from pathlib import Path from natsort import natsorted import numpy as np from typing import List, Tuple from matplotlib import pyplot as plt from tqdm import tqdm from monai.visualize.utils import blend_images import torch from monai.bundle import ConfigParser from monai.data import ThreadDataLoader from surg_seg.Datasets.SegmentationLabelParser import ( SegmentationLabelParser, YamlSegMapReader, ) from surg_seg.Datasets.ImageDataset import ( ImageDirParser, ImageSegmentationDataset, ) from surg_seg.ImageTransforms.ImageTransforms import ImageTransforms from surg_seg.Metrics.MetricsUtils import AggregatedMetricTable, IOUStats from surg_seg.Networks.Models import FlexibleUnet1InferencePipe, create_FlexibleUnet from surg_seg.Trainers.Trainer import ModelTrainer ################################################################## # Concrete implementation of abstract classes ################################################################## class CustomImageDirParser(ImageDirParser): def __init__(self, root_dirs: List[Path]): super().__init__(root_dirs) self.parse_image_dir(root_dirs[0]) def parse_image_dir(self, root_dir: Path): self.images_list = natsorted(list((root_dir / "raw").glob("*.png"))) self.labels_list = natsorted(list((root_dir / "label").glob("*.png"))) ################################################################## # Auxiliary functions ################################################################## def create_label_parser(config: ConfigParser) -> SegmentationLabelParser: train_config = config.get_parsed_content("ambf_train_config") mapping_file = train_config["mapping_file"] label_info_reader = YamlSegMapReader(mapping_file) label_parser = SegmentationLabelParser(label_info_reader) return label_parser def create_train_dataset_and_dataloader( config: ConfigParser, label_parser: SegmentationLabelParser, batch_size: int ) -> Tuple[ImageSegmentationDataset, ThreadDataLoader]: train_config = config.get_parsed_content("ambf_train_config") train_dir_list = train_config["train_dir_list"] train_data_reader = CustomImageDirParser(train_dir_list) ds = ImageSegmentationDataset( label_parser, train_data_reader, color_transforms=ImageTransforms.img_transforms, geometric_transforms=ImageTransforms.geometric_transforms, ) dl = ThreadDataLoader(ds, batch_size=batch_size, num_workers=2, shuffle=True) return ds, dl def create_valid_dataset_and_dataloader( config: ConfigParser, label_parser: SegmentationLabelParser, batch_size: int ) -> Tuple[ImageSegmentationDataset, ThreadDataLoader]: train_config = config.get_parsed_content("ambf_train_config") valid_dir_list = train_config["val_dir_list"] valid_data_reader = CustomImageDirParser(valid_dir_list) val_ds = ImageSegmentationDataset( label_parser, valid_data_reader, color_transforms=ImageTransforms.img_transforms ) val_dl = ThreadDataLoader(val_ds, batch_size=batch_size, num_workers=2, shuffle=True) return val_ds, val_dl ################################################################## # Main functions ################################################################## def train_with_image_dataset(config: ConfigParser): train_config = config.get_parsed_content("ambf_train_config") device = train_config["device"] # Load data label_parser = create_label_parser(config) ds, dl = create_train_dataset_and_dataloader(config, label_parser, batch_size=8) val_ds, val_dl = create_valid_dataset_and_dataloader(config, label_parser, batch_size=8) print(f"Training dataset size: {len(ds)}") print(f"Validation dataset size: {len(val_ds)}") print(f"Number of output clases: {label_parser.mask_num}") # Load model pretrained_weights_path = train_config["pretrained_weights_path"] model = create_FlexibleUnet(device, pretrained_weights_path, label_parser.mask_num) # Load trainer training_output_path = train_config["training_output_path"] epochs = train_config["epochs"] learning_rate = train_config["learning_rate"] optimizer = torch.optim.Adam(model.parameters(), learning_rate) trainer = ModelTrainer(device=device, max_epochs=epochs) model, training_stats = trainer.train_model(model, optimizer, dl, validation_dl=val_dl) # Save model training_output_path.mkdir(exist_ok=True) torch.save(model.state_dict(), training_output_path / "myweights.pt") training_stats.to_pickle(training_output_path) training_stats.plot_stats(file_path=training_output_path) print(f"Last train IOU {training_stats.iou_list[-1]}") print(f"Last validation IOU {training_stats.validation_iou_list[-1]}") def show_images(config: ConfigParser, show_valid: str = False): train_config = config.get_parsed_content("ambf_train_config") label_parser = create_label_parser(config) if show_valid: print("Showing validation images") ds, dl = create_valid_dataset_and_dataloader(config, label_parser, batch_size=1) else: print("Showing training images") ds, dl = create_valid_dataset_and_dataloader(config, label_parser, batch_size=1) fig, axes = plt.subplots(3, 3, figsize=(8, 8)) fig.set_tight_layout(True) fig.subplots_adjust(hspace=0, wspace=0) for i, ax in enumerate(axes.flat): pair = next(iter(dl)) im = pair["image"][0] lb = pair["label"][0] im = ImageTransforms.inv_transforms(im) lb = label_parser.convert_onehot_to_single_ch(lb) blended = blend_images(im, lb, cmap="viridis", alpha=0.7) blended = blended.numpy().transpose(1, 2, 0) blended = (blended * 255).astype(np.uint8) ax.imshow(blended) ax.axis("off") plt.show() def show_inference_samples(config: ConfigParser): device = "cuda" path2weights = config.get_parsed_content("test#weights") label_parser = create_label_parser(config) ds, dl = create_valid_dataset_and_dataloader(config, label_parser, batch_size=1) model_pipe = FlexibleUnet1InferencePipe( path2weights, device, out_channels=label_parser.mask_num ) model_pipe.model.eval() fig, axes = plt.subplots(3, 3, figsize=(8, 8)) fig.set_tight_layout(True) fig.subplots_adjust(hspace=0, wspace=0) for i, ax in enumerate(axes.flat): # pair = next(iter(dl)) pair = ds.__getitem__(i, transform=False) im = pair["image"] lb = pair["label"] print(im.shape) input_tensor, inferred_single_ch = model_pipe.infer(im) inferred_single_ch = inferred_single_ch.detach().cpu() input_tensor = input_tensor.detach().cpu()[0] blended = blend_images(input_tensor, inferred_single_ch, cmap="viridis", alpha=0.8).numpy() blended = (np.transpose(blended, (1, 2, 0)) * 254).astype(np.uint8) # im = ImageTransforms.inv_transforms(im) # lb = label_parser.convert_onehot_to_single_ch(lb) # blended = blend_images(im, lb, cmap="viridis", alpha=0.7) # blended = blended.numpy().transpose(1, 2, 0) # blended = (blended * 255).astype(np.uint8) ax.imshow(blended) ax.axis("off") plt.show() def calculate_metrics_on_valid(config: ConfigParser): device = "cuda" path2weights = config.get_parsed_content("test#weights") label_parser = create_label_parser(config) ds, dl = create_valid_dataset_and_dataloader(config, label_parser, batch_size=1) model_pipe = FlexibleUnet1InferencePipe( path2weights, device, out_channels=label_parser.mask_num ) model_pipe.model.eval() iou_stats = IOUStats(label_parser) for batch in tqdm(dl, desc="Calculating metrics"): img = batch["image"].to(device) label = batch["label"] img_paths = ["empty"] * label.shape[0] prediction = model_pipe.model(img).detach().cpu() onehot_prediction = ImageTransforms.predictions_transforms(prediction) iou_stats.calculate_metrics_from_batch(onehot_prediction, label, img_paths) # img = img.detach().cpu()[0] # # img = ImageTransforms.inv_transforms(img).type(torch.uint8)[0].numpy() # single_ch_prediction = onehot_prediction[0].argmax(dim=0, keepdim=True) # blended = blend_images(img, single_ch_prediction, cmap="viridis", alpha=0.8).numpy() # blended = (np.transpose(blended, (1, 2, 0)) * 254).astype(np.uint8) # fig, ax = plt.subplots(1, 1) # ax.imshow(blended) # # ax.imshow(np.transpose(img, (1, 2, 0))) # plt.show() iou_stats.calculate_aggregated_stats() table = AggregatedMetricTable(iou_stats) table.fill_table() table.print_table() def main(): # Config parameters config = ConfigParser() config.read_config("./training_configs/thin7/dvrk_train_config.yaml") # show_images(config, show_valid=True) train_with_image_dataset(config) # show_inference_samples(config) # calculate_metrics_on_valid(config) if __name__ == "__main__": main()