I am sorry I asked several times byt I am stuck with TAO detectnet_v2.
I got a lot of YOLO format data and image data.
3 0.498760 0.490741 0.859623 0.768519
0 0.728671 0.213955 0.252976 0.082672
1 0.478423 0.380622 0.730655 0.085317
2 0.544643 0.530093 0.416667 0.184524
However, it is said TAO Toolkit can not recognize YOLO format data, so I converted the YOLO format data to KITTI format data.
card 0 0 0 99.28583999999995 204.44448 1337.14296 1680.0009599999998 0 0 0 0 0 0 0
barcode 0 0 0 867.1435199999999 331.42848000000004 1231.42896 490.15871999999996 0 0 0 0 0 0 0
name 0 0 0 162.85751999999994 648.8899200000001 1215.00072 812.69856 0 0 0 0 0 0 0
price 0 0 0 484.28567999999996 840.63552 1084.2861599999999 1194.9216 0 0 0 0 0 0 0
Then I used TAO Dataset Convert Tool to convert KITTI data to TFRecords files.
# Setting file
!cat $LOCAL_SPECS_DIR/spec_tfrecords_kitti.txt
kitti_config {
root_directory_path: "/workspace/tao-experiments/data/"
image_dir_name: "training/images_aikata"
label_dir_name: "training/labels_aikata"
image_extension: ".jpg"
partition_mode: "random"
num_partitions: 2
val_split: 20
num_shards: 10
}
image_directory_path: "/workspace/tao-experiments/data/"
target_class_mapping {
key: "barcode"
value: "barcode"
}
target_class_mapping {
key: "name"
value: "name"
}
target_class_mapping {
key: "price"
value: "price"
}
target_class_mapping {
key: "card"
value: "card"
}
target_class_mapping {
key: "card7p"
value: "card7p"
}
target_class_mapping {
key: "unknown"
value: "unknown"
}
# Setting file
!cat $LOCAL_SPECS_DIR/spec_train_kitti.txt
random_seed: 42
dataset_config {
data_sources {
tfrecords_path: "/workspace/tao-experiments/data/tfrecords_aikata/*"
image_directory_path: "/workspace/tao-experiments/data/training"
}
image_extension: "jpg"
target_class_mapping {
key: "barcode"
value: "barcode"
}
target_class_mapping {
key: "name"
value: "name"
}
target_class_mapping {
key: "price"
value: "price"
}
target_class_mapping {
key: "card"
value: "card"
}
target_class_mapping {
key: "card7p"
value: "card7p"
}
target_class_mapping {
key: "unknown"
value: "unknown"
}
validation_fold: 0
}
model_config {
pretrained_model_file: "/workspace/tao-experiments/detectnet_v2_test/pretrained_resnet18/pretrained_detectnet_v2_vresnet18/resnet18.hdf5"
num_layers: 18
use_batch_norm: true
objective_set {
bbox {
scale: 35.0
offset: 0.5
}
cov {
}
}
arch: "resnet"
}
augmentation_config {
preprocessing {
output_image_width: 1440
output_image_height: 1920
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: 0.20000000298
contrast_scale_max: 0.10000000149
contrast_center: 0.5
}
}
postprocessing_config {
target_class_config {
key: "barcode"
value {
clustering_config {
clustering_algorithm: DBSCAN
dbscan_confidence_threshold: 0.9
coverage_threshold: 0.00499999988824
dbscan_eps: 0.20000000298
dbscan_min_samples: 1
minimum_bounding_box_height: 20
}
}
}
target_class_config {
key: "name"
value {
clustering_config {
clustering_algorithm: DBSCAN
dbscan_confidence_threshold: 0.9
coverage_threshold: 0.00499999988824
dbscan_eps: 0.15000000596
dbscan_min_samples: 1
minimum_bounding_box_height: 20
}
}
}
target_class_config {
key: "price"
value {
clustering_config {
clustering_algorithm: DBSCAN
dbscan_confidence_threshold: 0.9
coverage_threshold: 0.00749999983236
dbscan_eps: 0.230000004172
dbscan_min_samples: 1
minimum_bounding_box_height: 20
}
}
}
target_class_config {
key: "card"
value {
clustering_config {
clustering_algorithm: DBSCAN
dbscan_confidence_threshold: 0.9
coverage_threshold: 0.00749999983236
dbscan_eps: 0.230000004172
dbscan_min_samples: 1
minimum_bounding_box_height: 20
}
}
}
target_class_config {
key: "card7p"
value {
clustering_config {
clustering_algorithm: DBSCAN
dbscan_confidence_threshold: 0.9
coverage_threshold: 0.00749999983236
dbscan_eps: 0.230000004172
dbscan_min_samples: 1
minimum_bounding_box_height: 20
}
}
}
target_class_config {
key: "unknown"
value {
clustering_config {
clustering_algorithm: DBSCAN
dbscan_confidence_threshold: 0.9
coverage_threshold: 0.00749999983236
dbscan_eps: 0.230000004172
dbscan_min_samples: 1
minimum_bounding_box_height: 20
}
}
}
}
evaluation_config {
validation_period_during_training: 10
first_validation_epoch: 30
minimum_detection_ground_truth_overlap {
key: "barcode"
value: 0.699999988079
}
minimum_detection_ground_truth_overlap {
key: "name"
value: 0.5
}
minimum_detection_ground_truth_overlap {
key: "price"
value: 0.5
}
minimum_detection_ground_truth_overlap {
key: "card"
value: 0.5
}
minimum_detection_ground_truth_overlap {
key: "card7p"
value: 0.5
}
minimum_detection_ground_truth_overlap {
key: "unknown"
value: 0.5
}
evaluation_box_config {
key: "barcode"
value {
minimum_height: 20
maximum_height: 9999
minimum_width: 10
maximum_width: 9999
}
}
evaluation_box_config {
key: "name"
value {
minimum_height: 20
maximum_height: 9999
minimum_width: 10
maximum_width: 9999
}
}
evaluation_box_config {
key: "price"
value {
minimum_height: 20
maximum_height: 9999
minimum_width: 10
maximum_width: 9999
}
}
evaluation_box_config {
key: "card"
value {
minimum_height: 20
maximum_height: 9999
minimum_width: 10
maximum_width: 9999
}
}
evaluation_box_config {
key: "card7p"
value {
minimum_height: 20
maximum_height: 9999
minimum_width: 10
maximum_width: 9999
}
}
evaluation_box_config {
key: "unknown"
value {
minimum_height: 20
maximum_height: 9999
minimum_width: 10
maximum_width: 9999
}
}
average_precision_mode: INTEGRATE
}
cost_function_config {
target_classes {
name: "barcode"
class_weight: 1.0
coverage_foreground_weight: 0.0500000007451
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: "name"
class_weight: 8.0
coverage_foreground_weight: 0.0500000007451
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: "price"
class_weight: 4.0
coverage_foreground_weight: 0.0500000007451
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: "card"
class_weight: 4.0
coverage_foreground_weight: 0.0500000007451
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: "card7p"
class_weight: 4.0
coverage_foreground_weight: 0.0500000007451
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: "unknown"
class_weight: 4.0
coverage_foreground_weight: 0.0500000007451
objectives {
name: "cov"
initial_weight: 1.0
weight_target: 1.0
}
objectives {
name: "bbox"
initial_weight: 10.0
weight_target: 10.0
}
}
enable_autoweighting: false
max_objective_weight: 0.999899983406
min_objective_weight: 9.99999974738e-05
}
training_config {
batch_size_per_gpu: 4
num_epochs: 120
learning_rate {
soft_start_annealing_schedule {
min_learning_rate: 5e-07
max_learning_rate: 5e-05
soft_start: 0.10000000149
annealing: 0.699999988079
}
}
regularizer {
type: L1
weight: 3.00000002618e-09
}
optimizer {
adam {
epsilon: 9.99999993923e-09
beta1: 0.899999976158
beta2: 0.999000012875
}
}
cost_scaling {
initial_exponent: 20.0
increment: 0.005
decrement: 1.0
}
visualizer{
enabled: true
num_images: 3
scalar_logging_frequency: 50
infrequent_logging_frequency: 5
target_class_config {
key: "barcode"
value: {
coverage_threshold: 0.005
}
}
target_class_config {
key: "name"
value: {
coverage_threshold: 0.005
}
}
target_class_config {
key: "price"
value: {
coverage_threshold: 0.005
}
}
target_class_config {
key: "card"
value: {
coverage_threshold: 0.005
}
}
target_class_config {
key: "card7p"
value: {
coverage_threshold: 0.005
}
}
target_class_config {
key: "unknown"
value: {
coverage_threshold: 0.005
}
}
clearml_config{
project: "TAO Toolkit ClearML Demo"
task: "detectnet_v2_resnet18_clearml"
tags: "detectnet_v2"
tags: "training"
tags: "resnet18"
tags: "unpruned"
}
wandb_config{
project: "TAO Toolkit Wandb Demo"
name: "detectnet_v2_resnet18_wandb"
tags: "detectnet_v2"
tags: "training"
tags: "resnet18"
tags: "unpruned"
}
}
checkpoint_interval: 10
}
bbox_rasterizer_config {
target_class_config {
key: "barcode"
value {
cov_center_x: 0.5
cov_center_y: 0.5
cov_radius_x: 0.40000000596
cov_radius_y: 0.40000000596
bbox_min_radius: 1.0
}
}
target_class_config {
key: "name"
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: "price"
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: "card"
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: "card7p"
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: "unknown"
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: 0.400000154972
# TAO Dataset Converter
!tao model detectnet_v2 dataset_convert -d /workspace/tao-experiments/detectnet_v2/specs/spec_tfrecords_kitti.txt \
-o /workspace/tao-experiments/data/tfrecords_aikata
...
2024-06-03 09:04:18,874 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.dataio.dataset_converter_lib 105: Class map.
Label in GT: Label in tfrecords file
b'card': b'card'
b'name': b'name'
b'price': b'price'
b'barcode': b'barcode'
b'card7p': b'card7p'
b'unknown': b'unknown'
For the dataset_config in the experiment_spec, please use labels in the tfrecords file, while writing the classmap.
2024-06-03 09:04:18,874 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.dataio.dataset_converter_lib 114: Tfrecords generation complete.
Execution status: PASS
2024-06-03 18:04:23,922 [TAO Toolkit] [INFO] nvidia_tao_cli.components.docker_handler.docker_handler 363: Stopping container.
So, I thought it’s ready to train dataset with TAO and run these commands, but FAILED.
!tao model detectnet_v2 train -e $SPECS_DIR/spec_train_kitti.txt \
-r $USER_EXPERIMENT_DIR/experiment_dir_unpruned \
-k aikata \
-n resnet18_detector \
--gpus $NUM_GPUS
2024-06-03 18:10:45,022 [TAO Toolkit] [INFO] root 160: Registry: ['nvcr.io']
2024-06-03 18:10:45,086 [TAO Toolkit] [INFO] nvidia_tao_cli.components.instance_handler.local_instance 360: Running command in container: nvcr.io/nvidia/tao/tao-toolkit:5.0.0-tf1.15.5
2024-06-03 18:10:45,321 [TAO Toolkit] [INFO] nvidia_tao_cli.components.docker_handler.docker_handler 301: Printing tty value True
2024-06-03 09:10:46.078291: I tensorflow/stream_executor/platform/default/dso_loader.cc:50] Successfully opened dynamic library libcudart.so.12
2024-06-03 09:10:46,128 [TAO Toolkit] [WARNING] tensorflow 40: Deprecation warnings have been disabled. Set TF_ENABLE_DEPRECATION_WARNINGS=1 to re-enable them.
Using TensorFlow backend.
2024-06-03 09:10:47,178 [TAO Toolkit] [WARNING] tensorflow 43: TensorFlow will not use sklearn by default. This improves performance in some cases. To enable sklearn export the environment variable TF_ALLOW_IOLIBS=1.
2024-06-03 09:10:47,205 [TAO Toolkit] [WARNING] tensorflow 42: TensorFlow will not use Dask by default. This improves performance in some cases. To enable Dask export the environment variable TF_ALLOW_IOLIBS=1.
2024-06-03 09:10:47,209 [TAO Toolkit] [WARNING] tensorflow 43: TensorFlow will not use Pandas by default. This improves performance in some cases. To enable Pandas export the environment variable TF_ALLOW_IOLIBS=1.
2024-06-03 09:10:48,220 [TAO Toolkit] [WARNING] matplotlib 500: Matplotlib created a temporary config/cache directory at /tmp/matplotlib-9thmcfat because the default path (/.config/matplotlib) is not a writable directory; it is highly recommended to set the MPLCONFIGDIR environment variable to a writable directory, in particular to speed up the import of Matplotlib and to better support multiprocessing.
2024-06-03 09:10:48,390 [TAO Toolkit] [INFO] matplotlib.font_manager 1633: generated new fontManager
WARNING:tensorflow:Deprecation warnings have been disabled. Set TF_ENABLE_DEPRECATION_WARNINGS=1 to re-enable them.
Using TensorFlow backend.
WARNING:tensorflow:TensorFlow will not use sklearn by default. This improves performance in some cases. To enable sklearn export the environment variable TF_ALLOW_IOLIBS=1.
...
2024-06-03 09:10:53,326 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 133: Loading weights from pretrained model file. /workspace/tao-experiments/detectnet_v2_test/pretrained_resnet18/pretrained_detectnet_v2_vresnet18/resnet18.hdf5
2024-06-03 09:10:53,326 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer input_1 weights set from pre-trained model.
2024-06-03 09:10:53,446 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer conv1 weights set from pre-trained model.
2024-06-03 09:10:53,560 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer bn_conv1 weights set from pre-trained model.
2024-06-03 09:10:53,560 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer activation_1 weights set from pre-trained model.
2024-06-03 09:10:53,675 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer block_1a_conv_1 weights set from pre-trained model.
2024-06-03 09:10:53,815 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer block_1a_bn_1 weights set from pre-trained model.
2024-06-03 09:10:53,962 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer block_1a_conv_2 weights set from pre-trained model.
2024-06-03 09:10:54,099 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer block_1a_conv_shortcut weights set from pre-trained model.
2024-06-03 09:10:54,225 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer block_1a_bn_2 weights set from pre-trained model.
2024-06-03 09:10:54,342 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer block_1a_bn_shortcut weights set from pre-trained model.
2024-06-03 09:10:54,342 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer add_1 weights set from pre-trained model.
2024-06-03 09:10:54,455 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer block_1b_conv_1 weights set from pre-trained model.
2024-06-03 09:10:54,632 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer block_1b_bn_1 weights set from pre-trained model.
2024-06-03 09:10:54,770 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer block_1b_conv_2 weights set from pre-trained model.
2024-06-03 09:10:54,885 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer block_1b_bn_2 weights set from pre-trained model.
2024-06-03 09:10:54,885 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer add_2 weights set from pre-trained model.
2024-06-03 09:10:54,996 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer block_2a_conv_1 weights set from pre-trained model.
2024-06-03 09:10:55,111 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer block_2a_bn_1 weights set from pre-trained model.
2024-06-03 09:10:55,225 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer block_2a_conv_2 weights set from pre-trained model.
2024-06-03 09:10:55,337 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer block_2a_conv_shortcut weights set from pre-trained model.
2024-06-03 09:10:55,454 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer block_2a_bn_2 weights set from pre-trained model.
2024-06-03 09:10:55,572 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer block_2a_bn_shortcut weights set from pre-trained model.
2024-06-03 09:10:55,572 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer add_3 weights set from pre-trained model.
2024-06-03 09:10:55,685 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer block_2b_conv_1 weights set from pre-trained model.
2024-06-03 09:10:55,808 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer block_2b_bn_1 weights set from pre-trained model.
2024-06-03 09:10:55,931 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer block_2b_conv_2 weights set from pre-trained model.
2024-06-03 09:10:56,051 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer block_2b_bn_2 weights set from pre-trained model.
2024-06-03 09:10:56,051 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer add_4 weights set from pre-trained model.
2024-06-03 09:10:56,168 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer block_3a_conv_1 weights set from pre-trained model.
2024-06-03 09:10:56,360 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer block_3a_bn_1 weights set from pre-trained model.
2024-06-03 09:10:56,570 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer block_3a_conv_2 weights set from pre-trained model.
2024-06-03 09:10:56,770 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer block_3a_conv_shortcut weights set from pre-trained model.
2024-06-03 09:10:56,969 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer block_3a_bn_2 weights set from pre-trained model.
2024-06-03 09:10:57,139 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer block_3a_bn_shortcut weights set from pre-trained model.
2024-06-03 09:10:57,139 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer add_5 weights set from pre-trained model.
2024-06-03 09:10:57,278 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer block_3b_conv_1 weights set from pre-trained model.
2024-06-03 09:10:57,399 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer block_3b_bn_1 weights set from pre-trained model.
2024-06-03 09:10:57,534 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer block_3b_conv_2 weights set from pre-trained model.
2024-06-03 09:10:57,749 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer block_3b_bn_2 weights set from pre-trained model.
2024-06-03 09:10:57,749 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer add_6 weights set from pre-trained model.
2024-06-03 09:10:57,887 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer block_4a_conv_1 weights set from pre-trained model.
2024-06-03 09:10:58,022 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer block_4a_bn_1 weights set from pre-trained model.
2024-06-03 09:10:58,152 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer block_4a_conv_2 weights set from pre-trained model.
2024-06-03 09:10:58,271 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer block_4a_conv_shortcut weights set from pre-trained model.
2024-06-03 09:10:58,402 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer block_4a_bn_2 weights set from pre-trained model.
2024-06-03 09:10:58,537 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer block_4a_bn_shortcut weights set from pre-trained model.
2024-06-03 09:10:58,537 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer add_7 weights set from pre-trained model.
2024-06-03 09:10:58,689 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer block_4b_conv_1 weights set from pre-trained model.
2024-06-03 09:10:58,903 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer block_4b_bn_1 weights set from pre-trained model.
2024-06-03 09:10:59,074 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer block_4b_conv_2 weights set from pre-trained model.
2024-06-03 09:10:59,198 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer block_4b_bn_2 weights set from pre-trained model.
2024-06-03 09:10:59,198 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.model.detectnet_model 142: Layer add_8 weights set from pre-trained model.
2024-06-03 09:10:59,274 [TAO Toolkit] [INFO] nvidia_tao_tf1.cv.detectnet_v2.objectives.bbox_objective 78: Default L1 loss function will be used.
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) (None, 3, 1920, 1440 0
__________________________________________________________________________________________________
conv1 (Conv2D) (None, 64, 960, 720) 9472 input_1[0][0]
__________________________________________________________________________________________________
bn_conv1 (BatchNormalization) (None, 64, 960, 720) 256 conv1[0][0]
__________________________________________________________________________________________________
activation_1 (Activation) (None, 64, 960, 720) 0 bn_conv1[0][0]
__________________________________________________________________________________________________
block_1a_conv_1 (Conv2D) (None, 64, 480, 360) 36928 activation_1[0][0]
__________________________________________________________________________________________________
block_1a_bn_1 (BatchNormalizati (None, 64, 480, 360) 256 block_1a_conv_1[0][0]
__________________________________________________________________________________________________
block_1a_relu_1 (Activation) (None, 64, 480, 360) 0 block_1a_bn_1[0][0]
__________________________________________________________________________________________________
block_1a_conv_2 (Conv2D) (None, 64, 480, 360) 36928 block_1a_relu_1[0][0]
__________________________________________________________________________________________________
block_1a_conv_shortcut (Conv2D) (None, 64, 480, 360) 4160 activation_1[0][0]
__________________________________________________________________________________________________
block_1a_bn_2 (BatchNormalizati (None, 64, 480, 360) 256 block_1a_conv_2[0][0]
__________________________________________________________________________________________________
block_1a_bn_shortcut (BatchNorm (None, 64, 480, 360) 256 block_1a_conv_shortcut[0][0]
__________________________________________________________________________________________________
add_1 (Add) (None, 64, 480, 360) 0 block_1a_bn_2[0][0]
block_1a_bn_shortcut[0][0]
__________________________________________________________________________________________________
block_1a_relu (Activation) (None, 64, 480, 360) 0 add_1[0][0]
__________________________________________________________________________________________________
block_1b_conv_1 (Conv2D) (None, 64, 480, 360) 36928 block_1a_relu[0][0]
__________________________________________________________________________________________________
block_1b_bn_1 (BatchNormalizati (None, 64, 480, 360) 256 block_1b_conv_1[0][0]
__________________________________________________________________________________________________
block_1b_relu_1 (Activation) (None, 64, 480, 360) 0 block_1b_bn_1[0][0]
__________________________________________________________________________________________________
block_1b_conv_2 (Conv2D) (None, 64, 480, 360) 36928 block_1b_relu_1[0][0]
__________________________________________________________________________________________________
block_1b_bn_2 (BatchNormalizati (None, 64, 480, 360) 256 block_1b_conv_2[0][0]
__________________________________________________________________________________________________
add_2 (Add) (None, 64, 480, 360) 0 block_1b_bn_2[0][0]
block_1a_relu[0][0]
__________________________________________________________________________________________________
block_1b_relu (Activation) (None, 64, 480, 360) 0 add_2[0][0]
__________________________________________________________________________________________________
block_2a_conv_1 (Conv2D) (None, 128, 240, 180 73856 block_1b_relu[0][0]
__________________________________________________________________________________________________
block_2a_bn_1 (BatchNormalizati (None, 128, 240, 180 512 block_2a_conv_1[0][0]
__________________________________________________________________________________________________
block_2a_relu_1 (Activation) (None, 128, 240, 180 0 block_2a_bn_1[0][0]
__________________________________________________________________________________________________
block_2a_conv_2 (Conv2D) (None, 128, 240, 180 147584 block_2a_relu_1[0][0]
__________________________________________________________________________________________________
block_2a_conv_shortcut (Conv2D) (None, 128, 240, 180 8320 block_1b_relu[0][0]
__________________________________________________________________________________________________
block_2a_bn_2 (BatchNormalizati (None, 128, 240, 180 512 block_2a_conv_2[0][0]
__________________________________________________________________________________________________
block_2a_bn_shortcut (BatchNorm (None, 128, 240, 180 512 block_2a_conv_shortcut[0][0]
__________________________________________________________________________________________________
add_3 (Add) (None, 128, 240, 180 0 block_2a_bn_2[0][0]
block_2a_bn_shortcut[0][0]
__________________________________________________________________________________________________
block_2a_relu (Activation) (None, 128, 240, 180 0 add_3[0][0]
__________________________________________________________________________________________________
block_2b_conv_1 (Conv2D) (None, 128, 240, 180 147584 block_2a_relu[0][0]
__________________________________________________________________________________________________
block_2b_bn_1 (BatchNormalizati (None, 128, 240, 180 512 block_2b_conv_1[0][0]
__________________________________________________________________________________________________
block_2b_relu_1 (Activation) (None, 128, 240, 180 0 block_2b_bn_1[0][0]
__________________________________________________________________________________________________
block_2b_conv_2 (Conv2D) (None, 128, 240, 180 147584 block_2b_relu_1[0][0]
__________________________________________________________________________________________________
block_2b_bn_2 (BatchNormalizati (None, 128, 240, 180 512 block_2b_conv_2[0][0]
__________________________________________________________________________________________________
add_4 (Add) (None, 128, 240, 180 0 block_2b_bn_2[0][0]
block_2a_relu[0][0]
__________________________________________________________________________________________________
block_2b_relu (Activation) (None, 128, 240, 180 0 add_4[0][0]
__________________________________________________________________________________________________
block_3a_conv_1 (Conv2D) (None, 256, 120, 90) 295168 block_2b_relu[0][0]
__________________________________________________________________________________________________
block_3a_bn_1 (BatchNormalizati (None, 256, 120, 90) 1024 block_3a_conv_1[0][0]
__________________________________________________________________________________________________
block_3a_relu_1 (Activation) (None, 256, 120, 90) 0 block_3a_bn_1[0][0]
__________________________________________________________________________________________________
block_3a_conv_2 (Conv2D) (None, 256, 120, 90) 590080 block_3a_relu_1[0][0]
__________________________________________________________________________________________________
block_3a_conv_shortcut (Conv2D) (None, 256, 120, 90) 33024 block_2b_relu[0][0]
__________________________________________________________________________________________________
block_3a_bn_2 (BatchNormalizati (None, 256, 120, 90) 1024 block_3a_conv_2[0][0]
__________________________________________________________________________________________________
block_3a_bn_shortcut (BatchNorm (None, 256, 120, 90) 1024 block_3a_conv_shortcut[0][0]
__________________________________________________________________________________________________
add_5 (Add) (None, 256, 120, 90) 0 block_3a_bn_2[0][0]
block_3a_bn_shortcut[0][0]
__________________________________________________________________________________________________
block_3a_relu (Activation) (None, 256, 120, 90) 0 add_5[0][0]
__________________________________________________________________________________________________
block_3b_conv_1 (Conv2D) (None, 256, 120, 90) 590080 block_3a_relu[0][0]
__________________________________________________________________________________________________
block_3b_bn_1 (BatchNormalizati (None, 256, 120, 90) 1024 block_3b_conv_1[0][0]
__________________________________________________________________________________________________
block_3b_relu_1 (Activation) (None, 256, 120, 90) 0 block_3b_bn_1[0][0]
__________________________________________________________________________________________________
block_3b_conv_2 (Conv2D) (None, 256, 120, 90) 590080 block_3b_relu_1[0][0]
__________________________________________________________________________________________________
block_3b_bn_2 (BatchNormalizati (None, 256, 120, 90) 1024 block_3b_conv_2[0][0]
__________________________________________________________________________________________________
add_6 (Add) (None, 256, 120, 90) 0 block_3b_bn_2[0][0]
block_3a_relu[0][0]
__________________________________________________________________________________________________
block_3b_relu (Activation) (None, 256, 120, 90) 0 add_6[0][0]
__________________________________________________________________________________________________
block_4a_conv_1 (Conv2D) (None, 512, 120, 90) 1180160 block_3b_relu[0][0]
__________________________________________________________________________________________________
block_4a_bn_1 (BatchNormalizati (None, 512, 120, 90) 2048 block_4a_conv_1[0][0]
__________________________________________________________________________________________________
block_4a_relu_1 (Activation) (None, 512, 120, 90) 0 block_4a_bn_1[0][0]
__________________________________________________________________________________________________
block_4a_conv_2 (Conv2D) (None, 512, 120, 90) 2359808 block_4a_relu_1[0][0]
__________________________________________________________________________________________________
block_4a_conv_shortcut (Conv2D) (None, 512, 120, 90) 131584 block_3b_relu[0][0]
__________________________________________________________________________________________________
block_4a_bn_2 (BatchNormalizati (None, 512, 120, 90) 2048 block_4a_conv_2[0][0]
__________________________________________________________________________________________________
block_4a_bn_shortcut (BatchNorm (None, 512, 120, 90) 2048 block_4a_conv_shortcut[0][0]
__________________________________________________________________________________________________
add_7 (Add) (None, 512, 120, 90) 0 block_4a_bn_2[0][0]
block_4a_bn_shortcut[0][0]
__________________________________________________________________________________________________
block_4a_relu (Activation) (None, 512, 120, 90) 0 add_7[0][0]
__________________________________________________________________________________________________
block_4b_conv_1 (Conv2D) (None, 512, 120, 90) 2359808 block_4a_relu[0][0]
__________________________________________________________________________________________________
block_4b_bn_1 (BatchNormalizati (None, 512, 120, 90) 2048 block_4b_conv_1[0][0]
__________________________________________________________________________________________________
block_4b_relu_1 (Activation) (None, 512, 120, 90) 0 block_4b_bn_1[0][0]
__________________________________________________________________________________________________
block_4b_conv_2 (Conv2D) (None, 512, 120, 90) 2359808 block_4b_relu_1[0][0]
__________________________________________________________________________________________________
block_4b_bn_2 (BatchNormalizati (None, 512, 120, 90) 2048 block_4b_conv_2[0][0]
__________________________________________________________________________________________________
add_8 (Add) (None, 512, 120, 90) 0 block_4b_bn_2[0][0]
block_4a_relu[0][0]
__________________________________________________________________________________________________
block_4b_relu (Activation) (None, 512, 120, 90) 0 add_8[0][0]
__________________________________________________________________________________________________
output_bbox (Conv2D) (None, 24, 120, 90) 12312 block_4b_relu[0][0]
__________________________________________________________________________________________________
output_cov (Conv2D) (None, 6, 120, 90) 3078 block_4b_relu[0][0]
==================================================================================================
Total params: 11,210,718
Trainable params: 11,200,990
Non-trainable params: 9,728
__________________________________________________________________________________________________
2024-06-03 09:10:59,297 [TAO Toolkit] [INFO] root 2102: DetectNet V2 model built.
2024-06-03 09:10:59,298 [TAO Toolkit] [INFO] root 2102: Building rasterizer.
2024-06-03 09:10:59,298 [TAO Toolkit] [INFO] root 2102: Rasterizers built.
WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/nvidia_tao_tf1/cv/detectnet_v2/training/training_proto_utilities.py:102: The name tf.train.get_or_create_global_step is deprecated. Please use tf.compat.v1.train.get_or_create_global_step instead.
...
INFO:tensorflow:Graph was finalized.
2024-06-03 09:11:10,956 [TAO Toolkit] [INFO] tensorflow 240: Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmpjaunkjin/model.ckpt-120
2024-06-03 09:11:10,958 [TAO Toolkit] [INFO] tensorflow 1284: Restoring parameters from /tmp/tmpjaunkjin/model.ckpt-120
2024-06-03 09:11:11,739 [TAO Toolkit] [INFO] root 2102: Restoring from checkpoint failed. This is most likely due to a Variable name or other graph key that is missing from the checkpoint. Please ensure that you have not altered the graph expected based on the checkpoint. Original error:
2 root error(s) found.
(0) Not found: Key cost_sums/barcode-bbox not found in checkpoint
[[node save/RestoreV2 (defined at /tensorflow_core/python/framework/ops.py:1748) ]]
(1) Not found: Key cost_sums/barcode-bbox not found in checkpoint
[[node save/RestoreV2 (defined at /tensorflow_core/python/framework/ops.py:1748) ]]
[[save/RestoreV2/_793]]
0 successful operations.
0 derived errors ignored.
...
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.8/dist-packages/tensorflow_core/python/training/saver.py", line 1300, in restore
names_to_keys = object_graph_key_mapping(save_path)
File "/usr/local/lib/python3.8/dist-packages/tensorflow_core/python/training/saver.py", line 1618, in object_graph_key_mapping
object_graph_string = reader.get_tensor(trackable.OBJECT_GRAPH_PROTO_KEY)
File "/usr/local/lib/python3.8/dist-packages/tensorflow_core/python/pywrap_tensorflow_internal.py", line 915, in get_tensor
return CheckpointReader_GetTensor(self, compat.as_bytes(tensor_str))
tensorflow.python.framework.errors_impl.NotFoundError: Key _CHECKPOINTABLE_OBJECT_GRAPH not found in checkpoint
...
2024-06-03 09:11:12,086 [TAO Toolkit] [ERROR] tensorflow 70: ==================================
Object was never used (type <class 'tensorflow.python.framework.ops.Tensor'>):
<tf.Tensor 'IsVariableInitialized_302:0' shape=() dtype=bool>
If you want to mark it as used call its "mark_used()" method.
It was originally created here:
File "/usr/local/lib/python3.8/dist-packages/nvidia_tao_tf1/cv/detectnet_v2/training/utilities.py", line 154, in get_singular_monitored_session
return tf.train.SingularMonitoredSession(hooks=hooks, File "/usr/local/lib/python3.8/dist-packages/tensorflow_core/python/training/monitored_session.py", line 1100, in __init__
super(SingularMonitoredSession, self).__init__( File "/usr/local/lib/python3.8/dist-packages/tensorflow_core/python/training/monitored_session.py", line 727, in __init__
self._sess = self._coordinated_creator.create_session() File "/usr/local/lib/python3.8/dist-packages/nvidia_tao_tf1/core/hooks/hooks.py", line 286, in begin
self._variables_initialized.append( File "/usr/local/lib/python3.8/dist-packages/tensorflow_core/python/util/tf_should_use.py", line 198, in wrapped
return _add_should_use_warning(fn(*args, **kwargs))
==================================
Execution status: FAIL
2024-06-03 18:11:16,861 [TAO Toolkit] [INFO] nvidia_tao_cli.components.docker_handler.docker_handler 363: Stopping container.
It shows FAIL, but I do not have any clue from the stacktrace.
What might be the reason to be failed?
Because of my setting files or KITTI files ?
2 posts - 2 participants