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* Add test for iOS package (#7816) * Add test for iOS package * Add readme * fix pep8 warning * Addressed CR comments, fixed CI failure * Address CR comments * Update readme.md * Update package name and readme, added comments to the podspec * Add podspec template for ios package, update build settings (#7907) * Add podspec template for ios package * minor formatting update * Add spec.source_files for header files * Update spec.public_header_files to spec.source_files * minor update * Add iOS packaging pipeline (#8264) Create a pipeline to produce the iOS package artifacts. * [iOS] Packaging pipeline improvements. (#8324) Updates to the iOS packaging pipeline: - Make it harder to overwrite package archives accidentally when uploading (fails if the archive already exists) - Only upload package archives for release builds - Some clean up * Add metadata_props to ORT model (#8340) * Add metadata_props to ORT model * Minor update * Update python binding, and increase the minimal pipeline size threshold * Fixed a small bug in serializing ir_version * Remove temp ort.py.fbs and add it to .gitignore * Add iOS/macOS static framework (#8357) * Add ability to generate ios static framework * Fix typos * Add pod cache clean, update some comments of previous commit * Fix CI failure with newly added cpuinfo library * Update test model (CoreML requires node has a name) * Addressed CR comments * Fix iOS packaging pipeline failure (#8433) * Fix optimizer crash (#8274) * Update iOS packaging script to default build static framework, disable bitcode (#8533) * default package build to static, disable bitcode * fix pipeline failure * Address CR comments * Add HardSigmoid to mobile packages. Used by PyTorch MobileNet v3 (#8552) * bump the version number to 1.8.2 * Change Windows GPU machine pool to onnxruntime-win-cuda11-0 * [Objective-C API] Fix ORTIsCoreMLExecutionProviderAvailable link error when used from Swift. (#8350) Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com> Co-authored-by: RandySheriffH <48490400+RandySheriffH@users.noreply.github.com> Co-authored-by: Scott McKay <skottmckay@gmail.com> Co-authored-by: Changming Sun <chasun@microsoft.com>
82 lines
4.3 KiB
Text
82 lines
4.3 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 and v3 from pytorch
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- https://pytorch.org/vision/stable/models.html
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- pytorch.mobilenet_v2_float.onnx
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- pytorch.mobilenet_v2_uint8.onnx
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- pytorch.mobilenet_v3_small.onnx
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Other assorted pytorch models
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- Huggingface mobilebert-uncased (https://huggingface.co/transformers/serialization.html, https://huggingface.co/google/mobilebert-uncased)
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- SuperResolution (https://pytorch.org/tutorials/advanced/super_resolution_with_onnxruntime.html)
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- DeepLabV3 (https://pytorch.org/tutorials/beginner/deeplabv3_on_android.html)
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- EfficientNet (https://github.com/lukemelas/EfficientNet-PyTorch)
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- SSD Mobilenet V1 and V2 (https://github.com/qfgaohao/pytorch-ssd)
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- Wav2Vec 2.0 (adapted from https://github.com/pytorch/ios-demo-app/blob/f2b9aa196821c136d3299b99c5dd592de1fa1776/SpeechRecognition/create_wav2vec2.py)
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