# 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, Linear, BatchNorm from paddle.fluid.dygraph.base import to_variable from paddle.fluid.layer_helper import LayerHelper import math from test_dist_base import runtime_main, TestParallelDyGraphRunnerBase batch_size = 64 momentum_rate = 0.9 l2_decay = 1.2e-4 train_parameters = { "input_size": [3, 224, 224], "input_mean": [0.485, 0.456, 0.406], "input_std": [0.229, 0.224, 0.225], "learning_strategy": { "name": "cosine_decay", "batch_size": batch_size, "epochs": [40, 80, 100], "steps": [0.1, 0.01, 0.001, 0.0001] }, "batch_size": batch_size, "lr": 0.0125, "total_images": 6149, "num_epochs": 200 } def optimizer_setting(params, parameter_list=None): ls = params["learning_strategy"] if "total_images" not in params: total_images = 6149 else: total_images = params["total_images"] batch_size = ls["batch_size"] step = int(math.ceil(float(total_images) / batch_size)) bd = [step * e for e in ls["epochs"]] lr = params["lr"] num_epochs = params["num_epochs"] if fluid.in_dygraph_mode(): optimizer = fluid.optimizer.Momentum( learning_rate=fluid.layers.cosine_decay( learning_rate=lr, step_each_epoch=step, epochs=num_epochs), momentum=momentum_rate, regularization=fluid.regularizer.L2Decay(l2_decay), parameter_list=parameter_list) else: optimizer = fluid.optimizer.Momentum( learning_rate=fluid.layers.cosine_decay( learning_rate=lr, step_each_epoch=step, epochs=num_epochs), momentum=momentum_rate, regularization=fluid.regularizer.L2Decay(l2_decay)) return optimizer class ConvBNLayer(fluid.dygraph.Layer): def __init__(self, num_channels, num_filters, filter_size, stride=1, groups=1, act=None): super(ConvBNLayer, self).__init__() self._conv = Conv2D( num_channels=num_channels, num_filters=num_filters, filter_size=filter_size, stride=stride, padding=(filter_size - 1) // 2, groups=groups, act=None, bias_attr=False) # disable BatchNorm in multi-card. disable LayerNorm because of complex input_shape # self._batch_norm = BatchNorm(num_filters, act=act) def forward(self, inputs): y = self._conv(inputs) # y = self._batch_norm(y) return y class SqueezeExcitation(fluid.dygraph.Layer): def __init__(self, num_channels, reduction_ratio): super(SqueezeExcitation, self).__init__() self._num_channels = num_channels self._pool = Pool2D(pool_size=0, pool_type='avg', global_pooling=True) stdv = 1.0 / math.sqrt(num_channels * 1.0) self._squeeze = Linear( num_channels, num_channels // reduction_ratio, param_attr=fluid.ParamAttr( initializer=fluid.initializer.Uniform(-stdv, stdv)), act='relu') stdv = 1.0 / math.sqrt(num_channels / 16.0 * 1.0) self._excitation = Linear( num_channels // reduction_ratio, num_channels, param_attr=fluid.ParamAttr( initializer=fluid.initializer.Uniform(-stdv, stdv)), act='sigmoid') def forward(self, input): y = self._pool(input) y = fluid.layers.reshape(y, shape=[-1, self._num_channels]) 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, num_channels, num_filters, stride, cardinality, reduction_ratio, shortcut=True): super(BottleneckBlock, self).__init__() self.conv0 = ConvBNLayer( num_channels=num_channels, num_filters=num_filters, filter_size=1, act="relu") self.conv1 = ConvBNLayer( num_channels=num_filters, num_filters=num_filters, filter_size=3, stride=stride, groups=cardinality, act="relu") self.conv2 = ConvBNLayer( num_channels=num_filters, num_filters=num_filters * 2, filter_size=1, act=None) self.scale = SqueezeExcitation( num_channels=num_filters * 2, reduction_ratio=reduction_ratio) if not shortcut: self.short = ConvBNLayer( num_channels=num_channels, num_filters=num_filters * 2, filter_size=1, stride=stride) self.shortcut = shortcut self._num_channels_out = num_filters * 2 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, act='relu') return y class SeResNeXt(fluid.dygraph.Layer): def __init__(self, layers=50, class_dim=102): super(SeResNeXt, self).__init__() 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( num_channels=3, num_filters=64, filter_size=7, stride=2, act='relu') self.pool = Pool2D( 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( num_channels=3, num_filters=64, filter_size=7, stride=2, act='relu') self.pool = Pool2D( 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( num_channels=3, num_filters=64, filter_size=3, stride=2, act='relu') self.conv1 = ConvBNLayer( num_channels=64, num_filters=64, filter_size=3, stride=1, act='relu') self.conv2 = ConvBNLayer( num_channels=64, num_filters=128, filter_size=3, stride=1, act='relu') self.pool = Pool2D( 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( 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( pool_size=7, pool_type='avg', global_pooling=True) stdv = 1.0 / math.sqrt(2048 * 1.0) self.pool2d_avg_output = num_filters[len(num_filters) - 1] * 2 * 1 * 1 self.out = Linear( self.pool2d_avg_output, class_dim, param_attr=fluid.param_attr.ParamAttr( initializer=fluid.initializer.Uniform(-stdv, stdv))) def forward(self, inputs): 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) y = fluid.layers.reshape(y, shape=[-1, self.pool2d_avg_output]) y = self.out(y) return y class TestSeResNeXt(TestParallelDyGraphRunnerBase): def get_model(self): model = SeResNeXt() train_reader = paddle.batch( paddle.dataset.flowers.test(use_xmap=False), batch_size=train_parameters["batch_size"], drop_last=True) optimizer = optimizer_setting( train_parameters, parameter_list=model.parameters()) return model, train_reader, optimizer 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 out = model(img) softmax_out = fluid.layers.softmax(out, use_cudnn=False) loss = fluid.layers.cross_entropy(input=softmax_out, label=label) avg_loss = fluid.layers.mean(x=loss) return avg_loss if __name__ == "__main__": runtime_main(TestSeResNeXt)