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231 lines
9.2 KiB
231 lines
9.2 KiB
# Copyright (c) 2022 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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import paddle.nn as nn |
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import paddle.nn.functional as F |
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from paddle import ParamAttr |
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from paddle.nn.initializer import XavierUniform |
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from paddlers.models.ppdet.core.workspace import register, serializable |
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from paddlers.models.ppdet.modeling.layers import ConvNormLayer |
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from ..shape_spec import ShapeSpec |
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__all__ = ['FPN'] |
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@register |
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@serializable |
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class FPN(nn.Layer): |
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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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in_channels (list[int]): input channels of each level which can be |
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derived from the output shape of backbone by from_config |
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out_channel (int): output channel of each level |
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spatial_scales (list[float]): the spatial scales between input feature |
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maps and original input image which can be derived from the output |
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shape of backbone by from_config |
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has_extra_convs (bool): whether to add extra conv to the last level. |
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default False |
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extra_stage (int): the number of extra stages added to the last level. |
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default 1 |
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use_c5 (bool): Whether to use c5 as the input of extra stage, |
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otherwise p5 is used. default True |
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norm_type (string|None): The normalization type in FPN module. If |
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norm_type is None, norm will not be used after conv and if |
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norm_type is string, bn, gn, sync_bn are available. default None |
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norm_decay (float): weight decay for normalization layer weights. |
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default 0. |
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freeze_norm (bool): whether to freeze normalization layer. |
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default False |
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relu_before_extra_convs (bool): whether to add relu before extra convs. |
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default False |
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""" |
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def __init__(self, |
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in_channels, |
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out_channel, |
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spatial_scales=[0.25, 0.125, 0.0625, 0.03125], |
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has_extra_convs=False, |
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extra_stage=1, |
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use_c5=True, |
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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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relu_before_extra_convs=True): |
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super(FPN, self).__init__() |
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self.out_channel = out_channel |
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for s in range(extra_stage): |
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spatial_scales = spatial_scales + [spatial_scales[-1] / 2.] |
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self.spatial_scales = spatial_scales |
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self.has_extra_convs = has_extra_convs |
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self.extra_stage = extra_stage |
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self.use_c5 = use_c5 |
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self.relu_before_extra_convs = relu_before_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.freeze_norm = freeze_norm |
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self.lateral_convs = [] |
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self.fpn_convs = [] |
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fan = out_channel * 3 * 3 |
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# stage index 0,1,2,3 stands for res2,res3,res4,res5 on ResNet Backbone |
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# 0 <= st_stage < ed_stage <= 3 |
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st_stage = 4 - len(in_channels) |
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ed_stage = st_stage + len(in_channels) - 1 |
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for i in range(st_stage, ed_stage + 1): |
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if i == 3: |
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lateral_name = 'fpn_inner_res5_sum' |
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else: |
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lateral_name = 'fpn_inner_res{}_sum_lateral'.format(i + 2) |
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in_c = in_channels[i - st_stage] |
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if self.norm_type is not None: |
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lateral = self.add_sublayer( |
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lateral_name, |
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ConvNormLayer( |
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ch_in=in_c, |
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ch_out=out_channel, |
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filter_size=1, |
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stride=1, |
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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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initializer=XavierUniform(fan_out=in_c))) |
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else: |
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lateral = self.add_sublayer( |
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lateral_name, |
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nn.Conv2D( |
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in_channels=in_c, |
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out_channels=out_channel, |
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kernel_size=1, |
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weight_attr=ParamAttr( |
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initializer=XavierUniform(fan_out=in_c)))) |
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self.lateral_convs.append(lateral) |
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fpn_name = 'fpn_res{}_sum'.format(i + 2) |
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if self.norm_type is not None: |
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fpn_conv = self.add_sublayer( |
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fpn_name, |
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ConvNormLayer( |
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ch_in=out_channel, |
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ch_out=out_channel, |
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filter_size=3, |
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stride=1, |
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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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initializer=XavierUniform(fan_out=fan))) |
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else: |
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fpn_conv = self.add_sublayer( |
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fpn_name, |
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nn.Conv2D( |
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in_channels=out_channel, |
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out_channels=out_channel, |
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kernel_size=3, |
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padding=1, |
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weight_attr=ParamAttr( |
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initializer=XavierUniform(fan_out=fan)))) |
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self.fpn_convs.append(fpn_conv) |
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# add extra conv levels for RetinaNet(use_c5)/FCOS(use_p5) |
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if self.has_extra_convs: |
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for i in range(self.extra_stage): |
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lvl = ed_stage + 1 + i |
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if i == 0 and self.use_c5: |
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in_c = in_channels[-1] |
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else: |
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in_c = out_channel |
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extra_fpn_name = 'fpn_{}'.format(lvl + 2) |
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if self.norm_type is not None: |
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extra_fpn_conv = self.add_sublayer( |
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extra_fpn_name, |
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ConvNormLayer( |
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ch_in=in_c, |
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ch_out=out_channel, |
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filter_size=3, |
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stride=2, |
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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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initializer=XavierUniform(fan_out=fan))) |
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else: |
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extra_fpn_conv = self.add_sublayer( |
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extra_fpn_name, |
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nn.Conv2D( |
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in_channels=in_c, |
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out_channels=out_channel, |
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kernel_size=3, |
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stride=2, |
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padding=1, |
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weight_attr=ParamAttr( |
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initializer=XavierUniform(fan_out=fan)))) |
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self.fpn_convs.append(extra_fpn_conv) |
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@classmethod |
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def from_config(cls, cfg, input_shape): |
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return { |
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'in_channels': [i.channels for i in input_shape], |
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'spatial_scales': [1.0 / i.stride for i in input_shape], |
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} |
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def forward(self, body_feats): |
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laterals = [] |
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num_levels = len(body_feats) |
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for i in range(num_levels): |
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laterals.append(self.lateral_convs[i](body_feats[i])) |
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for i in range(1, num_levels): |
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lvl = num_levels - i |
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upsample = F.interpolate( |
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laterals[lvl], |
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scale_factor=2., |
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mode='nearest', ) |
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laterals[lvl - 1] += upsample |
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fpn_output = [] |
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for lvl in range(num_levels): |
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fpn_output.append(self.fpn_convs[lvl](laterals[lvl])) |
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if self.extra_stage > 0: |
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# use max pool to get more levels on top of outputs (Faster R-CNN, Mask R-CNN) |
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if not self.has_extra_convs: |
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assert self.extra_stage == 1, 'extra_stage should be 1 if FPN has not extra convs' |
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fpn_output.append(F.max_pool2d(fpn_output[-1], 1, stride=2)) |
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# add extra conv levels for RetinaNet(use_c5)/FCOS(use_p5) |
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else: |
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if self.use_c5: |
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extra_source = body_feats[-1] |
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else: |
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extra_source = fpn_output[-1] |
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fpn_output.append(self.fpn_convs[num_levels](extra_source)) |
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for i in range(1, self.extra_stage): |
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if self.relu_before_extra_convs: |
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fpn_output.append(self.fpn_convs[num_levels + i](F.relu( |
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fpn_output[-1]))) |
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else: |
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fpn_output.append(self.fpn_convs[num_levels + i]( |
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fpn_output[-1])) |
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return fpn_output |
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@property |
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def out_shape(self): |
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return [ |
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ShapeSpec( |
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channels=self.out_channel, stride=1. / s) |
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for s in self.spatial_scales |
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]
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