| 10 | from ram.archs.swinir_arch import SwinIR |
| 11 | import os.path as osp |
| 12 | def define_model(args): |
| 13 | if args.model =="ram_promptir": |
| 14 | model = PromptIR(decoder=True) |
| 15 | elif args.model == 'ram_swinir': |
| 16 | model = SwinIR( |
| 17 | patch_size = 1, |
| 18 | in_chans = 3, |
| 19 | embed_dim = 180, |
| 20 | depths = [ 6, 6, 6, 6, 6, 6], |
| 21 | num_heads = [ 6, 6, 6, 6, 6, 6 ], |
| 22 | mlp_ratio = 2, |
| 23 | window_size = 8, |
| 24 | finetune_type = None, |
| 25 | upscale = 1 |
| 26 | ) |
| 27 | else: |
| 28 | raise NotImplementedError |
| 29 | loadnet = torch.load(args.model_path) |
| 30 | if 'params_ema' in loadnet: |
| 31 | keyname = 'params_ema' |
| 32 | else: |
| 33 | keyname = 'params' |
| 34 | model.load_state_dict(loadnet[keyname], strict=False) |
| 35 | return model |
| 36 | |
| 37 | def process_image(img_path, model, device, args): |
| 38 | imgname = osp.splitext(osp.basename(img_path))[0] |