def main():
trained_model = './trained_model.pth'
test_batch_dir = './cifar-10/test_batch'
classifier = CNNModel()
classifier.load_state_dict(torch.load(trained_model))
classifier.cuda()
classifier.eval()
test_x, test_y = unpickle(test_batch_dir)
test_x, test_y = torch.tensor(np.reshape(
test_x, (len(test_x), 3, 32, 32))).to(
'cuda', dtype=torch.float), torch.tensor(test_y).cuda()
classes = [
'Airplane', 'Automobile', 'Bird', 'Cat', 'Deer', 'Dog', 'Frog',
'Horse', 'Ship', 'Truck'
]
# calculating the accuracy of our classifier;
print("Calculating accuracy...")
correct = 0
total = len(test_x)
with torch.no_grad():
out = classifier(test_x)
_, predicted = torch.max(out, 1)
# calculate the total accuracy
correct += (predicted == test_y).sum().item()
print('Accuracy: %5d %%' % (correct / total * 100))
total_loss += loss.data[0]
total_acc += (y_pred == y_batch).data.sum() - (y_batch
== 0).data.sum()
# Update progressbar
widgets[0] = FormatLabel('{}/{}'.format(
(i * batch_size + x_batch.size(0)), x_train.shape[0]))
widgets[-1] = FormatLabel(
'train_loss:{:.4f}, train_acc:{:.4f}'.format(
total_loss / (i + 1), total_acc / nonzeros))
pbar.update(i * batch_size + x_batch.size(0))
pbar.finish()
model.eval()
# Validation test
total_loss, total_acc, total_ed, nonzeros = 0, 0, 0, 0
for i, (x_batch, y_batch) in enumerate(valid_loader):
x_batch = Variable(x_batch).cuda()
y_batch = Variable(y_batch).cuda()
output = model(x_batch)
loss = loss_func(output.view(-1, output.size(-1)),
y_batch.view(-1))
y_pred = torch.max(output, -1)[1]
y_pred = y_pred.masked_fill_((y_batch == 0), 0)
nonzeros += (y_batch != 0).data.sum()
total_loss += loss.data[0]
