Prompt Title: SwinIR to TensorRT

SWINIR (Swin Transformer for Image Restoration) is an image restoration model based on the Swin Transformer architecture. TensorRT is an optimization tool provided by NVIDIA for deep learning models, which can be used to accelerate inference on NVIDIA GPUs. Here are the general steps for converting a SWINIR model to TensorRT:

1. Convert the SWINIR model to an ONNX format using a tool such as the ONNX exporter in PyTorch.

2. Install the TensorRT package and the corresponding version of CUDA on your system.

3. Use the TensorRT builder API to create a TensorRT engine from the ONNX model.

4. Optimize the TensorRT engine by setting the precision, batch size, and other parameters.

5. Serialize the optimized TensorRT engine to a file that can be loaded during inference.

Here's an example of how to convert a SWINIR model to TensorRT using the TensorRT Python API:

makefileCopy code
import tensorrt as trt
import onnx

# Load the SWINIR model as an ONNX file
model = onnx.load("swinir.onnx")

# Create a TensorRT builder object
builder = trt.Builder()

# Set the maximum batch size and the maximum workspace size
builder.max_batch_size = 1
builder.max_workspace_size = 1 << 30  # 1 GB

# Create a TensorRT network from the ONNX model
network = builder.create_network_from_onnx_model(model)

# Set the input and output data types and dimensions
input_shape = (3, 256, 256)
input_dtype = trt.float32
output_shape = (3, 256, 256)
output_dtype = trt.float32
input_tensor = network.add_input(name="input", dtype=input_dtype, shape=input_shape)
output_tensor = network.add_output(name="output", dtype=output_dtype, shape=output_shape)

# Set the precision mode to INT8 or FP16 if desired
builder.fp16_mode = True
builder.int8_mode = False

# Build the TensorRT engine
engine = builder.build_cuda_engine(network)

# Serialize the engine to a file
with open("swinir.trt", "wb") as f:
    f.write(engine.serialize())

Note that the exact steps may vary depending on the specific version of SWINIR and TensorRT that you are using, and you may need to adjust the parameters to optimize the performance of the model on your hardware.