Please provide the following information when requesting support.
• Hardware (NVIDIA RTX 3080Ti)
• Network Type (Detectnet_v2)
• TLT Version (TAO 5.5.0)
• Training spec file(random_seed: 42
dataset_config {
data_sources {
tfrecords_path: “/workspace/tao-experiments/test_detectnet_v2/dataset/train/tfrecords/tfrecords*”
image_directory_path: “/workspace/tao-experiments/test_detectnet_v2/dataset”
}
image_extension: “png”
target_class_mapping {
key: “car”
value: “car”
}
target_class_mapping {
key: “cyclist”
value: “cyclist”
}
target_class_mapping {
key: “pedestrian”
value: “pedestrian”
}
target_class_mapping {
key: “person_sitting”
value: “pedestrian”
}
target_class_mapping {
key: “truck”
value: “car”
}
target_class_mapping {
key: “van”
value: “car”
}
validation_data_source {
tfrecords_path: “/workspace/tao-experiments/test_detectnet_v2/dataset/valid/tfrecords/tfrecords*”
image_directory_path: “/workspace/tao-experiments/test_detectnet_v2/dataset”
}
}
augmentation_config {
preprocessing {
output_image_width: 1248
output_image_height: 384
min_bbox_width: 1.0
min_bbox_height: 1.0
output_image_channel: 3
}
spatial_augmentation {
hflip_probability: 0.5
zoom_min: 1.0
zoom_max: 1.0
translate_max_x: 8.0
translate_max_y: 8.0
}
color_augmentation {
hue_rotation_max: 25.0
saturation_shift_max: 0.20000000298023224
contrast_scale_max: 0.10000000149011612
contrast_center: 0.5
}
}
postprocessing_config {
target_class_config {
key: “car”
value {
clustering_config {
coverage_threshold: 0.004999999888241291
minimum_bounding_box_height: 20
dbscan_eps: 0.15000000596046448
dbscan_min_samples: 1
}
}
}
target_class_config {
key: “cyclist”
value {
clustering_config {
coverage_threshold: 0.004999999888241291
minimum_bounding_box_height: 20
dbscan_eps: 0.15000000596046448
dbscan_min_samples: 1
}
}
}
target_class_config {
key: “pedestrian”
value {
clustering_config {
coverage_threshold: 0.004999999888241291
minimum_bounding_box_height: 20
dbscan_eps: 0.15000000596046448
dbscan_min_samples: 1
}
}
}
}
model_config {
pretrained_model_file: “/workspace/tao-experiments/test_detectnet_v2/pretrained_model.hdf5”
num_layers: 18
use_batch_norm: true
objective_set {
bbox {
scale: 35.0
offset: 0.5
}
cov {
}
}
freeze_blocks: 0.0
freeze_blocks: 1.0
arch: “resnet”
all_projections: true
}
evaluation_config {
validation_period_during_training: 10
first_validation_epoch: 30
minimum_detection_ground_truth_overlap {
key: “car”
value: 0.699999988079071
}
minimum_detection_ground_truth_overlap {
key: “cyclist”
value: 0.5
}
minimum_detection_ground_truth_overlap {
key: “pedestrian”
value: 0.5
}
evaluation_box_config {
key: “car”
value {
minimum_height: 20
maximum_height: 9999
minimum_width: 10
maximum_width: 9999
}
}
evaluation_box_config {
key: “cyclist”
value {
minimum_height: 20
maximum_height: 9999
minimum_width: 10
maximum_width: 9999
}
}
evaluation_box_config {
key: “pedestrian”
value {
minimum_height: 20
maximum_height: 9999
minimum_width: 10
maximum_width: 9999
}
}
average_precision_mode: INTEGRATE
}
cost_function_config {
target_classes {
name: “car”
class_weight: 1.0
coverage_foreground_weight: 0.05000000074505806
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: 0.05000000074505806
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: 0.05000000074505806
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: 0.9998999834060669
min_objective_weight: 9.999999747378752e-05
}
training_config {
batch_size_per_gpu: 8
num_epochs: 1
learning_rate {
soft_start_annealing_schedule {
min_learning_rate: 4.999999873689376e-06
max_learning_rate: 0.0005000000237487257
soft_start: 0.10000000149011612
annealing: 0.699999988079071
}
}
regularizer {
type: L1
weight: 3.000000026176508e-09
}
optimizer {
adam {
epsilon: 9.99999993922529e-09
beta1: 0.8999999761581421
beta2: 0.9990000128746033
}
}
cost_scaling {
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: 0.004999999888241291
}
}
target_class_config {
key: “pedestrian”
value {
coverage_threshold: 0.004999999888241291
}
}
}
}
bbox_rasterizer_config {
target_class_config {
key: “car”
value {
cov_center_x: 0.5
cov_center_y: 0.5
cov_radius_x: 0.4000000059604645
cov_radius_y: 0.4000000059604645
bbox_min_radius: 1.0
}
}
target_class_config {
key: “cyclist”
value {
cov_center_x: 0.5
cov_center_y: 0.5
cov_radius_x: 1.0
cov_radius_y: 1.0
bbox_min_radius: 1.0
}
}
target_class_config {
key: “pedestrian”
value {
cov_center_x: 0.5
cov_center_y: 0.5
cov_radius_x: 1.0
cov_radius_y: 1.0
bbox_min_radius: 1.0
}
}
deadzone_radius: 0.4000001549720764
})
• How to reproduce the issue ?
$tao model detectnet_v2 prune -m /workspace/tao-experiments/test_detectnet_v2/results/model.epoch-1.hdf5 -o /workspace/tao-experiments/test_detectnet_v2/results/pruned/pruned_model.hdf5 -pth 0.7
The detectnet v2 model is trained and pruned with threshold (0.6, 0.7,0.9)
but the size of the model is not reduced even after retraining and exporting the onnx model with load_graph=True .