def test_map_semantic_img_fast_pytorch_uint16_stress_test():
"""
Test fast method on large example. Map 60,000 classes to a
different 60,000 classes with a fast array conversion.
np.int16 cannot hold anything over 32767 / 32768.
Should take less than 1 millisecond.
"""
semantic_img = np.array(range(60000)).reshape(3000, 20).astype(np.int64)
semantic_img = torch.from_numpy(semantic_img).type(torch.LongTensor)
label_mapping = {}
for i in range(60000):
label_mapping[i] = i + 1
label_mapping_copy = copy.deepcopy(label_mapping)
label_mapping_arr = form_label_mapping_array_pytorch(label_mapping)
label_mapping_arr_copy = label_mapping_arr.clone()
assert label_mapping == label_mapping_copy
start = time.time()
mapped_img = map_semantic_img_fast_pytorch(semantic_img, label_mapping_arr)
end = time.time()
assert torch.allclose(label_mapping_arr_copy, label_mapping_arr)
assert mapped_img.shape == torch.Size([3000, 20])
assert mapped_img.dtype == torch.int64
print(f'Took {end-start} sec.')
gt_mapped_img = np.array(range(60000)).reshape(3000, 20).astype(
np.uint16) + 1
assert np.allclose(gt_mapped_img, mapped_img.numpy())
def test_map_semantic_img_fast_pytorch():
"""
Test fast method on simple conversion from 2x3 grayscale -> 2x3 grayscale.
"""
semantic_img = np.array([[254, 0, 1], [7, 8, 9]], dtype=np.uint8)
semantic_img = torch.from_numpy(semantic_img).type(torch.LongTensor)
label_mapping = {254: 253, 0: 255, 1: 0, 7: 6, 8: 7, 9: 8}
label_mapping_arr = form_label_mapping_array_pytorch(label_mapping)
mapped_img = map_semantic_img_fast_pytorch(semantic_img, label_mapping_arr)
gt_mapped_img = np.array([[253, 255, 0], [6, 7, 8]], dtype=np.uint8)
assert np.allclose(gt_mapped_img, mapped_img.numpy())
assert mapped_img.dtype == torch.int64
def transform_label(self, label: torch.Tensor,
dataset: str) -> Mapping[int, torch.Tensor]:
""" Function to be called externally for training.
Perform fast grayscale->grayscale mapping for training data transformation.
Args:
- label: Pytorch tensor on the cpu with dtype Torch.LongTensor,
representing a semantic image, according to PNG/dataloader format.
- dataset: string representing dataset's name
Returns:
- labels: vector label for each depth in the tree, compatible with softmax
"""
from mseg.utils.mask_utils import map_semantic_img_fast_pytorch
label = map_semantic_img_fast_pytorch(
label, self.label_mapping_arr_dict[dataset])
return label
def transform_label(self, label: torch.Tensor,
dataset: str) -> Mapping[int, torch.Tensor]:
"""Perform fast grayscale->grayscale mapping for training data transformation.
Note: Function to be called externally for training.
Args:
label: Pytorch tensor on the cpu of shape (H,W) with dtype Torch.LongTensor,
representing a semantic image, according to PNG/dataloader format.
dataset: string representing dataset's name
Returns:
label: tensor also of shape (H,W), representing semantic classes in new taxonomy at each pixel
"""
from mseg.utils.mask_utils import map_semantic_img_fast_pytorch
label = map_semantic_img_fast_pytorch(
label, self.label_mapping_arr_dict[dataset])
return label
