forked from PulseFocusPlatform/PulseFocusPlatform
125 lines
4.6 KiB
Python
125 lines
4.6 KiB
Python
# 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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import math
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from paddle import fluid
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from paddle.fluid.param_attr import ParamAttr
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from ppdet.experimental import mixed_precision_global_state
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from ppdet.core.workspace import register, serializable
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from .resnext import ResNeXt
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__all__ = ['SENet', 'SENetC5']
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@register
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@serializable
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class SENet(ResNeXt):
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"""
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Squeeze-and-Excitation Networks, see https://arxiv.org/abs/1709.01507
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Args:
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depth (int): SENet 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=0.,
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variant='d',
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feature_maps=[2, 3, 4, 5],
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dcn_v2_stages=[],
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std_senet=False,
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weight_prefix_name=''):
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super(SENet, self).__init__(depth, groups, group_width, freeze_at,
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norm_type, freeze_norm, norm_decay, variant,
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feature_maps)
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if depth < 152:
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self.stage_filters = [128, 256, 512, 1024]
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else:
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self.stage_filters = [256, 512, 1024, 2048]
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self.reduction_ratio = 16
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self.std_senet = std_senet
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self._c1_out_chan_num = 128
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self._model_type = 'SEResNeXt'
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self.dcn_v2_stages = dcn_v2_stages
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def _squeeze_excitation(self, input, num_channels, name=None):
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mixed_precision_enabled = mixed_precision_global_state() is not None
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pool = fluid.layers.pool2d(
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input=input,
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pool_size=0,
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pool_type='avg',
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global_pooling=True,
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use_cudnn=mixed_precision_enabled)
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stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
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squeeze = fluid.layers.fc(
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input=pool,
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size=int(num_channels / self.reduction_ratio),
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act='relu',
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param_attr=fluid.param_attr.ParamAttr(
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initializer=fluid.initializer.Uniform(-stdv, stdv),
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name=name + '_sqz_weights'),
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bias_attr=ParamAttr(name=name + '_sqz_offset'))
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stdv = 1.0 / math.sqrt(squeeze.shape[1] * 1.0)
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excitation = fluid.layers.fc(
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input=squeeze,
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size=num_channels,
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act='sigmoid',
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param_attr=fluid.param_attr.ParamAttr(
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initializer=fluid.initializer.Uniform(-stdv, stdv),
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name=name + '_exc_weights'),
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bias_attr=ParamAttr(name=name + '_exc_offset'))
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scale = fluid.layers.elementwise_mul(x=input, y=excitation, axis=0)
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return scale
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@register
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@serializable
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class SENetC5(SENet):
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__doc__ = SENet.__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=0.,
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variant='d',
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feature_maps=[5],
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weight_prefix_name=''):
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super(SENetC5, 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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