(self, input_data, device=None)
| 233 | return data_output |
| 234 | |
| 235 | def val_func_process(self, input_data, device=None): |
| 236 | input_data = np.ascontiguousarray(input_data[None, :, :, :], dtype=np.float32) |
| 237 | # input_data = torch.FloatTensor(input_data).to(device) |
| 238 | # input_data = torch.tensor(input_data, device=device) |
| 239 | input_data = torch.from_numpy(input_data).to(device) |
| 240 | # print(torch.cuda.current_device(), device) |
| 241 | |
| 242 | with torch.cuda.device(input_data.get_device()): |
| 243 | self.val_func.eval() |
| 244 | self.val_func.to(input_data.get_device()) |
| 245 | with torch.no_grad(): |
| 246 | score = self.val_func(input_data) |
| 247 | score = score[0] |
| 248 | |
| 249 | if self.is_flip: |
| 250 | input_data = input_data.flip(-1) |
| 251 | score_flip = self.val_func(input_data) |
| 252 | score_flip = score_flip[0] |
| 253 | score += score_flip.flip(-1) |
| 254 | score = torch.exp(score) |
| 255 | # score = score.data |
| 256 | |
| 257 | return score |
| 258 | |
| 259 | def process_image(self, img, crop_size=None): |
| 260 | p_img = img |
no outgoing calls
no test coverage detected