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from argparse import Namespace |
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import os |
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import time |
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from tqdm import tqdm |
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from PIL import Image |
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import numpy as np |
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import torch |
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from torch.utils.data import DataLoader |
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import sys |
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sys.path.append(".") |
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sys.path.append("..") |
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from configs import data_configs |
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from datasets.inference_dataset import InferenceDataset |
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from datasets.augmentations import AgeTransformer |
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from utils.common import tensor2im, log_image |
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from options.test_options import TestOptions |
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from models.psp import pSp |
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def run(): |
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test_opts = TestOptions().parse() |
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out_path_results = os.path.join(test_opts.exp_dir, 'inference_side_by_side') |
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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 = pSp(opts) |
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net.eval() |
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net.cuda() |
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age_transformers = [AgeTransformer(target_age=age) for age in opts.target_age.split(',')] |
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print(f'Loading dataset for {opts.dataset_type}') |
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dataset_args = data_configs.DATASETS[opts.dataset_type] |
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transforms_dict = dataset_args['transforms'](opts).get_transforms() |
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dataset = InferenceDataset(root=opts.data_path, |
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transform=transforms_dict['transform_inference'], |
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opts=opts, |
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return_path=True) |
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dataloader = DataLoader(dataset, |
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batch_size=opts.test_batch_size, |
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shuffle=False, |
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num_workers=int(opts.test_workers), |
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drop_last=False) |
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if opts.n_images is None: |
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opts.n_images = len(dataset) |
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global_time = [] |
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global_i = 0 |
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for input_batch, image_paths in tqdm(dataloader): |
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if global_i >= opts.n_images: |
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break |
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batch_results = {} |
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for idx, age_transformer in enumerate(age_transformers): |
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with torch.no_grad(): |
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input_age_batch = [age_transformer(img.cpu()).to('cuda') for img in input_batch] |
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input_age_batch = torch.stack(input_age_batch) |
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input_cuda = input_age_batch.cuda().float() |
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tic = time.time() |
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result_batch = run_on_batch(input_cuda, net, opts) |
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toc = time.time() |
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global_time.append(toc - tic) |
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resize_amount = (256, 256) if opts.resize_outputs else (1024, 1024) |
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for i in range(len(input_batch)): |
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result = tensor2im(result_batch[i]) |
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im_path = image_paths[i] |
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input_im = log_image(input_batch[i], opts) |
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if im_path not in batch_results.keys(): |
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batch_results[im_path] = np.array(input_im.resize(resize_amount)) |
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batch_results[im_path] = np.concatenate([batch_results[im_path], |
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np.array(result.resize(resize_amount))], |
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axis=1) |
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for im_path, res in batch_results.items(): |
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image_name = os.path.basename(im_path) |
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im_save_path = os.path.join(out_path_results, image_name) |
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Image.fromarray(np.array(res)).save(im_save_path) |
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global_i += 1 |
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def run_on_batch(inputs, net, opts): |
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result_batch = net(inputs, randomize_noise=False, resize=opts.resize_outputs) |
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return result_batch |
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if __name__ == '__main__': |
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run() |
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