def __init__(self, model_type,layer_name,model):
self.model_arch = model
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.feature, 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)
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()
self.attention = 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
# eps = 7./3. - 4./3. - 1
# input_eps = input[0]
# input_eps += (input[0] == 0) * eps
# self.attention['value'] = output / input_eps
self.attention['value'] = output / input[0]
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) # not required for attention mapping
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 __init__(self, model_dict, verbose=False):
model_type = model_dict['type']
layer_name = model_dict['layer_name']
self.model_arch = model_dict['arch']
self.activation_function = model_dict['activation_function']
if self.activation_function not in ('sigmoid', 'exp'):
raise ValueError("Invalid activation function")
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 __init__(self, model_dict):
model_type = model_dict['type']
layer_name = model_dict['layer_name']
self.model_arch = model_dict['arch']
self.model_arch.eval()
if torch.cuda.is_available():
self.model_arch.cuda()
self.gradients = dict()
self.activations = dict()
def backward_hook(module, grad_input, grad_output):
if torch.cuda.is_available():
self.gradients['value'] = grad_output[0].cuda()
else:
self.gradients['value'] = grad_output[0]
return None
def forward_hook(module, input, output):
if torch.cuda.is_available():
self.activations['value'] = output.cuda()
else:
self.activations['value'] = output
return None
if 'vgg' in model_type.lower():
self.target_layer = find_vgg_layer(self.model_arch, layer_name)
elif 'resnet' in model_type.lower():
self.target_layer = find_resnet_layer(self.model_arch, layer_name)
elif 'densenet' in model_type.lower():
self.target_layer = find_densenet_layer(self.model_arch, layer_name)
elif 'alexnet' in model_type.lower():
self.target_layer = find_alexnet_layer(self.model_arch, layer_name)
elif 'squeezenet' in model_type.lower():
self.target_layer = find_squeezenet_layer(self.model_arch, layer_name)
elif 'googlenet' in model_type.lower():
self.target_layer = find_googlenet_layer(self.model_arch, layer_name)
elif 'shufflenet' in model_type.lower():
self.target_layer = find_shufflenet_layer(self.model_arch, layer_name)
elif 'mobilenet' in model_type.lower():
self.target_layer = find_mobilenet_layer(self.model_arch, layer_name)
else:
self.target_layer = find_layer(self.model_arch, layer_name)
self.target_layer.register_forward_hook(forward_hook)
self.target_layer.register_backward_hook(backward_hook)