forked from PulseFocusPlatform/PulseFocusPlatform
90 lines
3.3 KiB
Python
90 lines
3.3 KiB
Python
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# Copyright (c) 2019 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 absolute_import
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from __future__ import division
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from __future__ import print_function
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from ppdet.core.workspace import register, serializable
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from .resnet import ResNet
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__all__ = ['ResNeXt']
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@register
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@serializable
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class ResNeXt(ResNet):
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"""
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ResNeXt, see https://arxiv.org/abs/1611.05431
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Args:
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depth (int): network depth, should be 50, 101, 152.
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groups (int): group convolution cardinality
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group_width (int): width of each group convolution
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freeze_at (int): freeze the backbone at which stage
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norm_type (str): normalization type, 'bn', 'sync_bn' or 'affine_channel'
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freeze_norm (bool): freeze normalization layers
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norm_decay (float): weight decay for normalization layer weights
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variant (str): ResNet variant, supports 'a', 'b', 'c', 'd' currently
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feature_maps (list): index of the stages whose feature maps are returned
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dcn_v2_stages (list): index of stages who select deformable conv v2
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"""
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def __init__(self,
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depth=50,
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groups=64,
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group_width=4,
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freeze_at=2,
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norm_type='affine_channel',
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freeze_norm=True,
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norm_decay=True,
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variant='a',
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feature_maps=[2, 3, 4, 5],
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dcn_v2_stages=[],
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weight_prefix_name=''):
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assert depth in [50, 101, 152], "depth {} should be 50, 101 or 152"
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super(ResNeXt, self).__init__(depth, freeze_at, norm_type, freeze_norm,
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norm_decay, variant, feature_maps)
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self.depth_cfg = {
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50: ([3, 4, 6, 3], self.bottleneck),
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101: ([3, 4, 23, 3], self.bottleneck),
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152: ([3, 8, 36, 3], self.bottleneck)
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}
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self.stage_filters = [256, 512, 1024, 2048]
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self.groups = groups
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self.group_width = group_width
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self._model_type = 'ResNeXt'
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self.dcn_v2_stages = dcn_v2_stages
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@register
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@serializable
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class ResNeXtC5(ResNeXt):
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__doc__ = ResNeXt.__doc__
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def __init__(self,
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depth=50,
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groups=64,
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group_width=4,
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freeze_at=2,
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norm_type='affine_channel',
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freeze_norm=True,
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norm_decay=True,
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variant='a',
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feature_maps=[5],
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weight_prefix_name=''):
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super(ResNeXtC5, self).__init__(depth, groups, group_width, freeze_at,
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norm_type, freeze_norm, norm_decay,
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variant, feature_maps)
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self.severed_head = True
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