# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. # # 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. from __future__ import print_function import os import contextlib import unittest import numpy as np import six import pickle import sys import paddle import paddle.fluid as fluid import paddle.fluid.dygraph as dygraph from paddle.fluid import core from paddle.fluid.optimizer import SGDOptimizer from paddle.fluid.dygraph.nn import Conv2D, Pool2D, FC, BatchNorm from paddle.fluid.dygraph.base import to_variable from paddle.fluid.layer_helper import LayerHelper from test_dist_base import runtime_main, TestParallelDyGraphRunnerBase class ConvBNLayer(fluid.dygraph.Layer): def __init__(self, name_scope, num_channels, num_filters, filter_size, stride=1, groups=1, act=None): super(ConvBNLayer, self).__init__(name_scope) self._conv = Conv2D( self.full_name(), num_filters=num_filters, filter_size=filter_size, stride=stride, padding=(filter_size - 1) // 2, groups=groups, act=None, bias_attr=None) self._batch_norm = BatchNorm( self.full_name(), num_filters, act=act, momentum=0.1) self._layer_norm = fluid.dygraph.nn.LayerNorm( self.full_name(), begin_norm_axis=1) def forward(self, inputs): y = self._conv(inputs) # FIXME(zcd): when compare the result of multi-card and single-card, # we should replace batch_norm with layer_norm. y = self._layer_norm(y) # y = self._batch_norm(y) return y class SqueezeExcitation(fluid.dygraph.Layer): def __init__(self, name_scope, num_channels, reduction_ratio): super(SqueezeExcitation, self).__init__(name_scope) self._pool = Pool2D( self.full_name(), pool_size=0, pool_type='avg', global_pooling=True) self._squeeze = FC( self.full_name(), size=num_channels // reduction_ratio, param_attr=fluid.ParamAttr( initializer=fluid.initializer.Constant(value=0.05)), act='relu') self._excitation = FC( self.full_name(), size=num_channels, param_attr=fluid.ParamAttr( initializer=fluid.initializer.Constant(value=0.05)), act='sigmoid') def forward(self, input): y = self._pool(input) y = self._squeeze(y) y = self._excitation(y) y = fluid.layers.elementwise_mul(x=input, y=y, axis=0) return y class BottleneckBlock(fluid.dygraph.Layer): def __init__(self, name_scope, num_channels, num_filters, stride, cardinality, reduction_ratio, shortcut=True): super(BottleneckBlock, self).__init__(name_scope) self.conv0 = ConvBNLayer( self.full_name(), num_channels=num_channels, num_filters=num_filters, filter_size=1) self.conv1 = ConvBNLayer( self.full_name(), num_channels=num_filters, num_filters=num_filters, filter_size=3, stride=stride, groups=cardinality) self.conv2 = ConvBNLayer( self.full_name(), num_channels=num_filters, num_filters=num_filters * 4, filter_size=1, act='relu') self.scale = SqueezeExcitation( self.full_name(), num_channels=num_filters * 4, reduction_ratio=reduction_ratio) if not shortcut: self.short = ConvBNLayer( self.full_name(), num_channels=num_channels, num_filters=num_filters * 4, filter_size=1, stride=stride) self.shortcut = shortcut self._num_channels_out = num_filters * 4 def forward(self, inputs): y = self.conv0(inputs) conv1 = self.conv1(y) conv2 = self.conv2(conv1) scale = self.scale(conv2) if self.shortcut: short = inputs else: short = self.short(inputs) y = fluid.layers.elementwise_add(x=short, y=scale) layer_helper = LayerHelper(self.full_name(), act='relu') y = layer_helper.append_activation(y) return y class SeResNeXt(fluid.dygraph.Layer): def __init__(self, name_scope, layers=50, class_dim=102): super(SeResNeXt, self).__init__(name_scope) self.layers = layers supported_layers = [50, 101, 152] assert layers in supported_layers, \ "supported layers are {} but input layer is {}".format(supported_layers, layers) if layers == 50: cardinality = 32 reduction_ratio = 16 depth = [3, 4, 6, 3] num_filters = [128, 256, 512, 1024] self.conv0 = ConvBNLayer( self.full_name(), num_channels=3, num_filters=64, filter_size=7, stride=2, act='relu') self.pool = Pool2D( self.full_name(), pool_size=3, pool_stride=2, pool_padding=1, pool_type='max') elif layers == 101: cardinality = 32 reduction_ratio = 16 depth = [3, 4, 23, 3] num_filters = [128, 256, 512, 1024] self.conv0 = ConvBNLayer( self.full_name(), num_channels=3, num_filters=3, filter_size=7, stride=2, act='relu') self.pool = Pool2D( self.full_name(), pool_size=3, pool_stride=2, pool_padding=1, pool_type='max') elif layers == 152: cardinality = 64 reduction_ratio = 16 depth = [3, 8, 36, 3] num_filters = [128, 256, 512, 1024] self.conv0 = ConvBNLayer( self.full_name(), num_channels=3, num_filters=3, filter_size=7, stride=2, act='relu') self.conv1 = ConvBNLayer( self.full_name(), num_channels=64, num_filters=3, filter_size=7, stride=2, act='relu') self.conv2 = ConvBNLayer( self.full_name(), num_channels=64, num_filters=3, filter_size=7, stride=2, act='relu') self.pool = Pool2D( self.full_name(), pool_size=3, pool_stride=2, pool_padding=1, pool_type='max') self.bottleneck_block_list = [] num_channels = 64 for block in range(len(depth)): shortcut = False for i in range(depth[block]): bottleneck_block = self.add_sublayer( 'bb_%d_%d' % (block, i), BottleneckBlock( self.full_name(), num_channels=num_channels, num_filters=num_filters[block], stride=2 if i == 0 and block != 0 else 1, cardinality=cardinality, reduction_ratio=reduction_ratio, shortcut=shortcut)) num_channels = bottleneck_block._num_channels_out self.bottleneck_block_list.append(bottleneck_block) shortcut = True self.pool2d_avg = Pool2D( self.full_name(), pool_size=7, pool_type='avg', global_pooling=True) import math stdv = 1.0 / math.sqrt(2048 * 1.0) self.fc = FC(self.full_name(), size=class_dim, act='softmax', param_attr=fluid.param_attr.ParamAttr( initializer=fluid.initializer.Uniform(-stdv, stdv))) def forward(self, inputs, label): if self.layers == 50 or self.layers == 101: y = self.conv0(inputs) y = self.pool(y) elif self.layers == 152: y = self.conv0(inputs) y = self.conv1(inputs) y = self.conv2(inputs) y = self.pool(y) for bottleneck_block in self.bottleneck_block_list: y = bottleneck_block(y) y = self.pool2d_avg(y) # FIXME(zcd): the dropout should be removed when compare the # result of multi-card and single-card. # y = fluid.layers.dropout(y, dropout_prob=0.2, seed=1) cost = self.fc(y) loss = fluid.layers.cross_entropy(cost, label) avg_loss = fluid.layers.mean(loss) return avg_loss class TestSeResNeXt(TestParallelDyGraphRunnerBase): def get_model(self): model = SeResNeXt("se-resnext") train_reader = paddle.batch( paddle.dataset.flowers.test(use_xmap=False), batch_size=4, drop_last=True) opt = fluid.optimizer.SGD(learning_rate=1e-3) return model, train_reader, opt def run_one_loop(self, model, opt, data): bs = len(data) dy_x_data = np.array([x[0].reshape(3, 224, 224) for x in data]).astype('float32') y_data = np.array([x[1] for x in data]).astype('int64').reshape(bs, 1) img = to_variable(dy_x_data) label = to_variable(y_data) label.stop_gradient = True loss = model(img, label) return loss if __name__ == "__main__": runtime_main(TestSeResNeXt)