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- import os
- import shutil
- import torch
- import math
- from torch_fidelity import calculate_metrics
- from torchvision.utils import save_image
- from tqdm import tqdm
- import copy
- import argparse
- import shutil
- import curriculums
- if __name__ == '__main__':
- parser = argparse.ArgumentParser()
- parser.add_argument('generator_file', type=str)
- parser.add_argument('--real_image_dir', type=str, required=True)
- parser.add_argument('--output_dir', type=str, default='temp')
- parser.add_argument('--num_images', type=int, default=2048)
- parser.add_argument('--max_batch_size', type=int, default=94800000)
- parser.add_argument('--curriculum', type=str, default='CELEBA')
- opt = parser.parse_args()
- device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
- if os.path.exists(opt.output_dir) and os.path.isdir(opt.output_dir):
- shutil.rmtree(opt.output_dir)
-
- os.makedirs(opt.output_dir, exist_ok=False)
- generator = torch.load(opt.generator_file, map_location=device)
- generator.set_device(device)
- ema_file = opt.generator_file.split('generator')[0] + 'ema.pth'
- ema = torch.load(ema_file)
- ema.copy_to(generator.parameters())
- generator.eval()
-
- curriculum = curriculums.extract_metadata(getattr(curriculums, opt.curriculum), generator.step)
- curriculum['img_size'] = 128
- curriculum['psi'] = 1
- curriculum['last_back'] = curriculum.get('eval_last_back', False)
- curriculum['nerf_noise'] = 0
-
- for img_counter in tqdm(range(opt.num_images)):
- z = torch.randn(1, 256, device=device)
- with torch.no_grad():
- img = generator.staged_forward(z, max_batch_size=opt.max_batch_size, **curriculum)[0].to(device)
- save_image(img, os.path.join(opt.output_dir, f'{img_counter:0>5}.jpg'), normalize=True, range=(-1, 1))
- metrics_dict = calculate_metrics(opt.output_dir, opt.real_image_dir, cuda=True, isc=True, fid=True, kid=True, verbose=False)
- print(metrics_dict)
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