def test_item2tensor_empty(item):
item = item.replace(bboxes=[], labels=[], iscrowds=[])
x, y = item2tensor(item)
assert (y['boxes'] == tensor([_fake_box], dtype=torch.float)).all()
assert y['labels'] == tensor([0])
assert y['iscrowd'] == tensor([0])
assert y['area'] == tensor([4])
def test_mask_rcnn_dataloader(records):
dataset = Dataset(records)
dl = MantisMaskRCNN.dataloader(dataset, batch_size=3)
xb, yb = first(dl)
assert len(xb) == 3
assert len(yb) == 3
x = xb[2]
assert x.shape == (3, 427, 640)
assert isinstance(x, Tensor)
y = yb[2]
assert y["image_id"] == tensor(128372)
assert allequal([isinstance(o, Tensor) for o in y.values()])
assert (y["labels"] == tensor(
[6, 1, 1, 1, 1, 1, 1, 1, 31, 31, 1, 3, 31, 1, 31, 31])).all()
assert (y["boxes"][0] == tensor([0.0000, 73.8900, 416.4400,
379.0200])).all()
assert y["masks"].shape == (16, 427, 640)
assert not (y["masks"] == 0).all()
assert allequal(lmap(len, [y["labels"], y["boxes"], y["masks"]]))
def test_build_training_sample_fasterrcnn(data_sample):
x, y = MantisFasterRCNN.build_training_sample(**data_sample)
assert x.dtype == torch.float32
assert x.shape == (3, 427, 640)
assert isinstance(y, dict)
assert set(y.keys()) == {"image_id", "labels", "boxes"}
assert y["image_id"].dtype == torch.int64
assert y["labels"].dtype == torch.int64
assert y["labels"].shape == (16, )
assert (y["labels"] == tensor(
[6, 1, 1, 1, 1, 1, 1, 1, 31, 31, 1, 3, 31, 1, 31, 31])).all()
assert y["boxes"].dtype == torch.float32
assert y["boxes"].shape == (16, 4)
def test_item2tensor(item):
x, y = item2tensor(item)
assert x.dtype == torch.float32
assert x.shape == (3, 427, 640)
assert isinstance(y, dict)
assert list(y.keys()) == [
'image_id', 'labels', 'iscrowd', 'boxes', 'area', 'masks'
]
assert y['image_id'].dtype == torch.int64
assert y['labels'].dtype == torch.int64
assert y['labels'].shape == (16, )
assert (y['labels'] == tensor(
[6, 1, 1, 1, 1, 1, 1, 1, 27, 27, 1, 3, 27, 1, 27, 27])).all()
assert y['iscrowd'].dtype == torch.uint8
assert y['iscrowd'].shape == (16, )
assert (y['iscrowd'] == tensor(
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])).all()
assert y['boxes'].dtype == torch.float32
assert y['boxes'].shape == (16, 4)
assert y['area'].shape == (16, )
assert y['masks'].dtype == torch.uint8
assert y['masks'].shape == (16, 427, 640)
def test_rcnn_dataloader():
train_dl, valid_dl = test_utils.sample_rcnn_dataloaders()
xb, yb = first(train_dl)
assert len(xb) == 2
assert len(yb) == 2
x = xb[0]
assert x.shape == (3, 427, 640)
assert isinstance(x, Tensor)
y = yb[0]
assert y['image_id'] == tensor(0)
assert allequal([isinstance(o, Tensor) for o in y.values()])
assert (y['labels'] == tensor(
[6, 1, 1, 1, 1, 1, 1, 1, 27, 27, 1, 3, 27, 1, 27, 27])).all()
assert (y['iscrowd'] == tensor(
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])).all()
assert (y['boxes'][0] == tensor([0.0000, 73.8900, 416.4400,
379.0200])).all()
assert y['masks'].shape == (16, 427, 640)
assert not (y['masks'] == 0).all()
assert allequal(
lmap(len, [y['labels'], y['iscrowd'], y['boxes'], y['masks']]))
def test_rcnn_empty_training_sample(data_sample):
data_sample["bbox"] = []
data_sample["label"] = []
x, y = MantisMaskRCNN.build_training_sample(**data_sample)
assert (y["boxes"] == tensor([_fake_box], dtype=torch.float)).all()
assert y["labels"] == tensor([0])