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thingi10k_test.py
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from torch_geometric.data import Dataset
from lightconvpoint.datasets.data import Data
import os
import numpy as np
import torch
import logging
class Thingi10kTest(Dataset):
def __init__(self, root, split="training", transform=None, filter_name=None, num_non_manifold_points=2048, variant_directory="thingi10k_scans_original", dataset_size=None, **kwargs):
super().__init__(root, transform, None)
logging.info(f"Dataset - Thingi10k Test - Test only - {dataset_size}")
self.root = os.path.join(self.root, variant_directory)
self.filenames = []
split_file = os.path.join(self.root, "testset.txt")
with open(split_file) as f:
content = f.readlines()
content = [line.split("\n")[0] for line in content]
content = [os.path.join(self.root, "04_pts", line) for line in content]
self.filenames += content
self.filenames.sort()
if dataset_size is not None:
self.filenames = self.filenames[:dataset_size]
logging.info(f"Dataset - len {len(self.filenames)}")
def get_category(self, f_id):
return self.filenames[f_id].split("/")[-2]
def get_object_name(self, f_id):
return self.filenames[f_id].split("/")[-1]
def get_class_name(self, f_id):
return self.metadata[self.get_category(f_id)]["name"]
@property
def raw_file_names(self):
return []
@property
def processed_file_names(self):
return []
def _download(self): # override _download to remove makedirs
pass
def download(self):
pass
def process(self):
pass
def _process(self):
pass
def len(self):
return len(self.filenames)
def get_data_for_evaluation(self, idx):
filename = self.filenames[idx]
raise NotImplementedError
data_shape = np.load(os.path.join(filename, "pointcloud.npz"))
data_space = np.load(os.path.join(filename, "points.npz"))
return data_shape, data_space
def get(self, idx):
"""Get item."""
filename = self.filenames[idx]
pts_shp = np.load(filename+".xyz.npy")
# np.savetxt("/root/no_backup/test.xyz", np.concatenate([pts_space, occupancies[:,np.newaxis]], axis=1))
# exit()
pts_shp = torch.tensor(pts_shp, dtype=torch.float)
pts_space = torch.ones((1,3), dtype=torch.float)
occupancies = torch.ones((1,), dtype=torch.long)
data = Data(x = torch.ones_like(pts_shp),
shape_id=idx,
pos=pts_shp,
pos_non_manifold=pts_space, occupancies=occupancies, #
)
return data
class Thingi10kTestNoiseFree(Thingi10kTest):
def __init__(self, root, split="training", transform=None, filter_name=None, num_non_manifold_points=2048, variant_directory="thingi10k_scans_noisefree", dataset_size=None, **kwargs):
super().__init__(root,
split=split,
transform=transform,
filter_name=filter_name,
num_non_manifold_points=num_non_manifold_points,
variant_directory=variant_directory,
dataset_size=dataset_size, **kwargs)
class Thingi10kTestExtraNoisy(Thingi10kTest):
def __init__(self, root, split="training", transform=None, filter_name=None, num_non_manifold_points=2048, variant_directory="thingi10k_scans_extra_noisy", dataset_size=None, **kwargs):
super().__init__(root,
split=split,
transform=transform,
filter_name=filter_name,
num_non_manifold_points=num_non_manifold_points,
variant_directory=variant_directory,
dataset_size=dataset_size, **kwargs)
class Thingi10kTestSparse(Thingi10kTest):
def __init__(self, root, split="training", transform=None, filter_name=None, num_non_manifold_points=2048, variant_directory="thingi10k_scans_sparse", dataset_size=None, **kwargs):
super().__init__(root,
split=split,
transform=transform,
filter_name=filter_name,
num_non_manifold_points=num_non_manifold_points,
variant_directory=variant_directory,
dataset_size=dataset_size, **kwargs)
class Thingi10kTestDense(Thingi10kTest):
def __init__(self, root, split="training", transform=None, filter_name=None, num_non_manifold_points=2048, variant_directory="thingi10k_scans_dense", dataset_size=None, **kwargs):
super().__init__(root,
split=split,
transform=transform,
filter_name=filter_name,
num_non_manifold_points=num_non_manifold_points,
variant_directory=variant_directory,
dataset_size=dataset_size, **kwargs)