DetectNet_v2

            

            
tao model detectnet_v2 <sub_task> <args_per_subtask>
        

            

            
kitti_config {
  root_directory_path: "/workspace/tao-experiments/data/"
  image_dir_name: "training/image_2"
  label_dir_name: "training/label_2"
  image_extension: ".png"
  partition_mode: "random"
  num_partitions: 2
  val_split: 0
  num_shards: 10
}
image_directory_path: "/workspace/tao-experiments/data/"
  target_class_mapping {
      key: "car"
      value: "car"
  }
  target_class_mapping {
      key: "pedestrian"
      value: "pedestrian"
  }
  target_class_mapping {
      key: "cyclist"
      value: "cyclist"
  }
  target_class_mapping {
      key: "van"
      value: "car"
  }
  target_class_mapping {
      key: "person_sitting"
      value: "pedestrian"
  }
  target_class_mapping {
      key: "truck"
      value: "car"
  }
        

            

            
coco_config {
  root_directory_path: "/workspace/tao-experiments/data/coco"
  image_dir_names: ["val2017", "train2017"]
  annotation_files: ["annotations/instances_val2017.json", "annotations/instances_train2017.json"]
  num_partitions: 2
  num_shards: [32, 256]
}
image_directory_path: "/workspace/tao-experiments/data/coco"
        

            

            
tao model detectnet_v2 dataset_convert [-h] -d DATASET_EXPORT_SPEC -o OUTPUT_FILENAME
                 [-f VALIDATION_FOLD]
        

            

            
tao model detectnet_v2 dataset_convert  [-h] -d <path_to_tfrecords_conversion_spec>
                                       -o <path_to_output_tfrecords>
        

            

            
Using TensorFlow backend.
2019-07-16 01:30:59,073 - iva.detectnet_v2.dataio.build_converter - INFO - Instantiating a kitti converter
2019-07-16 01:30:59,243 - iva.detectnet_v2.dataio.kitti_converter_lib - INFO - Num images in
Train: 10786    Val: 2696
2019-07-16 01:30:59,243 - iva.detectnet_v2.dataio.kitti_converter_lib - INFO - Validation data in partition 0. Hence, while choosing the validation set during training choose validation_fold 0.
2019-07-16 01:30:59,251 - 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:265: VisibleDeprecationWarning: Reading unicode strings without specifying the encoding argument is deprecated. Set the encoding, use None for the system default.
2019-07-16 01:31:01,226 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Writing partition 0, shard 1
. .
sheep: 242
bottle: 205
..
boat: 171
car: 418
2019-07-16 01:31:20,772 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Writing partition 1, shard 0
..
2019-07-16 01:32:40,338 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Writing partition 1, shard 9
2019-07-16 01:32:49,063 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO -
Wrote the following numbers of objects:
sheep: 695
..
car: 1770

2019-07-16 01:32:49,064 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Cumulative object statistics
2019-07-16 01:32:49,064 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO -
Wrote the following numbers of objects:
sheep: 937
..
car: 2188
2019-07-16 01:32:49,064 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Class map.
Label in GT: Label in tfrecords file
sheep: sheep
..

boat: boat
For the dataset_config in the experiment_spec, please use labels in the tfrecords file, while writing the classmap.



2019-07-16 01:32:49,064 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Tfrecords generation complete.
        

            

            
# Sample model config for to instantiate a resnet18 model with pretrained weights and freeze blocks 0, 1
# with all shortcuts having projection layers.
model_config {
  arch: "resnet"
  pretrained_model_file: <path_to_model_file>
  freeze_blocks: 0
  freeze_blocks: 1
  all_projections: True
  num_layers: 18
  use_pooling: False
  use_batch_norm: True
  dropout_rate: 0.0
  objective_set: {
    cov {}
    bbox {
      scale: 35.0
      offset: 0.5
    }
  }
}
        

            

            
# Sample rasterizer configs to instantiate a 3 class bbox rasterizer
bbox_rasterizer_config {
  target_class_config {
    key: "car"
    value: {
      cov_center_x: 0.5
      cov_center_y: 0.5
      cov_radius_x: 0.4
      cov_radius_y: 0.4
      bbox_min_radius: 1.0
    }
  }
  target_class_config {
    key: "cyclist"
    value: {
      cov_center_x: 0.5
      cov_center_y: 0.5
      cov_radius_x: 0.4
      cov_radius_y: 0.4
      bbox_min_radius: 1.0
    }
  }
  target_class_config {
    key: "pedestrian"
    value: {
      cov_center_x: 0.5
      cov_center_y: 0.5
      cov_radius_x: 0.4
      cov_radius_y: 0.4
      bbox_min_radius: 1.0
    }
  }
  deadzone_radius: 0.67
}
        

            

            
postprocessing_config {
  target_class_config {
    key: "car"
    value: {
      clustering_config {
        coverage_threshold: 0.005
        dbscan_eps: 0.15
        dbscan_min_samples: 1
        minimum_bounding_box_height: 20
      }
    }
  }
  target_class_config {
    key: "cyclist"
    value: {
      clustering_config {
        coverage_threshold: 0.005
        dbscan_eps: 0.15
        dbscan_min_samples: 1
        minimum_bounding_box_height: 20
      }
    }
  }
  target_class_config {
    key: "pedestrian"
    value: {
      clustering_config {
        coverage_threshold: 0.005
        dbscan_eps: 0.15
        dbscan_min_samples: 1
        minimum_bounding_box_height: 20
      }
    }
 }
}
        

            

            
cost_function_config {
  target_classes {
    name: "car"
    class_weight: 1.0
    coverage_foreground_weight: 0.05
    objectives {
      name: "cov"
      initial_weight: 1.0
      weight_target: 1.0
    }
    objectives {
      name: "bbox"
      initial_weight: 10.0
      weight_target: 10.0
    }
  }
  target_classes {
    name: "cyclist"
    class_weight: 1.0
    coverage_foreground_weight: 0.05
    objectives {
      name: "cov"
      initial_weight: 1.0
      weight_target: 1.0
    }
    objectives {
      name: "bbox"
      initial_weight: 10.0
      weight_target: 1.0
    }
  }
  target_classes {
    name: "pedestrian"
    class_weight: 1.0
    coverage_foreground_weight: 0.05
    objectives {
      name: "cov"
      initial_weight: 1.0
      weight_target: 1.0
    }
    objectives {
      name: "bbox"
      initial_weight: 10.0
      weight_target: 10.0
    }
  }
  enable_autoweighting: True
  max_objective_weight: 0.9999
  min_objective_weight: 0.0001
}
        

            

            
training_config {
  batch_size_per_gpu: 16
    num_epochs: 80
    learning_rate {
    soft_start_annealing_schedule {
      min_learning_rate: 5e-6
      max_learning_rate: 5e-4
      soft_start: 0.1
      annealing: 0.7
    }
  }
  regularizer {
    type: L1
    weight: 3e-9
  }
  optimizer {
    adam {
      epsilon: 1e-08
      beta1: 0.9
      beta2: 0.999
    }
  }
  cost_scaling {
    enabled: False
    initial_exponent: 20.0
    increment: 0.005
    decrement: 1.0
  }
  visualizer {
    enabled: true
    num_images: 3
    scalar_logging_frequency: 10
    infrequent_logging_frequency: 1
    target_class_config {
      key: "car"
      value: {
        coverage_threshold: 0.005
      }
    }
    target_class_config {
      key: "pedestrian"
      value: {
        coverage_threshold: 0.005
      }
    }
  }
}
        

            

            
# Sample augementation config for
augmentation_config {
  preprocessing {
    output_image_width: 960
    output_image_height: 544
    output_image_channel: 3
    min_bbox_width: 1.0
    min_bbox_height: 1.0
  }
  spatial_augmentation {

    hflip_probability: 0.5
    vflip_probability: 0.0
    zoom_min: 1.0
    zoom_max: 1.0
    translate_max_x: 8.0
    translate_max_y: 8.0
  }
  color_augmentation {
    color_shift_stddev: 0.0
    hue_rotation_max: 25.0
    saturation_shift_max: 0.2
    contrast_scale_max: 0.1
    contrast_center: 0.5
  }
}
        

            

            
# Sample evaluation config to run evaluation in integrate mode for the given 3 class model,
# at every 10th epoch starting from the epoch 1.
evaluation_config {
  average_precision_mode: INTEGRATE
  validation_period_during_training: 10
  first_validation_epoch: 1
  minimum_detection_ground_truth_overlap {
    key: "car"
    value: 0.7
  }
  minimum_detection_ground_truth_overlap {
    key: "person"
    value: 0.5
  }
  minimum_detection_ground_truth_overlap {
    key: "bicycle"
    value: 0.5
  }
  evaluation_box_config {
    key: "car"
    value {
      minimum_height: 4
      maximum_height: 9999
      minimum_width: 4
      maximum_width: 9999
    }
  }
  evaluation_box_config {
    key: "person"
    value {
      minimum_height: 4
      maximum_height: 9999
      minimum_width: 4
      maximum_width: 9999
    }
  }
  evaluation_box_config {
    key: "bicycle"
    value {
      minimum_height: 4
      maximum_height: 9999
      minimum_width: 4
      maximum_width: 9999
    }
  }
}
        

            

            
dataset_config {
  data_sources: {
    tfrecords_path: "<path to the training tfrecords root/tfrecords train pattern>"
    image_directory_path: "<path to the training data source>"
  }
  image_extension: "jpg"
  target_class_mapping {
      key: "car"
      value: "car"
  }
  target_class_mapping {
      key: "automobile"
      value: "car"
  }
  target_class_mapping {
      key: "heavy_truck"
      value: "car"
  }
  target_class_mapping {
      key: "person"
      value: "pedestrian"
  }
  target_class_mapping {
      key: "rider"
      value: "cyclist"
  }
  validation_fold: 0
}
        

            

            
validation_data_source: {
    tfrecords_path: " <path to tfrecords to validate on>/tfrecords validation pattern>"
    image_directory_path: " <path to validation data source>"
}
        

            

            
inferencer_config{
  # defining target class names for the experiment.
  # Note: This must be mentioned in order of the networks classes.
  target_classes: "car"
  target_classes: "cyclist"
  target_classes: "pedestrian"
  # Inference dimensions.
  image_width: 1248
  image_height: 384
  # Must match what the model was trained for.
  image_channels: 3
  batch_size: 16
  gpu_index: 0
  # model handler config
  tensorrt_config{
    parser:  ETLT
    etlt_model: "/path/to/model.etlt"
    backend_data_type: INT8
    save_engine: true
    trt_engine: "/path/to/trt/engine/file"
    calibrator_config{
        calibration_cache: "/path/to/calibration/cache"
        n_batches: 10
        batch_size: 16
    }
  }
}
        

            

            
bbox_handler_config{
  kitti_dump: true
  disable_overlay: false
  overlay_linewidth: 2
  classwise_bbox_handler_config{
    key:"car"
    value: {
      confidence_model: "aggregate_cov"
      output_map: "car"
      bbox_color{
        R: 0
        G: 255
        B: 0
      }
      clustering_config{
        coverage_threshold: 0.005
        dbscan_eps: 0.3
        dbscan_min_samples: 1
        dbscan_confidence_threshold: 0.9
        minimum_bounding_box_height: 4
      }
    }
  }
  classwise_bbox_handler_config{
    key:"default"
    value: {
      confidence_model: "aggregate_cov"
      bbox_color{
        R: 255
        G: 0
        B: 0
      }
      clustering_config{
        coverage_threshold: 0.005
        dbscan_eps: 0.3
        dbscan_min_samples: 1
        dbscan_confidence_threshold: 0.9
        minimum_bounding_box_height: 4
      }
    }
  }
}
        

            

            
tao model detectnet_v2 train [-h] -k <key>
                            -r <result directory>
                            -e <spec_file>
                            [-n <name_string_for_the_model>]
                            [--gpus <num GPUs>]
                            [--gpu_index <comma separate gpu indices>]
                            [--use_amp]
                            [--log_file <log_file>]
        

            

            
tao model detectnet_v2 train -e <path_to_spec_file>
                       -r <path_to_experiment_output>
                       -k <key_to_load_the_model>
                       -n <name_string_for_the_model>
                       --gpus 2
        

            

            
tao model detectnet_v2 evaluate [-h] -e <experiment_spec
                               -m <model_file>
                               -k <key>
                               [--use_training_set]
                               [--gpu_index]
        

            

            
tao model detectnet_v2 evaluate -e <path to training spec file>
                          -m <path to the model>
                          -k <key to load the model>
        

            

            
dataset_config {
  data_sources: {
    tfrecords_path: "<path to training tfrecords root>/<tfrecords_name*>"
    image_directory_path: "<path to training data root>"
  }
  image_extension: "jpg"
  target_class_mapping {
      key: "car"
      value: "car"
  }
  target_class_mapping {
      key: "automobile"
      value: "car"
  }
  ..
  ..
  ..
  target_class_mapping {
      key: "person"
      value: "pedestrian"
  }
  target_class_mapping {
      key: "rider"
      value: "cyclist"
  }
  validation_data_source: {
    tfrecords_path: "<path to testing tfrecords root>/<tfrecords_name*>"
    image_directory_path: "<path to testing data root>"
  }
}
        

            

            
tao model detectnet_v2 inference [-h] -e </path/to/inference/spec/file>
                                -i </path/to/inference/input>
                                -o </path/to/inference/output>
                                -k <model key>
        

            

            
tao model detectnet_v2 prune [-h] -pm <pretrained_model>
                            -o <output_file>
                            -k <key>
                            [-n <normalizer>]
                            [-eq <equalization_criterion>]
                            [-pg <pruning_granularity>]
                            [-pth <pruning threshold>]
                            [-nf <min_num_filters>]
                            [-el [<excluded_list>]
        

            

            
tao model detectnet_v2 prune -m /workspace/output/weights/resnet_003.tlt
                       -o /workspace/output/weights/resnet_003_pruned.tlt
                       -eq union
                       -pth 0.7 -k $KEY
        

            

            
tao model detectnet_v2 calibration_tensorfile [-h] -e <path to training experiment spec file>
                                             -o <path to output tensorfile>
                                             -m <maximum number of batches to serialize>
                                            [--use_validation_set]
        

            

            
tao model detectnet_v2 calibration_tensorfile
                  -e $SPECS_DIR/classification_retrain_spec.cfg
                  -m 10
                  -o $USER_EXPERIMENT_DIR/export/calibration.tensor
        

            

            
tao model detectnet_v2 export [-h] -m <path to the .tlt model file generated by tao model train>
                             [-k <key>]
                             [-o <path to output file [.onnx]>]
                             [--cal_data_file <path to tensor file>]
                             [--cal_image_dir <path to the directory images to calibrate the model]
                             [--cal_cache_file <path to output calibration file>]
                             [--data_type <Data type for the TensorRT backend during export>]
                             [--batches <Number of batches to calibrate over>]
                             [--max_batch_size <maximum trt batch size>]
                             [--max_workspace_size <maximum workspace size]
                             [--batch_size <batch size to TensorRT engine>]
                             [--experiment_spec <path to experiment spec file>]
                             [--engine_file <path to the TensorRT engine file>]
                             [--verbose Verbosity of the logger]
                             [--force_ptq Flag to force PTQ]
                             [--gen_ds_config Generate DeepStream config]
                             [--onnx_route Backend library to be used to export the model.]
        

            

            
tao model detectnet_v2 export
     -e $USER_EXPERIMENT_DIR/experiment_dir_retrain/experiment_spec.txt
     -m $USER_EXPERIMENT_DIR/experiment_dir_retrain/weights/resnet18_detector_pruned.tlt
     -o $USER_EXPERIMENT_DIR/experiment_dir_final/resnet18_detector.onnx
     -k $KEY
        

            

            
tao model detectnet_v2 export
      -m $USER_EXPERIMENT_DIR/detectnet_v2/model.tlt
      -o $USER_EXPERIMENT_DIR/detectnet_v2/model.int8.onnx
      -e $SPECS_DIR/detectnet_v2_kitti_retrain_spec.txt
      --gen_ds_config