Optimize redundant comments

README.md

@@ -3,7 +3,7 @@
 This is the implementation of Spatio-temporal Differential Equation Network (STDEN) in the following paper:
 Jiahao Ji, Jingyuan Wang, Zhe Jiang, Jiawei Jiang, and Hu Zhang, Towards Physics-guided Neural Networks for Traffic Flow Prediction, AAAI 2022. 
 
-Thanks [chnsh](https://github.com/chnsh/DCRNN_PyTorch) for the model training framework of this project.
+The training framework of this project comes from [chnsh](https://github.com/chnsh/DCRNN_PyTorch). Thanks a lot :)
 
 ## Requirement
 

lib/metrics.py

@@ -1,7 +1,6 @@
 import torch
 
 def masked_mae_loss(y_pred, y_true):
-    # print('y_pred: ', y_pred.shape, 'y_true: ', y_true.shape)
     y_true[y_true < 1e-4] = 0
     mask = (y_true != 0).float()
     mask /= mask.mean() # assign the sample weights of zeros to nonzero-values 
@@ -12,23 +11,19 @@ def masked_mae_loss(y_pred, y_true):
     return loss.mean()
 
 def masked_mape_loss(y_pred, y_true):
-    # print('y_pred: ', y_pred.shape, 'y_true: ', y_true.shape)
     y_true[y_true < 1e-4] = 0
     mask = (y_true != 0).float()
     mask /= mask.mean() 
     loss = torch.abs((y_pred - y_true) / y_true)
     loss = loss * mask
-    # trick for nans: https://discuss.pytorch.org/t/how-to-set-nan-in-tensor-to-0/3918/3
     loss[loss != loss] = 0
     return loss.mean()
 
 def masked_rmse_loss(y_pred, y_true):
     y_true[y_true < 1e-4] = 0
-    # print('y_pred: ', y_pred.shape, 'y_true: ', y_true.shape)
     mask = (y_true != 0).float()
     mask /= mask.mean() 
     loss = torch.pow(y_pred - y_true, 2)
     loss = loss * mask
-    # trick for nans: https://discuss.pytorch.org/t/how-to-set-nan-in-tensor-to-0/3918/3
     loss[loss != loss] = 0
     return torch.sqrt(loss.mean())

lib/utils.py

@@ -2,16 +2,11 @@ import logging
 import numpy as np
 import os
 import time
-import pickle
 import scipy.sparse as sp
 import sys
-# import tensorflow as tf
 import torch 
 import torch.nn as nn
 
-from scipy.sparse import linalg
-
-
 class DataLoader(object):
     def __init__(self, xs, ys, batch_size, pad_with_last_sample=True, shuffle=False):
         """

model/stden_supervisor.py

@@ -9,7 +9,7 @@ from torch.utils.tensorboard import SummaryWriter
 
 from lib import utils
 from model.stden_model import STDENModel
-from lib.metrics import masked_mae_loss, masked_mape_loss, masked_mse_loss, masked_rmse_loss
+from lib.metrics import masked_mae_loss, masked_mape_loss, masked_rmse_loss
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")