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import torch
import torch.nn as nn
from torch.nn import functional as F
from nets.CSPdarknet_tiny import darknet_tiny
class BasicConv(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1):
super(BasicConv, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, kernel_size//2, bias=False)
self.bn = nn.BatchNorm2d(out_channels)
self.activation = nn.LeakyReLU(0.1)
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
x = self.activation(x)
return x
class Upsample(nn.Module):
def __init__(self, in_channels, out_channels):
super(Upsample, self).__init__()
self.upsample = nn.Sequential(
BasicConv(in_channels, out_channels, 1),
nn.Upsample(scale_factor=2, mode='nearest')
)
def forward(self, x,):
x = self.upsample(x)
return x
def yolo_head(filters_list, in_filters):
m = nn.Sequential(
BasicConv(in_filters, filters_list[0], 3),
nn.Conv2d(filters_list[0], filters_list[1], 1),
)
return m
class ConvBNReLU(nn.Module):
'''Module for the Conv-BN-ReLU tuple.'''
def __init__(self, c_in, c_out, kernel_size, stride, padding, dilation,
use_relu=True):
super(ConvBNReLU, self).__init__()
self.conv = nn.Conv2d(
c_in, c_out, kernel_size=kernel_size, stride=stride,
padding=padding, dilation=dilation, bias=False)
self.bn = nn.BatchNorm2d(c_out)
if use_relu:
self.relu = nn.ReLU(inplace=True)
else:
self.relu = None
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
if self.relu is not None:
x = self.relu(x)
return x
class CARAFE(nn.Module):
def __init__(self, c, c_mid=64, scale=2, k_up=5, k_enc=3):
""" The unofficial implementation of the CARAFE module.
The details are in "https://arxiv.org/abs/1905.02188".
Args:
c: The channel number of the input and the output.
c_mid: The channel number after compression.
scale: The expected upsample scale.
k_up: The size of the reassembly kernel.
k_enc: The kernel size of the encoder.
Returns:
X: The upsampled feature map.
"""
super(CARAFE, self).__init__()
self.scale = scale
self.comp = ConvBNReLU(c, c_mid, kernel_size=1, stride=1,
padding=0, dilation=1)
self.enc = ConvBNReLU(c_mid, (scale * k_up) ** 2, kernel_size=k_enc,
stride=1, padding=k_enc // 2, dilation=1,
use_relu=False)
self.pix_shf = nn.PixelShuffle(scale)
self.upsmp = nn.Upsample(scale_factor=scale, mode='nearest')
self.unfold = nn.Unfold(kernel_size=k_up, dilation=scale,
padding=k_up // 2 * scale)
def forward(self, X):
b, c, h, w = X.size()
h_, w_ = h * self.scale, w * self.scale
W = self.comp(X) # b * m * h * w
W = self.enc(W) # b * 100 * h * w
W = self.pix_shf(W) # b * 25 * h_ * w_
W = F.softmax(W, dim=1) # b * 25 * h_ * w_
X = self.upsmp(X) # b * c * h_ * w_
X = self.unfold(X) # b * 25c * h_ * w_
X = X.view(b, c, -1, h_, w_) # b * 25 * c * h_ * w_
X = torch.einsum('bkhw,bckhw->bchw', [W, X]) # b * c * h_ * w_
return X
#---------------------------------------------------#
# yolo_body--MSFNet
#---------------------------------------------------#
class YoloBody(nn.Module):
def __init__(self, anchors_mask, num_classes, phi=0, backbone ='tiny', pretrained=False):
super(YoloBody, self).__init__()
if backbone == 'tiny':
self.backbone = darknet_tiny(pretrained)
self.conv_for_P5 = BasicConv(512,256,1)
self.yolo_headP5 = yolo_head([512, len(anchors_mask[0]) * (5 + num_classes)],256)
self.upsample_1 = Upsample(256,128)
self.conv1 = BasicConv(256,128,1)
self.upsample_2 = CARAFE(128)
self.yolo_headP4 = yolo_head([256, len(anchors_mask[1]) * (5 + num_classes)],384)
def forward(self, x):
feat1, feat2 = self.backbone(x)
# 13,13,512 -> 13,13,256
P5 = self.conv_for_P5(feat2)
# 13,13,256 -> 13,13,512 -> 13,13,255
out0 = self.yolo_headP5(P5)
P6 = self.conv_for_P5(feat2)
P6_Upsample = self.upsample_1(P6)
# 13,13,256 -> 13,13,128 -> 26,26,128
P5 = self.conv1(P5)
P5_Upsample = self.upsample_2(P5)
sum = P5_Upsample + P6_Upsample
# 26,26,256 + 26,26,128 -> 26,26,384
# if 1 <= self.phi and self.phi <= 4:
# P5_Upsample = self.upsample_att(P5_Upsample)
# P4 = torch.cat([P5_Upsample, feat1],axis=1)
P4 = torch.cat([sum, feat1],axis=1)
# 26,26,384 -> 26,26,256 -> 26,26,255
out1 = self.yolo_headP4(P4)
return out0, out1
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