forked from PulseFocusPlatform/PulseFocusPlatform
240 lines
9.5 KiB
Python
240 lines
9.5 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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import copy
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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.initializer import Xavier
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from paddle.fluid.regularizer import L2Decay
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from ppdet.core.workspace import register
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from ppdet.modeling.ops import ConvNorm
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__all__ = ['FPN']
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@register
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class FPN(object):
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"""
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Feature Pyramid Network, see https://arxiv.org/abs/1612.03144
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Args:
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num_chan (int): number of feature channels
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min_level (int): lowest level of the backbone feature map to use
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max_level (int): highest level of the backbone feature map to use
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spatial_scale (list): feature map scaling factor
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has_extra_convs (bool): whether has extral convolutions in higher levels
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norm_type (str|None): normalization type, 'bn'/'sync_bn'/'affine_channel'
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norm_decay (float): weight decay for normalization layer weights.
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reverse_out (bool): whether to flip the output.
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"""
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__shared__ = ['norm_type', 'freeze_norm']
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def __init__(self,
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num_chan=256,
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min_level=2,
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max_level=6,
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spatial_scale=[1. / 32., 1. / 16., 1. / 8., 1. / 4.],
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has_extra_convs=False,
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norm_type=None,
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norm_decay=0.,
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freeze_norm=False,
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use_c5=True,
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reverse_out=False):
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self.freeze_norm = freeze_norm
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self.num_chan = num_chan
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self.min_level = min_level
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self.max_level = max_level
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self.spatial_scale = spatial_scale
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self.has_extra_convs = has_extra_convs
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self.norm_type = norm_type
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self.norm_decay = norm_decay
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self.use_c5 = use_c5
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self.reverse_out = reverse_out
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def _add_topdown_lateral(self, body_name, body_input, upper_output):
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lateral_name = 'fpn_inner_' + body_name + '_lateral'
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topdown_name = 'fpn_topdown_' + body_name
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fan = body_input.shape[1]
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if self.norm_type:
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initializer = Xavier(fan_out=fan)
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lateral = ConvNorm(
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body_input,
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self.num_chan,
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1,
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initializer=initializer,
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norm_type=self.norm_type,
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norm_decay=self.norm_decay,
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freeze_norm=self.freeze_norm,
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name=lateral_name,
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norm_name=lateral_name)
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else:
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lateral = fluid.layers.conv2d(
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body_input,
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self.num_chan,
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1,
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param_attr=ParamAttr(
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name=lateral_name + "_w", initializer=Xavier(fan_out=fan)),
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bias_attr=ParamAttr(
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name=lateral_name + "_b",
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learning_rate=2.,
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regularizer=L2Decay(0.)),
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name=lateral_name)
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if body_input.shape[2] == -1 and body_input.shape[3] == -1:
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topdown = fluid.layers.resize_nearest(
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upper_output, scale=2., name=topdown_name)
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else:
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topdown = fluid.layers.resize_nearest(
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upper_output,
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out_shape=[body_input.shape[2], body_input.shape[3]],
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name=topdown_name)
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return fluid.layers.elementwise_add(lateral, topdown)
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def get_output(self, body_dict):
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"""
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Add FPN onto backbone.
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Args:
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body_dict(OrderedDict): Dictionary of variables and each element is the
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output of backbone.
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Return:
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fpn_dict(OrderedDict): A dictionary represents the output of FPN with
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their name.
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spatial_scale(list): A list of multiplicative spatial scale factor.
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"""
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spatial_scale = copy.deepcopy(self.spatial_scale)
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body_name_list = list(body_dict.keys())[::-1]
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num_backbone_stages = len(body_name_list)
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self.fpn_inner_output = [[] for _ in range(num_backbone_stages)]
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fpn_inner_name = 'fpn_inner_' + body_name_list[0]
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body_input = body_dict[body_name_list[0]]
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fan = body_input.shape[1]
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if self.norm_type:
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initializer = Xavier(fan_out=fan)
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self.fpn_inner_output[0] = ConvNorm(
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body_input,
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self.num_chan,
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1,
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initializer=initializer,
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norm_type=self.norm_type,
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norm_decay=self.norm_decay,
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freeze_norm=self.freeze_norm,
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name=fpn_inner_name,
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norm_name=fpn_inner_name)
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else:
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self.fpn_inner_output[0] = fluid.layers.conv2d(
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body_input,
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self.num_chan,
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1,
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param_attr=ParamAttr(
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name=fpn_inner_name + "_w",
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initializer=Xavier(fan_out=fan)),
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bias_attr=ParamAttr(
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name=fpn_inner_name + "_b",
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learning_rate=2.,
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regularizer=L2Decay(0.)),
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name=fpn_inner_name)
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for i in range(1, num_backbone_stages):
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body_name = body_name_list[i]
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body_input = body_dict[body_name]
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top_output = self.fpn_inner_output[i - 1]
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fpn_inner_single = self._add_topdown_lateral(body_name, body_input,
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top_output)
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self.fpn_inner_output[i] = fpn_inner_single
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fpn_dict = {}
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fpn_name_list = []
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for i in range(num_backbone_stages):
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fpn_name = 'fpn_' + body_name_list[i]
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fan = self.fpn_inner_output[i].shape[1] * 3 * 3
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if self.norm_type:
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initializer = Xavier(fan_out=fan)
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fpn_output = ConvNorm(
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self.fpn_inner_output[i],
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self.num_chan,
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3,
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initializer=initializer,
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norm_type=self.norm_type,
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norm_decay=self.norm_decay,
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freeze_norm=self.freeze_norm,
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name=fpn_name,
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norm_name=fpn_name)
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else:
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fpn_output = fluid.layers.conv2d(
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self.fpn_inner_output[i],
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self.num_chan,
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filter_size=3,
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padding=1,
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param_attr=ParamAttr(
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name=fpn_name + "_w", initializer=Xavier(fan_out=fan)),
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bias_attr=ParamAttr(
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name=fpn_name + "_b",
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learning_rate=2.,
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regularizer=L2Decay(0.)),
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name=fpn_name)
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fpn_dict[fpn_name] = fpn_output
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fpn_name_list.append(fpn_name)
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if not self.has_extra_convs and self.max_level - self.min_level == len(
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spatial_scale):
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body_top_name = fpn_name_list[0]
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body_top_extension = fluid.layers.pool2d(
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fpn_dict[body_top_name],
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1,
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'max',
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pool_stride=2,
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name=body_top_name + '_subsampled_2x')
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fpn_dict[body_top_name + '_subsampled_2x'] = body_top_extension
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fpn_name_list.insert(0, body_top_name + '_subsampled_2x')
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spatial_scale.insert(0, spatial_scale[0] * 0.5)
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# Coarser FPN levels introduced for RetinaNet
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highest_backbone_level = self.min_level + len(spatial_scale) - 1
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if self.has_extra_convs and self.max_level > highest_backbone_level:
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if self.use_c5:
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fpn_blob = body_dict[body_name_list[0]]
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else:
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fpn_blob = fpn_dict[fpn_name_list[0]]
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for i in range(highest_backbone_level + 1, self.max_level + 1):
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fpn_blob_in = fpn_blob
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fpn_name = 'fpn_' + str(i)
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if i > highest_backbone_level + 1:
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fpn_blob_in = fluid.layers.relu(fpn_blob)
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fan = fpn_blob_in.shape[1] * 3 * 3
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fpn_blob = fluid.layers.conv2d(
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input=fpn_blob_in,
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num_filters=self.num_chan,
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filter_size=3,
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stride=2,
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padding=1,
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param_attr=ParamAttr(
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name=fpn_name + "_w", initializer=Xavier(fan_out=fan)),
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bias_attr=ParamAttr(
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name=fpn_name + "_b",
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learning_rate=2.,
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regularizer=L2Decay(0.)),
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name=fpn_name)
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fpn_dict[fpn_name] = fpn_blob
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fpn_name_list.insert(0, fpn_name)
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spatial_scale.insert(0, spatial_scale[0] * 0.5)
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if self.reverse_out:
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fpn_name_list = fpn_name_list[::-1]
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res_dict = OrderedDict([(k, fpn_dict[k]) for k in fpn_name_list])
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return res_dict, spatial_scale
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