multiple of 4.""" + + # assert up == [2, 2], " ".join(temp.split()) + + # neck = Yolov8Neck(filters=neck_filters, up=up) + # out_neck = neck(y[1][0], y[1][1], out_sppf) + + # head_filters = ( + # out_neck[0].shape[1], + # out_neck[1].shape[1], + # out_neck[2].shape[1], + # ) + # # keep only the heads we want + # head_filters = [head for heads, head in zip(heads, head_filters) if heads] + + # return (c1, c2), neck_filters, up, head_filters + + def preprocess_batch(self, batch): + """Preprocesses a batch of images by scaling and converting to float.""" + preprocessed_batch = {} + preprocessed_batch["img"] = ( + batch["img"].to(self.device, non_blocking=True).float() / 255 + ) + for k in batch: + if isinstance(batch[k], torch.Tensor) and k != "img": + preprocessed_batch[k] = batch[k].to(self.device) + + return preprocessed_batch + + def forward(self, batch): + """Runs the forward method by calling every module.""" + preprocessed_batch = self.preprocess_batch(batch) + # backbone = self.modules["backbone"](preprocessed_batch["img"].to(self.device)) + # neck_input = backbone[1] + # neck_input.append(self.modules["sppf"](backbone[0])) + # neck = self.modules["neck"](*neck_input) + # head = self.modules["head"](neck) + + # return head + return self.modules["yolov8"](preprocessed_batch["img"].to(self.device)) + + def compute_loss(self, pred, batch): + """Computes the loss.""" + preprocessed_batch = self.preprocess_batch(batch) + + lossi_sum, lossi = self.criterion( + pred, + preprocessed_batch, + ) + + return lossi_sum + + def configure_optimizers(self): + """Configures the optimizer and the scheduler.""" + opt = torch.optim.SGD(self.modules.parameters(), lr=1e-2, weight_decay=0.0005) + sched = torch.optim.lr_scheduler.CosineAnnealingLR( + opt, T_max=14000, eta_min=1e-3 + ) + # sched = torch.optim.lr_scheduler.ReduceLROnPlateau( + # opt, factor=0.7, patience=5, threshold=0.1 + # ) + return opt, sched + + @torch.no_grad() + def mAP(self, pred, batch): + """Compute the mean average precision (mAP) for a batch of predictions. + + Arguments + --------- + pred : torch.Tensor + Model predictions for the batch. + batch : dict + A dictionary containing batch information, including bounding boxes, + classes and shapes. + + Returns + ------- + torch.Tensor + A tensor containing the computed mean average precision (mAP) for the batch. + """ + preprocessed_batch = self.preprocess_batch(batch) + post_predictions = postprocess( + preds=pred[0], img=preprocessed_batch, orig_imgs=batch + ) + + batch_bboxes_xyxy = xywh2xyxy(batch["bboxes"]) + dim = batch["resized_shape"][0][0] + batch_bboxes_xyxy[:, :4] *= dim + + batch_bboxes = [] + for i in range(len(batch["batch_idx"])): + for b in range(len(batch_bboxes_xyxy[batch["batch_idx"] == i, :])): + batch_bboxes.append( + scale_boxes( + batch["resized_shape"][i], + batch_bboxes_xyxy[batch["batch_idx"] == i, :][b], + batch["ori_shape"][i], + ) + ) + + batch_bboxes = torch.stack(batch_bboxes).to(self.device) + mmAP = mean_average_precision( + post_predictions, batch, batch_bboxes, data_cfg["nc"] + ) + + return torch.Tensor([mmAP]) + + +def replace_datafolder(hparams, data_cfg): + """Replaces the data root folder, if told to do so from the configuration.""" + data_cfg["path"] = str(data_cfg["path"]) + data_cfg["path"] = ( + data_cfg["path"][:-1] if data_cfg["path"][-1] == "/" else data_cfg["path"] + ) + for key in ["train", "val"]: + if not isinstance(data_cfg[key], list): + data_cfg[key] = [data_cfg[key]] + new_list = [] + for tmp in data_cfg[key]: + if hasattr(hparams, "data_dir"): + if hparams.data_dir != data_cfg["path"]: + tmp = str(tmp).replace(data_cfg["path"], "") + tmp = tmp[1:] if tmp[0] == "/" else tmp + tmp = os.path.join(hparams.data_dir, tmp) + new_list.append(tmp) + data_cfg[key] = new_list + + data_cfg["path"] = hparams.data_dir + + return data_cfg + + +if __name__ == "__main__": + assert len(sys.argv) > 1, "Please pass the configuration file to the script." + hparams = parse_configuration(sys.argv[1]) + if len(hparams.input_shape) != 3: + hparams.input_shape = [ + int(x) for x in "".join(hparams.input_shape).split(",") + ] # temp solution + print(f"Setting input shape to {hparams.input_shape}.") + + m_cfg, data_cfg = load_config(hparams.data_cfg) + + # check if specified path for images is different, correct it in case + data_cfg = replace_datafolder(hparams, data_cfg) + m_cfg.imgsz = hparams.input_shape[-1] # temp solution + + train_loader, val_loader = create_loaders(m_cfg, data_cfg, hparams.batch_size) + + exp_folder = mm.utils.checkpointer.create_experiment_folder( + hparams.output_folder, hparams.experiment_name + ) + + checkpointer = mm.utils.checkpointer.Checkpointer( + exp_folder, hparams=hparams, key="loss" + ) + + yolo_mind = YOLO(m_cfg, hparams=hparams) + + mAP = mm.Metric("mAP", yolo_mind.mAP, eval_only=True, eval_period=1) + + yolo_mind.train( + epochs=hparams.epochs, + datasets={"train": train_loader, "val": val_loader}, + metrics=[mAP], + checkpointer=checkpointer, + debug=hparams.debug, + ) + # yolo_mind.test( + # #dataset={"test": } + # metrics=[mAP], + # ) From 4b20b82aff2fd7292be8c287ce96b7c464cd8ca9 Mon Sep 17 00:00:00 2001 From: matteobeltrami Date: Thu, 15 Feb 2024 10:22:32 +0100 Subject: [PATCH 04/31] some progress --- micromind/core.py | 20 +- recipes/object_detection/inference.py | 33 ++- recipes/object_detection/test_only.py | 280 ++++++++++++++++++++++++++ recipes/object_detection/train.py | 10 +- recipes/object_detection/yolo_loss.py | 39 +++- 5 files changed, 360 insertions(+), 22 deletions(-) create mode 100644 recipes/object_detection/test_only.py diff --git a/micromind/core.py b/micromind/core.py index 70746e1..c38ba0b 100644 --- a/micromind/core.py +++ b/micromind/core.py @@ -331,7 +331,7 @@ def add_forward_to_modules(self): self.modules.device = self.device @torch.no_grad() - def compute_params(self): + def compute_params(self, str="total"): """Computes the number of parameters for the modules inside `self.modules`. Returns a dictionary with the parameter count for each module. @@ -341,8 +341,12 @@ def compute_params(self): """ self.eval() params = {} - for k, m in self.modules.items(): - params[k] = summary(m, verbose=0).total_params + if str == "total": + for k, m in self.modules.items(): + params[k] = summary(m, verbose=0).total_params + if str == "trainable": + for k, m in self.modules.items(): + params[k] = summary(m, verbose=0).trainable_params return params @@ -460,6 +464,7 @@ def train( datasets: Dict = {}, metrics: List[Metric] = [], checkpointer: Optional[Checkpointer] = None, + max_norm=10.0, debug: Optional[bool] = False, ) -> None: """ @@ -525,12 +530,11 @@ def train( loss_epoch += loss.item() self.accelerator.backward(loss) + self.accelerator.clip_grad_norm_(self.modules.parameters(), max_norm=max_norm) self.opt.step() loss_epoch += loss.item() - if hasattr(self, "lr_sched"): - # ok for cos_lr - self.lr_sched.step() + for m in self.metrics: if ( @@ -578,6 +582,10 @@ def train( if e >= 1 and self.debug: break + if hasattr(self, "lr_sched"): + # ok for cos_lr + self.lr_sched.step(val_metrics["val_loss"]) + self.on_train_end() return None diff --git a/recipes/object_detection/inference.py b/recipes/object_detection/inference.py index 48cbd38..e76a80d 100644 --- a/recipes/object_detection/inference.py +++ b/recipes/object_detection/inference.py @@ -27,11 +27,17 @@ preprocess, ) from train import YOLO +from micromind.networks.yolo import YOLOv8 +from micromind.utils.yolo import get_variant_multiples +import micromind as mm -class Inference(YOLO): +class Inference(mm.MicroMind): def __init__(self, hparams): - super().__init__(hparams=hparams, m_cfg={}) + super().__init__(hparams=hparams) + w, r, d = get_variant_multiples("n") + self.modules["yolov8n"] = YOLOv8(w,r,d, 80) + self.modules["yolov8n"].load_state_dict(torch.load("usable_yolov8n.pt")) def forward(self, img): """Executes the detection network. @@ -45,12 +51,16 @@ def forward(self, img): ------- Output of the detection network : torch.Tensor """ - backbone = self.modules["backbone"](img) - neck_input = backbone[1] - neck_input.append(self.modules["sppf"](backbone[0])) - neck = self.modules["neck"](*neck_input) - head = self.modules["head"](neck) - return head + # backbone = self.modules["backbone"](img) + # neck_input = backbone[1] + # neck_input.append(self.modules["sppf"](backbone[0])) + # neck = self.modules["neck"](*neck_input) + # head = self.modules["head"](neck) + model = self.modules["yolov8n"] + return model + + def compute_loss(self, pred, batch): + pass if __name__ == "__main__": @@ -97,8 +107,11 @@ def forward(self, img): with torch.no_grad(): st = time.time() - predictions = model(pre_processed_image) + ones = torch.ones([1, 3, 448, 640]) + predictions = model.forward(pre_processed_image) + predictions = predictions(ones) print(f"Inference took {int(round(((time.time() - st) * 1000)))}ms") + breakpoint() post_predictions = postprocess( preds=predictions[0], img=pre_processed_image, orig_imgs=image ) @@ -112,4 +125,4 @@ def forward(self, img): ) # Exporting onnx model. - model.export("model.onnx", "onnx", hparams.input_shape) + #model.export("model.onnx", "onnx", hparams.input_shape) diff --git a/recipes/object_detection/test_only.py b/recipes/object_detection/test_only.py new file mode 100644 index 0000000..2fb39a1 --- /dev/null +++ b/recipes/object_detection/test_only.py @@ -0,0 +1,280 @@ +""" +YOLO training. + +This code allows you to train an object detection model with the YOLOv8 neck and loss. + +To run this script, you can start it with: + python train.py cfg/yolo_phinet.py + +Authors: + - Matteo Beltrami, 2023 + - Francesco Paissan, 2023 +""" + +import torch +from prepare_data import create_loaders +from ultralytics.utils.ops import scale_boxes, xywh2xyxy +from yolo_loss import Loss + +import micromind as mm +from micromind.networks import PhiNet +from micromind.networks.yolo import SPPF, DetectionHead, Yolov8Neck, Yolov8NeckOpt +from micromind.utils import parse_configuration +from micromind.utils.yolo import ( + load_config, + mean_average_precision, + postprocess, +) +from micromind.networks.yolo import YOLOv8 +import sys +import os +from micromind.utils.yolo import get_variant_multiples + + +class YOLO(mm.MicroMind): + def __init__(self, m_cfg, hparams, *args, **kwargs): + """Initializes the YOLO model.""" + super().__init__(*args, **kwargs) + self.m_cfg = m_cfg + w, r, d = get_variant_multiples("n") + self.modules["yolov8"] = YOLOv8(w,r,d, 80) + self.modules["yolov8"].load_state_dict(torch.load("usable_yolov8n.pt")) + + # self.modules["backbone"] = PhiNet( + # input_shape=hparams.input_shape, + # alpha=hparams.alpha, + # num_layers=hparams.num_layers, + # beta=hparams.beta, + # t_zero=hparams.t_zero, + # include_top=False, + # compatibility=False, + # divisor=hparams.divisor, + # downsampling_layers=hparams.downsampling_layers, + # return_layers=hparams.return_layers, + # ) + + # sppf_ch, neck_filters, up, head_filters = self.get_parameters( + # heads=hparams.heads + # ) + + # self.modules["sppf"] = SPPF(*sppf_ch) + # self.modules["neck"] = Yolov8NeckOpt( + # filters=neck_filters, up=up, heads=hparams.heads + # ) + # self.modules["head"] = DetectionHead(filters=head_filters, heads=hparams.heads) + + self.criterion = Loss(self.m_cfg, self.modules["yolov8"].head, self.device) + + print("Number of parameters for each module:") + print(self.compute_params()) + + # def get_parameters(self, heads=[True, True, True]): + # """ + # Gets the parameters with which to initialize the network detection part + # (SPPF block, Yolov8Neck, DetectionHead). + + # Arguments + # --------- + # heads : Optional[List] + # List indicating whether each detection head is active. + # Default: [True, True, True]. + + # Returns + # ------- + # Tuple containing the parameters for initializing the network detection part. + # Contains + # - Tuple (c1, c2): Tuple of input channel sizes for the SPPF block. + # - List neck_filters: List of filter sizes for Yolov8Neck. + # - List up: List of upsampling factors for Yolov8Neck. + # - List head_filters: List of filter sizes for DetectionHead. : Tuple + # """ + # in_shape = self.modules["backbone"].input_shape + # x = torch.randn(1, *in_shape) + # y = self.modules["backbone"](x) + + # c1 = c2 = y[0].shape[1] + # sppf = SPPF(c1, c2) + # out_sppf = sppf(y[0]) + + # neck_filters = [y[1][0].shape[1], y[1][1].shape[1], out_sppf.shape[1]] + # up = [2, 2] + # up[0] = y[1][1].shape[2] / out_sppf.shape[2] + # up[1] = y[1][0].shape[2] / (up[0] * out_sppf.shape[2]) + # temp = """The layers you selected are not valid. \ + # Please choose only layers between which the spatial resolution \ + # doubles every time. Eventually, you can achieve this by \ + # changing the downsampling layers. If you are trying to change \ + # the input resolution, make sure you also change it in the \ + # dataset configuration file and that it is a multiple of 4.""" + + # assert up == [2, 2], " ".join(temp.split()) + + # neck = Yolov8Neck(filters=neck_filters, up=up) + # out_neck = neck(y[1][0], y[1][1], out_sppf) + + # head_filters = ( + # out_neck[0].shape[1], + # out_neck[1].shape[1], + # out_neck[2].shape[1], + # ) + # # keep only the heads we want + # head_filters = [head for heads, head in zip(heads, head_filters) if heads] + + # return (c1, c2), neck_filters, up, head_filters + + def preprocess_batch(self, batch): + """Preprocesses a batch of images by scaling and converting to float.""" + preprocessed_batch = {} + preprocessed_batch["img"] = ( + batch["img"].to(self.device, non_blocking=True).float() / 255 + #batch["img"].to(self.device, non_blocking=True).float() *0 +1 + ) + for k in batch: + if isinstance(batch[k], torch.Tensor) and k != "img": + preprocessed_batch[k] = batch[k].to(self.device) + + return preprocessed_batch + + def forward(self, batch): + """Runs the forward method by calling every module.""" + preprocessed_batch = self.preprocess_batch(batch) + # backbone = self.modules["backbone"](preprocessed_batch["img"].to(self.device)) + # neck_input = backbone[1] + # neck_input.append(self.modules["sppf"](backbone[0])) + # neck = self.modules["neck"](*neck_input) + # head = self.modules["head"](neck) + + # return head + return self.modules["yolov8"](preprocessed_batch["img"].to(self.device)) + + def compute_loss(self, pred, batch): + """Computes the loss.""" + preprocessed_batch = self.preprocess_batch(batch) + + lossi_sum, lossi = self.criterion( + pred, + preprocessed_batch, + ) + + return lossi_sum + + def configure_optimizers(self): + """Configures the optimizer and the scheduler.""" + opt = torch.optim.SGD(self.modules.parameters(), lr=1e-2, weight_decay=0.0005) + # sched = torch.optim.lr_scheduler.CosineAnnealingLR( + # opt, T_max=14000, eta_min=1e-3 + # ) + # sched = torch.optim.lr_scheduler.ReduceLROnPlateau( + # opt, factor=0.7, patience=5, threshold=0.1 + # ) + return opt#, sched + + @torch.no_grad() + def mAP(self, pred, batch): + """Compute the mean average precision (mAP) for a batch of predictions. + + Arguments + --------- + pred : torch.Tensor + Model predictions for the batch. + batch : dict + A dictionary containing batch information, including bounding boxes, + classes and shapes. + + Returns + ------- + torch.Tensor + A tensor containing the computed mean average precision (mAP) for the batch. + """ + preprocessed_batch = self.preprocess_batch(batch) + post_predictions = postprocess( + preds=pred[0], img=preprocessed_batch, orig_imgs=batch + ) + + batch_bboxes_xyxy = xywh2xyxy(batch["bboxes"]) + dim = batch["resized_shape"][0][0] + batch_bboxes_xyxy[:, :4] *= dim + + batch_bboxes = [] + for i in range(len(batch["batch_idx"])): + for b in range(len(batch_bboxes_xyxy[batch["batch_idx"] == i, :])): + batch_bboxes.append( + scale_boxes( + batch["resized_shape"][i], + batch_bboxes_xyxy[batch["batch_idx"] == i, :][b], + batch["ori_shape"][i], + ) + ) + + batch_bboxes = torch.stack(batch_bboxes).to(self.device) + mmAP = mean_average_precision( + post_predictions, batch, batch_bboxes, data_cfg["nc"] + ) + + return torch.Tensor([mmAP]) + + +def replace_datafolder(hparams, data_cfg): + """Replaces the data root folder, if told to do so from the configuration.""" + data_cfg["path"] = str(data_cfg["path"]) + data_cfg["path"] = ( + data_cfg["path"][:-1] if data_cfg["path"][-1] == "/" else data_cfg["path"] + ) + for key in ["train", "val"]: + if not isinstance(data_cfg[key], list): + data_cfg[key] = [data_cfg[key]] + new_list = [] + for tmp in data_cfg[key]: + if hasattr(hparams, "data_dir"): + if hparams.data_dir != data_cfg["path"]: + tmp = str(tmp).replace(data_cfg["path"], "") + tmp = tmp[1:] if tmp[0] == "/" else tmp + tmp = os.path.join(hparams.data_dir, tmp) + new_list.append(tmp) + data_cfg[key] = new_list + + data_cfg["path"] = hparams.data_dir + + return data_cfg + + +if __name__ == "__main__": + assert len(sys.argv) > 1, "Please pass the configuration file to the script." + hparams = parse_configuration(sys.argv[1]) + if len(hparams.input_shape) != 3: + hparams.input_shape = [ + int(x) for x in "".join(hparams.input_shape).split(",") + ] # temp solution + print(f"Setting input shape to {hparams.input_shape}.") + + m_cfg, data_cfg = load_config(hparams.data_cfg) + + # check if specified path for images is different, correct it in case + data_cfg = replace_datafolder(hparams, data_cfg) + m_cfg.imgsz = hparams.input_shape[-1] # temp solution + + train_loader, val_loader = create_loaders(m_cfg, data_cfg, hparams.batch_size) + + exp_folder = mm.utils.checkpointer.create_experiment_folder( + hparams.output_folder, hparams.experiment_name + ) + + checkpointer = mm.utils.checkpointer.Checkpointer( + exp_folder, hparams=hparams, key="loss" + ) + + yolo_mind = YOLO(m_cfg, hparams=hparams) + + mAP = mm.Metric("mAP", yolo_mind.mAP, eval_only=True, eval_period=1) + + yolo_mind.train( + epochs=hparams.epochs, + datasets={"train": train_loader, "val": val_loader}, + metrics=[mAP], + checkpointer=checkpointer, + debug=hparams.debug, + ) + # yolo_mind.test( + # datasets={"test": val_loader}, + # metrics=[mAP], + # ) diff --git a/recipes/object_detection/train.py b/recipes/object_detection/train.py index dfc6f91..b94ea03 100644 --- a/recipes/object_detection/train.py +++ b/recipes/object_detection/train.py @@ -56,8 +56,7 @@ def __init__(self, m_cfg, hparams, *args, **kwargs): self.modules["neck"] = Yolov8NeckOpt( filters=neck_filters, up=up, heads=hparams.heads ) - self.modules["head"] = DetectionHead(filters=head_filters, heads=hparams.heads) - + self.modules["head"] = DetectionHead(hparams.num_classes, filters=head_filters, heads=hparams.heads) self.criterion = Loss(self.m_cfg, self.modules["head"], self.device) print("Number of parameters for each module:") @@ -154,8 +153,11 @@ def compute_loss(self, pred, batch): def configure_optimizers(self): """Configures the optimizer and the scheduler.""" opt = torch.optim.SGD(self.modules.parameters(), lr=1e-2, weight_decay=0.0005) - sched = torch.optim.lr_scheduler.CosineAnnealingLR( - opt, T_max=14000, eta_min=1e-3 + # sched = torch.optim.lr_scheduler.CosineAnnealingLR( + # opt, T_max=14000, eta_min=1e-3 + # ) + sched = torch.optim.lr_scheduler.ReduceLROnPlateau( + opt, factor=0.7, patience=5, threshold=0.1 ) return opt, sched diff --git a/recipes/object_detection/yolo_loss.py b/recipes/object_detection/yolo_loss.py index 29650a6..4d6b64b 100644 --- a/recipes/object_detection/yolo_loss.py +++ b/recipes/object_detection/yolo_loss.py @@ -76,8 +76,11 @@ def __call__(self, preds, batch): [xi.view(feats[0].shape[0], self.no, -1) for xi in feats], 2 ).split((self.reg_max * 4, self.nc), 1) + # breakpoint() pred_scores = pred_scores.permute(0, 2, 1).contiguous() pred_distri = pred_distri.permute(0, 2, 1).contiguous() + # print("pred scores shape ", pred_scores.shape) # x, 8400, 80 + # print("pred distri shape ", pred_distri.shape) # x, 8400, 64 (reg_max * 4) dtype = pred_scores.dtype batch_size = pred_scores.shape[0] @@ -85,6 +88,7 @@ def __call__(self, preds, batch): torch.tensor(feats[0].shape[2:], device=self.device, dtype=dtype) * self.stride[0] ) # image size (h,w) + # print(imgsz) anchor_points, stride_tensor = make_anchors(feats, self.stride, 0.5) # Targets @@ -95,21 +99,29 @@ def __call__(self, preds, batch): targets = self.preprocess( targets.to(self.device), batch_size, scale_tensor=imgsz[[1, 0, 1, 0]] ) + gt_labels, gt_bboxes = targets.split((1, 4), 2) # cls, xyxy mask_gt = gt_bboxes.sum(2, keepdim=True).gt_(0) # Pboxes pred_bboxes = self.bbox_decode(anchor_points, pred_distri) # xyxy, (b, h*w, 4) + # print("pred bboxes") + # print(pred_bboxes.shape) + # print(pred_bboxes[0, 0]) _, target_bboxes, target_scores, fg_mask, _ = self.assigner( - pred_scores.detach().sigmoid(), - (pred_bboxes.detach() * stride_tensor).type(gt_bboxes.dtype), + pred_scores.clone().detach().sigmoid(), + (pred_bboxes.clone().detach() * stride_tensor).type(gt_bboxes.dtype), anchor_points * stride_tensor, gt_labels, gt_bboxes, mask_gt, ) + # print("target bboxes") + # print(target_bboxes.shape) + # print(target_bboxes[0, 0]) + target_scores_sum = max(target_scores.sum(), 1) # Cls loss @@ -117,9 +129,14 @@ def __call__(self, preds, batch): self.bce(pred_scores, target_scores.to(dtype)).sum() / target_scores_sum ) # BCE + # print("classification loss", loss[1]) # Bbox loss if fg_mask.sum(): target_bboxes /= stride_tensor + # print("target bboxes w norm") + # print(target_bboxes.shape) + # print(target_bboxes[0, 0]) + loss[0], loss[2] = self.bbox_loss( pred_distri, pred_bboxes, @@ -134,4 +151,22 @@ def __call__(self, preds, batch): loss[1] *= self.hyp.cls # cls gain loss[2] *= self.hyp.dfl # dfl gain + + with open("dump.txt", "a") as f: + f.write("bbox loss {}".format(loss[0].item())) + f.write("\n") + f.write("cls loss {}".format(loss[1].item())) + f.write("\n") + f.write("dfl loss {}".format(loss[2].item())) + f.write("\n") + f.write("total {}".format(loss.sum().item())) + f.write("\n") + f.write("total * batch_size {}".format(loss.sum().item() * batch_size)) + f.write("\n") + + breakpoint() + + # print(torch.std_mean(batch["img"])) + # breakpoint() + return loss.sum() * batch_size, loss.detach() # loss(box, cls, dfl) From a0696974c51460c8baf3e7e3a838e2cc4611dbab Mon Sep 17 00:00:00 2001 From: matteobeltrami Date: Thu, 15 Feb 2024 09:52:35 +0000 Subject: [PATCH 05/31] weights --- recipes/object_detection/usable_yolov8n.pt | Bin 0 -> 6435511 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 recipes/object_detection/usable_yolov8n.pt diff --git a/recipes/object_detection/usable_yolov8n.pt b/recipes/object_detection/usable_yolov8n.pt new file mode 100644 index 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