{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"!pip --quiet install onnx onnxruntime onnxsim\n!pip install onnx-tf","metadata":{"execution":{"iopub.status.busy":"2023-02-25T03:32:42.866929Z","iopub.execute_input":"2023-02-25T03:32:42.867730Z","iopub.status.idle":"2023-02-25T03:33:11.869636Z","shell.execute_reply.started":"2023-02-25T03:32:42.867678Z","shell.execute_reply":"2023-02-25T03:33:11.868141Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\n# import os\n# for dirname, _, filenames in os.walk('/kaggle/input'):\n#     for filename in filenames:\n#         print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session\n\n\nimport torch\nfrom torchvision.models import mobilenet_v2\n\nfrom onnx_tf.backend import prepare\nimport onnx\n\nimport tensorflow as tf","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-02-25T03:35:43.517334Z","iopub.execute_input":"2023-02-25T03:35:43.517861Z","iopub.status.idle":"2023-02-25T03:35:43.525939Z","shell.execute_reply.started":"2023-02-25T03:35:43.517817Z","shell.execute_reply":"2023-02-25T03:35:43.524349Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# PyTorch -> ONNX","metadata":{}},{"cell_type":"code","source":"img_size = (640, 640)\nbatch_size = 1\nonnx_model_path = 'model.onnx'\n\nmodel = mobilenet_v2()\nmodel.eval()\n\nsample_input = torch.rand((batch_size, 3, *img_size))\n\ny = model(sample_input)\n\ntorch.onnx.export(\n    model,\n    sample_input, \n    onnx_model_path,\n    verbose=False,\n    input_names=['input'],\n    output_names=['output'],\n    opset_version=12\n)","metadata":{"execution":{"iopub.status.busy":"2023-02-25T03:34:25.648268Z","iopub.execute_input":"2023-02-25T03:34:25.648744Z","iopub.status.idle":"2023-02-25T03:34:27.206675Z","shell.execute_reply.started":"2023-02-25T03:34:25.648706Z","shell.execute_reply":"2023-02-25T03:34:27.205142Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# ONNX -> TensorFlow","metadata":{}},{"cell_type":"code","source":"from onnx_tf.backend import prepare\nimport onnx\n\nonnx_model_path = 'model.onnx'\ntf_model_path = 'model_tf'\n\nonnx_model = onnx.load(onnx_model_path)\ntf_rep = prepare(onnx_model)\ntf_rep.export_graph(tf_model_path)","metadata":{"execution":{"iopub.status.busy":"2023-02-25T03:35:18.561891Z","iopub.execute_input":"2023-02-25T03:35:18.562904Z","iopub.status.idle":"2023-02-25T03:35:34.276406Z","shell.execute_reply.started":"2023-02-25T03:35:18.562854Z","shell.execute_reply":"2023-02-25T03:35:34.274871Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# TensorFlow -> TFLite","metadata":{}},{"cell_type":"code","source":"saved_model_dir = 'model_tf'\ntflite_model_path = 'model.tflite'\n\n# Convert the model\nconverter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)\ntflite_model = converter.convert()\n\n# Save the model\nwith open(tflite_model_path, 'wb') as f:\n    f.write(tflite_model)","metadata":{"execution":{"iopub.status.busy":"2023-02-25T03:35:57.175374Z","iopub.execute_input":"2023-02-25T03:35:57.175843Z","iopub.status.idle":"2023-02-25T03:35:59.168601Z","shell.execute_reply.started":"2023-02-25T03:35:57.175800Z","shell.execute_reply":"2023-02-25T03:35:59.167200Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# TFLite Inference","metadata":{}},{"cell_type":"code","source":"tflite_model_path = 'model.tflite'\n# Load the TFLite model and allocate tensors\ninterpreter = tf.lite.Interpreter(model_path=tflite_model_path)\ninterpreter.allocate_tensors()\n\n# Get input and output tensors\ninput_details = interpreter.get_input_details()\noutput_details = interpreter.get_output_details()\n\n# Test the model on random input data\ninput_shape = input_details[0]['shape']\ninput_data = np.array(np.random.random_sample(input_shape), dtype=np.float32)\ninterpreter.set_tensor(input_details[0]['index'], input_data)\n\ninterpreter.invoke()\n\n# get_tensor() returns a copy of the tensor data\n# use tensor() in order to get a pointer to the tensor\noutput_data = interpreter.get_tensor(output_details[0]['index'])\nprint(output_data.shape)","metadata":{"execution":{"iopub.status.busy":"2023-02-25T03:36:38.609275Z","iopub.execute_input":"2023-02-25T03:36:38.609703Z","iopub.status.idle":"2023-02-25T03:36:39.099102Z","shell.execute_reply.started":"2023-02-25T03:36:38.609664Z","shell.execute_reply":"2023-02-25T03:36:39.097641Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}