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class Model(nn.Module):

def forward(self, img1, img2):

# Calculate the mean of the two input tensors

mean1 = torch.mean(img1, dim=0)

mean2 = torch.mean(img2, dim=0)

# Calculate the absolute difference between the two mean tensors

diff = torch.sqrt(torch.pow(mean1 - mean2, 2)).float()

print(diff.shape)

threshold = 30.0

# Create a binary mask where differences are higher than the threshold

mask = torch.where(diff > threshold, torch.tensor(1.0), torch.tensor(0.0))

print(mask.shape)

# Count the number of moving pixels

movingPx = torch.sum(mask)

print(movingPx)

# Calculate the total number of pixels

totalPx = torch.tensor(mask.shape[0] * mask.shape[1], dtype=torch.float32)

# Calculate the ratio of moving pixels to the total number of pixels

movingRatio = movingPx / totalPx

return movingRatio.unsqueeze(0) # Ensure the output is a tensor with an added dimension

class Model(nn.Module):

def forward(self, img1, img2):

# Calculate the mean of the two input tensors

mean1 = torch.mean(img1, dim=1, keepdim=True)

mean2 = torch.mean(img2, dim=1, keepdim=True)

# Calculate the absolute difference between the two mean tensors

diff = torch.sqrt(torch.pow(mean1 - mean2, 2)).float()

print(diff.shape)

threshold = 30.0

# Create a binary mask where differences are higher than the threshold

mask = torch.where(diff > threshold, torch.tensor(1.0), torch.tensor(0.0))

print(mask.shape)

# Count the number of moving pixels

movingPx = torch.sum(mask).view(1,1,1,1)

print(movingPx)

# Calculate the total number of pixels

totalPx = torch.tensor(mask.shape[0] * mask.shape[1], dtype=torch.float32)

# Calculate the ratio of moving pixels to the total number of pixels

movingRatio = movingPx / totalPx

return movingRatio # Ensure the output is a tensor with an added dimension

model = Model()

torch.onnx.export(model, (torch.randn(1,3,720,720), torch.randn(1,3,720,720)), "model_diff.onnx", opset_version=16)