Python mmcv.slice_list() Examples
The following are 21
code examples of mmcv.slice_list().
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Example #1
| Source File: utils.py From IoU-Uniform-R-CNN with Apache License 2.0 | 5 votes |
|
def split_combined_polys(polys, poly_lens, polys_per_mask):
"""Split the combined 1-D polys into masks.
A mask is represented as a list of polys, and a poly is represented as
a 1-D array. In dataset, all masks are concatenated into a single 1-D
tensor. Here we need to split the tensor into original representations.
Args:
polys (list): a list (length = image num) of 1-D tensors
poly_lens (list): a list (length = image num) of poly length
polys_per_mask (list): a list (length = image num) of poly number
of each mask
Returns:
list: a list (length = image num) of list (length = mask num) of
list (length = poly num) of numpy array
"""
mask_polys_list = []
for img_id in range(len(polys)):
polys_single = polys[img_id]
polys_lens_single = poly_lens[img_id].tolist()
polys_per_mask_single = polys_per_mask[img_id].tolist()
split_polys = mmcv.slice_list(polys_single, polys_lens_single)
mask_polys = mmcv.slice_list(split_polys, polys_per_mask_single)
mask_polys_list.append(mask_polys)
return mask_polys_list
Example #2
| Source File: utils.py From AugFPN with Apache License 2.0 | 5 votes |
|
def split_combined_polys(polys, poly_lens, polys_per_mask):
"""Split the combined 1-D polys into masks.
A mask is represented as a list of polys, and a poly is represented as
a 1-D array. In dataset, all masks are concatenated into a single 1-D
tensor. Here we need to split the tensor into original representations.
Args:
polys (list): a list (length = image num) of 1-D tensors
poly_lens (list): a list (length = image num) of poly length
polys_per_mask (list): a list (length = image num) of poly number
of each mask
Returns:
list: a list (length = image num) of list (length = mask num) of
list (length = poly num) of numpy array
"""
mask_polys_list = []
for img_id in range(len(polys)):
polys_single = polys[img_id]
polys_lens_single = poly_lens[img_id].tolist()
polys_per_mask_single = polys_per_mask[img_id].tolist()
split_polys = mmcv.slice_list(polys_single, polys_lens_single)
mask_polys = mmcv.slice_list(split_polys, polys_per_mask_single)
mask_polys_list.append(mask_polys)
return mask_polys_list
Example #3
| Source File: utils.py From ttfnet with Apache License 2.0 | 5 votes |
|
def split_combined_polys(polys, poly_lens, polys_per_mask):
"""Split the combined 1-D polys into masks.
A mask is represented as a list of polys, and a poly is represented as
a 1-D array. In dataset, all masks are concatenated into a single 1-D
tensor. Here we need to split the tensor into original representations.
Args:
polys (list): a list (length = image num) of 1-D tensors
poly_lens (list): a list (length = image num) of poly length
polys_per_mask (list): a list (length = image num) of poly number
of each mask
Returns:
list: a list (length = image num) of list (length = mask num) of
list (length = poly num) of numpy array
"""
mask_polys_list = []
for img_id in range(len(polys)):
polys_single = polys[img_id]
polys_lens_single = poly_lens[img_id].tolist()
polys_per_mask_single = polys_per_mask[img_id].tolist()
split_polys = mmcv.slice_list(polys_single, polys_lens_single)
mask_polys = mmcv.slice_list(split_polys, polys_per_mask_single)
mask_polys_list.append(mask_polys)
return mask_polys_list
Example #4
| Source File: utils.py From CenterNet with Apache License 2.0 | 5 votes |
|
def split_combined_polys(polys, poly_lens, polys_per_mask):
"""Split the combined 1-D polys into masks.
A mask is represented as a list of polys, and a poly is represented as
a 1-D array. In dataset, all masks are concatenated into a single 1-D
tensor. Here we need to split the tensor into original representations.
Args:
polys (list): a list (length = image num) of 1-D tensors
poly_lens (list): a list (length = image num) of poly length
polys_per_mask (list): a list (length = image num) of poly number
of each mask
Returns:
list: a list (length = image num) of list (length = mask num) of
list (length = poly num) of numpy array
"""
mask_polys_list = []
for img_id in range(len(polys)):
polys_single = polys[img_id]
polys_lens_single = poly_lens[img_id].tolist()
polys_per_mask_single = polys_per_mask[img_id].tolist()
split_polys = mmcv.slice_list(polys_single, polys_lens_single)
mask_polys = mmcv.slice_list(split_polys, polys_per_mask_single)
mask_polys_list.append(mask_polys)
return mask_polys_list
Example #5
| Source File: utils.py From hrnet with MIT License | 5 votes |
|
def split_combined_polys(polys, poly_lens, polys_per_mask):
"""Split the combined 1-D polys into masks.
A mask is represented as a list of polys, and a poly is represented as
a 1-D array. In dataset, all masks are concatenated into a single 1-D
tensor. Here we need to split the tensor into original representations.
Args:
polys (list): a list (length = image num) of 1-D tensors
poly_lens (list): a list (length = image num) of poly length
polys_per_mask (list): a list (length = image num) of poly number
of each mask
Returns:
list: a list (length = image num) of list (length = mask num) of
list (length = poly num) of numpy array
"""
mask_polys_list = []
for img_id in range(len(polys)):
polys_single = polys[img_id]
polys_lens_single = poly_lens[img_id].tolist()
polys_per_mask_single = polys_per_mask[img_id].tolist()
split_polys = mmcv.slice_list(polys_single, polys_lens_single)
mask_polys = mmcv.slice_list(split_polys, polys_per_mask_single)
mask_polys_list.append(mask_polys)
return mask_polys_list
Example #6
| Source File: utils.py From kaggle-imaterialist with MIT License | 5 votes |
|
def split_combined_polys(polys, poly_lens, polys_per_mask):
"""Split the combined 1-D polys into masks.
A mask is represented as a list of polys, and a poly is represented as
a 1-D array. In dataset, all masks are concatenated into a single 1-D
tensor. Here we need to split the tensor into original representations.
Args:
polys (list): a list (length = image num) of 1-D tensors
poly_lens (list): a list (length = image num) of poly length
polys_per_mask (list): a list (length = image num) of poly number
of each mask
Returns:
list: a list (length = image num) of list (length = mask num) of
list (length = poly num) of numpy array
"""
mask_polys_list = []
for img_id in range(len(polys)):
polys_single = polys[img_id]
polys_lens_single = poly_lens[img_id].tolist()
polys_per_mask_single = polys_per_mask[img_id].tolist()
split_polys = mmcv.slice_list(polys_single, polys_lens_single)
mask_polys = mmcv.slice_list(split_polys, polys_per_mask_single)
mask_polys_list.append(mask_polys)
return mask_polys_list
Example #7
| Source File: utils.py From Feature-Selective-Anchor-Free-Module-for-Single-Shot-Object-Detection with Apache License 2.0 | 5 votes |
|
def split_combined_polys(polys, poly_lens, polys_per_mask):
"""Split the combined 1-D polys into masks.
A mask is represented as a list of polys, and a poly is represented as
a 1-D array. In dataset, all masks are concatenated into a single 1-D
tensor. Here we need to split the tensor into original representations.
Args:
polys (list): a list (length = image num) of 1-D tensors
poly_lens (list): a list (length = image num) of poly length
polys_per_mask (list): a list (length = image num) of poly number
of each mask
Returns:
list: a list (length = image num) of list (length = mask num) of
list (length = poly num) of numpy array
"""
mask_polys_list = []
for img_id in range(len(polys)):
polys_single = polys[img_id]
polys_lens_single = poly_lens[img_id].tolist()
polys_per_mask_single = polys_per_mask[img_id].tolist()
split_polys = mmcv.slice_list(polys_single, polys_lens_single)
mask_polys = mmcv.slice_list(split_polys, polys_per_mask_single)
mask_polys_list.append(mask_polys)
return mask_polys_list
Example #8
| Source File: utils.py From Cascade-RPN with Apache License 2.0 | 5 votes |
|
def split_combined_polys(polys, poly_lens, polys_per_mask):
"""Split the combined 1-D polys into masks.
A mask is represented as a list of polys, and a poly is represented as
a 1-D array. In dataset, all masks are concatenated into a single 1-D
tensor. Here we need to split the tensor into original representations.
Args:
polys (list): a list (length = image num) of 1-D tensors
poly_lens (list): a list (length = image num) of poly length
polys_per_mask (list): a list (length = image num) of poly number
of each mask
Returns:
list: a list (length = image num) of list (length = mask num) of
list (length = poly num) of numpy array
"""
mask_polys_list = []
for img_id in range(len(polys)):
polys_single = polys[img_id]
polys_lens_single = poly_lens[img_id].tolist()
polys_per_mask_single = polys_per_mask[img_id].tolist()
split_polys = mmcv.slice_list(polys_single, polys_lens_single)
mask_polys = mmcv.slice_list(split_polys, polys_per_mask_single)
mask_polys_list.append(mask_polys)
return mask_polys_list
Example #9
| Source File: utils.py From FoveaBox with Apache License 2.0 | 5 votes |
|
def split_combined_polys(polys, poly_lens, polys_per_mask):
"""Split the combined 1-D polys into masks.
A mask is represented as a list of polys, and a poly is represented as
a 1-D array. In dataset, all masks are concatenated into a single 1-D
tensor. Here we need to split the tensor into original representations.
Args:
polys (list): a list (length = image num) of 1-D tensors
poly_lens (list): a list (length = image num) of poly length
polys_per_mask (list): a list (length = image num) of poly number
of each mask
Returns:
list: a list (length = image num) of list (length = mask num) of
list (length = poly num) of numpy array
"""
mask_polys_list = []
for img_id in range(len(polys)):
polys_single = polys[img_id]
polys_lens_single = poly_lens[img_id].tolist()
polys_per_mask_single = polys_per_mask[img_id].tolist()
split_polys = mmcv.slice_list(polys_single, polys_lens_single)
mask_polys = mmcv.slice_list(split_polys, polys_per_mask_single)
mask_polys_list.append(mask_polys)
return mask_polys_list
Example #10
| Source File: utils.py From Libra_R-CNN with Apache License 2.0 | 5 votes |
|
def split_combined_polys(polys, poly_lens, polys_per_mask):
"""Split the combined 1-D polys into masks.
A mask is represented as a list of polys, and a poly is represented as
a 1-D array. In dataset, all masks are concatenated into a single 1-D
tensor. Here we need to split the tensor into original representations.
Args:
polys (list): a list (length = image num) of 1-D tensors
poly_lens (list): a list (length = image num) of poly length
polys_per_mask (list): a list (length = image num) of poly number
of each mask
Returns:
list: a list (length = image num) of list (length = mask num) of
list (length = poly num) of numpy array
"""
mask_polys_list = []
for img_id in range(len(polys)):
polys_single = polys[img_id]
polys_lens_single = poly_lens[img_id].tolist()
polys_per_mask_single = polys_per_mask[img_id].tolist()
split_polys = mmcv.slice_list(polys_single, polys_lens_single)
mask_polys = mmcv.slice_list(split_polys, polys_per_mask_single)
mask_polys_list.append(mask_polys)
return mask_polys_list
Example #11
| Source File: utils.py From Reasoning-RCNN with Apache License 2.0 | 5 votes |
|
def split_combined_polys(polys, poly_lens, polys_per_mask):
"""Split the combined 1-D polys into masks.
A mask is represented as a list of polys, and a poly is represented as
a 1-D array. In dataset, all masks are concatenated into a single 1-D
tensor. Here we need to split the tensor into original representations.
Args:
polys (list): a list (length = image num) of 1-D tensors
poly_lens (list): a list (length = image num) of poly length
polys_per_mask (list): a list (length = image num) of poly number
of each mask
Returns:
list: a list (length = image num) of list (length = mask num) of
list (length = poly num) of numpy array
"""
mask_polys_list = []
for img_id in range(len(polys)):
polys_single = polys[img_id]
polys_lens_single = poly_lens[img_id].tolist()
polys_per_mask_single = polys_per_mask[img_id].tolist()
split_polys = mmcv.slice_list(polys_single, polys_lens_single)
mask_polys = mmcv.slice_list(split_polys, polys_per_mask_single)
mask_polys_list.append(mask_polys)
return mask_polys_list
Example #12
| Source File: utils.py From mmdetection with Apache License 2.0 | 5 votes |
|
def split_combined_polys(polys, poly_lens, polys_per_mask):
"""Split the combined 1-D polys into masks.
A mask is represented as a list of polys, and a poly is represented as
a 1-D array. In dataset, all masks are concatenated into a single 1-D
tensor. Here we need to split the tensor into original representations.
Args:
polys (list): a list (length = image num) of 1-D tensors
poly_lens (list): a list (length = image num) of poly length
polys_per_mask (list): a list (length = image num) of poly number
of each mask
Returns:
list: a list (length = image num) of list (length = mask num) of
list (length = poly num) of numpy array
"""
mask_polys_list = []
for img_id in range(len(polys)):
polys_single = polys[img_id]
polys_lens_single = poly_lens[img_id].tolist()
polys_per_mask_single = polys_per_mask[img_id].tolist()
split_polys = mmcv.slice_list(polys_single, polys_lens_single)
mask_polys = mmcv.slice_list(split_polys, polys_per_mask_single)
mask_polys_list.append(mask_polys)
return mask_polys_list
# TODO: move this function to more proper place
Example #13
| Source File: utils.py From RDSNet with Apache License 2.0 | 5 votes |
|
def split_combined_polys(polys, poly_lens, polys_per_mask):
"""Split the combined 1-D polys into masks.
A mask is represented as a list of polys, and a poly is represented as
a 1-D array. In dataset, all masks are concatenated into a single 1-D
tensor. Here we need to split the tensor into original representations.
Args:
polys (list): a list (length = image num) of 1-D tensors
poly_lens (list): a list (length = image num) of poly length
polys_per_mask (list): a list (length = image num) of poly number
of each mask
Returns:
list: a list (length = image num) of list (length = mask num) of
list (length = poly num) of numpy array
"""
mask_polys_list = []
for img_id in range(len(polys)):
polys_single = polys[img_id]
polys_lens_single = poly_lens[img_id].tolist()
polys_per_mask_single = polys_per_mask[img_id].tolist()
split_polys = mmcv.slice_list(polys_single, polys_lens_single)
mask_polys = mmcv.slice_list(split_polys, polys_per_mask_single)
mask_polys_list.append(mask_polys)
return mask_polys_list
Example #14
| Source File: utils.py From Grid-R-CNN with Apache License 2.0 | 5 votes |
|
def split_combined_polys(polys, poly_lens, polys_per_mask):
"""Split the combined 1-D polys into masks.
A mask is represented as a list of polys, and a poly is represented as
a 1-D array. In dataset, all masks are concatenated into a single 1-D
tensor. Here we need to split the tensor into original representations.
Args:
polys (list): a list (length = image num) of 1-D tensors
poly_lens (list): a list (length = image num) of poly length
polys_per_mask (list): a list (length = image num) of poly number
of each mask
Returns:
list: a list (length = image num) of list (length = mask num) of
list (length = poly num) of numpy array
"""
mask_polys_list = []
for img_id in range(len(polys)):
polys_single = polys[img_id]
polys_lens_single = poly_lens[img_id].tolist()
polys_per_mask_single = polys_per_mask[img_id].tolist()
split_polys = mmcv.slice_list(polys_single, polys_lens_single)
mask_polys = mmcv.slice_list(split_polys, polys_per_mask_single)
mask_polys_list.append(mask_polys)
return mask_polys_list
Example #15
| Source File: utils.py From kaggle-kuzushiji-recognition with MIT License | 5 votes |
|
def split_combined_polys(polys, poly_lens, polys_per_mask):
"""Split the combined 1-D polys into masks.
A mask is represented as a list of polys, and a poly is represented as
a 1-D array. In dataset, all masks are concatenated into a single 1-D
tensor. Here we need to split the tensor into original representations.
Args:
polys (list): a list (length = image num) of 1-D tensors
poly_lens (list): a list (length = image num) of poly length
polys_per_mask (list): a list (length = image num) of poly number
of each mask
Returns:
list: a list (length = image num) of list (length = mask num) of
list (length = poly num) of numpy array
"""
mask_polys_list = []
for img_id in range(len(polys)):
polys_single = polys[img_id]
polys_lens_single = poly_lens[img_id].tolist()
polys_per_mask_single = polys_per_mask[img_id].tolist()
split_polys = mmcv.slice_list(polys_single, polys_lens_single)
mask_polys = mmcv.slice_list(split_polys, polys_per_mask_single)
mask_polys_list.append(mask_polys)
return mask_polys_list
Example #16
| Source File: utils.py From PolarMask with Apache License 2.0 | 5 votes |
|
def split_combined_polys(polys, poly_lens, polys_per_mask):
"""Split the combined 1-D polys into masks.
A mask is represented as a list of polys, and a poly is represented as
a 1-D array. In dataset, all masks are concatenated into a single 1-D
tensor. Here we need to split the tensor into original representations.
Args:
polys (list): a list (length = image num) of 1-D tensors
poly_lens (list): a list (length = image num) of poly length
polys_per_mask (list): a list (length = image num) of poly number
of each mask
Returns:
list: a list (length = image num) of list (length = mask num) of
list (length = poly num) of numpy array
"""
mask_polys_list = []
for img_id in range(len(polys)):
polys_single = polys[img_id]
polys_lens_single = poly_lens[img_id].tolist()
polys_per_mask_single = polys_per_mask[img_id].tolist()
split_polys = mmcv.slice_list(polys_single, polys_lens_single)
mask_polys = mmcv.slice_list(split_polys, polys_per_mask_single)
mask_polys_list.append(mask_polys)
return mask_polys_list
Example #17
| Source File: utils.py From mmdetection_with_SENet154 with Apache License 2.0 | 5 votes |
|
def split_combined_polys(polys, poly_lens, polys_per_mask):
"""Split the combined 1-D polys into masks.
A mask is represented as a list of polys, and a poly is represented as
a 1-D array. In dataset, all masks are concatenated into a single 1-D
tensor. Here we need to split the tensor into original representations.
Args:
polys (list): a list (length = image num) of 1-D tensors
poly_lens (list): a list (length = image num) of poly length
polys_per_mask (list): a list (length = image num) of poly number
of each mask
Returns:
list: a list (length = image num) of list (length = mask num) of
list (length = poly num) of numpy array
"""
mask_polys_list = []
for img_id in range(len(polys)):
polys_single = polys[img_id]
polys_lens_single = poly_lens[img_id].tolist()
polys_per_mask_single = polys_per_mask[img_id].tolist()
split_polys = mmcv.slice_list(polys_single, polys_lens_single)
mask_polys = mmcv.slice_list(split_polys, polys_per_mask_single)
mask_polys_list.append(mask_polys)
return mask_polys_list
Example #18
| Source File: test_misc.py From mmcv with Apache License 2.0 | 5 votes |
|
def test_slice_list():
in_list = [1, 2, 3, 4, 5, 6]
assert mmcv.slice_list(in_list, [1, 2, 3]) == [[1], [2, 3], [4, 5, 6]]
assert mmcv.slice_list(in_list, [len(in_list)]) == [in_list]
with pytest.raises(TypeError):
mmcv.slice_list(in_list, 2.0)
with pytest.raises(ValueError):
mmcv.slice_list(in_list, [1, 2])
Example #19
| Source File: utils.py From mmdetection-annotated with Apache License 2.0 | 5 votes |
|
def split_combined_polys(polys, poly_lens, polys_per_mask):
"""Split the combined 1-D polys into masks.
A mask is represented as a list of polys, and a poly is represented as
a 1-D array. In dataset, all masks are concatenated into a single 1-D
tensor. Here we need to split the tensor into original representations.
Args:
polys (list): a list (length = image num) of 1-D tensors
poly_lens (list): a list (length = image num) of poly length
polys_per_mask (list): a list (length = image num) of poly number
of each mask
Returns:
list: a list (length = image num) of list (length = mask num) of
list (length = poly num) of numpy array
"""
mask_polys_list = []
for img_id in range(len(polys)):
polys_single = polys[img_id]
polys_lens_single = poly_lens[img_id].tolist()
polys_per_mask_single = polys_per_mask[img_id].tolist()
split_polys = mmcv.slice_list(polys_single, polys_lens_single)
mask_polys = mmcv.slice_list(split_polys, polys_per_mask_single)
mask_polys_list.append(mask_polys)
return mask_polys_list
Example #20
| Source File: utils.py From GCNet with Apache License 2.0 | 5 votes |
|
def split_combined_polys(polys, poly_lens, polys_per_mask):
"""Split the combined 1-D polys into masks.
A mask is represented as a list of polys, and a poly is represented as
a 1-D array. In dataset, all masks are concatenated into a single 1-D
tensor. Here we need to split the tensor into original representations.
Args:
polys (list): a list (length = image num) of 1-D tensors
poly_lens (list): a list (length = image num) of poly length
polys_per_mask (list): a list (length = image num) of poly number
of each mask
Returns:
list: a list (length = image num) of list (length = mask num) of
list (length = poly num) of numpy array
"""
mask_polys_list = []
for img_id in range(len(polys)):
polys_single = polys[img_id]
polys_lens_single = poly_lens[img_id].tolist()
polys_per_mask_single = polys_per_mask[img_id].tolist()
split_polys = mmcv.slice_list(polys_single, polys_lens_single)
mask_polys = mmcv.slice_list(split_polys, polys_per_mask_single)
mask_polys_list.append(mask_polys)
return mask_polys_list
Example #21
| Source File: utils.py From AerialDetection with Apache License 2.0 | 5 votes |
|
def split_combined_polys(polys, poly_lens, polys_per_mask):
"""Split the combined 1-D polys into masks.
A mask is represented as a list of polys, and a poly is represented as
a 1-D array. In dataset, all masks are concatenated into a single 1-D
tensor. Here we need to split the tensor into original representations.
Args:
polys (list): a list (length = image num) of 1-D tensors
poly_lens (list): a list (length = image num) of poly length
polys_per_mask (list): a list (length = image num) of poly number
of each mask
Returns:
list: a list (length = image num) of list (length = mask num) of
list (length = poly num) of numpy array
"""
mask_polys_list = []
for img_id in range(len(polys)):
polys_single = polys[img_id]
polys_lens_single = poly_lens[img_id].tolist()
polys_per_mask_single = polys_per_mask[img_id].tolist()
split_polys = mmcv.slice_list(polys_single, polys_lens_single)
mask_polys = mmcv.slice_list(split_polys, polys_per_mask_single)
mask_polys_list.append(mask_polys)
return mask_polys_list
