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{
  "text": "I am new to pytorch. I want to use imagenet images to understand how much each pixel contributes to the gradient. For this, I am trying to construct attention maps for my images. However, while doing so, I am encountering the following error:\n\n`<ipython-input-89-08560ac86bab>:2: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).   images_tensor = torch.tensor(images, requires_grad=True) <ipython-input-89-08560ac86bab>:3: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).   labels_tensor = torch.tensor(labels) --------------------------------------------------------------------------- RuntimeError                              Traceback (most recent call last) <ipython-input-90-49bfbb2b28f0> in <cell line: 20>()      18 plt.show()      19  ---> 20 show_attention_maps(X, y)  9 frames/usr/local/lib/python3.10/dist-packages/torch/nn/functional.py in batch_norm(input, running_mean, running_var, weight, bias, training, momentum, eps)    2480 _verify_batch_size(input.size())    2481  -> 2482     return torch.batch_norm(    2483 input, weight, bias, running_mean, running_var, training, momentum, eps, torch.backends.cudnn.enabled    2484     )`  \n\n`RuntimeError: running_mean should contain 1 elements not 64`\n\nI have tried changing the image size in preprocessing and changing the model to resnet152 instead of resnet18. My understanding from the research I have done is that the batchnorm in the first layer expects input size 1, but I have 64. I am not sure how that can be changed.\n\nMy code is here:\n\n`model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet18', pretrained=True)` \n\n`import torch.nn as nn new_conv1 = nn.Conv2d(15, 1, kernel_size=1, stride=1, padding=112)      nn.init.constant_(new_conv1.weight, 1)`\n\n `model.conv1 = new_conv1 model.eval()` \n\n`for param in model.parameters():`    \n\n`param.requires_grad = False` \n\n`def compute_attention_maps(images, labels, model):`     \n\n`images_tensor = torch.tensor(images, requires_grad=True)`     \n\n`labels_tensor = torch.tensor(labels)`     \n\n`predictions = model(images_tensor.unsqueeze(0))`     \n\n`criterion = torch.nn.CrossEntropyLoss()`     \n\n`loss = criterion(predictions, labels_tensor)`     \n\n`model.zero_grad()     loss.backward()`     \n\n`gradients = images_tensor.grad`     \n\n`attention_maps = torch.mean(gradients.abs(), dim=1)`     \n\n`return attention_maps`\n\n&#x200B;\n\n`def show_attention_maps(X, y): X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0)` \n\n`y_tensor = torch.LongTensor(y) attention = compute_attention_maps(X_tensor, y_tensor, model) attention = attention.numpy()`  \n\n`N = X.shape[0]` \n\n`for i in range(N):`     \n\n`plt.subplot(2, N, i + 1)`      \n\n`plt.imshow(X[i])     plt.axis('off')`     \n\n`plt.title(class_names[y[i]])`     \n\n`plt.subplot(2, N, N + i + 1)`     \n\n`plt.imshow(attention[i], cmap=plt.cm.gray)`     \n\n`plt.axis('off')`     \n\n`plt.gcf().set_size_inches(12, 5)` \n\n`plt.suptitle('Attention maps')` \n\n`plt.show()  show_attention_maps(X, y)`\n\nThank you very much in advance. Your help would help me learn and understand pytorch programming better!\n\n&#x200B;\n\n&#x200B;\n\n  \n",
  "label": "r/pytorch",
  "dataType": "post",
  "communityName": "r/pytorch",
  "datetime": "2024-04-10",
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}

Entry Information

• Entry ID: 4396
• Repository: Axioma AXP
• Dataset: arrmlet/reddit_dataset_36
• Total Entries: 100,000