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from collections import defaultdict
from pathlib import Path
import numpy as np
import torch
import torch.utils.data as torch_data
from ..utils import common_utils
from .augmentor.data_augmentor import DataAugmentor
from .processor.data_processor import DataProcessor
from .processor.point_feature_encoder import PointFeatureEncoder
class DatasetTemplate(torch_data.Dataset):
def __init__(self, dataset_cfg=None, class_names=None, training=True, root_path=None, logger=None):
super().__init__()
self.dataset_cfg = dataset_cfg
self.training = training
self.class_names = class_names
self.logger = logger
self.root_path = root_path if root_path is not None else Path(self.dataset_cfg.DATA_PATH)
self.logger = logger
if self.dataset_cfg is None or class_names is None:
return
self.point_cloud_range = np.array(self.dataset_cfg.POINT_CLOUD_RANGE, dtype=np.float32)
self.point_feature_encoder = PointFeatureEncoder(
self.dataset_cfg.POINT_FEATURE_ENCODING,
point_cloud_range=self.point_cloud_range
)
self.data_augmentor = DataAugmentor(
self.root_path, self.dataset_cfg.DATA_AUGMENTOR, self.class_names, logger=self.logger
) if self.training else None
self.data_processor = DataProcessor(
self.dataset_cfg.DATA_PROCESSOR, point_cloud_range=self.point_cloud_range,
training=self.training, num_point_features=self.point_feature_encoder.num_point_features
)
self.grid_size = self.data_processor.grid_size
self.voxel_size = self.data_processor.voxel_size
self.total_epochs = 0
self._merge_all_iters_to_one_epoch = False
if hasattr(self.data_processor, "depth_downsample_factor"):
self.depth_downsample_factor = self.data_processor.depth_downsample_factor
else:
self.depth_downsample_factor = None
@property
def mode(self):
return 'train' if self.training else 'test'
def __getstate__(self):
d = dict(self.__dict__)
del d['logger']
return d
def __setstate__(self, d):
self.__dict__.update(d)
def generate_prediction_dicts(self, batch_dict, pred_dicts, class_names, output_path=None):
"""
Args:
batch_dict:
frame_id:
pred_dicts: list of pred_dicts
pred_boxes: (N, 7 or 9), Tensor
pred_scores: (N), Tensor
pred_labels: (N), Tensor
class_names:
output_path:
Returns:
"""
def get_template_prediction(num_samples):
box_dim = 9 if self.dataset_cfg.get('TRAIN_WITH_SPEED', False) else 7
ret_dict = {
'name': np.zeros(num_samples), 'score': np.zeros(num_samples),
'boxes_lidar': np.zeros([num_samples, box_dim]), 'pred_labels': np.zeros(num_samples)
}
return ret_dict
def generate_single_sample_dict(box_dict):
pred_scores = box_dict['pred_scores'].cpu().numpy()
pred_boxes = box_dict['pred_boxes'].cpu().numpy()
pred_labels = box_dict['pred_labels'].cpu().numpy()
pred_dict = get_template_prediction(pred_scores.shape[0])
if pred_scores.shape[0] == 0:
return pred_dict
pred_dict['name'] = np.array(class_names)[pred_labels - 1]
pred_dict['score'] = pred_scores
pred_dict['boxes_lidar'] = pred_boxes
pred_dict['pred_labels'] = pred_labels
return pred_dict
annos = []
for index, box_dict in enumerate(pred_dicts):
single_pred_dict = generate_single_sample_dict(box_dict)
single_pred_dict['frame_id'] = batch_dict['frame_id'][index]
if 'metadata' in batch_dict:
single_pred_dict['metadata'] = batch_dict['metadata'][index]
annos.append(single_pred_dict)
return annos
def merge_all_iters_to_one_epoch(self, merge=True, epochs=None):
if merge:
self._merge_all_iters_to_one_epoch = True
self.total_epochs = epochs
else:
self._merge_all_iters_to_one_epoch = False
def __len__(self):
raise NotImplementedError
def __getitem__(self, index):
"""
To support a custom dataset, implement this function to load the raw data (and labels), then transform them to
the unified normative coordinate and call the function self.prepare_data() to process the data and send them
to the model.
Args:
index:
Returns:
"""
raise NotImplementedError
def set_lidar_aug_matrix(self, data_dict):
"""
Get lidar augment matrix (4 x 4), which are used to recover orig point coordinates.
"""
lidar_aug_matrix = np.eye(4)
if 'flip_y' in data_dict.keys():
flip_x = data_dict['flip_x']
flip_y = data_dict['flip_y']
if flip_x:
lidar_aug_matrix[:3,:3] = np.array([[1, 0, 0], [0, -1, 0], [0, 0, 1]]) @ lidar_aug_matrix[:3,:3]
if flip_y:
lidar_aug_matrix[:3,:3] = np.array([[-1, 0, 0], [0, 1, 0], [0, 0, 1]]) @ lidar_aug_matrix[:3,:3]
if 'noise_rot' in data_dict.keys():
noise_rot = data_dict['noise_rot']
lidar_aug_matrix[:3,:3] = common_utils.angle2matrix(torch.tensor(noise_rot)) @ lidar_aug_matrix[:3,:3]
if 'noise_scale' in data_dict.keys():
noise_scale = data_dict['noise_scale']
lidar_aug_matrix[:3,:3] *= noise_scale
if 'noise_translate' in data_dict.keys():
noise_translate = data_dict['noise_translate']
lidar_aug_matrix[:3,3:4] = noise_translate.T
data_dict['lidar_aug_matrix'] = lidar_aug_matrix
return data_dict
def prepare_data(self, data_dict):
"""
Args:
data_dict:
points: optional, (N, 3 + C_in)
gt_boxes: optional, (N, 7 + C) [x, y, z, dx, dy, dz, heading, ...]
gt_names: optional, (N), string
...
Returns:
data_dict:
frame_id: string
points: (N, 3 + C_in)
gt_boxes: optional, (N, 7 + C) [x, y, z, dx, dy, dz, heading, ...]
gt_names: optional, (N), string
use_lead_xyz: bool
voxels: optional (num_voxels, max_points_per_voxel, 3 + C)
voxel_coords: optional (num_voxels, 3)
voxel_num_points: optional (num_voxels)
...
"""
if self.training:
assert 'gt_boxes' in data_dict, 'gt_boxes should be provided for training'
gt_boxes_mask = np.array([n in self.class_names for n in data_dict['gt_names']], dtype=np.bool_)
if 'calib' in data_dict:
calib = data_dict['calib']
data_dict = self.data_augmentor.forward(
data_dict={
**data_dict,
'gt_boxes_mask': gt_boxes_mask
}
)
if 'calib' in data_dict:
data_dict['calib'] = calib
data_dict = self.set_lidar_aug_matrix(data_dict)
if data_dict.get('gt_boxes', None) is not None:
selected = common_utils.keep_arrays_by_name(data_dict['gt_names'], self.class_names)
data_dict['gt_boxes'] = data_dict['gt_boxes'][selected]
data_dict['gt_names'] = data_dict['gt_names'][selected]
gt_classes = np.array([self.class_names.index(n) + 1 for n in data_dict['gt_names']], dtype=np.int32)
gt_boxes = np.concatenate((data_dict['gt_boxes'], gt_classes.reshape(-1, 1).astype(np.float32)), axis=1)
data_dict['gt_boxes'] = gt_boxes
if data_dict.get('gt_boxes2d', None) is not None:
data_dict['gt_boxes2d'] = data_dict['gt_boxes2d'][selected]
if data_dict.get('points', None) is not None:
data_dict = self.point_feature_encoder.forward(data_dict)
data_dict = self.data_processor.forward(
data_dict=data_dict
)
if self.training and len(data_dict['gt_boxes']) == 0:
new_index = np.random.randint(self.__len__())
return self.__getitem__(new_index)
data_dict.pop('gt_names', None)
return data_dict
@staticmethod
def collate_batch(batch_list, _unused=False):
data_dict = defaultdict(list)
for cur_sample in batch_list:
for key, val in cur_sample.items():
data_dict[key].append(val)
batch_size = len(batch_list)
ret = {}
batch_size_ratio = 1
for key, val in data_dict.items():
try:
if key in ['voxels', 'voxel_num_points', 'geometric_features', 'voxel_centers']:
if isinstance(val[0], list):
batch_size_ratio = len(val[0])
val = [i for item in val for i in item]
try:
ret[key] = np.concatenate(val, axis=0)
except ValueError:
# Handle case where arrays have different shapes
print(f"Warning: Could not concatenate {key} due to shape mismatch. Skipping.")
continue
elif key in ['points', 'voxel_coords']:
coors = []
if isinstance(val[0], list):
val = [i for item in val for i in item]
for i, coor in enumerate(val):
coor_pad = np.pad(coor, ((0, 0), (1, 0)), mode='constant', constant_values=i)
coors.append(coor_pad)
ret[key] = np.concatenate(coors, axis=0)
elif key in ['gt_boxes']:
max_gt = max([len(x) for x in val])
batch_gt_boxes3d = np.zeros((batch_size, max_gt, val[0].shape[-1]), dtype=np.float32)
for k in range(batch_size):
batch_gt_boxes3d[k, :val[k].__len__(), :] = val[k]
ret[key] = batch_gt_boxes3d
elif key in ['roi_boxes']:
max_gt = max([x.shape[1] for x in val])
batch_gt_boxes3d = np.zeros((batch_size, val[0].shape[0], max_gt, val[0].shape[-1]), dtype=np.float32)
for k in range(batch_size):
batch_gt_boxes3d[k,:, :val[k].shape[1], :] = val[k]
ret[key] = batch_gt_boxes3d
elif key in ['roi_scores', 'roi_labels']:
max_gt = max([x.shape[1] for x in val])
batch_gt_boxes3d = np.zeros((batch_size, val[0].shape[0], max_gt), dtype=np.float32)
for k in range(batch_size):
batch_gt_boxes3d[k,:, :val[k].shape[1]] = val[k]
ret[key] = batch_gt_boxes3d
elif key in ['gt_boxes2d']:
max_boxes = 0
max_boxes = max([len(x) for x in val])
batch_boxes2d = np.zeros((batch_size, max_boxes, val[0].shape[-1]), dtype=np.float32)
for k in range(batch_size):
if val[k].size > 0:
batch_boxes2d[k, :val[k].__len__(), :] = val[k]
ret[key] = batch_boxes2d
elif key in ["images", "depth_maps"]:
# Get largest image size (H, W)
max_h = 0
max_w = 0
for image in val:
max_h = max(max_h, image.shape[0])
max_w = max(max_w, image.shape[1])
# Change size of images
images = []
for image in val:
pad_h = common_utils.get_pad_params(desired_size=max_h, cur_size=image.shape[0])
pad_w = common_utils.get_pad_params(desired_size=max_w, cur_size=image.shape[1])
pad_width = (pad_h, pad_w)
pad_value = 0
if key == "images":
pad_width = (pad_h, pad_w, (0, 0))
elif key == "depth_maps":
pad_width = (pad_h, pad_w)
image_pad = np.pad(image,
pad_width=pad_width,
mode='constant',
constant_values=pad_value)
images.append(image_pad)
ret[key] = np.stack(images, axis=0)
elif key in ['calib']:
ret[key] = val
elif key in ["points_2d"]:
max_len = max([len(_val) for _val in val])
pad_value = 0
points = []
for _points in val:
pad_width = ((0, max_len-len(_points)), (0,0))
points_pad = np.pad(_points,
pad_width=pad_width,
mode='constant',
constant_values=pad_value)
points.append(points_pad)
ret[key] = np.stack(points, axis=0)
elif key in ['camera_imgs']:
ret[key] = torch.stack([torch.stack(imgs,dim=0) for imgs in val],dim=0)
else:
ret[key] = np.stack(val, axis=0)
except Exception as e:
print(f'Error in collate_batch: key={key}, error={str(e)}')
# Skip this key instead of raising an error
continue
ret['batch_size'] = batch_size * batch_size_ratio
return ret
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