This commit is contained in:
337
ppocr/modeling/necks/csp_pan.py
Executable file
337
ppocr/modeling/necks/csp_pan.py
Executable file
@@ -0,0 +1,337 @@
|
||||
# Copyright (c) 2021 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.
|
||||
|
||||
# The code is based on:
|
||||
# https://github.com/PaddlePaddle/PaddleDetection/blob/release%2F2.3/ppdet/modeling/necks/csp_pan.py
|
||||
|
||||
import paddle
|
||||
import paddle.nn as nn
|
||||
import paddle.nn.functional as F
|
||||
from paddle import ParamAttr
|
||||
|
||||
__all__ = ["CSPPAN"]
|
||||
|
||||
|
||||
class ConvBNLayer(nn.Layer):
|
||||
def __init__(
|
||||
self,
|
||||
in_channel=96,
|
||||
out_channel=96,
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
groups=1,
|
||||
act="leaky_relu",
|
||||
):
|
||||
super(ConvBNLayer, self).__init__()
|
||||
initializer = nn.initializer.KaimingUniform()
|
||||
self.act = act
|
||||
assert self.act in ["leaky_relu", "hard_swish"]
|
||||
self.conv = nn.Conv2D(
|
||||
in_channels=in_channel,
|
||||
out_channels=out_channel,
|
||||
kernel_size=kernel_size,
|
||||
groups=groups,
|
||||
padding=(kernel_size - 1) // 2,
|
||||
stride=stride,
|
||||
weight_attr=ParamAttr(initializer=initializer),
|
||||
bias_attr=False,
|
||||
)
|
||||
self.bn = nn.BatchNorm2D(out_channel)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.bn(self.conv(x))
|
||||
if self.act == "leaky_relu":
|
||||
x = F.leaky_relu(x)
|
||||
elif self.act == "hard_swish":
|
||||
x = F.hardswish(x)
|
||||
return x
|
||||
|
||||
|
||||
class DPModule(nn.Layer):
|
||||
"""
|
||||
Depth-wise and point-wise module.
|
||||
Args:
|
||||
in_channel (int): The input channels of this Module.
|
||||
out_channel (int): The output channels of this Module.
|
||||
kernel_size (int): The conv2d kernel size of this Module.
|
||||
stride (int): The conv2d's stride of this Module.
|
||||
act (str): The activation function of this Module,
|
||||
Now support `leaky_relu` and `hard_swish`.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self, in_channel=96, out_channel=96, kernel_size=3, stride=1, act="leaky_relu"
|
||||
):
|
||||
super(DPModule, self).__init__()
|
||||
initializer = nn.initializer.KaimingUniform()
|
||||
self.act = act
|
||||
self.dwconv = nn.Conv2D(
|
||||
in_channels=in_channel,
|
||||
out_channels=out_channel,
|
||||
kernel_size=kernel_size,
|
||||
groups=out_channel,
|
||||
padding=(kernel_size - 1) // 2,
|
||||
stride=stride,
|
||||
weight_attr=ParamAttr(initializer=initializer),
|
||||
bias_attr=False,
|
||||
)
|
||||
self.bn1 = nn.BatchNorm2D(out_channel)
|
||||
self.pwconv = nn.Conv2D(
|
||||
in_channels=out_channel,
|
||||
out_channels=out_channel,
|
||||
kernel_size=1,
|
||||
groups=1,
|
||||
padding=0,
|
||||
weight_attr=ParamAttr(initializer=initializer),
|
||||
bias_attr=False,
|
||||
)
|
||||
self.bn2 = nn.BatchNorm2D(out_channel)
|
||||
|
||||
def act_func(self, x):
|
||||
if self.act == "leaky_relu":
|
||||
x = F.leaky_relu(x)
|
||||
elif self.act == "hard_swish":
|
||||
x = F.hardswish(x)
|
||||
return x
|
||||
|
||||
def forward(self, x):
|
||||
x = self.act_func(self.bn1(self.dwconv(x)))
|
||||
x = self.act_func(self.bn2(self.pwconv(x)))
|
||||
return x
|
||||
|
||||
|
||||
class DarknetBottleneck(nn.Layer):
|
||||
"""The basic bottleneck block used in Darknet.
|
||||
Each Block consists of two ConvModules and the input is added to the
|
||||
final output. Each ConvModule is composed of Conv, BN, and act.
|
||||
The first convLayer has filter size of 1x1 and the second one has the
|
||||
filter size of 3x3.
|
||||
Args:
|
||||
in_channels (int): The input channels of this Module.
|
||||
out_channels (int): The output channels of this Module.
|
||||
expansion (int): The kernel size of the convolution. Default: 0.5
|
||||
add_identity (bool): Whether to add identity to the out.
|
||||
Default: True
|
||||
use_depthwise (bool): Whether to use depthwise separable convolution.
|
||||
Default: False
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
in_channels,
|
||||
out_channels,
|
||||
kernel_size=3,
|
||||
expansion=0.5,
|
||||
add_identity=True,
|
||||
use_depthwise=False,
|
||||
act="leaky_relu",
|
||||
):
|
||||
super(DarknetBottleneck, self).__init__()
|
||||
hidden_channels = int(out_channels * expansion)
|
||||
conv_func = DPModule if use_depthwise else ConvBNLayer
|
||||
self.conv1 = ConvBNLayer(
|
||||
in_channel=in_channels, out_channel=hidden_channels, kernel_size=1, act=act
|
||||
)
|
||||
self.conv2 = conv_func(
|
||||
in_channel=hidden_channels,
|
||||
out_channel=out_channels,
|
||||
kernel_size=kernel_size,
|
||||
stride=1,
|
||||
act=act,
|
||||
)
|
||||
self.add_identity = add_identity and in_channels == out_channels
|
||||
|
||||
def forward(self, x):
|
||||
identity = x
|
||||
out = self.conv1(x)
|
||||
out = self.conv2(out)
|
||||
|
||||
if self.add_identity:
|
||||
return out + identity
|
||||
else:
|
||||
return out
|
||||
|
||||
|
||||
class CSPLayer(nn.Layer):
|
||||
"""Cross Stage Partial Layer.
|
||||
Args:
|
||||
in_channels (int): The input channels of the CSP layer.
|
||||
out_channels (int): The output channels of the CSP layer.
|
||||
expand_ratio (float): Ratio to adjust the number of channels of the
|
||||
hidden layer. Default: 0.5
|
||||
num_blocks (int): Number of blocks. Default: 1
|
||||
add_identity (bool): Whether to add identity in blocks.
|
||||
Default: True
|
||||
use_depthwise (bool): Whether to depthwise separable convolution in
|
||||
blocks. Default: False
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
in_channels,
|
||||
out_channels,
|
||||
kernel_size=3,
|
||||
expand_ratio=0.5,
|
||||
num_blocks=1,
|
||||
add_identity=True,
|
||||
use_depthwise=False,
|
||||
act="leaky_relu",
|
||||
):
|
||||
super().__init__()
|
||||
mid_channels = int(out_channels * expand_ratio)
|
||||
self.main_conv = ConvBNLayer(in_channels, mid_channels, 1, act=act)
|
||||
self.short_conv = ConvBNLayer(in_channels, mid_channels, 1, act=act)
|
||||
self.final_conv = ConvBNLayer(2 * mid_channels, out_channels, 1, act=act)
|
||||
|
||||
self.blocks = nn.Sequential(
|
||||
*[
|
||||
DarknetBottleneck(
|
||||
mid_channels,
|
||||
mid_channels,
|
||||
kernel_size,
|
||||
1.0,
|
||||
add_identity,
|
||||
use_depthwise,
|
||||
act=act,
|
||||
)
|
||||
for _ in range(num_blocks)
|
||||
]
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
x_short = self.short_conv(x)
|
||||
|
||||
x_main = self.main_conv(x)
|
||||
x_main = self.blocks(x_main)
|
||||
|
||||
x_final = paddle.concat((x_main, x_short), axis=1)
|
||||
return self.final_conv(x_final)
|
||||
|
||||
|
||||
class Channel_T(nn.Layer):
|
||||
def __init__(self, in_channels=[116, 232, 464], out_channels=96, act="leaky_relu"):
|
||||
super(Channel_T, self).__init__()
|
||||
self.convs = nn.LayerList()
|
||||
for i in range(len(in_channels)):
|
||||
self.convs.append(ConvBNLayer(in_channels[i], out_channels, 1, act=act))
|
||||
|
||||
def forward(self, x):
|
||||
outs = [self.convs[i](x[i]) for i in range(len(x))]
|
||||
return outs
|
||||
|
||||
|
||||
class CSPPAN(nn.Layer):
|
||||
"""Path Aggregation Network with CSP module.
|
||||
Args:
|
||||
in_channels (List[int]): Number of input channels per scale.
|
||||
out_channels (int): Number of output channels (used at each scale)
|
||||
kernel_size (int): The conv2d kernel size of this Module.
|
||||
num_csp_blocks (int): Number of bottlenecks in CSPLayer. Default: 1
|
||||
use_depthwise (bool): Whether to depthwise separable convolution in
|
||||
blocks. Default: True
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
in_channels,
|
||||
out_channels,
|
||||
kernel_size=5,
|
||||
num_csp_blocks=1,
|
||||
use_depthwise=True,
|
||||
act="hard_swish",
|
||||
):
|
||||
super(CSPPAN, self).__init__()
|
||||
self.in_channels = in_channels
|
||||
self.out_channels = [out_channels] * len(in_channels)
|
||||
conv_func = DPModule if use_depthwise else ConvBNLayer
|
||||
|
||||
self.conv_t = Channel_T(in_channels, out_channels, act=act)
|
||||
|
||||
# build top-down blocks
|
||||
self.upsample = nn.Upsample(scale_factor=2, mode="nearest")
|
||||
self.top_down_blocks = nn.LayerList()
|
||||
for idx in range(len(in_channels) - 1, 0, -1):
|
||||
self.top_down_blocks.append(
|
||||
CSPLayer(
|
||||
out_channels * 2,
|
||||
out_channels,
|
||||
kernel_size=kernel_size,
|
||||
num_blocks=num_csp_blocks,
|
||||
add_identity=False,
|
||||
use_depthwise=use_depthwise,
|
||||
act=act,
|
||||
)
|
||||
)
|
||||
|
||||
# build bottom-up blocks
|
||||
self.downsamples = nn.LayerList()
|
||||
self.bottom_up_blocks = nn.LayerList()
|
||||
for idx in range(len(in_channels) - 1):
|
||||
self.downsamples.append(
|
||||
conv_func(
|
||||
out_channels,
|
||||
out_channels,
|
||||
kernel_size=kernel_size,
|
||||
stride=2,
|
||||
act=act,
|
||||
)
|
||||
)
|
||||
self.bottom_up_blocks.append(
|
||||
CSPLayer(
|
||||
out_channels * 2,
|
||||
out_channels,
|
||||
kernel_size=kernel_size,
|
||||
num_blocks=num_csp_blocks,
|
||||
add_identity=False,
|
||||
use_depthwise=use_depthwise,
|
||||
act=act,
|
||||
)
|
||||
)
|
||||
|
||||
def forward(self, inputs):
|
||||
"""
|
||||
Args:
|
||||
inputs (tuple[Tensor]): input features.
|
||||
Returns:
|
||||
tuple[Tensor]: CSPPAN features.
|
||||
"""
|
||||
assert len(inputs) == len(self.in_channels)
|
||||
inputs = self.conv_t(inputs)
|
||||
|
||||
# top-down path
|
||||
inner_outs = [inputs[-1]]
|
||||
for idx in range(len(self.in_channels) - 1, 0, -1):
|
||||
feat_heigh = inner_outs[0]
|
||||
feat_low = inputs[idx - 1]
|
||||
upsample_feat = F.upsample(
|
||||
feat_heigh, size=feat_low.shape[2:4], mode="nearest"
|
||||
)
|
||||
|
||||
inner_out = self.top_down_blocks[len(self.in_channels) - 1 - idx](
|
||||
paddle.concat([upsample_feat, feat_low], 1)
|
||||
)
|
||||
inner_outs.insert(0, inner_out)
|
||||
|
||||
# bottom-up path
|
||||
outs = [inner_outs[0]]
|
||||
for idx in range(len(self.in_channels) - 1):
|
||||
feat_low = outs[-1]
|
||||
feat_height = inner_outs[idx + 1]
|
||||
downsample_feat = self.downsamples[idx](feat_low)
|
||||
out = self.bottom_up_blocks[idx](
|
||||
paddle.concat([downsample_feat, feat_height], 1)
|
||||
)
|
||||
outs.append(out)
|
||||
|
||||
return tuple(outs)
|
||||
Reference in New Issue
Block a user