| 368 | |
| 369 | |
| 370 | def test_coco_empty_annotations_poly(): |
| 371 | file_root = os.path.join(test_data_root, "db", "coco_dummy", "images") |
| 372 | train_annotations = os.path.join(test_data_root, "db", "coco_dummy", "instances.json") |
| 373 | |
| 374 | @pipeline_def(batch_size=1, device_id=0, num_threads=4) |
| 375 | def coco_pipe(): |
| 376 | _, _, _, poly, vert, ids = fn.readers.coco( |
| 377 | file_root=file_root, |
| 378 | annotations_file=train_annotations, |
| 379 | image_ids=True, |
| 380 | polygon_masks=True, |
| 381 | ) |
| 382 | return poly, vert, ids |
| 383 | |
| 384 | pipe = coco_pipe() |
| 385 | number_of_samples = pipe.epoch_size() |
| 386 | for k in number_of_samples: |
| 387 | # there is only one reader |
| 388 | number_of_samples = number_of_samples[k] |
| 389 | break |
| 390 | |
| 391 | annotations = None |
| 392 | with open(train_annotations) as file: |
| 393 | annotations = json.load(file) |
| 394 | |
| 395 | anno_mapping = {} |
| 396 | for elm in annotations["annotations"]: |
| 397 | image_id = elm["image_id"] |
| 398 | anno_mapping[image_id] = anno_mapping.get(image_id, False) or "segmentation" in elm |
| 399 | |
| 400 | for _ in range(number_of_samples): |
| 401 | poly, vert, image_ids = pipe.run() |
| 402 | image_ids = int(image_ids.as_array().item()) |
| 403 | poly = np.array(poly.as_tensor()).size |
| 404 | vert = np.array(vert.as_tensor()).size |
| 405 | assert (poly != 0 and image_ids in anno_mapping and anno_mapping[image_ids]) or ( |
| 406 | vert == 0 and not (image_ids in anno_mapping and anno_mapping[image_ids]) |
| 407 | ) |
| 408 | |
| 409 | |
| 410 | def test_coco_pix_mask_ratio(): |