You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
86 lines
2.8 KiB
86 lines
2.8 KiB
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. |
|
# |
|
# Licensed under the Apache License, Version 2.0 (the "License"); |
|
# you may not use this file except in compliance with the License. |
|
# You may obtain a copy of the License at |
|
# |
|
# http://www.apache.org/licenses/LICENSE-2.0 |
|
# |
|
# Unless required by applicable law or agreed to in writing, software |
|
# distributed under the License is distributed on an "AS IS" BASIS, |
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
|
# See the License for the specific language governing permissions and |
|
# limitations under the License. |
|
|
|
from __future__ import division |
|
from __future__ import print_function |
|
|
|
import paddle |
|
import paddle.nn as nn |
|
from paddle.vision.models.resnet import ResNet |
|
from paddle.vision.models.resnet import BottleneckBlock |
|
|
|
from paddle.utils.download import get_weights_path_from_url |
|
|
|
__all__ = ['resnext101_32x8d_wsl'] |
|
|
|
|
|
class ResNetEx(ResNet): |
|
"""ResNet extention model, support ResNeXt. |
|
""" |
|
def __init__(self, |
|
block, |
|
depth, |
|
num_classes=1000, |
|
with_pool=True, |
|
groups=1, |
|
width_per_group=64): |
|
self.groups = groups |
|
self.base_width = width_per_group |
|
|
|
super(ResNetEx, self).__init__(block, depth, num_classes, with_pool) |
|
|
|
def _make_layer(self, block, planes, blocks, stride=1, dilate=False): |
|
norm_layer = self._norm_layer |
|
downsample = None |
|
previous_dilation = self.dilation |
|
if dilate: |
|
self.dilation *= stride |
|
stride = 1 |
|
if stride != 1 or self.inplanes != planes * block.expansion: |
|
downsample = nn.Sequential( |
|
nn.Conv2D(self.inplanes, |
|
planes * block.expansion, |
|
1, |
|
stride=stride, |
|
bias_attr=False), |
|
norm_layer(planes * block.expansion), |
|
) |
|
|
|
layers = [] |
|
layers.append( |
|
block(self.inplanes, planes, stride, downsample, self.groups, |
|
self.base_width, previous_dilation, norm_layer)) |
|
self.inplanes = planes * block.expansion |
|
for _ in range(1, blocks): |
|
layers.append( |
|
block(self.inplanes, |
|
planes, |
|
groups=self.groups, |
|
base_width=self.base_width, |
|
norm_layer=norm_layer)) |
|
|
|
return nn.Sequential(*layers) |
|
|
|
|
|
def _resnext(arch, Block, depth, **kwargs): |
|
model = ResNetEx(Block, depth, **kwargs) |
|
return model |
|
|
|
|
|
def resnext101_32x8d_wsl(**kwargs): |
|
"""ResNet101 32x8d wsl model |
|
""" |
|
kwargs['groups'] = 32 |
|
kwargs['width_per_group'] = 8 |
|
return _resnext('resnet101_32x8d', BottleneckBlock, 101, **kwargs)
|
|
|