Please provide the following information when requesting support.
• Hardware (ubuntu18.04+rtx3060)
• Network Type (Faster_rcnn)
• TLT Version (Please run “tlt info --verbose” and share “docker_tag” here)
Configuration of the TLT Instance
dockers:
nvidia/tlt-streamanalytics:
docker_registry: nvcr.io
docker_tag: v3.0-py3
tasks:
1. augment
2. bpnet
3. classification
4. detectnet_v2
5. dssd
6. emotionnet
7. faster_rcnn
8. fpenet
9. gazenet
10. gesturenet
11. heartratenet
12. lprnet
13. mask_rcnn
14. multitask_classification
15. retinanet
16. ssd
17. unet
18. yolo_v3
19. yolo_v4
20. tlt-converter
nvidia/tlt-pytorch:
docker_registry: nvcr.io
docker_tag: v3.0-py3
tasks:
1. speech_to_text
2. speech_to_text_citrinet
3. text_classification
4. question_answering
5. token_classification
6. intent_slot_classification
7. punctuation_and_capitalization
format_version: 1.0
tlt_version: 3.0
published_date: 04/16/2021
• Training spec file(If have, please share here)
Copyright (c) 2017-2020, NVIDIA CORPORATION. All rights reserved.
random_seed: 42
enc_key:‘tlt’
verbose: True
model_config {
input_image_config {
image_type: RGB
image_channel_order: ‘bgr’
size_height_width {
height: 384
width: 1248
}
image_channel_mean {
key: ‘b’
value: 103.939
}
image_channel_mean {
key: ‘g’
value: 116.779
}
image_channel_mean {
key: ‘r’
value: 123.68
}
image_scaling_factor: 1.0
max_objects_num_per_image: 100
}
arch: “resnet:18”
anchor_box_config {
scale: 64.0
scale: 128.0
scale: 256.0
ratio: 1.0
ratio: 0.5
ratio: 2.0
}
freeze_bn: True
freeze_blocks: 0
freeze_blocks: 1
roi_mini_batch: 256
rpn_stride: 16
use_bias: False
roi_pooling_config {
pool_size: 7
pool_size_2x: False
}
all_projections: True
use_pooling:False
}
dataset_config {
data_sources: {
tfrecords_path: “/workspace/tlt-experiments/data/tfrecords/kitti_trainval/kitti_trainval*”
image_directory_path: “/workspace/tlt-experiments/data/training”
}
image_extension: ‘png’
target_class_mapping {
key: ‘car’
value: ‘car’
}
target_class_mapping {
key: ‘van’
value: ‘car’
}
target_class_mapping {
key: ‘pedestrian’
value: ‘person’
}
target_class_mapping {
key: ‘person_sitting’
value: ‘person’
}
target_class_mapping {
key: ‘cyclist’
value: ‘cyclist’
}
validation_fold: 0
}
augmentation_config {
preprocessing {
output_image_width: 1248
output_image_height: 384
output_image_channel: 3
min_bbox_width: 1.0
min_bbox_height: 1.0
enable_auto_resize: True
}
spatial_augmentation {
hflip_probability: 0.5
vflip_probability: 0.0
zoom_min: 1.0
zoom_max: 1.0
translate_max_x: 0
translate_max_y: 0
}
color_augmentation {
hue_rotation_max: 0.0
saturation_shift_max: 0.0
contrast_scale_max: 0.0
contrast_center: 0.5
}
}
training_config {
enable_augmentation: True
enable_qat: False
batch_size_per_gpu: 8
num_epochs: 12
pretrained_weights: “/workspace/tlt-experiments/faster_rcnn/resnet_18.hdf5”
#resume_from_model: “/workspace/tlt-experiments/faster_rcnn/frcnn_kitti_resnet18.epoch2.tlt”
output_model: “/workspace/tlt-experiments/faster_rcnn/frcnn_kitti_resnet18.tlt”
rpn_min_overlap: 0.3
rpn_max_overlap: 0.7
classifier_min_overlap: 0.0
classifier_max_overlap: 0.5
gt_as_roi: False
std_scaling: 1.0
classifier_regr_std {
key: ‘x’
value: 10.0
}
classifier_regr_std {
key: ‘y’
value: 10.0
}
classifier_regr_std {
key: ‘w’
value: 5.0
}
classifier_regr_std {
key: ‘h’
value: 5.0
}
rpn_mini_batch: 256
rpn_pre_nms_top_N: 12000
rpn_nms_max_boxes: 2000
rpn_nms_overlap_threshold: 0.7
regularizer {
type: L2
weight: 1e-4
}
optimizer {
sgd {
lr: 0.02
momentum: 0.9
decay: 0.0
nesterov: False
}
}
learning_rate {
soft_start {
base_lr: 0.02
start_lr: 0.002
soft_start: 0.1
annealing_points: 0.8
annealing_points: 0.9
annealing_divider: 10.0
}
}
lambda_rpn_regr: 1.0
lambda_rpn_class: 1.0
lambda_cls_regr: 1.0
lambda_cls_class: 1.0
}
inference_config {
images_dir: ‘/workspace/tlt-experiments/data/testing/image_2’
model: ‘/workspace/tlt-experiments/faster_rcnn/frcnn_kitti_resnet18.epoch12.tlt’
batch_size: 1
detection_image_output_dir: ‘/workspace/tlt-experiments/faster_rcnn/inference_results_imgs’
labels_dump_dir: ‘/workspace/tlt-experiments/faster_rcnn/inference_dump_labels’
rpn_pre_nms_top_N: 6000
rpn_nms_max_boxes: 300
rpn_nms_overlap_threshold: 0.7
object_confidence_thres: 0.0001
bbox_visualize_threshold: 0.6
classifier_nms_max_boxes: 100
classifier_nms_overlap_threshold: 0.3
}
evaluation_config {
model: ‘/workspace/tlt-experiments/faster_rcnn/frcnn_kitti_resnet18.epoch12.tlt’
batch_size: 1
validation_period_during_training: 1
rpn_pre_nms_top_N: 6000
rpn_nms_max_boxes: 300
rpn_nms_overlap_threshold: 0.7
classifier_nms_max_boxes: 100
classifier_nms_overlap_threshold: 0.3
object_confidence_thres: 0.0001
use_voc07_11point_metric:False
gt_matching_iou_threshold: 0.5
}
• How to reproduce the issue ? (This is for errors. Please share the command line and the detailed log here.)
Using the official faster-rcnn example, there is no problem in the previous steps. In step 6, the key error is prompted. What is the reason?
