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convert_script.txt
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convert_script.txt
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xhost +local: && \
docker run --gpus all -it --rm \
-v `pwd`:/home/user/workdir \
-v /tmp/.X11-unix/:/tmp/.X11-unix:rw \
--device /dev/video0:/dev/video0:mwr \
--net=host \
-e XDG_RUNTIME_DIR=$XDG_RUNTIME_DIR \
-e DISPLAY=$DISPLAY \
--privileged \
pinto0309/openvino2tensorflow:latest
cd workdir
MODEL=yolox_nano
H=320
W=320
$INTEL_OPENVINO_DIR/deployment_tools/model_optimizer/mo.py \
--input_model ${MODEL}_${H}x${W}.onnx \
--data_type FP32 \
--output_dir openvino/${MODEL}/${H}x${W}/FP32
$INTEL_OPENVINO_DIR/deployment_tools/model_optimizer/mo.py \
--input_model ${MODEL}_${H}x${W}.onnx \
--data_type FP16 \
--output_dir openvino/${MODEL}/${H}x${W}/FP16
mkdir -p openvino/${MODEL}/${H}x${W}/myriad
${INTEL_OPENVINO_DIR}/deployment_tools/inference_engine/lib/intel64/myriad_compile \
-m openvino/${MODEL}/${H}x${W}/FP16/${MODEL}_${H}x${W}.xml \
-ip U8 \
-VPU_NUMBER_OF_SHAVES 4 \
-VPU_NUMBER_OF_CMX_SLICES 4 \
-o openvino/${MODEL}/${H}x${W}/myriad/${MODEL}_${H}x${W}.blob
H=416
W=416
$INTEL_OPENVINO_DIR/deployment_tools/model_optimizer/mo.py \
--input_model ${MODEL}_${H}x${W}.onnx \
--data_type FP32 \
--output_dir openvino/${MODEL}/${H}x${W}/FP32
$INTEL_OPENVINO_DIR/deployment_tools/model_optimizer/mo.py \
--input_model ${MODEL}_${H}x${W}.onnx \
--data_type FP16 \
--output_dir openvino/${MODEL}/${H}x${W}/FP16
mkdir -p openvino/${MODEL}/${H}x${W}/myriad
${INTEL_OPENVINO_DIR}/deployment_tools/inference_engine/lib/intel64/myriad_compile \
-m openvino/${MODEL}/${H}x${W}/FP16/${MODEL}_${H}x${W}.xml \
-ip U8 \
-VPU_NUMBER_OF_SHAVES 4 \
-VPU_NUMBER_OF_CMX_SLICES 4 \
-o openvino/${MODEL}/${H}x${W}/myriad/${MODEL}_${H}x${W}.blob
H=480
W=640
$INTEL_OPENVINO_DIR/deployment_tools/model_optimizer/mo.py \
--input_model ${MODEL}_${H}x${W}.onnx \
--data_type FP32 \
--output_dir openvino/${MODEL}/${H}x${W}/FP32
$INTEL_OPENVINO_DIR/deployment_tools/model_optimizer/mo.py \
--input_model ${MODEL}_${H}x${W}.onnx \
--data_type FP16 \
--output_dir openvino/${MODEL}/${H}x${W}/FP16
mkdir -p openvino/${MODEL}/${H}x${W}/myriad
${INTEL_OPENVINO_DIR}/deployment_tools/inference_engine/lib/intel64/myriad_compile \
-m openvino/${MODEL}/${H}x${W}/FP16/${MODEL}_${H}x${W}.xml \
-ip U8 \
-VPU_NUMBER_OF_SHAVES 4 \
-VPU_NUMBER_OF_CMX_SLICES 4 \
-o openvino/${MODEL}/${H}x${W}/myriad/${MODEL}_${H}x${W}.blob
H=544
W=960
$INTEL_OPENVINO_DIR/deployment_tools/model_optimizer/mo.py \
--input_model ${MODEL}_${H}x${W}.onnx \
--data_type FP32 \
--output_dir openvino/${MODEL}/${H}x${W}/FP32
$INTEL_OPENVINO_DIR/deployment_tools/model_optimizer/mo.py \
--input_model ${MODEL}_${H}x${W}.onnx \
--data_type FP16 \
--output_dir openvino/${MODEL}/${H}x${W}/FP16
mkdir -p openvino/${MODEL}/${H}x${W}/myriad
${INTEL_OPENVINO_DIR}/deployment_tools/inference_engine/lib/intel64/myriad_compile \
-m openvino/${MODEL}/${H}x${W}/FP16/${MODEL}_${H}x${W}.xml \
-ip U8 \
-VPU_NUMBER_OF_SHAVES 4 \
-VPU_NUMBER_OF_CMX_SLICES 4 \
-o openvino/${MODEL}/${H}x${W}/myriad/${MODEL}_${H}x${W}.blob
H=736
W=1280
$INTEL_OPENVINO_DIR/deployment_tools/model_optimizer/mo.py \
--input_model ${MODEL}_${H}x${W}.onnx \
--data_type FP32 \
--output_dir openvino/${MODEL}/${H}x${W}/FP32
$INTEL_OPENVINO_DIR/deployment_tools/model_optimizer/mo.py \
--input_model ${MODEL}_${H}x${W}.onnx \
--data_type FP16 \
--output_dir openvino/${MODEL}/${H}x${W}/FP16
mkdir -p openvino/${MODEL}/${H}x${W}/myriad
${INTEL_OPENVINO_DIR}/deployment_tools/inference_engine/lib/intel64/myriad_compile \
-m openvino/${MODEL}/${H}x${W}/FP16/${MODEL}_${H}x${W}.xml \
-ip U8 \
-VPU_NUMBER_OF_SHAVES 4 \
-VPU_NUMBER_OF_CMX_SLICES 4 \
-o openvino/${MODEL}/${H}x${W}/myriad/${MODEL}_${H}x${W}.blob
H=1088
W=1920
$INTEL_OPENVINO_DIR/deployment_tools/model_optimizer/mo.py \
--input_model ${MODEL}_${H}x${W}.onnx \
--data_type FP32 \
--output_dir openvino/${MODEL}/${H}x${W}/FP32
$INTEL_OPENVINO_DIR/deployment_tools/model_optimizer/mo.py \
--input_model ${MODEL}_${H}x${W}.onnx \
--data_type FP16 \
--output_dir openvino/${MODEL}/${H}x${W}/FP16
mkdir -p openvino/${MODEL}/${H}x${W}/myriad
${INTEL_OPENVINO_DIR}/deployment_tools/inference_engine/lib/intel64/myriad_compile \
-m openvino/${MODEL}/${H}x${W}/FP16/${MODEL}_${H}x${W}.xml \
-ip U8 \
-VPU_NUMBER_OF_SHAVES 4 \
-VPU_NUMBER_OF_CMX_SLICES 4 \
-o openvino/${MODEL}/${H}x${W}/myriad/${MODEL}_${H}x${W}.blob
MODEL=yolox_tiny
H=320
W=320
$INTEL_OPENVINO_DIR/deployment_tools/model_optimizer/mo.py \
--input_model ${MODEL}_${H}x${W}.onnx \
--data_type FP32 \
--output_dir openvino/${MODEL}/${H}x${W}/FP32
$INTEL_OPENVINO_DIR/deployment_tools/model_optimizer/mo.py \
--input_model ${MODEL}_${H}x${W}.onnx \
--data_type FP16 \
--output_dir openvino/${MODEL}/${H}x${W}/FP16
mkdir -p openvino/${MODEL}/${H}x${W}/myriad
${INTEL_OPENVINO_DIR}/deployment_tools/inference_engine/lib/intel64/myriad_compile \
-m openvino/${MODEL}/${H}x${W}/FP16/${MODEL}_${H}x${W}.xml \
-ip U8 \
-VPU_NUMBER_OF_SHAVES 4 \
-VPU_NUMBER_OF_CMX_SLICES 4 \
-o openvino/${MODEL}/${H}x${W}/myriad/${MODEL}_${H}x${W}.blob
H=416
W=416
$INTEL_OPENVINO_DIR/deployment_tools/model_optimizer/mo.py \
--input_model ${MODEL}_${H}x${W}.onnx \
--data_type FP32 \
--output_dir openvino/${MODEL}/${H}x${W}/FP32
$INTEL_OPENVINO_DIR/deployment_tools/model_optimizer/mo.py \
--input_model ${MODEL}_${H}x${W}.onnx \
--data_type FP16 \
--output_dir openvino/${MODEL}/${H}x${W}/FP16
mkdir -p openvino/${MODEL}/${H}x${W}/myriad
${INTEL_OPENVINO_DIR}/deployment_tools/inference_engine/lib/intel64/myriad_compile \
-m openvino/${MODEL}/${H}x${W}/FP16/${MODEL}_${H}x${W}.xml \
-ip U8 \
-VPU_NUMBER_OF_SHAVES 4 \
-VPU_NUMBER_OF_CMX_SLICES 4 \
-o openvino/${MODEL}/${H}x${W}/myriad/${MODEL}_${H}x${W}.blob
H=480
W=640
$INTEL_OPENVINO_DIR/deployment_tools/model_optimizer/mo.py \
--input_model ${MODEL}_${H}x${W}.onnx \
--data_type FP32 \
--output_dir openvino/${MODEL}/${H}x${W}/FP32
$INTEL_OPENVINO_DIR/deployment_tools/model_optimizer/mo.py \
--input_model ${MODEL}_${H}x${W}.onnx \
--data_type FP16 \
--output_dir openvino/${MODEL}/${H}x${W}/FP16
mkdir -p openvino/${MODEL}/${H}x${W}/myriad
${INTEL_OPENVINO_DIR}/deployment_tools/inference_engine/lib/intel64/myriad_compile \
-m openvino/${MODEL}/${H}x${W}/FP16/${MODEL}_${H}x${W}.xml \
-ip U8 \
-VPU_NUMBER_OF_SHAVES 4 \
-VPU_NUMBER_OF_CMX_SLICES 4 \
-o openvino/${MODEL}/${H}x${W}/myriad/${MODEL}_${H}x${W}.blob
H=544
W=960
$INTEL_OPENVINO_DIR/deployment_tools/model_optimizer/mo.py \
--input_model ${MODEL}_${H}x${W}.onnx \
--data_type FP32 \
--output_dir openvino/${MODEL}/${H}x${W}/FP32
$INTEL_OPENVINO_DIR/deployment_tools/model_optimizer/mo.py \
--input_model ${MODEL}_${H}x${W}.onnx \
--data_type FP16 \
--output_dir openvino/${MODEL}/${H}x${W}/FP16
mkdir -p openvino/${MODEL}/${H}x${W}/myriad
${INTEL_OPENVINO_DIR}/deployment_tools/inference_engine/lib/intel64/myriad_compile \
-m openvino/${MODEL}/${H}x${W}/FP16/${MODEL}_${H}x${W}.xml \
-ip U8 \
-VPU_NUMBER_OF_SHAVES 4 \
-VPU_NUMBER_OF_CMX_SLICES 4 \
-o openvino/${MODEL}/${H}x${W}/myriad/${MODEL}_${H}x${W}.blob
H=736
W=1280
$INTEL_OPENVINO_DIR/deployment_tools/model_optimizer/mo.py \
--input_model ${MODEL}_${H}x${W}.onnx \
--data_type FP32 \
--output_dir openvino/${MODEL}/${H}x${W}/FP32
$INTEL_OPENVINO_DIR/deployment_tools/model_optimizer/mo.py \
--input_model ${MODEL}_${H}x${W}.onnx \
--data_type FP16 \
--output_dir openvino/${MODEL}/${H}x${W}/FP16
mkdir -p openvino/${MODEL}/${H}x${W}/myriad
${INTEL_OPENVINO_DIR}/deployment_tools/inference_engine/lib/intel64/myriad_compile \
-m openvino/${MODEL}/${H}x${W}/FP16/${MODEL}_${H}x${W}.xml \
-ip U8 \
-VPU_NUMBER_OF_SHAVES 4 \
-VPU_NUMBER_OF_CMX_SLICES 4 \
-o openvino/${MODEL}/${H}x${W}/myriad/${MODEL}_${H}x${W}.blob
H=1088
W=1920
$INTEL_OPENVINO_DIR/deployment_tools/model_optimizer/mo.py \
--input_model ${MODEL}_${H}x${W}.onnx \
--data_type FP32 \
--output_dir openvino/${MODEL}/${H}x${W}/FP32
$INTEL_OPENVINO_DIR/deployment_tools/model_optimizer/mo.py \
--input_model ${MODEL}_${H}x${W}.onnx \
--data_type FP16 \
--output_dir openvino/${MODEL}/${H}x${W}/FP16
mkdir -p openvino/${MODEL}/${H}x${W}/myriad
${INTEL_OPENVINO_DIR}/deployment_tools/inference_engine/lib/intel64/myriad_compile \
-m openvino/${MODEL}/${H}x${W}/FP16/${MODEL}_${H}x${W}.xml \
-ip U8 \
-VPU_NUMBER_OF_SHAVES 4 \
-VPU_NUMBER_OF_CMX_SLICES 4 \
-o openvino/${MODEL}/${H}x${W}/myriad/${MODEL}_${H}x${W}.blob
MODEL=yolox_nano
H=320
W=320
openvino2tensorflow \
--model_path openvino/${MODEL}/${H}x${W}/FP32/${MODEL}_${H}x${W}.xml \
--output_saved_model \
--output_pb \
--output_no_quant_float32_tflite \
--output_weight_quant_tflite \
--output_float16_quant_tflite \
--output_integer_quant_tflite \
--string_formulas_for_normalization 'data / 255' \
--output_integer_quant_type 'uint8' \
--output_tfjs \
--output_coreml \
--weight_replacement_config replace_nano.json
mv saved_model saved_model_${MODEL}_${H}x${W}
openvino2tensorflow \
--model_path openvino/${MODEL}/${H}x${W}/FP32/${MODEL}_${H}x${W}.xml \
--output_saved_model \
--output_pb \
--string_formulas_for_normalization 'data / 255' \
--output_integer_quant_type 'uint8' \
--output_edgetpu \
--weight_replacement_config replace_nano.json
cp saved_model/model_full_integer_quant.tflite saved_model_${MODEL}_${H}x${W}
cp saved_model/model_full_integer_quant_edgetpu.tflite saved_model_${MODEL}_${H}x${W}
rm -rf saved_model
H=416
W=416
openvino2tensorflow \
--model_path openvino/${MODEL}/${H}x${W}/FP32/${MODEL}_${H}x${W}.xml \
--output_saved_model \
--output_pb \
--output_no_quant_float32_tflite \
--output_weight_quant_tflite \
--output_float16_quant_tflite \
--output_integer_quant_tflite \
--string_formulas_for_normalization 'data / 255' \
--output_integer_quant_type 'uint8' \
--output_tfjs \
--output_coreml \
--weight_replacement_config replace_nano.json
mv saved_model saved_model_${MODEL}_${H}x${W}
openvino2tensorflow \
--model_path openvino/${MODEL}/${H}x${W}/FP32/${MODEL}_${H}x${W}.xml \
--output_saved_model \
--output_pb \
--string_formulas_for_normalization 'data / 255' \
--output_integer_quant_type 'uint8' \
--output_edgetpu \
--weight_replacement_config replace_nano.json
cp saved_model/model_full_integer_quant.tflite saved_model_${MODEL}_${H}x${W}
cp saved_model/model_full_integer_quant_edgetpu.tflite saved_model_${MODEL}_${H}x${W}
rm -rf saved_model
H=480
W=640
openvino2tensorflow \
--model_path openvino/${MODEL}/${H}x${W}/FP32/${MODEL}_${H}x${W}.xml \
--output_saved_model \
--output_pb \
--output_no_quant_float32_tflite \
--output_weight_quant_tflite \
--output_float16_quant_tflite \
--output_integer_quant_tflite \
--string_formulas_for_normalization 'data / 255' \
--output_integer_quant_type 'uint8' \
--output_tfjs \
--output_coreml \
--weight_replacement_config replace_nano.json
mv saved_model saved_model_${MODEL}_${H}x${W}
openvino2tensorflow \
--model_path openvino/${MODEL}/${H}x${W}/FP32/${MODEL}_${H}x${W}.xml \
--output_saved_model \
--output_pb \
--string_formulas_for_normalization 'data / 255' \
--output_integer_quant_type 'uint8' \
--output_edgetpu \
--weight_replacement_config replace_nano.json
cp saved_model/model_full_integer_quant.tflite saved_model_${MODEL}_${H}x${W}
cp saved_model/model_full_integer_quant_edgetpu.tflite saved_model_${MODEL}_${H}x${W}
rm -rf saved_model
H=544
W=960
openvino2tensorflow \
--model_path openvino/${MODEL}/${H}x${W}/FP32/${MODEL}_${H}x${W}.xml \
--output_saved_model \
--output_pb \
--output_no_quant_float32_tflite \
--output_weight_quant_tflite \
--output_float16_quant_tflite \
--output_integer_quant_tflite \
--string_formulas_for_normalization 'data / 255' \
--output_integer_quant_type 'uint8' \
--output_tfjs \
--output_coreml \
--weight_replacement_config replace_nano.json
mv saved_model saved_model_${MODEL}_${H}x${W}
H=736
W=1280
openvino2tensorflow \
--model_path openvino/${MODEL}/${H}x${W}/FP32/${MODEL}_${H}x${W}.xml \
--output_saved_model \
--output_pb \
--output_no_quant_float32_tflite \
--output_weight_quant_tflite \
--output_float16_quant_tflite \
--output_integer_quant_tflite \
--string_formulas_for_normalization 'data / 255' \
--output_integer_quant_type 'uint8' \
--output_tfjs \
--output_coreml \
--weight_replacement_config replace_nano.json
mv saved_model saved_model_${MODEL}_${H}x${W}
H=1088
W=1920
openvino2tensorflow \
--model_path openvino/${MODEL}/${H}x${W}/FP32/${MODEL}_${H}x${W}.xml \
--output_saved_model \
--output_pb \
--output_no_quant_float32_tflite \
--output_weight_quant_tflite \
--output_float16_quant_tflite \
--output_integer_quant_tflite \
--string_formulas_for_normalization 'data / 255' \
--output_integer_quant_type 'uint8' \
--output_tfjs \
--output_coreml \
--weight_replacement_config replace_nano.json
mv saved_model saved_model_${MODEL}_${H}x${W}
=========================================================
MODEL=yolox_tiny
H=320
W=320
openvino2tensorflow \
--model_path openvino/${MODEL}/${H}x${W}/FP32/${MODEL}_${H}x${W}.xml \
--output_saved_model \
--output_pb \
--output_no_quant_float32_tflite \
--output_weight_quant_tflite \
--output_float16_quant_tflite \
--output_tfjs \
--output_coreml \
--weight_replacement_config replace_tiny.json
mv saved_model saved_model_${MODEL}_${H}x${W}
H=416
W=416
openvino2tensorflow \
--model_path openvino/${MODEL}/${H}x${W}/FP32/${MODEL}_${H}x${W}.xml \
--output_saved_model \
--output_pb \
--output_no_quant_float32_tflite \
--output_weight_quant_tflite \
--output_float16_quant_tflite \
--output_tfjs \
--output_coreml \
--weight_replacement_config replace_tiny.json
mv saved_model saved_model_${MODEL}_${H}x${W}
H=480
W=640
openvino2tensorflow \
--model_path openvino/${MODEL}/${H}x${W}/FP32/${MODEL}_${H}x${W}.xml \
--output_saved_model \
--output_pb \
--output_no_quant_float32_tflite \
--output_weight_quant_tflite \
--output_float16_quant_tflite \
--output_tfjs \
--output_coreml \
--weight_replacement_config replace_tiny.json
mv saved_model saved_model_${MODEL}_${H}x${W}
H=544
W=960
openvino2tensorflow \
--model_path openvino/${MODEL}/${H}x${W}/FP32/${MODEL}_${H}x${W}.xml \
--output_saved_model \
--output_pb \
--output_no_quant_float32_tflite \
--output_weight_quant_tflite \
--output_float16_quant_tflite \
--output_tfjs \
--output_coreml \
--weight_replacement_config replace_tiny.json
mv saved_model saved_model_${MODEL}_${H}x${W}
H=736
W=1280
openvino2tensorflow \
--model_path openvino/${MODEL}/${H}x${W}/FP32/${MODEL}_${H}x${W}.xml \
--output_saved_model \
--output_pb \
--output_no_quant_float32_tflite \
--output_weight_quant_tflite \
--output_float16_quant_tflite \
--output_tfjs \
--output_coreml \
--weight_replacement_config replace_tiny.json
mv saved_model saved_model_${MODEL}_${H}x${W}
H=1088
W=1920
openvino2tensorflow \
--model_path openvino/${MODEL}/${H}x${W}/FP32/${MODEL}_${H}x${W}.xml \
--output_saved_model \
--output_pb \
--output_no_quant_float32_tflite \
--output_weight_quant_tflite \
--output_float16_quant_tflite \
--output_tfjs \
--output_coreml \
--weight_replacement_config replace_tiny.json
mv saved_model saved_model_${MODEL}_${H}x${W}
================================================
xhost +local: && \
docker run --gpus all -it --rm \
-v `pwd`:/home/user/workdir \
-v /tmp/.X11-unix/:/tmp/.X11-unix:rw \
--device /dev/video0:/dev/video0:mwr \
--net=host \
-e XDG_RUNTIME_DIR=$XDG_RUNTIME_DIR \
-e DISPLAY=$DISPLAY \
--privileged \
pinto0309/openvino2tensorflow:latest
cd workdir
MODEL=yolox_nano
H=320
W=320
openvino2tensorflow \
--model_path openvino/${MODEL}/${H}x${W}/FP32/${MODEL}_${H}x${W}.xml \
--output_tftrt \
--weight_replacement_config replace_nano.json
H=416
W=416
openvino2tensorflow \
--model_path openvino/${MODEL}/${H}x${W}/FP32/${MODEL}_${H}x${W}.xml \
--output_tftrt \
--weight_replacement_config replace_nano.json
H=480
W=640
openvino2tensorflow \
--model_path openvino/${MODEL}/${H}x${W}/FP32/${MODEL}_${H}x${W}.xml \
--output_tftrt \
--weight_replacement_config replace_nano.json
H=544
W=960
openvino2tensorflow \
--model_path openvino/${MODEL}/${H}x${W}/FP32/${MODEL}_${H}x${W}.xml \
--output_tftrt \
--weight_replacement_config replace_nano.json
H=736
W=1280
openvino2tensorflow \
--model_path openvino/${MODEL}/${H}x${W}/FP32/${MODEL}_${H}x${W}.xml \
--output_tftrt \
--weight_replacement_config replace_nano.json
H=1088
W=1920
openvino2tensorflow \
--model_path openvino/${MODEL}/${H}x${W}/FP32/${MODEL}_${H}x${W}.xml \
--output_tftrt \
--weight_replacement_config replace_nano.json