init - 初始化项目
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Lee Nony
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# Conversion of PyTorch Classification Models and Launch with OpenCV C++ {#pytorch_cls_c_tutorial_dnn_conversion}
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@prev_tutorial{pytorch_cls_tutorial_dnn_conversion}
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| | |
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| -: | :- |
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| Original author | Anastasia Murzova |
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| Compatibility | OpenCV >= 4.5 |
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## Goals
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In this tutorial you will learn how to:
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* convert PyTorch classification models into ONNX format
|
||||
* run converted PyTorch model with OpenCV C/C++ API
|
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* provide model inference
|
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|
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We will explore the above-listed points by the example of ResNet-50 architecture.
|
||||
|
||||
## Introduction
|
||||
Let's briefly view the key concepts involved in the pipeline of PyTorch models transition with OpenCV API. The initial step in conversion of PyTorch models into cv::dnn::Net
|
||||
is model transferring into [ONNX](https://onnx.ai/about.html) format. ONNX aims at the interchangeability of the neural networks between various frameworks. There is a built-in function in PyTorch for ONNX conversion: [``torch.onnx.export``](https://pytorch.org/docs/stable/onnx.html#torch.onnx.export).
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Further the obtained ``.onnx`` model is passed into cv::dnn::readNetFromONNX or cv::dnn::readNet.
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## Requirements
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To be able to experiment with the below code you will need to install a set of libraries. We will use a virtual environment with python3.7+ for this:
|
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```console
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virtualenv -p /usr/bin/python3.7 <env_dir_path>
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source <env_dir_path>/bin/activate
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```
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||||
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||||
For OpenCV-Python building from source, follow the corresponding instructions from the @ref tutorial_py_table_of_contents_setup.
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Before you start the installation of the libraries, you can customize the [requirements.txt](https://github.com/opencv/opencv/tree/master/samples/dnn/dnn_model_runner/dnn_conversion/requirements.txt), excluding or including (for example, ``opencv-python``) some dependencies.
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The below line initiates requirements installation into the previously activated virtual environment:
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```console
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pip install -r requirements.txt
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```
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## Practice
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In this part we are going to cover the following points:
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1. create a classification model conversion pipeline
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2. provide the inference, process prediction results
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### Model Conversion Pipeline
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The code in this subchapter is located in the ``samples/dnn/dnn_model_runner`` module and can be executed with the line:
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```console
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python -m dnn_model_runner.dnn_conversion.pytorch.classification.py_to_py_resnet50_onnx
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```
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The following code contains the description of the below-listed steps:
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1. instantiate PyTorch model
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2. convert PyTorch model into ``.onnx``
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```python
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# initialize PyTorch ResNet-50 model
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original_model = models.resnet50(pretrained=True)
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# get the path to the converted into ONNX PyTorch model
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full_model_path = get_pytorch_onnx_model(original_model)
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print("PyTorch ResNet-50 model was successfully converted: ", full_model_path)
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```
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``get_pytorch_onnx_model(original_model)`` function is based on ``torch.onnx.export(...)`` call:
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```python
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# define the directory for further converted model save
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onnx_model_path = "models"
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# define the name of further converted model
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onnx_model_name = "resnet50.onnx"
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# create directory for further converted model
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os.makedirs(onnx_model_path, exist_ok=True)
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# get full path to the converted model
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full_model_path = os.path.join(onnx_model_path, onnx_model_name)
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# generate model input
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generated_input = Variable(
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torch.randn(1, 3, 224, 224)
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)
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# model export into ONNX format
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torch.onnx.export(
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original_model,
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generated_input,
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full_model_path,
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verbose=True,
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input_names=["input"],
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output_names=["output"],
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opset_version=11
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)
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```
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After the successful execution of the above code we will get the following output:
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```console
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PyTorch ResNet-50 model was successfully converted: models/resnet50.onnx
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```
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The proposed in ``dnn/samples`` module ``dnn_model_runner`` allows us to reproduce the above conversion steps for the following PyTorch classification models:
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* alexnet
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* vgg11
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* vgg13
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* vgg16
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* vgg19
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* resnet18
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* resnet34
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* resnet50
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* resnet101
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* resnet152
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* squeezenet1_0
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* squeezenet1_1
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* resnext50_32x4d
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* resnext101_32x8d
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* wide_resnet50_2
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* wide_resnet101_2
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To obtain the converted model, the following line should be executed:
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```
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python -m dnn_model_runner.dnn_conversion.pytorch.classification.py_to_py_cls --model_name <pytorch_cls_model_name> --evaluate False
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```
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For the ResNet-50 case the below line should be run:
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```
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python -m dnn_model_runner.dnn_conversion.pytorch.classification.py_to_py_cls --model_name resnet50 --evaluate False
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```
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The default root directory for the converted model storage is defined in module ``CommonConfig``:
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```python
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@dataclass
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class CommonConfig:
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output_data_root_dir: str = "dnn_model_runner/dnn_conversion"
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```
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Thus, the converted ResNet-50 will be saved in ``dnn_model_runner/dnn_conversion/models``.
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### Inference Pipeline
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Now we can use ```models/resnet50.onnx``` for the inference pipeline using OpenCV C/C++ API. The implemented pipeline can be found in [samples/dnn/classification.cpp](https://github.com/opencv/opencv/blob/master/samples/dnn/classification.cpp).
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After the build of samples (``BUILD_EXAMPLES`` flag value should be ``ON``), the appropriate ``example_dnn_classification`` executable file will be provided.
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To provide model inference we will use the below [squirrel photo](https://www.pexels.com/photo/brown-squirrel-eating-1564292) (under [CC0](https://www.pexels.com/terms-of-service/) license) corresponding to ImageNet class ID 335:
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```console
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fox squirrel, eastern fox squirrel, Sciurus niger
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```
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For the label decoding of the obtained prediction, we also need ``imagenet_classes.txt`` file, which contains the full list of the ImageNet classes.
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In this tutorial we will run the inference process for the converted PyTorch ResNet-50 model from the build (``samples/build``) directory:
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```
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./dnn/example_dnn_classification --model=../dnn/models/resnet50.onnx --input=../data/squirrel_cls.jpg --width=224 --height=224 --rgb=true --scale="0.003921569" --mean="123.675 116.28 103.53" --std="0.229 0.224 0.225" --crop=true --initial_width=256 --initial_height=256 --classes=../data/dnn/classification_classes_ILSVRC2012.txt
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```
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Let's explore ``classification.cpp`` key points step by step:
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1. read the model with cv::dnn::readNet, initialize the network:
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```cpp
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Net net = readNet(model, config, framework);
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```
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The ``model`` parameter value is taken from ``--model`` key. In our case, it is ``resnet50.onnx``.
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* preprocess input image:
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```cpp
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if (rszWidth != 0 && rszHeight != 0)
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{
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resize(frame, frame, Size(rszWidth, rszHeight));
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}
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// Create a 4D blob from a frame
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blobFromImage(frame, blob, scale, Size(inpWidth, inpHeight), mean, swapRB, crop);
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// Check std values.
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if (std.val[0] != 0.0 && std.val[1] != 0.0 && std.val[2] != 0.0)
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{
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// Divide blob by std.
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divide(blob, std, blob);
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}
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```
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In this step we use cv::dnn::blobFromImage function to prepare model input.
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We set ``Size(rszWidth, rszHeight)`` with ``--initial_width=256 --initial_height=256`` for the initial image resize as it's described in [PyTorch ResNet inference pipeline](https://pytorch.org/hub/pytorch_vision_resnet/).
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It should be noted that firstly in cv::dnn::blobFromImage mean value is subtracted and only then pixel values are multiplied by scale.
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Thus, we use ``--mean="123.675 116.28 103.53"``, which is equivalent to ``[0.485, 0.456, 0.406]`` multiplied by ``255.0`` to reproduce the original image preprocessing order for PyTorch classification models:
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```python
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img /= 255.0
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img -= [0.485, 0.456, 0.406]
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img /= [0.229, 0.224, 0.225]
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```
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* make forward pass:
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```cpp
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net.setInput(blob);
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Mat prob = net.forward();
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```
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* process the prediction:
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```cpp
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Point classIdPoint;
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double confidence;
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minMaxLoc(prob.reshape(1, 1), 0, &confidence, 0, &classIdPoint);
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int classId = classIdPoint.x;
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```
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Here we choose the most likely object class. The ``classId`` result for our case is 335 - fox squirrel, eastern fox squirrel, Sciurus niger:
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@@ -0,0 +1,362 @@
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# Conversion of PyTorch Classification Models and Launch with OpenCV Python {#pytorch_cls_tutorial_dnn_conversion}
|
||||
|
||||
@prev_tutorial{tutorial_dnn_OCR}
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||||
@next_tutorial{pytorch_cls_c_tutorial_dnn_conversion}
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||||
|
||||
| | |
|
||||
| -: | :- |
|
||||
| Original author | Anastasia Murzova |
|
||||
| Compatibility | OpenCV >= 4.5 |
|
||||
|
||||
## Goals
|
||||
In this tutorial you will learn how to:
|
||||
* convert PyTorch classification models into ONNX format
|
||||
* run converted PyTorch model with OpenCV Python API
|
||||
* obtain an evaluation of the PyTorch and OpenCV DNN models.
|
||||
|
||||
We will explore the above-listed points by the example of the ResNet-50 architecture.
|
||||
|
||||
## Introduction
|
||||
Let's briefly view the key concepts involved in the pipeline of PyTorch models transition with OpenCV API. The initial step in conversion of PyTorch models into cv.dnn.Net
|
||||
is model transferring into [ONNX](https://onnx.ai/about.html) format. ONNX aims at the interchangeability of the neural networks between various frameworks. There is a built-in function in PyTorch for ONNX conversion: [``torch.onnx.export``](https://pytorch.org/docs/stable/onnx.html#torch.onnx.export).
|
||||
Further the obtained ``.onnx`` model is passed into cv.dnn.readNetFromONNX.
|
||||
|
||||
## Requirements
|
||||
To be able to experiment with the below code you will need to install a set of libraries. We will use a virtual environment with python3.7+ for this:
|
||||
|
||||
```console
|
||||
virtualenv -p /usr/bin/python3.7 <env_dir_path>
|
||||
source <env_dir_path>/bin/activate
|
||||
```
|
||||
|
||||
For OpenCV-Python building from source, follow the corresponding instructions from the @ref tutorial_py_table_of_contents_setup.
|
||||
|
||||
Before you start the installation of the libraries, you can customize the [requirements.txt](https://github.com/opencv/opencv/tree/master/samples/dnn/dnn_model_runner/dnn_conversion/requirements.txt), excluding or including (for example, ``opencv-python``) some dependencies.
|
||||
The below line initiates requirements installation into the previously activated virtual environment:
|
||||
|
||||
```console
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
## Practice
|
||||
In this part we are going to cover the following points:
|
||||
1. create a classification model conversion pipeline and provide the inference
|
||||
2. evaluate and test classification models
|
||||
|
||||
If you'd like merely to run evaluation or test model pipelines, the "Model Conversion Pipeline" part can be skipped.
|
||||
|
||||
### Model Conversion Pipeline
|
||||
The code in this subchapter is located in the ``dnn_model_runner`` module and can be executed with the line:
|
||||
|
||||
```console
|
||||
python -m dnn_model_runner.dnn_conversion.pytorch.classification.py_to_py_resnet50
|
||||
```
|
||||
|
||||
The following code contains the description of the below-listed steps:
|
||||
1. instantiate PyTorch model
|
||||
2. convert PyTorch model into ``.onnx``
|
||||
3. read the transferred network with OpenCV API
|
||||
4. prepare input data
|
||||
5. provide inference
|
||||
|
||||
```python
|
||||
# initialize PyTorch ResNet-50 model
|
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original_model = models.resnet50(pretrained=True)
|
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|
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# get the path to the converted into ONNX PyTorch model
|
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full_model_path = get_pytorch_onnx_model(original_model)
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# read converted .onnx model with OpenCV API
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opencv_net = cv2.dnn.readNetFromONNX(full_model_path)
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print("OpenCV model was successfully read. Layer IDs: \n", opencv_net.getLayerNames())
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# get preprocessed image
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input_img = get_preprocessed_img("../data/squirrel_cls.jpg")
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# get ImageNet labels
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imagenet_labels = get_imagenet_labels("../data/dnn/classification_classes_ILSVRC2012.txt")
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# obtain OpenCV DNN predictions
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get_opencv_dnn_prediction(opencv_net, input_img, imagenet_labels)
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# obtain original PyTorch ResNet50 predictions
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get_pytorch_dnn_prediction(original_model, input_img, imagenet_labels)
|
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```
|
||||
|
||||
To provide model inference we will use the below [squirrel photo](https://www.pexels.com/photo/brown-squirrel-eating-1564292) (under [CC0](https://www.pexels.com/terms-of-service/) license) corresponding to ImageNet class ID 335:
|
||||
```console
|
||||
fox squirrel, eastern fox squirrel, Sciurus niger
|
||||
```
|
||||
|
||||
|
||||
|
||||
For the label decoding of the obtained prediction, we also need ``imagenet_classes.txt`` file, which contains the full list of the ImageNet classes.
|
||||
|
||||
Let's go deeper into each step by the example of pretrained PyTorch ResNet-50:
|
||||
* instantiate PyTorch ResNet-50 model:
|
||||
|
||||
```python
|
||||
# initialize PyTorch ResNet-50 model
|
||||
original_model = models.resnet50(pretrained=True)
|
||||
```
|
||||
|
||||
* convert PyTorch model into ONNX:
|
||||
|
||||
```python
|
||||
# define the directory for further converted model save
|
||||
onnx_model_path = "models"
|
||||
# define the name of further converted model
|
||||
onnx_model_name = "resnet50.onnx"
|
||||
|
||||
# create directory for further converted model
|
||||
os.makedirs(onnx_model_path, exist_ok=True)
|
||||
|
||||
# get full path to the converted model
|
||||
full_model_path = os.path.join(onnx_model_path, onnx_model_name)
|
||||
|
||||
# generate model input
|
||||
generated_input = Variable(
|
||||
torch.randn(1, 3, 224, 224)
|
||||
)
|
||||
|
||||
# model export into ONNX format
|
||||
torch.onnx.export(
|
||||
original_model,
|
||||
generated_input,
|
||||
full_model_path,
|
||||
verbose=True,
|
||||
input_names=["input"],
|
||||
output_names=["output"],
|
||||
opset_version=11
|
||||
)
|
||||
```
|
||||
|
||||
After the successful execution of the above code, we will get ``models/resnet50.onnx``.
|
||||
|
||||
* read the transferred network with cv.dnn.readNetFromONNX passing the obtained in the previous step ONNX model into it:
|
||||
|
||||
```python
|
||||
# read converted .onnx model with OpenCV API
|
||||
opencv_net = cv2.dnn.readNetFromONNX(full_model_path)
|
||||
```
|
||||
|
||||
* prepare input data:
|
||||
|
||||
```python
|
||||
# read the image
|
||||
input_img = cv2.imread(img_path, cv2.IMREAD_COLOR)
|
||||
input_img = input_img.astype(np.float32)
|
||||
|
||||
input_img = cv2.resize(input_img, (256, 256))
|
||||
|
||||
# define preprocess parameters
|
||||
mean = np.array([0.485, 0.456, 0.406]) * 255.0
|
||||
scale = 1 / 255.0
|
||||
std = [0.229, 0.224, 0.225]
|
||||
|
||||
# prepare input blob to fit the model input:
|
||||
# 1. subtract mean
|
||||
# 2. scale to set pixel values from 0 to 1
|
||||
input_blob = cv2.dnn.blobFromImage(
|
||||
image=input_img,
|
||||
scalefactor=scale,
|
||||
size=(224, 224), # img target size
|
||||
mean=mean,
|
||||
swapRB=True, # BGR -> RGB
|
||||
crop=True # center crop
|
||||
)
|
||||
# 3. divide by std
|
||||
input_blob[0] /= np.asarray(std, dtype=np.float32).reshape(3, 1, 1)
|
||||
```
|
||||
|
||||
In this step we read the image and prepare model input with cv.dnn.blobFromImage function, which returns 4-dimensional blob.
|
||||
It should be noted that firstly in cv.dnn.blobFromImage mean value is subtracted and only then pixel values are multiplied by scale. Thus, ``mean`` is multiplied by ``255.0`` to reproduce the original image preprocessing order:
|
||||
|
||||
```python
|
||||
img /= 255.0
|
||||
img -= [0.485, 0.456, 0.406]
|
||||
img /= [0.229, 0.224, 0.225]
|
||||
```
|
||||
|
||||
* OpenCV cv.dnn.Net inference:
|
||||
|
||||
```python
|
||||
# set OpenCV DNN input
|
||||
opencv_net.setInput(preproc_img)
|
||||
|
||||
# OpenCV DNN inference
|
||||
out = opencv_net.forward()
|
||||
print("OpenCV DNN prediction: \n")
|
||||
print("* shape: ", out.shape)
|
||||
|
||||
# get the predicted class ID
|
||||
imagenet_class_id = np.argmax(out)
|
||||
|
||||
# get confidence
|
||||
confidence = out[0][imagenet_class_id]
|
||||
print("* class ID: {}, label: {}".format(imagenet_class_id, imagenet_labels[imagenet_class_id]))
|
||||
print("* confidence: {:.4f}".format(confidence))
|
||||
```
|
||||
|
||||
After the above code execution we will get the following output:
|
||||
|
||||
```console
|
||||
OpenCV DNN prediction:
|
||||
* shape: (1, 1000)
|
||||
* class ID: 335, label: fox squirrel, eastern fox squirrel, Sciurus niger
|
||||
* confidence: 14.8308
|
||||
```
|
||||
|
||||
* PyTorch ResNet-50 model inference:
|
||||
|
||||
```python
|
||||
original_net.eval()
|
||||
preproc_img = torch.FloatTensor(preproc_img)
|
||||
|
||||
# inference
|
||||
out = original_net(preproc_img)
|
||||
print("\nPyTorch model prediction: \n")
|
||||
print("* shape: ", out.shape)
|
||||
|
||||
# get the predicted class ID
|
||||
imagenet_class_id = torch.argmax(out, axis=1).item()
|
||||
print("* class ID: {}, label: {}".format(imagenet_class_id, imagenet_labels[imagenet_class_id]))
|
||||
|
||||
# get confidence
|
||||
confidence = out[0][imagenet_class_id]
|
||||
print("* confidence: {:.4f}".format(confidence.item()))
|
||||
```
|
||||
|
||||
After the above code launching we will get the following output:
|
||||
|
||||
```console
|
||||
PyTorch model prediction:
|
||||
* shape: torch.Size([1, 1000])
|
||||
* class ID: 335, label: fox squirrel, eastern fox squirrel, Sciurus niger
|
||||
* confidence: 14.8308
|
||||
```
|
||||
|
||||
The inference results of the original ResNet-50 model and cv.dnn.Net are equal. For the extended evaluation of the models we can use ``py_to_py_cls`` of the ``dnn_model_runner`` module. This module part will be described in the next subchapter.
|
||||
|
||||
### Evaluation of the Models
|
||||
|
||||
The proposed in ``samples/dnn`` ``dnn_model_runner`` module allows to run the full evaluation pipeline on the ImageNet dataset and test execution for the following PyTorch classification models:
|
||||
* alexnet
|
||||
* vgg11
|
||||
* vgg13
|
||||
* vgg16
|
||||
* vgg19
|
||||
* resnet18
|
||||
* resnet34
|
||||
* resnet50
|
||||
* resnet101
|
||||
* resnet152
|
||||
* squeezenet1_0
|
||||
* squeezenet1_1
|
||||
* resnext50_32x4d
|
||||
* resnext101_32x8d
|
||||
* wide_resnet50_2
|
||||
* wide_resnet101_2
|
||||
|
||||
This list can be also extended with further appropriate evaluation pipeline configuration.
|
||||
|
||||
#### Evaluation Mode
|
||||
|
||||
The below line represents running of the module in the evaluation mode:
|
||||
|
||||
```console
|
||||
python -m dnn_model_runner.dnn_conversion.pytorch.classification.py_to_py_cls --model_name <pytorch_cls_model_name>
|
||||
```
|
||||
|
||||
Chosen from the list classification model will be read into OpenCV cv.dnn.Net object. Evaluation results of PyTorch and OpenCV models (accuracy, inference time, L1) will be written into the log file. Inference time values will be also depicted in a chart to generalize the obtained model information.
|
||||
|
||||
Necessary evaluation configurations are defined in the [test_config.py](https://github.com/opencv/opencv/tree/master/samples/dnn/dnn_model_runner/dnn_conversion/common/test/configs/test_config.py) and can be modified in accordance with actual paths of data location:
|
||||
|
||||
```python
|
||||
@dataclass
|
||||
class TestClsConfig:
|
||||
batch_size: int = 50
|
||||
frame_size: int = 224
|
||||
img_root_dir: str = "./ILSVRC2012_img_val"
|
||||
# location of image-class matching
|
||||
img_cls_file: str = "./val.txt"
|
||||
bgr_to_rgb: bool = True
|
||||
```
|
||||
|
||||
To initiate the evaluation of the PyTorch ResNet-50, run the following line:
|
||||
|
||||
```console
|
||||
python -m dnn_model_runner.dnn_conversion.pytorch.classification.py_to_py_cls --model_name resnet50
|
||||
```
|
||||
|
||||
After script launch, the log file with evaluation data will be generated in ``dnn_model_runner/dnn_conversion/logs``:
|
||||
|
||||
```console
|
||||
The model PyTorch resnet50 was successfully obtained and converted to OpenCV DNN resnet50
|
||||
===== Running evaluation of the model with the following params:
|
||||
* val data location: ./ILSVRC2012_img_val
|
||||
* log file location: dnn_model_runner/dnn_conversion/logs/PyTorch_resnet50_log.txt
|
||||
```
|
||||
|
||||
#### Test Mode
|
||||
|
||||
The below line represents running of the module in the test mode, namely it provides the steps for the model inference:
|
||||
|
||||
```console
|
||||
python -m dnn_model_runner.dnn_conversion.pytorch.classification.py_to_py_cls --model_name <pytorch_cls_model_name> --test True --default_img_preprocess <True/False> --evaluate False
|
||||
```
|
||||
|
||||
Here ``default_img_preprocess`` key defines whether you'd like to parametrize the model test process with some particular values or use the default values, for example, ``scale``, ``mean`` or ``std``.
|
||||
|
||||
Test configuration is represented in [test_config.py](https://github.com/opencv/opencv/tree/master/samples/dnn/dnn_model_runner/dnn_conversion/common/test/configs/test_config.py) ``TestClsModuleConfig`` class:
|
||||
|
||||
```python
|
||||
@dataclass
|
||||
class TestClsModuleConfig:
|
||||
cls_test_data_dir: str = "../data"
|
||||
test_module_name: str = "classification"
|
||||
test_module_path: str = "classification.py"
|
||||
input_img: str = os.path.join(cls_test_data_dir, "squirrel_cls.jpg")
|
||||
model: str = ""
|
||||
|
||||
frame_height: str = str(TestClsConfig.frame_size)
|
||||
frame_width: str = str(TestClsConfig.frame_size)
|
||||
scale: str = "1.0"
|
||||
mean: List[str] = field(default_factory=lambda: ["0.0", "0.0", "0.0"])
|
||||
std: List[str] = field(default_factory=list)
|
||||
crop: str = "False"
|
||||
rgb: str = "True"
|
||||
rsz_height: str = ""
|
||||
rsz_width: str = ""
|
||||
classes: str = os.path.join(cls_test_data_dir, "dnn", "classification_classes_ILSVRC2012.txt")
|
||||
```
|
||||
|
||||
The default image preprocessing options are defined in [default_preprocess_config.py](https://github.com/opencv/opencv/tree/master/samples/dnn/dnn_model_runner/dnn_conversion/common/test/configs/default_preprocess_config.py). For instance:
|
||||
|
||||
```python
|
||||
BASE_IMG_SCALE_FACTOR = 1 / 255.0
|
||||
PYTORCH_RSZ_HEIGHT = 256
|
||||
PYTORCH_RSZ_WIDTH = 256
|
||||
|
||||
pytorch_resize_input_blob = {
|
||||
"mean": ["123.675", "116.28", "103.53"],
|
||||
"scale": str(BASE_IMG_SCALE_FACTOR),
|
||||
"std": ["0.229", "0.224", "0.225"],
|
||||
"crop": "True",
|
||||
"rgb": "True",
|
||||
"rsz_height": str(PYTORCH_RSZ_HEIGHT),
|
||||
"rsz_width": str(PYTORCH_RSZ_WIDTH)
|
||||
}
|
||||
```
|
||||
|
||||
The basis of the model testing is represented in [samples/dnn/classification.py](https://github.com/opencv/opencv/blob/master/samples/dnn/classification.py). ``classification.py`` can be executed autonomously with provided converted model in ``--input`` and populated parameters for cv.dnn.blobFromImage.
|
||||
|
||||
To reproduce from scratch the described in "Model Conversion Pipeline" OpenCV steps with ``dnn_model_runner`` execute the below line:
|
||||
|
||||
```console
|
||||
python -m dnn_model_runner.dnn_conversion.pytorch.classification.py_to_py_cls --model_name resnet50 --test True --default_img_preprocess True --evaluate False
|
||||
```
|
||||
|
||||
The network prediction is depicted in the top left corner of the output window:
|
||||
|
||||
|
||||
@@ -0,0 +1,360 @@
|
||||
# Conversion of TensorFlow Classification Models and Launch with OpenCV Python {#tf_cls_tutorial_dnn_conversion}
|
||||
|
||||
| | |
|
||||
| -: | :- |
|
||||
| Original author | Anastasia Murzova |
|
||||
| Compatibility | OpenCV >= 4.5 |
|
||||
|
||||
## Goals
|
||||
In this tutorial you will learn how to:
|
||||
* obtain frozen graphs of TensorFlow (TF) classification models
|
||||
* run converted TensorFlow model with OpenCV Python API
|
||||
* obtain an evaluation of the TensorFlow and OpenCV DNN models
|
||||
|
||||
We will explore the above-listed points by the example of MobileNet architecture.
|
||||
|
||||
## Introduction
|
||||
Let's briefly view the key concepts involved in the pipeline of TensorFlow models transition with OpenCV API. The initial step in conversion of TensorFlow models into cv.dnn.Net
|
||||
is obtaining the frozen TF model graph. Frozen graph defines the combination of the model graph structure with kept values of the required variables, for example, weights. Usually the frozen graph is saved in [protobuf](https://en.wikipedia.org/wiki/Protocol_Buffers) (```.pb```) files.
|
||||
After the model ``.pb`` file was generated it can be read with cv.dnn.readNetFromTensorflow function.
|
||||
|
||||
## Requirements
|
||||
To be able to experiment with the below code you will need to install a set of libraries. We will use a virtual environment with python3.7+ for this:
|
||||
|
||||
```console
|
||||
virtualenv -p /usr/bin/python3.7 <env_dir_path>
|
||||
source <env_dir_path>/bin/activate
|
||||
```
|
||||
|
||||
For OpenCV-Python building from source, follow the corresponding instructions from the @ref tutorial_py_table_of_contents_setup.
|
||||
|
||||
Before you start the installation of the libraries, you can customize the [requirements.txt](https://github.com/opencv/opencv/tree/master/samples/dnn/dnn_model_runner/dnn_conversion/requirements.txt), excluding or including (for example, ``opencv-python``) some dependencies.
|
||||
The below line initiates requirements installation into the previously activated virtual environment:
|
||||
|
||||
```console
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
## Practice
|
||||
In this part we are going to cover the following points:
|
||||
1. create a TF classification model conversion pipeline and provide the inference
|
||||
2. evaluate and test TF classification models
|
||||
|
||||
If you'd like merely to run evaluation or test model pipelines, the "Model Conversion Pipeline" tutorial part can be skipped.
|
||||
|
||||
### Model Conversion Pipeline
|
||||
The code in this subchapter is located in the ``dnn_model_runner`` module and can be executed with the line:
|
||||
|
||||
```console
|
||||
python -m dnn_model_runner.dnn_conversion.tf.classification.py_to_py_mobilenet
|
||||
```
|
||||
|
||||
The following code contains the description of the below-listed steps:
|
||||
1. instantiate TF model
|
||||
2. create TF frozen graph
|
||||
3. read TF frozen graph with OpenCV API
|
||||
4. prepare input data
|
||||
5. provide inference
|
||||
|
||||
```python
|
||||
# initialize TF MobileNet model
|
||||
original_tf_model = MobileNet(
|
||||
include_top=True,
|
||||
weights="imagenet"
|
||||
)
|
||||
|
||||
# get TF frozen graph path
|
||||
full_pb_path = get_tf_model_proto(original_tf_model)
|
||||
|
||||
# read frozen graph with OpenCV API
|
||||
opencv_net = cv2.dnn.readNetFromTensorflow(full_pb_path)
|
||||
print("OpenCV model was successfully read. Model layers: \n", opencv_net.getLayerNames())
|
||||
|
||||
# get preprocessed image
|
||||
input_img = get_preprocessed_img("../data/squirrel_cls.jpg")
|
||||
|
||||
# get ImageNet labels
|
||||
imagenet_labels = get_imagenet_labels("../data/dnn/classification_classes_ILSVRC2012.txt")
|
||||
|
||||
# obtain OpenCV DNN predictions
|
||||
get_opencv_dnn_prediction(opencv_net, input_img, imagenet_labels)
|
||||
|
||||
# obtain TF model predictions
|
||||
get_tf_dnn_prediction(original_tf_model, input_img, imagenet_labels)
|
||||
```
|
||||
|
||||
To provide model inference we will use the below [squirrel photo](https://www.pexels.com/photo/brown-squirrel-eating-1564292) (under [CC0](https://www.pexels.com/terms-of-service/) license) corresponding to ImageNet class ID 335:
|
||||
```console
|
||||
fox squirrel, eastern fox squirrel, Sciurus niger
|
||||
```
|
||||
|
||||
|
||||
|
||||
For the label decoding of the obtained prediction, we also need ``imagenet_classes.txt`` file, which contains the full list of the ImageNet classes.
|
||||
|
||||
Let's go deeper into each step by the example of pretrained TF MobileNet:
|
||||
* instantiate TF model:
|
||||
|
||||
```python
|
||||
# initialize TF MobileNet model
|
||||
original_tf_model = MobileNet(
|
||||
include_top=True,
|
||||
weights="imagenet"
|
||||
)
|
||||
```
|
||||
|
||||
* create TF frozen graph
|
||||
|
||||
```python
|
||||
# define the directory for .pb model
|
||||
pb_model_path = "models"
|
||||
|
||||
# define the name of .pb model
|
||||
pb_model_name = "mobilenet.pb"
|
||||
|
||||
# create directory for further converted model
|
||||
os.makedirs(pb_model_path, exist_ok=True)
|
||||
|
||||
# get model TF graph
|
||||
tf_model_graph = tf.function(lambda x: tf_model(x))
|
||||
|
||||
# get concrete function
|
||||
tf_model_graph = tf_model_graph.get_concrete_function(
|
||||
tf.TensorSpec(tf_model.inputs[0].shape, tf_model.inputs[0].dtype))
|
||||
|
||||
# obtain frozen concrete function
|
||||
frozen_tf_func = convert_variables_to_constants_v2(tf_model_graph)
|
||||
# get frozen graph
|
||||
frozen_tf_func.graph.as_graph_def()
|
||||
|
||||
# save full tf model
|
||||
tf.io.write_graph(graph_or_graph_def=frozen_tf_func.graph,
|
||||
logdir=pb_model_path,
|
||||
name=pb_model_name,
|
||||
as_text=False)
|
||||
```
|
||||
|
||||
After the successful execution of the above code, we will get a frozen graph in ``models/mobilenet.pb``.
|
||||
|
||||
* read TF frozen graph with with cv.dnn.readNetFromTensorflow passing the obtained in the previous step ``mobilenet.pb`` into it:
|
||||
|
||||
```python
|
||||
# get TF frozen graph path
|
||||
full_pb_path = get_tf_model_proto(original_tf_model)
|
||||
```
|
||||
|
||||
* prepare input data with cv2.dnn.blobFromImage function:
|
||||
|
||||
```python
|
||||
# read the image
|
||||
input_img = cv2.imread(img_path, cv2.IMREAD_COLOR)
|
||||
input_img = input_img.astype(np.float32)
|
||||
|
||||
# define preprocess parameters
|
||||
mean = np.array([1.0, 1.0, 1.0]) * 127.5
|
||||
scale = 1 / 127.5
|
||||
|
||||
# prepare input blob to fit the model input:
|
||||
# 1. subtract mean
|
||||
# 2. scale to set pixel values from 0 to 1
|
||||
input_blob = cv2.dnn.blobFromImage(
|
||||
image=input_img,
|
||||
scalefactor=scale,
|
||||
size=(224, 224), # img target size
|
||||
mean=mean,
|
||||
swapRB=True, # BGR -> RGB
|
||||
crop=True # center crop
|
||||
)
|
||||
print("Input blob shape: {}\n".format(input_blob.shape))
|
||||
```
|
||||
|
||||
Please, pay attention at the preprocessing order in the cv2.dnn.blobFromImage function. Firstly, the mean value is subtracted and only then pixel values are multiplied by the defined scale.
|
||||
Therefore, to reproduce the image preprocessing pipeline from the TF [``mobilenet.preprocess_input``](https://github.com/tensorflow/tensorflow/blob/02032fb477e9417197132648ec81e75beee9063a/tensorflow/python/keras/applications/mobilenet.py#L443-L445) function, we multiply ``mean`` by ``127.5``.
|
||||
|
||||
As a result, 4-dimensional ``input_blob`` was obtained:
|
||||
|
||||
``Input blob shape: (1, 3, 224, 224)``
|
||||
|
||||
* provide OpenCV cv.dnn.Net inference:
|
||||
|
||||
```python
|
||||
# set OpenCV DNN input
|
||||
opencv_net.setInput(preproc_img)
|
||||
|
||||
# OpenCV DNN inference
|
||||
out = opencv_net.forward()
|
||||
print("OpenCV DNN prediction: \n")
|
||||
print("* shape: ", out.shape)
|
||||
|
||||
# get the predicted class ID
|
||||
imagenet_class_id = np.argmax(out)
|
||||
|
||||
# get confidence
|
||||
confidence = out[0][imagenet_class_id]
|
||||
print("* class ID: {}, label: {}".format(imagenet_class_id, imagenet_labels[imagenet_class_id]))
|
||||
print("* confidence: {:.4f}\n".format(confidence))
|
||||
```
|
||||
|
||||
After the above code execution we will get the following output:
|
||||
|
||||
```console
|
||||
OpenCV DNN prediction:
|
||||
* shape: (1, 1000)
|
||||
* class ID: 335, label: fox squirrel, eastern fox squirrel, Sciurus niger
|
||||
* confidence: 0.9525
|
||||
```
|
||||
|
||||
* provide TF MobileNet inference:
|
||||
|
||||
```python
|
||||
# inference
|
||||
preproc_img = preproc_img.transpose(0, 2, 3, 1)
|
||||
print("TF input blob shape: {}\n".format(preproc_img.shape))
|
||||
|
||||
out = original_net(preproc_img)
|
||||
|
||||
print("\nTensorFlow model prediction: \n")
|
||||
print("* shape: ", out.shape)
|
||||
|
||||
# get the predicted class ID
|
||||
imagenet_class_id = np.argmax(out)
|
||||
print("* class ID: {}, label: {}".format(imagenet_class_id, imagenet_labels[imagenet_class_id]))
|
||||
|
||||
# get confidence
|
||||
confidence = out[0][imagenet_class_id]
|
||||
print("* confidence: {:.4f}".format(confidence))
|
||||
```
|
||||
|
||||
To fit TF model input, ``input_blob`` was transposed:
|
||||
|
||||
```console
|
||||
TF input blob shape: (1, 224, 224, 3)
|
||||
```
|
||||
|
||||
TF inference results are the following:
|
||||
|
||||
```console
|
||||
TensorFlow model prediction:
|
||||
* shape: (1, 1000)
|
||||
* class ID: 335, label: fox squirrel, eastern fox squirrel, Sciurus niger
|
||||
* confidence: 0.9525
|
||||
```
|
||||
|
||||
As it can be seen from the experiments OpenCV and TF inference results are equal.
|
||||
|
||||
### Evaluation of the Models
|
||||
|
||||
The proposed in ``dnn/samples`` ``dnn_model_runner`` module allows to run the full evaluation pipeline on the ImageNet dataset and test execution for the following TensorFlow classification models:
|
||||
* vgg16
|
||||
* vgg19
|
||||
* resnet50
|
||||
* resnet101
|
||||
* resnet152
|
||||
* densenet121
|
||||
* densenet169
|
||||
* densenet201
|
||||
* inceptionresnetv2
|
||||
* inceptionv3
|
||||
* mobilenet
|
||||
* mobilenetv2
|
||||
* nasnetlarge
|
||||
* nasnetmobile
|
||||
* xception
|
||||
|
||||
This list can be also extended with further appropriate evaluation pipeline configuration.
|
||||
|
||||
#### Evaluation Mode
|
||||
|
||||
To below line represents running of the module in the evaluation mode:
|
||||
|
||||
```console
|
||||
python -m dnn_model_runner.dnn_conversion.tf.classification.py_to_py_cls --model_name <tf_cls_model_name>
|
||||
```
|
||||
|
||||
Chosen from the list classification model will be read into OpenCV ``cv.dnn_Net`` object. Evaluation results of TF and OpenCV models (accuracy, inference time, L1) will be written into the log file. Inference time values will be also depicted in a chart to generalize the obtained model information.
|
||||
|
||||
Necessary evaluation configurations are defined in the [test_config.py](https://github.com/opencv/opencv/tree/master/samples/dnn/dnn_model_runner/dnn_conversion/common/test/configs/test_config.py) and can be modified in accordance with actual paths of data location::
|
||||
|
||||
```python
|
||||
@dataclass
|
||||
class TestClsConfig:
|
||||
batch_size: int = 50
|
||||
frame_size: int = 224
|
||||
img_root_dir: str = "./ILSVRC2012_img_val"
|
||||
# location of image-class matching
|
||||
img_cls_file: str = "./val.txt"
|
||||
bgr_to_rgb: bool = True
|
||||
```
|
||||
|
||||
The values from ``TestClsConfig`` can be customized in accordance with chosen model.
|
||||
|
||||
To initiate the evaluation of the TensorFlow MobileNet, run the following line:
|
||||
|
||||
```console
|
||||
python -m dnn_model_runner.dnn_conversion.tf.classification.py_to_py_cls --model_name mobilenet
|
||||
```
|
||||
|
||||
After script launch, the log file with evaluation data will be generated in ``dnn_model_runner/dnn_conversion/logs``:
|
||||
|
||||
```console
|
||||
===== Running evaluation of the model with the following params:
|
||||
* val data location: ./ILSVRC2012_img_val
|
||||
* log file location: dnn_model_runner/dnn_conversion/logs/TF_mobilenet_log.txt
|
||||
```
|
||||
|
||||
#### Test Mode
|
||||
|
||||
The below line represents running of the module in the test mode, namely it provides the steps for the model inference:
|
||||
|
||||
```console
|
||||
python -m dnn_model_runner.dnn_conversion.tf.classification.py_to_py_cls --model_name <tf_cls_model_name> --test True --default_img_preprocess <True/False> --evaluate False
|
||||
```
|
||||
|
||||
Here ``default_img_preprocess`` key defines whether you'd like to parametrize the model test process with some particular values or use the default values, for example, ``scale``, ``mean`` or ``std``.
|
||||
|
||||
Test configuration is represented in [test_config.py](https://github.com/opencv/opencv/tree/master/samples/dnn/dnn_model_runner/dnn_conversion/common/test/configs/test_config.py) ``TestClsModuleConfig`` class:
|
||||
|
||||
```python
|
||||
@dataclass
|
||||
class TestClsModuleConfig:
|
||||
cls_test_data_dir: str = "../data"
|
||||
test_module_name: str = "classification"
|
||||
test_module_path: str = "classification.py"
|
||||
input_img: str = os.path.join(cls_test_data_dir, "squirrel_cls.jpg")
|
||||
model: str = ""
|
||||
|
||||
frame_height: str = str(TestClsConfig.frame_size)
|
||||
frame_width: str = str(TestClsConfig.frame_size)
|
||||
scale: str = "1.0"
|
||||
mean: List[str] = field(default_factory=lambda: ["0.0", "0.0", "0.0"])
|
||||
std: List[str] = field(default_factory=list)
|
||||
crop: str = "False"
|
||||
rgb: str = "True"
|
||||
rsz_height: str = ""
|
||||
rsz_width: str = ""
|
||||
classes: str = os.path.join(cls_test_data_dir, "dnn", "classification_classes_ILSVRC2012.txt")
|
||||
```
|
||||
|
||||
The default image preprocessing options are defined in ``default_preprocess_config.py``. For instance, for MobileNet:
|
||||
|
||||
```python
|
||||
tf_input_blob = {
|
||||
"mean": ["127.5", "127.5", "127.5"],
|
||||
"scale": str(1 / 127.5),
|
||||
"std": [],
|
||||
"crop": "True",
|
||||
"rgb": "True"
|
||||
}
|
||||
```
|
||||
|
||||
The basis of the model testing is represented in [samples/dnn/classification.py](https://github.com/opencv/opencv/blob/master/samples/dnn/classification.py). ``classification.py`` can be executed autonomously with provided converted model in ``--input`` and populated parameters for cv.dnn.blobFromImage.
|
||||
|
||||
To reproduce from scratch the described in "Model Conversion Pipeline" OpenCV steps with ``dnn_model_runner`` execute the below line:
|
||||
|
||||
```console
|
||||
python -m dnn_model_runner.dnn_conversion.tf.classification.py_to_py_cls --model_name mobilenet --test True --default_img_preprocess True --evaluate False
|
||||
```
|
||||
|
||||
The network prediction is depicted in the top left corner of the output window:
|
||||
|
||||
|
||||
Reference in New Issue
Block a user