gaussian_record = open('test/gaussian_record.txt', 'w')
for word_list in generate_words:
for i, word in enumerate(word_list):
if (i + 1) % 4 != 0:
word += ', '
else:
word += '\n'
print(word, file=gaussian_record, end='')
print('Gaussian score: ', gaussian_score, file=gaussian_record)
gaussian_record.close()
test_dataset = WordDataset('test')
max_length = test_dataset.max_length
dataloader = DataLoader(test_dataset, batch_size=1, shuffle=False)
tense_list = dataloader.dataset.tense2idx.values()
transformer = WordTransoformer()
# Epoch 57 is the best
loadmodel = 'model/cycle_500/checkpoint57.pkl'
model = CVAE(max_length)
model = model.cuda()
state_dict = torch.load(loadmodel)
model.load_state_dict(state_dict)
average_bleu_score, predict_list, gaussian_score, generate_words = evaluate(
model, dataloader, tense_list)
record_score(average_bleu_score, gaussian_score, predict_list, generate_words,
dataloader, transformer)
if __name__ == '__main__':
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Dataset
datasets, dataloaders, dataset_sizes = get_data(num_quadrant_inputs=1,
batch_size=128)
baseline_net = BaselineNet(500, 500)
baseline_net.load_state_dict(
torch.load('/Users/carlossouza/Downloads/baseline_net_q1.pth',
map_location='cpu'))
baseline_net.eval()
cvae_net = CVAE(200, 500, 500, baseline_net)
cvae_net.load_state_dict(
torch.load('/Users/carlossouza/Downloads/cvae_net_q1.pth',
map_location='cpu'))
cvae_net.eval()
visualize(device=device,
num_quadrant_inputs=1,
pre_trained_baseline=baseline_net,
pre_trained_cvae=cvae_net,
num_images=10,
num_samples=10)
# df = generate_table(
# device=device,
# num_quadrant_inputs=1,
# pre_trained_baseline=baseline_net,
# pre_trained_cvae=cvae_net,