save_images(label[:, -1, ...].cpu(), pred_maps[:, -1, ...].cpu(),epoch, img_name, save_dir)

I'm sorry, as an AI language model, I do not have access to the specific implementation details of your code. Can you please provide more context and information about the code snippet you have provided?

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loss_final = sum_mse_loss(pred_maps[:, -1, ...].cpu(), label[:, -1, ...].cpu())

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.

def error_compoute(y_pred_upper, y_pred_lower, y_tes, alpha): # Ravel for ease of computation y_pred_upper = y_pred_upper.ravel() y_pred_lower = y_pred_lower.ravel() y_tes = y_tes.ravel() # Find out of bound indices for WS idx_oobl = np.where((y_tes < y_pred_lower))[0] # 低于下限 # idx_oolu = np.where((y_tes >= y_pred_lower & y_tes <= y_pred_upper))[0] # 介于上下限之间 idx_oobu = np.where((y_tes > y_pred_upper))[0] # 高于上限 picp = np.sum((y_tes > y_pred_lower) & (y_tes <= y_pred_upper)) / len(y_tes) * 100 ace = picp - (1 - alpha) * 100 pinaw = np.sum(y_pred_upper - y_pred_lower) / (np.max(y_tes) - np.min(y_tes)) / len(y_tes) ws = np.sum(np.sum(-2 * alpha * (y_pred_upper - y_pred_lower)) + np.sum(-4 * (y_pred_lower[idx_oobl] - y_tes[idx_oobl])) + np.sum(-4 * (y_tes[idx_oobu] - y_pred_upper[idx_oobu]))) / len(y_tes) print("PICP: {:.4f}%".format(picp)) print("ACE: {:.4f}".format(ace)) print("PINAW: {:.4f}".format(pinaw)) print("WS: {:.4f}".format(ws))

这段代码定义了一个名为 `error_compute()` 的函数，用于计算预测结果的误差指标。函数接受以下参数：

- `y_pred_upper`：预测结果的上限值
- `y_pred_lower`：预测结果的下限值
- `y_tes`：真实的目标值
- `alpha`：置信水平

首先，通过 `ravel()` 函数对输入的数组进行展平操作，以便于后续计算。然后，根据真实目标值和上下限值，找到超出边界的索引。其中，`idx_oobl` 存储低于下限的索引，`idx_oobu` 存储高于上限的索引。

接下来，通过计算 `(y_tes > y_pred_lower) & (y_tes <= y_pred_upper)` 来确定落在上下限之间的样本数量，并将其除以样本总数得到覆盖概率（PICP）。ACE 是覆盖概率与置信水平之差。

PINAW 是上下限范围的平均宽度与真实目标值范围的比值。

最后，计算 WS（预测误差分数），其中包括覆盖范围内的损失和超出范围的损失。

函数会打印出 PICP、ACE、PINAW 和 WS 的值。

请注意，函数中有一行被注释掉了，即 `idx_oolu` 的计算，可能是因为没有在后续计算中使用到。