Notes

To convert a TensorFlow 2 saved .h5 file to TensorRT, you can use the TensorFlow-ONNX-TensorRT (TF-TRT) integration. Here's a general outline of the process:

Convert .h5 to ONNX:
Use the tf2onnx tool to convert your TensorFlow 2 .h5 model to ONNX format. Install it using:
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pip install tf2onnx
Convert the model:
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python -m tf2onnx.convert --saved-model /path/to/saved_model --output /path/to/model.onnx
Convert ONNX to TensorRT:
Install the ONNX-TensorRT package:
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pip install onnx-tensorrt
Convert the ONNX model to TensorRT using the following code:
python
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import onnx
from onnx_tensorrt import backend as trt_backend

onnx_model = onnx.load('/path/to/model.onnx')
engine, _ = trt_backend.prepare(onnx_model, max_batch_size=1)
engine.save_to_file('/path/to/model.trt')
Now, you have successfully converted your TensorFlow 2 saved .h5 model to TensorRT format. Make sure to adjust paths and configurations according to your specific model and environment.


Certainly! After converting your model to TensorRT, you can use the NVIDIA TensorRT Python API for inference. Below is a basic example code for image inference:

python
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import pycuda.driver as cuda
import pycuda.autoinit
import numpy as np
import cv2
import onnx
import onnx_tensorrt.backend as backend

# Load TensorRT engine
engine_path = '/path/to/model.trt'
trt_logger = trt.Logger(trt.Logger.INFO)
trt_runtime = trt.Runtime(trt_logger)
with open(engine_path, 'rb') as f:
engine_bytes = f.read()
engine = trt_runtime.deserialize_cuda_engine(engine_bytes)

# Create TensorRT context
context = engine.create_execution_context()

# Prepare input and output bindings
host_input = cuda.pagelocked_empty(trt.volume(engine.get_binding_shape(0)), dtype=np.float32)
host_output = cuda.pagelocked_empty(trt.volume(engine.get_binding_shape(1)), dtype=np.float32)
cuda_mem_input = cuda.mem_alloc(host_input.nbytes)
cuda_mem_output = cuda.mem_alloc(host_output.nbytes)

# Load image
image_path = '/path/to/your/image.jpg'
image = cv2.imread(image_path)
# Preprocess the image according to your model's requirements
# ...

# Copy input data to device
cuda.memcpy_htod(cuda_mem_input, host_input)

# Run inference
context.execute(bindings=[int(cuda_mem_input), int(cuda_mem_output)])

# Copy output data to host
cuda.memcpy_dtoh(host_output, cuda_mem_output)

# Post-process the output according to your model's requirements
# ...

# Print or use the final result as needed
print("Inference result:", host_output)

# Release resources
cuda_mem_input.free()
cuda_mem_output.free()
Make sure to adjust the paths and preprocessing steps according to your specific model and use case. This example assumes that your model has one input and one output. If you have multiple inputs/outputs, you'll need to adjust the code accordingly.