Image Detection on EDGE

OpenVINO (Open Visual Inference and Neural network Optimization) and TensorRT are two popular frameworks for optimizing and deploying deep learning models on edge devices such as GPUs, FPGAs, and other accelerators.

OpenVINO is an open-source toolkit developed by Intel that helps developers optimize and deploy pre-trained models on edge devices. The toolkit includes a range of pre-trained models, model optimization tools, and runtime libraries to enable inference on a variety of edge devices. OpenVINO also includes support for multiple frameworks such as TensorFlow, PyTorch, and MXNet.

The optimization tools in OpenVINO enable developers to convert pre-trained models to an optimized format that is better suited for deployment on edge devices. This includes quantization, which reduces the precision of model weights and activations to improve computational efficiency, and model pruning, which removes unnecessary weights and connections to reduce model size and inference time.

TensorRT, on the other hand, is a high-performance deep learning inference engine developed by NVIDIA. TensorRT is designed to optimize and deploy deep learning models on NVIDIA GPUs. It includes a deep learning model optimizer, a runtime library for inference, and a set of tools for model conversion, calibration, and validation.

Like OpenVINO, TensorRT includes support for a range of deep learning frameworks such as TensorFlow, PyTorch, and ONNX. TensorRT also includes optimizations such as kernel fusion, which combines multiple kernel operations into a single operation to reduce memory bandwidth and improve inference performance, and dynamic tensor memory management, which enables efficient memory allocation and reuse during inference.

Both OpenVINO and TensorRT are popular choices for optimizing and deploying deep learning models on edge devices. The choice between them depends on the specific use case and the hardware platform being used.

PyTorch and TensorFlow are two of the most popular deep learning frameworks used by researchers and developers worldwide. Both frameworks have their own strengths and weaknesses, and the choice between them depends on the specific use case and the preference of the user.

PyTorch is a deep learning framework developed by Facebook’s AI Research team. PyTorch is known for its dynamic computational graph, which enables developers to easily define and modify complex models. The dynamic nature of PyTorch makes it a good choice for researchers who want to experiment with different model architectures and optimization techniques. PyTorch also has excellent support for GPU acceleration and offers a range of tools for model deployment and training on distributed systems.

TensorFlow, on the other hand, is a deep learning framework developed by Google. TensorFlow is known for its static computational graph, which makes it easier to optimize models and deploy them on a variety of hardware platforms. TensorFlow also has a large and active community of developers and users, which has contributed to the development of many powerful tools and libraries for deep learning. TensorFlow supports a wide range of use cases, from research to production, and has excellent support for model deployment on cloud and edge devices.

In general, PyTorch is often preferred for its ease of use, flexibility, and ability to rapidly prototype new ideas, while TensorFlow is often preferred for its scalability, performance, and ease of deployment. However, both frameworks are powerful tools for developing and deploying deep learning models and have their own unique advantages and disadvantages. Ultimately, the choice between PyTorch and TensorFlow depends on the specific use case and the preference of the user.

Deep neural network (DNN) inference optimizations are techniques used to improve the performance and efficiency of deep learning models during inference on CPUs, GPUs, and other accelerators. Some of the most popular DNN inference optimizations include:

1. Quantization: Quantization is a technique used to reduce the precision of the weights and activations in a deep learning model. By reducing the precision of the parameters, the model requires fewer bits to represent each value, which reduces the memory footprint and improves the computational efficiency.
2. Pruning: Pruning is a technique used to remove unnecessary weights and connections from a deep learning model. By removing these parameters, the model size is reduced, which can improve inference speed and reduce memory usage.
3. Kernel Fusion: Kernel fusion is a technique used to combine multiple kernel operations into a single operation. By fusing operations, the number of memory accesses and data transfers is reduced, which can improve computational efficiency.
4. Parallelism: Parallelism is a technique used to split the inference workload across multiple processing units, such as CPU cores, GPU threads, or multiple GPUs. By utilizing multiple processing units in parallel, the inference time can be reduced.
5. Data Format Optimization: Data format optimization involves choosing the optimal data format for the input and output tensors of the deep learning model. By using the optimal data format, the amount of data transferred between the CPU and GPU can be minimized, which can improve inference speed.
6. Compiler Optimizations: Compiler optimizations involve using specialized compilers and programming languages to generate optimized code for the target hardware platform. These compilers can apply a range of optimizations, such as loop unrolling, function inlining, and instruction scheduling, to improve the performance of the deep learning model.

Overall, DNN inference optimizations are critical for achieving high performance and efficiency in deep learning models, particularly when deploying models on edge devices and other resource-constrained platforms.

We can use TFLite EDGE converted model from Tensorflow Keras model.

To convert and use a TensorFlow Lite (TFLite) edge model, you can follow these general steps:

1. Train your model: First, train your deep learning model on your dataset using TensorFlow or another deep learning framework. Once you have a trained model, you can convert it to the TFLite format for deployment on edge devices.
2. Convert the model to TFLite format: To convert your model to the TFLite format, you can use the TensorFlow Lite Converter tool. This tool takes a TensorFlow model as input and produces a TFLite model that can be deployed on edge devices. The TFLite Converter supports a wide range of conversion options, including quantization, pruning, and other optimizations that can improve the performance and efficiency of the model.
3. Test the TFLite model: Once you have converted your model to the TFLite format, you can test it using the TensorFlow Lite Interpreter. The interpreter allows you to load and run the TFLite model on a variety of edge devices, including Android and iOS devices, microcontrollers, and embedded systems.
4. Deploy the TFLite model: Once you have tested the TFLite model and verified that it is working correctly, you can deploy it on your edge device. The process for deploying the model will depend on the specific device and platform you are using.

In general, using TFLite edge models involves optimizing the model for efficient execution on resource-constrained devices while minimizing the loss in performance. Some common techniques used for this include quantization, pruning, and other optimizations that can reduce the memory and computation requirements of the model. Once the model is optimized, it can be deployed on a wide range of edge devices, from mobile phones to microcontrollers, for a variety of use cases, such as object detection, image classification, and speech recognition.

Some example code

rom keras.models import Mode
from keras.layers import Dense, Flatten, BatchNormalization


# Add your custom layers on top of the base model
model = Sequential()
model.add(resnet)
model.add(Dense(1024, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))

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model.compile(..................)
model.fit(..........................)
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# Save the trained model
model.save('test_model.h5')
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import tensorflow as tf

# Convert the model
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
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# Save the model.
with open('test_model.tflite', 'wb') as f:
  f.write(tflite_model)
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# A generator that provides a representative dataset
def representative_data_gen():
  dataset_list = tf.data.Dataset.list_files(test_dir + '/*/*')
  for i in range(100):
    image = next(iter(dataset_list))
    # file_type = os.path.splitext(image)[1]
    # if file_type not in ['.jpeg', '.jpg', '.png', '.bmp']:
    #   continue
    try:
      image = tf.io.read_file(image)
      image = tf.io.decode_jpeg(image, channels=3)
      image = tf.image.resize(image, [IMAGE_WIDTH, IMAGE_HEIGHT])
      image = tf.cast(image / 255., tf.float32)
      image = tf.expand_dims(image, 0)
    except tf.errors.InvalidArgumentError as e:
      continue
    yield [image]

converter = tf.lite.TFLiteConverter.from_keras_model(model)
# This enables quantization
converter.optimizations = [tf.lite.Optimize.DEFAULT]
# This sets the representative dataset for quantization
converter.representative_dataset = representative_data_gen
# This ensures that if any ops can't be quantized, the converter throws an error
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
# For full integer quantization, though supported types defaults to int8 only, we explicitly declare it for clarity.
converter.target_spec.supported_types = [tf.int8]
# These set the input and output tensors to uint8 (added in r2.3)
converter.inference_input_type = tf.uint8
converter.inference_output_type = tf.uint8
tflite_model = converter.convert()

with open('test_model_edge.tflite', 'wb') as f:
  f.write(tflite_model)
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! curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -

! echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | sudo tee /etc/apt/sources.list.d/coral-edgetpu.list

! sudo apt-get update

! sudo apt-get install edgetpu-compiler 
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! edgetpu_compiler test_model_edge.tflite
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print (train_generator.class_indices)

labels = '\n'.join(sorted(train_generator.class_indices.keys()))

with open('test_labels.txt', 'w') as f:
  f.write(labels)
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