TypeError: get_calibrator() got an unexpected keyword argument 'image_mean'

alphaxyd_1 · August 17, 2021, 1:17pm

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:

augment
bpnet
classification
detectnet_v2
dssd
emotionnet
faster_rcnn
fpenet
gazenet
gesturenet
heartratenet
lprnet
mask_rcnn
multitask_classification
retinanet
ssd
unet
yolo_v3
yolo_v4
tlt-converter
nvidia/tlt-pytorch:
docker_registry: nvcr.io
docker_tag: v3.0-py3
tasks:
speech_to_text
speech_to_text_citrinet
text_classification
question_answering
token_classification
intent_slot_classification
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 14,Error

Morganh · August 17, 2021, 2:25pm

There is no issue when export with fp16 mode. The etlt model is generated successfully.

When export with int8 mode, actually the etlt model is generated successfully.
But the cal.bin is not generated successfully due to above error. It is a regression issue in tlt 3.0-py3.
For workaround, please login 3.0-dp-py3 docker and run the same command to generate cal.bin.

Same as Problem in tlt export - #11

alphaxyd_1 · August 18, 2021, 9:59am

It seems that the problem as before！

**

Morganh · August 20, 2021, 12:36am

For above-mentioned workaround, according to the log, I assume you are still using tlt 3.0-py3 docker.

Please login 3.0-dp-py3 to generate cal.bin.
$ docker run --runtime=nvidia -it 3.0-dp-py3-docker /bin/bash

alphaxyd_1 · August 20, 2021, 9:21am

I login in to docker using the following command:

docker run --runtime=nvidia -it nvcr.io/nvidia/tlt-streamanalytics:v3.0-py3 /bin/bash

As shown in Figure 1：

Then I execute the following two commands，As shown in Figure 2：

execute “ls"command,there are only two files,
execute generate cal.bin command,An error occurred because there is no corresponding file

The screenshot of the first question is executed as follows:

1. Open another terminal and execute ”sudo docker PS“ to find the container number

2. Execute ”sudo docker exec - it < container number > / bin / Bash“

then execute generate cal.bin command

Morganh · August 20, 2021, 9:26am

I mean , for workaround to generate the cal.bin, please login 3.0-dp-py3 docker.
You can also add -v yourlocaldir:dockerdir if you want to mount the local dir into docker.

alphaxyd_1 · August 20, 2021, 12:48pm

The following is my operation record. Please help me see if there is any problem?

xiayd@xiayd-MS-7C82:~$ docker login nvcr.io
Authenticating with existing credentials...
WARNING! Your password will be stored unencrypted in /home/xiayd/.docker/config.json.
Configure a credential helper to remove this warning. See
https://docs.docker.com/engine/reference/commandline/login/#credentials-store

Login Succeeded
xiayd@xiayd-MS-7C82:~$ docker run --runtime=nvidia -it -v /home/xiayd/:/workspace/tlt-experiments nvcr.io/nvidia/tlt-streamanalytics:v3.0-py3 /bin/bash
--2021-08-20 12:25:01--  https://ngc.nvidia.com/downloads/ngccli_reg_linux.zip
Resolving ngc.nvidia.com (ngc.nvidia.com)... 143.204.128.103, 143.204.128.57, 143.204.128.11, ...
Connecting to ngc.nvidia.com (ngc.nvidia.com)|143.204.128.103|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 25071865 (24M) [application/zip]
Saving to: ‘/opt/ngccli/ngccli_reg_linux.zip’

ngccli_reg_linux.zi 100%[===================>]  23.91M  6.04MB/s    in 4.0s

2021-08-20 12:25:11 (6.04 MB/s) - ‘/opt/ngccli/ngccli_reg_linux.zip’ saved [25071865/25071865]

Archive:  /opt/ngccli/ngccli_reg_linux.zip
  inflating: /opt/ngccli/ngc
 extracting: /opt/ngccli/ngc.md5
root@f3250a75bdd4:/workspace# ls
EULA.pdf  README.md  tlt-experiments
root@f3250a75bdd4:/workspace# ls tlt-experiments/faster_rcnn/specs
default_spec_darknet19.txt        default_spec_resnet18.txt
default_spec_darknet53.txt        default_spec_resnet18_grayscale.txt
default_spec_efficientnet_b0.txt  default_spec_resnet18_retrain_spec.txt
default_spec_efficientnet_b1.txt  default_spec_resnet34.txt
default_spec_googlenet.txt        default_spec_resnet50.txt
default_spec_mobilenet_v1.txt     default_spec_vgg16.txt
default_spec_mobilenet_v2.txt     default_spec_vgg19.txt
default_spec_resnet10.txt         frcnn_tfrecords_kitti_trainval.txt
default_spec_resnet101.txt

root@f3250a75bdd4:/workspace# faster_rcnn export --gpu_index 0 
-m tlt-experiments/faster_rcnn/frcnn_kitti_resnet18_retrain.epoch12.tlt 
-o tlt-experiments/faster_rcnn/frcnn_kitti_resnet18_retrain_int8.etlt  
-e tlt-experiments/faster_rcnn/specs/default_spec_resnet18_retrain_spec.txt
-k tlt                        
 --data_type int8                        
 --batch_size 8                         
--batches 10                         
--cal_cache_file tlt-experiments/faster_rcnn/cal.bin

Using TensorFlow backend.
Using TensorFlow backend.
WARNING:tensorflow:Deprecation warnings have been disabled. Set TF_ENABLE_DEPRECATION_WARNINGS=1 to re-enable them.
2021-08-20 12:28:02,748 [INFO] iva.common.export.keras_exporter: Using input nodes: ['input_image']
2021-08-20 12:28:02,748 [INFO] iva.common.export.keras_exporter: Using output nodes: ['NMS']
2021-08-20 12:28:02,748 [INFO] iva.faster_rcnn.spec_loader.spec_loader: Loading experiment spec at tlt-experiments/faster_rcnn/specs/default_spec_resnet18_retrain_spec.txt.
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to   
==================================================================================================
input_image (InputLayer)        (None, 3, 384, 1248) 0                          
__________________________________________________________________________________________________
conv1 (Conv2D)                  (None, 48, 192, 624) 7056        input_image[0][0]
__________________________________________________________________________________________________
bn_conv1 (BatchNormalization)   (None, 48, 192, 624) 192         conv1[0][0]    
__________________________________________________________________________________________________
activation_1 (Activation)       (None, 48, 192, 624) 0           bn_conv1[0][0] 
__________________________________________________________________________________________________
block_1a_conv_1 (Conv2D)        (None, 64, 96, 312)  27648       activation_1[0][0]
__________________________________________________________________________________________________
block_1a_bn_1 (BatchNormalizati (None, 64, 96, 312)  256         block_1a_conv_1[0][0]
__________________________________________________________________________________________________
block_1a_relu_1 (Activation)    (None, 64, 96, 312)  0           block_1a_bn_1[0][0]
__________________________________________________________________________________________________
block_1a_conv_2 (Conv2D)        (None, 64, 96, 312)  36864       block_1a_relu_1[0][0]
__________________________________________________________________________________________________
block_1a_conv_shortcut (Conv2D) (None, 64, 96, 312)  3072        activation_1[0][0]
__________________________________________________________________________________________________
block_1a_bn_2 (BatchNormalizati (None, 64, 96, 312)  256         block_1a_conv_2[0][0]
__________________________________________________________________________________________________
block_1a_bn_shortcut (BatchNorm (None, 64, 96, 312)  256         block_1a_conv_shortcut[0][0]
__________________________________________________________________________________________________
add_1 (Add)                     (None, 64, 96, 312)  0           block_1a_bn_2[0][0]
                                                                 block_1a_bn_shortcut[0][0]
__________________________________________________________________________________________________
block_1a_relu (Activation)      (None, 64, 96, 312)  0           add_1[0][0]    
__________________________________________________________________________________________________
block_1b_conv_1 (Conv2D)        (None, 64, 96, 312)  36864       block_1a_relu[0][0]
__________________________________________________________________________________________________
block_1b_bn_1 (BatchNormalizati (None, 64, 96, 312)  256         block_1b_conv_1[0][0]
__________________________________________________________________________________________________
block_1b_relu_1 (Activation)    (None, 64, 96, 312)  0           block_1b_bn_1[0][0]
__________________________________________________________________________________________________
block_1b_conv_2 (Conv2D)        (None, 64, 96, 312)  36864       block_1b_relu_1[0][0]
__________________________________________________________________________________________________
block_1b_conv_shortcut (Conv2D) (None, 64, 96, 312)  4096        block_1a_relu[0][0]
__________________________________________________________________________________________________
block_1b_bn_2 (BatchNormalizati (None, 64, 96, 312)  256         block_1b_conv_2[0][0]
__________________________________________________________________________________________________
block_1b_bn_shortcut (BatchNorm (None, 64, 96, 312)  256         block_1b_conv_shortcut[0][0]
__________________________________________________________________________________________________
add_2 (Add)                     (None, 64, 96, 312)  0           block_1b_bn_2[0][0]
                                                                 block_1b_bn_shortcut[0][0]
__________________________________________________________________________________________________
block_1b_relu (Activation)      (None, 64, 96, 312)  0           add_2[0][0]    
__________________________________________________________________________________________________
block_2a_conv_1 (Conv2D)        (None, 128, 48, 156) 73728       block_1b_relu[0][0]
__________________________________________________________________________________________________
block_2a_bn_1 (BatchNormalizati (None, 128, 48, 156) 512         block_2a_conv_1[0][0]
__________________________________________________________________________________________________
block_2a_relu_1 (Activation)    (None, 128, 48, 156) 0           block_2a_bn_1[0][0]
__________________________________________________________________________________________________
block_2a_conv_2 (Conv2D)        (None, 128, 48, 156) 147456      block_2a_relu_1[0][0]
__________________________________________________________________________________________________
block_2a_conv_shortcut (Conv2D) (None, 128, 48, 156) 8192        block_1b_relu[0][0]
__________________________________________________________________________________________________
block_2a_bn_2 (BatchNormalizati (None, 128, 48, 156) 512         block_2a_conv_2[0][0]
__________________________________________________________________________________________________
block_2a_bn_shortcut (BatchNorm (None, 128, 48, 156) 512         block_2a_conv_shortcut[0][0]
__________________________________________________________________________________________________
add_3 (Add)                     (None, 128, 48, 156) 0           block_2a_bn_2[0][0]
                                                                 block_2a_bn_shortcut[0][0]
__________________________________________________________________________________________________
block_2a_relu (Activation)      (None, 128, 48, 156) 0           add_3[0][0]    
__________________________________________________________________________________________________
block_2b_conv_1 (Conv2D)        (None, 128, 48, 156) 147456      block_2a_relu[0][0]
__________________________________________________________________________________________________
block_2b_bn_1 (BatchNormalizati (None, 128, 48, 156) 512         block_2b_conv_1[0][0]
__________________________________________________________________________________________________
block_2b_relu_1 (Activation)    (None, 128, 48, 156) 0           block_2b_bn_1[0][0]
__________________________________________________________________________________________________
block_2b_conv_2 (Conv2D)        (None, 128, 48, 156) 147456      block_2b_relu_1[0][0]
__________________________________________________________________________________________________
block_2b_conv_shortcut (Conv2D) (None, 128, 48, 156) 16384       block_2a_relu[0][0]
__________________________________________________________________________________________________
block_2b_bn_2 (BatchNormalizati (None, 128, 48, 156) 512         block_2b_conv_2[0][0]
__________________________________________________________________________________________________
block_2b_bn_shortcut (BatchNorm (None, 128, 48, 156) 512         block_2b_conv_shortcut[0][0]
__________________________________________________________________________________________________
add_4 (Add)                     (None, 128, 48, 156) 0           block_2b_bn_2[0][0]
                                                                 block_2b_bn_shortcut[0][0]
__________________________________________________________________________________________________
block_2b_relu (Activation)      (None, 128, 48, 156) 0           add_4[0][0]    
__________________________________________________________________________________________________
block_3a_conv_1 (Conv2D)        (None, 256, 24, 78)  294912      block_2b_relu[0][0]
__________________________________________________________________________________________________
block_3a_bn_1 (BatchNormalizati (None, 256, 24, 78)  1024        block_3a_conv_1[0][0]
__________________________________________________________________________________________________
block_3a_relu_1 (Activation)    (None, 256, 24, 78)  0           block_3a_bn_1[0][0]
__________________________________________________________________________________________________
block_3a_conv_2 (Conv2D)        (None, 256, 24, 78)  589824      block_3a_relu_1[0][0]
__________________________________________________________________________________________________
block_3a_conv_shortcut (Conv2D) (None, 256, 24, 78)  32768       block_2b_relu[0][0]
__________________________________________________________________________________________________
block_3a_bn_2 (BatchNormalizati (None, 256, 24, 78)  1024        block_3a_conv_2[0][0]
__________________________________________________________________________________________________
block_3a_bn_shortcut (BatchNorm (None, 256, 24, 78)  1024        block_3a_conv_shortcut[0][0]
__________________________________________________________________________________________________
add_5 (Add)                     (None, 256, 24, 78)  0           block_3a_bn_2[0][0]
                                                                 block_3a_bn_shortcut[0][0]
__________________________________________________________________________________________________
block_3a_relu (Activation)      (None, 256, 24, 78)  0           add_5[0][0]    
__________________________________________________________________________________________________
block_3b_conv_1 (Conv2D)        (None, 256, 24, 78)  589824      block_3a_relu[0][0]
__________________________________________________________________________________________________
block_3b_bn_1 (BatchNormalizati (None, 256, 24, 78)  1024        block_3b_conv_1[0][0]
__________________________________________________________________________________________________
block_3b_relu_1 (Activation)    (None, 256, 24, 78)  0           block_3b_bn_1[0][0]
__________________________________________________________________________________________________
block_3b_conv_2 (Conv2D)        (None, 256, 24, 78)  589824      block_3b_relu_1[0][0]
__________________________________________________________________________________________________
block_3b_conv_shortcut (Conv2D) (None, 256, 24, 78)  65536       block_3a_relu[0][0]
__________________________________________________________________________________________________
block_3b_bn_2 (BatchNormalizati (None, 256, 24, 78)  1024        block_3b_conv_2[0][0]
__________________________________________________________________________________________________
block_3b_bn_shortcut (BatchNorm (None, 256, 24, 78)  1024        block_3b_conv_shortcut[0][0]
__________________________________________________________________________________________________
add_6 (Add)                     (None, 256, 24, 78)  0           block_3b_bn_2[0][0]
                                                                 block_3b_bn_shortcut[0][0]
__________________________________________________________________________________________________
block_3b_relu (Activation)      (None, 256, 24, 78)  0           add_6[0][0]    
__________________________________________________________________________________________________
rpn_conv1 (Conv2D)              (None, 512, 24, 78)  1180160     block_3b_relu[0][0]
__________________________________________________________________________________________________
rpn_out_class (Conv2D)          (None, 9, 24, 78)    4617        rpn_conv1[0][0]
__________________________________________________________________________________________________
rpn_out_regress (Conv2D)        (None, 36, 24, 78)   18468       rpn_conv1[0][0]
__________________________________________________________________________________________________
proposal_1 (Proposal)           (None, 300, 4)       0           rpn_out_class[0][0]
                                                                 rpn_out_regress[0][0]
                                                                 input_image[0][0]
__________________________________________________________________________________________________
crop_and_resize_1 (CropAndResiz (None, 300, 256, 7,  0           block_3b_relu[0][0]
                                                                 proposal_1[0][0]
                                                                 input_image[0][0]
__________________________________________________________________________________________________
time_distributed_1 (TimeDistrib (None, 300, 512, 7,  1179648     crop_and_resize_1[0][0]
__________________________________________________________________________________________________
time_distributed_2 (TimeDistrib (None, 300, 512, 7,  2048        time_distributed_1[0][0]
__________________________________________________________________________________________________
block_4a_relu_1 (Activation)    (None, 300, 512, 7,  0           time_distributed_2[0][0]
__________________________________________________________________________________________________
time_distributed_3 (TimeDistrib (None, 300, 512, 7,  2359296     block_4a_relu_1[0][0]
__________________________________________________________________________________________________
time_distributed_5 (TimeDistrib (None, 300, 512, 7,  131072      crop_and_resize_1[0][0]
__________________________________________________________________________________________________
time_distributed_4 (TimeDistrib (None, 300, 512, 7,  2048        time_distributed_3[0][0]
__________________________________________________________________________________________________
time_distributed_6 (TimeDistrib (None, 300, 512, 7,  2048        time_distributed_5[0][0]
__________________________________________________________________________________________________
add_7 (Add)                     (None, 300, 512, 7,  0           time_distributed_4[0][0]
                                                                 time_distributed_6[0][0]
__________________________________________________________________________________________________
block_4a_relu (Activation)      (None, 300, 512, 7,  0           add_7[0][0]    
__________________________________________________________________________________________________
time_distributed_7 (TimeDistrib (None, 300, 512, 7,  2359296     block_4a_relu[0][0]
__________________________________________________________________________________________________
time_distributed_8 (TimeDistrib (None, 300, 512, 7,  2048        time_distributed_7[0][0]
__________________________________________________________________________________________________
block_4b_relu_1 (Activation)    (None, 300, 512, 7,  0           time_distributed_8[0][0]
__________________________________________________________________________________________________
time_distributed_9 (TimeDistrib (None, 300, 512, 7,  2359296     block_4b_relu_1[0][0]
__________________________________________________________________________________________________
time_distributed_11 (TimeDistri (None, 300, 512, 7,  262144      block_4a_relu[0][0]
__________________________________________________________________________________________________
time_distributed_10 (TimeDistri (None, 300, 512, 7,  2048        time_distributed_9[0][0]
__________________________________________________________________________________________________
time_distributed_12 (TimeDistri (None, 300, 512, 7,  2048        time_distributed_11[0][0]
__________________________________________________________________________________________________
add_8 (Add)                     (None, 300, 512, 7,  0           time_distributed_10[0][0]
                                                                 time_distributed_12[0][0]
__________________________________________________________________________________________________
block_4b_relu (Activation)      (None, 300, 512, 7,  0           add_8[0][0]    
__________________________________________________________________________________________________
time_distributed_13 (TimeDistri (None, 300, 512, 1,  0           block_4b_relu[0][0]
__________________________________________________________________________________________________
time_distributed_flatten (TimeD (None, 300, 512)     0           time_distributed_13[0][0]
__________________________________________________________________________________________________
dense_class_td (TimeDistributed (None, 300, 4)       2052        time_distributed_flatten[0][0]
__________________________________________________________________________________________________
dense_regress_td (TimeDistribut (None, 300, 12)      6156        time_distributed_flatten[0][0]
__________________________________________________________________________________________________
nms_inputs_1 (NmsInputs)        [(None, 4800, 1, 1), 0           proposal_1[0][0]
                                                                 dense_class_td[0][0]
                                                                 dense_regress_td[0][0]
==================================================================================================
Total params: 12,741,261
Trainable params: 12,577,181
Non-trainable params: 164,080
__________________________________________________________________________________________________
NOTE: UFF has been tested with TensorFlow 1.14.0.
WARNING: The version of TensorFlow installed on this system is not guaranteed to work with UFF.
Warning: No conversion function registered for layer: NMS_TRT yet.
Converting NMS as custom op: NMS_TRT
Warning: No conversion function registered for layer: Proposal yet.
Converting proposal as custom op: Proposal
DEBUG: convert reshape to flatten node
Warning: No conversion function registered for layer: CropAndResize yet.
Converting roi_pooling_conv_1/CropAndResize_new as custom op: CropAndResize
DEBUG [/usr/local/lib/python3.6/dist-packages/uff/converters/tensorflow/converter.py:96] Marking ['NMS'] as outputs
Traceback (most recent call last):
  File "/opt/tlt/.cache/dazel/_dazel_tlt/2b81a5aac84a1d3b7a324f2a7a6f400b/execroot/ai_infra/bazel-out/k8-fastbuild/bin/magnet/packages/iva/build_wheel.runfiles/ai_infra/iva/faster_rcnn/scripts/export.py", line 12, in <module>
  File "/opt/tlt/.cache/dazel/_dazel_tlt/2b81a5aac84a1d3b7a324f2a7a6f400b/execroot/ai_infra/bazel-out/k8-fastbuild/bin/magnet/packages/iva/build_wheel.runfiles/ai_infra/iva/common/export/app.py", line 219, in launch_export
  File "/opt/tlt/.cache/dazel/_dazel_tlt/2b81a5aac84a1d3b7a324f2a7a6f400b/execroot/ai_infra/bazel-out/k8-fastbuild/bin/magnet/packages/iva/build_wheel.runfiles/ai_infra/iva/common/export/app.py", line 201, in run_export
  File "/opt/tlt/.cache/dazel/_dazel_tlt/2b81a5aac84a1d3b7a324f2a7a6f400b/execroot/ai_infra/bazel-out/k8-fastbuild/bin/magnet/packages/iva/build_wheel.runfiles/ai_infra/iva/common/export/keras_exporter.py", line 360, in export
TypeError: get_calibrator() got an unexpected keyword argument 'image_mean'
root@f3250a75bdd4:/workspace#

Morganh · August 20, 2021, 1:39pm

Please change below

docker run --runtime=nvidia -it -v /home/xiayd/:/workspace/tlt-experiments nvcr.io/nvidia/tlt-streamanalytics:v3.0-py3 /bin/bash

to

docker run --runtime=nvidia -it -v /home/xiayd/:/workspace/tlt-experiments nvcr.io/nvidia/tlt-streamanalytics:v3.0-dp-py3 /bin/bash

alphaxyd_1 · August 21, 2021, 1:43am

The .bin file is generated successfully,thanks!!