src/transformers/models/swin2sr/convert_swin2sr_original_to_pytorch.py/convert_swin2sr_checkpoint
def convert_swin2sr_checkpoint(checkpoint_url, pytorch_dump_folder_path, push_to_hub):
    config = get_config(checkpoint_url)
    model = Swin2SRForImageSuperResolution(config)
    model.eval()

    state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
    new_state_dict = convert_state_dict(state_dict, config)
    missing_keys, unexpected_keys = model.load_state_dict(new_state_dict, strict=False)

    if len(missing_keys) > 0:
        raise ValueError("Missing keys when converting: {}".format(missing_keys))
    for key in unexpected_keys:
        if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key):
            raise ValueError(f"Unexpected key {key} in state_dict")

    # verify values
    url = "https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true"
    image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
    processor = Swin2SRImageProcessor()
    # pixel_values = processor(image, return_tensors="pt").pixel_values

    image_size = 126 if "Jpeg" in checkpoint_url else 256
    transforms = Compose(
        [
            Resize((image_size, image_size)),
            ToTensor(),
            Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
        ]
    )
    pixel_values = transforms(image).unsqueeze(0)

    if config.num_channels == 1:
        pixel_values = pixel_values[:, 0, :, :].unsqueeze(1)

    outputs = model(pixel_values)

    # assert values
    if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
        expected_shape = torch.Size([1, 3, 512, 512])
        expected_slice = torch.tensor(
            [[-0.7087, -0.7138, -0.6721], [-0.8340, -0.8095, -0.7298], [-0.9149, -0.8414, -0.7940]]
        )
    elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
        expected_shape = torch.Size([1, 3, 1024, 1024])
        expected_slice = torch.tensor(
            [[-0.7775, -0.8105, -0.8933], [-0.7764, -0.8356, -0.9225], [-0.7976, -0.8686, -0.9579]]
        )
    elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
        # TODO values didn't match exactly here
        expected_shape = torch.Size([1, 3, 1024, 1024])
        expected_slice = torch.tensor(
            [[-0.8035, -0.7504, -0.7491], [-0.8538, -0.8124, -0.7782], [-0.8804, -0.8651, -0.8493]]
        )
    elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
        expected_shape = torch.Size([1, 3, 512, 512])
        expected_slice = torch.tensor(
            [[-0.7669, -0.8662, -0.8767], [-0.8810, -0.9962, -0.9820], [-0.9340, -1.0322, -1.1149]]
        )
    elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
        expected_shape = torch.Size([1, 3, 1024, 1024])
        expected_slice = torch.tensor(
            [[-0.5238, -0.5557, -0.6321], [-0.6016, -0.5903, -0.6391], [-0.6244, -0.6334, -0.6889]]
        )

    assert (
        outputs.reconstruction.shape == expected_shape
    ), f"Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}"
    assert torch.allclose(outputs.reconstruction[0, 0, :3, :3], expected_slice, atol=1e-3)
    print("Looks ok!")

    url_to_name = {
        "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth": (
            "swin2SR-classical-sr-x2-64"
        ),
        "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth": (
            "swin2SR-classical-sr-x4-64"
        ),
        "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth": (
            "swin2SR-compressed-sr-x4-48"
        ),
        "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth": (
            "swin2SR-lightweight-x2-64"
        ),
        "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth": (
            "swin2SR-realworld-sr-x4-64-bsrgan-psnr"
        ),
    }
    model_name = url_to_name[checkpoint_url]

    if pytorch_dump_folder_path is not None:
        print(f"Saving model {model_name} to {pytorch_dump_folder_path}")
        model.save_pretrained(pytorch_dump_folder_path)
        print(f"Saving image processor to {pytorch_dump_folder_path}")
        processor.save_pretrained(pytorch_dump_folder_path)

    if push_to_hub:
        model.push_to_hub(f"caidas/{model_name}")
        processor.push_to_hub(f"caidas/{model_name}")