342 lines
10 KiB
Python
342 lines
10 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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from paddle import ParamAttr, reshape, transpose, concat, split
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from paddle.nn import Layer, Conv2d, MaxPool2d, AdaptiveAvgPool2d, BatchNorm, Linear
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from paddle.nn.initializer import MSRA
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from paddle.nn.functional import swish
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__all__ = [
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"ShuffleNetV2_x0_25", "ShuffleNetV2_x0_33", "ShuffleNetV2_x0_5",
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"ShuffleNetV2", "ShuffleNetV2_x1_5", "ShuffleNetV2_x2_0",
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"ShuffleNetV2_swish"
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]
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def channel_shuffle(x, groups):
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batch_size, num_channels, height, width = x.shape[0:4]
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channels_per_group = num_channels // groups
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# reshape
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x = reshape(x=x, shape=[batch_size, groups, channels_per_group, height, width])
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# transpose
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x = transpose(x=x, perm=[0, 2, 1, 3, 4])
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# flatten
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x = reshape(x=x, shape=[batch_size, num_channels, height, width])
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return x
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class ConvBNLayer(Layer):
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def __init__(
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self,
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in_channels,
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out_channels,
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kernel_size,
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stride,
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padding,
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groups=1,
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act=None,
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name=None,
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):
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super(ConvBNLayer, self).__init__()
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self._conv = Conv2d(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=kernel_size,
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stride=stride,
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padding=padding,
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groups=groups,
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weight_attr=ParamAttr(initializer=MSRA(), name=name + "_weights"),
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bias_attr=False
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)
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self._batch_norm = BatchNorm(
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out_channels,
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param_attr=ParamAttr(name=name + "_bn_scale"),
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bias_attr=ParamAttr(name=name + "_bn_offset"),
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act=act,
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moving_mean_name=name + "_bn_mean",
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moving_variance_name=name + "_bn_variance"
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)
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def forward(self, 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 InvertedResidual(Layer):
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def __init__(
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self,
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in_channels,
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out_channels,
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stride,
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act="relu",
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name=None
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):
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super(InvertedResidual, self).__init__()
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self._conv_pw = ConvBNLayer(
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in_channels=in_channels // 2,
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out_channels=out_channels // 2,
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kernel_size=1,
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stride=1,
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padding=0,
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groups=1,
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act=act,
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name='stage_' + name + '_conv1'
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)
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self._conv_dw = ConvBNLayer(
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in_channels=out_channels // 2,
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out_channels=out_channels // 2,
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kernel_size=3,
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stride=stride,
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padding=1,
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groups=out_channels // 2,
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act=None,
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name='stage_' + name + '_conv2'
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)
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self._conv_linear = ConvBNLayer(
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in_channels=out_channels // 2,
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out_channels=out_channels // 2,
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kernel_size=1,
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stride=1,
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padding=0,
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groups=1,
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act=act,
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name='stage_' + name + '_conv3'
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)
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def forward(self, inputs):
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x1, x2 = split(inputs, num_or_sections=[inputs.shape[1] // 2, inputs.shape[1] // 2], axis=1)
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x2 = self._conv_pw(x2)
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x2 = self._conv_dw(x2)
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x2 = self._conv_linear(x2)
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out = concat([x1, x2], axis=1)
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return channel_shuffle(out, 2)
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class InvertedResidualDS(Layer):
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def __init__(
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self,
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in_channels,
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out_channels,
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stride,
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act="relu",
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name=None
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):
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super(InvertedResidualDS, self).__init__()
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# branch1
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self._conv_dw_1 = ConvBNLayer(
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in_channels=in_channels,
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out_channels=in_channels,
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kernel_size=3,
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stride=stride,
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padding=1,
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groups=in_channels,
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act=None,
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name='stage_' + name + '_conv4'
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)
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self._conv_linear_1 = ConvBNLayer(
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in_channels=in_channels,
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out_channels=out_channels // 2,
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kernel_size=1,
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stride=1,
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padding=0,
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groups=1,
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act=act,
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name='stage_' + name + '_conv5'
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)
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# branch2
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self._conv_pw_2 = ConvBNLayer(
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in_channels=in_channels,
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out_channels=out_channels // 2,
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kernel_size=1,
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stride=1,
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padding=0,
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groups=1,
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act=act,
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name='stage_' + name + '_conv1'
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)
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self._conv_dw_2 = ConvBNLayer(
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in_channels=out_channels // 2,
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out_channels=out_channels // 2,
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kernel_size=3,
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stride=stride,
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padding=1,
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groups=out_channels // 2,
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act=None,
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name='stage_' + name + '_conv2'
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)
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self._conv_linear_2 = ConvBNLayer(
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in_channels=out_channels // 2,
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out_channels=out_channels // 2,
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kernel_size=1,
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stride=1,
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padding=0,
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groups=1,
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act=act,
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name='stage_' + name + '_conv3'
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)
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def forward(self, inputs):
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x1 = self._conv_dw_1(inputs)
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x1 = self._conv_linear_1(x1)
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x2 = self._conv_pw_2(inputs)
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x2 = self._conv_dw_2(x2)
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x2 = self._conv_linear_2(x2)
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out = concat([x1, x2], axis=1)
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return channel_shuffle(out, 2)
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class ShuffleNet(Layer):
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def __init__(self, class_dim=1000, scale=1.0, act="relu"):
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super(ShuffleNet, self).__init__()
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self.scale = scale
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self.class_dim = class_dim
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stage_repeats = [4, 8, 4]
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if scale == 0.25:
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stage_out_channels = [-1, 24, 24, 48, 96, 512]
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elif scale == 0.33:
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stage_out_channels = [-1, 24, 32, 64, 128, 512]
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elif scale == 0.5:
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stage_out_channels = [-1, 24, 48, 96, 192, 1024]
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elif scale == 1.0:
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stage_out_channels = [-1, 24, 116, 232, 464, 1024]
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elif scale == 1.5:
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stage_out_channels = [-1, 24, 176, 352, 704, 1024]
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elif scale == 2.0:
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stage_out_channels = [-1, 24, 224, 488, 976, 2048]
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else:
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raise NotImplementedError("This scale size:[" + str(scale) +
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"] is not implemented!")
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# 1. conv1
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self._conv1 = ConvBNLayer(
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in_channels=3,
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out_channels=stage_out_channels[1],
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kernel_size=3,
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stride=2,
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padding=1,
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act=act,
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name='stage1_conv'
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)
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self._max_pool = MaxPool2d(
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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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# 2. bottleneck sequences
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self._block_list = []
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for stage_id, num_repeat in enumerate(stage_repeats):
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for i in range(num_repeat):
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if i == 0:
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block = self.add_sublayer(
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name=str(stage_id + 2) + '_' + str(i + 1),
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sublayer=InvertedResidualDS(
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in_channels=stage_out_channels[stage_id + 1],
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out_channels=stage_out_channels[stage_id + 2],
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stride=2,
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act=act,
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name=str(stage_id + 2) + '_' + str(i + 1)
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)
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)
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else:
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block = self.add_sublayer(
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name=str(stage_id + 2) + '_' + str(i + 1),
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sublayer=InvertedResidual(
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in_channels=stage_out_channels[stage_id + 2],
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out_channels=stage_out_channels[stage_id + 2],
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stride=1,
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act=act,
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name=str(stage_id + 2) + '_' + str(i + 1)
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)
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)
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self._block_list.append(block)
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# 3. last_conv
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self._last_conv = ConvBNLayer(
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in_channels=stage_out_channels[-2],
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out_channels=stage_out_channels[-1],
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kernel_size=1,
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stride=1,
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padding=0,
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act=act,
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name='conv5'
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)
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# 4. pool
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self._pool2d_avg = AdaptiveAvgPool2d(1)
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self._out_c = stage_out_channels[-1]
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# 5. fc
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self._fc = Linear(
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stage_out_channels[-1],
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class_dim,
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weight_attr=ParamAttr(name='fc6_weights'),
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bias_attr=ParamAttr(name='fc6_offset')
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)
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def forward(self, inputs):
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y = self._conv1(inputs)
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y = self._max_pool(y)
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for inv in self._block_list:
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y = inv(y)
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y = self._last_conv(y)
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y = self._pool2d_avg(y)
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y = reshape(y, shape=[-1, self._out_c])
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y = self._fc(y)
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return y
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def ShuffleNetV2_x0_25(**args):
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model = ShuffleNet(scale=0.25, **args)
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return model
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def ShuffleNetV2_x0_33(**args):
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model = ShuffleNet(scale=0.33, **args)
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return model
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def ShuffleNetV2_x0_5(**args):
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model = ShuffleNet(scale=0.5, **args)
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return model
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def ShuffleNetV2(**args):
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model = ShuffleNet(scale=1.0, **args)
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return model
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def ShuffleNetV2_x1_5(**args):
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model = ShuffleNet(scale=1.5, **args)
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return model
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def ShuffleNetV2_x2_0(**args):
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model = ShuffleNet(scale=2.0, **args)
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return model
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def ShuffleNetV2_swish(**args):
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model = ShuffleNet(scale=1.0, act=swish, **args)
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return model
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