diff --git a/dataset/shapenet.py b/dataset/shapenet.py index 480c04e..ffe829e 100644 --- a/dataset/shapenet.py +++ b/dataset/shapenet.py @@ -17,24 +17,28 @@ def save_names(name_list, path): with open(path, 'wb') as f: f.writelines(name_list) + class CustomShapeNet(InMemoryDataset): categories = {key: val for val, key in enumerate(['Box', 'Cone', 'Cylinder', 'Sphere'])} + modes = {key: val for val, key in enumerate(['train', 'test', 'predict'])} - def __init__(self, root, collate_per_segment=True, train=True, transform=None, pre_filter=None, pre_transform=None, - headers=True, **kwargs): - self.has_headers = headers - self.collate_per_element = collate_per_segment - self.train = train - super(CustomShapeNet, self).__init__(root, transform, pre_transform, pre_filter) - path = self.processed_paths[0] if train else self.processed_paths[-1] - self.data, self.slices = torch.load(path) + def __init__(self, root_dir, collate_per_segment=True, mode='train', transform=None, pre_filter=None, + pre_transform=None, headers=True, has_variations=False, refresh=False, labels_within=False): + assert mode in self.modes.keys(), f'"mode" must be one of {self.modes.keys()}' + assert not (collate_per_segment and has_variations), 'Either use each element or pointclouds - with variations' + + #Set the Dataset Parameters + self.has_headers, self.has_variations, self.labels_within = headers, has_variations, labels_within + self.collate_per_element, self.mode, self.refresh = collate_per_segment, mode, refresh + super(CustomShapeNet, self).__init__(root_dir, transform, pre_transform, pre_filter) + self.data, self.slices = self._load_dataset() print("Initialized") @property def raw_file_names(self): # Maybe add more data like validation sets - return ['train', 'test'] + return list(self.modes.keys()) @property def processed_file_names(self): @@ -53,9 +57,18 @@ class CustomShapeNet(InMemoryDataset): def _load_dataset(self): data, slices = None, None + filepath = self.processed_paths[self.modes[self.mode]] + if self.refresh: + try: + os.remove(filepath) + print('Processed Location "Refreshed" (We deleted the Files)') + except FileNotFoundError: + print('You meant to refresh the allready processed dataset, but there were none...') + print('continue processing') + pass + while True: try: - filepath = os.path.join(self.root, self.processed_dir, f'{"train" if self.train else "test"}.pt') data, slices = torch.load(filepath) print('Dataset Loaded') break @@ -77,7 +90,7 @@ class CustomShapeNet(InMemoryDataset): def process(self, delimiter=' '): datasets = defaultdict(list) - idx, data_folder = (0, self.raw_file_names[0]) if self.train else (1, self.raw_file_names[1]) + idx, data_folder = self.modes[self.mode], self.raw_file_names[self.modes[self.mode]] path_to_clouds = os.path.join(self.raw_dir, data_folder) if '.headers' in os.listdir(path_to_clouds): @@ -88,6 +101,8 @@ class CustomShapeNet(InMemoryDataset): pass for pointcloud in tqdm(os.scandir(path_to_clouds)): + if self.has_variations: + cloud_variations = defaultdict(list) if not os.path.isdir(pointcloud): continue data, paths = None, list() @@ -95,16 +110,11 @@ class CustomShapeNet(InMemoryDataset): paths.extend(glob.glob(os.path.join(pointcloud.path, f'*.{ext}'))) for element in paths: - if all([x not in os.path.split(element)[-1] for x in ['pc.dat', 'pc.xyz']]): - # Assign training data to the data container - # Following the original logic; - # y should be the label; - # pos should be the six dimensional vector describing: !its pos not points!! - # x,y,z,x_rot,y_rot,z_rot - - # Get the y - Label - y_raw = next(i for i, v in enumerate(self.categories.keys()) if v.lower() in element.lower()) - # y_raw = os.path.splitext(element)[0].split('_')[-2] + # This was build to filter all variations that aregreater then 25 + pattern = re.compile('^((6[0-1]|[1-5][0-9])_\w+?\d+?|pc|\d+?_pc)\.(xyz|dat)$') + if pattern.match(os.path.split(element)[-1]): + continue + else: with open(element,'r') as f: if self.has_headers: headers = f.__next__() @@ -118,21 +128,36 @@ class CustomShapeNet(InMemoryDataset): points = torch.tensor(src, dtype=None).squeeze() if not len(points.shape) > 1: continue - y_all = [y_raw] * points.shape[0] + # Place Fake Labels to hold the given structure + if self.labels_within: + y_all = points[:, -1] + points = points[:, :-1] + else: + # Get the y - Label + y_raw = next(i for i, v in enumerate(self.categories.keys()) if v.lower() in element.lower()) + y_all = ([y_raw] if self.mode != 'predict' else [-1]) * points.shape[0] + y = torch.as_tensor(y_all, dtype=torch.int) if self.collate_per_element: - data = Data(y=y, pos=points[:, :3], points=points, norm=points[:, 3:]) + data = Data(y=y, pos=points[:, :3]) # , points=points, norm=points[:, 3:]) else: if not data: data = defaultdict(list) - for key, val in dict(y=y, pos=points[:, :3], points=points, norm=points[:, 3:]).items(): + # points=points, norm=points[:, 3:] + for key, val in dict(y=y, pos=points[:, :3]).items(): data[key].append(val) data = self._transform_and_filter(data) if self.collate_per_element: datasets[data_folder].append(data) + if self.has_variations: + cloud_variations[int(os.path.split(element)[-1].split('_')[0])].append(data) if not self.collate_per_element: - datasets[data_folder].append(Data(**{key: torch.cat(data[key]) for key in data.keys()})) + if self.has_variations: + for variation in cloud_variations.keys(): + datasets[data_folder].append(Data(**{key: torch.cat(data[key]) for key in data.keys()})) + else: + datasets[data_folder].append(Data(**{key: torch.cat(data[key]) for key in data.keys()})) if datasets[data_folder]: os.makedirs(self.processed_dir, exist_ok=True) @@ -147,15 +172,15 @@ class ShapeNetPartSegDataset(Dataset): Resample raw point cloud to fixed number of points. Map raw label from range [1, N] to [0, N-1]. """ - def __init__(self, root_dir, collate_per_segment=True, train=True, transform=None, npoints=1024, headers=True): + def __init__(self, root_dir, npoints=1024, **kwargs): super(ShapeNetPartSegDataset, self).__init__() + kwargs.update(dict(root_dir=root_dir)) self.npoints = npoints - self.dataset = CustomShapeNet(root=root_dir, collate_per_segment=collate_per_segment, - train=train, transform=transform, headers=headers) + self.dataset = CustomShapeNet(**kwargs) def __getitem__(self, index): data = self.dataset[index] - points, labels, _, norm = data.pos, data.y, data.points, data.norm + points, labels = data.pos, data.y # , data.points, data.norm # Resample to fixed number of points try: @@ -163,14 +188,13 @@ class ShapeNetPartSegDataset(Dataset): except ValueError: choice = [] - points, labels, norm = points[choice, :], labels[choice], norm[choice] + points, labels = points[choice, :], labels[choice] labels -= 1 if self.num_classes() in labels else 0 # Map label from [1, C] to [0, C-1] sample = { 'points': points, # torch.Tensor (n, 3) - 'labels': labels, # torch.Tensor (n,) - 'normals': norm # torch.Tensor (n,) + 'labels': labels # torch.Tensor (n,) } return sample @@ -180,144 +204,3 @@ class ShapeNetPartSegDataset(Dataset): def num_classes(self): return self.dataset.num_classes - - -class PredictionShapeNet(InMemoryDataset): - - def __init__(self, root, transform=None, pre_filter=None, pre_transform=None, headers=True, refresh=False): - self.has_headers = headers - self.refresh = refresh - super(PredictionShapeNet, self).__init__(root, transform, pre_transform, pre_filter) - path = self.processed_paths[0] - self.data, self.slices = self._load_dataset() - print("Initialized") - - @property - def raw_file_names(self): - # Maybe add more data like validation sets - return ['predict'] - - @property - def processed_file_names(self): - return [f'{x}.pt' for x in self.raw_file_names] - - def download(self): - dir_count = len([name for name in os.listdir(self.raw_dir) if os.path.isdir(os.path.join(self.raw_dir, name))]) - print(f'{dir_count} folders have been found....') - if dir_count: - return dir_count - raise IOError("No raw pointclouds have been found.") - - @property - def num_classes(self): - return len(self.categories) - - def _load_dataset(self): - data, slices = None, None - filepath = os.path.join(self.processed_dir, self.processed_file_names[0]) - if self.refresh: - try: - os.remove(filepath) - print('Processed Location "Refreshed" (We deleted the Files)') - except FileNotFoundError: - print('You meant to refresh the allready processed dataset, but there were none...') - print('continue processing') - pass - - while True: - try: - data, slices = torch.load(filepath) - print('Dataset Loaded') - break - except FileNotFoundError: - self.process() - continue - return data, slices - - def process(self, delimiter=' '): - - datasets, filenames = defaultdict(list), [] - path_to_clouds = os.path.join(self.raw_dir, self.raw_file_names[0]) - - if '.headers' in os.listdir(path_to_clouds): - self.has_headers = True - elif 'no.headers' in os.listdir(path_to_clouds): - self.has_headers = False - else: - pass - - for pointcloud in tqdm(os.scandir(path_to_clouds)): - if not os.path.isdir(pointcloud): - continue - full_cloud_pattern = '(^\d+?_|^)pc\.(xyz|dat)' - pattern = re.compile(full_cloud_pattern) - for file in os.scandir(pointcloud.path): - if not pattern.match(file.name): - continue - with open(file, 'r') as f: - if self.has_headers: - headers = f.__next__() - # Check if there are no useable nodes in this file, header says 0. - if not int(headers.rstrip().split(delimiter)[0]): - continue - - # Iterate over all rows - src = [[float(x) if x not in ['-nan(ind)', 'nan(ind)'] else 0 - for x in line.rstrip().split(delimiter)[None:None]] for line in f if line != ''] - points = torch.tensor(src, dtype=None).squeeze() - if not len(points.shape) > 1: - continue - # pos = points[:, :3] - # norm = points[:, 3:] - y_fake_all = [-1] * points.shape[0] - y = torch.as_tensor(y_fake_all, dtype=torch.int) - # points = torch.as_tensor(points, dtype=torch.float) - # norm = torch.as_tensor(norm, dtype=torch.float) - data = Data(y=y, pos=points[:, :3], points=points, norm=points[:, 3:]) - # , points=points, norm=points[:3], ) - # ToDo: ANy filter to apply? Then do it here. - if self.pre_filter is not None and not self.pre_filter(data): - data = self.pre_filter(data) - raise NotImplementedError - # ToDo: ANy transformation to apply? Then do it here. - if self.pre_transform is not None: - data = self.pre_transform(data) - raise NotImplementedError - datasets[self.raw_file_names[0]].append(data) - filenames.append(file) - - os.makedirs(self.processed_dir, exist_ok=True) - torch.save(self.collate(datasets[self.raw_file_names[0]]), self.processed_paths[0]) - # save_names(filenames) - - def __repr__(self): - return f'{self.__class__.__name__}({len(self)})' - - -class PredictNetPartSegDataset(Dataset): - """ - Resample raw point cloud to fixed number of points. - Map raw label from range [1, N] to [0, N-1]. - """ - def __init__(self, root_dir, num_classes, transform=None, npoints=2048, headers=True, refresh=False): - super(PredictNetPartSegDataset, self).__init__() - self.npoints = npoints - self._num_classes = num_classes - self.dataset = PredictionShapeNet(root=root_dir, transform=transform, headers=headers, refresh=refresh) - - def __getitem__(self, index): - data = self.dataset[index] - points, labels, _, norm = data.pos, data.y, data.points, data.norm - - sample = { - 'points': points, # torch.Tensor (n, 3) - 'labels': labels, # torch.Tensor (n,) - 'normals': norm # torch.Tensor (n,) - } - return sample - - def __len__(self): - return len(self.dataset) - - def num_classes(self): - return self._num_classes diff --git a/main.py b/main.py index ba572bd..7c05350 100644 --- a/main.py +++ b/main.py @@ -33,11 +33,15 @@ parser.add_argument('--npoints', type=int, default=1024, help='resample points n parser.add_argument('--model', type=str, default='', help='model path') parser.add_argument('--nepoch', type=int, default=250, help='number of epochs to train for') parser.add_argument('--outf', type=str, default='checkpoint', help='output folder') +parser.add_argument('--labels_within', type=strtobool, default=False, help='defines the label location') parser.add_argument('--batch_size', type=int, default=8, help='input batch size') parser.add_argument('--test_per_batches', type=int, default=1000, help='run a test batch per training batches number') parser.add_argument('--num_workers', type=int, default=4, help='number of data loading workers') parser.add_argument('--headers', type=strtobool, default=True, help='if raw files come with headers') parser.add_argument('--collate_per_segment', type=strtobool, default=True, help='whether to look at pointclouds or sub') +parser.add_argument('--has_variations', type=strtobool, default=False, + help='whether a single pointcloud has variations ' + 'named int(id)_pc.(xyz|dat) look at pointclouds or sub') opt = parser.parse_args() @@ -53,28 +57,34 @@ torch.manual_seed(opt.manual_seed) torch.cuda.manual_seed(opt.manual_seed) if __name__ == '__main__': - # Dataset and transform print('Construct dataset ..') - if True: - rot_max_angle = 15 - trans_max_distance = 0.01 - RotTransform = GT.Compose([GT.RandomRotate(rot_max_angle, 0), - GT.RandomRotate(rot_max_angle, 1), - GT.RandomRotate(rot_max_angle, 2)] - ) - TransTransform = GT.RandomTranslate(trans_max_distance) + rot_max_angle = 15 + trans_max_distance = 0.01 - train_transform = GT.Compose([GT.NormalizeScale(), RotTransform, TransTransform]) - test_transform = GT.Compose([GT.NormalizeScale(), ]) + RotTransform = GT.Compose([GT.RandomRotate(rot_max_angle, 0), + GT.RandomRotate(rot_max_angle, 1), + GT.RandomRotate(rot_max_angle, 2)] + ) - dataset = ShapeNetPartSegDataset(root_dir=opt.dataset, collate_per_segment=opt.collate_per_segment, - train=True, transform=train_transform, npoints=opt.npoints, headers=opt.headers) + TransTransform = GT.RandomTranslate(trans_max_distance) + train_transform = GT.Compose([GT.NormalizeScale(), RotTransform, TransTransform]) + test_transform = GT.Compose([GT.NormalizeScale(), ]) + + params = dict(root_dir=opt.dataset, + collate_per_segment=opt.collate_per_segment, + transform=train_transform, + npoints=opt.npoints, + labels_within=opt.labels_within, + has_variations=opt.has_variations, + headers=opt.headers + ) + + dataset = ShapeNetPartSegDataset(mode='train', **params) dataLoader = DataLoader(dataset, batch_size=opt.batch_size, shuffle=True, num_workers=opt.num_workers) - test_dataset = ShapeNetPartSegDataset(root_dir=opt.dataset, collate_per_segment=opt.collate_per_segment, - train=False, transform=test_transform, npoints=opt.npoints, headers=opt.headers) + test_dataset = ShapeNetPartSegDataset(mode='test', **params) test_dataLoader = DataLoader(test_dataset, batch_size=opt.batch_size, shuffle=True, num_workers=opt.num_workers) num_classes = dataset.num_classes() @@ -118,7 +128,8 @@ if __name__ == '__main__': print('Epoch {}, total epoches {}'.format(epoch+1, opt.nepoch)) net.train() - + # ToDo: We need different dataloader here to train the network in multiple iterations, maybe move the loop down + # for dataloader in ... for batch_idx, sample in enumerate(dataLoader): # points: (batch_size, n, 3) # labels: (batch_size, n) diff --git a/predict/checkpoint/seg_model_custom_249.pth b/predict/checkpoint/seg_model_custom_249.pth deleted file mode 100644 index 13a8c8f..0000000 Binary files a/predict/checkpoint/seg_model_custom_249.pth and /dev/null differ diff --git a/predict/data/raw/predict/580_9/100_pc.xyz b/predict/data/raw/predict/580_9/100_pc.xyz deleted file mode 100644 index 118265b..0000000 --- a/predict/data/raw/predict/580_9/100_pc.xyz +++ /dev/null @@ -1,2048 +0,0 @@ -0.00639466 0.291468 0.240116 0.0689098 0.995458 0.0656904 --0.113605 -0.648532 -0.179884 0.0361247 -0.999252 0.0138094 -0.206395 -0.00853241 -0.239884 -0.555854 0.10388 -0.824764 --0.113605 0.651468 -0.199884 0.240351 0.0070698 -0.97066 --0.133605 -0.208532 0.120116 -0.154032 -0.118453 0.98094 -0.286395 -0.588532 0.120116 0.00196012 0.0196405 0.999805 -0.346395 0.0714676 0.140116 0.830808 -0.0411953 -0.247261 --0.153605 0.151468 -0.119884 -0.447329 0.249008 -0.859006 -0.206395 0.431468 -0.0798838 0.769651 0.0414865 -0.637115 -0.126395 -0.368532 -0.139884 -0.00196012 -0.0196405 -0.999805 -0.206395 -0.228532 0.180116 0.7131 -0.524651 0.465005 --0.213605 -0.308532 -0.179884 0.0811924 -0.41772 -0.904941 --0.173605 0.431468 0.0201162 -0.970174 0.034212 -0.239982 --0.0536053 -0.508532 0.0801162 -0.204339 0.00614069 0.978881 -0.0463947 0.0514676 0.0801162 -0.380173 0.850556 0.363349 -0.166395 -0.648532 -0.119884 0.341146 -0.939842 0.0177938 --0.0336053 -0.108532 -0.139884 0.154032 0.118453 -0.98094 --0.0336053 0.651468 0.0801162 0.0315116 0.99939 0.0150818 --0.0136053 0.351468 -0.179884 0.240351 0.0070698 -0.97066 -0.266395 0.191468 -0.0798838 0.851505 -0.521108 -0.0581876 -0.266395 -0.0885324 -0.0198838 0.380173 -0.850556 -0.363349 -0.246395 0.271468 0.220116 0.435781 -0.0892683 0.895615 -0.00639466 0.0714676 0.320116 -0.465386 -0.00656057 0.587612 --0.213605 -0.00853241 0.0401162 -0.0330891 0.821575 0.569139 -0.0663947 0.291468 0.0201162 0.401129 0.575921 0.712278 -0.0863947 -0.668532 0.120116 -0.940008 -0.341044 0.00854249 -0.146395 -0.428532 0.100116 0.00196012 0.0196405 0.999805 -0.0663947 -0.228532 0.0201162 0.128711 -0.986734 -0.0989418 -0.0663947 0.131468 -0.119884 -0.555854 0.10388 -0.824764 --0.233605 -0.368532 0.0201162 -0.996688 -0.0298757 -0.0756335 --0.113605 0.191468 0.0801162 -0.115568 0.583 0.80421 -0.00639466 0.531468 -0.0798838 0.970174 -0.034212 0.239982 --0.0536053 -0.448532 -0.179884 0.204339 -0.00614069 -0.978881 -0.106395 0.471468 0.100116 -0.0997791 0.384519 0.917708 --0.173605 -0.648532 0.0201162 -0.978233 -0.0381836 -0.203964 -0.146395 -0.168532 -0.139884 0.14296 0.427345 -0.892714 -0.246395 -0.488532 -0.0598838 0.940008 0.341044 -0.00854249 -0.166395 0.0714676 0.220116 -0.0689098 -0.995458 -0.0656904 --0.213605 -0.168532 -0.0598838 -0.998843 -0.00731931 -0.0475058 -0.0663947 -0.108532 0.160116 -0.154032 -0.118453 0.98094 -0.0463947 -0.528532 -0.0598838 -0.940008 -0.341044 0.00854249 -0.326395 0.271468 0.0601162 0.897411 -0.0330901 -0.439953 --0.153605 0.591468 -0.0398838 -0.970174 0.034212 -0.239982 --0.133605 0.471468 -0.159884 -0.970174 0.034212 -0.239982 --0.0136053 -0.368532 -0.0398838 0.996688 0.0298757 0.0756335 --0.0536053 -0.0685324 0.0201162 -0.128711 0.986734 0.0989418 --0.0136053 -0.268532 -0.179884 0.0680636 -0.190587 -0.82165 -0.226395 -0.248532 -0.0198838 0.903111 -0.340247 0.261959 --0.153605 -0.508532 -0.0598838 -0.978233 -0.0381836 -0.203964 --0.173605 0.311468 -0.0998838 -0.688672 0.239416 -0.684405 -0.206395 0.0514676 0.0601162 0.555854 -0.10388 0.824764 --0.0336053 0.151468 0.200116 -0.897411 0.0330901 0.439953 --0.0336053 0.391468 0.0601162 0.970174 -0.034212 0.239982 --0.0136053 -0.648532 0.000116245 0.516773 -0.46997 0.110823 --0.0336053 0.231468 -0.0798838 -0.851505 0.521108 0.0581876 --0.0336053 -0.328532 0.100116 -0.940008 -0.341044 0.00854249 --0.0536053 0.0514676 -0.0798838 0.438688 -0.665846 -0.603492 -0.106395 0.191468 0.280116 0.435781 -0.0892683 0.895615 -0.0863947 -0.0285324 -0.159884 -0.555854 0.10388 -0.824764 -0.0263947 0.651468 -0.159884 0.0315116 0.99939 0.0150818 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project root directory + +from dataset.shapenet import ShapeNetPartSegDataset from model.pointnet2_part_seg import PointNet2PartSegmentNet import torch_geometric.transforms as GT import torch @@ -237,7 +240,7 @@ sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/../') # add proj parser = argparse.ArgumentParser() parser.add_argument('--dataset', type=str, default='data', help='dataset path') parser.add_argument('--npoints', type=int, default=2048, help='resample points number') -parser.add_argument('--model', type=str, default='./checkpoint/seg_model_custom_241.pth', help='model path') +parser.add_argument('--model', type=str, default='./checkpoint/seg_model_custom_246.pth', help='model path') parser.add_argument('--sample_idx', type=int, default=0, help='select a sample to segment and view result') opt = parser.parse_args() print(opt) @@ -279,12 +282,12 @@ if __name__ == '__main__': print('load dataset ..') test_transform = GT.Compose([GT.NormalizeScale(), ]) - test_dataset = PredictNetPartSegDataset( + test_dataset = ShapeNetPartSegDataset( + mode='predict', root_dir=opt.dataset, - num_classes=4, transform=None, npoints=opt.npoints, - refresh=True + refresh=False ) num_classes = test_dataset.num_classes() diff --git a/vis/show_seg_res.py b/vis/show_seg_res.py index 72bc5a3..130d6a7 100644 --- a/vis/show_seg_res.py +++ b/vis/show_seg_res.py @@ -28,10 +28,10 @@ if __name__ == '__main__': print('Construct dataset ..') test_transform = GT.Compose([GT.NormalizeScale(),]) - test_dataset = PredictNetPartSegDataset( + test_dataset = ShapeNetPartSegDataset( root_dir=opt.dataset, collate_per_segment=False, - train=False, + mode='predict', transform=test_transform, npoints=opt.npoints )