# 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 numpy as np import argparse import time import math import paddle import paddle.fluid as fluid import paddle.fluid.profiler as profiler from paddle.fluid import core import unittest from multiprocessing import Process import os import sys import signal from test_dist_base import TestDistRunnerBase, runtime_main # Fix seed for test fluid.default_startup_program().random_seed = 1 fluid.default_main_program().random_seed = 1 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": "piecewise_decay", "epochs": [30, 60, 90], "steps": [0.1, 0.01, 0.001, 0.0001] } } class SE_ResNeXt(): def __init__(self, layers=50): self.params = train_parameters self.layers = layers def net(self, input, class_dim=1000): layers = self.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] conv = self.conv_bn_layer( input=input, num_filters=64, filter_size=7, stride=2, act='relu') conv = fluid.layers.pool2d( input=conv, 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] conv = self.conv_bn_layer( input=input, num_filters=64, filter_size=7, stride=2, act='relu') conv = fluid.layers.pool2d( input=conv, 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] conv = self.conv_bn_layer( input=input, num_filters=64, filter_size=3, stride=2, act='relu') conv = self.conv_bn_layer( input=conv, num_filters=64, filter_size=3, stride=1, act='relu') conv = self.conv_bn_layer( input=conv, num_filters=128, filter_size=3, stride=1, act='relu') conv = fluid.layers.pool2d( input=conv, pool_size=3, pool_stride=2, pool_padding=1, \ pool_type='max') for block in range(len(depth)): for i in range(depth[block]): conv = self.bottleneck_block( input=conv, num_filters=num_filters[block], stride=2 if i == 0 and block != 0 else 1, cardinality=cardinality, reduction_ratio=reduction_ratio) pool = fluid.layers.pool2d( input=conv, pool_size=7, pool_type='avg', global_pooling=True) drop = fluid.layers.dropout(x=pool, dropout_prob=0.2) stdv = 1.0 / math.sqrt(drop.shape[1] * 1.0) out = fluid.layers.fc(input=drop, size=class_dim, act='softmax') return out def shortcut(self, input, ch_out, stride): ch_in = input.shape[1] if ch_in != ch_out or stride != 1: filter_size = 1 return self.conv_bn_layer(input, ch_out, filter_size, stride) else: return input def bottleneck_block(self, input, num_filters, stride, cardinality, reduction_ratio): conv0 = self.conv_bn_layer( input=input, num_filters=num_filters, filter_size=1, act='relu') conv1 = self.conv_bn_layer( input=conv0, num_filters=num_filters, filter_size=3, stride=stride, groups=cardinality, act='relu') conv2 = self.conv_bn_layer( input=conv1, num_filters=num_filters * 2, filter_size=1, act=None) scale = self.squeeze_excitation( input=conv2, num_channels=num_filters * 2, reduction_ratio=reduction_ratio) short = self.shortcut(input, num_filters * 2, stride) return fluid.layers.elementwise_add(x=short, y=scale, act='relu') def conv_bn_layer(self, input, num_filters, filter_size, stride=1, groups=1, act=None): conv = fluid.layers.conv2d( input=input, num_filters=num_filters, filter_size=filter_size, stride=stride, padding=(filter_size - 1) // 2, groups=groups, act=None, # avoid pserver CPU init differs from GPU param_attr=fluid.ParamAttr( initializer=fluid.initializer.Constant()), bias_attr=False) return fluid.layers.batch_norm(input=conv, act=act) def squeeze_excitation(self, input, num_channels, reduction_ratio): pool = fluid.layers.pool2d( input=input, pool_size=0, pool_type='avg', global_pooling=True) stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0) squeeze = fluid.layers.fc(input=pool, size=num_channels // reduction_ratio, act='relu') stdv = 1.0 / math.sqrt(squeeze.shape[1] * 1.0) excitation = fluid.layers.fc(input=squeeze, size=num_channels, act='sigmoid') scale = fluid.layers.elementwise_mul(x=input, y=excitation, axis=0) return scale class DistSeResneXt2x2(TestDistRunnerBase): def get_model(self, batch_size=2): # Input data image = fluid.layers.data( name="data", shape=[3, 224, 224], dtype='float32') label = fluid.layers.data(name="int64", shape=[1], dtype='int64') # Train program model = SE_ResNeXt(layers=50) out = model.net(input=image, class_dim=102) cost = fluid.layers.cross_entropy(input=out, label=label) avg_cost = fluid.layers.mean(x=cost) acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1) acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5) # Evaluator test_program = fluid.default_main_program().clone(for_test=True) # Optimization total_images = 6149 # flowers epochs = [30, 60, 90] step = int(total_images / batch_size + 1) bd = [step * e for e in epochs] base_lr = 0.1 lr = [] lr = [base_lr * (0.1**i) for i in range(len(bd) + 1)] optimizer = fluid.optimizer.Momentum( # FIXME(typhoonzero): add back LR decay once ParallelExecutor fixed. #learning_rate=fluid.layers.piecewise_decay( # boundaries=bd, values=lr), learning_rate=base_lr, momentum=0.9, regularization=fluid.regularizer.L2Decay(1e-4)) optimizer.minimize(avg_cost) # Reader train_reader = paddle.batch( paddle.dataset.flowers.train(), batch_size=batch_size) test_reader = paddle.batch( paddle.dataset.flowers.test(use_xmap=False), batch_size=batch_size) return test_program, avg_cost, train_reader, test_reader, acc_top1, out if __name__ == "__main__": runtime_main(DistSeResneXt2x2)