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PaddleOCR/ppocr/modeling/necks/fce_fpn.py
zhiminzhang0830 bf7e085ea2 modify fcenet
2022-02-13 11:48:18 +08:00

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# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This code is refer from:
https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.3/ppdet/modeling/necks/fpn.py
"""
import paddle.nn as nn
import paddle.nn.functional as F
from paddle import ParamAttr
from paddle.nn.initializer import XavierUniform
from paddle.nn.initializer import Normal
from paddle.regularizer import L2Decay
__all__ = ['FCEFPN']
class ConvNormLayer(nn.Layer):
def __init__(self,
ch_in,
ch_out,
filter_size,
stride,
groups=1,
norm_type='bn',
norm_decay=0.,
norm_groups=32,
lr_scale=1.,
freeze_norm=False,
initializer=Normal(
mean=0., std=0.01)):
super(ConvNormLayer, self).__init__()
assert norm_type in ['bn', 'sync_bn', 'gn']
bias_attr = False
self.conv = nn.Conv2D(
in_channels=ch_in,
out_channels=ch_out,
kernel_size=filter_size,
stride=stride,
padding=(filter_size - 1) // 2,
groups=groups,
weight_attr=ParamAttr(
initializer=initializer, learning_rate=1.),
bias_attr=bias_attr)
norm_lr = 0. if freeze_norm else 1.
param_attr = ParamAttr(
learning_rate=norm_lr,
regularizer=L2Decay(norm_decay) if norm_decay is not None else None)
bias_attr = ParamAttr(
learning_rate=norm_lr,
regularizer=L2Decay(norm_decay) if norm_decay is not None else None)
if norm_type == 'bn':
self.norm = nn.BatchNorm2D(
ch_out, weight_attr=param_attr, bias_attr=bias_attr)
elif norm_type == 'sync_bn':
self.norm = nn.SyncBatchNorm(
ch_out, weight_attr=param_attr, bias_attr=bias_attr)
elif norm_type == 'gn':
self.norm = nn.GroupNorm(
num_groups=norm_groups,
num_channels=ch_out,
weight_attr=param_attr,
bias_attr=bias_attr)
def forward(self, inputs):
out = self.conv(inputs)
out = self.norm(out)
return out
class FCEFPN(nn.Layer):
"""
Feature Pyramid Network, see https://arxiv.org/abs/1612.03144
Args:
in_channels (list[int]): input channels of each level which can be
derived from the output shape of backbone by from_config
out_channels (list[int]): output channel of each level
spatial_scales (list[float]): the spatial scales between input feature
maps and original input image which can be derived from the output
shape of backbone by from_config
has_extra_convs (bool): whether to add extra conv to the last level.
default False
extra_stage (int): the number of extra stages added to the last level.
default 1
use_c5 (bool): Whether to use c5 as the input of extra stage,
otherwise p5 is used. default True
norm_type (string|None): The normalization type in FPN module. If
norm_type is None, norm will not be used after conv and if
norm_type is string, bn, gn, sync_bn are available. default None
norm_decay (float): weight decay for normalization layer weights.
default 0.
freeze_norm (bool): whether to freeze normalization layer.
default False
relu_before_extra_convs (bool): whether to add relu before extra convs.
default False
"""
def __init__(self,
in_channels,
out_channels,
spatial_scales=[0.25, 0.125, 0.0625, 0.03125],
has_extra_convs=False,
extra_stage=1,
use_c5=True,
norm_type=None,
norm_decay=0.,
freeze_norm=False,
relu_before_extra_convs=True):
super(FCEFPN, self).__init__()
self.out_channels = out_channels
for s in range(extra_stage):
spatial_scales = spatial_scales + [spatial_scales[-1] / 2.]
self.spatial_scales = spatial_scales
self.has_extra_convs = has_extra_convs
self.extra_stage = extra_stage
self.use_c5 = use_c5
self.relu_before_extra_convs = relu_before_extra_convs
self.norm_type = norm_type
self.norm_decay = norm_decay
self.freeze_norm = freeze_norm
self.lateral_convs = []
self.fpn_convs = []
fan = out_channels * 3 * 3
# stage index 0,1,2,3 stands for res2,res3,res4,res5 on ResNet Backbone
# 0 <= st_stage < ed_stage <= 3
st_stage = 4 - len(in_channels)
ed_stage = st_stage + len(in_channels) - 1
for i in range(st_stage, ed_stage + 1):
if i == 3:
lateral_name = 'fpn_inner_res5_sum'
else:
lateral_name = 'fpn_inner_res{}_sum_lateral'.format(i + 2)
in_c = in_channels[i - st_stage]
if self.norm_type is not None:
lateral = self.add_sublayer(
lateral_name,
ConvNormLayer(
ch_in=in_c,
ch_out=out_channels,
filter_size=1,
stride=1,
norm_type=self.norm_type,
norm_decay=self.norm_decay,
freeze_norm=self.freeze_norm,
initializer=XavierUniform(fan_out=in_c)))
else:
lateral = self.add_sublayer(
lateral_name,
nn.Conv2D(
in_channels=in_c,
out_channels=out_channels,
kernel_size=1,
weight_attr=ParamAttr(
initializer=XavierUniform(fan_out=in_c))))
self.lateral_convs.append(lateral)
for i in range(st_stage, ed_stage + 1):
fpn_name = 'fpn_res{}_sum'.format(i + 2)
if self.norm_type is not None:
fpn_conv = self.add_sublayer(
fpn_name,
ConvNormLayer(
ch_in=out_channels,
ch_out=out_channels,
filter_size=3,
stride=1,
norm_type=self.norm_type,
norm_decay=self.norm_decay,
freeze_norm=self.freeze_norm,
initializer=XavierUniform(fan_out=fan)))
else:
fpn_conv = self.add_sublayer(
fpn_name,
nn.Conv2D(
in_channels=out_channels,
out_channels=out_channels,
kernel_size=3,
padding=1,
weight_attr=ParamAttr(
initializer=XavierUniform(fan_out=fan))))
self.fpn_convs.append(fpn_conv)
# add extra conv levels for RetinaNet(use_c5)/FCOS(use_p5)
if self.has_extra_convs:
for i in range(self.extra_stage):
lvl = ed_stage + 1 + i
if i == 0 and self.use_c5:
in_c = in_channels[-1]
else:
in_c = out_channels
extra_fpn_name = 'fpn_{}'.format(lvl + 2)
if self.norm_type is not None:
extra_fpn_conv = self.add_sublayer(
extra_fpn_name,
ConvNormLayer(
ch_in=in_c,
ch_out=out_channels,
filter_size=3,
stride=2,
norm_type=self.norm_type,
norm_decay=self.norm_decay,
freeze_norm=self.freeze_norm,
initializer=XavierUniform(fan_out=fan)))
else:
extra_fpn_conv = self.add_sublayer(
extra_fpn_name,
nn.Conv2D(
in_channels=in_c,
out_channels=out_channels,
kernel_size=3,
stride=2,
padding=1,
weight_attr=ParamAttr(
initializer=XavierUniform(fan_out=fan))))
self.fpn_convs.append(extra_fpn_conv)
@classmethod
def from_config(cls, cfg, input_shape):
return {
'in_channels': [i.channels for i in input_shape],
'spatial_scales': [1.0 / i.stride for i in input_shape],
}
def forward(self, body_feats):
laterals = []
num_levels = len(body_feats)
for i in range(num_levels):
laterals.append(self.lateral_convs[i](body_feats[i]))
for i in range(1, num_levels):
lvl = num_levels - i
upsample = F.interpolate(
laterals[lvl],
scale_factor=2.,
mode='nearest', )
laterals[lvl - 1] += upsample
fpn_output = []
for lvl in range(num_levels):
fpn_output.append(self.fpn_convs[lvl](laterals[lvl]))
if self.extra_stage > 0:
# use max pool to get more levels on top of outputs (Faster R-CNN, Mask R-CNN)
if not self.has_extra_convs:
assert self.extra_stage == 1, 'extra_stage should be 1 if FPN has not extra convs'
fpn_output.append(F.max_pool2d(fpn_output[-1], 1, stride=2))
# add extra conv levels for RetinaNet(use_c5)/FCOS(use_p5)
else:
if self.use_c5:
extra_source = body_feats[-1]
else:
extra_source = fpn_output[-1]
fpn_output.append(self.fpn_convs[num_levels](extra_source))
for i in range(1, self.extra_stage):
if self.relu_before_extra_convs:
fpn_output.append(self.fpn_convs[num_levels + i](F.relu(
fpn_output[-1])))
else:
fpn_output.append(self.fpn_convs[num_levels + i](
fpn_output[-1]))
return fpn_output
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