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models.py
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models.py
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import torch
from torch import nn
import torch.nn.functional as F
from tool.torch_utils import *
from tool.yolo_layer import YoloLayer
class Mish(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
x = x * (torch.tanh(torch.nn.functional.softplus(x)))
return x
class Upsample(nn.Module):
def __init__(self):
super(Upsample, self).__init__()
def forward(self, x, target_size, inference=False):
assert (x.data.dim() == 4)
# _, _, tH, tW = target_size
if inference:
#B = x.data.size(0)
#C = x.data.size(1)
#H = x.data.size(2)
#W = x.data.size(3)
return x.view(x.size(0), x.size(1), x.size(2), 1, x.size(3), 1).\
expand(x.size(0), x.size(1), x.size(2), target_size[2] // x.size(2), x.size(3), target_size[3] // x.size(3)).\
contiguous().view(x.size(0), x.size(1), target_size[2], target_size[3])
else:
return F.interpolate(x, size=(target_size[2], target_size[3]), mode='nearest')
class Conv_Bn_Activation(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride, activation, bn=True, bias=False):
super().__init__()
pad = (kernel_size - 1) // 2
self.conv = nn.ModuleList()
if bias:
self.conv.append(nn.Conv2d(in_channels, out_channels, kernel_size, stride, pad))
else:
self.conv.append(nn.Conv2d(in_channels, out_channels, kernel_size, stride, pad, bias=False))
if bn:
self.conv.append(nn.BatchNorm2d(out_channels))
if activation == "mish":
self.conv.append(Mish())
elif activation == "relu":
self.conv.append(nn.ReLU(inplace=True))
elif activation == "leaky":
self.conv.append(nn.LeakyReLU(0.1, inplace=True))
elif activation == "linear":
pass
else:
print("activate error !!! {} {} {}".format(sys._getframe().f_code.co_filename,
sys._getframe().f_code.co_name, sys._getframe().f_lineno))
def forward(self, x):
for l in self.conv:
x = l(x)
return x
class ResBlock(nn.Module):
"""
Sequential residual blocks each of which consists of \
two convolution layers.
Args:
ch (int): number of input and output channels.
nblocks (int): number of residual blocks.
shortcut (bool): if True, residual tensor addition is enabled.
"""
def __init__(self, ch, nblocks=1, shortcut=True):
super().__init__()
self.shortcut = shortcut
self.module_list = nn.ModuleList()
for i in range(nblocks):
resblock_one = nn.ModuleList()
resblock_one.append(Conv_Bn_Activation(ch, ch, 1, 1, 'mish'))
resblock_one.append(Conv_Bn_Activation(ch, ch, 3, 1, 'mish'))
self.module_list.append(resblock_one)
def forward(self, x):
for module in self.module_list:
h = x
for res in module:
h = res(h)
x = x + h if self.shortcut else h
return x
class DownSample1(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = Conv_Bn_Activation(3, 32, 3, 1, 'mish')
self.conv2 = Conv_Bn_Activation(32, 64, 3, 2, 'mish')
self.conv3 = Conv_Bn_Activation(64, 64, 1, 1, 'mish')
# [route]
# layers = -2
self.conv4 = Conv_Bn_Activation(64, 64, 1, 1, 'mish')
self.conv5 = Conv_Bn_Activation(64, 32, 1, 1, 'mish')
self.conv6 = Conv_Bn_Activation(32, 64, 3, 1, 'mish')
# [shortcut]
# from=-3
# activation = linear
self.conv7 = Conv_Bn_Activation(64, 64, 1, 1, 'mish')
# [route]
# layers = -1, -7
self.conv8 = Conv_Bn_Activation(128, 64, 1, 1, 'mish')
def forward(self, input):
x1 = self.conv1(input)
x2 = self.conv2(x1)
x3 = self.conv3(x2)
# route -2
x4 = self.conv4(x2)
x5 = self.conv5(x4)
x6 = self.conv6(x5)
# shortcut -3
x6 = x6 + x4
x7 = self.conv7(x6)
# [route]
# layers = -1, -7
x7 = torch.cat([x7, x3], dim=1)
x8 = self.conv8(x7)
return x8
class DownSample2(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = Conv_Bn_Activation(64, 128, 3, 2, 'mish')
self.conv2 = Conv_Bn_Activation(128, 64, 1, 1, 'mish')
# r -2
self.conv3 = Conv_Bn_Activation(128, 64, 1, 1, 'mish')
self.resblock = ResBlock(ch=64, nblocks=2)
# s -3
self.conv4 = Conv_Bn_Activation(64, 64, 1, 1, 'mish')
# r -1 -10
self.conv5 = Conv_Bn_Activation(128, 128, 1, 1, 'mish')
def forward(self, input):
x1 = self.conv1(input)
x2 = self.conv2(x1)
x3 = self.conv3(x1)
r = self.resblock(x3)
x4 = self.conv4(r)
x4 = torch.cat([x4, x2], dim=1)
x5 = self.conv5(x4)
return x5
class DownSample3(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = Conv_Bn_Activation(128, 256, 3, 2, 'mish')
self.conv2 = Conv_Bn_Activation(256, 128, 1, 1, 'mish')
self.conv3 = Conv_Bn_Activation(256, 128, 1, 1, 'mish')
self.resblock = ResBlock(ch=128, nblocks=8)
self.conv4 = Conv_Bn_Activation(128, 128, 1, 1, 'mish')
self.conv5 = Conv_Bn_Activation(256, 256, 1, 1, 'mish')
def forward(self, input):
x1 = self.conv1(input)
x2 = self.conv2(x1)
x3 = self.conv3(x1)
r = self.resblock(x3)
x4 = self.conv4(r)
x4 = torch.cat([x4, x2], dim=1)
x5 = self.conv5(x4)
return x5
class DownSample4(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = Conv_Bn_Activation(256, 512, 3, 2, 'mish')
self.conv2 = Conv_Bn_Activation(512, 256, 1, 1, 'mish')
self.conv3 = Conv_Bn_Activation(512, 256, 1, 1, 'mish')
self.resblock = ResBlock(ch=256, nblocks=8)
self.conv4 = Conv_Bn_Activation(256, 256, 1, 1, 'mish')
self.conv5 = Conv_Bn_Activation(512, 512, 1, 1, 'mish')
def forward(self, input):
x1 = self.conv1(input)
x2 = self.conv2(x1)
x3 = self.conv3(x1)
r = self.resblock(x3)
x4 = self.conv4(r)
x4 = torch.cat([x4, x2], dim=1)
x5 = self.conv5(x4)
return x5
class DownSample5(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = Conv_Bn_Activation(512, 1024, 3, 2, 'mish')
self.conv2 = Conv_Bn_Activation(1024, 512, 1, 1, 'mish')
self.conv3 = Conv_Bn_Activation(1024, 512, 1, 1, 'mish')
self.resblock = ResBlock(ch=512, nblocks=4)
self.conv4 = Conv_Bn_Activation(512, 512, 1, 1, 'mish')
self.conv5 = Conv_Bn_Activation(1024, 1024, 1, 1, 'mish')
def forward(self, input):
x1 = self.conv1(input)
x2 = self.conv2(x1)
x3 = self.conv3(x1)
r = self.resblock(x3)
x4 = self.conv4(r)
x4 = torch.cat([x4, x2], dim=1)
x5 = self.conv5(x4)
return x5
class Neck(nn.Module):
def __init__(self, inference=False):
super().__init__()
self.inference = inference
self.conv1 = Conv_Bn_Activation(1024, 512, 1, 1, 'leaky')
self.conv2 = Conv_Bn_Activation(512, 1024, 3, 1, 'leaky')
self.conv3 = Conv_Bn_Activation(1024, 512, 1, 1, 'leaky')
# SPP
self.maxpool1 = nn.MaxPool2d(kernel_size=5, stride=1, padding=5 // 2)
self.maxpool2 = nn.MaxPool2d(kernel_size=9, stride=1, padding=9 // 2)
self.maxpool3 = nn.MaxPool2d(kernel_size=13, stride=1, padding=13 // 2)
# R -1 -3 -5 -6
# SPP
self.conv4 = Conv_Bn_Activation(2048, 512, 1, 1, 'leaky')
self.conv5 = Conv_Bn_Activation(512, 1024, 3, 1, 'leaky')
self.conv6 = Conv_Bn_Activation(1024, 512, 1, 1, 'leaky')
self.conv7 = Conv_Bn_Activation(512, 256, 1, 1, 'leaky')
# UP
self.upsample1 = Upsample()
# R 85
self.conv8 = Conv_Bn_Activation(512, 256, 1, 1, 'leaky')
# R -1 -3
self.conv9 = Conv_Bn_Activation(512, 256, 1, 1, 'leaky')
self.conv10 = Conv_Bn_Activation(256, 512, 3, 1, 'leaky')
self.conv11 = Conv_Bn_Activation(512, 256, 1, 1, 'leaky')
self.conv12 = Conv_Bn_Activation(256, 512, 3, 1, 'leaky')
self.conv13 = Conv_Bn_Activation(512, 256, 1, 1, 'leaky')
self.conv14 = Conv_Bn_Activation(256, 128, 1, 1, 'leaky')
# UP
self.upsample2 = Upsample()
# R 54
self.conv15 = Conv_Bn_Activation(256, 128, 1, 1, 'leaky')
# R -1 -3
self.conv16 = Conv_Bn_Activation(256, 128, 1, 1, 'leaky')
self.conv17 = Conv_Bn_Activation(128, 256, 3, 1, 'leaky')
self.conv18 = Conv_Bn_Activation(256, 128, 1, 1, 'leaky')
self.conv19 = Conv_Bn_Activation(128, 256, 3, 1, 'leaky')
self.conv20 = Conv_Bn_Activation(256, 128, 1, 1, 'leaky')
def forward(self, input, downsample4, downsample3, inference=False):
x1 = self.conv1(input)
x2 = self.conv2(x1)
x3 = self.conv3(x2)
# SPP
m1 = self.maxpool1(x3)
m2 = self.maxpool2(x3)
m3 = self.maxpool3(x3)
spp = torch.cat([m3, m2, m1, x3], dim=1)
# SPP end
x4 = self.conv4(spp)
x5 = self.conv5(x4)
x6 = self.conv6(x5)
x7 = self.conv7(x6)
# UP
up = self.upsample1(x7, downsample4.size(), self.inference)
# R 85
x8 = self.conv8(downsample4)
# R -1 -3
x8 = torch.cat([x8, up], dim=1)
x9 = self.conv9(x8)
x10 = self.conv10(x9)
x11 = self.conv11(x10)
x12 = self.conv12(x11)
x13 = self.conv13(x12)
x14 = self.conv14(x13)
# UP
up = self.upsample2(x14, downsample3.size(), self.inference)
# R 54
x15 = self.conv15(downsample3)
# R -1 -3
x15 = torch.cat([x15, up], dim=1)
x16 = self.conv16(x15)
x17 = self.conv17(x16)
x18 = self.conv18(x17)
x19 = self.conv19(x18)
x20 = self.conv20(x19)
return x20, x13, x6
class Yolov4Head(nn.Module):
def __init__(self, output_ch, n_classes, inference=False):
super().__init__()
self.inference = inference
self.conv1 = Conv_Bn_Activation(128, 256, 3, 1, 'leaky')
self.conv2 = Conv_Bn_Activation(256, output_ch, 1, 1, 'linear', bn=False, bias=True)
self.yolo1 = YoloLayer(
anchor_mask=[0, 1, 2], num_classes=n_classes,
anchors=[12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401],
num_anchors=9, stride=8)
# R -4
self.conv3 = Conv_Bn_Activation(128, 256, 3, 2, 'leaky')
# R -1 -16
self.conv4 = Conv_Bn_Activation(512, 256, 1, 1, 'leaky')
self.conv5 = Conv_Bn_Activation(256, 512, 3, 1, 'leaky')
self.conv6 = Conv_Bn_Activation(512, 256, 1, 1, 'leaky')
self.conv7 = Conv_Bn_Activation(256, 512, 3, 1, 'leaky')
self.conv8 = Conv_Bn_Activation(512, 256, 1, 1, 'leaky')
self.conv9 = Conv_Bn_Activation(256, 512, 3, 1, 'leaky')
self.conv10 = Conv_Bn_Activation(512, output_ch, 1, 1, 'linear', bn=False, bias=True)
self.yolo2 = YoloLayer(
anchor_mask=[3, 4, 5], num_classes=n_classes,
anchors=[12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401],
num_anchors=9, stride=16)
# R -4
self.conv11 = Conv_Bn_Activation(256, 512, 3, 2, 'leaky')
# R -1 -37
self.conv12 = Conv_Bn_Activation(1024, 512, 1, 1, 'leaky')
self.conv13 = Conv_Bn_Activation(512, 1024, 3, 1, 'leaky')
self.conv14 = Conv_Bn_Activation(1024, 512, 1, 1, 'leaky')
self.conv15 = Conv_Bn_Activation(512, 1024, 3, 1, 'leaky')
self.conv16 = Conv_Bn_Activation(1024, 512, 1, 1, 'leaky')
self.conv17 = Conv_Bn_Activation(512, 1024, 3, 1, 'leaky')
self.conv18 = Conv_Bn_Activation(1024, output_ch, 1, 1, 'linear', bn=False, bias=True)
self.yolo3 = YoloLayer(
anchor_mask=[6, 7, 8], num_classes=n_classes,
anchors=[12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401],
num_anchors=9, stride=32)
def forward(self, input1, input2, input3):
x1 = self.conv1(input1)
x2 = self.conv2(x1)
x3 = self.conv3(input1)
# R -1 -16
x3 = torch.cat([x3, input2], dim=1)
x4 = self.conv4(x3)
x5 = self.conv5(x4)
x6 = self.conv6(x5)
x7 = self.conv7(x6)
x8 = self.conv8(x7)
x9 = self.conv9(x8)
x10 = self.conv10(x9)
# R -4
x11 = self.conv11(x8)
# R -1 -37
x11 = torch.cat([x11, input3], dim=1)
x12 = self.conv12(x11)
x13 = self.conv13(x12)
x14 = self.conv14(x13)
x15 = self.conv15(x14)
x16 = self.conv16(x15)
x17 = self.conv17(x16)
x18 = self.conv18(x17)
if self.inference:
y1 = self.yolo1(x2)
y2 = self.yolo2(x10)
y3 = self.yolo3(x18)
return get_region_boxes([y1, y2, y3])
else:
return [x2, x10, x18]
class Yolov4(nn.Module):
def __init__(self, yolov4conv137weight=None, n_classes=80, inference=False):
super().__init__()
output_ch = (4 + 1 + n_classes) * 3
# backbone
self.down1 = DownSample1()
self.down2 = DownSample2()
self.down3 = DownSample3()
self.down4 = DownSample4()
self.down5 = DownSample5()
# neck
self.neck = Neck(inference)
# yolov4conv137
if yolov4conv137weight:
_model = nn.Sequential(self.down1, self.down2, self.down3, self.down4, self.down5, self.neck)
pretrained_dict = torch.load(yolov4conv137weight)
model_dict = _model.state_dict()
# 1. filter out unnecessary keys
pretrained_dict = {k1: v for (k, v), k1 in zip(pretrained_dict.items(), model_dict)}
# 2. overwrite entries in the existing state dict
model_dict.update(pretrained_dict)
_model.load_state_dict(model_dict)
# head
self.head = Yolov4Head(output_ch, n_classes, inference)
def forward(self, input):
d1 = self.down1(input)
d2 = self.down2(d1)
d3 = self.down3(d2)
d4 = self.down4(d3)
d5 = self.down5(d4)
x20, x13, x6 = self.neck(d5, d4, d3)
output = self.head(x20, x13, x6)
return output
if __name__ == "__main__":
import sys
import cv2
namesfile = None
if len(sys.argv) == 6:
n_classes = int(sys.argv[1])
weightfile = sys.argv[2]
imgfile = sys.argv[3]
height = int(sys.argv[4])
width = int(sys.argv[5])
elif len(sys.argv) == 7:
n_classes = int(sys.argv[1])
weightfile = sys.argv[2]
imgfile = sys.argv[3]
height = int(sys.argv[4])
width = int(sys.argv[5])
namesfile = sys.argv[6]
else:
print('Usage: ')
print(' python models.py num_classes weightfile imgfile namefile')
model = Yolov4(yolov4conv137weight=None, n_classes=n_classes, inference=True)
pretrained_dict = torch.load(weightfile, map_location=torch.device('cuda'))
model.load_state_dict(pretrained_dict)
use_cuda = True
if use_cuda:
model.cuda()
img = cv2.imread(imgfile)
# Inference input size is 416*416 does not mean training size is the same
# Training size could be 608*608 or even other sizes
# Optional inference sizes:
# Hight in {320, 416, 512, 608, ... 320 + 96 * n}
# Width in {320, 416, 512, 608, ... 320 + 96 * m}
sized = cv2.resize(img, (width, height))
sized = cv2.cvtColor(sized, cv2.COLOR_BGR2RGB)
from tool.utils import load_class_names, plot_boxes_cv2
from tool.torch_utils import do_detect
for i in range(2): # This 'for' loop is for speed check
# Because the first iteration is usually longer
boxes = do_detect(model, sized, 0.4, 0.6, use_cuda)
if namesfile == None:
if n_classes == 20:
namesfile = 'data/voc.names'
elif n_classes == 80:
namesfile = 'data/coco.names'
else:
print("please give namefile")
class_names = load_class_names(namesfile)
plot_boxes_cv2(img, boxes[0], 'predictions.jpg', class_names)