| |
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 (.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:
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 Introduction to OpenCV.
Before you start the installation of the libraries, you can customize the requirements.txt, excluding or including (for example, opencv-python
) some dependencies. The below line initiates requirements installation into the previously activated virtual environment:
pip install -r requirements.txt
Practice
In this part we are going to cover the following points:
- create a TF classification model conversion pipeline and provide the inference
- 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:
python -m dnn_model_runner.dnn_conversion.tf.classification.py_to_py_mobilenet
The following code contains the description of the below-listed steps:
- instantiate TF model
- create TF frozen graph
- read TF frozen graph with OpenCV API
- prepare input data
- provide inference
original_tf_model = MobileNet(
include_top=True,
weights="imagenet"
)
full_pb_path = get_tf_model_proto(original_tf_model)
opencv_net = cv2.dnn.readNetFromTensorflow(full_pb_path)
print("OpenCV model was successfully read. Model layers: \n", opencv_net.getLayerNames())
input_img = get_preprocessed_img("../data/squirrel_cls.jpg")
imagenet_labels = get_imagenet_labels("../data/dnn/classification_classes_ILSVRC2012.txt")
get_opencv_dnn_prediction(opencv_net, input_img, imagenet_labels)
get_tf_dnn_prediction(original_tf_model, input_img, imagenet_labels)
To provide model inference we will use the below squirrel photo (under CC0 license) corresponding to ImageNet class ID 335:
fox squirrel, eastern fox squirrel, Sciurus niger
Classification model input image
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:
original_tf_model = MobileNet(
include_top=True,
weights="imagenet"
)
pb_model_path = "models"
pb_model_name = "mobilenet.pb"
os.makedirs(pb_model_path, exist_ok=True)
tf_model_graph = tf.function(lambda x: tf_model(x))
tf_model_graph = tf_model_graph.get_concrete_function(
tf.TensorSpec(tf_model.inputs[0].shape, tf_model.inputs[0].dtype))
frozen_tf_func = convert_variables_to_constants_v2(tf_model_graph)
frozen_tf_func.graph.as_graph_def()
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
.
full_pb_path = get_tf_model_proto(original_tf_model)
- prepare input data with cv2.dnn.blobFromImage function:
input_img = cv2.imread(img_path, cv2.IMREAD_COLOR)
input_img = input_img.astype(np.float32)
mean = np.array([1.0, 1.0, 1.0]) * 127.5
scale = 1 / 127.5
input_blob = cv2.dnn.blobFromImage(
image=input_img,
scalefactor=scale,
size=(224, 224),
mean=mean,
swapRB=True,
crop=True
)
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
function, we multiply mean
by 127.5
.
As a result, 4-dimensional input_blob
was obtained:
Input blob shape: (1, 3, 224, 224)
opencv_net.setInput(preproc_img)
out = opencv_net.forward()
print("OpenCV DNN prediction: \n")
print("* shape: ", out.shape)
imagenet_class_id = np.argmax(out)
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:
OpenCV DNN prediction:
* shape: (1, 1000)
* class ID: 335, label: fox squirrel, eastern fox squirrel, Sciurus niger
* confidence: 0.9525
- provide TF MobileNet 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)
imagenet_class_id = np.argmax(out)
print("* class ID: {}, label: {}".format(imagenet_class_id, imagenet_labels[imagenet_class_id]))
confidence = out[0][imagenet_class_id]
print("* confidence: {:.4f}".format(confidence))
To fit TF model input, input_blob
was transposed:
TF input blob shape: (1, 224, 224, 3)
TF inference results are the following:
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:
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 and can be modified in accordance with actual paths of data location::
@dataclass
class TestClsConfig:
batch_size: int = 50
frame_size: int = 224
img_root_dir: str = "./ILSVRC2012_img_val"
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:
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
:
===== 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:
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 TestClsModuleConfig
class:
@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:
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. 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:
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:
TF MobileNet OpenCV inference output