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Runtime error
Runtime error
| import torch | |
| def run_inversion(inputs, net, opts, return_intermediate_results=False): | |
| y_hat, latent, weights_deltas, codes = None, None, None, None | |
| if return_intermediate_results: | |
| results_batch = {idx: [] for idx in range(inputs.shape[0])} | |
| results_latent = {idx: [] for idx in range(inputs.shape[0])} | |
| results_deltas = {idx: [] for idx in range(inputs.shape[0])} | |
| else: | |
| results_batch, results_latent, results_deltas = None, None, None | |
| for iter in range(opts.n_iters_per_batch): | |
| y_hat, latent, weights_deltas, codes, _ = net.forward(inputs, | |
| y_hat=y_hat, | |
| codes=codes, | |
| weights_deltas=weights_deltas, | |
| return_latents=True, | |
| resize=opts.resize_outputs, | |
| randomize_noise=False, | |
| return_weight_deltas_and_codes=True) | |
| if "cars" in opts.dataset_type: | |
| if opts.resize_outputs: | |
| y_hat = y_hat[:, :, 32:224, :] | |
| else: | |
| y_hat = y_hat[:, :, 64:448, :] | |
| if return_intermediate_results: | |
| store_intermediate_results(results_batch, results_latent, results_deltas, y_hat, latent, weights_deltas) | |
| # resize input to 256 before feeding into next iteration | |
| if "cars" in opts.dataset_type: | |
| y_hat = torch.nn.AdaptiveAvgPool2d((192, 256))(y_hat) | |
| else: | |
| y_hat = net.face_pool(y_hat) | |
| if return_intermediate_results: | |
| return results_batch, results_latent, results_deltas | |
| return y_hat, latent, weights_deltas, codes | |
| def store_intermediate_results(results_batch, results_latent, results_deltas, y_hat, latent, weights_deltas): | |
| for idx in range(y_hat.shape[0]): | |
| results_batch[idx].append(y_hat[idx]) | |
| results_latent[idx].append(latent[idx].cpu().numpy()) | |
| results_deltas[idx].append([w[idx].cpu().numpy() if w is not None else None for w in weights_deltas]) | |