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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()
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