RuntimeError: Given groups=1, weight of size [64, 64, 1, 1], expected input[1, 32, 128, 128] to have 64 channels, but got 32 channels instead

[dayeone]

`# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license

Parameters

nc: 2 # number of classes
depth_multiple: 0.33 # model depth multiple
width_multiple: 0.50 # layer channel multiple
anchors:

• [10, 13, 16, 30, 33, 23] # P3/8
• [30, 61, 62, 45, 59, 119] # P4/16
• [116, 90, 156, 198, 373, 326] # P5/32

YOLOv5 v6.0 backbone

backbone:
[
[-1, 1, Conv, [64, 3, 1]], # Conv:
[-1, 1, MaxPool2d, [3, 2, 1]], # MaxPool2d
[-1, 1, BottleneckRES, [64, 64, 1]], # BottleneckRES:
[-1, 2, BottleneckRES, [256, 64, 1]], # BottleneckRES:
[-1, 1, BottleneckRES, [256, 128, 2]], # BottleneckRES:
[-1, 4, BottleneckRES, [512, 128, 1]], # BottleneckRES:
[-1, 6, BottleneckRES, [512, 256, 2]], # BottleneckRES
[-1, 3, BottleneckRES, [1024, 256, 1]], # BottleneckRES:
[-1, 3, BottleneckRES, [1024, 512, 2]], # BottleneckRES:
[-1, 1, BottleneckRES, [2048, 512, 1]], # BottleneckRES:
]

YOLOv5 v6.0 head

head:
[
[-1, 1, BottleneckCSP, [2048, 512, 1]], # BottleneckCSP:
[-1, 1, Conv, [256, 1, 1]], # Conv:
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
[[-1, 6], 1, Concat, [1]], # cat backbone P4
[-1, 3, C3, [512, 256, 1]], # C3:

[-1, 1, Conv, [128, 1, 1]],  # Conv: 
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
[[-1, 4], 1, Concat, [1]],  # cat backbone P3
[-1, 3, C3, [256, 128, 1]],  # C3: 

[-1, 1, Conv, [256, 3, 2]],  # Conv: 
[[-1, 14], 1, Concat, [1]],  # cat head P4
[-1, 3, C3, [512, 256, 1]],  # C3: 

[-1, 1, Conv, [512, 3, 2]],  # Conv: 
[[-1, 10], 1, Concat, [1]],  # cat head P5
[-1, 3, C3, [1024, 512, 1]],  # C3: 

[[17, 20, 23], 1, Detect, [nc, anchors]],  # Detect: P3, P4, P5

]
`
This is my YAML file. I have connected the ResNet50 backbone to the YOLOv5 head

and
`class BottleneckRES(nn.Module):
expansion = 4

def __init__(self, in_planes, planes, stride=1):
    super(BottleneckRES, self).__init__()
    out_planes = planes * self.expansion
    
    self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
    self.bn1 = nn.BatchNorm2d(planes)
    
    self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
    self.bn2 = nn.BatchNorm2d(planes)
    
    self.conv3 = nn.Conv2d(planes, out_planes, kernel_size=1, bias=False)
    self.bn3 = nn.BatchNorm2d(out_planes)

    self.relu = nn.ReLU(inplace=True)

    self.shortcut = nn.Sequential()
    if stride != 1 or in_planes != out_planes:
        self.shortcut = nn.Sequential(
            nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False),
            nn.BatchNorm2d(out_planes)
        )

def forward(self, x):
    identity = x
    
    out = self.relu(self.bn1(self.conv1(x)))
    print(f"After conv1: {out.shape}")
    out = self.relu(self.bn2(self.conv2(out)))
    print(f"After conv2: {out.shape}")
    out = self.bn3(self.conv3(out))
    print(f"After conv3: {out.shape}")
    
    identity = self.shortcut(identity)
    
    out += identity
    out = self.relu(out)
    print(f"After shortcut and add: {out.shape}")
    return out`

defined the bottleneckRES function to use ResNet50

Traceback (most recent call last):
File “train.py”, line 858, in
main(opt)
File “train.py”, line 632, in main
train(opt.hyp, opt, device, callbacks)
File “train.py”, line 199, in train
model = Model(cfg, ch=3, nc=nc, anchors=hyp.get(“anchors”)).to(device) # create
File “/home/firo-msi/RESNET/yolov5/models/yolo.py”, line 253, in init
m.stride = torch.tensor([s / x.shape[-2] for x in forward(torch.zeros(1, ch, s, s))]) # forward
File “/home/firo-msi/RESNET/yolov5/models/yolo.py”, line 252, in
forward = lambda x: self.forward(x)[0] if isinstance(m, Segment) else self.forward(x)
File “/home/firo-msi/RESNET/yolov5/models/yolo.py”, line 268, in forward
return self._forward_once(x, profile, visualize) # single-scale inference, train
File “/home/firo-msi/RESNET/yolov5/models/yolo.py”, line 171, in _forward_once
x = m(x) # run
File “/home/firo-msi/anaconda3/envs/yolov5/lib/python3.8/site-packages/torch/nn/modules/module.py”, line 1511, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File “/home/firo-msi/anaconda3/envs/yolov5/lib/python3.8/site-packages/torch/nn/modules/module.py”, line 1520, in _call_impl
return forward_call(*args, **kwargs)
File “/home/firo-msi/RESNET/yolov5/models/common.py”, line 1163, in forward
out = self.relu(self.bn1(self.conv1(x)))
File “/home/firo-msi/anaconda3/envs/yolov5/lib/python3.8/site-packages/torch/nn/modules/module.py”, line 1511, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File “/home/firo-msi/anaconda3/envs/yolov5/lib/python3.8/site-packages/torch/nn/modules/module.py”, line 1520, in _call_impl
return forward_call(*args, **kwargs)
File “/home/firo-msi/anaconda3/envs/yolov5/lib/python3.8/site-packages/torch/nn/modules/conv.py”, line 460, in forward
return self._conv_forward(input, self.weight, self.bias)
File “/home/firo-msi/anaconda3/envs/yolov5/lib/python3.8/site-packages/torch/nn/modules/conv.py”, line 456, in _conv_forward
return F.conv2d(input, weight, bias, self.stride,
RuntimeError: Given groups=1, weight of size [64, 64, 1, 1], expected input[1, 32, 128, 128] to have 64 channels, but got 32 channels instead

However, I am encountering an error that seems related to incorrect channels. Is there a solution for this? PLEASE ㅠㅠ