redo
This commit is contained in:
Si11ium
parent
30525c954e
commit
54a5b48ddc
@ -21,23 +21,25 @@ def save_names(name_list, path):
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class CustomShapeNet(InMemoryDataset):
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categories = {key: val for val, key in enumerate(['Box', 'Cone', 'Cylinder', 'Sphere'])}
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modes = {key: val for val, key in enumerate(['train', 'test', 'predict'])}
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def __init__(self, root, collate_per_segment=True, train=True, transform=None, pre_filter=None, pre_transform=None,
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headers=True, has_variations=False):
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def __init__(self, root, collate_per_segment=True, mode='train', transform=None, pre_filter=None, pre_transform=None,
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headers=True, has_variations=False, refresh=False):
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assert mode in self.modes.keys(), f'"mode" must be one of {self.modes.keys()}'
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assert not (collate_per_segment and has_variations), 'Either use each element or pointclouds - with variations'
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self.has_headers = headers
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self.has_variations = has_variations
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self.collate_per_element = collate_per_segment
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self.train = train
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self.mode = mode
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self.refresh = refresh
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super(CustomShapeNet, self).__init__(root, transform, pre_transform, pre_filter)
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path = self.processed_paths[0] if train else self.processed_paths[-1]
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self.data, self.slices = torch.load(path)
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self.data, self.slices = self._load_dataset()
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print("Initialized")
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@property
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def raw_file_names(self):
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# Maybe add more data like validation sets
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return ['train', 'test']
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return list(self.modes.keys())
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@property
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def processed_file_names(self):
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@ -56,9 +58,18 @@ class CustomShapeNet(InMemoryDataset):
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def _load_dataset(self):
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data, slices = None, None
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filepath = self.processed_paths[self.modes[self.mode]]
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if self.refresh:
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try:
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os.remove(filepath)
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print('Processed Location "Refreshed" (We deleted the Files)')
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except FileNotFoundError:
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print('You meant to refresh the allready processed dataset, but there were none...')
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print('continue processing')
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pass
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while True:
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try:
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filepath = os.path.join(self.root, self.processed_dir, f'{"train" if self.train else "test"}.pt')
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data, slices = torch.load(filepath)
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print('Dataset Loaded')
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break
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@ -80,7 +91,7 @@ class CustomShapeNet(InMemoryDataset):
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def process(self, delimiter=' '):
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datasets = defaultdict(list)
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idx, data_folder = (0, self.raw_file_names[0]) if self.train else (1, self.raw_file_names[1])
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idx, data_folder = self.modes[self.mode], self.raw_file_names[self.modes[self.mode]]
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path_to_clouds = os.path.join(self.raw_dir, data_folder)
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if '.headers' in os.listdir(path_to_clouds):
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@ -131,7 +142,7 @@ class CustomShapeNet(InMemoryDataset):
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points = torch.tensor(src, dtype=None).squeeze()
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if not len(points.shape) > 1:
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continue
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y_all = [y_raw] * points.shape[0]
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y_all = ([y_raw] if self.mode != 'predict' else [-1]) * points.shape[0]
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y = torch.as_tensor(y_all, dtype=torch.int)
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if self.collate_per_element:
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data = Data(y=y, pos=points[:, :3]) # , points=points, norm=points[:, 3:])
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@ -167,12 +178,12 @@ class ShapeNetPartSegDataset(Dataset):
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Resample raw point cloud to fixed number of points.
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Map raw label from range [1, N] to [0, N-1].
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"""
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def __init__(self, root_dir, collate_per_segment=True, train=True, transform=None,
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def __init__(self, root_dir, collate_per_segment=True, mode='train', transform=None, refresh=False,
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has_variations=False, npoints=1024, headers=True):
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super(ShapeNetPartSegDataset, self).__init__()
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self.npoints = npoints
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self.dataset = CustomShapeNet(root=root_dir, collate_per_segment=collate_per_segment,
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train=train, transform=transform, headers=headers, has_variations=has_variations)
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self.dataset = CustomShapeNet(root=root_dir, collate_per_segment=collate_per_segment, refresh=refresh,
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mode=mode, transform=transform, headers=headers, has_variations=has_variations)
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def __getitem__(self, index):
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data = self.dataset[index]
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@ -200,144 +211,3 @@ class ShapeNetPartSegDataset(Dataset):
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def num_classes(self):
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return self.dataset.num_classes
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class PredictionShapeNet(InMemoryDataset):
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def __init__(self, root, transform=None, pre_filter=None, pre_transform=None, headers=True, refresh=False):
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self.has_headers = headers
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self.refresh = refresh
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super(PredictionShapeNet, self).__init__(root, transform, pre_transform, pre_filter)
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path = self.processed_paths[0]
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self.data, self.slices = self._load_dataset()
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print("Initialized")
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@property
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def raw_file_names(self):
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# Maybe add more data like validation sets
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return ['predict']
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@property
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def processed_file_names(self):
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return [f'{x}.pt' for x in self.raw_file_names]
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def download(self):
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dir_count = len([name for name in os.listdir(self.raw_dir) if os.path.isdir(os.path.join(self.raw_dir, name))])
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print(f'{dir_count} folders have been found....')
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if dir_count:
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return dir_count
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raise IOError("No raw pointclouds have been found.")
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@property
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def num_classes(self):
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return len(self.categories)
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def _load_dataset(self):
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data, slices = None, None
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filepath = os.path.join(self.processed_dir, self.processed_file_names[0])
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if self.refresh:
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try:
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os.remove(filepath)
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print('Processed Location "Refreshed" (We deleted the Files)')
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except FileNotFoundError:
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print('You meant to refresh the allready processed dataset, but there were none...')
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print('continue processing')
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pass
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while True:
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try:
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data, slices = torch.load(filepath)
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print('Dataset Loaded')
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break
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except FileNotFoundError:
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self.process()
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continue
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return data, slices
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def process(self, delimiter=' '):
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datasets, filenames = defaultdict(list), []
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path_to_clouds = os.path.join(self.raw_dir, self.raw_file_names[0])
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if '.headers' in os.listdir(path_to_clouds):
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self.has_headers = True
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elif 'no.headers' in os.listdir(path_to_clouds):
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self.has_headers = False
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else:
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pass
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for pointcloud in tqdm(os.scandir(path_to_clouds)):
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if not os.path.isdir(pointcloud):
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continue
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full_cloud_pattern = '(^\d+?_|^)pc\.(xyz|dat)'
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pattern = re.compile(full_cloud_pattern)
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for file in os.scandir(pointcloud.path):
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if not pattern.match(file.name):
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continue
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with open(file, 'r') as f:
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if self.has_headers:
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headers = f.__next__()
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# Check if there are no useable nodes in this file, header says 0.
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if not int(headers.rstrip().split(delimiter)[0]):
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continue
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# Iterate over all rows
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src = [[float(x) if x not in ['-nan(ind)', 'nan(ind)'] else 0
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for x in line.rstrip().split(delimiter)[None:None]] for line in f if line != '']
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points = torch.tensor(src, dtype=None).squeeze()
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if not len(points.shape) > 1:
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continue
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# pos = points[:, :3]
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# norm = points[:, 3:]
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y_fake_all = [-1] * points.shape[0]
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y = torch.as_tensor(y_fake_all, dtype=torch.int)
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# points = torch.as_tensor(points, dtype=torch.float)
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# norm = torch.as_tensor(norm, dtype=torch.float)
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data = Data(y=y, pos=points[:, :3], points=points, norm=points[:, 3:])
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# , points=points, norm=points[:3], )
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# ToDo: ANy filter to apply? Then do it here.
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if self.pre_filter is not None and not self.pre_filter(data):
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data = self.pre_filter(data)
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raise NotImplementedError
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# ToDo: ANy transformation to apply? Then do it here.
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if self.pre_transform is not None:
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data = self.pre_transform(data)
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raise NotImplementedError
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datasets[self.raw_file_names[0]].append(data)
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filenames.append(file)
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os.makedirs(self.processed_dir, exist_ok=True)
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torch.save(self.collate(datasets[self.raw_file_names[0]]), self.processed_paths[0])
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# save_names(filenames)
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def __repr__(self):
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return f'{self.__class__.__name__}({len(self)})'
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class PredictNetPartSegDataset(Dataset):
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"""
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Resample raw point cloud to fixed number of points.
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Map raw label from range [1, N] to [0, N-1].
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"""
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def __init__(self, root_dir, num_classes, transform=None, npoints=2048, headers=True, refresh=False):
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super(PredictNetPartSegDataset, self).__init__()
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self.npoints = npoints
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self._num_classes = num_classes
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self.dataset = PredictionShapeNet(root=root_dir, transform=transform, headers=headers, refresh=refresh)
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def __getitem__(self, index):
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data = self.dataset[index]
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points, labels, _, norm = data.pos, data.y, data.points, data.norm
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sample = {
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'points': points, # torch.Tensor (n, 3)
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'labels': labels, # torch.Tensor (n,)
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'normals': norm # torch.Tensor (n,)
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}
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return sample
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def __len__(self):
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return len(self.dataset)
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def num_classes(self):
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return self._num_classes
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4
main.py
4
main.py
@ -73,12 +73,12 @@ if __name__ == '__main__':
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test_transform = GT.Compose([GT.NormalizeScale(), ])
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dataset = ShapeNetPartSegDataset(root_dir=opt.dataset, collate_per_segment=opt.collate_per_segment,
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train=True, transform=train_transform, npoints=opt.npoints,
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mode='train', transform=train_transform, npoints=opt.npoints,
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has_variations=opt.has_variations, headers=opt.headers)
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dataLoader = DataLoader(dataset, batch_size=opt.batch_size, shuffle=True, num_workers=opt.num_workers)
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test_dataset = ShapeNetPartSegDataset(root_dir=opt.dataset, collate_per_segment=opt.collate_per_segment,
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train=False, transform=test_transform, npoints=opt.npoints,
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mode='test', transform=test_transform, npoints=opt.npoints,
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has_variations=opt.has_variations, headers=opt.headers)
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test_dataLoader = DataLoader(test_dataset, batch_size=opt.batch_size, shuffle=True, num_workers=opt.num_workers)
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@ -2,7 +2,7 @@ import sys
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import os
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sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/../') # add project root directory
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from dataset.shapenet import PredictNetPartSegDataset, ShapeNetPartSegDataset
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from dataset.shapenet import ShapeNetPartSegDataset
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from model.pointnet2_part_seg import PointNet2PartSegmentNet
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import torch_geometric.transforms as GT
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import torch
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@ -13,7 +13,7 @@ import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument('--dataset', type=str, default='data', help='dataset path')
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parser.add_argument('--npoints', type=int, default=2048, help='resample points number')
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parser.add_argument('--model', type=str, default='./checkpoint/seg_model_custom_249.pth', help='model path')
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parser.add_argument('--model', type=str, default='./checkpoint/seg_model_custom_246.pth', help='model path')
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parser.add_argument('--sample_idx', type=int, default=0, help='select a sample to segment and view result')
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opt = parser.parse_args()
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print(opt)
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@ -24,9 +24,9 @@ if __name__ == '__main__':
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print('Construct dataset ..')
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test_transform = GT.Compose([GT.NormalizeScale(),])
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test_dataset = PredictNetPartSegDataset(
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test_dataset = ShapeNetPartSegDataset(
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mode='predict',
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root_dir=opt.dataset,
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num_classes=4,
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transform=None,
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npoints=opt.npoints,
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refresh=True
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@ -28,10 +28,10 @@ if __name__ == '__main__':
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print('Construct dataset ..')
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test_transform = GT.Compose([GT.NormalizeScale(),])
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test_dataset = PredictNetPartSegDataset(
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test_dataset = ShapeNetPartSegDataset(
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root_dir=opt.dataset,
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collate_per_segment=False,
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train=False,
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mode='predict',
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transform=test_transform,
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npoints=opt.npoints
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)
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