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* Include ORT format model conversion scripts and infrastructure in ORT python package. - tweak existing script setup so it can be easily run directly and from the ORT python package Add config file and readme for Android minimal build package Update ORT Mobile doco Disable warning if 'all' optimizations are enabled but NCHWc transformer is excluded (device specific optimizations don't apply in this scenario so the warning is moot). * Address PR comments
73 lines
3.6 KiB
Text
73 lines
3.6 KiB
Text
The required operators config file was generated from a number of models (details below), with optimizations run using 'all', 'extended' and 'basic'.
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Following that, some additional operators were added, as per the comments in the config file.
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The global types to support were selected to support quantized and float32 models
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Additionally there is internal 'required' type support for int32 and int64_t in selected operators that work with the dimensions in a shape or indices so that we don't need to enable those types at a global level.
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Models used as input (Converted using tf2onnx in early March 2021):
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Models from TF Lite Examples https://www.tensorflow.org/lite/examples
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- lite-model_deeplabv3_1_metadata_2.tflite.onnx
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- lite-model_esrgan-tf2_1.tflite.onnx
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- lite-model_mobilebert_1_metadata_1.tflite.onnx
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- mnist.tflite.onnx
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- mobilenet_v1_1.0_224_quant.tflite.onnx
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- model_history10_top100.tflite.onnx
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- posenet_mobilenet_float_075_1_default_1.tflite.onnx
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- posenet_mobilenet_v1_100_257x257_multi_kpt_stripped.tflite.onnx
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- ssd_mobilenet_v1_1_metadata_1.tflite.onnx
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- text_classification_v2.tflite.onnx
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Assorted models from TF Hub that were able to be converted with tf2onnx
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TFLite v1 https://tfhub.dev/s?deployment-format=lite&tf-version=tf1
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- efficientnet_lite1_fp32_2.tflite.onnx
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- efficientnet_lite1_int8_2.tflite.onnx
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- efficientnet_lite4_fp32_2.tflite.onnx
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- efficientnet_lite4_int8_2.tflite.onnx
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- lite-model_aiy_vision_classifier_birds_V1_3.tflite.onnx
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- lite-model_aiy_vision_classifier_food_V1_1.tflite.onnx
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- lite-model_aiy_vision_classifier_plants_V1_3.tflite.onnx
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- lite-model_midas_v2_1_small_1_lite_1.tflite.onnx
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- lite-model_object_detection_mobile_object_labeler_v1_1.tflite.onnx
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- magenta_arbitrary-image-stylization-v1-256_int8_prediction_1.tflite.onnx
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- magenta_arbitrary-image-stylization-v1-256_int8_transfer_1.tflite.onnx
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- object_detection_mobile_object_localizer_v1_1_default_1.tflite.onnx
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TFLite v2 https://tfhub.dev/s?deployment-format=lite&tf-version=tf2
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- tf2\albert_lite_base_squadv1_1.tflite.onnx
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- tf2\lite-model_disease-classification_1.tflite.onnx
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- tf2\lite-model_efficientdet_lite0_detection_default_1.tflite.onnx
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- tf2\lite-model_efficientdet_lite0_int8_1.tflite.onnx
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- tf2\lite-model_efficientdet_lite1_detection_default_1.tflite.onnx
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- tf2\lite-model_efficientdet_lite2_detection_default_1.tflite.onnx
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- tf2\lite-model_efficientdet_lite3_detection_default_1.tflite.onnx
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- tf2\lite-model_efficientdet_lite4_detection_default_1.tflite.onnx
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- tf2\lite-model_esrgan-tf2_1.tflite.onnx
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- tf2\lite-model_german-mbmelgan_lite_1.tflite.onnx
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- tf2\lite-model_nonsemantic-speech-benchmark_trill-distilled_1.tflite.onnx
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- tf2\lite-model_yamnet_tflite_1.tflite.onnx
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Models from MLPerf Mobile
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(mainly models converted from TFLite and quantized in different ways, but some from TF for completeness as those also have batch handling)
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- deeplabv3_mnv2_ade20k_float-int8.onnx
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- deeplabv3_mnv2_ade20k_float.onnx
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- deeplabv3_mnv2_ade20k-qdq.onnx
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- mobilebert-int8.onnx
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- mobilebert-qdq.onnx
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- mobilebert.onnx
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- mobiledet-int8.onnx
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- mobiledet-qdq.onnx
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- mobiledet.onnx
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- mobilenet_edgetpu_224_1.0_float-int8.onnx
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- mobilenet_edgetpu_224_1.0_float.onnx
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- mobilenet_edgetpu_224_1.0-qdq.onnx
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- mobilenet_v1_1.0_224.opset12.onnx
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- resnet50_v1-int8.onnx
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- resnet50_v1.onnx
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- ssd_mobilenet_v2_300_float-int8.onnx
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- ssd_mobilenet_v2_300_float.onnx
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- ssd_mobilenet_v2_300-qdq.onnx
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Other
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Mobilenet v2 from pytortch
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- pytorch.mobilenet_v2_float.onnx
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- pytorch.mobilenet_v2_uint8.onnx
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