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replace input with input_img #47

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Dec 26, 2020
Merged

replace input with input_img #47

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56 changes: 28 additions & 28 deletions gradcam.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,7 @@
from torchvision import models

class FeatureExtractor():
""" Class for extracting activations and
""" Class for extracting activations and
registering gradients from targetted intermediate layers """

def __init__(self, model, target_layers):
Expand Down Expand Up @@ -52,7 +52,7 @@ def __call__(self, x):
x = x.view(x.size(0),-1)
else:
x = module(x)

return target_activations, x


Expand All @@ -68,8 +68,8 @@ def preprocess_image(img):
np.ascontiguousarray(np.transpose(preprocessed_img, (2, 0, 1)))
preprocessed_img = torch.from_numpy(preprocessed_img)
preprocessed_img.unsqueeze_(0)
input = preprocessed_img.requires_grad_(True)
return input
input_img = preprocessed_img.requires_grad_(True)
return input_img


def show_cam_on_image(img, mask):
Expand All @@ -91,14 +91,14 @@ def __init__(self, model, feature_module, target_layer_names, use_cuda):

self.extractor = ModelOutputs(self.model, self.feature_module, target_layer_names)

def forward(self, input):
return self.model(input)
def forward(self, input_img):
return self.model(input_img)

def __call__(self, input, index=None):
def __call__(self, input_img, index=None):
if self.cuda:
features, output = self.extractor(input.cuda())
features, output = self.extractor(input_img.cuda())
else:
features, output = self.extractor(input)
features, output = self.extractor(input_img)

if index == None:
index = np.argmax(output.cpu().data.numpy())
Expand Down Expand Up @@ -127,7 +127,7 @@ def __call__(self, input, index=None):
cam += w * target[i, :, :]

cam = np.maximum(cam, 0)
cam = cv2.resize(cam, input.shape[2:])
cam = cv2.resize(cam, input_img.shape[2:])
cam = cam - np.min(cam)
cam = cam / np.max(cam)
return cam
Expand All @@ -136,21 +136,21 @@ def __call__(self, input, index=None):
class GuidedBackpropReLU(Function):

@staticmethod
def forward(self, input):
positive_mask = (input > 0).type_as(input)
output = torch.addcmul(torch.zeros(input.size()).type_as(input), input, positive_mask)
self.save_for_backward(input, output)
def forward(self, input_img):
positive_mask = (input_img > 0).type_as(input_img)
output = torch.addcmul(torch.zeros(input_img.size()).type_as(input_img), input_img, positive_mask)
self.save_for_backward(input_img, output)
return output

@staticmethod
def backward(self, grad_output):
input, output = self.saved_tensors
input_img, output = self.saved_tensors
grad_input = None

positive_mask_1 = (input > 0).type_as(grad_output)
positive_mask_1 = (input_img > 0).type_as(grad_output)
positive_mask_2 = (grad_output > 0).type_as(grad_output)
grad_input = torch.addcmul(torch.zeros(input.size()).type_as(input),
torch.addcmul(torch.zeros(input.size()).type_as(input), grad_output,
grad_input = torch.addcmul(torch.zeros(input_img.size()).type_as(input_img),
torch.addcmul(torch.zeros(input_img.size()).type_as(input_img), grad_output,
positive_mask_1), positive_mask_2)

return grad_input
Expand All @@ -169,18 +169,18 @@ def recursive_relu_apply(module_top):
recursive_relu_apply(module)
if module.__class__.__name__ == 'ReLU':
module_top._modules[idx] = GuidedBackpropReLU.apply

# replace ReLU with GuidedBackpropReLU
recursive_relu_apply(self.model)

def forward(self, input):
return self.model(input)
def forward(self, input_img):
return self.model(input_img)

def __call__(self, input, index=None):
def __call__(self, input_img, index=None):
if self.cuda:
output = self.forward(input.cuda())
output = self.forward(input_img.cuda())
else:
output = self.forward(input)
output = self.forward(input_img)

if index == None:
index = np.argmax(output.cpu().data.numpy())
Expand All @@ -197,7 +197,7 @@ def __call__(self, input, index=None):
# self.model.classifier.zero_grad()
one_hot.backward(retain_graph=True)

output = input.grad.cpu().data.numpy()
output = input_img.grad.cpu().data.numpy()
output = output[0, :, :, :]

return output
Expand Down Expand Up @@ -247,18 +247,18 @@ def deprocess_image(img):

img = cv2.imread(args.image_path, 1)
img = np.float32(cv2.resize(img, (224, 224))) / 255
input = preprocess_image(img)
input_img = preprocess_image(img)

# If None, returns the map for the highest scoring category.
# Otherwise, targets the requested index.
target_index = None
mask = grad_cam(input, target_index)
mask = grad_cam(input_img, target_index)

show_cam_on_image(img, mask)

gb_model = GuidedBackpropReLUModel(model=model, use_cuda=args.use_cuda)
print(model._modules.items())
gb = gb_model(input, index=target_index)
gb = gb_model(input_img, index=target_index)
gb = gb.transpose((1, 2, 0))
cam_mask = cv2.merge([mask, mask, mask])
cam_gb = deprocess_image(cam_mask*gb)
Expand Down