def vis_crop_aug_sampler(crop_filename, dataset):
sampler = CropAugSampler(crop_filename)
for frame_idx in range(10):
labels = dataset.get_label(frame_idx)
cam_rgb_points = dataset.get_cam_points_in_image_with_rgb(frame_idx)
cam_rgb_points, labels = sampler.crop_aug(
cam_rgb_points,
labels,
sample_rate={
"Car": 2,
"Pedestrian": 10,
"Cyclist": 10
},
parser_kwargs={
'max_overlap_num_allowed': 50,
'max_trails': 100,
'method_name': 'normal',
'yaw_std': np.pi / 16,
'expand_factor': (1.1, 1.1, 1.1),
'auto_box_height': True,
'overlap_mode': 'box_and_point',
'max_overlap_rate': 1e-6,
'appr_factor': 100,
'must_have_ground': True,
})
aug_configs = [{
'method_name': 'random_box_global_rotation',
'method_kwargs': {
'max_overlap_num_allowed': 100,
'max_trails': 100,
'appr_factor': 100,
'method_name': 'normal',
'yaw_std': np.pi / 8,
'expend_factor': (1.1, 1.1, 1.1)
}
}]
aug_fn = preprocess.get_data_aug(aug_configs)
cam_rgb_points, labels = aug_fn(cam_rgb_points, labels)
dataset.vis_points(cam_rgb_points,
labels,
expend_factor=(1.1, 1.1, 1.1))
num_classes=config['num_classes'])
NUM_CLASSES = dataset.num_classes
if 'NUM_TEST_SAMPLE' not in train_config:
NUM_TEST_SAMPLE = dataset.num_files
else:
if train_config['NUM_TEST_SAMPLE'] < 0:
NUM_TEST_SAMPLE = dataset.num_files
else:
NUM_TEST_SAMPLE = train_config['NUM_TEST_SAMPLE']
BOX_ENCODING_LEN = get_encoding_len(config['box_encoding_method'])
box_encoding_fn = get_box_encoding_fn(config['box_encoding_method'])
box_decoding_fn = get_box_decoding_fn(config['box_encoding_method'])
aug_fn = preprocess.get_data_aug(train_config['data_aug_configs'])
if 'crop_aug' in train_config:
sampler = CropAugSampler(train_config['crop_aug']['crop_filename'])
def fetch_data(frame_idx):
cam_rgb_points = dataset.get_cam_points_in_image_with_rgb(
frame_idx, config['downsample_by_voxel_size'])
box_label_list = dataset.get_label(frame_idx)
if 'crop_aug' in train_config:
cam_rgb_points, box_label_list = sampler.crop_aug(
cam_rgb_points,
box_label_list,
sample_rate=train_config['crop_aug']['sample_rate'],
parser_kwargs=train_config['crop_aug']['parser_kwargs'])
def fetch_data(dataset, frame_idx, train_config, config):
aug_fn = preprocess.get_data_aug(train_config['data_aug_configs'])
BOX_ENCODING_LEN = get_encoding_len(config['box_encoding_method'])
box_encoding_fn = get_box_encoding_fn(config['box_encoding_method'])
box_decoding_fn = get_box_decoding_fn(config['box_encoding_method'])
graph_generate_fn = get_graph_generate_fn(config['graph_gen_method'])
cam_rgb_points = dataset.get_cam_points_in_image_with_rgb(
frame_idx, config['downsample_by_voxel_size'])
box_label_list = dataset.get_label(frame_idx)
if 'crop_aug' in train_config:
cam_rgb_points, box_label_list = sampler.crop_aug(
cam_rgb_points,
box_label_list,
sample_rate=train_config['crop_aug']['sample_rate'],
parser_kwargs=train_config['crop_aug']['parser_kwargs'])
cam_rgb_points, box_label_list = aug_fn(cam_rgb_points, box_label_list)
(vertex_coord_list, keypoint_indices_list, edges_list) = \
graph_generate_fn(cam_rgb_points.xyz, **config['graph_gen_kwargs'])
if config['input_features'] == 'irgb':
input_v = cam_rgb_points.attr
elif config['input_features'] == '0rgb':
input_v = np.hstack([
np.zeros((cam_rgb_points.attr.shape[0], 1)),
cam_rgb_points.attr[:, 1:]
])
elif config['input_features'] == '0000':
input_v = np.zeros_like(cam_rgb_points.attr)
elif config['input_features'] == 'i000':
input_v = np.hstack([
cam_rgb_points.attr[:, [0]],
np.zeros((cam_rgb_points.attr.shape[0], 3))
])
elif config['input_features'] == 'i':
input_v = cam_rgb_points.attr[:, [0]]
elif config['input_features'] == '0':
input_v = np.zeros((cam_rgb_points.attr.shape[0], 1))
last_layer_graph_level = config['model_kwargs']['layer_configs'][-1][
'graph_level']
last_layer_points_xyz = vertex_coord_list[last_layer_graph_level + 1]
if config['label_method'] == 'yaw':
cls_labels, boxes_3d, valid_boxes, label_map = \
dataset.assign_classaware_label_to_points(box_label_list,
last_layer_points_xyz,
expend_factor=train_config.get('expend_factor', (1.0, 1.0, 1.0)))
if config['label_method'] == 'Car':
cls_labels, boxes_3d, valid_boxes, label_map = \
dataset.assign_classaware_car_label_to_points(box_label_list,
last_layer_points_xyz,
expend_factor=train_config.get('expend_factor', (1.0, 1.0, 1.0)))
if config['label_method'] == 'Pedestrian_and_Cyclist':
(cls_labels, boxes_3d, valid_boxes, label_map) =\
dataset.assign_classaware_ped_and_cyc_label_to_points(
box_label_list, last_layer_points_xyz,
expend_factor=train_config.get('expend_factor', (1.0, 1.0, 1.0)))
encoded_boxes = box_encoding_fn(cls_labels, last_layer_points_xyz,
boxes_3d, label_map)
input_v = input_v.astype(np.float32)
vertex_coord_list = [p.astype(np.float32) for p in vertex_coord_list]
keypoint_indices_list = [e.astype(np.int32) for e in keypoint_indices_list]
edges_list = [e.astype(np.int32) for e in edges_list]
cls_labels = cls_labels.astype(np.int32)
encoded_boxes = encoded_boxes.astype(np.float32)
valid_boxes = valid_boxes.astype(np.float32)
return (input_v, vertex_coord_list, keypoint_indices_list, edges_list,
cls_labels, encoded_boxes, valid_boxes)
num_classes=config['num_classes'])
NUM_CLASSES = dataset.num_classes
print(dataset)
if 'NUM_TEST_SAMPLE' not in eval_config:
NUM_TEST_SAMPLE = dataset.num_files
else:
if eval_config['NUM_TEST_SAMPLE'] < 0:
NUM_TEST_SAMPLE = dataset.num_files
else:
NUM_TEST_SAMPLE = eval_config['NUM_TEST_SAMPLE']
BOX_ENCODING_LEN = get_encoding_len(config['box_encoding_method'])
box_encoding_fn = get_box_encoding_fn(config['box_encoding_method'])
box_decoding_fn = get_box_decoding_fn(config['box_encoding_method'])
aug_fn = preprocess.get_data_aug(eval_config['data_aug_configs'])
def fetch_data(frame_idx):
cam_points = dataset.get_cam_points_in_image(
frame_idx, config['downsample_by_voxel_size'])
box_label_list = dataset.get_label(frame_idx)
cam_points, box_label_list = aug_fn(cam_points, box_label_list)
graph_generate_fn = get_graph_generate_fn(config['graph_gen_method'])
(vertex_coord_list, keypoint_indices_list,
edges_list) = graph_generate_fn(cam_points.xyz,
**config['graph_gen_kwargs'])
input_v = cam_points.attr[:, [0]]
last_layer_graph_level = config['model_kwargs']['layer_configs'][-1][
'graph_level']
last_layer_points_xyz = vertex_coord_list[last_layer_graph_level + 1]
