mirror of
https://github.com/saymrwulf/onnxruntime.git
synced 2026-05-18 21:21:17 +00:00
b1e21312b5
This is an update to https://github.com/microsoft/onnxruntime/pull/8079 The sample application motivating the original update changed to use an updated version of the model. Now, fewer ops are required. This change removes the previously added ops which are no longer needed.
80 lines
4.2 KiB
Text
80 lines
4.2 KiB
Text
The required operators config file was generated from a number of models (details below), with optimizations run using 'all', 'extended' and 'basic'.
|
|
Following that, some additional operators were added, as per the comments in the config file.
|
|
|
|
The global types to support were selected to support quantized and float32 models
|
|
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.
|
|
|
|
Models used as input (Converted using tf2onnx in early March 2021):
|
|
Models from TF Lite Examples https://www.tensorflow.org/lite/examples
|
|
- lite-model_deeplabv3_1_metadata_2.tflite.onnx
|
|
- lite-model_esrgan-tf2_1.tflite.onnx
|
|
- lite-model_mobilebert_1_metadata_1.tflite.onnx
|
|
- mnist.tflite.onnx
|
|
- mobilenet_v1_1.0_224_quant.tflite.onnx
|
|
- model_history10_top100.tflite.onnx
|
|
- posenet_mobilenet_float_075_1_default_1.tflite.onnx
|
|
- posenet_mobilenet_v1_100_257x257_multi_kpt_stripped.tflite.onnx
|
|
- ssd_mobilenet_v1_1_metadata_1.tflite.onnx
|
|
- text_classification_v2.tflite.onnx
|
|
|
|
Assorted models from TF Hub that were able to be converted with tf2onnx
|
|
TFLite v1 https://tfhub.dev/s?deployment-format=lite&tf-version=tf1
|
|
- efficientnet_lite1_fp32_2.tflite.onnx
|
|
- efficientnet_lite1_int8_2.tflite.onnx
|
|
- efficientnet_lite4_fp32_2.tflite.onnx
|
|
- efficientnet_lite4_int8_2.tflite.onnx
|
|
- lite-model_aiy_vision_classifier_birds_V1_3.tflite.onnx
|
|
- lite-model_aiy_vision_classifier_food_V1_1.tflite.onnx
|
|
- lite-model_aiy_vision_classifier_plants_V1_3.tflite.onnx
|
|
- lite-model_midas_v2_1_small_1_lite_1.tflite.onnx
|
|
- lite-model_object_detection_mobile_object_labeler_v1_1.tflite.onnx
|
|
- magenta_arbitrary-image-stylization-v1-256_int8_prediction_1.tflite.onnx
|
|
- magenta_arbitrary-image-stylization-v1-256_int8_transfer_1.tflite.onnx
|
|
- object_detection_mobile_object_localizer_v1_1_default_1.tflite.onnx
|
|
|
|
TFLite v2 https://tfhub.dev/s?deployment-format=lite&tf-version=tf2
|
|
- tf2\albert_lite_base_squadv1_1.tflite.onnx
|
|
- tf2\lite-model_disease-classification_1.tflite.onnx
|
|
- tf2\lite-model_efficientdet_lite0_detection_default_1.tflite.onnx
|
|
- tf2\lite-model_efficientdet_lite0_int8_1.tflite.onnx
|
|
- tf2\lite-model_efficientdet_lite1_detection_default_1.tflite.onnx
|
|
- tf2\lite-model_efficientdet_lite2_detection_default_1.tflite.onnx
|
|
- tf2\lite-model_efficientdet_lite3_detection_default_1.tflite.onnx
|
|
- tf2\lite-model_efficientdet_lite4_detection_default_1.tflite.onnx
|
|
- tf2\lite-model_esrgan-tf2_1.tflite.onnx
|
|
- tf2\lite-model_german-mbmelgan_lite_1.tflite.onnx
|
|
- tf2\lite-model_nonsemantic-speech-benchmark_trill-distilled_1.tflite.onnx
|
|
- tf2\lite-model_yamnet_tflite_1.tflite.onnx
|
|
|
|
Models from MLPerf Mobile
|
|
(mainly models converted from TFLite and quantized in different ways, but some from TF for completeness as those also have batch handling)
|
|
- deeplabv3_mnv2_ade20k_float-int8.onnx
|
|
- deeplabv3_mnv2_ade20k_float.onnx
|
|
- deeplabv3_mnv2_ade20k-qdq.onnx
|
|
- mobilebert-int8.onnx
|
|
- mobilebert-qdq.onnx
|
|
- mobilebert.onnx
|
|
- mobiledet-int8.onnx
|
|
- mobiledet-qdq.onnx
|
|
- mobiledet.onnx
|
|
- mobilenet_edgetpu_224_1.0_float-int8.onnx
|
|
- mobilenet_edgetpu_224_1.0_float.onnx
|
|
- mobilenet_edgetpu_224_1.0-qdq.onnx
|
|
- mobilenet_v1_1.0_224.opset12.onnx
|
|
- resnet50_v1-int8.onnx
|
|
- resnet50_v1.onnx
|
|
- ssd_mobilenet_v2_300_float-int8.onnx
|
|
- ssd_mobilenet_v2_300_float.onnx
|
|
- ssd_mobilenet_v2_300-qdq.onnx
|
|
|
|
Other
|
|
Mobilenet v2 from pytorch
|
|
- pytorch.mobilenet_v2_float.onnx
|
|
- pytorch.mobilenet_v2_uint8.onnx
|
|
Other assorted pytorch models
|
|
- Huggingface mobilebert-uncased (https://huggingface.co/transformers/serialization.html, https://huggingface.co/google/mobilebert-uncased)
|
|
- SuperResolution (https://pytorch.org/tutorials/advanced/super_resolution_with_onnxruntime.html)
|
|
- DeepLabV3 (https://pytorch.org/tutorials/beginner/deeplabv3_on_android.html)
|
|
- EfficientNet (https://github.com/lukemelas/EfficientNet-PyTorch)
|
|
- SSD Mobilenet V1 and V2 (https://github.com/qfgaohao/pytorch-ssd)
|
|
- Wav2Vec 2.0 (adapted from https://github.com/pytorch/ios-demo-app/blob/f2b9aa196821c136d3299b99c5dd592de1fa1776/SpeechRecognition/create_wav2vec2.py)
|