262 lines
8.5 KiB
Python
262 lines
8.5 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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# reference: https://arxiv.org/abs/1409.1556
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from __future__ import absolute_import, division, print_function
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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 MaxPool2D
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from ..base.theseus_layer import TheseusLayer
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from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
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MODEL_URLS = {
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"VGG11":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/VGG11_pretrained.pdparams",
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"VGG13":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/VGG13_pretrained.pdparams",
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"VGG16":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/VGG16_pretrained.pdparams",
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"VGG19":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/VGG19_pretrained.pdparams",
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}
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MODEL_STAGES_PATTERN = {
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"VGG": [
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"conv_block_1", "conv_block_2", "conv_block_3", "conv_block_4",
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"conv_block_5"
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]
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}
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__all__ = MODEL_URLS.keys()
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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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class ConvBlock(TheseusLayer):
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def __init__(self, input_channels, output_channels, groups):
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super().__init__()
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self.groups = groups
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self.conv1 = 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.conv2 = 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.conv3 = 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.conv4 = 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.max_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.conv1(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.conv2(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.conv3(x)
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x = self.relu(x)
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if self.groups == 4:
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x = self.conv4(x)
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x = self.relu(x)
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x = self.max_pool(x)
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return x
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class VGGNet(TheseusLayer):
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"""
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VGGNet
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Args:
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config: list. VGGNet config.
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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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class_num: int=1000. The number of classes.
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Returns:
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model: nn.Layer. Specific VGG model depends on args.
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"""
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def __init__(self,
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config,
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stages_pattern,
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stop_grad_layers=0,
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class_num=1000,
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return_patterns=None,
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return_stages=None):
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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.fc3 = Linear(4096, class_num)
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super().init_res(
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stages_pattern,
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return_patterns=return_patterns,
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return_stages=return_stages)
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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.fc3(x)
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return x
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def _load_pretrained(pretrained, model, model_url, use_ssld):
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if pretrained is False:
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pass
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elif pretrained is True:
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load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
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elif isinstance(pretrained, str):
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load_dygraph_pretrain(model, pretrained)
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else:
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raise RuntimeError(
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"pretrained type is not available. Please use `string` or `boolean` type."
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)
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def VGG11(pretrained=False, use_ssld=False, **kwargs):
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"""
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VGG11
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Args:
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pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
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If str, means the path of the pretrained model.
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use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
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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(
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config=NET_CONFIG[11],
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stages_pattern=MODEL_STAGES_PATTERN["VGG"],
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**kwargs)
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_load_pretrained(pretrained, model, MODEL_URLS["VGG11"], use_ssld)
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return model
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def VGG13(pretrained=False, use_ssld=False, **kwargs):
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"""
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VGG13
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Args:
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pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
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If str, means the path of the pretrained model.
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use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
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Returns:
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model: nn.Layer. Specific `VGG13` model depends on args.
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"""
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model = VGGNet(
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config=NET_CONFIG[13],
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stages_pattern=MODEL_STAGES_PATTERN["VGG"],
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**kwargs)
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_load_pretrained(pretrained, model, MODEL_URLS["VGG13"], use_ssld)
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return model
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def VGG16(pretrained=False, use_ssld=False, **kwargs):
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"""
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VGG16
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Args:
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pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
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If str, means the path of the pretrained model.
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use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
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Returns:
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model: nn.Layer. Specific `VGG16` model depends on args.
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"""
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model = VGGNet(
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config=NET_CONFIG[16],
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stages_pattern=MODEL_STAGES_PATTERN["VGG"],
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**kwargs)
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_load_pretrained(pretrained, model, MODEL_URLS["VGG16"], use_ssld)
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return model
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def VGG19(pretrained=False, use_ssld=False, **kwargs):
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"""
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VGG19
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Args:
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pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
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If str, means the path of the pretrained model.
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use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
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Returns:
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model: nn.Layer. Specific `VGG19` model depends on args.
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"""
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model = VGGNet(
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config=NET_CONFIG[19],
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stages_pattern=MODEL_STAGES_PATTERN["VGG"],
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**kwargs)
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_load_pretrained(pretrained, model, MODEL_URLS["VGG19"], use_ssld)
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return model
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