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# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
from mmcv.parallel import DataContainer as DC
from mmdet3d.core.bbox import BaseInstance3DBoxes
from mmdet3d.core.points import BasePoints
from mmdet3d.datasets.builder import PIPELINES
from mmdet.datasets.pipelines import to_tensor
from mmdet3d.datasets.pipelines import DefaultFormatBundle3D
@PIPELINES.register_module()
class CustomDefaultFormatBundle3D(DefaultFormatBundle3D):
"""Default formatting bundle.
It simplifies the pipeline of formatting common fields for voxels,
including "proposals", "gt_bboxes", "gt_labels", "gt_masks" and
"gt_semantic_seg".
These fields are formatted as follows.
- img: (1)transpose, (2)to tensor, (3)to DataContainer (stack=True)
- proposals: (1)to tensor, (2)to DataContainer
- gt_bboxes: (1)to tensor, (2)to DataContainer
- gt_bboxes_ignore: (1)to tensor, (2)to DataContainer
- gt_labels: (1)to tensor, (2)to DataContainer
"""
def __call__(self, results):
"""Call function to transform and format common fields in results.
Args:
results (dict): Result dict contains the data to convert.
Returns:
dict: The result dict contains the data that is formatted with
default bundle.
"""
# Format 3D data
results = super(CustomDefaultFormatBundle3D, self).__call__(results)
results["gt_map_masks"] = DC(to_tensor(results["gt_map_masks"]), stack=True)
return results
@PIPELINES.register_module()
class CarlaDefaultFormatBundle(object):
"""Default formatting bundle.
It simplifies the pipeline of formatting common fields, including "img",
"proposals", "gt_bboxes", "gt_labels", "gt_masks" and "gt_semantic_seg".
These fields are formatted as follows.
- img: (1)transpose, (2)to tensor, (3)to DataContainer (stack=True)
- proposals: (1)to tensor, (2)to DataContainer
- gt_bboxes: (1)to tensor, (2)to DataContainer
- gt_bboxes_ignore: (1)to tensor, (2)to DataContainer
- gt_labels: (1)to tensor, (2)to DataContainer
- gt_masks: (1)to tensor, (2)to DataContainer (cpu_only=True)
- gt_semantic_seg: (1)unsqueeze dim-0 (2)to tensor,
(3)to DataContainer (stack=True)
"""
def __init__(
self,
):
return
def __call__(self, results):
"""Call function to transform and format common fields in results.
Args:
results (dict): Result dict contains the data to convert.
Returns:
dict: The result dict contains the data that is formatted with
default bundle.
"""
if "img" in results:
if isinstance(results["img"], list):
# process multiple imgs in single frame
imgs = [img.transpose(2, 0, 1) for img in results["img"]]
imgs = np.ascontiguousarray(np.stack(imgs, axis=0))
results["img"] = DC(to_tensor(imgs), stack=True)
else:
img = np.ascontiguousarray(results["img"].transpose(2, 0, 1))
results["img"] = DC(to_tensor(img), stack=True)
for key in [
"proposals",
"gt_bboxes",
"gt_bboxes_ignore",
"gt_labels",
"gt_labels_3d",
"attr_labels",
"pts_instance_mask",
"pts_semantic_mask",
"centers2d",
"depths",
"light_hazard",
"light_inrange",
"sign_inrange",
]:
if key not in results:
continue
if isinstance(results[key], list):
results[key] = DC([to_tensor(res) for res in results[key]])
else:
results[key] = DC(to_tensor(results[key]))
if "gt_bboxes_3d" in results:
if isinstance(results["gt_bboxes_3d"], BaseInstance3DBoxes):
results["gt_bboxes_3d"] = DC(results["gt_bboxes_3d"], cpu_only=True)
else:
results["gt_bboxes_3d"] = DC(to_tensor(results["gt_bboxes_3d"]))
if "gt_masks" in results:
results["gt_masks"] = DC(results["gt_masks"], cpu_only=True)
if "gt_semantic_seg" in results:
results["gt_semantic_seg"] = DC(
to_tensor(results["gt_semantic_seg"][None, ...]), stack=True
)
return results
def __repr__(self):
return self.__class__.__name__
@PIPELINES.register_module()
class CarlaCollect3D(object):
"""Collect data from the loader relevant to the specific task.
This is usually the last stage of the data loader pipeline. Typically keys
is set to some subset of "img", "proposals", "gt_bboxes",
"gt_bboxes_ignore", "gt_labels", and/or "gt_masks".
The "img_meta" item is always populated. The contents of the "img_meta"
dictionary depends on "meta_keys". By default this includes:
- 'img_shape': shape of the image input to the network as a tuple
(h, w, c). Note that images may be zero padded on the
bottom/right if the batch tensor is larger than this shape.
- 'scale_factor': a float indicating the preprocessing scale
- 'flip': a boolean indicating if image flip transform was used
- 'filename': path to the image file
- 'ori_shape': original shape of the image as a tuple (h, w, c)
- 'pad_shape': image shape after padding
- 'lidar2img': transform from lidar to image
- 'depth2img': transform from depth to image
- 'cam2img': transform from camera to image
- 'pcd_horizontal_flip': a boolean indicating if point cloud is
flipped horizontally
- 'pcd_vertical_flip': a boolean indicating if point cloud is
flipped vertically
- 'box_mode_3d': 3D box mode
- 'box_type_3d': 3D box type
- 'img_norm_cfg': a dict of normalization information:
- mean: per channel mean subtraction
- std: per channel std divisor
- to_rgb: bool indicating if bgr was converted to rgb
- 'pcd_trans': point cloud transformations
- 'sample_idx': sample index
- 'pcd_scale_factor': point cloud scale factor
- 'pcd_rotation': rotation applied to point cloud
- 'pts_filename': path to point cloud file.
Args:
keys (Sequence[str]): Keys of results to be collected in ``data``.
meta_keys (Sequence[str], optional): Meta keys to be converted to
``mmcv.DataContainer`` and collected in ``data[img_metas]``.
Default: ('filename', 'ori_shape', 'img_shape', 'lidar2img',
'depth2img', 'cam2img', 'pad_shape', 'scale_factor', 'flip',
'pcd_horizontal_flip', 'pcd_vertical_flip', 'box_mode_3d',
'box_type_3d', 'img_norm_cfg', 'pcd_trans',
'sample_idx', 'pcd_scale_factor', 'pcd_rotation', 'pts_filename')
"""
def __init__(
self,
keys,
meta_keys=(
"filename",
"ori_shape",
"img_shape",
"lidar2img",
"depth2img",
"cam2img",
"pad_shape",
"scale_factor",
"flip",
"pcd_horizontal_flip",
"pcd_vertical_flip",
"box_mode_3d",
"box_type_3d",
"img_norm_cfg",
"pcd_trans",
"sample_idx",
"pcd_scale_factor",
"pcd_rotation",
"pcd_rotation_angle",
"pts_filename",
"transformation_3d_flow",
"trans_mat",
"affine_aug",
"light_hazard",
"light_inrange",
"sign_inrange",
),
):
self.keys = keys
self.meta_keys = meta_keys
def __call__(self, results):
"""Call function to collect keys in results. The keys in ``meta_keys``
will be converted to :obj:`mmcv.DataContainer`.
Args:
results (dict): Result dict contains the data to collect.
Returns:
dict: The result dict contains the following keys
- keys in ``self.keys``
- ``img_metas``
"""
data = {}
img_metas = {}
for key in self.meta_keys:
if key in results:
img_metas[key] = results[key]
data["img_metas"] = DC(img_metas, cpu_only=True)
for key in self.keys:
data[key] = results[key]
return data
def __repr__(self):
"""str: Return a string that describes the module."""
return (
self.__class__.__name__ + f"(keys={self.keys}, meta_keys={self.meta_keys})"
)
@PIPELINES.register_module()
class CarlaDefaultFormatBundle3D(CarlaDefaultFormatBundle):
"""Default formatting bundle.
It simplifies the pipeline of formatting common fields for voxels,
including "proposals", "gt_bboxes", "gt_labels", "gt_masks" and
"gt_semantic_seg".
These fields are formatted as follows.
- img: (1)transpose, (2)to tensor, (3)to DataContainer (stack=True)
- proposals: (1)to tensor, (2)to DataContainer
- gt_bboxes: (1)to tensor, (2)to DataContainer
- gt_bboxes_ignore: (1)to tensor, (2)to DataContainer
- gt_labels: (1)to tensor, (2)to DataContainer
"""
def __init__(self, class_names, with_gt=True, with_label=True):
super(CarlaDefaultFormatBundle3D, self).__init__()
self.class_names = class_names
self.with_gt = with_gt
self.with_label = with_label
def __call__(self, results):
"""Call function to transform and format common fields in results.
Args:
results (dict): Result dict contains the data to convert.
Returns:
dict: The result dict contains the data that is formatted with
default bundle.
"""
# Format 3D data
if "points" in results:
assert isinstance(results["points"], BasePoints)
results["points"] = DC(results["points"].tensor)
for key in ["voxels", "coors", "voxel_centers", "num_points"]:
if key not in results:
continue
results[key] = DC(to_tensor(results[key]), stack=False)
if self.with_gt:
# Clean GT bboxes in the final
if "gt_bboxes_3d_mask" in results:
gt_bboxes_3d_mask = results["gt_bboxes_3d_mask"]
results["gt_bboxes_3d"] = results["gt_bboxes_3d"][gt_bboxes_3d_mask]
if "gt_names_3d" in results:
results["gt_names_3d"] = results["gt_names_3d"][gt_bboxes_3d_mask]
if "centers2d" in results:
results["centers2d"] = results["centers2d"][gt_bboxes_3d_mask]
if "depths" in results:
results["depths"] = results["depths"][gt_bboxes_3d_mask]
if "gt_bboxes_mask" in results:
gt_bboxes_mask = results["gt_bboxes_mask"]
if "gt_bboxes" in results:
results["gt_bboxes"] = results["gt_bboxes"][gt_bboxes_mask]
results["gt_names"] = results["gt_names"][gt_bboxes_mask]
if self.with_label:
if "gt_names" in results and len(results["gt_names"]) == 0:
results["gt_labels"] = np.array([], dtype=np.int64)
results["attr_labels"] = np.array([], dtype=np.int64)
elif "gt_names" in results and isinstance(results["gt_names"][0], list):
# gt_labels might be a list of list in multi-view setting
results["gt_labels"] = [
np.array(
[self.class_names.index(n) for n in res], dtype=np.int64
)
for res in results["gt_names"]
]
elif "gt_names" in results:
results["gt_labels"] = np.array(
[self.class_names.index(n) for n in results["gt_names"]],
dtype=np.int64,
)
# we still assume one pipeline for one frame LiDAR
# thus, the 3D name is list[string]
if "gt_names_3d" in results:
results["gt_labels_3d"] = np.array(
[self.class_names.index(n) for n in results["gt_names_3d"]],
dtype=np.int64,
)
# traffic signals
if "light_hazard" in results["ann_info"]:
results["light_hazard"] = results["ann_info"]["light_hazard"]
if "light_inrange" in results["ann_info"]:
results["light_inrange"] = results["ann_info"]["light_inrange"]
if "sign_inrange" in results["ann_info"]:
results["sign_inrange"] = results["ann_info"]["sign_inrange"]
results = super(CarlaDefaultFormatBundle3D, self).__call__(results)
return results
def __repr__(self):
"""str: Return a string that describes the module."""
repr_str = self.__class__.__name__
repr_str += f"(class_names={self.class_names}, "
repr_str += f"with_gt={self.with_gt}, with_label={self.with_label})"
return repr_str
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