Compare text

Find the difference between two text files

Real-time diff

Unified diff

Collapse lines

Highlight change

Syntax highlighting

Tools

Diffchecker Desktop The most secure way to run Diffchecker. Get the Diffchecker Desktop app: your diffs never leave your computer!Get Desktop

model_diff_help1

Created about a year agoDiff never expires

Lines
Total
Removed

Words
Total
Removed

To continue using this feature, upgrade to Diffchecker Pro View Pricing

26 lines

Lines
Total
Added

Words
Total
Added

To continue using this feature, upgrade to Diffchecker Pro View Pricing

31 lines

class Model(nn.Module):

def forward(self, img1, img2):

# Calculate the mean of the two input tensors

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

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

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

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)

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

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)

Saved diffs

Original text

Open file

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

Changed text

Open file

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)