Hi Everyone,
I am currently using TLT to train a resnet50 faster rcnn. I have trained the model but during evaluation, I am getting the error ValueError: Image file not found for label. I am training for a new class which is go-kart. Below is the image file present in the validation folder and also the label file for which the error occurs. As can be seen from the image below it is also .jpg format.
Also my spec file is:
random_seed: 42
enc_key: ‘$KEY’
verbose: True
network_config {
input_image_config {
image_type: RGB
image_channel_order: ‘bgr’
size_height_width {
height: 384
width: 1280
}
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
}
feature_extractor: “resnet:50”
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: 128
rpn_stride: 16
conv_bn_share_bias: False
roi_pooling_config {
pool_size: 7
pool_size_2x: False
}
all_projections: True
use_pooling:False
}
training_config {
kitti_data_config {
images_dir : ‘/workspace/tlt-experiments/data/KITTI/training/images’
labels_dir: ‘/workspace/tlt-experiments/data/KITTI/training/labels’
}
training_data_parser: ‘raw_kitti’
data_augmentation {
use_augmentation: 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 {
color_shift_stddev: 0.0
hue_rotation_max: 0.0
saturation_shift_max: 0.0
contrast_scale_max: 0.0
contrast_center: 0.5
}
}
num_epochs: 100
class_mapping {
key: ‘Car’
value: 0
}
class_mapping {
key: ‘Van’
value: 0
}
class_mapping {
key: “Pedestrian”
value: 1
}
class_mapping {
key: “Person_sitting”
value: 1
}
class_mapping {
key: ‘Cyclist’
value: 2
}
class_mapping{
key: “go-kart”
value: 3
}
class_mapping {
key: “DontCare”
value: -1
}
class_mapping {
key: “Truck”
value: -1
}
class_mapping {
key: “Misc”
value: -1
}
class_mapping {
key: “Tram”
value: -1
}
class_mapping {
key: “background”
value: 4
}
pretrained_weights: “/workspace/tlt-experiments/data/faster_rcnn/resnet50.h5”
pretrained_model: “”
output_weights: “/workspace/tlt-experiments/data/faster_rcnn/frcnn_kitti.tltw”
output_model: “/workspace/tlt-experiments/data/faster_rcnn/frcnn_kitti.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: 0.7reg_config {
reg_type: ‘L2’
weight_decay: 1e-4
}optimizer {
adam {
lr: 0.00001
beta_1: 0.9
beta_2: 0.999
decay: 0.0
}
}lr_scheduler {
step {
base_lr: 0.00001
gamma: 1.0
step_size: 30
}
}lambda_rpn_regr: 1.0
lambda_rpn_class: 1.0
lambda_cls_regr: 1.0
lambda_cls_class: 1.0inference_config {
images_dir: ‘/workspace/tlt-experiments/data/testing/’
model: ‘/workspace/tlt-experiments/data/faster_rcnn/frcnn_kitti.epoch100.tlt’
detection_image_output_dir: ‘/workspace/tlt-experiments/data/faster_rcnn/inference_results_imgs’
labels_dump_dir: ‘/workspace/tlt-experiments/data/faster_rcnn/inference_dump_labels’
rpn_pre_nms_top_N: 6000
rpn_nms_max_boxes: 300
rpn_nms_overlap_threshold: 0.7
bbox_visualize_threshold: 0.6
classifier_nms_max_boxes: 300
classifier_nms_overlap_threshold: 0.3
}evaluation_config {
dataset {
images_dir : ‘/workspace/tlt-experiments/data/KITTI/val/image_2’
labels_dir: ‘/workspace/tlt-experiments/data/KITTI/val/label_2’
}
data_parser: ‘raw_kitti’
model: ‘/workspace/tlt-experiments/data/faster_rcnn/frcnn_kitti.epoch100.tlt’
labels_dump_dir: ‘/workspace/tlt-experiments/data/faster_rcnn/test_dump_labels’
rpn_pre_nms_top_N: 6000
rpn_nms_max_boxes: 300
rpn_nms_overlap_threshold: 0.7
classifier_nms_max_boxes: 300
classifier_nms_overlap_threshold: 0.3
object_confidence_thres: 0.0001
use_voc07_11point_metric:False
}}
Any help would be much appreciated!
Thank You.