Gradients and activations shape in the VIT example code #511
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Did you resolve this issue? I have a similar problem: |
I have a similar problem, too. I tried to run it for DINOv2: File "/code/finetune-v2/visualize_gradcam.py", line 208, in gradcam_dataset
gradcam_one_img(img_path)
File "/code/finetune-v2/visualize_gradcam.py", line 152, in gradcam_one_img
grayscale_cam = cam(input_tensor=img_tensor, targets=targets)
File "/opt/conda/envs/dinov2/lib/python3.9/site-packages/pytorch_grad_cam/base_cam.py", line 186, in __call__
return self.forward(input_tensor, targets, eigen_smooth)
File "/opt/conda/envs/dinov2/lib/python3.9/site-packages/pytorch_grad_cam/base_cam.py", line 110, in forward
cam_per_layer = self.compute_cam_per_layer(input_tensor, targets, eigen_smooth)
File "/opt/conda/envs/dinov2/lib/python3.9/site-packages/pytorch_grad_cam/base_cam.py", line 141, in compute_cam_per_layer
cam = self.get_cam_image(input_tensor, target_layer, targets, layer_activations, layer_grads, eigen_smooth)
File "/opt/conda/envs/dinov2/lib/python3.9/site-packages/pytorch_grad_cam/base_cam.py", line 66, in get_cam_image
weights = self.get_cam_weights(input_tensor, target_layer, targets, activations, grads)
File "/opt/conda/envs/dinov2/lib/python3.9/site-packages/pytorch_grad_cam/grad_cam.py", line 23, in get_cam_weights
if len(grads.shape) == 4:
AttributeError: 'NoneType' object has no attribute 'shape'here is my code: class Dino(nn.Module):
def __init__(self, type, img_size, cls_num):
super().__init__()
# get feature model
model = torch.hub.load(
'', type, source='local'
).to(device)
autocast_ctx = partial(
torch.cuda.amp.autocast, enabled=True, dtype=torch.float16
)
self.feature_model = ModelWithIntermediateLayers(
model, n_last_blocks=1, autocast_ctx=autocast_ctx
).to(device)
with torch.no_grad():
sample_input = torch.randn(1, 3, *img_size).to(device)
sample_output = self.feature_model(sample_input)
# get linear readout
out_dim = create_linear_input(
sample_output, use_n_blocks=1, use_avgpool=True
).shape[1]
self.classifier = LinearClassifier(
out_dim, use_n_blocks=1, use_avgpool=True, num_classes=cls_num
).to(device)
def forward(self, x):
x = self.feature_model(x)
x = self.classifier(x)
return x
data_transforms = {
'train': transforms.Compose([
transforms.Resize(args.image_size),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
'val': transforms.Compose([
transforms.Resize(args.image_size),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
}
model = Dino(args.arch, args.image_size, 2)
# print(model)
checkpoint_path = f'/code/finetune-v2/best_models/best_model_{model_name}.pth'
checkpoint = torch.load(checkpoint_path, map_location='cpu')
model.load_state_dict(checkpoint)
model.eval()
model = model.cuda()
target_layers = [model.feature_model.feature_model.blocks[-1].norm1]
# target_layers = [model.feature_model.feature_model.blocks[10]]
print(target_layers[0])
targets = [ClassifierOutputTarget(0)]
def reshape_transform(tensor):
result = tensor[:, 1:, :].reshape(tensor.size(0),
args.image_size[0] // 14, args.image_size[1] // 14, tensor.size(2))
# Bring the channels to the first dimension,
# like in CNNs.
result = result.transpose(2, 3).transpose(1, 2)
return result
def gradcam_one_img(img_path):
with GradCAM(model=model, target_layers=target_layers, reshape_transform=reshape_transform) as cam:
rgb_img = Image.open(img_path).convert('RGB')
img_tensor = data_transforms['val'](rgb_img).unsqueeze(0).cuda()
print(img_tensor.shape)
# outputs = model(img_tensor)
grayscale_cam = cam(input_tensor=img_tensor, targets=targets)
# grayscale_cam = cam(input_tensor=img_tensor, targets=targets)
grayscale_cam = grayscale_cam[0, :]
visualization = show_cam_on_image(rgb_img, grayscale_cam, use_rgb=True)
print(visualization)
return cam.outputs, visualization
gradcam_one_img(img_path) |
Hello, did you resolve this issue? I have a similar problem! |
|
I met the same problem!! |
I tried to run the vit example code, however, I noticed the following errors.
Python 3.9.18 (main, Sep 11 2023, 13:41:44)
[GCC 11.2.0] :: Anaconda, Inc. on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from pytorch_grad_cam import GradCAM
>>> import torch
>>> model = torch.hub.load('facebookresearch/deit:main',
... 'deit_tiny_patch16_224', pretrained=True)
Using cache found in /home/.cache/torch/hub/facebookresearch_deit_main
>>> target_layers = [model.blocks[-1].norm1]
>>> image = torch.rand(1,3,224,224)
>>> cam = GradCAM(model=model, target_layers=target_layers)
>>> grayscale_cam = cam(input_tensor=image, targets=None)
(1, 197, 192)
Traceback (most recent call last):
File "", line 1, in
File "/data4/user/miniconda3/envs/anti-dreambooth/lib/python3.9/site-packages/pytorch_grad_cam/base_cam.py", line 188, in call
return self.forward(input_tensor, targets, eigen_smooth)
File "/data4/user/miniconda3/envs/anti-dreambooth/lib/python3.9/site-packages/pytorch_grad_cam/base_cam.py", line 112, in forward
cam_per_layer = self.compute_cam_per_layer(input_tensor, targets, eigen_smooth)
File "/data4/user/miniconda3/envs/anti-dreambooth/lib/python3.9/site-packages/pytorch_grad_cam/base_cam.py", line 143, in compute_cam_per_layer
cam = self.get_cam_image(input_tensor, target_layer, targets, layer_activations, layer_grads, eigen_smooth)
File "/data4/user/miniconda3/envs/anti-dreambooth/lib/python3.9/site-packages/pytorch_grad_cam/base_cam.py", line 66, in get_cam_image
weights = self.get_cam_weights(input_tensor, target_layer, targets, activations, grads)
File "/data4/user/miniconda3/envs/anti-dreambooth/lib/python3.9/site-packages/pytorch_grad_cam/grad_cam.py", line 32, in get_cam_weights
raise ValueError("Invalid grads shape."
ValueError: Invalid grads shape.Shape of grads should be 4 (2D image) or 5 (3D image).
If I comment out the error throwing logic in grad_cam.py, then the shape check in
pytorch-grad-cam/pytorch_grad_cam/base_cam.py
Line 74 in 1ff3f58
Does the shape really matter? Is there a way to fix this or just work around it? Thanks!
pytorch '2.1.0+cu121', grad-cam 1.5.2, ubuntu 18.04
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