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,