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Unverified Commit cbc2491f authored by Tai-Wang's avatar Tai-Wang Committed by GitHub
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Add code-spell pre-commit hook and fix typos (#995)

parent 6b1602f1
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Showing with 35 additions and 32 deletions
mmdet3d/ops/ball_query/ball_query.py
View file @ cbc2491f
......@@ -23,7 +23,7 @@ class BallQuery(Function):
center_xyz (Tensor): (B, npoint, 3) centers of the ball query.
Returns:
Tensor: (B, npoint, nsample) tensor with the indicies of
Tensor: (B, npoint, nsample) tensor with the indices of
the features that form the query balls.
"""
assert center_xyz.is_contiguous()
......
mmdet3d/ops/group_points/group_points.py
View file @ cbc2491f
......@@ -183,7 +183,7 @@ class GroupingOperation(Function):
Args:
features (Tensor): (B, C, N) tensor of features to group.
indices (Tensor): (B, npoint, nsample) the indicies of
indices (Tensor): (B, npoint, nsample) the indices of
features to group with.
Returns:
......
mmdet3d/ops/knn/knn.py
View file @ cbc2491f
......@@ -27,11 +27,11 @@ class KNN(Function):
center_xyz (Tensor): (B, npoint, 3) if transposed == False,
else (B, 3, npoint). centers of the knn query.
transposed (bool): whether the input tensors are transposed.
defaults to False. Should not expicitly use this keyword
defaults to False. Should not explicitly use this keyword
when calling knn (=KNN.apply), just add the fourth param.
Returns:
Tensor: (B, k, npoint) tensor with the indicies of
Tensor: (B, k, npoint) tensor with the indices of
the features that form k-nearest neighbours.
"""
assert k > 0
......
mmdet3d/ops/norm.py
View file @ cbc2491f
......@@ -26,7 +26,7 @@ class AllReduce(Function):
@NORM_LAYERS.register_module('naiveSyncBN1d')
class NaiveSyncBatchNorm1d(nn.BatchNorm1d):
"""Syncronized Batch Normalization for 3D Tensors.
"""Synchronized Batch Normalization for 3D Tensors.
Note:
This implementation is modified from
......@@ -37,7 +37,7 @@ class NaiveSyncBatchNorm1d(nn.BatchNorm1d):
when the batch size on each worker is quite different
(e.g., when scale augmentation is used).
In 3D detection, different workers has points of different shapes,
whish also cause instability.
which also cause instability.
Use this implementation before `nn.SyncBatchNorm` is fixed.
It is slower than `nn.SyncBatchNorm`.
......@@ -80,7 +80,7 @@ class NaiveSyncBatchNorm1d(nn.BatchNorm1d):
@NORM_LAYERS.register_module('naiveSyncBN2d')
class NaiveSyncBatchNorm2d(nn.BatchNorm2d):
"""Syncronized Batch Normalization for 4D Tensors.
"""Synchronized Batch Normalization for 4D Tensors.
Note:
This implementation is modified from
......
mmdet3d/ops/paconv/paconv.py
View file @ cbc2491f
......@@ -83,7 +83,7 @@ class ScoreNet(nn.Module):
Args:
xyz_features (torch.Tensor): (B, C, N, K), features constructed
from xyz coordinates of point pairs. May contain relative
positions, Euclidian distance, etc.
positions, Euclidean distance, etc.
Returns:
torch.Tensor: (B, N, K, M), predicted scores for `M` kernels.
......@@ -174,7 +174,7 @@ class PAConv(nn.Module):
# (grouped_xyz - center_xyz, grouped_xyz)
self.scorenet_in_channels = 6
elif scorenet_input == 'w_neighbor_dist':
# (center_xyz, grouped_xyz - center_xyz, Euclidian distance)
# (center_xyz, grouped_xyz - center_xyz, Euclidean distance)
self.scorenet_in_channels = 7
else:
raise NotImplementedError(
......
mmdet3d/ops/paconv/utils.py
View file @ cbc2491f
......@@ -2,14 +2,14 @@ import torch
def calc_euclidian_dist(xyz1, xyz2):
"""Calculate the Euclidian distance between two sets of points.
"""Calculate the Euclidean distance between two sets of points.
Args:
xyz1 (torch.Tensor): (N, 3), the first set of points.
xyz2 (torch.Tensor): (N, 3), the second set of points.
Returns:
torch.Tensor: (N, ), the Euclidian distance between each point pair.
torch.Tensor: (N, ), the Euclidean distance between each point pair.
"""
assert xyz1.shape[0] == xyz2.shape[0], 'number of points are not the same'
assert xyz1.shape[1] == xyz2.shape[1] == 3, \
......
mmdet3d/ops/pointnet_modules/paconv_sa_module.py
View file @ cbc2491f
......@@ -28,7 +28,7 @@ class PAConvSAModuleMSG(BasePointSAModule):
- 'w_neighbor': Use xyz coordinates and the difference with center
points as input.
- 'w_neighbor_dist': Use xyz coordinates, the difference with
center points and the Euclidian distance as input.
center points and the Euclidean distance as input.
scorenet_cfg (dict, optional): Config of the ScoreNet module, which
may contain the following keys and values:
......
mmdet3d/ops/spconv/conv.py
View file @ cbc2491f
......@@ -143,16 +143,16 @@ class SparseConvolution(SparseModule):
out_tensor.indice_dict = input.indice_dict
out_tensor.grid = input.grid
return out_tensor
datas = input.find_indice_pair(self.indice_key)
data = input.find_indice_pair(self.indice_key)
if self.inverse:
assert datas is not None and self.indice_key is not None
_, outids, indice_pairs, indice_pair_num, out_spatial_shape = datas
assert data is not None and self.indice_key is not None
_, outids, indice_pairs, indice_pair_num, out_spatial_shape = data
assert indice_pairs.shape[0] == np.prod(
self.kernel_size
), 'inverse conv must have same kernel size as its couple conv'
else:
if self.indice_key is not None and datas is not None:
outids, _, indice_pairs, indice_pair_num, _ = datas
if self.indice_key is not None and data is not None:
outids, _, indice_pairs, indice_pair_num, _ = data
else:
outids, indice_pairs, indice_pair_num = ops.get_indice_pairs(
indices,
......
mmdet3d/ops/spconv/include/prettyprint.h
View file @ cbc2491f
......@@ -93,7 +93,7 @@ struct delimiters {
};
// Functor to print containers. You can use this directly if you want
// to specificy a non-default delimiters type. The printing logic can
// to specify a non-default delimiters type. The printing logic can
// be customized by specializing the nested template.
template <typename T, typename TChar = char,
......
mmdet3d/ops/spconv/include/tensorview/tensorview.h
View file @ cbc2491f
......@@ -73,7 +73,7 @@ void sstream_print(SStream &ss, T val, TArgs... args) {
if (!(expr)) { \
std::stringstream __macro_s; \
__macro_s << __FILE__ << " " << __LINE__ << "\n"; \
__macro_s << #expr << " assert faild. "; \
__macro_s << #expr << " assert failed. "; \
tv::sstream_print(__macro_s, __VA_ARGS__); \
throw std::runtime_error(__macro_s.str()); \
} \
......@@ -84,7 +84,7 @@ void sstream_print(SStream &ss, T val, TArgs... args) {
if (!(expr)) { \
std::stringstream __macro_s; \
__macro_s << __FILE__ << " " << __LINE__ << "\n"; \
__macro_s << #expr << " assert faild. "; \
__macro_s << #expr << " assert failed. "; \
tv::sstream_print(__macro_s, __VA_ARGS__); \
throw std::invalid_argument(__macro_s.str()); \
} \
......
mmdet3d/ops/voxel/src/voxelization_cuda.cu
View file @ cbc2491f
......@@ -305,7 +305,7 @@ int hard_voxelize_gpu(const at::Tensor& points, at::Tensor& voxels,
cudaDeviceSynchronize();
AT_CUDA_CHECK(cudaGetLastError());
// 3. determin voxel num and voxel's coor index
// 3. determine voxel num and voxel's coor index
// make the logic in the CUDA device could accelerate about 10 times
auto coor_to_voxelidx = -at::ones(
{
......@@ -316,7 +316,7 @@ int hard_voxelize_gpu(const at::Tensor& points, at::Tensor& voxels,
{
1,
},
points.options().dtype(at::kInt)); // must be zero from the begining
points.options().dtype(at::kInt)); // must be zero from the beginning
AT_DISPATCH_ALL_TYPES(
temp_coors.scalar_type(), "determin_duplicate", ([&] {
......
requirements/runtime.txt
View file @ cbc2491f
lyft_dataset_sdk
networkx>=2.2,<2.3
# we may unlock the verion of numba in the future
# we may unlock the version of numba in the future
numba==0.48.0
numpy<1.20.0
nuscenes-devkit
......
setup.cfg
View file @ cbc2491f
......@@ -11,3 +11,6 @@ known_first_party = mmdet,mmseg,mmdet3d
known_third_party = cv2,imageio,indoor3d_util,load_scannet_data,lyft_dataset_sdk,m2r,matplotlib,mmcv,nuimages,numba,numpy,nuscenes,pandas,plyfile,pycocotools,pyquaternion,pytest,pytorch_sphinx_theme,recommonmark,scannet_utils,scipy,seaborn,shapely,skimage,tensorflow,terminaltables,torch,trimesh,waymo_open_dataset
no_lines_before = STDLIB,LOCALFOLDER
default_section = THIRDPARTY
[codespell]
ignore-words-list = ans,refridgerator,crate,hist,formating,dout,wan,nd,fo
tests/data/kitti/kitti_infos_mono3d.coco.json
View file @ cbc2491f
{"images": [{"file_name": "training/image_2/000007.png", "id": 7, "Tri2v": [[0.9999976, 0.0007553071, -0.002035826, -0.8086759], [-0.0007854027, 0.9998898, -0.01482298, 0.3195559], [0.002024406, 0.01482454, 0.9998881, -0.7997231], [0.0, 0.0, 0.0, 1.0]], "Trv2c": [[0.007533745, -0.9999714, -0.000616602, -0.004069766], [0.01480249, 0.0007280733, -0.9998902, -0.07631618], [0.9998621, 0.00752379, 0.01480755, -0.2717806], [0.0, 0.0, 0.0, 1.0]], "rect": [[0.9999239, 0.00983776, -0.007445048, 0.0], [-0.009869795, 0.9999421, -0.004278459, 0.0], [0.007402527, 0.004351614, 0.9999631, 0.0], [0.0, 0.0, 0.0, 1.0]], "cam_intrinsic": [[721.5377, 0.0, 609.5593, 44.85728], [0.0, 721.5377, 172.854, 0.2163791], [0.0, 0.0, 1.0, 0.002745884], [0.0, 0.0, 0.0, 1.0]], "width": 1242, "height": 375}], "annotations": [{"file_name": "training/image_2/000007.png", "image_id": 7, "area": 2556.023616260146, "category_name": "Car", "category_id": 2, "bbox": [565.4822720402807, 175.01202566042497, 51.17323679197273, 49.94844525177848], "iscrowd": 0, "bbox_cam3d": [-0.627830982208252, 0.8849999904632568, 25.010000228881836, 3.200000047683716, 1.6100000143051147, 1.659999966621399, -1.590000033378601], "velo_cam3d": -1, "center2d": [591.3814672167642, 198.3730937263457, 25.012745884], "attribute_name": -1, "attribute_id": -1, "segmentation": [], "id": 2}, {"file_name": "training/image_2/000007.png", "image_id": 7, "area": 693.1538564468428, "category_name": "Car", "category_id": 2, "bbox": [481.8496708488522, 179.85710612050596, 30.55976691329198, 22.681909139344754], "iscrowd": 0, "bbox_cam3d": [-7.367831230163574, 1.1799999475479126, 47.54999923706055, 3.700000047683716, 1.399999976158142, 1.5099999904632568, 1.5499999523162842], "velo_cam3d": -1, "center2d": [497.72892067550754, 190.75320250122618, 47.552745884], "attribute_name": -1, "attribute_id": -1, "segmentation": [], "id": 3}, {"file_name": "training/image_2/000007.png", "image_id": 7, "area": 419.21693566410073, "category_name": "Car", "category_id": 2, "bbox": [542.2247151650495, 175.73341152322814, 23.019633917835904, 18.211277258379255], "iscrowd": 0, "bbox_cam3d": [-4.647830963134766, 0.9800000190734863, 60.52000045776367, 4.050000190734863, 1.4600000381469727, 1.659999966621399, 1.559999942779541], "velo_cam3d": -1, "center2d": [554.1213152040074, 184.53305847203026, 60.522745884], "attribute_name": -1, "attribute_id": -1, "segmentation": [], "id": 4}, {"file_name": "training/image_2/000007.png", "image_id": 7, "area": 928.9555081918186, "category_name": "Cyclist", "category_id": 1, "bbox": [330.84191493374504, 176.13804311926262, 24.65593879860404, 37.67674456769879], "iscrowd": 0, "bbox_cam3d": [-12.567831039428711, 1.0199999809265137, 34.09000015258789, 1.9500000476837158, 1.7200000286102295, 0.5, 1.5399999618530273], "velo_cam3d": -1, "center2d": [343.52506265845847, 194.43366972124528, 34.092745884], "attribute_name": -1, "attribute_id": -1, "segmentation": [], "id": 5}], "categories": [{"id": 0, "name": "Pedestrian"}, {"id": 1, "name": "Cyclist"}, {"id": 2, "name": "Car"}]}
\ No newline at end of file
{"images": [{"file_name": "training/image_2/000007.png", "id": 7, "Tri2v": [[0.9999976, 0.0007553071, -0.002035826, -0.8086759], [-0.0007854027, 0.9998898, -0.01482298, 0.3195559], [0.002024406, 0.01482454, 0.9998881, -0.7997231], [0.0, 0.0, 0.0, 1.0]], "Trv2c": [[0.007533745, -0.9999714, -0.000616602, -0.004069766], [0.01480249, 0.0007280733, -0.9998902, -0.07631618], [0.9998621, 0.00752379, 0.01480755, -0.2717806], [0.0, 0.0, 0.0, 1.0]], "rect": [[0.9999239, 0.00983776, -0.007445048, 0.0], [-0.009869795, 0.9999421, -0.004278459, 0.0], [0.007402527, 0.004351614, 0.9999631, 0.0], [0.0, 0.0, 0.0, 1.0]], "cam_intrinsic": [[721.5377, 0.0, 609.5593, 44.85728], [0.0, 721.5377, 172.854, 0.2163791], [0.0, 0.0, 1.0, 0.002745884], [0.0, 0.0, 0.0, 1.0]], "width": 1242, "height": 375}], "annotations": [{"file_name": "training/image_2/000007.png", "image_id": 7, "area": 2556.023616260146, "category_name": "Car", "category_id": 2, "bbox": [565.4822720402807, 175.01202566042497, 51.17323679197273, 49.94844525177848], "iscrowd": 0, "bbox_cam3d": [-0.627830982208252, 0.8849999904632568, 25.010000228881836, 3.200000047683716, 1.6100000143051147, 1.659999966621399, -1.590000033378601], "velo_cam3d": -1, "center2d": [591.3814672167642, 198.3730937263457, 25.012745884], "attribute_name": -1, "attribute_id": -1, "segmentation": [], "id": 2}, {"file_name": "training/image_2/000007.png", "image_id": 7, "area": 693.1538564468428, "category_name": "Car", "category_id": 2, "bbox": [481.8496708488522, 179.85710612050596, 30.55976691329198, 22.681909139344754], "iscrowd": 0, "bbox_cam3d": [-7.367831230163574, 1.1799999475479126, 47.54999923706055, 3.700000047683716, 1.399999976158142, 1.5099999904632568, 1.5499999523162842], "velo_cam3d": -1, "center2d": [497.72892067550754, 190.75320250122618, 47.552745884], "attribute_name": -1, "attribute_id": -1, "segmentation": [], "id": 3}, {"file_name": "training/image_2/000007.png", "image_id": 7, "area": 419.21693566410073, "category_name": "Car", "category_id": 2, "bbox": [542.2247151650495, 175.73341152322814, 23.019633917835904, 18.211277258379255], "iscrowd": 0, "bbox_cam3d": [-4.647830963134766, 0.9800000190734863, 60.52000045776367, 4.050000190734863, 1.4600000381469727, 1.659999966621399, 1.559999942779541], "velo_cam3d": -1, "center2d": [554.1213152040074, 184.53305847203026, 60.522745884], "attribute_name": -1, "attribute_id": -1, "segmentation": [], "id": 4}, {"file_name": "training/image_2/000007.png", "image_id": 7, "area": 928.9555081918186, "category_name": "Cyclist", "category_id": 1, "bbox": [330.84191493374504, 176.13804311926262, 24.65593879860404, 37.67674456769879], "iscrowd": 0, "bbox_cam3d": [-12.567831039428711, 1.0199999809265137, 34.09000015258789, 1.9500000476837158, 1.7200000286102295, 0.5, 1.5399999618530273], "velo_cam3d": -1, "center2d": [343.52506265845847, 194.43366972124528, 34.092745884], "attribute_name": -1, "attribute_id": -1, "segmentation": [], "id": 5}], "categories": [{"id": 0, "name": "Pedestrian"}, {"id": 1, "name": "Cyclist"}, {"id": 2, "name": "Car"}]}
tests/data/nuscenes/nus_infos_mono3d.coco.json
View file @ cbc2491f
{"images": [{"file_name": "samples/CAM_BACK_LEFT/n015-2018-07-18-11-07-57+0800__CAM_BACK_LEFT__1531883530447423.jpg", "id": "86e6806d626b4711a6d0f5015b090116", "token": "e93e98b63d3b40209056d129dc53ceee", "cam2ego_rotation": [0.6924185592174665, -0.7031619420114925, -0.11648342771943819, 0.11203317912370753], "cam2ego_translation": [1.03569100218, 0.484795032713, 1.59097014818], "ego2global_rotation": [-0.7495886280607293, -0.0077695335695504636, 0.00829759813869316, -0.6618063711504101], "ego2global_translation": [1010.1328353833223, 610.8111652918716, 0.0], "cam_intrinsic": [[1256.7414812095406, 0.0, 792.1125740759628], [0.0, 1256.7414812095406, 492.7757465151356], [0.0, 0.0, 1.0]], "width": 1600, "height": 900}, {"file_name": "samples/CAM_FRONT/n015-2018-07-18-11-07-57+0800__CAM_FRONT__1531883530412470.jpg", "id": "020d7b4f858147558106c504f7f31bef", "token": "e93e98b63d3b40209056d129dc53ceee", "cam2ego_rotation": [0.4998015430569128, -0.5030316162024876, 0.4997798114386805, -0.49737083824542755], "cam2ego_translation": [1.70079118954, 0.0159456324149, 1.51095763913], "ego2global_rotation": [-0.7495886280607293, -0.0077695335695504636, 0.00829759813869316, -0.6618063711504101], "ego2global_translation": [1010.1328353833223, 610.8111652918716, 0.0], "cam_intrinsic": [[1266.417203046554, 0.0, 816.2670197447984], [0.0, 1266.417203046554, 491.50706579294757], [0.0, 0.0, 1.0]], "width": 1600, "height": 900}, {"file_name": "samples/CAM_FRONT_RIGHT/n015-2018-07-18-11-07-57+0800__CAM_FRONT_RIGHT__1531883530420339.jpg", "id": "16d39ff22a8545b0a4ee3236a0fe1c20", "token": "e93e98b63d3b40209056d129dc53ceee", "cam2ego_rotation": [0.2060347966337182, -0.2026940577919598, 0.6824507824531167, -0.6713610884174485], "cam2ego_translation": [1.5508477543, -0.493404796419, 1.49574800619], "ego2global_rotation": [-0.7495886280607293, -0.0077695335695504636, 0.00829759813869316, -0.6618063711504101], "ego2global_translation": [1010.1328353833223, 610.8111652918716, 0.0], 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"samples/CAM_BACK/n015-2018-07-18-11-07-57+0800__CAM_BACK__1531883530437525.jpg", "image_id": "aab35aeccbda42de82b2ff5c278a0d48", "area": 31981.88483023472, "category_name": "car", "category_id": 0, "bbox": [652.8710695836726, 487.2457293359287, 256.3734471348506, 124.74725907715583], "iscrowd": 0, "bbox_cam3d": [-0.48041137691585667, 0.8426032188612489, 12.27160016308813, 1.871, 4.478, 1.456, -2.0402647554154876], "velo_cam3d": [-2.4043357184501866, -4.232358489028598], "center2d": [797.5400340802389, 537.3418550489371, 12.27160016308813], "attribute_name": "vehicle.moving", "attribute_id": 5, "segmentation": [], "id": 4}, {"file_name": "samples/CAM_BACK_LEFT/n015-2018-07-18-11-07-57+0800__CAM_BACK_LEFT__1531883530447423.jpg", "image_id": "86e6806d626b4711a6d0f5015b090116", "area": 1874.1656394574547, "category_name": "traffic_cone", "category_id": 8, "bbox": [1084.536273989852, 513.7567766430512, 30.043100006470013, 62.382565016720605], "iscrowd": 0, "bbox_cam3d": [3.745641322414848, 0.6321604510604618, 15.319339525420224, 0.3, 0.291, 0.734, 1.4550554479430875], "velo_cam3d": [0.028202672296939114, -0.001622377193634249], "center2d": [1099.3910188026568, 544.635832278593, 15.319339525420224], "attribute_name": "None", "attribute_id": 8, "segmentation": [], "id": 5}, {"file_name": "samples/CAM_BACK_LEFT/n015-2018-07-18-11-07-57+0800__CAM_BACK_LEFT__1531883530447423.jpg", "image_id": "86e6806d626b4711a6d0f5015b090116", "area": 1641.3529623313364, "category_name": "traffic_cone", "category_id": 8, "bbox": [823.5058461203419, 512.0451382733748, 27.545987206560085, 59.58591899514306], "iscrowd": 0, "bbox_cam3d": [0.558956408408079, 0.6054486006477211, 15.607344275188172, 0.315, 0.338, 0.712, 1.5596704833049395], "velo_cam3d": [0.07717355032092023, -0.0013264953734539453], "center2d": [837.1211093045397, 541.5279466177432, 15.607344275188172], "attribute_name": "None", "attribute_id": 8, "segmentation": [], "id": 6}, {"file_name": 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"bbox_cam3d": [4.7798492054669035, 0.1162134030605403, 14.880252178422799, 0.665, 0.544, 1.739, 1.4550554479430875], "velo_cam3d": [0.08665208940588605, -0.12554131041835265], "center2d": [1195.8043058026105, 502.5907820768639, 14.880252178422799], "attribute_name": "pedestrian.sitting_lying_down", "attribute_id": 4, "segmentation": [], "id": 8}, {"file_name": "samples/CAM_BACK_LEFT/n015-2018-07-18-11-07-57+0800__CAM_BACK_LEFT__1531883530447423.jpg", "image_id": "86e6806d626b4711a6d0f5015b090116", "area": 1840.396836351825, "category_name": "traffic_cone", "category_id": 8, "bbox": [976.5016497372175, 515.0039595028874, 30.627062877370918, 60.09054292018379], "iscrowd": 0, "bbox_cam3d": [2.4596094747766615, 0.6404788797338883, 15.49228428713527, 0.338, 0.309, 0.712, 1.461625206011101], "velo_cam3d": [0.02389033738396964, -0.0027892907804445547], "center2d": [991.6372663187118, 544.7316983348808, 15.49228428713527], "attribute_name": "None", "attribute_id": 8, "segmentation": [], "id": 9}, {"file_name": "samples/CAM_BACK_RIGHT/n015-2018-07-18-11-07-57+0800__CAM_BACK_RIGHT__1531883530427893.jpg", "image_id": "ec7096278e484c9ebe6894a2ad5682e9", "area": 130637.82232697189, "category_name": "car", "category_id": 0, "bbox": [806.290660237549, 470.86948127698895, 564.486943265249, 231.42753589888787], "iscrowd": 0, "bbox_cam3d": [2.041080764231013, 0.5400087467741127, 10.16381197333443, 1.638, 4.25, 1.44, 2.3008777344302445], "velo_cam3d": [-3.11975390859937, 4.71824099865795], "center2d": [1060.1864774468488, 568.1144351228712, 10.16381197333443], "attribute_name": "vehicle.moving", "attribute_id": 5, "segmentation": [], "id": 10}], "categories": [{"id": 0, "name": "car"}, {"id": 1, "name": "truck"}, {"id": 2, "name": "trailer"}, {"id": 3, "name": "bus"}, {"id": 4, "name": "construction_vehicle"}, {"id": 5, "name": "bicycle"}, {"id": 6, "name": "motorcycle"}, {"id": 7, "name": "pedestrian"}, {"id": 8, "name": "traffic_cone"}, {"id": 9, "name": "barrier"}]}
tests/test_metrics/test_losses.py
View file @ cbc2491f
......@@ -95,7 +95,7 @@ def test_paconv_regularization_loss():
set_random_seed(0, True)
model = ToyModel()
# reduction shoule be in ['none', 'mean', 'sum']
# reduction should be in ['none', 'mean', 'sum']
with pytest.raises(AssertionError):
paconv_corr_loss = PAConvRegularizationLoss(reduction='l2')
......@@ -116,7 +116,7 @@ def test_paconv_regularization_loss():
def test_uncertain_smooth_l1_loss():
from mmdet3d.models.losses import UncertainL1Loss, UncertainSmoothL1Loss
# reduction shoule be in ['none', 'mean', 'sum']
# reduction should be in ['none', 'mean', 'sum']
with pytest.raises(AssertionError):
uncertain_l1_loss = UncertainL1Loss(reduction='l2')
with pytest.raises(AssertionError):
......
tests/test_utils/test_box3d.py
View file @ cbc2491f
......@@ -656,7 +656,7 @@ def test_boxes_conversion():
dtype=torch.float32)
rt_mat = rect @ Trv2c
# test coversion with Box type
# test conversion with Box type
cam_to_lidar_box = Box3DMode.convert(camera_boxes, Box3DMode.CAM,
Box3DMode.LIDAR, rt_mat.inverse())
assert torch.allclose(cam_to_lidar_box.tensor, expected_tensor)
......
tools/data_converter/kitti_converter.py
View file @ cbc2491f
......@@ -493,7 +493,7 @@ def get_2d_boxes(info, occluded, mono3d=True):
def generate_record(ann_rec, x1, y1, x2, y2, sample_data_token, filename):
"""Generate one 2D annotation record given various informations on top of
"""Generate one 2D annotation record given various information on top of
the 2D bounding box coordinates.
Args:
......@@ -508,7 +508,7 @@ def generate_record(ann_rec, x1, y1, x2, y2, sample_data_token, filename):
Returns:
dict: A sample 2D annotation record.
- file_name (str): flie name
- file_name (str): file name
- image_id (str): sample data token
- area (float): 2d box area
- category_name (str): category name
......
tools/data_converter/nuscenes_converter.py
View file @ cbc2491f
......@@ -565,7 +565,7 @@ def post_process_coords(
def generate_record(ann_rec: dict, x1: float, y1: float, x2: float, y2: float,
sample_data_token: str, filename: str) -> OrderedDict:
"""Generate one 2D annotation record given various informations on top of
"""Generate one 2D annotation record given various information on top of
the 2D bounding box coordinates.
Args:
......@@ -580,7 +580,7 @@ def generate_record(ann_rec: dict, x1: float, y1: float, x2: float, y2: float,
Returns:
dict: A sample 2D annotation record.
- file_name (str): flie name
- file_name (str): file name
- image_id (str): sample data token
- area (float): 2d box area
- category_name (str): category name
......
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