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There are separate targets for\nquantized and non-quantized version of the same bundle so that both can\nbe built in the same build tree.\n\nTesting:\nConfigured fresh build and ran bundle running cmake targets.","distinct":false,"url":"https://api.github.com/repos/jfix71/glow/commits/e47c695a9d5aa920ff2446dea5973de55ee4523a"},{"sha":"3d61091e50b140e9b682b58c7f9a871a2031b200","author":{"name":"Roman Dzhabarov","email":"79cb2cd851e8ac09176e6cad897c2c40e03260f0@fb.com"},"message":"[build] Do not build bundles by default. It takes 40min on CI.","distinct":false,"url":"https://api.github.com/repos/jfix71/glow/commits/3d61091e50b140e9b682b58c7f9a871a2031b200"},{"sha":"d8eb94d76b12affd19b6bd15d8f9817bbdaf4b9e","author":{"name":"zakk.chen","email":"2a3fce6a2571e0253be53cbff4731d80edbed832@bitmain.com"},"message":"IRBuilder: support create weight with quantize type","distinct":false,"url":"https://api.github.com/repos/jfix71/glow/commits/d8eb94d76b12affd19b6bd15d8f9817bbdaf4b9e"},{"sha":"e13578b0aa0a5167d468a31b9e0a5b995cfe9369","author":{"name":"Roman Dzhabarov","email":"79cb2cd851e8ac09176e6cad897c2c40e03260f0@fb.com"},"message":"[onnx loaders] Support Gather ops in onnxifi loader.","distinct":false,"url":"https://api.github.com/repos/jfix71/glow/commits/e13578b0aa0a5167d468a31b9e0a5b995cfe9369"},{"sha":"10133843a2c279db37298591429a3975d199677f","author":{"name":"Roman Dzhabarov","email":"79cb2cd851e8ac09176e6cad897c2c40e03260f0@fb.com"},"message":"[opencl ci] Test opencl on CI.","distinct":false,"url":"https://api.github.com/repos/jfix71/glow/commits/10133843a2c279db37298591429a3975d199677f"},{"sha":"8f7e87ece32c6c12afbf292e96a5f474df5ee70c","author":{"name":"Roman Dzhabarov","email":"79cb2cd851e8ac09176e6cad897c2c40e03260f0@fb.com"},"message":"[opencl] Better reporting for profiling. Fix reporting for copying outputs.","distinct":false,"url":"https://api.github.com/repos/jfix71/glow/commits/8f7e87ece32c6c12afbf292e96a5f474df5ee70c"},{"sha":"aaad75c5f47d0108d1410c5894e32a61a7b2cd92","author":{"name":"Roman Dzhabarov","email":"79cb2cd851e8ac09176e6cad897c2c40e03260f0@fb.com"},"message":"[onnx] Update onnx submodule.","distinct":false,"url":"https://api.github.com/repos/jfix71/glow/commits/aaad75c5f47d0108d1410c5894e32a61a7b2cd92"},{"sha":"eb27c0d5efb5c67ced480d97e6d00a6aa1ffbec3","author":{"name":"Jordan Fix","email":"a553a4f4a354259566a27829ce25fdc28049418d@fb.com"},"message":"[utils] Add driver for determining imagenet topk accuracy\n\n*Description*: This script is used as a driver to calculate Top-1 and Top-5 accuracy across Imagenet. It will resize+center-crop images prior to inference. This resize+center-crop can also be done to pre-process the images without performing inference, so that the images can be used many times repeatedly without having to resize+center-crop them over and over again.\n\nOn Resnet50, here is what I found (showing results with and without specialized imagenet normalization https://github.com/pytorch/glow/pull/2007):\n\n- Float:\n  - Interpreter/CPU Backend (same results):\n    - Standard normalization:\n      - Top-1 accuracy: 0.6846\n      - Top-5 accuracy: 0.8863\n    - Specialized Imagenet normalization:\n      - Top-1 accuracy: 0.7613\n      - Top-5 accuracy: 0.9293\n\n- Quantized:\n  - Interpreter:\n    - Standard normalization:\n      - Top-1 accuracy: 0.6682\n      - Top-5 accuracy: 0.8767\n    - Specialized Imagenet normalization:\n      - Top-1 accuracy: 0.7500\n      - Top-5 accuracy: 0.9236\n  - CPU Backend:\n    - Standard normalization:\n      - Top-1 accuracy: 0.6731\n      - Top-5 accuracy: 0.8795\n    - Specialized Imagenet normalization:\n      - Top-1 accuracy: 0.7497\n      - Top-5 accuracy: 0.9236\n\n*Testing*: Tested it with many different inputs.\n\n*Documentation*: Added to `Testing.md`","distinct":true,"url":"https://api.github.com/repos/jfix71/glow/commits/eb27c0d5efb5c67ced480d97e6d00a6aa1ffbec3"}]}
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