generate a kaggle submission file

Author: tugstugi

.gitignore

@@ -1,9 +1,5 @@
 __pycache__
 *.pth
+*.csv
 pretrained/
 runs/
-
-checkpoint/
-train.py
-main.py
-utils.py

test_speech_commands.py

@@ -5,6 +5,8 @@
 
 import argparse
 import time
+import csv
+import os
 
 from tqdm import *
 
@@ -125,3 +127,9 @@ def test():
 probabilities, predictions = test()
 if args.generate_kaggle_submission:
     print("generating kaggle submission file '%s'..." % args.output)
+    with open(args.output, 'w') as outfile:
+        fieldnames = ['fname', 'label']
+        writer = csv.DictWriter(outfile, fieldnames=fieldnames)
+        writer.writeheader()
+        for fname, pred in predictions.items():
+            writer.writerow({'fname': os.path.basename(fname), 'label': test_dataset.classes[pred]})

train_speech_commands.py

@@ -31,7 +31,7 @@
 parser.add_argument("--batch-size", type=int, default=128, help='batch size')
 parser.add_argument("--dataload-workers-nums", type=int, default=6, help='number of workers for dataloader')
 parser.add_argument("--weight-decay", type=float, default=1e-2, help='weight decay')
-parser.add_argument("--optim", choices=['sgd', 'adam'], default='adam', help='choices of optimization algorithms')
+parser.add_argument("--optim", choices=['sgd', 'adam'], default='sgd', help='choices of optimization algorithms')
 parser.add_argument("--learning-rate", type=float, default=1e-4, help='learning rate for optimization')
 parser.add_argument("--lr-scheduler", choices=['plateau', 'step'], default='plateau', help='method to adjust learning rate')
 parser.add_argument("--lr-scheduler-patience", type=int, default=5, help='lr scheduler plateau: Number of epochs with no improvement after which learning rate will be reduced')