wehub-resource-sync
271 lines
8.8 KiB
Python
271 lines
8.8 KiB
Python
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import math
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from test_dist_base import TestDistRunnerBase, runtime_main
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import paddle
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from paddle import base
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paddle.enable_static()
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# Fix seed for test
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paddle.seed(1)
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train_parameters = {
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"input_size": [3, 224, 224],
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"input_mean": [0.485, 0.456, 0.406],
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"input_std": [0.229, 0.224, 0.225],
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"learning_strategy": {
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"name": "piecewise_decay",
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"epochs": [30, 60, 90],
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"steps": [0.1, 0.01, 0.001, 0.0001],
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},
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}
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class SE_ResNeXt:
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def __init__(self, layers=50):
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self.params = train_parameters
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self.layers = layers
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def net(self, input, class_dim=1000):
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layers = self.layers
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supported_layers = [50, 101, 152]
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assert layers in supported_layers, (
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f"supported layers are {supported_layers} but input layer is {layers}"
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)
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if layers == 50:
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cardinality = 32
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reduction_ratio = 16
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depth = [3, 4, 6, 3]
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num_filters = [128, 256, 512, 1024]
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conv = self.conv_bn_layer(
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input=input, num_filters=64, filter_size=7, stride=2, act='relu'
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)
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conv = paddle.nn.functional.max_pool2d(
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x=conv,
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kernel_size=3,
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stride=2,
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padding=1,
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)
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elif layers == 101:
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cardinality = 32
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reduction_ratio = 16
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depth = [3, 4, 23, 3]
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num_filters = [128, 256, 512, 1024]
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conv = self.conv_bn_layer(
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input=input, num_filters=64, filter_size=7, stride=2, act='relu'
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)
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conv = paddle.nn.functional.max_pool2d(
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x=conv,
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kernel_size=3,
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stride=2,
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padding=1,
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)
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elif layers == 152:
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cardinality = 64
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reduction_ratio = 16
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depth = [3, 8, 36, 3]
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num_filters = [128, 256, 512, 1024]
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conv = self.conv_bn_layer(
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input=input, num_filters=64, filter_size=3, stride=2, act='relu'
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)
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conv = self.conv_bn_layer(
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input=conv, num_filters=64, filter_size=3, stride=1, act='relu'
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)
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conv = self.conv_bn_layer(
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input=conv, num_filters=128, filter_size=3, stride=1, act='relu'
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)
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conv = paddle.nn.functional.max_pool2d(
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x=conv,
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kernel_size=3,
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stride=2,
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padding=1,
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)
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for block in range(len(depth)):
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for i in range(depth[block]):
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conv = self.bottleneck_block(
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input=conv,
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num_filters=num_filters[block],
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stride=2 if i == 0 and block != 0 else 1,
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cardinality=cardinality,
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reduction_ratio=reduction_ratio,
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)
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pool = paddle.nn.functional.adaptive_avg_pool2d(x=conv, output_size=1)
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drop = paddle.nn.functional.dropout(x=pool, p=0.2)
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stdv = 1.0 / math.sqrt(drop.shape[1] * 1.0)
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out = paddle.static.nn.fc(
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x=drop,
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size=class_dim,
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activation='softmax',
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weight_attr=base.ParamAttr(
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initializer=paddle.nn.initializer.Constant(value=0.05)
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),
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)
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return out
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def shortcut(self, input, ch_out, stride):
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ch_in = input.shape[1]
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if ch_in != ch_out or stride != 1:
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filter_size = 1
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return self.conv_bn_layer(input, ch_out, filter_size, stride)
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else:
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return input
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def bottleneck_block(
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self, input, num_filters, stride, cardinality, reduction_ratio
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):
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conv0 = self.conv_bn_layer(
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input=input, num_filters=num_filters, filter_size=1, act='relu'
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)
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conv1 = self.conv_bn_layer(
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input=conv0,
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num_filters=num_filters,
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filter_size=3,
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stride=stride,
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groups=cardinality,
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act='relu',
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)
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conv2 = self.conv_bn_layer(
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input=conv1, num_filters=num_filters * 2, filter_size=1, act=None
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)
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scale = self.squeeze_excitation(
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input=conv2,
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num_channels=num_filters * 2,
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reduction_ratio=reduction_ratio,
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)
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short = self.shortcut(input, num_filters * 2, stride)
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return paddle.nn.functional.relu(paddle.add(x=short, y=scale))
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def conv_bn_layer(
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self, input, num_filters, filter_size, stride=1, groups=1, act=None
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):
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conv = paddle.static.nn.conv2d(
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input=input,
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num_filters=num_filters,
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filter_size=filter_size,
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stride=stride,
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padding=(filter_size - 1) // 2,
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groups=groups,
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act=None,
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# avoid pserver CPU init differs from GPU
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param_attr=base.ParamAttr(
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initializer=paddle.nn.initializer.Constant(value=0.05)
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),
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bias_attr=False,
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)
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return paddle.static.nn.batch_norm(input=conv, act=act)
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def squeeze_excitation(self, input, num_channels, reduction_ratio):
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pool = paddle.nn.functional.adaptive_avg_pool2d(x=input, output_size=1)
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stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
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squeeze = paddle.static.nn.fc(
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x=pool,
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size=num_channels // reduction_ratio,
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weight_attr=base.ParamAttr(
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initializer=paddle.nn.initializer.Constant(value=0.05)
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),
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activation='relu',
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)
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stdv = 1.0 / math.sqrt(squeeze.shape[1] * 1.0)
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excitation = paddle.static.nn.fc(
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x=squeeze,
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size=num_channels,
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weight_attr=base.ParamAttr(
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initializer=paddle.nn.initializer.Constant(value=0.05)
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),
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activation='sigmoid',
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)
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scale = paddle.tensor.math._multiply_with_axis(
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x=input, y=excitation, axis=0
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)
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return scale
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class DistSeResneXt2x2(TestDistRunnerBase):
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def get_model(self, batch_size=2, use_dgc=False):
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# Input data
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image = paddle.static.data(
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name="data", shape=[-1, 3, 224, 224], dtype='float32'
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)
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label = paddle.static.data(name="int64", shape=[-1, 1], dtype='int64')
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# Train program
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model = SE_ResNeXt(layers=50)
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out = model.net(input=image, class_dim=102)
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cost = paddle.nn.functional.cross_entropy(
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input=out, label=label, reduction='none', use_softmax=True
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)
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avg_cost = paddle.mean(x=cost)
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acc_top1 = paddle.static.accuracy(input=out, label=label, k=1)
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acc_top5 = paddle.static.accuracy(input=out, label=label, k=5)
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# Evaluator
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test_program = base.default_main_program().clone(for_test=True)
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# Optimization
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total_images = 6149 # flowers
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epochs = [30, 60, 90]
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step = int(total_images / batch_size + 1)
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bd = [step * e for e in epochs]
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base_lr = 0.1
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lr = [base_lr * (0.1**i) for i in range(len(bd) + 1)]
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if not use_dgc:
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optimizer = paddle.optimizer.Momentum(
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learning_rate=paddle.optimizer.lr.PiecewiseDecay(
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boundaries=bd, values=lr
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),
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momentum=0.9,
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weight_decay=paddle.regularizer.L2Decay(1e-4),
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)
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else:
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optimizer = (
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paddle.distributed.fleet.meta_optimizers.DGCMomentumOptimizer(
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learning_rate=paddle.optimizer.lr.piecewise_decay(
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boundaries=bd, values=lr
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),
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momentum=0.9,
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rampup_begin_step=0,
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regularization=paddle.regularizer.L2Decay(1e-4),
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)
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)
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optimizer.minimize(avg_cost)
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# Reader
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train_reader = paddle.batch(
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paddle.dataset.flowers.test(use_xmap=False), batch_size=batch_size
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)
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test_reader = paddle.batch(
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paddle.dataset.flowers.test(use_xmap=False), batch_size=batch_size
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)
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return test_program, avg_cost, train_reader, test_reader, acc_top1, out
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if __name__ == "__main__":
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runtime_main(DistSeResneXt2x2)
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