I thank you for the quick answer.
To point 1: If I don’t set or omit the spatial_augmentation and color_augmentation in the experiment spec file, the following error occurs anyway:
augmentation_config {
preprocessing {
output_image_width: 672
output_image_height: 384
output_image_channel: 3
crop_right: 672
crop_bottom: 384
min_bbox_width: 1.0
min_bbox_height: 1.0
}
}
2020-06-09 09:37:33,719 [INFO] iva.retinanet.scripts.train: Loading pretrained weights. This may take a while...
Traceback (most recent call last):
File "/usr/local/bin/tlt-train-g1", line 8, in <module>
sys.exit(main())
File "./common/magnet_train.py", line 40, in main
File "./retinanet/scripts/train.py", line 247, in main
File "./retinanet/scripts/train.py", line 112, in run_experiment
File "./retinanet/builders/input_builder.py", line 69, in build
File "./retinanet/builders/data_generator.py", line 44, in __init__
File "./detectnet_v2/dataloader/build_dataloader.py", line 90, in build_dataloader
File "./detectnet_v2/dataloader/augmentation/build_augmentation_config.py", line 89, in build_augmentation_config
File "./detectnet_v2/dataloader/augmentation/build_augmentation_config.py", line 54, in build_spatial_augmentation_config
File "./detectnet_v2/dataloader/augmentation/augmentation_config.py", line 107, in __init__
ValueError: zoom_min must be > 0.0
To point 2: When I set the num_partitions to 1 in the TFrecords conversion spec file for training, the following error message appears:
2020-06-09 09:49:35,820 - iva.detectnet_v2.dataio.build_converter - INFO - Instantiating a kitti converter
Traceback (most recent call last):
File "/usr/local/bin/tlt-dataset-convert", line 8, in <module>
sys.exit(main())
File "./detectnet_v2/scripts/dataset_convert.py", line 63, in main
File "./detectnet_v2/dataio/build_converter.py", line 76, in build_converter
File "./detectnet_v2/dataio/kitti_converter_lib.py", line 89, in __init__
AssertionError: Invalid number of partitions (1) for random split mode.
and if I set val_split to 0 then it ignores this setting and still divides the dataset into train and val
kitti_config {
root_directory_path: "/workspace/Git_Repos_Datasets/GDS_V1/"
image_dir_name: "images/train"
label_dir_name: "annotations/train"
image_extension: ".jpg"
partition_mode: "random"
num_partitions: 2
val_split: 0
num_shards: 1
}
image_directory_path: "/workspace/Git_Repos_Datasets/GDS_V1/"
2020-06-09 09:51:58,936 - iva.detectnet_v2.dataio.build_converter - INFO - Instantiating a kitti converter
2020-06-09 09:51:58,952 - iva.detectnet_v2.dataio.kitti_converter_lib - INFO - Num images in
Train: 3500 Val: 874
2020-06-09 09:51:58,952 - iva.detectnet_v2.dataio.kitti_converter_lib - INFO - Validation data in partition 0. Hence, while choosing the validationset during training choose validation_fold 0.
2020-06-09 09:51:58,954 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Writing partition 0, shard 0
/usr/local/lib/python2.7/dist-packages/iva/detectnet_v2/dataio/kitti_converter_lib.py:266: VisibleDeprecationWarning: Reading unicode strings without specifying the encoding argument is deprecated. Set the encoding, use None for the system default.
/usr/local/lib/python2.7/dist-packages/iva/detectnet_v2/dataio/kitti_converter_lib.py:266: UserWarning: genfromtxt: Empty input file: "/workspace/Git_Repos_Datasets/GDS_V1/annotations/train/0973.txt"
2020-06-09 09:51:59,940 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO -
Wrote the following numbers of objects:
cars: 1138
2020-06-09 09:51:59,940 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Writing partition 1, shard 0
2020-06-09 09:52:03,795 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO -
Wrote the following numbers of objects:
cars: 4476
2020-06-09 09:52:03,795 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Cumulative object statistics
2020-06-09 09:52:03,796 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO -
Wrote the following numbers of objects:
cars: 5614
2020-06-09 09:52:03,796 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Class map.
Label in GT: Label in tfrecords file
cars: cars
For the dataset_config in the experiment_spec, please use labels in the tfrecords file, while writing the classmap.
2020-06-09 09:52:03,796 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Tfrecords generation complete.