# Raise self.exc\_type(msg) ValueError: Caught ValueError in DataLoader worker process 0 **URL:** \ **Category:** Uncategorized\ **Created:** [June 3, 2021, 10:52am UTC](https://discuss.pytorch.org/t/raise-self-exc-type-msg-valueerror-caught-valueerror-in-dataloader-worker-process-0/123168 "2021-06-03T10:52:26Z")\ **Posts on this page:** 9\ **Page:** 1
**Author:** ![Florentino](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/florentino/32/39270_2.png) [@Florentino](https://discuss.pytorch.org/u/Florentino)\ **Post date:** [June 3, 2021, 10:52am UTC](https://discuss.pytorch.org/t/raise-self-exc-type-msg-valueerror-caught-valueerror-in-dataloader-worker-process-0/123168/1 "2021-06-03T10:52:26Z")
Hello, I am training a UNet, and the input and output images are grayscale. When I run it, I don’t know how to fixed the error ```auto import torch import torch.nn as nn import torchvision.transforms.functional as TF class DoubleConv(nn.Module): def __init__ (self, in_channels, out_channels): super(DoubleConv, self). __init__ () self.conv = nn.Sequential( nn.Conv2d(in_channels, out_channels, 3, 1, 1, bias=False), nn.BatchNorm2d(out_channels), nn.ReLU(inplace=True), nn.Conv2d(out_channels, out_channels, 3, 1, 1, bias=False), nn.BatchNorm2d(out_channels), nn.ReLU(inplace=True), ) def forward(self, x): return self.conv(x) class UNET(nn.Module): def __init__ (self, in_channels=3, out_channels=1, features=[64, 128, 256, 512]): super(UNET, self). __init__ () self.ups = nn.ModuleList() self.downs = nn.ModuleList() self.pool = nn.MaxPool2d(kernel_size=2, stride=2) # Down part of UNET for feature in features: self.downs.append(DoubleConv(in_channels, feature)) in_channels = feature # Up part of UNET for feature in reversed(features): self.ups.append( nn.ConvTranspose2d(feature * 2, feature, kernel_size=2, stride=2), ) self.ups.append(DoubleConv(feature * 2, feature)) self.bottleneck = DoubleConv(features[-1], features[-1] * 2) self.final_conv = nn.Conv2d(features[0], out_channels, kernel_size=1) def forward(self, x): skip_connections = [] for down in self.downs: x = down(x) skip_connections.append(x) x = self.pool(x) x = self.bottleneck(x) skip_connections = skip_connections[::-1] # reverse sort for idx in range(0, len(self.ups), 2): x = self.ups[idx](x) skip_connection = skip_connections[idx // 2] # floor division if x.shape != skip_connection.shape: x = TF.resize(x, size=skip_connection.shape[2:]) concat_skip = torch.cat((skip_connection, x), dim=1) x = self.ups[idx + 1](concat_skip) return self.final_conv(x) def test(): x = torch.randn((3, 1, 600, 600)) model = UNET(in_channels=1, out_channels=1) preds = model(x) print(x.shape) assert preds.shape == x.shape if __name__ == " __main__": test() ``` It gives the error: ```auto `Traceback (most recent call last):` File "C:/DeepLearning/UNet/train.py", line 120, in main() File "C:/DeepLearning/UNet/train.py", line 98, in main check_accuracy(val_loader, model, device=DEVICE) File "C:\DeepLearning\UNet\utils.py", line 62, in check_accuracy for x, y in loader: File "C:\Users\Tsai\anaconda3\envs\tf\lib\site-packages\torch\utils\data\dataloader.py", line 517, in __next__ data = self._next_data() File "C:\Users\Tsai\anaconda3\envs\tf\lib\site-packages\torch\utils\data\dataloader.py", line 1199, in _next_data return self._process_data(data) File "C:\Users\Tsai\anaconda3\envs\tf\lib\site-packages\torch\utils\data\dataloader.py", line 1225, in _process_data data.reraise() File "C:\Users\Tsai\anaconda3\envs\tf\lib\site-packages\torch\_utils.py", line 429, in reraise raise self.exc_type(msg) ValueError: Caught ValueError in DataLoader worker process 0. Original Traceback (most recent call last): File "C:\Users\Tsai\anaconda3\envs\tf\lib\site-packages\torch\utils\data\_utils\worker.py", line 202, in _worker_loop data = fetcher.fetch(index) File "C:\Users\Tsai\anaconda3\envs\tf\lib\site-packages\torch\utils\data\_utils\fetch.py", line 44, in fetch data = [self.dataset[idx] for idx in possibly_batched_index] File "C:\Users\Tsai\anaconda3\envs\tf\lib\site-packages\torch\utils\data\_utils\fetch.py", line 44, in data = [self.dataset[idx] for idx in possibly_batched_index] File "C:\DeepLearning\UNet\dataset.py", line 24, in __getitem__ augmentations = self.transform(image=image, mask=mask) File "C:\Users\Tsai\anaconda3\envs\tf\lib\site-packages\albumentations\core\composition.py", line 182, in __call__ data = t(force_apply=force_apply, **data) File "C:\Users\Tsai\anaconda3\envs\tf\lib\site-packages\albumentations\core\transforms_interface.py", line 89, in __call__ return self.apply_with_params(params, **kwargs) File "C:\Users\Tsai\anaconda3\envs\tf\lib\site-packages\albumentations\core\transforms_interface.py", line 102, in apply_with_params res[key] = target_function(arg, **dict(params,**target_dependencies)) File "C:\Users\Tsai\anaconda3\envs\tf\lib\site-packages\albumentations\augmentations\transforms.py", line 1496, in apply return F.normalize(image, self.mean, self.std, self.max_pixel_value) File "C:\Users\Tsai\anaconda3\envs\tf\lib\site-packages\albumentations\augmentations\functional.py", line 141, in normalize img -= mean ValueError: operands could not be broadcast together with shapes (600,600,2) (3,) (600,600,2) ``` ---
**Author:** ![Florentino](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/florentino/32/39270_2.png) [@Florentino](https://discuss.pytorch.org/u/Florentino)\ **Post date:** [June 3, 2021, 10:54am UTC](https://discuss.pytorch.org/t/raise-self-exc-type-msg-valueerror-caught-valueerror-in-dataloader-worker-process-0/123168/2 "2021-06-03T10:54:01Z")
The error source utils.py: ```auto import torch import torchvision from dataset import CarvanaDataset from torch.utils.data import DataLoader def save_checkpoint(state, filename="my_checkpoint.pth.tar"): print("=> Saving checkpoint") torch.save(state, filename) def load_checkpoint(checkpoint, model): print("=> Loading checkpoint") model.load_state_dict(checkpoint["state_dict"]) def get_loaders( train_dir, train_maskdir, val_dir, val_maskdir, batch_size, train_transform, val_transform, num_workers=4, pin_memory=True, ): train_ds = CarvanaDataset( image_dir=train_dir, mask_dir=train_maskdir, transform=train_transform, ) train_loader = DataLoader( train_ds, batch_size=batch_size, num_workers=num_workers, pin_memory=pin_memory, shuffle=True, ) val_ds = CarvanaDataset( image_dir=val_dir, mask_dir=val_maskdir, transform=val_transform, ) val_loader = DataLoader( val_ds, batch_size=batch_size, num_workers=num_workers, pin_memory=pin_memory, shuffle=False, ) return train_loader, val_loader def check_accuracy(loader, model, device="cuda"): num_correct = 0 num_pixels = 0 dice_score = 0 model.eval() with torch.no_grad(): for x, y in loader: x = x.to(device) y = y.to(device).unsqueeze(1) preds = torch.sigmoid(model(x)) preds = (preds > 0.5).float() # 0, 1 matrix num_correct += (preds == y).sum() num_pixels += torch.numel(preds) dice_score += (2 * (preds * y).sum())/( (preds+y).sum() + 1e-8 ) print( f"Got {num_correct}/{num_pixels} with acc {num_correct/num_pixels*100:.2f}" ) print(f"Dice score: {dice_score/len(loader)}") model.train() def save_predictions_as_imgs( loader, model, folder="saved_images/", device="cuda" ): model.eval() for idx, (x, y) in enumerate(loader): x = x.to(device=device) with torch.no_grad(): preds = torch.sigmoid(model(x)) preds = (preds > 0.5).float() torchvision.utils.save_image( preds, f"{folder}/pred_{idx}.png" ) torchvision.utils.save_image(y.unsqueeze(1), f"{folder}{idx}.png") model.train() ``` ---
**Author:** ![ejguan](https://discuss.pytorch.org/letter_avatar_proxy/v4/letter/e/5f8ce5/32.png) [@ejguan](https://discuss.pytorch.org/u/ejguan)\ **Post date:** [June 4, 2021, 7:30pm UTC](https://discuss.pytorch.org/t/raise-self-exc-type-msg-valueerror-caught-valueerror-in-dataloader-worker-process-0/123168/3 "2021-06-04T19:30:24Z")
> [@Florentino](#): > > ```auto > Original Traceback (most recent call last): > File "C:\Users\Tsai\anaconda3\envs\tf\lib\site-packages\torch\utils\data\_utils\worker.py", line 202, in _worker_loop > data = fetcher.fetch(index) > File "C:\Users\Tsai\anaconda3\envs\tf\lib\site-packages\torch\utils\data\_utils\fetch.py", line 44, in fetch > data = [self.dataset[idx] for idx in possibly_batched_index] > File "C:\Users\Tsai\anaconda3\envs\tf\lib\site-packages\torch\utils\data\_utils\fetch.py", line 44, in > data = [self.dataset[idx] for idx in possibly_batched_index] > File "C:\DeepLearning\UNet\dataset.py", line 24, in __getitem__ > augmentations = self.transform(image=image, mask=mask) > File "C:\Users\Tsai\anaconda3\envs\tf\lib\site-packages\albumentations\core\composition.py", line 182, in __call__ > data = t(force_apply=force_apply, **data) > File "C:\Users\Tsai\anaconda3\envs\tf\lib\site-packages\albumentations\core\transforms_interface.py", line 89, in __call__ > return self.apply_with_params(params, **kwargs) > File "C:\Users\Tsai\anaconda3\envs\tf\lib\site-packages\albumentations\core\transforms_interface.py", line 102, in apply_with_params > res[key] = target_function(arg, **dict(params,**target_dependencies)) > File "C:\Users\Tsai\anaconda3\envs\tf\lib\site-packages\albumentations\augmentations\transforms.py", line 1496, in apply > return F.normalize(image, self.mean, self.std, self.max_pixel_value) > File "C:\Users\Tsai\anaconda3\envs\tf\lib\site-packages\albumentations\augmentations\functional.py", line 141, in normalize > img -= mean > ValueError: operands could not be broadcast together with shapes (600,600,2) (3,) (600,600,2) > > ``` The error has already been printed out. Can you please check your transform function? ---
**Author:** ![Florentino](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/florentino/32/39270_2.png) [@Florentino](https://discuss.pytorch.org/u/Florentino)\ **Post date:** [June 9, 2021, 10:52am UTC](https://discuss.pytorch.org/t/raise-self-exc-type-msg-valueerror-caught-valueerror-in-dataloader-worker-process-0/123168/4 "2021-06-09T10:52:14Z")
@ejguan I have no idea how to adjust it. Hello, here is my Dataset class: ```auto from PIL import Image from torch.utils.data import Dataset import numpy as np import os class CarvanaDataset(Dataset): def __init__ (self, image_dir, mask_dir, transform=None): self.image_dir = image_dir self.mask_dir = mask_dir self.transform = transform self.images = os.listdir(image_dir) def __len__ (self): return len(self.images) def __getitem__ (self, index): img_path = os.path.join(self.image_dir, self.images[index]) mask_path = os.path.join(self.mask_dir, self.images[index].replace(".jpq", "_mask.gif")) image = np.array(Image.open(img_path).convert("L")) mask = np.array(Image.open(mask_path).convert("L"), dtype=np.float32) mask[mask == 255.0] = 1.0 if self.transform is not None: augmentations = self.transform(image=image, mask=mask) image = augmentations["image"] mask = augmentations["mask"] return image, mask ``` ---
**Author:** ![ejguan](https://discuss.pytorch.org/letter_avatar_proxy/v4/letter/e/5f8ce5/32.png) [@ejguan](https://discuss.pytorch.org/u/ejguan)\ **Post date:** [June 10, 2021, 3:45pm UTC](https://discuss.pytorch.org/t/raise-self-exc-type-msg-valueerror-caught-valueerror-in-dataloader-worker-process-0/123168/5 "2021-06-10T15:45:02Z")
Can you share your `transform`? The error comes from it. And, can you print out your the shape of `image` and `mask`? ---
**Author:** ![Florentino](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/florentino/32/39270_2.png) [@Florentino](https://discuss.pytorch.org/u/Florentino)\ **Post date:** [June 12, 2021, 1:43am UTC](https://discuss.pytorch.org/t/raise-self-exc-type-msg-valueerror-caught-valueerror-in-dataloader-worker-process-0/123168/6 "2021-06-12T01:43:07Z")
```auto import torch import albumentations as A from albumentations.pytorch import ToTensorV2 from tqdm import tqdm import torch.nn as nn import torch.optim as optim from model import UNET from utils import ( load_checkpoint, save_checkpoint, get_loaders, check_accuracy, save_predictions_as_imgs, ) # Hyperparameters etc. LEARNING_RATE = 1e-4 DEVICE = "cuda" if torch.cuda.is_available() else "cpu" BATCH_SIZE = 16 NUM_EPOCHS = 3 NUM_WORKERS = 2 IMAGE_HEIGHT = 600 # 1280 originally IMAGE_WIDTH = 600 # 1918 originally PIN_MEMORY = True LOAD_MODEL = False TRAIN_IMG_DIR = "C:\DeepLearning\\train\img" TRAIN_MASK_DIR = "C:\DeepLearning\\train\mask" VAL_IMG_DIR = "C:\DeepLearning\\test\img" VAL_MASK_DIR = "C:\DeepLearning\\test\mask" def train_fn(loader, model, optimizer, loss_fn, scalar): loop = tqdm(loader) for batch_idx, (data, targets) in enumerate(loop): data = data.to(DEVICE) targets = targets.float().unsqueeze(1).to(DEVICE) # forward with torch.cuda.amp.autocast(): predictions = model(data) loss = loss_fn(predictions, targets) # backward optimizer.zero_grad() scalar.scale(loss).backward() scalar.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.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=1, out_channels=1).to(DEVICE) loss_fn = nn.BCEWithLogitsLoss() # This loss combines a Sigmoid layer and the BCELoss in one single class opimizer = 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, opimizer, 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 examplea to a folder save_predictions_as_imgs( val_loader, model, folder="saved_images/", device=DEVICE ) if __name__ == " __main__": main() ``` ```auto from PIL import Image from torch.utils.data import Dataset import numpy as np import os class CarvanaDataset(Dataset): def __init__ (self, image_dir, mask_dir, transform=None): self.image_dir = image_dir self.mask_dir = mask_dir self.transform = transform self.images = os.listdir(image_dir) self.masks = os.listdir(mask_dir) def __len__ (self): return len(self.images) def __getitem__ (self, index): img_path = os.path.join(self.image_dir, self.images[index]) mask_path = os.path.join(self.mask_dir, self.masks[index]) image = np.array(Image.open(img_path).convert("L")) mask = np.array(Image.open(mask_path).convert("L"), dtype=np.float32) mask[mask == 255.0] = 1.0 if self.transform is not None: augmentations = self.transform(image=image, mask=mask) image = augmentations["image"] mask = augmentations["mask"] return image, mask if __name__ == " __main__": data = CarvanaDataset('C:\DeepLearning\\train\img', 'C:\DeepLearning\\train\mask') image, mask = data. __getitem__ (0) print(image.shape) print(mask.shape) ``` ```auto print(image.shape) ``` ---
**Author:** ![ejguan](https://discuss.pytorch.org/letter_avatar_proxy/v4/letter/e/5f8ce5/32.png) [@ejguan](https://discuss.pytorch.org/u/ejguan)\ **Post date:** [June 12, 2021, 1:57am UTC](https://discuss.pytorch.org/t/raise-self-exc-type-msg-valueerror-caught-valueerror-in-dataloader-worker-process-0/123168/7 "2021-06-12T01:57:40Z")
> [@Florentino](#): > > ```auto > image = np.array(Image.open(img_path).convert("L")) > > ``` You image has single channel. > [@Florentino](#): > > ```auto > 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.Normalize( > mean=[0.0, 0.0, 0.0], > std=[1.0, 1.0, 1.0], > max_pixel_value=255.0, > ), > ToTensorV2(), > ] > ) > > ``` But you transform function expects three channel inputs. You should either change your image or your transform function. ---
**Author:** ![Florentino](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/florentino/32/39270_2.png) [@Florentino](https://discuss.pytorch.org/u/Florentino)\ **Post date:** [June 12, 2021, 2:20am UTC](https://discuss.pytorch.org/t/raise-self-exc-type-msg-valueerror-caught-valueerror-in-dataloader-worker-process-0/123168/8 "2021-06-12T02:20:06Z")
After your advise, the result of the shapes become: ```auto from PIL import Image from torch.utils.data import Dataset import numpy as np import os class CarvanaDataset(Dataset): def __init__ (self, image_dir, mask_dir, transform=None): self.image_dir = image_dir self.mask_dir = mask_dir self.transform = transform self.images = os.listdir(image_dir) self.masks = os.listdir(mask_dir) def __len__ (self): return len(self.images) def __getitem__ (self, index): img_path = os.path.join(self.image_dir, self.images[index]) mask_path = os.path.join(self.mask_dir, self.masks[index]) image = np.array(Image.open(img_path).convert("L")) image = np.expand_dims(image, axis=0) mask = np.array(Image.open(mask_path).convert("L"), dtype=np.float32) mask = np.expand_dims(mask, axis=0) mask[mask == 255.0] = 1.0 if self.transform is not None: augmentations = self.transform(image=image, mask=mask) image = augmentations["image"] mask = augmentations["mask"] return image, mask if __name__ == " __main__": data = CarvanaDataset('C:\DeepLearning\\train\img', 'C:\DeepLearning\\train\mask') image, mask = data. __getitem__ (0) print(image.shape) print(mask.shape) --> (1, 600, 600) (1, 600, 600) ``` Another new error comes here when run train.py: ```auto Traceback (most recent call last): File "C:/DeepLearning/UNet/train.py", line 120, in main() File "C:/DeepLearning/UNet/train.py", line 98, in main check_accuracy(val_loader, model, device=DEVICE) File "C:\DeepLearning\UNet\utils.py", line 62, in check_accuracy for x, y in loader: File "C:\Users\Tsai\anaconda3\envs\tf\lib\site-packages\torch\utils\data\dataloader.py", line 517, in __next__ data = self._next_data() File "C:\Users\Tsai\anaconda3\envs\tf\lib\site-packages\torch\utils\data\dataloader.py", line 1199, in _next_data return self._process_data(data) File "C:\Users\Tsai\anaconda3\envs\tf\lib\site-packages\torch\utils\data\dataloader.py", line 1225, in _process_data data.reraise() File "C:\Users\Tsai\anaconda3\envs\tf\lib\site-packages\torch\_utils.py", line 429, in reraise raise self.exc_type(msg) ValueError: Caught ValueError in DataLoader worker process 0. Original Traceback (most recent call last): File "C:\Users\Tsai\anaconda3\envs\tf\lib\site-packages\torch\utils\data\_utils\worker.py", line 202, in _worker_loop data = fetcher.fetch(index) File "C:\Users\Tsai\anaconda3\envs\tf\lib\site-packages\torch\utils\data\_utils\fetch.py", line 44, in fetch data = [self.dataset[idx] for idx in possibly_batched_index] File "C:\Users\Tsai\anaconda3\envs\tf\lib\site-packages\torch\utils\data\_utils\fetch.py", line 44, in data = [self.dataset[idx] for idx in possibly_batched_index] File "C:\DeepLearning\UNet\dataset.py", line 26, in __getitem__ augmentations = self.transform(image=image, mask=mask) File "C:\Users\Tsai\anaconda3\envs\tf\lib\site-packages\albumentations\core\composition.py", line 182, in __call__ data = t(force_apply=force_apply, **data) File "C:\Users\Tsai\anaconda3\envs\tf\lib\site-packages\albumentations\core\transforms_interface.py", line 89, in __call__ return self.apply_with_params(params, **kwargs) File "C:\Users\Tsai\anaconda3\envs\tf\lib\site-packages\albumentations\core\transforms_interface.py", line 102, in apply_with_params res[key] = target_function(arg, **dict(params,**target_dependencies)) File "C:\Users\Tsai\anaconda3\envs\tf\lib\site-packages\albumentations\augmentations\transforms.py", line 1496, in apply return F.normalize(image, self.mean, self.std, self.max_pixel_value) File "C:\Users\Tsai\anaconda3\envs\tf\lib\site-packages\albumentations\augmentations\functional.py", line 141, in normalize img -= mean ValueError: operands could not be broadcast together with shapes (600,600,600) (3,) (600,600,600) ``` How should I adjust my input or transform content? Thanks ---
**Author:** ![ejguan](https://discuss.pytorch.org/letter_avatar_proxy/v4/letter/e/5f8ce5/32.png) [@ejguan](https://discuss.pytorch.org/u/ejguan)\ **Post date:** [June 14, 2021, 9:18pm UTC](https://discuss.pytorch.org/t/raise-self-exc-type-msg-valueerror-caught-valueerror-in-dataloader-worker-process-0/123168/9 "2021-06-14T21:18:36Z")
Emmm. You were changing the wrong part. Two options for you, you should choose **ONE** of them depending on your use case: 1. Change image to three-channel (RGB rather than L mode) ```auto image = np.array(Image.open(img_path).convert("RGB")) ``` 1. Change transform to single-channel ```auto 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.Normalize( mean=[0.0], std=[1.0], max_pixel_value=255.0, ), ToTensorV2(), ] ) ```