'time':
format_time(time.time() - st_time),
'eta':
format_time((time.time() - st_time) / (epoch + 1) *
(args.epochs - epoch - 1)),
})
print(row)
logger.writerow(row)
##end for epoch
torch.save({
'model': cnn.state_dict(),
}, ckpt_cnn_filename)
logger.close()
else:
print("\n Loading pretrained model...")
cnn.load_state_dict(torch.load(ckpt_cnn_filename)['model'])
cnn = cnn.cuda()
val_acc = test(test_loader) * 100.0
print(val_acc)
with open(log_filename, 'a') as f:
f.write(
"\n==================================================================================================="
)
eval_results_fullpath = ckpt_directory + "/test_result_" + test_id + ".txt"
if not os.path.isfile(eval_results_fullpath):
eval_results_logging_file = open(eval_results_fullpath, "w")
eval_results_logging_file.close()
with open(eval_results_fullpath, 'a') as eval_results_logging_file:
eval_results_logging_file.write(
with torch.no_grad():
pred = cnn(images)
if teacher is not None:
fs, pred = pred
pred = torch.max(pred.data, 1)[1]
total += labels.size(0)
correct += (pred == labels).sum().item()
val_acc = correct / total
cnn.train()
return val_acc
if args.test:
cnn.load_state_dict(torch.load(args.resume))
print(test(test_loader))
exit()
# train
best_acc = 0.0
st_time = time.time()
for epoch in range(args.epochs):
loss_avg = {}
correct = 0.
total = 0.
cnt_ft = {}
for i, (images, labels) in enumerate(train_loader):
images, labels = images.cuda(), labels.cuda()
cnn.zero_grad()