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230 lines
6.6 KiB
230 lines
6.6 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
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import paddle.nn as nn
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from paddle.utils.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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'89bbffc0f87d260be9b8cdc169c991c4')
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}
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class VGG(nn.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 (nn.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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with_pool (bool): Use pool before the last three fc layer or not. Default: True.
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Examples:
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.. code-block:: python
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from paddle.vision.models import VGG
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from paddle.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, features, num_classes=1000, with_pool=True):
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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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self.with_pool = with_pool
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if with_pool:
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self.avgpool = nn.AdaptiveAvgPool2D((7, 7))
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if num_classes > 0:
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self.classifier = nn.Sequential(
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nn.Linear(512 * 7 * 7, 4096),
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nn.ReLU(),
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nn.Dropout(),
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nn.Linear(4096, 4096),
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nn.ReLU(),
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nn.Dropout(),
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nn.Linear(4096, num_classes), )
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def forward(self, x):
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x = self.features(x)
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if self.with_pool:
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x = self.avgpool(x)
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if self.num_classes > 0:
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x = paddle.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 += [nn.MaxPool2D(kernel_size=2, stride=2)]
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else:
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conv2d = nn.Conv2D(in_channels, v, kernel_size=3, padding=1)
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if batch_norm:
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layers += [conv2d, nn.BatchNorm2D(v), nn.ReLU()]
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else:
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layers += [conv2d, nn.ReLU()]
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in_channels = v
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return nn.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(cfgs[cfg], batch_norm=batch_norm), **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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param = paddle.load(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.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.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.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.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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