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88 lines
2.8 KiB
88 lines
2.8 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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from __future__ import division |
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from __future__ import print_function |
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import paddle |
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import paddle.nn as nn |
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from paddle.vision.models.resnet import ResNet |
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from paddle.vision.models.resnet import BottleneckBlock |
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from paddle.utils.download import get_weights_path_from_url |
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__all__ = ['resnext101_32x8d_wsl'] |
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class ResNetEx(ResNet): |
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"""ResNet extention model, support ResNeXt. |
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""" |
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def __init__(self, |
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block, |
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depth, |
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num_classes=1000, |
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with_pool=True, |
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groups=1, |
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width_per_group=64): |
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self.groups = groups |
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self.base_width = width_per_group |
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super(ResNetEx, self).__init__(block, depth, num_classes, with_pool) |
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def _make_layer(self, block, planes, blocks, stride=1, dilate=False): |
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norm_layer = self._norm_layer |
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downsample = None |
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previous_dilation = self.dilation |
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if dilate: |
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self.dilation *= stride |
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stride = 1 |
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if stride != 1 or self.inplanes != planes * block.expansion: |
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downsample = nn.Sequential( |
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nn.Conv2D( |
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self.inplanes, |
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planes * block.expansion, |
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1, |
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stride=stride, |
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bias_attr=False), |
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norm_layer(planes * block.expansion), ) |
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layers = [] |
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layers.append( |
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block(self.inplanes, planes, stride, downsample, self.groups, |
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self.base_width, previous_dilation, norm_layer)) |
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self.inplanes = planes * block.expansion |
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for _ in range(1, blocks): |
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layers.append( |
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block( |
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self.inplanes, |
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planes, |
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groups=self.groups, |
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base_width=self.base_width, |
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norm_layer=norm_layer)) |
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return nn.Sequential(*layers) |
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def _resnext(arch, Block, depth, **kwargs): |
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model = ResNetEx(Block, depth, **kwargs) |
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return model |
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def resnext101_32x8d_wsl(**kwargs): |
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"""ResNet101 32x8d wsl model |
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""" |
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kwargs['groups'] = 32 |
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kwargs['width_per_group'] = 8 |
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return _resnext('resnet101_32x8d', BottleneckBlock, 101, **kwargs)
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