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import argparse
import torch
import torch.onnx
from basicsr.archs.rrdbnet_arch import RRDBNet
def main(args):
# An instance of the model
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
if args.params:
keyname = 'params'
else:
keyname = 'params_ema'
model.load_state_dict(torch.load(args.input)[keyname])
# set the train mode to false since we will only run the forward pass.
model.train(False)
model.cpu().eval()
# An example input
x = torch.rand(1, 3, 64, 64)
# Export the model
with torch.no_grad():
torch_out = torch.onnx._export(model, x, args.output, opset_version=11, export_params=True)
print(torch_out.shape)
if __name__ == '__main__':
"""Convert pytorch model to onnx models"""
parser = argparse.ArgumentParser()
parser.add_argument(
'--input', type=str, default='experiments/pretrained_models/RealESRGAN_x4plus.pth', help='Input model path')
parser.add_argument('--output', type=str, default='realesrgan-x4.onnx', help='Output onnx path')
parser.add_argument('--params', action='store_false', help='Use params instead of params_ema')
args = parser.parse_args()
main(args)
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