def _nuscenes_modify_step(config,
epochs,
eval_epoch,
data_sample_factor,
num_examples=28130):
input_cfg = config.train_input_reader
train_cfg = config.train_config
batch_size = input_cfg.batch_size
data_sample_factor_to_name = {
1: "NuScenesDataset",
2: "NuScenesDatasetD2",
3: "NuScenesDatasetD3",
4: "NuScenesDatasetD4",
5: "NuScenesDatasetD5",
6: "NuScenesDatasetD6",
7: "NuScenesDatasetD7",
8: "NuScenesDatasetD8",
}
dataset_name = data_sample_factor_to_name[data_sample_factor]
input_cfg.dataset.dataset_class_name = dataset_name
ds = get_dataset_class(dataset_name)(
root_path=input_cfg.dataset.kitti_root_path,
info_path=input_cfg.dataset.kitti_info_path,
)
num_examples_after_sample = len(ds)
step_per_epoch = _div_up(num_examples_after_sample, batch_size)
step_per_eval = step_per_epoch * eval_epoch
total_step = step_per_epoch * epochs
train_cfg.steps = total_step
train_cfg.steps_per_eval = step_per_eval
def readinfo():
global BACKEND
instance = request.json
root_path = Path(instance["root_path"])
response = {"status": "normal"}
BACKEND.root_path = root_path
info_path = Path(instance["info_path"])
dataset_class_name = instance["dataset_class_name"]
BACKEND.dataset = get_dataset_class(dataset_class_name)(root_path=root_path, info_path=info_path)
BACKEND.image_idxes = list(range(len(BACKEND.dataset)))
response["image_indexes"] = BACKEND.image_idxes
response = jsonify(results=[response])
response.headers['Access-Control-Allow-Headers'] = '*'
return response
def set_train_step(config, epochs, eval_epoch):
input_cfg = config.train_input_reader
train_cfg = config.train_config
batch_size = input_cfg.batch_size
dataset_name = input_cfg.dataset.dataset_class_name
ds = get_dataset_class(dataset_name)(
root_path=input_cfg.dataset.kitti_root_path,
info_path=input_cfg.dataset.kitti_info_path,
)
num_examples_after_sample = len(ds)
step_per_epoch = _div_up(num_examples_after_sample, batch_size)
step_per_eval = step_per_epoch * eval_epoch
total_step = step_per_epoch * epochs
train_cfg.steps = total_step
train_cfg.steps_per_eval = step_per_eval
def readinfo():
app.logger.info("readinfo")
global BACKEND
instance = request.json
root_path = Path(instance["root_path"])
response = {"status": "normal"}
BACKEND.root_path = root_path
info_path = Path(instance["info_path"])
dataset_class_name = instance["dataset_class_name"]
BACKEND.dataset = get_dataset_class(dataset_class_name)(
root_path=root_path, info_path=info_path)
BACKEND.image_idxes = list(range(len(BACKEND.dataset)))
response["image_indexes"] = BACKEND.image_idxes
response = jsonify(results=[response])
return response
def build(input_reader_config,
model_config,
training,
voxel_generator,
target_assigner,
multi_gpu=False):
"""Builds a tensor dictionary based on the InputReader config.
Args:
input_reader_config: A input_reader_pb2.InputReader object.
Returns:
A tensor dict based on the input_reader_config.
Raises:
ValueError: On invalid input reader proto.
ValueError: If no input paths are specified.
"""
if not isinstance(input_reader_config, input_reader_pb2.InputReader):
raise ValueError('input_reader_config not of type '
'input_reader_pb2.InputReader.')
prep_cfg = input_reader_config.preprocess
dataset_cfg = input_reader_config.dataset
num_point_features = model_config.num_point_features
out_size_factor = get_downsample_factor(model_config)
assert out_size_factor > 0
cfg = input_reader_config
db_sampler_cfg = prep_cfg.database_sampler
db_sampler = None
if len(db_sampler_cfg.sample_groups) > 0: # enable sample
db_sampler = dbsampler_builder.build(db_sampler_cfg)
grid_size = voxel_generator.grid_size
# [352, 400]
feature_map_size = grid_size[:2] // out_size_factor
feature_map_size = [*feature_map_size, 1][::-1]
print("feature_map_size", feature_map_size)
assert all([n != '' for n in target_assigner.classes
]), "you must specify class_name in anchor_generators."
dataset_cls = get_dataset_class(dataset_cfg.dataset_class_name)
assert dataset_cls.NumPointFeatures >= 3, "you must set this to correct value"
assert dataset_cls.NumPointFeatures == num_point_features, "currently you need keep them same"
prep_func = partial(
prep_pointcloud,
root_path=dataset_cfg.kitti_root_path,
voxel_generator=voxel_generator,
target_assigner=target_assigner,
training=training,
max_voxels=prep_cfg.max_number_of_voxels,
remove_outside_points=False,
remove_unknown=prep_cfg.remove_unknown_examples,
create_targets=training,
shuffle_points=prep_cfg.shuffle_points,
gt_rotation_noise=list(prep_cfg.groundtruth_rotation_uniform_noise),
gt_loc_noise_std=list(prep_cfg.groundtruth_localization_noise_std),
global_rotation_noise=list(prep_cfg.global_rotation_uniform_noise),
global_scaling_noise=list(prep_cfg.global_scaling_uniform_noise),
global_random_rot_range=list(
prep_cfg.global_random_rotation_range_per_object),
global_translate_noise_std=list(prep_cfg.global_translate_noise_std),
db_sampler=db_sampler,
num_point_features=dataset_cls.NumPointFeatures,
anchor_area_threshold=prep_cfg.anchor_area_threshold,
gt_points_drop=prep_cfg.groundtruth_points_drop_percentage,
gt_drop_max_keep=prep_cfg.groundtruth_drop_max_keep_points,
remove_points_after_sample=prep_cfg.remove_points_after_sample,
remove_environment=prep_cfg.remove_environment,
use_group_id=prep_cfg.use_group_id,
out_size_factor=out_size_factor,
multi_gpu=multi_gpu)
ret = target_assigner.generate_anchors(feature_map_size)
class_names = target_assigner.classes
anchors_dict = target_assigner.generate_anchors_dict(feature_map_size)
anchors = ret["anchors"]
anchors = anchors.reshape([-1, 7])
matched_thresholds = ret["matched_thresholds"]
unmatched_thresholds = ret["unmatched_thresholds"]
anchors_bv = box_np_ops.rbbox2d_to_near_bbox(anchors[:, [0, 1, 3, 4, 6]])
anchor_cache = {
"anchors": anchors,
"anchors_bv": anchors_bv,
"matched_thresholds": matched_thresholds,
"unmatched_thresholds": unmatched_thresholds,
"anchors_dict": anchors_dict,
}
prep_func = partial(prep_func, anchor_cache=anchor_cache)
dataset = dataset_cls(info_path=dataset_cfg.kitti_info_path,
root_path=dataset_cfg.kitti_root_path,
class_names=class_names,
prep_func=prep_func)
return dataset