Image segmentation 25%

vision
1

Hello, I try to implement segmentation task, however, I am always getting 25% accuracy result. Here is my code:
LEARNING_RATE = 1e-4
DEVICE = “cuda” if torch.cuda.is_available() else “cpu”
BATCH_SIZE = 16
NUM_EPOCHS = 3
NUM_WORKERS = 2
IMAGE_HEIGHT = 200 # 1280 originally
IMAGE_WIDTH = 280# 1918 originally
PIN_MEMORY = True
LOAD_MODEL = False
TRAIN_IMG_DIR = “/kaggle/input/segmentation-bts/train/imgs”
TRAIN_MASK_DIR = “/kaggle/input/segmentation-bts/train/masks”
VAL_IMG_DIR = “/kaggle/input/segmentation-bts/val/imgs”
VAL_MASK_DIR = “/kaggle/input/segmentation-bts/val/masks”

def train_fn(loader, model, optimizer, loss_fn, scaler):
loop = tqdm(loader)

for batch_idx, (data, targets) in enumerate(loop):
    data = data.to(device=DEVICE)
    targets = targets.float().unsqueeze(1).to(device=DEVICE)

    # forward
    with torch.cuda.amp.autocast():
        predictions = model(data)
        loss = loss_fn(predictions, targets)

    # backward
    optimizer.zero_grad()
    scaler.scale(loss).backward()
    scaler.step(optimizer)
    scaler.update()

    # update tqdm loop
    loop.set_postfix(loss=loss.item())

def main():
train_transform = A.Compose(
[
A.Resize(height=IMAGE_HEIGHT, width=IMAGE_WIDTH),
A.Rotate(limit=35, p=1.0),
A.HorizontalFlip(p=0.5),
A.VerticalFlip(p=0.1),
A.Normalize(
mean=[0.0, 0.0, 0.0],
std=[1.0, 1.0, 1.0],
max_pixel_value=255.0,
),
ToTensorV2(),
],
)

val_transforms = A.Compose(
    [
        A.Resize(height=IMAGE_HEIGHT, width=IMAGE_WIDTH),
        A.Normalize(
            mean=[0.0, 0.0, 0.0],
            std=[1.0, 1.0, 1.0],
            max_pixel_value=255.0,
        ),
        ToTensorV2(),
    ],
)

model = UNET(in_channels=3, out_channels=1).to(DEVICE)
loss_fn = nn.BCEWithLogitsLoss()
optimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE)

train_loader, val_loader = get_loaders(
    TRAIN_IMG_DIR,
    TRAIN_MASK_DIR,
    VAL_IMG_DIR,
    VAL_MASK_DIR,
    BATCH_SIZE,
    train_transform,
    val_transforms,
    NUM_WORKERS,
    PIN_MEMORY,
)

if LOAD_MODEL:
    load_checkpoint(torch.load("my_checkpoint.pth.tar"), model)


check_accuracy(val_loader, model, device=DEVICE)
scaler = torch.cuda.amp.GradScaler()

for epoch in range(NUM_EPOCHS):
    train_fn(train_loader, model, optimizer, loss_fn, scaler)

    # save model
    checkpoint = {
        "state_dict": model.state_dict(),
        "optimizer":optimizer.state_dict(),
    }
    save_checkpoint(checkpoint)

    # check accuracy
    check_accuracy(val_loader, model, device=DEVICE)

    # print some examples to a folder
save_predictions_as_imgs(
        val_loader, model, folder="saved_images/", device=DEVICE
    )

if name == “main”:
main()

LEARNING_RATE = 1e-4
DEVICE = “cuda” if torch.cuda.is_available() else “cpu”
BATCH_SIZE = 16
NUM_EPOCHS = 3
NUM_WORKERS = 2
IMAGE_HEIGHT = 200 # 1280 originally
IMAGE_WIDTH = 280# 1918 originally
PIN_MEMORY = True
LOAD_MODEL = False
TRAIN_IMG_DIR = “/kaggle/input/segmentation-bts/train/imgs”
TRAIN_MASK_DIR = “/kaggle/input/segmentation-bts/train/masks”
VAL_IMG_DIR = “/kaggle/input/segmentation-bts/val/imgs”
VAL_MASK_DIR = “/kaggle/input/segmentation-bts/val/masks”

def train_fn(loader, model, optimizer, loss_fn, scaler):
loop = tqdm(loader)

for batch_idx, (data, targets) in enumerate(loop):
    data = data.to(device=DEVICE)
    targets = targets.float().unsqueeze(1).to(device=DEVICE)

    # forward
    with torch.cuda.amp.autocast():
        predictions = model(data)
        loss = loss_fn(predictions, targets)

    # backward
    optimizer.zero_grad()
    scaler.scale(loss).backward()
    scaler.step(optimizer)
    scaler.update()

    # update tqdm loop
    loop.set_postfix(loss=loss.item())

def main():
train_transform = A.Compose(
[
A.Resize(height=IMAGE_HEIGHT, width=IMAGE_WIDTH),
A.Rotate(limit=35, p=1.0),
A.HorizontalFlip(p=0.5),
A.VerticalFlip(p=0.1),
A.Normalize(
mean=[0.0, 0.0, 0.0],
std=[1.0, 1.0, 1.0],
max_pixel_value=255.0,
),
ToTensorV2(),
],
)

val_transforms = A.Compose(
    [
        A.Resize(height=IMAGE_HEIGHT, width=IMAGE_WIDTH),
        A.Normalize(
            mean=[0.0, 0.0, 0.0],
            std=[1.0, 1.0, 1.0],
            max_pixel_value=255.0,
        ),
        ToTensorV2(),
    ],
)

model = UNET(in_channels=3, out_channels=1).to(DEVICE)
loss_fn = nn.BCEWithLogitsLoss()
optimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE)

train_loader, val_loader = get_loaders(
    TRAIN_IMG_DIR,
    TRAIN_MASK_DIR,
    VAL_IMG_DIR,
    VAL_MASK_DIR,
    BATCH_SIZE,
    train_transform,
    val_transforms,
    NUM_WORKERS,
    PIN_MEMORY,
)

if LOAD_MODEL:
    load_checkpoint(torch.load("my_checkpoint.pth.tar"), model)


check_accuracy(val_loader, model, device=DEVICE)
scaler = torch.cuda.amp.GradScaler()

for epoch in range(NUM_EPOCHS):
    train_fn(train_loader, model, optimizer, loss_fn, scaler)

    # save model
    checkpoint = {
        "state_dict": model.state_dict(),
        "optimizer":optimizer.state_dict(),
    }
    save_checkpoint(checkpoint)

    # check accuracy
    check_accuracy(val_loader, model, device=DEVICE)

    # print some examples to a folder
save_predictions_as_imgs(
        val_loader, model, folder="saved_images/", device=DEVICE
    )

if name == “main”:
main()

LEARNING_RATE = 1e-4
DEVICE = “cuda” if torch.cuda.is_available() else “cpu”
BATCH_SIZE = 16
NUM_EPOCHS = 3
NUM_WORKERS = 2
IMAGE_HEIGHT = 200 # 1280 originally
IMAGE_WIDTH = 280# 1918 originally
PIN_MEMORY = True
LOAD_MODEL = False
TRAIN_IMG_DIR = “/kaggle/input/segmentation-bts/train/imgs”
TRAIN_MASK_DIR = “/kaggle/input/segmentation-bts/train/masks”
VAL_IMG_DIR = “/kaggle/input/segmentation-bts/val/imgs”
VAL_MASK_DIR = “/kaggle/input/segmentation-bts/val/masks”

def train_fn(loader, model, optimizer, loss_fn, scaler):
loop = tqdm(loader)

for batch_idx, (data, targets) in enumerate(loop):
    data = data.to(device=DEVICE)
    targets = targets.float().unsqueeze(1).to(device=DEVICE)

    # forward
    with torch.cuda.amp.autocast():
        predictions = model(data)
        loss = loss_fn(predictions, targets)

    # backward
    optimizer.zero_grad()
    scaler.scale(loss).backward()
    scaler.step(optimizer)
    scaler.update()

    # update tqdm loop
    loop.set_postfix(loss=loss.item())

def main():
train_transform = A.Compose(
[
A.Resize(height=IMAGE_HEIGHT, width=IMAGE_WIDTH),
A.Rotate(limit=35, p=1.0),
A.HorizontalFlip(p=0.5),
A.VerticalFlip(p=0.1),
A.Normalize(
mean=[0.0, 0.0, 0.0],
std=[1.0, 1.0, 1.0],
max_pixel_value=255.0,
),
ToTensorV2(),
],
)

val_transforms = A.Compose(
    [
        A.Resize(height=IMAGE_HEIGHT, width=IMAGE_WIDTH),
        A.Normalize(
            mean=[0.0, 0.0, 0.0],
            std=[1.0, 1.0, 1.0],
            max_pixel_value=255.0,
        ),
        ToTensorV2(),
    ],
)

model = UNET(in_channels=3, out_channels=1).to(DEVICE)
loss_fn = nn.BCEWithLogitsLoss()
optimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE)

train_loader, val_loader = get_loaders(
    TRAIN_IMG_DIR,
    TRAIN_MASK_DIR,
    VAL_IMG_DIR,
    VAL_MASK_DIR,
    BATCH_SIZE,
    train_transform,
    val_transforms,
    NUM_WORKERS,
    PIN_MEMORY,
)

if LOAD_MODEL:
    load_checkpoint(torch.load("my_checkpoint.pth.tar"), model)


check_accuracy(val_loader, model, device=DEVICE)
scaler = torch.cuda.amp.GradScaler()

for epoch in range(NUM_EPOCHS):
    train_fn(train_loader, model, optimizer, loss_fn, scaler)

    # save model
    checkpoint = {
        "state_dict": model.state_dict(),
        "optimizer":optimizer.state_dict(),
    }
    save_checkpoint(checkpoint)

    # check accuracy
    check_accuracy(val_loader, model, device=DEVICE)

    # print some examples to a folder
save_predictions_as_imgs(
        val_loader, model, folder="saved_images/", device=DEVICE
    )

if name == “main”:
main()

Does anybody know what might be the problem?

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