File size: 3,396 Bytes
117e206 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 | from pathlib import Path
from tqdm import trange
import torch
import monai
from monai.data import ThreadDataLoader
from monai.networks.nets import FlexibleUNet
from surg_seg.Datasets.ImageDataset import ImageSegmentationDataset
from surg_seg.Datasets.VideoDatasets import CombinedVidDataset
from surg_seg.Trainers.Trainer import ModelTrainer
def create_FlexibleUnet(device, pretrained_weights_path: Path, out_channels: int):
model = FlexibleUNet(
in_channels=3,
out_channels=out_channels,
backbone="efficientnet-b0",
pretrained=True,
is_pad=False,
).to(device)
pretrained_weights = monai.bundle.load(
name="endoscopic_tool_segmentation", bundle_dir=pretrained_weights_path, version="0.2.0"
)
model_weight = model.state_dict()
weights_no_head = {k: v for k, v in pretrained_weights.items() if not "segmentation_head" in k}
model_weight.update(weights_no_head)
model.load_state_dict(model_weight)
return model
def train_with_video_dataset():
device = "cpu"
vid_root = Path("/home/juan1995/research_juan/accelnet_grant/data/rec01/")
vid_filepath = vid_root / "raw/rec01_seg_raw.avi"
seg_filepath = vid_root / "annotation2colors/rec01_seg_annotation2colors.avi"
ds = CombinedVidDataset(vid_filepath, seg_filepath)
dl = ThreadDataLoader(ds, batch_size=4, num_workers=0, shuffle=True)
pretrained_weigths_path = Path("./assets/weights/trained-weights.pt")
model = create_FlexibleUnet(device, pretrained_weigths_path, ds.label_channels)
optimizer = torch.optim.Adam(model.parameters(), 1e-2)
trainer = ModelTrainer(device=device, max_epochs=2)
model, training_stats = trainer.train_model(model, optimizer, dl)
training_stats.plot_stats()
model_path = "./assets/weights/myweights_video"
torch.save(model.state_dict(), model_path)
training_stats.to_pickle(model_path)
def train_with_image_dataset():
device = "cpu"
root = Path("/home/juan1995/research_juan/accelnet_grant/data")
train_dirs = [root / "rec01", root / "rec03", root / "rec05"]
val_dirs = [root / "rec02", root / "rec04"]
ds = ImageSegmentationDataset(train_dirs, "5colors")
dl = ThreadDataLoader(ds, batch_size=4, num_workers=0, shuffle=True)
val_ds = ImageSegmentationDataset(val_dirs, "5colors")
val_dl = ThreadDataLoader(val_ds, batch_size=4, num_workers=0, shuffle=True)
print(f"Training dataset size: {len(ds)}")
print(f"Validation dataset size: {len(val_ds)}")
pretrained_weigths_path = Path("./assets/weights/pretrained-weights")
model = create_FlexibleUnet(device, pretrained_weigths_path, ds.label_channels)
optimizer = torch.optim.Adam(model.parameters(), 1e-2)
trainer = ModelTrainer(device=device, max_epochs=2)
model, training_stats = trainer.train_model(model, optimizer, dl, validation_dl=val_dl)
model_path = Path("./assets/weights/myweights_image")
model_path.mkdir(exist_ok=True)
torch.save(model.state_dict(), model_path / "myweights.pt")
training_stats.to_pickle(model_path)
training_stats.plot_stats(file_path=model_path)
print(f"Last train IOU {training_stats.iou_list[-1]}")
print(f"Last validation IOU {training_stats.validation_iou_list[-1]}")
def main():
# train_with_video_dataset()
train_with_image_dataset()
if __name__ == "__main__":
main()
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