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245 lines
7.4 KiB
245 lines
7.4 KiB
# Copyright (c) 2020 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 paddle.fluid as fluid
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from paddle.nn import Conv2d, Pool2D, BatchNorm, Linear, ReLU
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from paddle.fluid.dygraph.container import Sequential
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from ...download import get_weights_path_from_url
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__all__ = [
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'VGG',
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'vgg11',
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'vgg13',
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'vgg16',
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'vgg19',
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]
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model_urls = {
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'vgg16': ('https://paddle-hapi.bj.bcebos.com/models/vgg16.pdparams',
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'c788f453a3b999063e8da043456281ee')
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}
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class Classifier(fluid.dygraph.Layer):
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def __init__(self, num_classes, classifier_activation='softmax'):
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super(Classifier, self).__init__()
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self.linear1 = Linear(512 * 7 * 7, 4096)
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self.linear2 = Linear(4096, 4096)
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self.linear3 = Linear(4096, num_classes, act=classifier_activation)
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def forward(self, x):
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x = self.linear1(x)
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x = fluid.layers.relu(x)
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x = fluid.layers.dropout(x, 0.5)
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x = self.linear2(x)
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x = fluid.layers.relu(x)
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x = fluid.layers.dropout(x, 0.5)
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out = self.linear3(x)
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return out
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class VGG(fluid.dygraph.Layer):
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"""VGG model from
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`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_
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Args:
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features (fluid.dygraph.Layer): vgg features create by function make_layers.
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num_classes (int): output dim of last fc layer. If num_classes <=0, last fc layer
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will not be defined. Default: 1000.
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classifier_activation (str): activation for the last fc layer. Default: 'softmax'.
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Examples:
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.. code-block:: python
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from paddle.incubate.hapi.vision.models import VGG
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from paddle.incubate.hapi.vision.models.vgg import make_layers
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vgg11_cfg = [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M']
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features = make_layers(vgg11_cfg)
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vgg11 = VGG(features)
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"""
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def __init__(self,
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features,
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num_classes=1000,
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classifier_activation='softmax'):
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super(VGG, self).__init__()
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self.features = features
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self.num_classes = num_classes
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if num_classes > 0:
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classifier = Classifier(num_classes, classifier_activation)
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self.classifier = self.add_sublayer("classifier",
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Sequential(classifier))
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def forward(self, x):
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x = self.features(x)
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if self.num_classes > 0:
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x = fluid.layers.flatten(x, 1)
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x = self.classifier(x)
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return x
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def make_layers(cfg, batch_norm=False):
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layers = []
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in_channels = 3
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for v in cfg:
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if v == 'M':
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layers += [Pool2D(pool_size=2, pool_stride=2)]
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else:
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if batch_norm:
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conv2d = Conv2d(in_channels, v, kernel_size=3, padding=1)
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layers += [conv2d, BatchNorm(v), ReLU()]
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else:
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conv2d = Conv2d(in_channels, v, kernel_size=3, padding=1)
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layers += [conv2d, ReLU()]
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in_channels = v
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return Sequential(*layers)
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cfgs = {
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'A': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
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'B':
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[64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
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'D': [
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64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512,
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512, 512, 'M'
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],
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'E': [
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64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512,
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'M', 512, 512, 512, 512, 'M'
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],
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}
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def _vgg(arch, cfg, batch_norm, pretrained, **kwargs):
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model = VGG(make_layers(
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cfgs[cfg], batch_norm=batch_norm),
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num_classes=1000,
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**kwargs)
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if pretrained:
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assert arch in model_urls, "{} model do not have a pretrained model now, you should set pretrained=False".format(
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arch)
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weight_path = get_weights_path_from_url(model_urls[arch][0],
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model_urls[arch][1])
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assert weight_path.endswith(
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'.pdparams'), "suffix of weight must be .pdparams"
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param, _ = fluid.load_dygraph(weight_path)
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model.load_dict(param)
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return model
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def vgg11(pretrained=False, batch_norm=False, **kwargs):
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"""VGG 11-layer model
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet. Default: False.
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batch_norm (bool): If True, returns a model with batch_norm layer. Default: False.
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Examples:
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.. code-block:: python
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from paddle.incubate.hapi.vision.models import vgg11
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# build model
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model = vgg11()
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# build vgg11 model with batch_norm
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model = vgg11(batch_norm=True)
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"""
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model_name = 'vgg11'
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if batch_norm:
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model_name += ('_bn')
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return _vgg(model_name, 'A', batch_norm, pretrained, **kwargs)
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def vgg13(pretrained=False, batch_norm=False, **kwargs):
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"""VGG 13-layer model
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet. Default: False.
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batch_norm (bool): If True, returns a model with batch_norm layer. Default: False.
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Examples:
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.. code-block:: python
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from paddle.incubate.hapi.vision.models import vgg13
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# build model
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model = vgg13()
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# build vgg13 model with batch_norm
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model = vgg13(batch_norm=True)
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"""
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model_name = 'vgg13'
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if batch_norm:
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model_name += ('_bn')
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return _vgg(model_name, 'B', batch_norm, pretrained, **kwargs)
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def vgg16(pretrained=False, batch_norm=False, **kwargs):
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"""VGG 16-layer model
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet. Default: False.
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batch_norm (bool): If True, returns a model with batch_norm layer. Default: False.
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Examples:
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.. code-block:: python
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from paddle.incubate.hapi.vision.models import vgg16
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# build model
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model = vgg16()
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# build vgg16 model with batch_norm
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model = vgg16(batch_norm=True)
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"""
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model_name = 'vgg16'
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if batch_norm:
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model_name += ('_bn')
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return _vgg(model_name, 'D', batch_norm, pretrained, **kwargs)
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def vgg19(pretrained=False, batch_norm=False, **kwargs):
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"""VGG 19-layer model
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet. Default: False.
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batch_norm (bool): If True, returns a model with batch_norm layer. Default: False.
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Examples:
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.. code-block:: python
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from paddle.incubate.hapi.vision.models import vgg19
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# build model
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model = vgg19()
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# build vgg19 model with batch_norm
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model = vgg19(batch_norm=True)
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"""
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model_name = 'vgg19'
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if batch_norm:
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model_name += ('_bn')
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return _vgg(model_name, 'E', batch_norm, pretrained, **kwargs)
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