def extract(cls, node): pb = node.parameters read_learning_info(pb) weights = read_binary_vector(pb) mapping_rule = {} embed_input(mapping_rule, 1, 'weights', weights) ScaleShiftOp.update_node_stat(node, mapping_rule) return cls.enabled
def extract(cls, node): pb = node.parameters read_learning_info(pb) biases = read_binary_vector(pb) bias_term = True mapping_rule = {'bias_term': bias_term} embed_input(mapping_rule, 1, 'weights', np.ones(biases.shape)) embed_input(mapping_rule, 2, 'biases', biases) ScaleShiftOp.update_node_stat(node, mapping_rule) return cls.enabled
def extract(cls, node: Node) -> bool: """ Extract conv parameters from node.parameters. node.parameters like file descriptor object. :param node: Convolution node :return: """ pb = node.parameters kernel = read_token_value(pb, b'<PatchDim>') stride = read_token_value(pb, b'<PatchStep>') patch_stride = read_token_value(pb, b'<PatchStride>') read_learning_info(pb) collect_until_whitespace(pb) weights, weights_shape = read_binary_matrix(pb) collect_until_whitespace(pb) biases = read_binary_vector(pb) if (patch_stride - kernel) % stride != 0: raise Error( 'Kernel size and stride does not correspond to `patch_stride` attribute of Convolution layer. ' + refer_to_faq_msg(93)) output = biases.shape[0] if weights_shape[0] != output: raise Error('Weights shape does not correspond to the `output` attribute of Convolution layer. ' + refer_to_faq_msg(93)) mapping_rule = { 'output': output, 'patch_stride': patch_stride, 'bias_term': None, 'pad': np.array([[0, 0], [0, 0], [0, 0], [0, 0]], dtype=np.int64), 'pad_spatial_shape': np.array([[0, 0], [0, 0]], dtype=np.int64), 'dilation': np.array([1, 1, 1, 1], dtype=np.int64), 'kernel': np.array([1, 1, 1, kernel], dtype=np.int64), 'stride': np.array([1, 1, 1, stride], dtype=np.int64), 'kernel_spatial': np.array([1, kernel], dtype=np.int64), 'input_feature_channel': 1, 'output_feature_channel': 0, 'kernel_spatial_idx': [2, 3], 'group': 1, 'reshape_kernel': True, } mapping_rule.update(layout_attrs()) embed_input(mapping_rule, 1, 'weights', weights) embed_input(mapping_rule, 2, 'biases', biases) mapping_rule['bias_addable'] = len(biases) > 0 Convolution.update_node_stat(node, mapping_rule) return cls.enabled
def extract(cls, node): pb = node.parameters read_learning_info(pb) weights, weights_shape = read_binary_matrix(pb) biases = read_binary_vector(pb) mapping_rule = { 'out-size': weights_shape[0], 'transpose_weights': True, } embed_input(mapping_rule, 1, 'weights', weights) embed_input(mapping_rule, 2, 'biases', biases) FullyConnected.update_node_stat(node, mapping_rule) return cls.enabled
def extract(cls, node: Node): read_learning_info(node.parameters) return FixedAffineComponentFrontExtractor.extract(node)