346 lines
11 KiB
Python
346 lines
11 KiB
Python
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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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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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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import paddle
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from paddle import ParamAttr
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import paddle.nn as nn
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from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
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from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
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from paddle.nn.initializer import Uniform
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import math
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__all__ = [
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"ResNet18_vd", "ResNet34_vd", "ResNet50_vd", "ResNet101_vd", "ResNet152_vd"
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]
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class ConvBNLayer(nn.Layer):
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def __init__(self,
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num_channels,
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num_filters,
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filter_size,
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stride=1,
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groups=1,
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is_vd_mode=False,
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act=None,
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lr_mult=1.0,
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name=None):
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super(ConvBNLayer, self).__init__()
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self.is_vd_mode = is_vd_mode
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self._pool2d_avg = AvgPool2d(
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kernel_size=2, stride=2, padding=0, ceil_mode=True)
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self._conv = Conv2d(
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in_channels=num_channels,
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out_channels=num_filters,
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kernel_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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weight_attr=ParamAttr(
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name=name + "_weights", learning_rate=lr_mult),
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bias_attr=False)
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if name == "conv1":
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bn_name = "bn_" + name
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else:
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bn_name = "bn" + name[3:]
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self._batch_norm = BatchNorm(
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num_filters,
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act=act,
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param_attr=ParamAttr(name=bn_name + '_scale'),
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bias_attr=ParamAttr(bn_name + '_offset'),
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moving_mean_name=bn_name + '_mean',
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moving_variance_name=bn_name + '_variance')
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def forward(self, inputs):
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if self.is_vd_mode:
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inputs = self._pool2d_avg(inputs)
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y = self._conv(inputs)
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y = self._batch_norm(y)
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return y
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class BottleneckBlock(nn.Layer):
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def __init__(self,
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num_channels,
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num_filters,
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stride,
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shortcut=True,
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if_first=False,
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lr_mult=1.0,
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name=None):
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super(BottleneckBlock, self).__init__()
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self.conv0 = ConvBNLayer(
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num_channels=num_channels,
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num_filters=num_filters,
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filter_size=1,
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act='relu',
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lr_mult=lr_mult,
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name=name + "_branch2a")
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self.conv1 = ConvBNLayer(
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num_channels=num_filters,
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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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act='relu',
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lr_mult=lr_mult,
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name=name + "_branch2b")
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self.conv2 = ConvBNLayer(
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num_channels=num_filters,
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num_filters=num_filters * 4,
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filter_size=1,
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act=None,
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lr_mult=lr_mult,
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name=name + "_branch2c")
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if not shortcut:
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self.short = ConvBNLayer(
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num_channels=num_channels,
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num_filters=num_filters * 4,
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filter_size=1,
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stride=1,
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is_vd_mode=False if if_first else True,
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name=name + "_branch1")
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self.shortcut = shortcut
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def forward(self, inputs):
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y = self.conv0(inputs)
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conv1 = self.conv1(y)
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conv2 = self.conv2(conv1)
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if self.shortcut:
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short = inputs
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else:
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short = self.short(inputs)
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y = paddle.elementwise_add(x=short, y=conv2, act='relu')
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return y
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class BasicBlock(nn.Layer):
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def __init__(self,
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num_channels,
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num_filters,
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stride,
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shortcut=True,
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if_first=False,
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lr_mult=1.0,
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name=None):
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super(BasicBlock, self).__init__()
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self.stride = stride
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self.conv0 = ConvBNLayer(
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num_channels=num_channels,
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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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act='relu',
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name=name + "_branch2a")
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self.conv1 = ConvBNLayer(
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num_channels=num_filters,
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num_filters=num_filters,
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filter_size=3,
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act=None,
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name=name + "_branch2b")
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if not shortcut:
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self.short = ConvBNLayer(
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num_channels=num_channels,
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num_filters=num_filters,
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filter_size=1,
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stride=1,
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is_vd_mode=False if if_first else True,
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name=name + "_branch1")
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self.shortcut = shortcut
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def forward(self, inputs):
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y = self.conv0(inputs)
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conv1 = self.conv1(y)
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if self.shortcut:
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short = inputs
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else:
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short = self.short(inputs)
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y = paddle.elementwise_add(x=short, y=conv1, act='relu')
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return y
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class ResNet_vd(nn.Layer):
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def __init__(self,
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layers=50,
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class_dim=1000,
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lr_mult_list=[1.0, 1.0, 1.0, 1.0, 1.0]):
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super(ResNet_vd, self).__init__()
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self.layers = layers
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supported_layers = [18, 34, 50, 101, 152, 200]
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assert layers in supported_layers, \
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"supported layers are {} but input layer is {}".format(
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supported_layers, layers)
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self.lr_mult_list = lr_mult_list
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assert isinstance(self.lr_mult_list, (
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list, tuple
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)), "lr_mult_list should be in (list, tuple) but got {}".format(
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type(self.lr_mult_list))
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assert len(
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self.lr_mult_list
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) == 5, "lr_mult_list length should should be 5 but got {}".format(
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len(self.lr_mult_list))
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if layers == 18:
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depth = [2, 2, 2, 2]
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elif layers == 34 or layers == 50:
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depth = [3, 4, 6, 3]
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elif layers == 101:
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depth = [3, 4, 23, 3]
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elif layers == 152:
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depth = [3, 8, 36, 3]
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elif layers == 200:
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depth = [3, 12, 48, 3]
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num_channels = [64, 256, 512,
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1024] if layers >= 50 else [64, 64, 128, 256]
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num_filters = [64, 128, 256, 512]
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self.conv1_1 = ConvBNLayer(
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num_channels=3,
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num_filters=32,
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filter_size=3,
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stride=2,
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act='relu',
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lr_mult=self.lr_mult_list[0],
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name="conv1_1")
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self.conv1_2 = ConvBNLayer(
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num_channels=32,
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num_filters=32,
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filter_size=3,
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stride=1,
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act='relu',
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lr_mult=self.lr_mult_list[0],
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name="conv1_2")
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self.conv1_3 = ConvBNLayer(
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num_channels=32,
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num_filters=64,
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filter_size=3,
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stride=1,
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act='relu',
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lr_mult=self.lr_mult_list[0],
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name="conv1_3")
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self.pool2d_max = MaxPool2d(kernel_size=3, stride=2, padding=1)
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self.block_list = []
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if layers >= 50:
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for block in range(len(depth)):
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shortcut = False
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for i in range(depth[block]):
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if layers in [101, 152] and block == 2:
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if i == 0:
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conv_name = "res" + str(block + 2) + "a"
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else:
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conv_name = "res" + str(block + 2) + "b" + str(i)
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else:
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conv_name = "res" + str(block + 2) + chr(97 + i)
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bottleneck_block = self.add_sublayer(
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'bb_%d_%d' % (block, i),
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BottleneckBlock(
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num_channels=num_channels[block]
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if i == 0 else num_filters[block] * 4,
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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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shortcut=shortcut,
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if_first=block == i == 0,
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lr_mult=self.lr_mult_list[block + 1],
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name=conv_name))
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self.block_list.append(bottleneck_block)
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shortcut = True
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else:
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for block in range(len(depth)):
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shortcut = False
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for i in range(depth[block]):
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conv_name = "res" + str(block + 2) + chr(97 + i)
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basic_block = self.add_sublayer(
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'bb_%d_%d' % (block, i),
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BasicBlock(
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num_channels=num_channels[block]
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if i == 0 else num_filters[block],
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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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shortcut=shortcut,
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if_first=block == i == 0,
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name=conv_name,
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lr_mult=lr_mult))
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self.block_list.append(basic_block)
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shortcut = True
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self.pool2d_avg = AdaptiveAvgPool2d(1)
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self.pool2d_avg_channels = num_channels[-1] * 2
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stdv = 1.0 / math.sqrt(self.pool2d_avg_channels * 1.0)
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self.out = Linear(
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self.pool2d_avg_channels,
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class_dim,
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weight_attr=ParamAttr(
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initializer=Uniform(-stdv, stdv), name="fc_0.w_0"),
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bias_attr=ParamAttr(name="fc_0.b_0"))
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def forward(self, inputs):
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y = self.conv1_1(inputs)
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y = self.conv1_2(y)
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y = self.conv1_3(y)
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y = self.pool2d_max(y)
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for block in self.block_list:
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y = block(y)
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y = self.pool2d_avg(y)
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y = paddle.reshape(y, shape=[-1, self.pool2d_avg_channels])
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y = self.out(y)
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return y
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def ResNet18_vd(**args):
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model = ResNet_vd(layers=18, **args)
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return model
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def ResNet34_vd(**args):
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model = ResNet_vd(layers=34, **args)
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return model
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def ResNet50_vd(**args):
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model = ResNet_vd(layers=50, **args)
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return model
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def ResNet101_vd(**args):
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model = ResNet_vd(layers=101, **args)
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return model
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def ResNet152_vd(**args):
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model = ResNet_vd(layers=152, **args)
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return model
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def ResNet200_vd(**args):
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model = ResNet_vd(layers=200, **args)
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return model
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