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- import torch
- import torch.onnx
- from basicsr.archs.rrdbnet_arch import RRDBNet
- # An instance of your model
- model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32)
- model.load_state_dict(torch.load('experiments/pretrained_models/RealESRGAN_x4plus.pth')['params_ema'])
- # set the train mode to false since we will only run the forward pass.
- model.train(False)
- model.cpu().eval()
- # An example input you would normally provide to your model's forward() method
- x = torch.rand(1, 3, 64, 64)
- # Export the model
- with torch.no_grad():
- torch_out = torch.onnx._export(model, x, 'realesrgan-x4.onnx', opset_version=11, export_params=True)
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