6761a29af5
* update-notebooks * Update docs/nbdoc/nbdoc.py Co-authored-by: bstankix <bartoszx.stankiewicz@intel.com> * Update docs/nbdoc/nbdoc.py Co-authored-by: bstankix <bartoszx.stankiewicz@intel.com> --------- Co-authored-by: bstankix <bartoszx.stankiewicz@intel.com>
263 lines
8.3 KiB
ReStructuredText
263 lines
8.3 KiB
ReStructuredText
Convert a TensorFlow Model to OpenVINO™
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=======================================
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This short tutorial shows how to convert a TensorFlow
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`MobileNetV3 <https://docs.openvino.ai/2023.0/omz_models_model_mobilenet_v3_small_1_0_224_tf.html>`__
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image classification model to OpenVINO `Intermediate
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Representation <https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_IR_and_opsets.html>`__
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(OpenVINO IR) format, using `Model
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Optimizer <https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html>`__.
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After creating the OpenVINO IR, load the model in `OpenVINO
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Runtime <https://docs.openvino.ai/nightly/openvino_docs_OV_UG_OV_Runtime_User_Guide.html>`__
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and do inference with a sample image.
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Imports
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-------
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.. code:: ipython3
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import time
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from pathlib import Path
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import cv2
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import matplotlib.pyplot as plt
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import numpy as np
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import tensorflow as tf
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from openvino.runtime import Core, serialize
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from openvino.tools import mo
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.. parsed-literal::
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2023-07-11 22:24:31.659014: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
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2023-07-11 22:24:31.694466: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
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To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
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2023-07-11 22:24:32.210417: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
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Settings
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--------
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.. code:: ipython3
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# The paths of the source and converted models.
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model_dir = Path("model")
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model_dir.mkdir(exist_ok=True)
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model_path = Path("model/v3-small_224_1.0_float")
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ir_path = Path("model/v3-small_224_1.0_float.xml")
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Download model
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--------------
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Load model using `tf.keras.applications
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api <https://www.tensorflow.org/api_docs/python/tf/keras/applications/MobileNetV3Small>`__
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and save it to the disk.
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.. code:: ipython3
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model = tf.keras.applications.MobileNetV3Small()
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model.save(model_path)
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.. parsed-literal::
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WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not 224. Weights for input shape (224, 224) will be loaded as the default.
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.. parsed-literal::
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2023-07-11 22:24:33.117393: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1956] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
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Skipping registering GPU devices...
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.. parsed-literal::
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WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
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.. parsed-literal::
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2023-07-11 22:24:37.315661: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,1,1,1024]
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[[{{node inputs}}]]
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2023-07-11 22:24:40.473876: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,1,1,1024]
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[[{{node inputs}}]]
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WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 54). These functions will not be directly callable after loading.
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.. parsed-literal::
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INFO:tensorflow:Assets written to: model/v3-small_224_1.0_float/assets
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.. parsed-literal::
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INFO:tensorflow:Assets written to: model/v3-small_224_1.0_float/assets
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Convert a Model to OpenVINO IR Format
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-------------------------------------
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Convert a TensorFlow Model to OpenVINO IR Format
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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Call the OpenVINO Model Optimizer Python API to convert the TensorFlow
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model to OpenVINO IR. ``mo.convert_model`` function accept path to saved
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model directory and returns OpenVINO Model class instance which
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represents this model. Obtained model is ready to use and loading on
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device using ``compile_model`` or can be saved on disk using
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``serialize`` function. See the `Model Optimizer Developer
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Guide <https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_TensorFlow.html>`__
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for more information about Model Optimizer and TensorFlow models
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conversion.
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.. code:: ipython3
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# Run Model Optimizer if the IR model file does not exist
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if not ir_path.exists():
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print("Exporting TensorFlow model to IR... This may take a few minutes.")
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ov_model = mo.convert_model(saved_model_dir=model_path, input_shape=[[1, 224, 224, 3]], compress_to_fp16=True)
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serialize(ov_model, ir_path)
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else:
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print(f"IR model {ir_path} already exists.")
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.. parsed-literal::
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Exporting TensorFlow model to IR... This may take a few minutes.
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Test Inference on the Converted Model
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-------------------------------------
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Load the Model
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~~~~~~~~~~~~~~
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.. code:: ipython3
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core = Core()
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model = core.read_model(ir_path)
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Select inference device
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-----------------------
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select device from dropdown list for running inference using OpenVINO
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.. code:: ipython3
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import ipywidgets as widgets
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device = widgets.Dropdown(
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options=core.available_devices + ["AUTO"],
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value='AUTO',
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description='Device:',
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disabled=False,
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)
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device
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.. parsed-literal::
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Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')
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.. code:: ipython3
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compiled_model = core.compile_model(model=model, device_name=device.value)
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Get Model Information
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~~~~~~~~~~~~~~~~~~~~~
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.. code:: ipython3
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input_key = compiled_model.input(0)
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output_key = compiled_model.output(0)
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network_input_shape = input_key.shape
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Load an Image
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~~~~~~~~~~~~~
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Load an image, resize it, and convert it to the input shape of the
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network.
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.. code:: ipython3
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# The MobileNet network expects images in RGB format.
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image = cv2.cvtColor(cv2.imread(filename="../data/image/coco.jpg"), code=cv2.COLOR_BGR2RGB)
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# Resize the image to the network input shape.
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resized_image = cv2.resize(src=image, dsize=(224, 224))
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# Transpose the image to the network input shape.
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input_image = np.expand_dims(resized_image, 0)
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plt.imshow(image);
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.. image:: 101-tensorflow-classification-to-openvino-with-output_files/101-tensorflow-classification-to-openvino-with-output_18_0.png
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Do Inference
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~~~~~~~~~~~~
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.. code:: ipython3
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result = compiled_model(input_image)[output_key]
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result_index = np.argmax(result)
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.. code:: ipython3
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# Convert the inference result to a class name.
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imagenet_classes = open("../data/datasets/imagenet/imagenet_2012.txt").read().splitlines()
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imagenet_classes[result_index]
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.. parsed-literal::
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'n02099267 flat-coated retriever'
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Timing
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------
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Measure the time it takes to do inference on thousand images. This gives
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an indication of performance. For more accurate benchmarking, use the
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`Benchmark
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Tool <https://docs.openvino.ai/2023.0/openvino_inference_engine_tools_benchmark_tool_README.html>`__
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in OpenVINO. Note that many optimizations are possible to improve the
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performance.
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.. code:: ipython3
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num_images = 1000
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start = time.perf_counter()
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for _ in range(num_images):
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compiled_model([input_image])
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end = time.perf_counter()
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time_ir = end - start
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print(
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f"IR model in OpenVINO Runtime/CPU: {time_ir/num_images:.4f} "
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f"seconds per image, FPS: {num_images/time_ir:.2f}"
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)
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.. parsed-literal::
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IR model in OpenVINO Runtime/CPU: 0.0010 seconds per image, FPS: 998.88
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