Running demo

[mnbrshd]

I get the following shape mismatch error whenever I try to run the demo:

Disable distributed. load net keys <built-in method keys of collections.OrderedDict object at 0x7f04c6da2540> Traceback (most recent call last): File "basicsr/demo.py", line 46, in main() File "basicsr/demo.py", line 40, in main model = create_model(opt) File "/media/spoodermun/YoData/FISH/noise_mitigation/HINet/basicsr/models/init.py", line 44, in create_model model = model_cls(opt) File "/media/spoodermun/YoData/FISH/noise_mitigation/HINet/basicsr/models/image_restoration_model.py", line 36, in init self.load_network(self.net_g, load_path, File "/media/spoodermun/YoData/FISH/noise_mitigation/HINet/basicsr/models/base_model.py", line 287, in load_network net.load_state_dict(load_net, strict=strict) File "/media/spoodermun/YoData/morph/morphology_detection/YOLO/pytorch-env/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1223, in load_state_dict raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format( RuntimeError: Error(s) in loading state_dict for HINet: size mismatch for down_path_1.0.identity.weight: copying a param with shape torch.Size([64, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 1, 1]). size mismatch for down_path_1.0.identity.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([32]). size mismatch for down_path_1.0.conv_1.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).

The demo config is as follows:

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Copyright (c) 2021 megvii-model. All Rights Reserved.

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Modified from BasicSR (https://github.com/xinntao/BasicSR)

Copyright 2018-2020 BasicSR Authors

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general settings

name: demo model_type: ImageRestorationModel scale: 1 num_gpu: 1 # set num_gpu: 0 for cpu mode manual_seed: 10

single image inference and save image

img_path: input_img: ./noise_mitigation/test_images/enhanced/1p36_0_0.png output_img: ./demo/demo1.png

network structures

network_g: type: HINet wf: 32 hin_position_left: 0 hin_position_right: 4

path

path: pretrain_network_g: ./noise_mitigation/HINet/HINet-SIDD-1x.pth strict_load_g: true resume_state: ~

validation settings

val: grids: true crop_size: 256

dist training settings

dist_params: backend: nccl port: 29500

[mayorx]

Hi mnbrshd,

Thanks for your attention to HINet !

I think the problem might because of the inconsistency between network structure and the pre-trained model, and if you want to try pre-trained HINet-SIDD-1x, please refer to here for the network structure : )