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| # Copyright (c) OpenMMLab. All rights reserved. | |
| """pytest tests/test_loss_compatibility.py.""" | |
| import copy | |
| from os.path import dirname, exists, join | |
| import numpy as np | |
| import pytest | |
| import torch | |
| def _get_config_directory(): | |
| """Find the predefined detector config directory.""" | |
| try: | |
| # Assume we are running in the source mmdetection repo | |
| repo_dpath = dirname(dirname(dirname(__file__))) | |
| except NameError: | |
| # For IPython development when this __file__ is not defined | |
| import mmdet | |
| repo_dpath = dirname(dirname(mmdet.__file__)) | |
| config_dpath = join(repo_dpath, 'configs') | |
| if not exists(config_dpath): | |
| raise Exception('Cannot find config path') | |
| return config_dpath | |
| def _get_config_module(fname): | |
| """Load a configuration as a python module.""" | |
| from mmcv import Config | |
| config_dpath = _get_config_directory() | |
| config_fpath = join(config_dpath, fname) | |
| config_mod = Config.fromfile(config_fpath) | |
| return config_mod | |
| def _get_detector_cfg(fname): | |
| """Grab configs necessary to create a detector. | |
| These are deep copied to allow for safe modification of parameters without | |
| influencing other tests. | |
| """ | |
| config = _get_config_module(fname) | |
| model = copy.deepcopy(config.model) | |
| return model | |
| def test_bbox_loss_compatibility(loss_bbox): | |
| """Test loss_bbox compatibility. | |
| Using Faster R-CNN as a sample, modifying the loss function in the config | |
| file to verify the compatibility of Loss APIS | |
| """ | |
| # Faster R-CNN config dict | |
| config_path = '_base_/models/faster_rcnn_r50_fpn.py' | |
| cfg_model = _get_detector_cfg(config_path) | |
| input_shape = (1, 3, 256, 256) | |
| mm_inputs = _demo_mm_inputs(input_shape, num_items=[10]) | |
| imgs = mm_inputs.pop('imgs') | |
| img_metas = mm_inputs.pop('img_metas') | |
| if 'IoULoss' in loss_bbox['type']: | |
| cfg_model.roi_head.bbox_head.reg_decoded_bbox = True | |
| cfg_model.roi_head.bbox_head.loss_bbox = loss_bbox | |
| from mmdet.models import build_detector | |
| detector = build_detector(cfg_model) | |
| loss = detector.forward(imgs, img_metas, return_loss=True, **mm_inputs) | |
| assert isinstance(loss, dict) | |
| loss, _ = detector._parse_losses(loss) | |
| assert float(loss.item()) > 0 | |
| def test_cls_loss_compatibility(loss_cls): | |
| """Test loss_cls compatibility. | |
| Using Faster R-CNN as a sample, modifying the loss function in the config | |
| file to verify the compatibility of Loss APIS | |
| """ | |
| # Faster R-CNN config dict | |
| config_path = '_base_/models/faster_rcnn_r50_fpn.py' | |
| cfg_model = _get_detector_cfg(config_path) | |
| input_shape = (1, 3, 256, 256) | |
| mm_inputs = _demo_mm_inputs(input_shape, num_items=[10]) | |
| imgs = mm_inputs.pop('imgs') | |
| img_metas = mm_inputs.pop('img_metas') | |
| # verify class loss function compatibility | |
| # for loss_cls in loss_clses: | |
| cfg_model.roi_head.bbox_head.loss_cls = loss_cls | |
| from mmdet.models import build_detector | |
| detector = build_detector(cfg_model) | |
| loss = detector.forward(imgs, img_metas, return_loss=True, **mm_inputs) | |
| assert isinstance(loss, dict) | |
| loss, _ = detector._parse_losses(loss) | |
| assert float(loss.item()) > 0 | |
| def _demo_mm_inputs(input_shape=(1, 3, 300, 300), | |
| num_items=None, num_classes=10, | |
| with_semantic=False): # yapf: disable | |
| """Create a superset of inputs needed to run test or train batches. | |
| Args: | |
| input_shape (tuple): | |
| input batch dimensions | |
| num_items (None | List[int]): | |
| specifies the number of boxes in each batch item | |
| num_classes (int): | |
| number of different labels a box might have | |
| """ | |
| from mmdet.core import BitmapMasks | |
| (N, C, H, W) = input_shape | |
| rng = np.random.RandomState(0) | |
| imgs = rng.rand(*input_shape) | |
| img_metas = [{ | |
| 'img_shape': (H, W, C), | |
| 'ori_shape': (H, W, C), | |
| 'pad_shape': (H, W, C), | |
| 'filename': '<demo>.png', | |
| 'scale_factor': np.array([1.1, 1.2, 1.1, 1.2]), | |
| 'flip': False, | |
| 'flip_direction': None, | |
| } for _ in range(N)] | |
| gt_bboxes = [] | |
| gt_labels = [] | |
| gt_masks = [] | |
| for batch_idx in range(N): | |
| if num_items is None: | |
| num_boxes = rng.randint(1, 10) | |
| else: | |
| num_boxes = num_items[batch_idx] | |
| cx, cy, bw, bh = rng.rand(num_boxes, 4).T | |
| tl_x = ((cx * W) - (W * bw / 2)).clip(0, W) | |
| tl_y = ((cy * H) - (H * bh / 2)).clip(0, H) | |
| br_x = ((cx * W) + (W * bw / 2)).clip(0, W) | |
| br_y = ((cy * H) + (H * bh / 2)).clip(0, H) | |
| boxes = np.vstack([tl_x, tl_y, br_x, br_y]).T | |
| class_idxs = rng.randint(1, num_classes, size=num_boxes) | |
| gt_bboxes.append(torch.FloatTensor(boxes)) | |
| gt_labels.append(torch.LongTensor(class_idxs)) | |
| mask = np.random.randint(0, 2, (len(boxes), H, W), dtype=np.uint8) | |
| gt_masks.append(BitmapMasks(mask, H, W)) | |
| mm_inputs = { | |
| 'imgs': torch.FloatTensor(imgs).requires_grad_(True), | |
| 'img_metas': img_metas, | |
| 'gt_bboxes': gt_bboxes, | |
| 'gt_labels': gt_labels, | |
| 'gt_bboxes_ignore': None, | |
| 'gt_masks': gt_masks, | |
| } | |
| if with_semantic: | |
| # assume gt_semantic_seg using scale 1/8 of the img | |
| gt_semantic_seg = np.random.randint( | |
| 0, num_classes, (1, 1, H // 8, W // 8), dtype=np.uint8) | |
| mm_inputs.update( | |
| {'gt_semantic_seg': torch.ByteTensor(gt_semantic_seg)}) | |
| return mm_inputs | |