• Hardware
A100
• Network Type
Classification
• TLT Version
Configuration of the TLT Instance
dockers:
nvidia/tlt-streamanalytics:
docker_registry: nvcr.io
docker_tag: v3.0-py3
tasks:
1. augment
2. bpnet
3. classification
4. detectnet_v2
5. dssd
6. emotionnet
7. faster_rcnn
8. fpenet
9. gazenet
10. gesturenet
11. heartratenet
12. lprnet
13. mask_rcnn
14. multitask_classification
15. retinanet
16. ssd
17. unet
18. yolo_v3
19. yolo_v4
20. tlt-converter
nvidia/tlt-pytorch:
docker_registry: nvcr.io
docker_tag: v3.0-py3
tasks:
1. speech_to_text
2. speech_to_text_citrinet
3. text_classification
4. question_answering
5. token_classification
6. intent_slot_classification
7. punctuation_and_capitalization
format_version: 1.0
tlt_version: 3.0
published_date: 04/16/2021
• Training spec file
• How to reproduce the issue ?
follow the dev blog
https://developer.nvidia.com/blog/preparing-state-of-the-art-models-for-classification-and-object-detection-with-tlt/
tlt classification train -e /workspace/tlt-experiments/classification/darknet53/spec.txt -r /workspace/tlt-experiments/classification/darknet53 -k nvidia_tlt --gpus 8 --use_amp
error infos:
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/makenet/utils/mixup_generator.py”, line 82, in next
File “/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/iterator.py”, line 116, in next
return self._get_batches_of_transformed_samples(index_array)
File “/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/iterator.py”, line 239, in _get_batches_of_transformed_samples
x = self.image_data_generator.standardize(x)
File “/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/image_data_generator.py”, line 708, in standardize
x = self.preprocessing_function(x)
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/makenet/utils/preprocess_input.py”, line 246, in preprocess_input
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/makenet/utils/preprocess_input.py”, line 69, in _preprocess_numpy_input
NotImplementedError: torch mode doesn’t support custom image_mean