# 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. import math from test_dist_base import TestDistRunnerBase, runtime_main import paddle from paddle import fluid paddle.enable_static() # 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 = paddle.nn.functional.max_pool2d( x=conv, kernel_size=3, stride=2, padding=1, ) 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 = paddle.nn.functional.max_pool2d( x=conv, kernel_size=3, stride=2, padding=1, ) 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 = paddle.nn.functional.max_pool2d( x=conv, kernel_size=3, stride=2, padding=1, ) 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 = paddle.nn.functional.adaptive_avg_pool2d(x=conv, output_size=1) drop = paddle.nn.functional.dropout(x=pool, p=0.2) stdv = 1.0 / math.sqrt(drop.shape[1] * 1.0) out = paddle.static.nn.fc( x=drop, size=class_dim, activation='softmax', weight_attr=fluid.ParamAttr( initializer=paddle.nn.initializer.Constant(value=0.05) ), ) 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 paddle.nn.functional.relu(paddle.add(x=short, y=scale)) def conv_bn_layer( self, input, num_filters, filter_size, stride=1, groups=1, act=None ): conv = paddle.static.nn.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=paddle.nn.initializer.Constant(value=0.05) ), bias_attr=False, ) return paddle.static.nn.batch_norm(input=conv, act=act) def squeeze_excitation(self, input, num_channels, reduction_ratio): pool = paddle.nn.functional.adaptive_avg_pool2d(x=input, output_size=1) stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0) squeeze = paddle.static.nn.fc( x=pool, size=num_channels // reduction_ratio, weight_attr=fluid.ParamAttr( initializer=paddle.nn.initializer.Constant(value=0.05) ), activation='relu', ) stdv = 1.0 / math.sqrt(squeeze.shape[1] * 1.0) excitation = paddle.static.nn.fc( x=squeeze, size=num_channels, weight_attr=fluid.ParamAttr( initializer=paddle.nn.initializer.Constant(value=0.05) ), activation='sigmoid', ) scale = paddle.tensor.math._multiply_with_axis( x=input, y=excitation, axis=0 ) return scale class DistSeResneXt2x2(TestDistRunnerBase): def get_model(self, batch_size=2, use_dgc=False): # Input data image = paddle.static.data( name="data", shape=[-1, 3, 224, 224], dtype='float32' ) label = paddle.static.data(name="int64", shape=[-1, 1], dtype='int64') # Train program model = SE_ResNeXt(layers=50) out = model.net(input=image, class_dim=102) cost = paddle.nn.functional.cross_entropy( input=out, label=label, reduction='none', use_softmax=False ) avg_cost = paddle.mean(x=cost) acc_top1 = paddle.static.accuracy(input=out, label=label, k=1) acc_top5 = paddle.static.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 = [base_lr * (0.1**i) for i in range(len(bd) + 1)] if not use_dgc: optimizer = fluid.optimizer.Momentum( learning_rate=fluid.layers.piecewise_decay( boundaries=bd, values=lr ), momentum=0.9, regularization=paddle.regularizer.L2Decay(1e-4), ) else: optimizer = ( paddle.distributed.fleet.meta_optimizers.DGCMomentumOptimizer( learning_rate=fluid.layers.piecewise_decay( boundaries=bd, values=lr ), momentum=0.9, rampup_begin_step=0, regularization=paddle.regularizer.L2Decay(1e-4), ) ) optimizer.minimize(avg_cost) # Reader train_reader = paddle.batch( paddle.dataset.flowers.test(use_xmap=False), 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)