(self, cfg, classnames, clip_model)
| 195 | |
| 196 | class CustomCLIP(nn.Module): |
| 197 | def __init__(self, cfg, classnames, clip_model): |
| 198 | super().__init__() |
| 199 | self.n_cls = len(classnames) |
| 200 | self.prompt_learner = PromptLearner(cfg, classnames, clip_model) |
| 201 | self.tokenized_prompts = self.prompt_learner.tokenized_prompts |
| 202 | self.image_encoder = clip_model.visual |
| 203 | self.text_encoder = TextEncoder(clip_model) |
| 204 | self.logit_scale = clip_model.logit_scale |
| 205 | self.dtype = clip_model.dtype |
| 206 | self.device = torch.device("cuda:0") |
| 207 | self.device1 = torch.device("cuda") |
| 208 | self.N = cfg.TRAINER.PLOT.N |
| 209 | self.dataset = cfg.DATASET.NAME |
| 210 | self.use_uniform = True |
| 211 | self.eps = 0.1 |
| 212 | self.max_iter = 100 |
| 213 | |
| 214 | def Sinkhorn(self, K, u, v): |
| 215 | r = torch.ones_like(u) |
no test coverage detected