def extract(cls, node): pb = node.parameters mapping_rule = { 'context': list() } tag = find_next_tag(pb) if tag == '<LeftContext>': read_placeholder(pb, 1) l_context = read_binary_integer32_token(pb) tag = find_next_tag(pb) if tag != '<RightContext>': raise Error('Unknown token {} in SpliceComponent node {}'.format(tag, node.id)) read_placeholder(pb, 1) r_context = read_binary_integer32_token(pb) for i in range(-l_context, r_context + 1): mapping_rule['context'].append(i) elif tag == '<Context>': collect_until_whitespace(pb) mapping_rule['context'] = read_binary_vector(pb, False, dtype=np.int32) else: raise Error('Unknown token {} in SpliceComponent node {}'.format(tag, node.id)) tag = find_next_tag(pb) if tag == '<ConstComponentDim>': read_placeholder(pb, 1) const_dim = read_binary_integer32_token(pb) mapping_rule['const_dim'] = const_dim Splice.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): clip_value = 50 pb = node.parameters res = collect_until_whitespace(pb) if res == b'<CellClip>': clip_value = get_uint32(pb.read(4)) collect_until_token(pb, b'FM') gifo_x_weights, gifo_x_weights_shape = read_binary_matrix(pb, False) gifo_r_weights, gifo_r_weights_shape = read_binary_matrix(pb) gifo_biases = read_binary_vector(pb) input_gate_weights = read_binary_vector(pb) forget_gate_weights = read_binary_vector(pb) output_gate_weights = read_binary_vector(pb) projection_weights, projection_weights_shape = read_binary_matrix(pb) mapping_rule = { 'gifo_x_weights_shape': gifo_x_weights_shape, 'gifo_r_weights_shape': gifo_r_weights_shape, 'projection_weights_shape': projection_weights_shape, 'clip_value': clip_value, 'format': 'kaldi', } embed_input(mapping_rule, 1, 'gifo_x_weights', gifo_x_weights) embed_input(mapping_rule, 2, 'gifo_r_weights', gifo_r_weights) embed_input(mapping_rule, 3, 'gifo_biases', gifo_biases) embed_input(mapping_rule, 4, 'input_gate_weights', input_gate_weights) embed_input(mapping_rule, 5, 'forget_gate_weights', forget_gate_weights) embed_input(mapping_rule, 6, 'output_gate_weights', output_gate_weights) embed_input(mapping_rule, 7, 'projection_weights', projection_weights) LSTMCell.update_node_stat(node, mapping_rule) return cls.enabled