This repository has been archived by the owner on Jul 25, 2023. It is now read-only.
-
Notifications
You must be signed in to change notification settings - Fork 0
/
gradcam.py
250 lines (219 loc) · 9 KB
/
gradcam.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
import argparse
import cv2
import numpy as np
import torch
from torch.autograd import Function
from torchvision import models
class FeatureExtractor():
""" Class for extracting activations and
registering gradients from targetted intermediate layers """
def __init__(self, model, blob_name, target_layers):
self.model = model
self.blob_name = blob_name
self.target_layers = target_layers
self.gradients = []
def save_gradient(self, grad):
self.gradients.append(grad)
def __call__(self, x):
outputs = []
self.gradients = []
for idx, module in self.model._modules.items():
if idx != self.blob_name:
try:
x = module(x)
except:
x = x.view(x.size(0), -1)
x = module(x)
else:
for name, block in enumerate(getattr(self.model,self.blob_name)):
x = block(x)
if str(name) in self.target_layers:
x.register_hook(self.save_gradient)
outputs += [x]
return outputs, x
def preprocess_image(img):
means = [0.485, 0.456, 0.406]
stds = [0.229, 0.224, 0.225]
preprocessed_img = img.copy()[:, :, ::-1]
for i in range(3):
preprocessed_img[:, :, i] = preprocessed_img[:, :, i] - means[i]
preprocessed_img[:, :, i] = preprocessed_img[:, :, i] / stds[i]
preprocessed_img = \
np.ascontiguousarray(np.transpose(preprocessed_img, (2, 0, 1)))
preprocessed_img = torch.from_numpy(preprocessed_img)
preprocessed_img.unsqueeze_(0)
inputs = preprocessed_img.requires_grad_(True)
return inputs
def show_cams(img, mask_dic):
for name, mask in mask_dic.items():
show_cam_on_image(img, mask, name)
def show_cam_on_image(img, mask, name):
heatmap = cv2.applyColorMap(np.uint8(255 * mask), cv2.COLORMAP_JET)
heatmap = np.float32(heatmap) / 255
cam = heatmap + np.float32(img)
cam = cam / np.max(cam)
cv2.imwrite("cam{}.jpg".format(name), np.uint8(255 * cam))
class GradCam:
def __init__(self, model, blob_name, target_layer_names, use_cuda):
self.model = model
self.target_layer_names = target_layer_names
self.model.eval()
self.cuda = use_cuda
if self.cuda:
self.model = model.cuda()
self.extractor = FeatureExtractor(self.model, blob_name, target_layer_names)
def __call__(self, inputs, index=None):
cam_dic = {}
if self.cuda:
features, output = self.extractor(inputs.cuda())
else:
features, output = self.extractor(inputs)
if index == None:
index = np.argmax(output.cpu().data.numpy())
one_hot = np.zeros((1, output.size()[-1]), dtype=np.float32)
one_hot[0][index] = 1
one_hot = torch.from_numpy(one_hot).requires_grad_(True)
if self.cuda:
one_hot = torch.sum(one_hot.cuda() * output)
else:
one_hot = torch.sum(one_hot * output)
self.model.zero_grad()
one_hot.backward()
self.model.zero_grad()
for idx, feature in enumerate(features):
grads_val = self.extractor.gradients[len(features)-1-idx].cpu().data.numpy()
target = features[idx]
target = target.cpu().data.numpy()[0, :]
weights = np.mean(grads_val, axis=(2, 3))[0, :]
cam = np.zeros(target.shape[1:], dtype=np.float32)
for i, w in enumerate(weights):
cam += w * target[i, :, :]
cam = np.maximum(cam, 0)
cam = cv2.resize(cam, (456, 456))
cam = cam - np.min(cam)
cam = cam / np.max(cam)
cam_dic[self.target_layer_names[idx]] = cam
return cam_dic
class GuidedBackpropReLU(Function):
@staticmethod
def forward(self, i):
positive_mask = (i > 0).type_as(i)
output = torch.addcmul(torch.zeros(i.size()).type_as(i), i, positive_mask)
self.save_for_backward(i)
return output
@staticmethod
def backward(self, grad_output):
i = self.saved_tensors[0]
grad_input = None
positive_mask_1 = (i > 0).type_as(grad_output)
positive_mask_2 = (grad_output > 0).type_as(grad_output)
grad_input = torch.addcmul(torch.zeros(i.size()).type_as(i),
torch.addcmul(torch.zeros(i.size()).type_as(i), grad_output,
positive_mask_1), positive_mask_2)
return grad_input
class GuidedBackpropSwish(Function):
@staticmethod
def forward(self, i):
result = i * torch.sigmoid(i)
self.save_for_backward(i)
return result
@staticmethod
def backward(self, grad_output):
i = self.saved_tensors[0]
sigmoid_i = torch.sigmoid(i)
positive_mask_1 = (i > 0).type_as(grad_output)
positive_mask_2 = (grad_output > 0).type_as(grad_output)
grad_input = grad_output * (sigmoid_i * (1 + i * (1 - sigmoid_i))) * positive_mask_1 * positive_mask_2
return grad_input
class GuidedBackpropReLUModel:
def __init__(self, model, use_cuda, activation_layer_name = 'ReLU'):
self.model = model
self.model.eval()
self.cuda = use_cuda
if self.cuda:
self.model = model.cuda()
if activation_layer_name == 'MemoryEfficientSwish':
fb_func = GuidedBackpropSwish.apply
else:
fb_func = GuidedBackpropReLU.apply
for idx0, module0 in self.model._modules.items():
module0 = self.model._modules[idx0]
if module0.__class__.__name__ == activation_layer_name:
self.model._modules[idx0] = fb_func
for idx1, _ in module0._modules.items():
module1 = module0._modules[idx1]
if module1.__class__.__name__ == activation_layer_name:
self.model._modules[idx0]._modules[idx1] = fb_func
continue
for idx2, _ in module1._modules.items():
module2 = module1._modules[idx2]
if module2.__class__.__name__ == activation_layer_name:
self.model._modules[idx0]._modules[idx1]._modules[idx2] = fb_func
def forward(self, inputs):
return self.model(inputs)
def __call__(self, inputs, index=None):
if self.cuda:
output = self.forward(inputs.cuda())
else:
output = self.forward(inputs)
if index == None:
index = np.argmax(output.cpu().data.numpy())
one_hot = np.zeros((1, output.size()[-1]), dtype=np.float32)
one_hot[0][index] = 1
one_hot = torch.from_numpy(one_hot).requires_grad_(True)
if self.cuda:
one_hot = torch.sum(one_hot.cuda() * output)
else:
one_hot = torch.sum(one_hot * output)
one_hot.backward()
gradient = inputs.grad.cpu().data.numpy()
gradient = gradient[0, :, :, :]
return gradient
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--use-cuda', action='store_true', default=False,
help='Use NVIDIA GPU acceleration')
parser.add_argument('--image-path', type=str, default='./assets/dog.jpg',
help='Input image path')
args = parser.parse_args()
args.use_cuda = args.use_cuda and torch.cuda.is_available()
if args.use_cuda:
print("Using GPU for acceleration")
else:
print("Using CPU for computation")
return args
def deprocess_image(img):
""" see https://github.com/jacobgil/keras-grad-cam/blob/master/grad-cam.py#L65 """
img = img - np.mean(img)
img = img / (np.std(img) + 1e-5)
img = img * 0.1
img = img + 0.5
img = np.clip(img, 0, 1)
return np.uint8(img*255)
def show_gbs(inputs, gb_model, target_index, mask_dic):
gb = gb_model(inputs, index=target_index)
gb = gb.transpose((1, 2, 0))
z = []
for idx, mask in mask_dic.items():
cam_mask = cv2.merge([mask, mask, mask])
cam_gb = (cam_mask*gb)
z.append(cam_gb)
return z
if __name__ == '__main__':
""" python grad_cam.py <path_to_image>
1. Loads an image with opencv.
2. Preprocesses it for VGG19 and converts to a pytorch variable.
3. Makes a forward pass to find the category index with the highest score,
and computes intermediate activations.
Makes the visualization. """
grad_cam = GradCam(model=model, blob_name = 'features', target_layer_names=['35'], use_cuda=False)
img = cv2.imread(args.image_path, 1)
img = np.float32(cv2.resize(img, (224, 224))) / 255
inputs = preprocess_image(img)
# If None, returns the map for the highest scoring category.
# Otherwise, targets the requested index.
target_index = None
mask_dic = grad_cam(inputs, target_index)
show_cams(img, mask_dic)
gb_model = GuidedBackpropReLUModel(model=model, activation_layer_name = 'ReLU', use_cuda=False)
show_gbs(inputs, gb_model, mask_dic)