loss_final = sum_mse_loss(pred_maps[:, -1, ...].cpu(), label[:, -1, ...].cpu())
时间: 2024-05-25 21:18:11 浏览: 10
This line of code calculates the mean squared error (MSE) loss between the predicted maps and the ground truth labels for the last time step of the sequence.
The `pred_maps` tensor represents the predicted output of the model and has shape `(batch_size, num_timesteps, height, width)`. The `label` tensor represents the ground truth labels and has the same shape.
The `[:, -1, ...]` indexing selects the last time step of the sequence for both `pred_maps` and `label`. The `cpu()` method moves the tensors from the GPU to the CPU for calculation.
The `sum_mse_loss` function likely calculates the MSE loss between the two tensors and returns a scalar value. The `loss_final` variable stores this value.
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