194 lines
6.1 KiB
Python
194 lines
6.1 KiB
Python
# copyright (c) 2021 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, division, print_function
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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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import paddle.nn.functional as F
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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 ppcls.arch.backbone.base.theseus_layer import TheseusLayer
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__all__ = ["VGG11", "VGG13", "VGG16", "VGG19"]
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# VGG config
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# key: VGG network depth
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# value: conv num in different blocks
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NET_CONFIG = {
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11: [1, 1, 2, 2, 2],
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13: [2, 2, 2, 2, 2],
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16: [2, 2, 3, 3, 3],
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19: [2, 2, 4, 4, 4]
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}
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def VGG11(**args):
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"""
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VGG11
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Args:
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kwargs:
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class_num: int=1000. Output dim of last fc layer.
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stop_grad_layers: int=0. The parameters in blocks which index larger than `stop_grad_layers`, will be set `param.trainable=False`
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Returns:
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model: nn.Layer. Specific `VGG11` model depends on args.
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"""
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model = VGGNet(config=NET_CONFIG[11], **args)
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return model
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def VGG13(**args):
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"""
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VGG13
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Args:
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kwargs:
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class_num: int=1000. Output dim of last fc layer.
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stop_grad_layers: int=0. The parameters in blocks which index larger than `stop_grad_layers`, will be set `param.trainable=False`
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Returns:
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model: nn.Layer. Specific `VGG11` model depends on args.
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"""
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model = VGGNet(config=NET_CONFIG[13], **args)
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return model
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def VGG16(**args):
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"""
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VGG16
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Args:
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kwargs:
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class_num: int=1000. Output dim of last fc layer.
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stop_grad_layers: int=0. The parameters in blocks which index larger than `stop_grad_layers`, will be set `param.trainable=False`
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Returns:
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model: nn.Layer. Specific `VGG11` model depends on args.
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"""
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model = VGGNet(config=NET_CONFIG[16], **args)
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return model
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def VGG19(**args):
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"""
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VGG19
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Args:
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kwargs:
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class_num: int=1000. Output dim of last fc layer.
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stop_grad_layers: int=0. The parameters in blocks which index larger than `stop_grad_layers`, will be set `param.trainable=False`
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Returns:
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model: nn.Layer. Specific `VGG11` model depends on args.
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"""
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model = VGGNet(config=NET_CONFIG[19], **args)
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return model
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class ConvBlock(TheseusLayer):
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def __init__(self, input_channels, output_channels, groups):
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super(ConvBlock, self).__init__()
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self.groups = groups
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self._conv_1 = Conv2D(
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in_channels=input_channels,
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out_channels=output_channels,
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kernel_size=3,
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stride=1,
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padding=1,
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bias_attr=False)
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if groups == 2 or groups == 3 or groups == 4:
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self._conv_2 = Conv2D(
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in_channels=output_channels,
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out_channels=output_channels,
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kernel_size=3,
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stride=1,
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padding=1,
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bias_attr=False)
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if groups == 3 or groups == 4:
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self._conv_3 = Conv2D(
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in_channels=output_channels,
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out_channels=output_channels,
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kernel_size=3,
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stride=1,
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padding=1,
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bias_attr=False)
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if groups == 4:
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self._conv_4 = Conv2D(
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in_channels=output_channels,
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out_channels=output_channels,
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kernel_size=3,
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stride=1,
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padding=1,
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bias_attr=False)
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self._pool = MaxPool2D(kernel_size=2, stride=2, padding=0)
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self._relu = nn.ReLU()
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def forward(self, inputs):
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x = self._conv_1(inputs)
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x = self._relu(x)
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if self.groups == 2 or self.groups == 3 or self.groups == 4:
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x = self._conv_2(x)
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x = self._relu(x)
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if self.groups == 3 or self.groups == 4:
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x = self._conv_3(x)
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x = self._relu(x)
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if self.groups == 4:
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x = self._conv_4(x)
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x = self._relu(x)
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x = self._pool(x)
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return x
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class VGGNet(TheseusLayer):
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def __init__(self, config, stop_grad_layers=0, class_num=1000):
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super().__init__()
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self.stop_grad_layers = stop_grad_layers
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self._conv_block_1 = ConvBlock(3, 64, config[0])
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self._conv_block_2 = ConvBlock(64, 128, config[1])
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self._conv_block_3 = ConvBlock(128, 256, config[2])
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self._conv_block_4 = ConvBlock(256, 512, config[3])
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self._conv_block_5 = ConvBlock(512, 512, config[4])
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self._relu = nn.ReLU()
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self._flatten = nn.Flatten(start_axis=1, stop_axis=-1)
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for idx, block in enumerate([
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self._conv_block_1, self._conv_block_2, self._conv_block_3,
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self._conv_block_4, self._conv_block_5
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]):
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if self.stop_grad_layers >= idx + 1:
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for param in block.parameters():
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param.trainable = False
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self._drop = Dropout(p=0.5, mode="downscale_in_infer")
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self._fc1 = Linear(7 * 7 * 512, 4096)
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self._fc2 = Linear(4096, 4096)
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self._out = Linear(4096, class_num)
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def forward(self, inputs):
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x = self._conv_block_1(inputs)
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x = self._conv_block_2(x)
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x = self._conv_block_3(x)
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x = self._conv_block_4(x)
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x = self._conv_block_5(x)
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x = self._flatten(x)
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x = self._fc1(x)
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x = self._relu(x)
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x = self._drop(x)
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x = self._fc2(x)
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x = self._relu(x)
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x = self._drop(x)
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x = self._out(x)
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return x
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