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feat: almost there, remove scheduler next time

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Former-commit-id: 0a08b5a9559e46ca72f7d07ae84202c1412a63e9 [formerly 522877adbc8f7d132875405a86e594b4fb753850]
Former-commit-id: 2ea20809c265b8366ec2e0aa3867b13886cbd500
This commit is contained in:
Laurent Fainsin 2022-07-05 20:50:08 +02:00
parent d785a5c6be
commit c0772a390e
2 changed files with 13 additions and 91 deletions
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103
src/train.py
View file

@ -30,14 +30,14 @@ if __name__ == "__main__":
DIR_TEST_IMG="/home/lilian/data_disk/lfainsin/test/",
DIR_SPHERE_IMG="/home/lilian/data_disk/lfainsin/spheres/Images/",
DIR_SPHERE_MASK="/home/lilian/data_disk/lfainsin/spheres/Masks/",
FEATURES=[64, 128, 256, 512],
FEATURES=[16, 32, 64, 128],
N_CHANNELS=3,
N_CLASSES=1,
AMP=True,
PIN_MEMORY=True,
BENCHMARK=True,
DEVICE="cuda",
WORKERS=8,
WORKERS=7,
EPOCHS=5,
BATCH_SIZE=16,
LEARNING_RATE=1e-4,
@ -88,13 +88,10 @@ if __name__ == "__main__":
# 2. Create datasets
ds_train = SphereDataset(image_dir=wandb.config.DIR_TRAIN_IMG, transform=tf_train)
ds_valid = SphereDataset(image_dir=wandb.config.DIR_VALID_IMG, transform=tf_valid)
ds_test = SphereDataset(image_dir=wandb.config.DIR_TEST_IMG)
ds_valid = SphereDataset(image_dir=wandb.config.DIR_TEST_IMG)
# 2.5. Create subset, if uncommented
ds_train = torch.utils.data.Subset(ds_train, list(range(0, len(ds_train), len(ds_train) // 10000)))
ds_valid = torch.utils.data.Subset(ds_valid, list(range(0, len(ds_valid), len(ds_valid) // 1000)))
ds_test = torch.utils.data.Subset(ds_test, list(range(0, len(ds_test), len(ds_test) // 100)))
# 3. Create data loaders
train_loader = DataLoader(
@ -108,14 +105,6 @@ if __name__ == "__main__":
ds_valid,
shuffle=False,
drop_last=True,
batch_size=wandb.config.BATCH_SIZE,
num_workers=wandb.config.WORKERS,
pin_memory=wandb.config.PIN_MEMORY,
)
test_loader = DataLoader(
ds_test,
shuffle=False,
drop_last=False,
batch_size=1,
num_workers=wandb.config.WORKERS,
pin_memory=wandb.config.PIN_MEMORY,
@ -136,13 +125,13 @@ if __name__ == "__main__":
dummy_input = torch.randn(
1, wandb.config.N_CHANNELS, wandb.config.IMG_SIZE, wandb.config.IMG_SIZE, requires_grad=True
).to(device)
torch.onnx.export(net, dummy_input, "checkpoints/model-0.onnx")
torch.onnx.export(net, dummy_input, "checkpoints/model.onnx")
artifact = wandb.Artifact("onnx", type="model")
artifact.add_file("checkpoints/model-0.onnx")
artifact.add_file("checkpoints/model.onnx")
wandb.run.log_artifact(artifact)
# log gradients and weights four time per epoch
wandb.watch(net, criterion, log_freq=100)
wandb.watch(net, log_freq=100)
# print the config
logging.info(f"wandb config:\n{yaml.dump(wandb.config.as_dict())}")
@ -156,6 +145,7 @@ if __name__ == "__main__":
)
try:
global_step = 0
for epoch in range(1, wandb.config.EPOCHS + 1):
with tqdm(total=len(ds_train), desc=f"{epoch}/{wandb.config.EPOCHS}", unit="img") as pbar:
@ -195,7 +185,6 @@ if __name__ == "__main__":
# log metrics
wandb.log(
{
"epoch": epoch - 1 + step / len(train_loader),
"train/accuracy": accuracy,
"train/bce": train_loss,
"train/dice": dice,
@ -203,14 +192,16 @@ if __name__ == "__main__":
}
)
if step and (step % 250 == 0 or step == len(train_loader)):
global_step += 1
if global_step % 100 == 0:
# Evaluation round
net.eval()
accuracy = 0
val_loss = 0
dice = 0
mae = 0
with tqdm(val_loader, total=len(ds_valid), desc="val.", unit="img", leave=False) as pbar2:
with tqdm(val_loader, total=len(ds_valid), desc="val", unit="img", leave=False) as pbar2:
for images, masks_true in val_loader:
# transfer images to device
@ -228,8 +219,9 @@ if __name__ == "__main__":
accuracy += (masks_true == masks_pred_bin).float().mean()
dice += dice_coeff(masks_pred_bin, masks_true)
# update progress bar
# update progress bars
pbar2.update(images.shape[0])
pbar.refresh()
accuracy /= len(val_loader)
val_loss /= len(val_loader)
@ -285,75 +277,6 @@ if __name__ == "__main__":
artifact.add_file(f"checkpoints/model-{epoch}-{step}.onnx")
wandb.run.log_artifact(artifact)
# testing round
net.eval()
accuracy = 0
val_loss = 0
dice = 0
mae = 0
with tqdm(test_loader, total=len(ds_test), desc="test", unit="img", leave=False) as pbar3:
for images, masks_true in test_loader:
# transfer images to device
images = images.to(device=device)
masks_true = masks_true.unsqueeze(1).to(device=device)
# forward
with torch.inference_mode():
masks_pred = net(images)
# compute metrics
val_loss += criterion(masks_pred, masks_true)
masks_pred_bin = (torch.sigmoid(masks_pred) > 0.5).float()
mae += torch.nn.functional.l1_loss(masks_pred_bin, masks_true)
accuracy += (masks_true == masks_pred_bin).float().mean()
dice += dice_coeff(masks_pred_bin, masks_true)
# update progress bar
pbar3.update(images.shape[0])
accuracy /= len(test_loader)
val_loss /= len(test_loader)
dice /= len(test_loader)
mae /= len(test_loader)
# save the last validation batch to table
table = wandb.Table(columns=["ID", "image", "ground truth", "prediction"])
for i, (img, mask, pred, pred_bin) in enumerate(
zip(
images.cpu(),
masks_true.cpu(),
masks_pred.cpu(),
masks_pred_bin.cpu().squeeze(1).int().numpy(),
)
):
table.add_data(
i,
wandb.Image(img),
wandb.Image(mask),
wandb.Image(
pred,
masks={
"predictions": {
"mask_data": pred_bin,
"class_labels": class_labels,
},
},
),
)
# log validation metrics
wandb.log(
{
"test/predictions": table,
"test/accuracy": accuracy,
"test/bce": val_loss,
"test/dice": dice,
"test/mae": mae,
},
commit=False,
)
# stop wandb
wandb.run.finish()

1
src/unet/model.py
View file

@ -26,7 +26,6 @@ class UNet(nn.Module):
self.outc = OutConv(features[0], n_classes)
def forward(self, x):
skips = []
x = self.inc(x)