AP, precision and recall are remaining zero using the custom dataset. training the fasterRCNN with resnet_18

Please provide the following information when requesting support.

• Hardware (T4/V100/Xavier/Nano/etc)
T4
• Network Type (Detectnet_v2/Faster_rcnn/Yolo_v4/LPRnet/Mask_rcnn/Classification/etc)
Faster_rcnn
• TLT Version (Please run “tlt info --verbose” and share “docker_tag” here)
docker_tag: v3.21.08-py3
• Training spec file(If have, please share here)

Copyright (c) 2017-2020, NVIDIA CORPORATION. All rights reserved.

random_seed: 42
enc_key: ‘NXVodTI0MXNnZGtzdXBic2o0cTIwbmp0bnA6N2IwZDEyMGYtMGZiOS00MDNlLTllOGMtOGMzOTJiYmRlMzk0’
verbose: True
model_config {
input_image_config {
image_type: RGB
image_channel_order: ‘bgr’
size_height_width {
#height: 2160
#width: 3840
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: False
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: “/home/ubuntu/cv_samples_v1.2.0/data/tfrecords/kitti_trainval/kitti_trainval*”
image_directory_path: “/home/ubuntu/cv_samples_v1.2.0/data/training/”
}
image_extension: ‘jpg’
target_class_mapping {
key: ‘car’
value: ‘car’
}
target_class_mapping {
key: ‘hvac’
value: ‘hvac’
}
target_class_mapping {
key: ‘person’
value: ‘person’
}
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: 1
batch_size_per_gpu: 1
num_epochs: 50
pretrained_weights: “/home/ubuntu/cv_samples_v1.2.0/faster_rcnn/resnet_18.hdf5”
#resume_from_model: “/home/ubuntu/cv_samples_v1.2.0/faster_rcnn/frcnn_kitti_resnet18.epoch2.tlt”
output_model: “/home/ubuntu/cv_samples_v1.2.0/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: 0.002
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: ‘/home/ubuntu/cv_samples_v1.2.0/data/training/resize_images’
model: ‘/home/ubuntu/cv_samples_v1.2.0/faster_rcnn/frcnn_kitti_resnet18.epoch50.tlt’
batch_size: 1
detection_image_output_dir: ‘/home/ubuntu/cv_samples_v1.2.0/faster_rcnn/inference_results_imgs’
labels_dump_dir: ‘/home/ubuntu/cv_samples_v1.2.0/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: ‘/home/ubuntu/cv_samples_v1.2.0/faster_rcnn/frcnn_kitti_resnet18.epoch50.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.)
I can not share the data

Hi,

I am running the fasterRCNN with a custom dataset, I have three classes called car, person, and hvac, I barely change the configuration file except for the learning rate from 0.02 to 0.2, I have all the mapping lower case and trained the model for 50 epochs, but in all the epochs the AP precision and recall are zero. In total, I have 120 images and I am just going to fine-tune the model The following is the format of the labels in one image

car 0 0 0 621 98 638 91 0 0 0 0 0 0 0
car 0 0 0 473 33 477 30 0 0 0 0 0 0 0
car 0 0 0 493 51 498 47 0 0 0 0 0 0 0
image
The file names are starting from 0 and all resized to the default input of the model
image

Am I missing something?
However, I am wondering to know that if the model will resize images on fly or if I can set different image sizes ( change the model input and output size)
Regards

Refer to FasterRCNN — TAO Toolkit 3.0 documentation

Please follow the format of Data Annotation Format — TAO Toolkit 3.0 documentation ,
For example,
cyclist 0.00 0 0.00 665.45 160.00 717.93 217.99 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Thanks for your response
However The training stops with more data

ss_loss: 0.4698 - dense_class_td_loss: 1.3495 - dense_regress_td_loss: 0.0609 - dense_class_td_acc: 0.7056Traceback (most recent call last):
  File "/root/.cache/bazel/_bazel_root/ed34e6d125608f91724fda23656f1726/execroot/ai_infra/bazel-out/k8-fastbuild/bin/magnet/packages/iva/build_wheel.runfiles/ai_infra/iva/faster_rcnn/scripts/train.py", line 94, in <module>
  File "/root/.cache/bazel/_bazel_root/ed34e6d125608f91724fda23656f1726/execroot/ai_infra/bazel-out/k8-fastbuild/bin/magnet/packages/iva/build_wheel.runfiles/ai_infra/iva/faster_rcnn/scripts/train.py", line 82, in <module>
  File "/root/.cache/bazel/_bazel_root/ed34e6d125608f91724fda23656f1726/execroot/ai_infra/bazel-out/k8-fastbuild/bin/magnet/packages/iva/build_wheel.runfiles/ai_infra/iva/faster_rcnn/scripts/train.py", line 77, in main
  File "/root/.cache/bazel/_bazel_root/ed34e6d125608f91724fda23656f1726/execroot/ai_infra/bazel-out/k8-fastbuild/bin/magnet/packages/iva/build_wheel.runfiles/ai_infra/iva/faster_rcnn/models/model_builder.py", line 747, in train
  File "/usr/local/lib/python3.6/dist-packages/keras/engine/training.py", line 1039, in fit
    validation_steps=validation_steps)
  File "/usr/local/lib/python3.6/dist-packages/keras/engine/training_arrays.py", line 154, in fit_loop
    outs = f(ins)
  File "/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py", line 2715, in __call__
    return self._call(inputs)
  File "/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py", line 2675, in _call
    fetched = self._callable_fn(*array_vals)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py", line 1472, in __call__
    run_metadata_ptr)
tensorflow.python.framework.errors_impl.InvalidArgumentError: 2 root error(s) found.
  (0) Invalid argument: assertion failed: [Maximum number of objects in image exceeds the limit 100] [Condition x <= y did not hold element-wise:] [x (strided_slice_17:0) = ] [107] [y (assert_less_equal/y:0) = ] [100]
	 [[{{node assert_less_equal/Assert/Assert}}]]
	 [[proposal_target_1/cond_17/Min/Switch/_4487]]
  (1) Invalid argument: assertion failed: [Maximum number of objects in image exceeds the limit 100] [Condition x <= y did not hold element-wise:] [x (strided_slice_17:0) = ] [107] [y (assert_less_equal/y:0) = ] [100]
	 [[{{node assert_less_equal/Assert/Assert}}]]
0 successful operations.
0 derived errors ignored.

Regards

See FasterRCNN — TAO Toolkit 3.0 documentation

The maximum number of objects in an image depends on the dataset. It is important to set the parameter max_objects_num_per_image to be no less than this number. Otherwise, training will fail.

1 Like

I am following exactly the same format but still no data is written to TRF files:

image

Here is the kitti config:

kitti_config {
  root_directory_path: "/home/ubuntu/cv_samples_v1.2.0/data/training"
  image_dir_name: "images/"
  label_dir_name: "labels/"
  image_extension: ".jpg"
  partition_mode: "random"
  num_partitions: 2
  val_split: 1
  num_shards: 10
}
image_directory_path: "/home/ubuntu/cv_samples_v1.2.0/data/training/images"

Here is the output log

2021-10-19 16:57:16,102 [INFO] root: Registry: ['nvcr.io']
2021-10-19 16:57:16,178 [WARNING] tlt.components.docker_handler.docker_handler: 
Docker will run the commands as root. If you would like to retain your
local host permissions, please add the "user":"UID:GID" in the
DockerOptions portion of the "/home/ubuntu/.tao_mounts.json" file. You can obtain your
users UID and GID by using the "id -u" and "id -g" commands on the
terminal.
Using TensorFlow backend.
WARNING:tensorflow:Deprecation warnings have been disabled. Set TF_ENABLE_DEPRECATION_WARNINGS=1 to re-enable them.
Using TensorFlow backend.
2021-10-19 16:57:22,106 - iva.detectnet_v2.dataio.build_converter - INFO - Instantiating a kitti converter
2021-10-19 16:57:22,106 - iva.detectnet_v2.dataio.kitti_converter_lib - INFO - Num images in
Train: 119	Val: 1
2021-10-19 16:57:22,106 - iva.detectnet_v2.dataio.kitti_converter_lib - INFO - Validation data in partition 0. Hence, while choosing the validationset during training choose validation_fold 0.
2021-10-19 16:57:22,106 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Writing partition 0, shard 0
WARNING:tensorflow:From /root/.cache/bazel/_bazel_root/ed34e6d125608f91724fda23656f1726/execroot/ai_infra/bazel-out/k8-fastbuild/bin/magnet/packages/iva/build_wheel.runfiles/ai_infra/iva/detectnet_v2/dataio/dataset_converter_lib.py:142: The name tf.python_io.TFRecordWriter is deprecated. Please use tf.io.TFRecordWriter instead.

2021-10-19 16:57:22,107 - tensorflow - WARNING - From /root/.cache/bazel/_bazel_root/ed34e6d125608f91724fda23656f1726/execroot/ai_infra/bazel-out/k8-fastbuild/bin/magnet/packages/iva/build_wheel.runfiles/ai_infra/iva/detectnet_v2/dataio/dataset_converter_lib.py:142: The name tf.python_io.TFRecordWriter is deprecated. Please use tf.io.TFRecordWriter instead.

2021-10-19 16:57:22,107 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Writing partition 0, shard 1
2021-10-19 16:57:22,107 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Writing partition 0, shard 2
2021-10-19 16:57:22,107 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Writing partition 0, shard 3
2021-10-19 16:57:22,107 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Writing partition 0, shard 4
2021-10-19 16:57:22,107 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Writing partition 0, shard 5
2021-10-19 16:57:22,108 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Writing partition 0, shard 6
2021-10-19 16:57:22,108 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Writing partition 0, shard 7
2021-10-19 16:57:22,108 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Writing partition 0, shard 8
2021-10-19 16:57:22,108 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Writing partition 0, shard 9
/usr/local/lib/python3.6/dist-packages/iva/detectnet_v2/dataio/kitti_converter_lib.py:283: VisibleDeprecationWarning: Reading unicode strings without specifying the encoding argument is deprecated. Set the encoding, use None for the system default.
2021-10-19 16:57:22,114 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - 
Wrote the following numbers of objects:

2021-10-19 16:57:22,114 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Writing partition 1, shard 0
2021-10-19 16:57:22,123 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Writing partition 1, shard 1
2021-10-19 16:57:22,132 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Writing partition 1, shard 2
2021-10-19 16:57:22,141 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Writing partition 1, shard 3
2021-10-19 16:57:22,150 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Writing partition 1, shard 4
2021-10-19 16:57:22,159 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Writing partition 1, shard 5
2021-10-19 16:57:22,169 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Writing partition 1, shard 6
2021-10-19 16:57:22,178 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Writing partition 1, shard 7
2021-10-19 16:57:22,187 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Writing partition 1, shard 8
2021-10-19 16:57:22,196 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Writing partition 1, shard 9
2021-10-19 16:57:22,213 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - 
Wrote the following numbers of objects:

2021-10-19 16:57:22,213 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Cumulative object statistics
2021-10-19 16:57:22,213 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - 
Wrote the following numbers of objects:

2021-10-19 16:57:22,213 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Class map. 
Label in GT: Label in tfrecords file 
For the dataset_config in the experiment_spec, please use labels in the tfrecords file, while writing the classmap.

2021-10-19 16:57:22,213 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Tfrecords generation complete.
2021-10-19 16:57:23,022 [INFO] tlt.components.docker_handler.docker_handler: Stopping container.

Can you share one image and label ? Is your label file’s name same as image file’s name?

Yes sure here is the image and corresponding labels. The name of the images are identical to their corresponding lables.

frame1.txt (496 Bytes)

Can you modify above to below and retry?

  image_dir_name: "images"
  label_dir_name: "labels"

The results are the same
here is the output:

2021-10-21 02:00:14,934 [INFO] root: Registry: ['nvcr.io']
2021-10-21 02:00:15,016 [WARNING] tlt.components.docker_handler.docker_handler: 
Docker will run the commands as root. If you would like to retain your
local host permissions, please add the "user":"UID:GID" in the
DockerOptions portion of the "/home/ubuntu/.tao_mounts.json" file. You can obtain your
users UID and GID by using the "id -u" and "id -g" commands on the
terminal.
Using TensorFlow backend.
WARNING:tensorflow:Deprecation warnings have been disabled. Set TF_ENABLE_DEPRECATION_WARNINGS=1 to re-enable them.
Using TensorFlow backend.
2021-10-21 02:00:20,967 - iva.detectnet_v2.dataio.build_converter - INFO - Instantiating a kitti converter
2021-10-21 02:00:20,967 - iva.detectnet_v2.dataio.kitti_converter_lib - INFO - Num images in
Train: 119	Val: 1
2021-10-21 02:00:20,967 - iva.detectnet_v2.dataio.kitti_converter_lib - INFO - Validation data in partition 0. Hence, while choosing the validationset during training choose validation_fold 0.
2021-10-21 02:00:20,968 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Writing partition 0, shard 0
WARNING:tensorflow:From /root/.cache/bazel/_bazel_root/ed34e6d125608f91724fda23656f1726/execroot/ai_infra/bazel-out/k8-fastbuild/bin/magnet/packages/iva/build_wheel.runfiles/ai_infra/iva/detectnet_v2/dataio/dataset_converter_lib.py:142: The name tf.python_io.TFRecordWriter is deprecated. Please use tf.io.TFRecordWriter instead.

2021-10-21 02:00:20,968 - tensorflow - WARNING - From /root/.cache/bazel/_bazel_root/ed34e6d125608f91724fda23656f1726/execroot/ai_infra/bazel-out/k8-fastbuild/bin/magnet/packages/iva/build_wheel.runfiles/ai_infra/iva/detectnet_v2/dataio/dataset_converter_lib.py:142: The name tf.python_io.TFRecordWriter is deprecated. Please use tf.io.TFRecordWriter instead.

2021-10-21 02:00:20,968 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Writing partition 0, shard 1
2021-10-21 02:00:20,968 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Writing partition 0, shard 2
2021-10-21 02:00:20,968 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Writing partition 0, shard 3
2021-10-21 02:00:20,968 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Writing partition 0, shard 4
2021-10-21 02:00:20,969 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Writing partition 0, shard 5
2021-10-21 02:00:20,969 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Writing partition 0, shard 6
2021-10-21 02:00:20,969 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Writing partition 0, shard 7
2021-10-21 02:00:20,969 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Writing partition 0, shard 8
2021-10-21 02:00:20,969 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Writing partition 0, shard 9
/usr/local/lib/python3.6/dist-packages/iva/detectnet_v2/dataio/kitti_converter_lib.py:283: VisibleDeprecationWarning: Reading unicode strings without specifying the encoding argument is deprecated. Set the encoding, use None for the system default.
2021-10-21 02:00:20,975 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - 
Wrote the following numbers of objects:

2021-10-21 02:00:20,975 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Writing partition 1, shard 0
2021-10-21 02:00:20,984 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Writing partition 1, shard 1
2021-10-21 02:00:20,993 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Writing partition 1, shard 2
2021-10-21 02:00:21,002 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Writing partition 1, shard 3
2021-10-21 02:00:21,012 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Writing partition 1, shard 4
2021-10-21 02:00:21,022 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Writing partition 1, shard 5
2021-10-21 02:00:21,031 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Writing partition 1, shard 6
2021-10-21 02:00:21,040 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Writing partition 1, shard 7
2021-10-21 02:00:21,049 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Writing partition 1, shard 8
2021-10-21 02:00:21,059 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Writing partition 1, shard 9
2021-10-21 02:00:21,075 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - 
Wrote the following numbers of objects:

2021-10-21 02:00:21,075 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Cumulative object statistics
2021-10-21 02:00:21,075 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - 
Wrote the following numbers of objects:

2021-10-21 02:00:21,075 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Class map. 
Label in GT: Label in tfrecords file 
For the dataset_config in the experiment_spec, please use labels in the tfrecords file, while writing the classmap.

2021-10-21 02:00:21,075 - iva.detectnet_v2.dataio.dataset_converter_lib - INFO - Tfrecords generation complete.
2021-10-21 02:00:21,934 [INFO] tlt.components.docker_handler.docker_handler: Stopping container.

Can you share your command?

Here is my kitti config:

kitti_config {
  root_directory_path: "/home/ubuntu/cv_samples_v1.2.0/data/training"
  image_dir_name: "images"
  label_dir_name: "labels"
  image_extension: ".jpg"
  partition_mode: "random"
  num_partitions: 2
  val_split: 1
  num_shards: 10
}
image_directory_path: "/home/ubuntu/cv_samples_v1.2.0/data/training/images"

And this is my training config:

# Copyright (c) 2017-2020, NVIDIA CORPORATION.  All rights reserved.
random_seed: 42
enc_key: 'NXVodTI0MXNnZGtzdXBic2o0cTIwbmp0bnA6N2IwZDEyMGYtMGZiOS00MDNlLTllOGMtOGMzOTJiYmRlMzk0'
verbose: True
model_config {
input_image_config {
image_type: RGB
image_channel_order: 'bgr'
size_height_width {
#height: 2160
#width: 3840
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: 1000
}
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: "/home/ubuntu/cv_samples_v1.2.0/data/tfrecords/kitti_trainval/kitti_trainval*"
    image_directory_path: "/home/ubuntu/cv_samples_v1.2.0/data/training/images"
  }
image_extension: 'jpg'
target_class_mapping {
key: 'car'
value: 'car'
}
target_class_mapping {
key: 'van'
value: 'van'
}
target_class_mapping {
key: 'pedestrian'
value: 'pedestrian'
}
target_class_mapping {
key: 'people'
value: 'people'
}
target_class_mapping {
key: 'bicycle'
value: 'bicycle'
}
target_class_mapping {
key: 'truck'
value: 'truck'
}
target_class_mapping {
key: 'tricycle'
value: 'tricycle'
}
target_class_mapping {
key: 'awning-tricycle'
value: 'awning-tricycle'
}
target_class_mapping {
key: 'bus'
value: 'bus'
}
target_class_mapping {
key: 'motor'
value: 'motor'
}
target_class_mapping {
key: 'hvac'
value: 'hvac'
}
target_class_mapping {
key: 'person'
value: 'person'
}
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: 1
batch_size_per_gpu: 1
num_epochs: 50
#pretrained_weights: "/home/ubuntu/cv_samples_v1.2.0/faster_rcnn/resnet_18.hdf5"
resume_from_model: "/home/ubuntu/cv_samples_v1.2.0/faster_rcnn/frcnn_kitti_resnet18.epoch50.tlt"
output_model: "/home/ubuntu/cv_samples_v1.2.0/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_max_boxes: 5000
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: '/home/ubuntu/cv_samples_v1.2.0/data/training/images'
model: '/home/ubuntu/cv_samples_v1.2.0/faster_rcnn/frcnn_kitti_resnet18.epoch50.tlt'
batch_size: 1
detection_image_output_dir: '/home/ubuntu/cv_samples_v1.2.0/faster_rcnn/inference_results_imgs'
labels_dump_dir: '/home/ubuntu/cv_samples_v1.2.0/faster_rcnn/inference_dump_labels'
rpn_pre_nms_top_N: 6000
#rpn_nms_max_boxes: 300
rpn_nms_max_boxes: 1000
rpn_nms_overlap_threshold: 0.7
object_confidence_thres: 0.0001
bbox_visualize_threshold: 0.6
#classifier_nms_max_boxes: 100
classifier_nms_max_boxes: 500
classifier_nms_overlap_threshold: 0.3
}
evaluation_config {
model: '/home/ubuntu/cv_samples_v1.2.0/faster_rcnn/frcnn_kitti_resnet18.epoch50.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_max_boxes: 500
classifier_nms_overlap_threshold: 0.3
object_confidence_thres: 0.0001
use_voc07_11point_metric:False
gt_matching_iou_threshold: 0.5
}

And this is what I am running in jupyter notebook:

# Creating a new directory for the output tfrecords dump.
!mkdir -p $LOCAL_DATA_DIR/tfrecords/kitti_trainval && rm -rf $LOCAL_DATA_DIR/tfrecords/kitti_trainval/*
#KITTI trainval
!tao faster_rcnn dataset_convert --gpu_index $GPU_INDEX -d $SPECS_DIR/frcnn_tfrecords_kitti_trainval.txt \
                     -o $DATA_DOWNLOAD_DIR/tfrecords/kitti_trainval/kitti_trainval

Hi,
Your label file is as below. The x1,y1,x2, y2 is out of range for the resolution(1380x776) of your image. That’s the reason why there are no objects during tfrecords generation.

person 0.00 0 0.00 1462.00 1008.00 1492.00 938.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
car 0.00 0 0.00 1628.00 1241.00 1929.00 1091.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
car 0.00 0 0.00 1469.00 1538.00 1781.00 1386.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
car 0.00 0 0.00 1323.00 1816.00 1706.00 1608.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
car 0.00 0 0.00 2510.00 994.00 2767.00 882.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
car 0.00 0 0.00 3285.00 468.00 3386.00 362.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

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