""" Copyright (c) 2018 Intel Corporation Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ import networkx as nx import numpy as np from mo.graph.graph import create_edge from mo.middle.pattern_match import apply_pattern from mo.ops.op import Op, PermuteAttrs from mo.ops.reshape import Reshape def mean_to_avgpool_action(graph: nx.MultiDiGraph, matches: dict): if matches['axis'].value is None or matches['input'].shape is None: return dims = len(matches['input'].shape) ones = np.ones(dims, dtype=np.int64) mean = graph.node[matches['mean'].node] mean['stride'] = np.array(ones) # TODO: need to check axis with real layout spatial_dims = np.array(matches['axis'].value) mean['spatial_dims'] = spatial_dims mean['pad'] = np.zeros((dims, 2), np.int64) mean['pad_spatial_shape'] = np.array(mean['pad'][spatial_dims]) window = np.array(ones) window[spatial_dims] = matches['input'].shape[spatial_dims] mean['window'] = window mean['TF_op'] = mean['op'] mean['op'] = 'AvgPool' mean['pool_method'] = 'avg' mean['rounding_type'] = 'ceil' mean['exclude_pad'] = 'true' mean['kernel_spatial'] = window[spatial_dims] graph.remove_edge(matches['axis'].node, matches['mean'].node) mean['permute_attrs'] = PermuteAttrs().update_attrs(attrs=[('pad', 'input:0'), ('stride', 'input:0'), ('window', 'input:0'), ('spatial_dims', 'input:0')]) if matches['mean'].keep_dims == False: output = matches['mean'].out_node() pool_node = matches['mean'] # Keep dims for AvgPool shape = np.array(output.shape) for idx in spatial_dims: shape = np.insert(shape, idx, 1) graph.remove_edge(pool_node.id, output.id) # Create new data for pool with all dims pool_data = Op.create_data_node(graph, pool_node, {'shape': np.array(shape)}) # Create and connect reshape node reshape_op = Reshape(graph, {'dim': np.array(output.shape)}) reshape_node = reshape_op.create_node([pool_data], dict(name='Reshape_', permute_attrs=PermuteAttrs().update_attrs(attrs=[('dim', 'output:0')]))) create_edge(reshape_node, output) def mean_to_avgpool(graph: nx.MultiDiGraph): """ Translate Mean as a average pooling with kernel size equals to reduced dimensions and with no padding. """ apply_pattern( graph, nodes=[ ('input', dict(kind='data')), ('axis', dict(kind='data')), ('mean', dict(kind='op', op='Mean'))], edges=[ ('input', 'mean', {'in': 0}), ('axis', 'mean', {'in': 1})], action=mean_to_avgpool_action ) return graph