cnn_optimizer.step()
xentropy_loss_avg += xentropy_loss.item()
# Calculate running average of accuracy
pred = torch.max(pred.data, 1)[1]
total += labels.size(0)
correct += (pred == labels.data).sum().item()
accuracy = correct / total
progress_bar.set_postfix(xentropy='%.3f' % (xentropy_loss_avg /
(i + 1)),
acc='%.3f' % accuracy)
test_acc = test(test_loader)
flops = cnn.flops()
score = flops / mobilenet_flops + params / mobilenet_params
tqdm.write('test_acc: %.3f, flops: %s, parameters: %s, score %s' %
(test_acc, flops, params, score))
scheduler.step(epoch)
row = {
'epoch': str(epoch),
'train_acc': str(accuracy),
'test_acc': str(test_acc)
}
csv_logger.writerow(row)
torch.save(cnn.state_dict(), 'checkpoints/' + test_id + '.pt')
csv_logger.close()
progress_bar.set_postfix(
xentropy='%.3f' % (xentropy_loss_avg / (i + 1)),
acc='%.3f' % accuracy)
test_acc = test(test_loader)
tqdm.write('test_acc: %.3f' % (test_acc))
# scheduler.step(epoch) # Use this line for PyTorch <1.4
scheduler.step() # Use this line for PyTorch >=1.4
row = {'epoch': str(epoch), 'train_acc': str(accuracy), 'test_acc': str(test_acc)}
csv_logger.writerow(row)
checkpoints_dir = 'checkpoints'
os.makedirs(checkpoints_dir, exist_ok=True)
torch.save(cnn.state_dict(),
os.path.join(checkpoints_dir, '{}.pt'.format(test_id)))
dummy_input = torch.randn((20, 3, args.input_res, args.input_res),
requires_grad=False)
onnx_path = os.path.join('checkpoints', '{}.onnx'.format(test_id))
torch.onnx.export(cnn.cpu(),
dummy_input,
onnx_path,
opset_version=10,
do_constant_folding=True,
keep_initializers_as_inputs=True)
csv_logger.close()
xentropy_loss_avg += xentropy_loss.item()
# Calculate running average of accuracy
pred = torch.max(pred.data, 1)[1]
total += labels.size(0)
correct += (pred == labels.data).sum().item()
accuracy = correct / total
progress_bar.set_postfix(xentropy='%.3f' % (xentropy_loss_avg /
(i + 1)),
acc='%.3f' % accuracy)
test_acc = eval(cnn, test_loader)
if test_acc > max_acc:
max_acc = test_acc
torch.save(cnn.state_dict(), basic_path + 'checkpoints/max_acc.pt')
scheduler.step(epoch)
tqdm.write('test_acc: %.4f, max_acc: %.4f' % (test_acc, max_acc))
csv_logger.writerow({
'epoch': str(epoch),
'train_acc': str(accuracy),
'test_acc': str(test_acc),
'max_acc': str(max_acc)
})
torch.save(cnn.state_dict(), basic_path + 'checkpoints/last.pt')
csv_logger.close()
'loss': '%.5f' % (sum(loss_avg.values())),
}
loss_avg = {k: '%.5f' % loss_avg[k] for k in loss_avg}
row.update(loss_avg)
row.update({
'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"
'test_acc': str(test_acc)
}
csv_logger.writerow(row)
if test_acc > best_acc:
best_acc = test_acc
best_epoch = epoch
if test_acc > 0.1:
best_model = copy.deepcopy(cnn)
# keep csv of predictions
best_preds = df_preds
preds_dir = 'preds/' + test_id
os.makedirs(preds_dir, exist_ok=True)
df_preds.to_csv(
f'{preds_dir}/predictions_test_epoch{epoch}_acc{best_acc}.csv')
print("Best test acc:", str(best_acc))
print("Best Epoch:", str(best_epoch))
newdir = 'new_checkpoints/' + test_id + "/"
os.makedirs(newdir, exist_ok=True)
torch.save(cnn.state_dict(), newdir + str(args.epochs) + 'epoch' + '.pt')
torch.save(best_model.state_dict(),
newdir + 'best_model_at_epoch_' + str(best_epoch) + '.pt')
csv_logger.close()
best_preds.to_csv(
f'{preds_dir}/best_predictions_test_epoch{epoch}_acc{best_acc}.csv',
index=False)
accuracy = correct / total
progress_bar.set_postfix(xentropy='%.3f' % (xentropy_loss_avg /
(i + 1)),
acc='%.3f' % accuracy)
test_acc = test(test_loader)
tqdm.write('test_acc: %.3f' % (test_acc))
scheduler.step(epoch)
row = {
'epoch': str(epoch),
'train_acc': str(accuracy),
'test_acc': str(test_acc)
}
csv_logger.writerow(row)
#Saving checkpoint
is_best = test_acc > max_accuracy
max_accuracy = max(test_acc, max_accuracy)
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': cnn.state_dict(),
'best_prec1': max_accuracy,
}, is_best)
print("Best Accuracy", max_accuracy)
#torch.save(cnn.state_dict(), 'checkpoints/' + test_id + '.pt')
csv_logger.close()
test_acc = test(test_loader)
tqdm.write('test_acc: %.3f' % (test_acc))
scheduler.step()
row = {
'epoch': str(epoch),
'train_acc': str(accuracy),
'test_acc': str(test_acc),
}
csv_logger.writerow(row)
if args.tensorboard:
global_step = epoch + 1
writer.add_scalar('xentropy loss', xentropy_loss_avg / (i + 1),
global_step=global_step)
writer.add_scalar('train acc', accuracy, global_step=global_step)
writer.add_scalar('test acc', test_acc, global_step=global_step)
# Save model checkpoint.
checkpoint_path = misc_util.get_checkpoint_path(args.out_dir)
misc_util.create_directory(os.path.dirname(checkpoint_path))
torch.save(cnn.state_dict(), checkpoint_path)
# Close logger and SummaryWriter.
csv_logger.close()
if args.tensorboard:
writer.close()
val_acc = 0
scheduler.step(epoch)
row = {
'epoch': str(epoch),
'train_acc': str(accuracy),
'val_acc': str(val_acc)
}
csv_logger.writerow(row)
acc_train[epoch] = accuracy
acc_val[epoch] = val_acc
loss_train[epoch] = xentropy_loss_avg
torch.save(cnn.state_dict(), model_weights_file)
csv_logger.close()
print("Training took {}".format(datetime.now() - start_time))
# Plot Accuracies/losses
fig, ax_arr = plt.subplots(1, 2)
ax_arr[0].plot(np.arange(args.epochs),
loss_train / i,
label='train',
color='b')
ax_arr[0].set_xlabel("Epoch")
ax_arr[0].set_ylabel("Loss")
ax_arr[1].plot(np.arange(args.epochs), acc_train, label='train', color='b')
ax_arr[1].plot(np.arange(args.epochs), acc_val, label='validation', color='r')
ax_arr[1].set_xlabel("Epoch")
