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https://github.com/PaddlePaddle/PaddleOCR.git
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281 lines
11 KiB
Python
281 lines
11 KiB
Python
# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve.
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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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"""
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This code is refer from:
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https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.3/ppdet/modeling/necks/fpn.py
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"""
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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 paddle.nn.initializer import Normal
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from paddle.regularizer import L2Decay
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__all__ = ['FCEFPN']
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class ConvNormLayer(nn.Layer):
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def __init__(self,
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ch_in,
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ch_out,
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filter_size,
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stride,
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groups=1,
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norm_type='bn',
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norm_decay=0.,
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norm_groups=32,
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lr_scale=1.,
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freeze_norm=False,
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initializer=Normal(
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mean=0., std=0.01)):
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super(ConvNormLayer, self).__init__()
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assert norm_type in ['bn', 'sync_bn', 'gn']
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bias_attr = False
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self.conv = nn.Conv2D(
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in_channels=ch_in,
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out_channels=ch_out,
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kernel_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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weight_attr=ParamAttr(
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initializer=initializer, learning_rate=1.),
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bias_attr=bias_attr)
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norm_lr = 0. if freeze_norm else 1.
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param_attr = ParamAttr(
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learning_rate=norm_lr,
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regularizer=L2Decay(norm_decay) if norm_decay is not None else None)
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bias_attr = ParamAttr(
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learning_rate=norm_lr,
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regularizer=L2Decay(norm_decay) if norm_decay is not None else None)
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if norm_type == 'bn':
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self.norm = nn.BatchNorm2D(
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ch_out, weight_attr=param_attr, bias_attr=bias_attr)
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elif norm_type == 'sync_bn':
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self.norm = nn.SyncBatchNorm(
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ch_out, weight_attr=param_attr, bias_attr=bias_attr)
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elif norm_type == 'gn':
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self.norm = nn.GroupNorm(
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num_groups=norm_groups,
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num_channels=ch_out,
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weight_attr=param_attr,
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bias_attr=bias_attr)
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def forward(self, inputs):
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out = self.conv(inputs)
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out = self.norm(out)
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return out
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class FCEFPN(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_channels (list[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_channels,
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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(FCEFPN, self).__init__()
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self.out_channels = out_channels
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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_channels * 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_channels,
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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_channels,
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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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for i in range(st_stage, ed_stage + 1):
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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_channels,
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ch_out=out_channels,
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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_channels,
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out_channels=out_channels,
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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_channels
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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_channels,
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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_channels,
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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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