bytetrack / yolox /models /yolo_pafpn.py
AK391
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7734d5b
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
import torch
import torch.nn as nn
from .darknet import CSPDarknet
from .network_blocks import BaseConv, CSPLayer, DWConv
class YOLOPAFPN(nn.Module):
"""
YOLOv3 model. Darknet 53 is the default backbone of this model.
"""
def __init__(
self,
depth=1.0,
width=1.0,
in_features=("dark3", "dark4", "dark5"),
in_channels=[256, 512, 1024],
depthwise=False,
act="silu",
):
super().__init__()
self.backbone = CSPDarknet(depth, width, depthwise=depthwise, act=act)
self.in_features = in_features
self.in_channels = in_channels
Conv = DWConv if depthwise else BaseConv
self.upsample = nn.Upsample(scale_factor=2, mode="nearest")
self.lateral_conv0 = BaseConv(
int(in_channels[2] * width), int(in_channels[1] * width), 1, 1, act=act
)
self.C3_p4 = CSPLayer(
int(2 * in_channels[1] * width),
int(in_channels[1] * width),
round(3 * depth),
False,
depthwise=depthwise,
act=act,
) # cat
self.reduce_conv1 = BaseConv(
int(in_channels[1] * width), int(in_channels[0] * width), 1, 1, act=act
)
self.C3_p3 = CSPLayer(
int(2 * in_channels[0] * width),
int(in_channels[0] * width),
round(3 * depth),
False,
depthwise=depthwise,
act=act,
)
# bottom-up conv
self.bu_conv2 = Conv(
int(in_channels[0] * width), int(in_channels[0] * width), 3, 2, act=act
)
self.C3_n3 = CSPLayer(
int(2 * in_channels[0] * width),
int(in_channels[1] * width),
round(3 * depth),
False,
depthwise=depthwise,
act=act,
)
# bottom-up conv
self.bu_conv1 = Conv(
int(in_channels[1] * width), int(in_channels[1] * width), 3, 2, act=act
)
self.C3_n4 = CSPLayer(
int(2 * in_channels[1] * width),
int(in_channels[2] * width),
round(3 * depth),
False,
depthwise=depthwise,
act=act,
)
def forward(self, input):
"""
Args:
inputs: input images.
Returns:
Tuple[Tensor]: FPN feature.
"""
# backbone
out_features = self.backbone(input)
features = [out_features[f] for f in self.in_features]
[x2, x1, x0] = features
fpn_out0 = self.lateral_conv0(x0) # 1024->512/32
f_out0 = self.upsample(fpn_out0) # 512/16
f_out0 = torch.cat([f_out0, x1], 1) # 512->1024/16
f_out0 = self.C3_p4(f_out0) # 1024->512/16
fpn_out1 = self.reduce_conv1(f_out0) # 512->256/16
f_out1 = self.upsample(fpn_out1) # 256/8
f_out1 = torch.cat([f_out1, x2], 1) # 256->512/8
pan_out2 = self.C3_p3(f_out1) # 512->256/8
p_out1 = self.bu_conv2(pan_out2) # 256->256/16
p_out1 = torch.cat([p_out1, fpn_out1], 1) # 256->512/16
pan_out1 = self.C3_n3(p_out1) # 512->512/16
p_out0 = self.bu_conv1(pan_out1) # 512->512/32
p_out0 = torch.cat([p_out0, fpn_out0], 1) # 512->1024/32
pan_out0 = self.C3_n4(p_out0) # 1024->1024/32
outputs = (pan_out2, pan_out1, pan_out0)
return outputs