Converting a PyTorch RCAN Model

RCAN : Image Super-Resolution Using Very Deep Residual Channel Attention Networks

Downloading and Converting the Model to ONNX

To download the pretrained model or train the model yourself, refer to the instruction in the RCAN model repository. First, convert the model to ONNX format. Create and run the script with the following content in the root directory of the model repository:

from argparse import Namespace

import torch

from RCAN_TestCode.code.model.rcan import RCAN

config = Namespace(n_feats=64, n_resblocks=4, n_resgroups=2, reduction=16, scale=[2], data_train='DIV2K', res_scale=1,
                   n_colors=3, rgb_range=255)
net = RCAN(config)
dummy_input = torch.randn(1, 3, 360, 640)
torch.onnx.export(net, dummy_input, 'RCAN.onnx')

The script generates the ONNX model file RCAN.onnx. More information about model parameters (n_resblocks, n_resgroups, and others) and their different values can be found in the model repository. The model conversion was tested with the commit-SHA: 3339ebc59519c3bb2b5719b87dd36515ec7f3ba7.

Converting an ONNX RCAN Model to IR

mo --input_model RCAN.onnx