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Small Coreml utils for deep learning models\n### * utils are tested for pytorch but should work for any deep learning framework\n\n## Install\n```\npip install coreml-pytorch-utils\n```\n\n## Example Usage\n\n```python\nfrom coreml_torch_utils import InputEnumeratedShapeImage, OutputDynamicImage, RenameOutput, CoreExporter\nfrom torch import nn\nmodel = nn.Sequential(nn.Conv2d(3, 6, kernel_size=(1, 1)), nn.Conv2d(6, 3, kernel_size=(1, 1)))\nmodel.eval()\njitted = jit.trace(model, example_inputs=(torch.rand(1, 3, 128, 128),))\n\nbase_coreml_model = ct.convert(\n    jitted,\n    inputs=ct.ImageType(\n        \"name\": \"x\",\n        \"shape\": (1, 3, 128, 128),\n        ),\n    convert_to=\"neuralnetwork\",\n)\nutils = [\n    InputEnumeratedShapeImage([(64, 128), (128, 256)]),\n    OutputDynamicImage(\n    height_lower_bound = 64\n    height_upper_bound = 128\n    width_lower_bound = 128\n    width_upper_bound = 256),\n    RenameOutput(new_name=\"SampleOutput\")\n]\nexporter = CoreExporter(utils)\nnew_model = exporter(base_coreml_model)\n\n# New model output name =\u003e SampleOutput, New model output type =\u003e Image [Avaliable input shapes 64x128, 128x265]\n# Old model output name example =\u003e var23, Old model output type Array [Avaliable input shapes 128x128]\n```\n\n## Change inputs to images with enumerated shapes\n```python\ninp_enum = InputEnumeratedShapeImage([(64, 128), (128, 256)]) # [(H W), (H W)]\nexporter = CoreExporter([inp_enum])\nnew_model = exporter(base_coreml_model)\n```\n\n## Change output to images with static shape\n```python\nexporter = CoreExporter([OutputStaticImage(height=128, width=256)])\nnew_model = exporter(base_coreml_model)\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmachineko%2Fcoreml_torch_utils","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmachineko%2Fcoreml_torch_utils","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmachineko%2Fcoreml_torch_utils/lists"}