def _gradient_matching_test_assert(self, model: Module, output_layer: Module, test_input: Tensor) -> None: out = _forward_layer_eval(model, test_input, output_layer) # Select first element of tuple out = out[0] gradient_attrib = NeuronGradient(model, output_layer) self.assertFalse(gradient_attrib.multiplies_by_inputs) for i in range(cast(Tuple[int, ...], out.shape)[1]): neuron: Tuple[int, ...] = (i, ) while len(neuron) < len(out.shape) - 1: neuron = neuron + (0, ) input_attrib = Saliency(lambda x: _forward_layer_eval( model, x, output_layer, grad_enabled=True)[0][ (slice(None), *neuron)]) sal_vals = input_attrib.attribute(test_input, abs=False) grad_vals = gradient_attrib.attribute(test_input, neuron) # Verify matching sizes self.assertEqual(grad_vals.shape, sal_vals.shape) self.assertEqual(grad_vals.shape, test_input.shape) assertArraysAlmostEqual( sal_vals.reshape(-1).tolist(), grad_vals.reshape(-1).tolist(), delta=0.001, )
def forward_eval_layer_with_inputs_helper(self, model, inputs_to_test): # hard coding for simplicity # 0 if using args, 1 if using kwargs # => no 0s after first 1 (left to right) # # used to test utilization of args/kwargs use_args_or_kwargs = [ [[0], [1]], [ [0, 0], [0, 1], [1, 1], ], ] model = ModelInputWrapper(model) def forward_func(*args, args_or_kwargs=None): # convert to args or kwargs to test *args and **kwargs wrapping behavior new_args = [] new_kwargs = {} for args_or_kwarg, name, inp in zip(args_or_kwargs, inputs_to_test.keys(), args): if args_or_kwarg: new_kwargs[name] = inp else: new_args.append(inp) return model(*new_args, **new_kwargs) for args_or_kwargs in use_args_or_kwargs[len(inputs_to_test) - 1]: with self.subTest(args_or_kwargs=args_or_kwargs): inputs = _forward_layer_eval( functools.partial(forward_func, args_or_kwargs=args_or_kwargs), inputs=tuple(inputs_to_test.values()), layer=[ model.input_maps[name] for name in inputs_to_test.keys() ], ) inputs_with_attrib_to_inp = _forward_layer_eval( functools.partial(forward_func, args_or_kwargs=args_or_kwargs), inputs=tuple(inputs_to_test.values()), layer=[ model.input_maps[name] for name in inputs_to_test.keys() ], attribute_to_layer_input=True, ) for i1, i2, i3 in zip(inputs, inputs_with_attrib_to_inp, inputs_to_test.values()): self.assertTrue((i1[0] == i2[0]).all()) self.assertTrue((i1[0] == i3).all())
def neuron_forward_func(*args: Any): with torch.no_grad(): layer_eval = _forward_layer_eval( self.forward_func, args, self.layer, device_ids=self.device_ids, attribute_to_layer_input=attribute_to_neuron_input, ) return _verify_select_neuron(layer_eval, neuron_selector)
def attribute( self, inputs: Union[Tensor, Tuple[Tensor, ...]], baselines: BaselineType = None, target: TargetType = None, additional_forward_args: Any = None, n_steps: int = 50, method: str = "gausslegendre", internal_batch_size: Union[None, int] = None, return_convergence_delta: bool = False, attribute_to_layer_input: bool = False, ) -> Union[Union[Tensor, Tuple[Tensor, ...], List[Union[Tensor, Tuple[ Tensor, ...]]]], Tuple[Union[Tensor, Tuple[ Tensor, ...], List[Union[Tensor, Tuple[Tensor, ...]]]], Tensor, ], ]: r""" This method attributes the output of the model with given target index (in case it is provided, otherwise it assumes that output is a scalar) to layer inputs or outputs of the model, depending on whether `attribute_to_layer_input` is set to True or False, using the approach described above. In addition to that it also returns, if `return_convergence_delta` is set to True, integral approximation delta based on the completeness property of integrated gradients. Args: inputs (tensor or tuple of tensors): Input for which layer integrated gradients are computed. If forward_func takes a single tensor as input, a single input tensor should be provided. If forward_func takes multiple tensors as input, a tuple of the input tensors should be provided. It is assumed that for all given input tensors, dimension 0 corresponds to the number of examples, and if multiple input tensors are provided, the examples must be aligned appropriately. baselines (scalar, tensor, tuple of scalars or tensors, optional): Baselines define the starting point from which integral is computed and can be provided as: - a single tensor, if inputs is a single tensor, with exactly the same dimensions as inputs or the first dimension is one and the remaining dimensions match with inputs. - a single scalar, if inputs is a single tensor, which will be broadcasted for each input value in input tensor. - a tuple of tensors or scalars, the baseline corresponding to each tensor in the inputs' tuple can be: - either a tensor with matching dimensions to corresponding tensor in the inputs' tuple or the first dimension is one and the remaining dimensions match with the corresponding input tensor. - or a scalar, corresponding to a tensor in the inputs' tuple. This scalar value is broadcasted for corresponding input tensor. In the cases when `baselines` is not provided, we internally use zero scalar corresponding to each input tensor. Default: None target (int, tuple, tensor or list, optional): Output indices for which gradients are computed (for classification cases, this is usually the target class). If the network returns a scalar value per example, no target index is necessary. For general 2D outputs, targets can be either: - a single integer or a tensor containing a single integer, which is applied to all input examples - a list of integers or a 1D tensor, with length matching the number of examples in inputs (dim 0). Each integer is applied as the target for the corresponding example. For outputs with > 2 dimensions, targets can be either: - A single tuple, which contains #output_dims - 1 elements. This target index is applied to all examples. - A list of tuples with length equal to the number of examples in inputs (dim 0), and each tuple containing #output_dims - 1 elements. Each tuple is applied as the target for the corresponding example. Default: None additional_forward_args (any, optional): If the forward function requires additional arguments other than the inputs for which attributions should not be computed, this argument can be provided. It must be either a single additional argument of a Tensor or arbitrary (non-tuple) type or a tuple containing multiple additional arguments including tensors or any arbitrary python types. These arguments are provided to forward_func in order following the arguments in inputs. For a tensor, the first dimension of the tensor must correspond to the number of examples. It will be repeated for each of `n_steps` along the integrated path. For all other types, the given argument is used for all forward evaluations. Note that attributions are not computed with respect to these arguments. Default: None n_steps (int, optional): The number of steps used by the approximation method. Default: 50. method (string, optional): Method for approximating the integral, one of `riemann_right`, `riemann_left`, `riemann_middle`, `riemann_trapezoid` or `gausslegendre`. Default: `gausslegendre` if no method is provided. internal_batch_size (int, optional): Divides total #steps * #examples data points into chunks of size at most internal_batch_size, which are computed (forward / backward passes) sequentially. internal_batch_size must be at least equal to #examples. For DataParallel models, each batch is split among the available devices, so evaluations on each available device contain internal_batch_size / num_devices examples. If internal_batch_size is None, then all evaluations are processed in one batch. Default: None return_convergence_delta (bool, optional): Indicates whether to return convergence delta or not. If `return_convergence_delta` is set to True convergence delta will be returned in a tuple following attributions. Default: False attribute_to_layer_input (bool, optional): Indicates whether to compute the attribution with respect to the layer input or output. If `attribute_to_layer_input` is set to True then the attributions will be computed with respect to layer input, otherwise it will be computed with respect to layer output. Note that currently it is assumed that either the input or the output of internal layer, depending on whether we attribute to the input or output, is a single tensor. Support for multiple tensors will be added later. Default: False Returns: **attributions** or 2-element tuple of **attributions**, **delta**: - **attributions** (*tensor*, tuple of *tensors* or tuple of *tensors*): Integrated gradients with respect to `layer`'s inputs or outputs. Attributions will always be the same size and dimensionality as the input or output of the given layer, depending on whether we attribute to the inputs or outputs of the layer which is decided by the input flag `attribute_to_layer_input`. For a single layer, attributions are returned in a tuple if the layer inputs / outputs contain multiple tensors, otherwise a single tensor is returned. For multiple layers, attributions will always be returned as a list. Each element in this list will be equivalent to that of a single layer output, i.e. in the case that one layer, in the given layers, inputs / outputs multiple tensors: the corresponding output element will be a tuple of tensors. The ordering of the outputs will be the same order as the layers given in the constructor. - **delta** (*tensor*, returned if return_convergence_delta=True): The difference between the total approximated and true integrated gradients. This is computed using the property that the total sum of forward_func(inputs) - forward_func(baselines) must equal the total sum of the integrated gradient. Delta is calculated per example, meaning that the number of elements in returned delta tensor is equal to the number of of examples in inputs. Examples:: >>> # ImageClassifier takes a single input tensor of images Nx3x32x32, >>> # and returns an Nx10 tensor of class probabilities. >>> # It contains an attribute conv1, which is an instance of nn.conv2d, >>> # and the output of this layer has dimensions Nx12x32x32. >>> net = ImageClassifier() >>> lig = LayerIntegratedGradients(net, net.conv1) >>> input = torch.randn(2, 3, 32, 32, requires_grad=True) >>> # Computes layer integrated gradients for class 3. >>> # attribution size matches layer output, Nx12x32x32 >>> attribution = lig.attribute(input, target=3) """ inps, baselines = _format_input_baseline(inputs, baselines) _validate_input(inps, baselines, n_steps, method) baselines = _tensorize_baseline(inps, baselines) additional_forward_args = _format_additional_forward_args( additional_forward_args) def flatten_tuple(tup): return tuple( sum((list(x) if isinstance(x, (tuple, list)) else [x] for x in tup), [])) if self.device_ids is None: self.device_ids = getattr(self.forward_func, "device_ids", None) inputs_layer = _forward_layer_eval( self.forward_func, inps, self.layer, device_ids=self.device_ids, additional_forward_args=additional_forward_args, attribute_to_layer_input=attribute_to_layer_input, ) # if we have one output if not isinstance(self.layer, list): inputs_layer = (inputs_layer, ) num_outputs = [ 1 if isinstance(x, Tensor) else len(x) for x in inputs_layer ] num_outputs_cumsum = torch.cumsum( torch.IntTensor([0] + num_outputs), dim=0 # type: ignore ) inputs_layer = flatten_tuple(inputs_layer) baselines_layer = _forward_layer_eval( self.forward_func, baselines, self.layer, device_ids=self.device_ids, additional_forward_args=additional_forward_args, attribute_to_layer_input=attribute_to_layer_input, ) baselines_layer = flatten_tuple(baselines_layer) # inputs -> these inputs are scaled def gradient_func( forward_fn: Callable, inputs: Union[Tensor, Tuple[Tensor, ...]], target_ind: TargetType = None, additional_forward_args: Any = None, ) -> Tuple[Tensor, ...]: if self.device_ids is None or len(self.device_ids) == 0: scattered_inputs = (inputs, ) else: # scatter method does not have a precise enough return type in its # stub, so suppress the type warning. scattered_inputs = scatter( # type:ignore inputs, target_gpus=self.device_ids) scattered_inputs_dict = { scattered_input[0].device: scattered_input for scattered_input in scattered_inputs } with torch.autograd.set_grad_enabled(True): def layer_forward_hook(module, hook_inputs, hook_outputs=None, layer_idx=0): device = _extract_device(module, hook_inputs, hook_outputs) is_layer_tuple = ( isinstance(hook_outputs, tuple) # hook_outputs is None if attribute_to_layer_input == True if hook_outputs is not None else isinstance( hook_inputs, tuple)) if is_layer_tuple: return scattered_inputs_dict[device][ num_outputs_cumsum[layer_idx]:num_outputs_cumsum[ layer_idx + 1]] return scattered_inputs_dict[device][ num_outputs_cumsum[layer_idx]] hooks = [] try: layers = self.layer if not isinstance(layers, list): layers = [self.layer] for layer_idx, layer in enumerate(layers): hook = None # TODO: # Allow multiple attribute_to_layer_input flags for # each layer, i.e. attribute_to_layer_input[layer_idx] if attribute_to_layer_input: hook = layer.register_forward_pre_hook( functools.partial(layer_forward_hook, layer_idx=layer_idx)) else: hook = layer.register_forward_hook( functools.partial(layer_forward_hook, layer_idx=layer_idx)) hooks.append(hook) output = _run_forward(self.forward_func, tuple(), target_ind, additional_forward_args) finally: for hook in hooks: if hook is not None: hook.remove() assert output[0].numel() == 1, ( "Target not provided when necessary, cannot" " take gradient with respect to multiple outputs.") # torch.unbind(forward_out) is a list of scalar tensor tuples and # contains batch_size * #steps elements grads = torch.autograd.grad(torch.unbind(output), inputs) return grads self.ig.gradient_func = gradient_func all_inputs = ((inps + additional_forward_args) if additional_forward_args is not None else inps) attributions = self.ig.attribute.__wrapped__( # type: ignore self.ig, # self inputs_layer, baselines=baselines_layer, target=target, additional_forward_args=all_inputs, n_steps=n_steps, method=method, internal_batch_size=internal_batch_size, return_convergence_delta=False, ) # handle multiple outputs output: List[Tuple[Tensor, ...]] = [ tuple(attributions[int(num_outputs_cumsum[i] ):int(num_outputs_cumsum[i + 1])]) for i in range(len(num_outputs)) ] if return_convergence_delta: start_point, end_point = baselines, inps # computes approximation error based on the completeness axiom delta = self.compute_convergence_delta( attributions, start_point, end_point, additional_forward_args=additional_forward_args, target=target, ) return _format_outputs(isinstance(self.layer, list), output), delta return _format_outputs(isinstance(self.layer, list), output)
def attribute( # type: ignore self, inputs: Union[Tensor, Tuple[Tensor, ...]], baselines: Union[Tensor, Tuple[Tensor, ...]], target: TargetType = None, additional_forward_args: Any = None, return_convergence_delta: bool = False, attribute_to_layer_input: bool = False, ) -> Union[Tensor, Tuple[Tensor, ...], Tuple[Union[Tensor, Tuple[ Tensor, ...]], Tensor]]: inputs, baselines = _format_input_baseline(inputs, baselines) rand_coefficient = torch.tensor( np.random.uniform(0.0, 1.0, inputs[0].shape[0]), device=inputs[0].device, dtype=inputs[0].dtype, ) input_baseline_scaled = tuple( _scale_input(input, baseline, rand_coefficient) for input, baseline in zip(inputs, baselines)) grads, _ = compute_layer_gradients_and_eval( self.forward_func, self.layer, input_baseline_scaled, target, additional_forward_args, device_ids=self.device_ids, attribute_to_layer_input=attribute_to_layer_input, ) attr_baselines = _forward_layer_eval( self.forward_func, baselines, self.layer, additional_forward_args=additional_forward_args, device_ids=self.device_ids, attribute_to_layer_input=attribute_to_layer_input, ) attr_inputs = _forward_layer_eval( self.forward_func, inputs, self.layer, additional_forward_args=additional_forward_args, device_ids=self.device_ids, attribute_to_layer_input=attribute_to_layer_input, ) if self.multiplies_by_inputs: input_baseline_diffs = tuple( input - baseline for input, baseline in zip(attr_inputs, attr_baselines)) attributions = tuple(input_baseline_diff * grad for input_baseline_diff, grad in zip( input_baseline_diffs, grads)) else: attributions = grads return _compute_conv_delta_and_format_attrs( self, return_convergence_delta, attributions, baselines, inputs, additional_forward_args, target, cast(Union[Literal[True], Literal[False]], len(attributions) > 1), )
def attribute( self, inputs: Union[Tensor, Tuple[Tensor, ...]], layer_baselines: BaselineType = None, target: TargetType = None, additional_forward_args: Any = None, layer_mask: Union[None, Tensor, Tuple[Tensor, ...]] = None, attribute_to_layer_input: bool = False, perturbations_per_eval: int = 1, ) -> Union[Tensor, Tuple[Tensor, ...]]: r""" Args: inputs (tensor or tuple of tensors): Input for which layer attributions are computed. If forward_func takes a single tensor as input, a single input tensor should be provided. If forward_func takes multiple tensors as input, a tuple of the input tensors should be provided. It is assumed that for all given input tensors, dimension 0 corresponds to the number of examples, and if multiple input tensors are provided, the examples must be aligned appropriately. layer_baselines (scalar, tensor, tuple of scalars or tensors, optional): Layer baselines define reference values which replace each layer input / output value when ablated. Layer baselines should be a single tensor with dimensions matching the input / output of the target layer (or broadcastable to match it), based on whether we are attributing to the input or output of the target layer. In the cases when `baselines` is not provided, we internally use zero as the baseline for each neuron. Default: None target (int, tuple, tensor or list, optional): Output indices for which gradients are computed (for classification cases, this is usually the target class). If the network returns a scalar value per example, no target index is necessary. For general 2D outputs, targets can be either: - a single integer or a tensor containing a single integer, which is applied to all input examples - a list of integers or a 1D tensor, with length matching the number of examples in inputs (dim 0). Each integer is applied as the target for the corresponding example. For outputs with > 2 dimensions, targets can be either: - A single tuple, which contains #output_dims - 1 elements. This target index is applied to all examples. - A list of tuples with length equal to the number of examples in inputs (dim 0), and each tuple containing #output_dims - 1 elements. Each tuple is applied as the target for the corresponding example. Default: None additional_forward_args (any, optional): If the forward function requires additional arguments other than the inputs for which attributions should not be computed, this argument can be provided. It must be either a single additional argument of a Tensor or arbitrary (non-tuple) type or a tuple containing multiple additional arguments including tensors or any arbitrary python types. These arguments are provided to forward_func in order following the arguments in inputs. Note that attributions are not computed with respect to these arguments. Default: None layer_mask (tensor or tuple of tensors, optional): layer_mask defines a mask for the layer, grouping elements of the layer input / output which should be ablated together. layer_mask should be a single tensor with dimensions matching the input / output of the target layer (or broadcastable to match it), based on whether we are attributing to the input or output of the target layer. layer_mask should contain integers in the range 0 to num_groups - 1, and all elements with the same value are considered to be in the same group. If None, then a layer mask is constructed which assigns each neuron within the layer as a separate group, which is ablated independently. Default: None attribute_to_layer_input (bool, optional): Indicates whether to compute the attributions with respect to the layer input or output. If `attribute_to_layer_input` is set to True then the attributions will be computed with respect to layer's inputs, otherwise it will be computed with respect to layer's outputs. Note that currently it is assumed that either the input or the output of the layer, depending on whether we attribute to the input or output, is a single tensor. Support for multiple tensors will be added later. Default: False perturbations_per_eval (int, optional): Allows ablation of multiple neuron (groups) to be processed simultaneously in one call to forward_fn. Each forward pass will contain a maximum of perturbations_per_eval * #examples samples. For DataParallel models, each batch is split among the available devices, so evaluations on each available device contain at most (perturbations_per_eval * #examples) / num_devices samples. Default: 1 Returns: *tensor* or tuple of *tensors* of **attributions**: - **attributions** (*tensor* or tuple of *tensors*): Attribution of each neuron in given layer input or output. Attributions will always be the same size as the input or output of the given layer, depending on whether we attribute to the inputs or outputs of the layer which is decided by the input flag `attribute_to_layer_input` Attributions are returned in a tuple if the layer inputs / outputs contain multiple tensors, otherwise a single tensor is returned. Examples:: >>> # SimpleClassifier takes a single input tensor of size Nx4x4, >>> # and returns an Nx3 tensor of class probabilities. >>> # It contains an attribute conv1, which is an instance of nn.conv2d, >>> # and the output of this layer has dimensions Nx12x3x3. >>> net = SimpleClassifier() >>> # Generating random input with size 2 x 4 x 4 >>> input = torch.randn(2, 4, 4) >>> # Defining LayerFeatureAblation interpreter >>> ablator = LayerFeatureAblation(net, net.conv1) >>> # Computes ablation attribution, ablating each of the 108 >>> # neurons independently. >>> attr = ablator.attribute(input, target=1) >>> # Alternatively, we may want to ablate neurons in groups, e.g. >>> # grouping all the layer outputs in the same row. >>> # This can be done by creating a layer mask as follows, which >>> # defines the groups of layer inputs / outouts, e.g.: >>> # +---+---+---+ >>> # | 0 | 0 | 0 | >>> # +---+---+---+ >>> # | 1 | 1 | 1 | >>> # +---+---+---+ >>> # | 2 | 2 | 2 | >>> # +---+---+---+ >>> # With this mask, all the 36 neurons in a row / channel are ablated >>> # simultaneously, and the attribution for each neuron in the same >>> # group (0 - 2) per example are the same. >>> # The attributions can be calculated as follows: >>> # layer mask has dimensions 1 x 3 x 3 >>> layer_mask = torch.tensor([[[0,0,0],[1,1,1], >>> [2,2,2]]]) >>> attr = ablator.attribute(input, target=1, >>> layer_mask=layer_mask) """ def layer_forward_func(*args): layer_length = args[-1] layer_input = args[:layer_length] original_inputs = args[layer_length:-1] device_ids = self.device_ids if device_ids is None: device_ids = getattr(self.forward_func, "device_ids", None) all_layer_inputs = {} if device_ids is not None: scattered_layer_input = scatter(layer_input, target_gpus=device_ids) for device_tensors in scattered_layer_input: all_layer_inputs[device_tensors[0].device] = device_tensors else: all_layer_inputs[layer_input[0].device] = layer_input def forward_hook(module, inp, out=None): device = _extract_device(module, inp, out) is_layer_tuple = (isinstance(out, tuple) if out is not None else isinstance(inp, tuple)) if device not in all_layer_inputs: raise AssertionError( "Layer input not placed on appropriate " "device. If using a DataParallel model, either provide the " "DataParallel model as forward_func or provide device ids" " to the constructor.") if not is_layer_tuple: return all_layer_inputs[device][0] return all_layer_inputs[device] hook = None try: if attribute_to_layer_input: hook = self.layer.register_forward_pre_hook(forward_hook) else: hook = self.layer.register_forward_hook(forward_hook) eval = _run_forward(self.forward_func, original_inputs, target=target) finally: if hook is not None: hook.remove() return eval with torch.no_grad(): inputs = _format_tensor_into_tuples(inputs) additional_forward_args = _format_additional_forward_args( additional_forward_args) layer_eval = _forward_layer_eval( self.forward_func, inputs, self.layer, additional_forward_args, device_ids=self.device_ids, attribute_to_layer_input=attribute_to_layer_input, ) layer_eval_len = (len(layer_eval), ) all_inputs = ((inputs + additional_forward_args + layer_eval_len) if additional_forward_args is not None else inputs + layer_eval_len) ablator = FeatureAblation(layer_forward_func) layer_attribs = ablator.attribute.__wrapped__( ablator, # self layer_eval, baselines=layer_baselines, additional_forward_args=all_inputs, feature_mask=layer_mask, perturbations_per_eval=perturbations_per_eval, ) _attr = _format_output(len(layer_attribs) > 1, layer_attribs) return _attr
def attribute( self, inputs: Union[Tensor, Tuple[Tensor, ...]], additional_forward_args: Any = None, attribute_to_layer_input: bool = False, ) -> Union[Tensor, Tuple[Tensor, ...], List[Union[Tensor, Tuple[Tensor, ...]]]]: r""" Args: inputs (tensor or tuple of tensors): Input for which layer activation is computed. If forward_func takes a single tensor as input, a single input tensor should be provided. If forward_func takes multiple tensors as input, a tuple of the input tensors should be provided. It is assumed that for all given input tensors, dimension 0 corresponds to the number of examples, and if multiple input tensors are provided, the examples must be aligned appropriately. additional_forward_args (any, optional): If the forward function requires additional arguments other than the inputs for which attributions should not be computed, this argument can be provided. It must be either a single additional argument of a Tensor or arbitrary (non-tuple) type or a tuple containing multiple additional arguments including tensors or any arbitrary python types. These arguments are provided to forward_func in order following the arguments in inputs. Note that attributions are not computed with respect to these arguments. Default: None attribute_to_layer_input (bool, optional): Indicates whether to compute the attribution with respect to the layer input or output. If `attribute_to_layer_input` is set to True then the attributions will be computed with respect to layer input, otherwise it will be computed with respect to layer output. Note that currently it is assumed that either the input or the output of internal layer, depending on whether we attribute to the input or output, is a single tensor. Support for multiple tensors will be added later. Default: False Returns: *tensor* or tuple of *tensors* or *list* of **attributions**: - **attributions** (*tensor* or tuple of *tensors* or *list*): Activation of each neuron in given layer output. Attributions will always be the same size as the output of the given layer. Attributions are returned in a tuple if the layer inputs / outputs contain multiple tensors, otherwise a single tensor is returned. If multiple layers are provided, attributions are returned as a list, each element corresponding to the activations of the corresponding layer. Examples:: >>> # ImageClassifier takes a single input tensor of images Nx3x32x32, >>> # and returns an Nx10 tensor of class probabilities. >>> # It contains an attribute conv1, which is an instance of nn.conv2d, >>> # and the output of this layer has dimensions Nx12x32x32. >>> net = ImageClassifier() >>> layer_act = LayerActivation(net, net.conv1) >>> input = torch.randn(2, 3, 32, 32, requires_grad=True) >>> # Computes layer activation. >>> # attribution is layer output, with size Nx12x32x32 >>> attribution = layer_cond.attribute(input) """ with torch.no_grad(): layer_eval = _forward_layer_eval( self.forward_func, inputs, self.layer, additional_forward_args, device_ids=self.device_ids, attribute_to_layer_input=attribute_to_layer_input, ) if isinstance(self.layer, Module): return _format_output(len(layer_eval) > 1, layer_eval) else: return [ _format_output(len(single_layer_eval) > 1, single_layer_eval) for single_layer_eval in layer_eval ]