clean up + mask is long

run_classifier.py

@@ -22,10 +22,10 @@ import csv
 import os
 import logging
 import argparse
-
 import random
-import numpy as np
 from tqdm import tqdm, trange
+
+import numpy as np
 import torch
 from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
 from torch.utils.data.distributed import DistributedSampler
@@ -102,7 +102,7 @@ class MrpcProcessor(DataProcessor):
 
     def get_train_examples(self, data_dir):
         """See base class."""
-        print("LOOKING AT {}".format(os.path.join(data_dir, "train.tsv")))
+        logger.info("LOOKING AT {}".format(os.path.join(data_dir, "train.tsv")))
         return self._create_examples(
             self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
 
@@ -420,7 +420,7 @@ def main():
         n_gpu = 1
         # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
         torch.distributed.init_process_group(backend='nccl')
-    print("device", device, "n_gpu", n_gpu, "distributed training", bool(args.local_rank != -1))
+    logger.info("device", device, "n_gpu", n_gpu, "distributed training", bool(args.local_rank != -1))
 
     if args.accumulate_gradients < 1:
         raise ValueError("Invalid accumulate_gradients parameter: {}, should be >= 1".format(
@@ -516,7 +516,7 @@ def main():
             nb_tr_examples, nb_tr_steps = 0, 0
             for step, (input_ids, input_mask, segment_ids, label_ids) in enumerate(tqdm(train_dataloader, desc="Iteration")):
                 input_ids = input_ids.to(device)
-                input_mask = input_mask.float().to(device)
+                input_mask = input_mask.to(device)
                 segment_ids = segment_ids.to(device)
                 label_ids = label_ids.to(device)
 
@@ -559,7 +559,7 @@ def main():
         nb_eval_steps, nb_eval_examples = 0, 0
         for input_ids, input_mask, segment_ids, label_ids in eval_dataloader:
             input_ids = input_ids.to(device)
-            input_mask = input_mask.float().to(device)
+            input_mask = input_mask.to(device)
             segment_ids = segment_ids.to(device)
             label_ids = label_ids.to(device)