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| # Licensed to the Apache Software Foundation (ASF) under one | ||
| # or more contributor license agreements. See the NOTICE file | ||
| # distributed with this work for additional information | ||
| # regarding copyright ownership. The ASF licenses this file | ||
| # to you 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. | ||
|
|
||
| # coding: utf-8 | ||
| # pylint: disable= arguments-differ | ||
| """DenseNet, implemented in Gluon.""" | ||
|
|
||
| import sys | ||
| import os | ||
| import mxnet as mx | ||
| import mxnet.ndarray as nd | ||
| import mxnet.gluon as gluon | ||
| import mxnet.gluon.nn as nn | ||
| import mxnet.autograd as ag | ||
| import symbol_utils | ||
| sys.path.append(os.path.join(os.path.dirname(__file__), '..')) | ||
| from config import config | ||
|
|
||
| def Act(): | ||
| if config.net_act=='prelu': | ||
| return nn.PReLU() | ||
| else: | ||
| return nn.Activation(config.net_act) | ||
| # Helpers | ||
| def _make_dense_block(num_layers, bn_size, growth_rate, dropout, stage_index): | ||
| out = nn.HybridSequential(prefix='stage%d_'%stage_index) | ||
| with out.name_scope(): | ||
| for _ in range(num_layers): | ||
| out.add(_make_dense_layer(growth_rate, bn_size, dropout)) | ||
| return out | ||
|
|
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| def _make_dense_layer(growth_rate, bn_size, dropout): | ||
| new_features = nn.HybridSequential(prefix='') | ||
| new_features.add(nn.BatchNorm()) | ||
| #new_features.add(nn.Activation('relu')) | ||
| new_features.add(Act()) | ||
| new_features.add(nn.Conv2D(bn_size * growth_rate, kernel_size=1, use_bias=False)) | ||
| new_features.add(nn.BatchNorm()) | ||
| #new_features.add(nn.Activation('relu')) | ||
| new_features.add(Act()) | ||
| new_features.add(nn.Conv2D(growth_rate, kernel_size=3, padding=1, use_bias=False)) | ||
| if dropout: | ||
| new_features.add(nn.Dropout(dropout)) | ||
|
|
||
| out = gluon.contrib.nn.HybridConcurrent(axis=1, prefix='') | ||
| out.add(gluon.contrib.nn.Identity()) | ||
| out.add(new_features) | ||
|
|
||
| return out | ||
|
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| def _make_transition(num_output_features): | ||
| out = nn.HybridSequential(prefix='') | ||
| out.add(nn.BatchNorm()) | ||
| #out.add(nn.Activation('relu')) | ||
| out.add(Act()) | ||
| out.add(nn.Conv2D(num_output_features, kernel_size=1, use_bias=False)) | ||
| out.add(nn.AvgPool2D(pool_size=2, strides=2)) | ||
| return out | ||
|
|
||
| # Net | ||
| class DenseNet(nn.HybridBlock): | ||
| r"""Densenet-BC model from the | ||
| `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_ paper. | ||
| Parameters | ||
| ---------- | ||
| num_init_features : int | ||
| Number of filters to learn in the first convolution layer. | ||
| growth_rate : int | ||
| Number of filters to add each layer (`k` in the paper). | ||
| block_config : list of int | ||
| List of integers for numbers of layers in each pooling block. | ||
| bn_size : int, default 4 | ||
| Multiplicative factor for number of bottle neck layers. | ||
| (i.e. bn_size * k features in the bottleneck layer) | ||
| dropout : float, default 0 | ||
| Rate of dropout after each dense layer. | ||
| classes : int, default 1000 | ||
| Number of classification classes. | ||
| """ | ||
| def __init__(self, num_init_features, growth_rate, block_config, | ||
| bn_size=4, dropout=0, classes=1000, **kwargs): | ||
|
|
||
| super(DenseNet, self).__init__(**kwargs) | ||
| with self.name_scope(): | ||
| self.features = nn.HybridSequential(prefix='') | ||
| self.features.add(nn.Conv2D(num_init_features, kernel_size=3, | ||
| strides=1, padding=1, use_bias=False)) | ||
| self.features.add(nn.BatchNorm()) | ||
| self.features.add(nn.Activation('relu')) | ||
| self.features.add(nn.MaxPool2D(pool_size=3, strides=2, padding=1)) | ||
| # Add dense blocks | ||
| num_features = num_init_features | ||
| for i, num_layers in enumerate(block_config): | ||
| self.features.add(_make_dense_block(num_layers, bn_size, growth_rate, dropout, i+1)) | ||
| num_features = num_features + num_layers * growth_rate | ||
| if i != len(block_config) - 1: | ||
| self.features.add(_make_transition(num_features // 2)) | ||
| num_features = num_features // 2 | ||
| self.features.add(nn.BatchNorm()) | ||
| self.features.add(nn.Activation('relu')) | ||
| #self.features.add(nn.AvgPool2D(pool_size=7)) | ||
| #self.features.add(nn.Flatten()) | ||
|
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| #self.output = nn.Dense(classes) | ||
|
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| def hybrid_forward(self, F, x): | ||
| x = self.features(x) | ||
| #x = self.output(x) | ||
| return x | ||
|
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|
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||
| # Specification | ||
| densenet_spec = {121: (64, 32, [6, 12, 24, 16]), | ||
| 161: (96, 48, [6, 12, 36, 24]), | ||
| 169: (64, 32, [6, 12, 32, 32]), | ||
| 201: (64, 32, [6, 12, 48, 32])} | ||
|
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|
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| # Constructor | ||
| def get_symbol(): | ||
| num_layers = config.num_layers | ||
| num_init_features, growth_rate, block_config = densenet_spec[num_layers] | ||
| net = DenseNet(num_init_features, growth_rate, block_config) | ||
| data = mx.sym.Variable(name='data') | ||
| data = data-127.5 | ||
| data = data*0.0078125 | ||
| body = net(data) | ||
| fc1 = symbol_utils.get_fc1(body, config.emb_size, config.net_output) | ||
| return fc1 | ||
|
|
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