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6D prediction files now working

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This commit is contained in:
Si11ium 2020-06-26 08:33:59 +02:00
parent 2a7a236b89
commit 358d692699
3 changed files with 5 additions and 5 deletions
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4
_parameters.py
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@ -26,8 +26,8 @@ main_arg_parser.add_argument("--data_root", type=str, default='data', help="")
main_arg_parser.add_argument("--data_refresh", type=strtobool, default=False, help="")
main_arg_parser.add_argument("--data_dataset_type", type=str, default='ShapeNetPartSegDataset', help="")
main_arg_parser.add_argument("--data_cluster_type", type=str, default='grid', help="")
main_arg_parser.add_argument("--data_normals_as_cords", type=strtobool, default=False, help="")
main_arg_parser.add_argument("--data_poly_as_plane", type=strtobool, default=True, help="")
main_arg_parser.add_argument("--data_normals_as_cords", type=strtobool, default=True, help="")
main_arg_parser.add_argument("--data_poly_as_plane", type=strtobool, default=False, help="")
# Transformations
# main_arg_parser.add_argument("--transformations_to_tensor", type=strtobool, default=False, help="")

2
main.py
View File

@ -27,7 +27,7 @@ def run_lightning_loop(config_obj):
checkpoint_callback = ModelCheckpoint(
monitor='mean_loss',
filepath=str(logger.log_dir / 'ckpt_weights'),
verbose=True, save_top_k=10,
verbose=True, save_top_k=3,
)
# =============================================================================

4
main_pipeline.py
View File

@ -56,7 +56,7 @@ if __name__ == '__main__':
# input_pc_path = Path('data') / 'pc' / 'test.xyz'
model_path = Path('output') / 'PN2' / 'PN_14628b734c5b651b013ad9e36c406934' / 'version_0'
model_path = Path('output') / 'PN2' / 'PN_9843bf499399786cfd58fe79fa1b3db8' / 'version_0'
# config_filename = 'config.ini'
# config = ThisConfig()
# config.read_file((Path(model_path) / config_filename).open('r'))
@ -70,7 +70,7 @@ if __name__ == '__main__':
# TEST DATASET
transforms = Compose([NormalizeScale(), ])
test_dataset = ShapeNetPartSegDataset('data', mode=GlobalVar.data_split.predict, collate_per_segment=False,
refresh=True, transform=transforms)
refresh=True, transform=transforms) # , cluster_type='pc')
grid_clusters = cluster_cubes(test_dataset[0], [1, 1, 1], max_points_per_cluster=8192)