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import os |
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import pickle |
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from argparse import Namespace |
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import torchvision |
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import torch |
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import sys |
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import time |
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from configs import paths_config, global_config |
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from models.StyleCLIP.mapper.styleclip_mapper import StyleCLIPMapper |
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from utils.models_utils import load_tuned_G, load_old_G |
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sys.path.append(".") |
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sys.path.append("..") |
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def run(test_opts, model_id, image_name, use_multi_id_G): |
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out_path_results = os.path.join(test_opts.exp_dir, test_opts.data_dir_name) |
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os.makedirs(out_path_results, exist_ok=True) |
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out_path_results = os.path.join(out_path_results, test_opts.image_name) |
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os.makedirs(out_path_results, exist_ok=True) |
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ckpt = torch.load(test_opts.checkpoint_path, map_location='cpu') |
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opts = ckpt['opts'] |
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opts.update(vars(test_opts)) |
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opts = Namespace(**opts) |
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net = StyleCLIPMapper(opts, test_opts.run_id) |
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net.eval() |
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net.to(global_config.device) |
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generator_type = paths_config.multi_id_model_type if use_multi_id_G else image_name |
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new_G = load_tuned_G(model_id, generator_type) |
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old_G = load_old_G() |
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run_styleclip(net, new_G, opts, paths_config.pti_results_keyword, out_path_results, test_opts) |
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run_styleclip(net, old_G, opts, paths_config.e4e_results_keyword, out_path_results, test_opts) |
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def run_styleclip(net, G, opts, method, out_path_results, test_opts): |
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net.set_G(G) |
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out_path_results = os.path.join(out_path_results, method) |
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os.makedirs(out_path_results, exist_ok=True) |
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latent = torch.load(opts.latents_test_path) |
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global_i = 0 |
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global_time = [] |
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with torch.no_grad(): |
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input_cuda = latent.cuda().float() |
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tic = time.time() |
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result_batch = run_on_batch(input_cuda, net, test_opts.couple_outputs) |
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toc = time.time() |
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global_time.append(toc - tic) |
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for i in range(opts.test_batch_size): |
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im_path = f'{test_opts.image_name}_{test_opts.edit_name}' |
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if test_opts.couple_outputs: |
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couple_output = torch.cat([result_batch[2][i].unsqueeze(0), result_batch[0][i].unsqueeze(0)]) |
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torchvision.utils.save_image(couple_output, os.path.join(out_path_results, f"{im_path}.jpg"), |
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normalize=True, range=(-1, 1)) |
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else: |
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torchvision.utils.save_image(result_batch[0][i], os.path.join(out_path_results, f"{im_path}.jpg"), |
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normalize=True, range=(-1, 1)) |
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torch.save(result_batch[1][i].detach().cpu(), os.path.join(out_path_results, f"latent_{im_path}.pt")) |
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def run_on_batch(inputs, net, couple_outputs=False): |
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w = inputs |
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with torch.no_grad(): |
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w_hat = w + 0.06 * net.mapper(w) |
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x_hat = net.decoder.synthesis(w_hat, noise_mode='const', force_fp32=True) |
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result_batch = (x_hat, w_hat) |
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if couple_outputs: |
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x = net.decoder.synthesis(w, noise_mode='const', force_fp32=True) |
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result_batch = (x_hat, w_hat, x) |
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return result_batch |
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