151 lines
5.2 KiB
Python
151 lines
5.2 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# 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.
|
|
|
|
import paddle
|
|
import paddle.nn as nn
|
|
from paddle import ParamAttr
|
|
from paddle.regularizer import L2Decay
|
|
from paddle.nn.initializer import KaimingUniform, Constant, Normal
|
|
from ppdet.core.workspace import register, serializable
|
|
from ..shape_spec import ShapeSpec
|
|
|
|
__all__ = ['DilatedEncoder']
|
|
|
|
|
|
class Bottleneck(nn.Layer):
|
|
def __init__(self, in_channels, mid_channels, dilation):
|
|
super(Bottleneck, self).__init__()
|
|
self.conv1 = nn.Sequential(* [
|
|
nn.Conv2D(
|
|
in_channels,
|
|
mid_channels,
|
|
1,
|
|
padding=0,
|
|
weight_attr=ParamAttr(initializer=Normal(
|
|
mean=0, std=0.01)),
|
|
bias_attr=ParamAttr(initializer=Constant(0.0))),
|
|
nn.BatchNorm2D(
|
|
mid_channels,
|
|
weight_attr=ParamAttr(regularizer=L2Decay(0.0)),
|
|
bias_attr=ParamAttr(regularizer=L2Decay(0.0))),
|
|
nn.ReLU(),
|
|
])
|
|
self.conv2 = nn.Sequential(* [
|
|
nn.Conv2D(
|
|
mid_channels,
|
|
mid_channels,
|
|
3,
|
|
padding=dilation,
|
|
dilation=dilation,
|
|
weight_attr=ParamAttr(initializer=Normal(
|
|
mean=0, std=0.01)),
|
|
bias_attr=ParamAttr(initializer=Constant(0.0))),
|
|
nn.BatchNorm2D(
|
|
mid_channels,
|
|
weight_attr=ParamAttr(regularizer=L2Decay(0.0)),
|
|
bias_attr=ParamAttr(regularizer=L2Decay(0.0))),
|
|
nn.ReLU(),
|
|
])
|
|
self.conv3 = nn.Sequential(* [
|
|
nn.Conv2D(
|
|
mid_channels,
|
|
in_channels,
|
|
1,
|
|
padding=0,
|
|
weight_attr=ParamAttr(initializer=Normal(
|
|
mean=0, std=0.01)),
|
|
bias_attr=ParamAttr(initializer=Constant(0.0))),
|
|
nn.BatchNorm2D(
|
|
in_channels,
|
|
weight_attr=ParamAttr(regularizer=L2Decay(0.0)),
|
|
bias_attr=ParamAttr(regularizer=L2Decay(0.0))),
|
|
nn.ReLU(),
|
|
])
|
|
|
|
def forward(self, x):
|
|
identity = x
|
|
y = self.conv3(self.conv2(self.conv1(x)))
|
|
return y + identity
|
|
|
|
|
|
@register
|
|
class DilatedEncoder(nn.Layer):
|
|
"""
|
|
DilatedEncoder used in YOLOF
|
|
"""
|
|
|
|
def __init__(self,
|
|
in_channels=[2048],
|
|
out_channels=[512],
|
|
block_mid_channels=128,
|
|
num_residual_blocks=4,
|
|
block_dilations=[2, 4, 6, 8]):
|
|
super(DilatedEncoder, self).__init__()
|
|
self.in_channels = in_channels
|
|
self.out_channels = out_channels
|
|
assert len(self.in_channels) == 1, "YOLOF only has one level feature."
|
|
assert len(self.out_channels) == 1, "YOLOF only has one level feature."
|
|
|
|
self.block_mid_channels = block_mid_channels
|
|
self.num_residual_blocks = num_residual_blocks
|
|
self.block_dilations = block_dilations
|
|
|
|
out_ch = self.out_channels[0]
|
|
self.lateral_conv = nn.Conv2D(
|
|
self.in_channels[0],
|
|
out_ch,
|
|
1,
|
|
weight_attr=ParamAttr(initializer=KaimingUniform(
|
|
negative_slope=1, nonlinearity='leaky_relu')),
|
|
bias_attr=ParamAttr(initializer=Constant(value=0.0)))
|
|
self.lateral_norm = nn.BatchNorm2D(
|
|
out_ch,
|
|
weight_attr=ParamAttr(regularizer=L2Decay(0.0)),
|
|
bias_attr=ParamAttr(regularizer=L2Decay(0.0)))
|
|
|
|
self.fpn_conv = nn.Conv2D(
|
|
out_ch,
|
|
out_ch,
|
|
3,
|
|
padding=1,
|
|
weight_attr=ParamAttr(initializer=KaimingUniform(
|
|
negative_slope=1, nonlinearity='leaky_relu')))
|
|
self.fpn_norm = nn.BatchNorm2D(
|
|
out_ch,
|
|
weight_attr=ParamAttr(regularizer=L2Decay(0.0)),
|
|
bias_attr=ParamAttr(regularizer=L2Decay(0.0)))
|
|
|
|
encoder_blocks = []
|
|
for i in range(self.num_residual_blocks):
|
|
encoder_blocks.append(
|
|
Bottleneck(
|
|
out_ch,
|
|
self.block_mid_channels,
|
|
dilation=block_dilations[i]))
|
|
self.dilated_encoder_blocks = nn.Sequential(*encoder_blocks)
|
|
|
|
def forward(self, inputs, for_mot=False):
|
|
out = self.lateral_norm(self.lateral_conv(inputs[0]))
|
|
out = self.fpn_norm(self.fpn_conv(out))
|
|
out = self.dilated_encoder_blocks(out)
|
|
return [out]
|
|
|
|
@classmethod
|
|
def from_config(cls, cfg, input_shape):
|
|
return {'in_channels': [i.channels for i in input_shape], }
|
|
|
|
@property
|
|
def out_shape(self):
|
|
return [ShapeSpec(channels=c) for c in self.out_channels]
|