def _deconv_matching_assert(self, model, output_layer, test_input): out = model(test_input) attrib = Deconvolution(model) neuron_attrib = NeuronDeconvolution(model, output_layer) for i in range(out.shape[1]): deconv_vals = attrib.attribute(test_input, target=i) neuron_deconv_vals = neuron_attrib.attribute(test_input, (i, )) assertTensorAlmostEqual(self, deconv_vals, neuron_deconv_vals, delta=0.01)
def _deconv_test_assert(self, model, test_input, expected, additional_input=None): deconv = Deconvolution(model) attributions = deconv.attribute( test_input, target=0, additional_forward_args=additional_input) for i in range(len(test_input)): assertTensorAlmostEqual(self, attributions[i], expected[i], delta=0.01)
def _deconv_test_assert( self, model: Module, test_input: TensorOrTupleOfTensorsGeneric, expected: Tuple[List[List[float]], ...], additional_input: Any = None, ) -> None: deconv = Deconvolution(model) attributions = deconv.attribute( test_input, target=0, additional_forward_args=additional_input ) for i in range(len(test_input)): assertTensorAlmostEqual(self, attributions[i], expected[i], delta=0.01)
def _deconv_matching_assert( self, model: Module, output_layer: Module, test_input: TensorOrTupleOfTensorsGeneric, ) -> None: out = model(test_input) attrib = Deconvolution(model) self.assertFalse(attrib.multiplies_by_inputs) neuron_attrib = NeuronDeconvolution(model, output_layer) for i in range(out.shape[1]): deconv_vals = attrib.attribute(test_input, target=i) neuron_deconv_vals = neuron_attrib.attribute(test_input, (i,)) assertTensorAlmostEqual(self, deconv_vals, neuron_deconv_vals, delta=0.01)
def __init__(self, model: Module, layer: Module, device_ids: Union[None, List[int]] = None) -> None: r""" Args: model (Module): The reference to PyTorch model instance. layer (Module): Layer for which attributions are computed. Output size of attribute matches this layer's input or output dimensions, depending on whether we attribute to the inputs or outputs of the layer, corresponding to attribution of each neuron in the input or output of this layer. Currently, it is assumed that the inputs or the outputs of the layer, depending on which one is used for attribution, can only be a single tensor. device_ids (list(int)): Device ID list, necessary only if forward_func applies a DataParallel model. This allows reconstruction of intermediate outputs from batched results across devices. If forward_func is given as the DataParallel model itself, then it is not necessary to provide this argument. """ NeuronAttribution.__init__(self, model, layer, device_ids) GradientAttribution.__init__(self, model) self.deconv = Deconvolution(model)