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utils_grad_cam.py
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utils_grad_cam.py
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import cv2
import numpy as np
import torch
def visualize_cam(mask, img):
"""Make heatmap from mask and synthesize GradCAM result image using heatmap and img.
Args:
mask (torch.tensor): mask shape of (1, 1, H, W) and each element has value in range [0, 1]
img (torch.tensor): img shape of (1, 3, H, W) and each pixel value is in range [0, 1]
Return:
heatmap (torch.tensor): heatmap img shape of (3, H, W)
result (torch.tensor): synthesized GradCAM result of same shape with heatmap.
"""
heatmap = cv2.applyColorMap(np.uint8(255 * mask.cpu().squeeze()), cv2.COLORMAP_JET)
heatmap = torch.from_numpy(heatmap).permute(2, 0, 1).float().div(255)
b, g, r = heatmap.split(1)
heatmap = torch.cat([r, g, b])
result = heatmap+img.cpu()
result = result.div(result.max()).squeeze()
return heatmap, result
def find_resnet_layer(arch, target_layer_name):
"""Find resnet layer to calculate GradCAM and GradCAM++
Args:
arch: default torchvision densenet models
target_layer_name (str): the name of layer with its hierarchical information. please refer to usages below.
target_layer_name = 'conv1'
target_layer_name = 'layer1'
target_layer_name = 'layer1_basicblock0'
target_layer_name = 'layer1_basicblock0_relu'
target_layer_name = 'layer1_bottleneck0'
target_layer_name = 'layer1_bottleneck0_conv1'
target_layer_name = 'layer1_bottleneck0_downsample'
target_layer_name = 'layer1_bottleneck0_downsample_0'
target_layer_name = 'avgpool'
target_layer_name = 'fc'
Return:
target_layer: found layer. this layer will be hooked to get forward/backward pass information.
"""
if 'layer' in target_layer_name:
hierarchy = target_layer_name.split('_')
layer_num = int(hierarchy[0].lstrip('layer'))
# l = [module for module in arch.modules() if type(module) != torch.nn.Sequential]
# print(l)
if layer_num == 1:
target_layer = arch.layer1[2].conv2
elif layer_num == 2:
target_layer = arch.layer2[3].conv2
elif layer_num == 3:
target_layer = arch.layer3[5].conv2
elif layer_num == 4:
target_layer = arch.layer4[2].conv2
else:
raise ValueError('unknown layer : {}'.format(target_layer_name))
if len(hierarchy) >= 2:
bottleneck_num = int(hierarchy[1].lower().lstrip('bottleneck').lstrip('basicblock'))
target_layer = target_layer[bottleneck_num]
if len(hierarchy) >= 3:
target_layer = target_layer._modules[hierarchy[2]]
if len(hierarchy) == 4:
target_layer = target_layer._modules[hierarchy[3]]
else:
target_layer = arch._modules[target_layer_name]
return target_layer
def find_densenet_layer(arch, target_layer_name):
"""Find densenet layer to calculate GradCAM and GradCAM++
Args:
arch: default torchvision densenet models
target_layer_name (str): the name of layer with its hierarchical information. please refer to usages below.
target_layer_name = 'features'
target_layer_name = 'features_transition1'
target_layer_name = 'features_transition1_norm'
target_layer_name = 'features_denseblock2_denselayer12'
target_layer_name = 'features_denseblock2_denselayer12_norm1'
target_layer_name = 'features_denseblock2_denselayer12_norm1'
target_layer_name = 'classifier'
Return:
target_layer: found layer. this layer will be hooked to get forward/backward pass information.
"""
hierarchy = target_layer_name.split('_')
target_layer = arch._modules[hierarchy[0]]
if len(hierarchy) >= 2:
target_layer = target_layer._modules[hierarchy[1]]
if len(hierarchy) >= 3:
target_layer = target_layer._modules[hierarchy[2]]
if len(hierarchy) == 4:
target_layer = target_layer._modules[hierarchy[3]]
return target_layer
def find_vgg_layer(arch, target_layer_name):
"""Find vgg layer to calculate GradCAM and GradCAM++
Args:
arch: default torchvision densenet models
target_layer_name (str): the name of layer with its hierarchical information. please refer to usages below.
target_layer_name = 'features'
target_layer_name = 'features_42'
target_layer_name = 'classifier'
target_layer_name = 'classifier_0'
Return:
target_layer: found layer. this layer will be hooked to get forward/backward pass information.
"""
hierarchy = target_layer_name.split('_')
if len(hierarchy) >= 1:
target_layer = arch.features
if len(hierarchy) == 2:
target_layer = target_layer[int(hierarchy[1])]
return target_layer
def find_alexnet_layer(arch, target_layer_name):
"""Find alexnet layer to calculate GradCAM and GradCAM++
Args:
arch: default torchvision densenet models
target_layer_name (str): the name of layer with its hierarchical information. please refer to usages below.
target_layer_name = 'features'
target_layer_name = 'features_0'
target_layer_name = 'classifier'
target_layer_name = 'classifier_0'
Return:
target_layer: found layer. this layer will be hooked to get forward/backward pass information.
"""
hierarchy = target_layer_name.split('_')
if len(hierarchy) >= 1:
target_layer = arch.features
if len(hierarchy) == 2:
target_layer = target_layer[int(hierarchy[1])]
return target_layer
def find_squeezenet_layer(arch, target_layer_name):
"""Find squeezenet layer to calculate GradCAM and GradCAM++
Args:
arch: default torchvision densenet models
target_layer_name (str): the name of layer with its hierarchical information. please refer to usages below.
target_layer_name = 'features_12'
target_layer_name = 'features_12_expand3x3'
target_layer_name = 'features_12_expand3x3_activation'
Return:
target_layer: found layer. this layer will be hooked to get forward/backward pass information.
"""
hierarchy = target_layer_name.split('_')
target_layer = arch._modules[hierarchy[0]]
if len(hierarchy) >= 2:
target_layer = target_layer._modules[hierarchy[1]]
if len(hierarchy) == 3:
target_layer = target_layer._modules[hierarchy[2]]
elif len(hierarchy) == 4:
target_layer = target_layer._modules[hierarchy[2]+'_'+hierarchy[3]]
return target_layer
def denormalize(tensor, mean, std):
if not tensor.ndimension() == 4:
raise TypeError('tensor should be 4D')
mean = torch.FloatTensor(mean).view(1, 3, 1, 1).expand_as(tensor).to(tensor.device)
std = torch.FloatTensor(std).view(1, 3, 1, 1).expand_as(tensor).to(tensor.device)
return tensor.mul(std).add(mean)
def normalize(tensor, mean, std):
if not tensor.ndimension() == 4:
raise TypeError('tensor should be 4D')
mean = torch.FloatTensor(mean).view(1, 3, 1, 1).expand_as(tensor).to(tensor.device)
std = torch.FloatTensor(std).view(1, 3, 1, 1).expand_as(tensor).to(tensor.device)
return tensor.sub(mean).div(std)
class Normalize(object):
def __init__(self, mean, std):
self.mean = mean
self.std = std
def __call__(self, tensor):
return self.do(tensor)
def do(self, tensor):
return normalize(tensor, self.mean, self.std)
def undo(self, tensor):
return denormalize(tensor, self.mean, self.std)
def __repr__(self):
return self.__class__.__name__ + '(mean={0}, std={1})'.format(self.mean, self.std)