dataset.text_encoder_type)
# generate images from pre-extracted embeddings
if not cfg.B_VALIDATION:
# generate images for input test set (i.e. custom captions)
print(
'\nRunning on example captions...\n++++++++++++++++++++++++++++++'
)
root_dir_g = gen_example(dataset.wordtoix,
dataset.text_encoder_type, evaluator)
end_t = time.time()
print('Total time for running on example captions:',
end_t - start_t)
else:
# generate images for the whole valid dataset
print('\nValidating...\n+++++++++++++')
root_dir_g = evaluator.sampling(split_dir)
end_t = time.time()
print('Total time for validation:', end_t - start_t)
print()
# GAN Metrics
if cfg.B_FID or cfg.B_PPL: # or cfg.B_IS:
device = torch.device('cuda' if (
torch.cuda.is_available()) else 'cpu')
num_metrics = 0
final_dir_g = str(Path(root_dir_g).parent / 'metrics')
# compute FID
if cfg.B_FID:
print(
'++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++'
)
