| from model.utils import get_config, tensor2im |
| from model.inference_handler import InferenceHandler |
| from model.dataset import Image_Editing_Dataset |
|
|
| import torch |
| import cv2 |
|
|
| from torch.utils.data import DataLoader |
|
|
| def get_cfg(): |
| cfg = get_config("checkpoints/config.yaml") |
|
|
| cfg['lab_dim'] = 151 |
| cfg['max_epoch'] = 500 |
| cfg['test_freq'] = 20 |
|
|
| cfg["is_train"] = False |
| cfg["dataset_name"] = "flickr-landscape" |
| return cfg |
|
|
| def get_inference_handler(cfg): |
| inference_handler = InferenceHandler(cfg) |
| inference_handler.eval() |
| inference_handler.load_checkpoint(ckpt_filename="checkpoints/best.pth") |
| return inference_handler |
|
|
| def get_dataloader(cfg): |
| dataset_root = "gradio_files/samples" |
| dataset = Image_Editing_Dataset(cfg, dataset_root, split='test', dataset_name="flickr-landscape") |
| return DataLoader(dataset=dataset, batch_size=1, shuffle=False) |
|
|
| def start_inference(): |
| cfg = get_cfg() |
| inference_handler = get_inference_handler(cfg) |
| dataloader = get_dataloader(cfg) |
| cached_codes = torch.load("checkpoints/style_codes.pt", map_location=torch.device("cpu")) |
| save_path = 'gradio_files/samples/synthesized_image/result.png' |
| with torch.no_grad(): |
| cfg['mask_type'] = '0' |
| for i, data in enumerate(dataloader): |
| inference_handler.set_input(data) |
| inference_handler.forward(cached_codes) |
| result = inference_handler.get_results() |
| cv2.imwrite(save_path, tensor2im(result)) |
| return save_path |
|
|
| if __name__ == "__main__": |
| start_inference() |
|
|
