(model, image)
| 282 | return image |
| 283 | |
| 284 | def tta_model(model, image): |
| 285 | n_image = image |
| 286 | h_image = horizontal_flip(image) |
| 287 | v_image = vertical_flip(image) |
| 288 | |
| 289 | n_mask = model.predict(np.expand_dims(n_image, axis=0))[0] |
| 290 | h_mask = model.predict(np.expand_dims(h_image, axis=0))[0] |
| 291 | v_mask = model.predict(np.expand_dims(v_image, axis=0))[0] |
| 292 | |
| 293 | n_mask = n_mask |
| 294 | h_mask = horizontal_flip(h_mask) |
| 295 | v_mask = vertical_flip(v_mask) |
| 296 | |
| 297 | mean_mask = (n_mask + h_mask + v_mask) / 3.0 |
| 298 | return mean_mask |
| 299 | |
| 300 | def cal_params_flops(model, size, logger): |
| 301 | input = torch.randn(1, 3, size, size).cuda() |
nothing calls this directly
no test coverage detected