forked from PulseFocusPlatform/PulseFocusPlatform
452 lines
16 KiB
Python
452 lines
16 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 collections import OrderedDict
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from paddle import fluid
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from paddle.fluid.param_attr import ParamAttr
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from paddle.fluid.framework import Variable
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from paddle.fluid.regularizer import L2Decay
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from paddle.fluid.initializer import Constant
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from ppdet.core.workspace import register, serializable
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from numbers import Integral
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from .name_adapter import NameAdapter
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from .nonlocal_helper import add_space_nonlocal
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__all__ = ['CBResNet']
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@register
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@serializable
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class CBResNet(object):
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"""
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CBNet, see https://arxiv.org/abs/1909.03625
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Args:
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depth (int): ResNet depth, should be 18, 34, 50, 101, 152.
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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'/'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 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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nonlocal_stages (list): index of stages who select nonlocal networks
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repeat_num (int): number of repeat for backbone
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Attention:
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1. Here we set the ResNet as the base backbone.
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2. All the pretraned params are copied from corresponding names,
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but with different names to avoid name refliction.
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"""
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def __init__(self,
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depth=50,
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freeze_at=2,
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norm_type='bn',
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freeze_norm=True,
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norm_decay=0.,
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variant='b',
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feature_maps=[2, 3, 4, 5],
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dcn_v2_stages=[],
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nonlocal_stages=[],
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repeat_num=2,
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lr_mult_list=[1., 1., 1., 1.]):
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super(CBResNet, self).__init__()
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if isinstance(feature_maps, Integral):
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feature_maps = [feature_maps]
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assert depth in [18, 34, 50, 101, 152, 200], \
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"depth {} not in [18, 34, 50, 101, 152, 200]"
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assert variant in ['a', 'b', 'c', 'd'], "invalid ResNet variant"
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assert 0 <= freeze_at <= 4, "freeze_at should be 0, 1, 2, 3 or 4"
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assert len(feature_maps) > 0, "need one or more feature maps"
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assert norm_type in ['bn', 'sync_bn', 'affine_channel']
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assert not (len(nonlocal_stages)>0 and depth<50), \
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"non-local is not supported for resnet18 or resnet34"
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self.depth = depth
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self.dcn_v2_stages = dcn_v2_stages
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self.freeze_at = freeze_at
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self.norm_type = norm_type
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self.norm_decay = norm_decay
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self.freeze_norm = freeze_norm
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self.variant = variant
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self._model_type = 'ResNet'
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self.feature_maps = feature_maps
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self.repeat_num = repeat_num
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self.curr_level = 0
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self.depth_cfg = {
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18: ([2, 2, 2, 2], self.basicblock),
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34: ([3, 4, 6, 3], self.basicblock),
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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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200: ([3, 12, 48, 3], self.bottleneck),
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}
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self.nonlocal_stages = nonlocal_stages
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self.nonlocal_mod_cfg = {
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50: 2,
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101: 5,
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152: 8,
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200: 12,
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}
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self.lr_mult_list = lr_mult_list
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self.stage_num = -1
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self.stage_filters = [64, 128, 256, 512]
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self._c1_out_chan_num = 64
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self.na = NameAdapter(self)
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def _conv_offset(self,
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input,
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filter_size,
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stride,
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padding,
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act=None,
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name=None):
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out_channel = filter_size * filter_size * 3
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out = fluid.layers.conv2d(
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input,
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num_filters=out_channel,
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filter_size=filter_size,
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stride=stride,
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padding=padding,
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param_attr=ParamAttr(
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initializer=Constant(0.0), name=name + ".w_0"),
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bias_attr=ParamAttr(
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initializer=Constant(0.0), name=name + ".b_0"),
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act=act,
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name=name)
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return out
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def _conv_norm(self,
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input,
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num_filters,
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filter_size,
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stride=1,
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groups=1,
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act=None,
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name=None,
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dcn=False):
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# need fine lr for distilled model, default as 1.0
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lr_mult = 1.0
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mult_idx = max(self.stage_num - 2, 0)
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mult_idx = min(self.stage_num - 2, 3)
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lr_mult = self.lr_mult_list[mult_idx]
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if not dcn:
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conv = fluid.layers.conv2d(
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input=input,
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num_filters=num_filters,
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filter_size=filter_size,
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stride=stride,
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padding=(filter_size - 1) // 2,
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groups=groups,
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act=None,
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param_attr=ParamAttr(
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name=name + "_weights_" + str(self.curr_level),
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learning_rate=lr_mult),
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bias_attr=False)
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else:
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offset_mask = self._conv_offset(
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input=input,
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filter_size=filter_size,
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stride=stride,
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padding=(filter_size - 1) // 2,
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act=None,
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name=name + "_conv_offset_" + str(self.curr_level))
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offset_channel = filter_size**2 * 2
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mask_channel = filter_size**2
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offset, mask = fluid.layers.split(
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input=offset_mask,
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num_or_sections=[offset_channel, mask_channel],
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dim=1)
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mask = fluid.layers.sigmoid(mask)
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conv = fluid.layers.deformable_conv(
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input=input,
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offset=offset,
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mask=mask,
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num_filters=num_filters,
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filter_size=filter_size,
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stride=stride,
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padding=(filter_size - 1) // 2,
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groups=groups,
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deformable_groups=1,
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im2col_step=1,
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param_attr=ParamAttr(
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name=name + "_weights_" + str(self.curr_level),
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learning_rate=lr_mult),
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bias_attr=False)
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bn_name = self.na.fix_conv_norm_name(name)
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norm_lr = 0. if self.freeze_norm else lr_mult
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norm_decay = self.norm_decay
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pattr = ParamAttr(
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name=bn_name + '_scale_' + str(self.curr_level),
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learning_rate=norm_lr,
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regularizer=L2Decay(norm_decay))
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battr = ParamAttr(
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name=bn_name + '_offset_' + str(self.curr_level),
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learning_rate=norm_lr,
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regularizer=L2Decay(norm_decay))
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if self.norm_type in ['bn', 'sync_bn']:
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global_stats = True if self.freeze_norm else False
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out = fluid.layers.batch_norm(
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input=conv,
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act=act,
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name=bn_name + '.output.1_' + str(self.curr_level),
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param_attr=pattr,
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bias_attr=battr,
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moving_mean_name=bn_name + '_mean_' + str(self.curr_level),
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moving_variance_name=bn_name + '_variance_' +
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str(self.curr_level),
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use_global_stats=global_stats)
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scale = fluid.framework._get_var(pattr.name)
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bias = fluid.framework._get_var(battr.name)
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elif self.norm_type == 'affine_channel':
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assert False, "deprecated!!!"
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if self.freeze_norm:
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scale.stop_gradient = True
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bias.stop_gradient = True
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return out
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def _shortcut(self, input, ch_out, stride, is_first, name):
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max_pooling_in_short_cut = self.variant == 'd'
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ch_in = input.shape[1]
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# the naming rule is same as pretrained weight
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name = self.na.fix_shortcut_name(name)
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if ch_in != ch_out or stride != 1 or (self.depth < 50 and is_first):
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if max_pooling_in_short_cut and not is_first:
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input = fluid.layers.pool2d(
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input=input,
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pool_size=2,
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pool_stride=2,
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pool_padding=0,
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ceil_mode=True,
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pool_type='avg')
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return self._conv_norm(input, ch_out, 1, 1, name=name)
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return self._conv_norm(input, ch_out, 1, stride, name=name)
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else:
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return input
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def bottleneck(self, input, num_filters, stride, is_first, name, dcn=False):
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if self.variant == 'a':
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stride1, stride2 = stride, 1
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else:
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stride1, stride2 = 1, stride
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# ResNeXt
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groups = getattr(self, 'groups', 1)
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group_width = getattr(self, 'group_width', -1)
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if groups == 1:
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expand = 4
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elif (groups * group_width) == 256:
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expand = 1
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else: # FIXME hard code for now, handles 32x4d, 64x4d and 32x8d
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num_filters = num_filters // 2
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expand = 2
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conv_name1, conv_name2, conv_name3, \
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shortcut_name = self.na.fix_bottleneck_name(name)
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conv_def = [[num_filters, 1, stride1, 'relu', 1, conv_name1],
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[num_filters, 3, stride2, 'relu', groups, conv_name2],
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[num_filters * expand, 1, 1, None, 1, conv_name3]]
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residual = input
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for i, (c, k, s, act, g, _name) in enumerate(conv_def):
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residual = self._conv_norm(
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input=residual,
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num_filters=c,
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filter_size=k,
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stride=s,
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act=act,
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groups=g,
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name=_name,
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dcn=(i == 1 and dcn))
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short = self._shortcut(
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input,
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num_filters * expand,
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stride,
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is_first=is_first,
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name=shortcut_name)
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# Squeeze-and-Excitation
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if callable(getattr(self, '_squeeze_excitation', None)):
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residual = self._squeeze_excitation(
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input=residual, num_channels=num_filters, name='fc' + name)
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return fluid.layers.elementwise_add(x=short, y=residual, act='relu')
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def basicblock(self, input, num_filters, stride, is_first, name, dcn=False):
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assert dcn is False, "Not implemented yet."
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conv0 = self._conv_norm(
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input=input,
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num_filters=num_filters,
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filter_size=3,
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act='relu',
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stride=stride,
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name=name + "_branch2a")
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conv1 = self._conv_norm(
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input=conv0,
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num_filters=num_filters,
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filter_size=3,
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act=None,
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name=name + "_branch2b")
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short = self._shortcut(
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input, num_filters, stride, is_first, name=name + "_branch1")
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return fluid.layers.elementwise_add(x=short, y=conv1, act='relu')
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def layer_warp(self, input, stage_num):
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"""
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Args:
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input (Variable): input variable.
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stage_num (int): the stage number, should be 2, 3, 4, 5
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Returns:
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The last variable in endpoint-th stage.
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"""
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assert stage_num in [2, 3, 4, 5]
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self.stage_num = stage_num
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stages, block_func = self.depth_cfg[self.depth]
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count = stages[stage_num - 2]
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ch_out = self.stage_filters[stage_num - 2]
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is_first = False if stage_num != 2 else True
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dcn = True if stage_num in self.dcn_v2_stages else False
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nonlocal_mod = 1000
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if stage_num in self.nonlocal_stages:
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nonlocal_mod = self.nonlocal_mod_cfg[
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self.depth] if stage_num == 4 else 2
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# Make the layer name and parameter name consistent
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# with ImageNet pre-trained model
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conv = input
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for i in range(count):
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conv_name = self.na.fix_layer_warp_name(stage_num, count, i)
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if self.depth < 50:
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is_first = True if i == 0 and stage_num == 2 else False
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conv = block_func(
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input=conv,
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num_filters=ch_out,
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stride=2 if i == 0 and stage_num != 2 else 1,
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is_first=is_first,
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name=conv_name,
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dcn=dcn)
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# add non local model
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dim_in = conv.shape[1]
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||
|
|
nonlocal_name = "nonlocal_conv{}_lvl{}".format(stage_num,
|
||
|
|
self.curr_level)
|
||
|
|
if i % nonlocal_mod == nonlocal_mod - 1:
|
||
|
|
conv = add_space_nonlocal(conv, dim_in, dim_in,
|
||
|
|
nonlocal_name + '_{}'.format(i),
|
||
|
|
int(dim_in / 2))
|
||
|
|
|
||
|
|
return conv
|
||
|
|
|
||
|
|
def c1_stage(self, input):
|
||
|
|
out_chan = self._c1_out_chan_num
|
||
|
|
|
||
|
|
conv1_name = self.na.fix_c1_stage_name()
|
||
|
|
|
||
|
|
if self.variant in ['c', 'd']:
|
||
|
|
conv1_1_name = "conv1_1"
|
||
|
|
conv1_2_name = "conv1_2"
|
||
|
|
conv1_3_name = "conv1_3"
|
||
|
|
conv_def = [
|
||
|
|
[out_chan // 2, 3, 2, conv1_1_name],
|
||
|
|
[out_chan // 2, 3, 1, conv1_2_name],
|
||
|
|
[out_chan, 3, 1, conv1_3_name],
|
||
|
|
]
|
||
|
|
else:
|
||
|
|
conv_def = [[out_chan, 7, 2, conv1_name]]
|
||
|
|
|
||
|
|
for (c, k, s, _name) in conv_def:
|
||
|
|
input = self._conv_norm(
|
||
|
|
input=input,
|
||
|
|
num_filters=c,
|
||
|
|
filter_size=k,
|
||
|
|
stride=s,
|
||
|
|
act='relu',
|
||
|
|
name=_name)
|
||
|
|
|
||
|
|
output = fluid.layers.pool2d(
|
||
|
|
input=input,
|
||
|
|
pool_size=3,
|
||
|
|
pool_stride=2,
|
||
|
|
pool_padding=1,
|
||
|
|
pool_type='max')
|
||
|
|
return output
|
||
|
|
|
||
|
|
def connect(self, left, right, name):
|
||
|
|
ch_right = right.shape[1]
|
||
|
|
conv = self._conv_norm(
|
||
|
|
left,
|
||
|
|
num_filters=ch_right,
|
||
|
|
filter_size=1,
|
||
|
|
stride=1,
|
||
|
|
act="relu",
|
||
|
|
name=name + "_connect")
|
||
|
|
shape = fluid.layers.shape(right)
|
||
|
|
shape_hw = fluid.layers.slice(shape, axes=[0], starts=[2], ends=[4])
|
||
|
|
out_shape_ = shape_hw
|
||
|
|
out_shape = fluid.layers.cast(out_shape_, dtype='int32')
|
||
|
|
out_shape.stop_gradient = True
|
||
|
|
conv = fluid.layers.resize_nearest(conv, scale=2., out_shape=out_shape)
|
||
|
|
|
||
|
|
output = fluid.layers.elementwise_add(x=right, y=conv)
|
||
|
|
return output
|
||
|
|
|
||
|
|
def __call__(self, input):
|
||
|
|
assert isinstance(input, Variable)
|
||
|
|
assert not (set(self.feature_maps) - set([2, 3, 4, 5])), \
|
||
|
|
"feature maps {} not in [2, 3, 4, 5]".format(self.feature_maps)
|
||
|
|
|
||
|
|
res_endpoints = []
|
||
|
|
|
||
|
|
self.curr_level = 0
|
||
|
|
res = self.c1_stage(input)
|
||
|
|
feature_maps = range(2, max(self.feature_maps) + 1)
|
||
|
|
for i in feature_maps:
|
||
|
|
res = self.layer_warp(res, i)
|
||
|
|
if i in self.feature_maps:
|
||
|
|
res_endpoints.append(res)
|
||
|
|
|
||
|
|
for num in range(1, self.repeat_num):
|
||
|
|
self.stage_num = -1
|
||
|
|
self.curr_level = num
|
||
|
|
res = self.c1_stage(input)
|
||
|
|
for i in range(len(res_endpoints)):
|
||
|
|
res = self.connect(res_endpoints[i], res, "test_c" + str(i + 1))
|
||
|
|
res = self.layer_warp(res, i + 2)
|
||
|
|
res_endpoints[i] = res
|
||
|
|
if self.freeze_at >= i + 2:
|
||
|
|
res.stop_gradient = True
|
||
|
|
|
||
|
|
return OrderedDict([('res{}_sum'.format(self.feature_maps[idx]), feat)
|
||
|
|
for idx, feat in enumerate(res_endpoints)])
|