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Zero
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"""
Default Datasets
Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
Please cite our work if the code is helpful to you.
"""
import os
import glob
import numpy as np
import torch
from copy import deepcopy
from torch.utils.data import Dataset
from collections.abc import Sequence
from pointcept.utils.logger import get_root_logger
from pointcept.utils.cache import shared_dict
from .builder import DATASETS, build_dataset
from .transform import Compose, TRANSFORMS
@DATASETS.register_module()
class DefaultDataset(Dataset):
VALID_ASSETS = [
"coord",
"color",
"normal",
"strength",
"segment",
"instance",
"pose",
]
def __init__(
self,
split="train",
data_root="data/dataset",
transform=None,
test_mode=False,
test_cfg=None,
cache=False,
ignore_index=-1,
loop=1,
):
super(DefaultDataset, self).__init__()
self.data_root = data_root
self.split = split
self.transform = Compose(transform)
self.cache = cache
self.ignore_index = ignore_index
self.loop = (
loop if not test_mode else 1
) # force make loop = 1 while in test mode
self.test_mode = test_mode
self.test_cfg = test_cfg if test_mode else None
if test_mode:
self.test_voxelize = TRANSFORMS.build(self.test_cfg.voxelize)
self.test_crop = (
TRANSFORMS.build(self.test_cfg.crop) if self.test_cfg.crop else None
)
self.post_transform = Compose(self.test_cfg.post_transform)
self.aug_transform = [Compose(aug) for aug in self.test_cfg.aug_transform]
self.data_list = self.get_data_list()
logger = get_root_logger()
logger.info(
"Totally {} x {} samples in {} set.".format(
len(self.data_list), self.loop, split
)
)
def get_data_list(self):
if isinstance(self.split, str):
data_list = glob.glob(os.path.join(self.data_root, self.split, "*"))
elif isinstance(self.split, Sequence):
data_list = []
for split in self.split:
data_list += glob.glob(os.path.join(self.data_root, split, "*"))
else:
raise NotImplementedError
return data_list
def get_data(self, idx):
data_path = self.data_list[idx % len(self.data_list)]
name = self.get_data_name(idx)
if self.cache:
cache_name = f"pointcept-{name}"
return shared_dict(cache_name)
data_dict = {}
assets = os.listdir(data_path)
for asset in assets:
if not asset.endswith(".npy"):
continue
if asset[:-4] not in self.VALID_ASSETS:
continue
data_dict[asset[:-4]] = np.load(os.path.join(data_path, asset))
data_dict["name"] = name
if "coord" in data_dict.keys():
data_dict["coord"] = data_dict["coord"].astype(np.float32)
if "color" in data_dict.keys():
data_dict["color"] = data_dict["color"].astype(np.float32)
if "normal" in data_dict.keys():
data_dict["normal"] = data_dict["normal"].astype(np.float32)
if "segment" in data_dict.keys():
data_dict["segment"] = data_dict["segment"].reshape([-1]).astype(np.int32)
else:
data_dict["segment"] = (
np.ones(data_dict["coord"].shape[0], dtype=np.int32) * -1
)
if "instance" in data_dict.keys():
data_dict["instance"] = data_dict["instance"].reshape([-1]).astype(np.int32)
else:
data_dict["instance"] = (
np.ones(data_dict["coord"].shape[0], dtype=np.int32) * -1
)
return data_dict
def get_data_name(self, idx):
return os.path.basename(self.data_list[idx % len(self.data_list)])
def prepare_train_data(self, idx):
# load data
data_dict = self.get_data(idx)
data_dict = self.transform(data_dict)
return data_dict
def prepare_test_data(self, idx):
# load data
data_dict = self.get_data(idx)
data_dict = self.transform(data_dict)
result_dict = dict(segment=data_dict.pop("segment"), name=data_dict.pop("name"))
if "origin_segment" in data_dict:
assert "inverse" in data_dict
result_dict["origin_segment"] = data_dict.pop("origin_segment")
result_dict["inverse"] = data_dict.pop("inverse")
data_dict_list = []
for aug in self.aug_transform:
data_dict_list.append(aug(deepcopy(data_dict)))
fragment_list = []
for data in data_dict_list:
if self.test_voxelize is not None:
data_part_list = self.test_voxelize(data)
else:
data["index"] = np.arange(data["coord"].shape[0])
data_part_list = [data]
for data_part in data_part_list:
if self.test_crop is not None:
data_part = self.test_crop(data_part)
else:
data_part = [data_part]
fragment_list += data_part
for i in range(len(fragment_list)):
fragment_list[i] = self.post_transform(fragment_list[i])
result_dict["fragment_list"] = fragment_list
return result_dict
def __getitem__(self, idx):
if self.test_mode:
return self.prepare_test_data(idx)
else:
return self.prepare_train_data(idx)
def __len__(self):
return len(self.data_list) * self.loop
@DATASETS.register_module()
class ConcatDataset(Dataset):
def __init__(self, datasets, loop=1):
super(ConcatDataset, self).__init__()
self.datasets = [build_dataset(dataset) for dataset in datasets]
self.loop = loop
self.data_list = self.get_data_list()
logger = get_root_logger()
logger.info(
"Totally {} x {} samples in the concat set.".format(
len(self.data_list), self.loop
)
)
def get_data_list(self):
data_list = []
for i in range(len(self.datasets)):
data_list.extend(
zip(
np.ones(len(self.datasets[i])) * i, np.arange(len(self.datasets[i]))
)
)
return data_list
def get_data(self, idx):
dataset_idx, data_idx = self.data_list[idx % len(self.data_list)]
return self.datasets[dataset_idx][data_idx]
def get_data_name(self, idx):
dataset_idx, data_idx = self.data_list[idx % len(self.data_list)]
return self.datasets[dataset_idx].get_data_name(data_idx)
def __getitem__(self, idx):
return self.get_data(idx)
def __len__(self):
return len(self.data_list) * self.loop
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