def __call__(self, inputs, targets):
"""Call function for `GradCAM`."""
self._verify_data(inputs, targets)
self._hook_cell()
with ForwardProbe(self._saliency_cell) as probe:
inputs = unify_inputs(inputs)
targets = unify_targets(targets)
weights = get_bp_weights(self._backward_model, *inputs, targets)
grad_net = GradNet(self._backward_model)
gradients = grad_net(*inputs, weights)
# get intermediate activation
activation = (probe.value,)
if self._layer == "":
activation = inputs
self._intermediate_grad = unify_inputs(gradients)
if self._intermediate_grad is not None:
# average pooling on gradients
intermediate_grad = unify_inputs(
self._avgpool(self._intermediate_grad[0], (2, 3)))
else:
raise ValueError("Gradient for intermediate layer is not "
"obtained")
mul = op.Mul()
attribution = self._aggregation_fn(
mul(*intermediate_grad, *activation))
if self._resize:
attribution = self._resize_fn(attribution, *inputs,
mode=self._resize_mode)
self._intermediate_grad = None
return attribution
def __call__(self, inputs, targets):
"""
Call function for `ModifiedReLU`, inherited by "Deconvolution" and "GuidedBackprop".
Args:
inputs (Tensor): The input data to be explained, a 4D tensor of shape :math:`(N, C, H, W)`.
targets (Tensor, int): The label of interest. It should be a 1D or 0D tensor, or an integer.
If it is a 1D tensor, its length should be the same as `inputs`.
Returns:
Tensor, a 4D tensor of shape :math:`(N, 1, H, W)`.
Examples:
>>> inputs = ms.Tensor(np.random.rand(1, 3, 224, 224), ms.float32)
>>> label = 5
>>> # explainer is a "Deconvolution" or "GuidedBackprop" object, parse data and the target label to be
>>> # explained and get the attribution
>>> saliency = explainer(inputs, label)
"""
self._verify_data(inputs, targets)
inputs = unify_inputs(inputs)
targets = unify_targets(targets)
weights = get_bp_weights(self._backward_model, inputs, targets)
gradients = self._grad_net(*inputs, weights)
saliency = self._aggregation_fn(gradients)
return saliency
def __call__(self, inputs, targets):
"""Call function for `Gradient`."""
self._verify_data(inputs, targets)
inputs = unify_inputs(inputs)
targets = unify_targets(targets)
weights = get_bp_weights(self._backward_model, *inputs, targets)
gradient = self._grad_net(*inputs, weights)
saliency = self._aggregation_fn(gradient)
return saliency
def __call__(self, inputs, targets):
"""
Call function for `ModifiedReLU`, inherited by "Deconvolution" and "GuidedBackprop".
Args:
inputs (Tensor): The input data to be explained, a 4D tensor of shape :math:`(N, C, H, W)`.
targets (Tensor, int): The label of interest. It should be a 1D or 0D tensor, or an integer.
If it is a 1D tensor, its length should be the same as `inputs`.
Returns:
Tensor, a 4D tensor of shape :math:`(N, 1, H, W)`.
"""
self._verify_data(inputs, targets)
inputs = unify_inputs(inputs)
targets = unify_targets(targets)
weights = get_bp_weights(self._backward_model, inputs, targets)
gradients = self._grad_net(*inputs, weights)
saliency = self._aggregation_fn(gradients)
return saliency
def get_bp_weights(model, inputs, targets=None, weights=None):
r"""
Compute the gradient of output w.r.t input.
Args:
model (Cell): Differentiable black-box model.
inputs (Tensor): Input to calculate gradient and explanation.
targets (int, optional): Target label id specifying which category to compute gradient. Default: None.
weights (Tensor, optional): Custom weights for computing gradients. The shape of weights should match the model
outputs. If None is provided, an one-hot weights with one in targets positions will be used instead.
Default: None.
Returns:
Tensor, signal to be back-propagated to the input.
"""
inputs = unify_inputs(inputs)
if targets is None and weights is None:
raise ValueError('Must provide one of targets or weights')
if weights is None:
targets = unify_targets(targets)
output = model(*inputs)
num_categories = output.shape[-1]
weights = generate_one_hot(targets, num_categories)
return weights