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gradcam.py
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gradcam.py
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import torch
import torch.nn.functional as F
from utils_grad_cam import find_alexnet_layer, find_vgg_layer, find_resnet_layer, find_densenet_layer, find_squeezenet_layer
class GradCAM(object):
"""Calculate GradCAM salinecy map.
A simple example:
# initialize a model, model_dict and gradcam
resnet = torchvision.models.resnet101(pretrained=True)
resnet.eval()
model_dict = dict(model_type='resnet', arch=resnet, layer_name='layer4', input_size=(224, 224))
gradcam = GradCAM(model_dict)
# get an image and normalize with mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)
img = load_img()
normed_img = normalizer(img)
# get a GradCAM saliency map on the class index 10.
mask, logit = gradcam(normed_img, class_idx=10)
# make heatmap from mask and synthesize saliency map using heatmap and img
heatmap, cam_result = visualize_cam(mask, img)
Args:
model_dict (dict): a dictionary that contains 'model_type', 'arch', layer_name', 'input_size'(optional) as keys.
verbose (bool): whether to print output size of the saliency map givien 'layer_name' and 'input_size' in model_dict.
"""
def __init__(self, model_dict, verbose=False):
model_type = model_dict['type']
layer_name = model_dict['layer_name']
self.model_arch = model_dict['arch']
self.gradients = dict()
self.activations = dict()
def backward_hook(module, grad_input, grad_output):
self.gradients['value'] = grad_output[0]
return None
def forward_hook(module, input, output):
self.activations['value'] = output
return None
if 'vgg' in model_type.lower():
target_layer = find_vgg_layer(self.model_arch, layer_name)
elif 'resnet' in model_type.lower():
target_layer = find_resnet_layer(self.model_arch, layer_name)
elif 'densenet' in model_type.lower():
target_layer = find_densenet_layer(self.model_arch, layer_name)
elif 'alexnet' in model_type.lower():
target_layer = find_alexnet_layer(self.model_arch, layer_name)
elif 'squeezenet' in model_type.lower():
target_layer = find_squeezenet_layer(self.model_arch, layer_name)
target_layer.register_forward_hook(forward_hook)
target_layer.register_backward_hook(backward_hook)
if verbose:
try:
input_size = model_dict['input_size']
except KeyError:
print("please specify size of input image in model_dict. e.g. {'input_size':(224, 224)}")
pass
else:
device = 'cuda' if next(self.model_arch.parameters()).is_cuda else 'cpu'
self.model_arch(torch.zeros(1, 3, *(input_size), device=device))
print('saliency_map size :', self.activations['value'].shape[2:])
def forward(self, input, class_idx=None, retain_graph=False):
"""
Args:
input: input image with shape of (1, 3, H, W)
class_idx (int): class index for calculating GradCAM.
If not specified, the class index that makes the highest model prediction score will be used.
Return:
mask: saliency map of the same spatial dimension with input
logit: model output
"""
b, c, h, w = input.size()
logit = self.model_arch(input)
if class_idx is None:
score = logit[:, logit.max(1)[-1]].squeeze()
else:
score = logit[:, class_idx].squeeze()
self.model_arch.zero_grad()
score.backward(retain_graph=retain_graph)
gradients = self.gradients['value']
activations = self.activations['value']
b, k, u, v = gradients.size()
alpha = gradients.view(b, k, -1).mean(2)
#alpha = F.relu(gradients.view(b, k, -1)).mean(2)
weights = alpha.view(b, k, 1, 1)
saliency_map = (weights*activations).sum(1, keepdim=True)
saliency_map = F.relu(saliency_map)
saliency_map = F.upsample(saliency_map, size=(h, w), mode='bilinear', align_corners=False)
saliency_map_min, saliency_map_max = saliency_map.min(), saliency_map.max()
saliency_map = (saliency_map - saliency_map_min).div(saliency_map_max - saliency_map_min).data
return saliency_map, logit
def __call__(self, input, class_idx=None, retain_graph=False):
return self.forward(input, class_idx, retain_graph)
class GradCAMpp(GradCAM):
"""Calculate GradCAM++ salinecy map.
A simple example:
# initialize a model, model_dict and gradcampp
resnet = torchvision.models.resnet101(pretrained=True)
resnet.eval()
model_dict = dict(model_type='resnet', arch=resnet, layer_name='layer4', input_size=(224, 224))
gradcampp = GradCAMpp(model_dict)
# get an image and normalize with mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)
img = load_img()
normed_img = normalizer(img)
# get a GradCAM saliency map on the class index 10.
mask, logit = gradcampp(normed_img, class_idx=10)
# make heatmap from mask and synthesize saliency map using heatmap and img
heatmap, cam_result = visualize_cam(mask, img)
Args:
model_dict (dict): a dictionary that contains 'model_type', 'arch', layer_name', 'input_size'(optional) as keys.
verbose (bool): whether to print output size of the saliency map givien 'layer_name' and 'input_size' in model_dict.
"""
def __init__(self, model_dict, verbose=False):
super(GradCAMpp, self).__init__(model_dict, verbose)
def forward(self, input, class_idx=None, retain_graph=False):
"""
Args:
input: input image with shape of (1, 3, H, W)
class_idx (int): class index for calculating GradCAM.
If not specified, the class index that makes the highest model prediction score will be used.
Return:
mask: saliency map of the same spatial dimension with input
logit: model output
"""
b, c, h, w = input.size()
logit = self.model_arch(input)
if class_idx is None:
score = logit[:, logit.max(1)[-1]].squeeze()
else:
score = logit[:, class_idx].squeeze()
self.model_arch.zero_grad()
score.backward(retain_graph=retain_graph)
gradients = self.gradients['value'] # dS/dA
activations = self.activations['value'] # A
b, k, u, v = gradients.size()
alpha_num = gradients.pow(2)
alpha_denom = gradients.pow(2).mul(2) + \
activations.mul(gradients.pow(3)).view(b, k, u*v).sum(-1, keepdim=True).view(b, k, 1, 1)
alpha_denom = torch.where(alpha_denom != 0.0, alpha_denom, torch.ones_like(alpha_denom))
alpha = alpha_num.div(alpha_denom+1e-7)
positive_gradients = F.relu(score.exp()*gradients) # ReLU(dY/dA) == ReLU(exp(S)*dS/dA))
weights = (alpha*positive_gradients).view(b, k, u*v).sum(-1).view(b, k, 1, 1)
saliency_map = (weights*activations).sum(1, keepdim=True)
saliency_map = F.relu(saliency_map)
saliency_map = F.upsample(saliency_map, size=(224, 224), mode='bilinear', align_corners=False)
saliency_map_min, saliency_map_max = saliency_map.min(), saliency_map.max()
saliency_map = (saliency_map-saliency_map_min).div(saliency_map_max-saliency_map_min).data
return saliency_map, logit