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eval running - offline logger implemented -> Test it!

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Si11ium
2020-05-30 18:12:42 +02:00
parent ba7c0280ae
commit 8d0577b756
9 changed files with 212 additions and 102 deletions
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13
datasets/_point_dataset.py
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@@ -5,7 +5,7 @@ from abc import ABC
from pathlib import Path
from torch.utils.data import Dataset
from ml_lib.point_toolset.sampling import FarthestpointSampling
from ml_lib.point_toolset.sampling import FarthestpointSampling, RandomSampling
import numpy as np
@@ -22,16 +22,21 @@ class _Point_Dataset(ABC, Dataset):
raise NotImplementedError
headers = ['x', 'y', 'z', 'xn', 'yn', 'zn', 'label', 'cl_idx']
samplers = dict(fps=FarthestpointSampling, rnd=RandomSampling)
def __init__(self, root=Path('data'), sampling_k=2048, transforms=None, load_preprocessed=True, *args, **kwargs):
def __init__(self, root=Path('data'), norm_as_feature=True, sampling_k=2048, sampling='rnd',
transforms=None, load_preprocessed=True, split='train', dense_output=False, *args, **kwargs):
super(_Point_Dataset, self).__init__()
self.dense_output = dense_output
self.split = split
self.norm_as_feature = norm_as_feature
self.load_preprocessed = load_preprocessed
self.transforms = transforms if transforms else lambda x: x
self.sampling_k = sampling_k
self.sampling = FarthestpointSampling(K=self.sampling_k)
self.sampling = self.samplers[sampling](K=self.sampling_k)
self.root = Path(root)
self.raw = self.root / 'raw'
self.raw = self.root / 'raw' / self.split
self.processed_ext = '.pik'
self.raw_ext = '.xyz'
self.processed = self.root / self.setting

21
datasets/full_pointclouds.py
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@@ -9,6 +9,7 @@ from ._point_dataset import _Point_Dataset
class FullCloudsDataset(_Point_Dataset):
setting = 'pc'
split: str
def __init__(self, *args, **kwargs):
super(FullCloudsDataset, self).__init__(*args, **kwargs)
@@ -21,13 +22,15 @@ class FullCloudsDataset(_Point_Dataset):
with processed_file_path.open('rb') as processed_file:
pointcloud = pickle.load(processed_file)
points = np.stack((pointcloud['x'], pointcloud['y'], pointcloud['z'],
pointcloud['xn'], pointcloud['yn'], pointcloud['zn']
),
axis=-1)
# When yopu want to return points and normal seperately
# normal = np.stack((pointcloud['xn'], pointcloud['yn'], pointcloud['zn']), axis=-1)
label = pointcloud['label']
sample_idxs = self.sampling(points)
return points[sample_idxs].astype(np.float), label[sample_idxs].astype(np.int)
position = np.stack((pointcloud['x'], pointcloud['y'], pointcloud['z']), axis=-1)
normal = np.stack((pointcloud['xn'], pointcloud['yn'], pointcloud['zn']), axis=-1)
label = pointcloud['label']
sample_idxs = self.sampling(position)
return (normal[sample_idxs].astype(np.float),
position[sample_idxs].astype(np.float),
label[sample_idxs].astype(np.int))

2
datasets/grid_clustered.py
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@@ -26,7 +26,7 @@ class FullCloudsDataset(_Point_Dataset):
# When yopu want to return points and normal seperately
# normal = np.stack((pointcloud['xn'], pointcloud['yn'], pointcloud['zn']), axis=-1)
label = np.stack((pointcloud['label'], pointcloud['cl_idx']))
label = pointcloud['cl_idx']
sample_idxs = self.sampling(points)
return points[sample_idxs], label[sample_idxs]

2
datasets/prim_clustered.py
View File

@@ -26,7 +26,7 @@ class FullCloudsDataset(_Point_Dataset):
# When yopu want to return points and normal seperately
# normal = np.stack((pointcloud['xn'], pointcloud['yn'], pointcloud['zn']), axis=-1)
label = np.stack((pointcloud['label'], pointcloud['cl_idx']))
label = pointcloud['cl_idx']
sample_idxs = self.sampling(points)
return points[sample_idxs], label[sample_idxs]