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feat: ajout des données de test

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Former-commit-id: c582ae71d296afe90d25127f541c696052172a2a [formerly b5fe53254d424e3d6ea74573378a716ccd429d84]
Former-commit-id: f0ae70c1025d70af43b0f172e5abaeeba819999f
This commit is contained in:
Laurent Fainsin 2022-07-04 14:38:48 +02:00
parent 2571e5c6d3
commit 0d6f85518e
2 changed files with 110 additions and 27 deletions
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118
src/train.py
View file

@ -27,6 +27,7 @@ if __name__ == "__main__":
config=dict(
DIR_TRAIN_IMG="/home/lilian/data_disk/lfainsin/train/",
DIR_VALID_IMG="/home/lilian/data_disk/lfainsin/val/",
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],
@ -88,10 +89,12 @@ 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)
# 2.5. Create subset, if uncommented
ds_train = torch.utils.data.Subset(ds_train, list(range(0, len(ds_train), len(ds_train) // 5000)))
ds_valid = torch.utils.data.Subset(ds_valid, list(range(0, len(ds_valid), len(ds_valid) // 100)))
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(
@ -109,6 +112,14 @@ if __name__ == "__main__":
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,
)
# 4. Set up the optimizer, the loss, the learning rate scheduler and the loss scaling for amp
optimizer = torch.optim.RMSprop(
@ -121,12 +132,6 @@ if __name__ == "__main__":
grad_scaler = torch.cuda.amp.GradScaler(enabled=wandb.config.AMP)
criterion = torch.nn.BCEWithLogitsLoss()
# save model.pth
torch.save(net.state_dict(), "checkpoints/model-0.pth")
artifact = wandb.Artifact("pth", type="model")
artifact.add_file("checkpoints/model-0.pth")
wandb.run.log_artifact(artifact)
# save model.onxx
dummy_input = torch.randn(
1, wandb.config.N_CHANNELS, wandb.config.IMG_SIZE, wandb.config.IMG_SIZE, requires_grad=True
@ -143,14 +148,6 @@ if __name__ == "__main__":
logging.info(f"wandb config:\n{yaml.dump(wandb.config.as_dict())}")
try:
# wandb init log
# wandb.log(
# {
# "train/learning_rate": scheduler.get_lr(),
# },
# commit=False,
# )
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:
@ -205,7 +202,7 @@ if __name__ == "__main__":
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
@ -217,10 +214,10 @@ if __name__ == "__main__":
masks_pred = net(images)
# compute metrics
val_loss += criterion(pred_masks, true_masks)
mae += torch.nn.functional.l1_loss(pred_masks_bin, true_masks)
val_loss += criterion(pred_masks, masks_true)
masks_pred_bin = (torch.sigmoid(masks_pred) > 0.5).float()
accuracy += (true_masks == pred_masks_bin).float().mean()
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
@ -235,10 +232,10 @@ if __name__ == "__main__":
table = wandb.Table(columns=["ID", "image", "ground truth", "prediction"])
for i, (img, mask, pred, pred_bin) in enumerate(
zip(
images.to("cpu"),
masks_true.to("cpu"),
masks_pred.to("cpu"),
masks_pred_bin.to("cpu").squeeze().int().numpy(),
images.cpu(),
masks_true.cpu(),
masks_pred.cpu(),
masks_pred_bin.cpu().squeeze(1).int().numpy(),
)
):
table.add_data(
@ -259,7 +256,7 @@ if __name__ == "__main__":
# log validation metrics
wandb.log(
{
"predictions": table,
"val/predictions": table,
"train/learning_rate": optimizer.state_dict()["param_groups"][0]["lr"],
"val/accuracy": accuracy,
"val/bce": val_loss,
@ -280,9 +277,80 @@ 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()
except KeyboardInterrupt:
torch.save(net.state_dict(), "INTERRUPTED.pth")
raise
# sapin de noel

19
src/utils/dataset.py
View file

@ -1,6 +1,8 @@
from pathlib import Path
import albumentations as A
import numpy as np
from albumentations.pytorch import ToTensorV2
from PIL import Image
from torch.utils.data import Dataset
@ -16,12 +18,25 @@ class SphereDataset(Dataset):
def __getitem__(self, index):
image = np.array(Image.open(self.images[index]).convert("RGB"), dtype=np.uint8)
mask = np.zeros((image.shape[0], image.shape[1]), dtype=np.uint8)
if self.transform is not None:
mask = np.zeros((image.shape[0], image.shape[1]), dtype=np.uint8)
augmentations = self.transform(image=image, mask=mask)
image = augmentations["image"]
mask = augmentations["mask"]
else:
mask_path = self.images[index].parent.joinpath("MASK.PNG")
mask = np.array(Image.open(mask_path).convert("L"), dtype=np.uint8) / 255
preprocess = A.Compose(
[
A.SmallestMaxSize(1024),
A.ToFloat(max_value=255),
ToTensorV2(),
],
)
augmentations = preprocess(image=image, mask=mask)
image = augmentations["image"]
mask = augmentations["mask"]
# make sure image and mask are floats
image = image.float()