Comparar texto

Encuentra la diferencia entre dos archivos de texto

Editor live

Ocultar sin cambios

Sin ajuste de línea

Vista

Nivel de detalle

Resaltado de sintaxis

Diffchecker Desktop La forma más segura de usar Diffchecker. ¡Obtén la app de Diffchecker Desktop: tus diffs nunca salen de tu computadora!Obtener Desktop

model_diff_help1

Creado hace 2 añosEl diff nunca expira

3 eliminaciones

Líneas
Total
Eliminado

Caracteres
Total
Eliminado

Para continuar usando esta función, actualice a Diffchecker Pro Ver precios

26 líneas

9 adiciones

Líneas
Total
Añadido

Caracteres
Total
Añadido

Para continuar usando esta función, actualice a Diffchecker Pro Ver precios

31 líneas

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)

Diferencias guardadas

Texto original

Abrir archivo

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

Texto modificado

Abrir archivo

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)