AttributeError: 'numpy.ndarray' object has no attribute 'unsqueeze'

Question

I'm running a training code using pyhtorch and numpy.

This is the plot_example function:

def plot_example(low_res_folder, gen):
    files=os.listdir(low_res_folder)
    
    gen.eval()
    for file in files:
        image=Image.open("test_images/" + file)
        with torch.no_grad():
            upscaled_img=gen(
                config1.both_transform(image=np.asarray(image))["image"]
                .unsqueeze(0)
                .to(config1.DEVICE)
            )
        save_image(upscaled_img * 0.5 + 0.5, f"saved/{file}")
    gen.train()

The problem I have is that the unsqueeze attribute raises the error:

File "E:\Downloads\esrgan-tf2-masteren\modules\train1.py", line 58, in train_fn
    plot_example("test_images/", gen)
    
File "E:\Downloads\esrgan-tf2-masteren\modules\utils1.py", line 46, in plot_example
    config1.both_transform(image=np.asarray(image))["image"]
    
AttributeError: 'numpy.ndarray' object has no attribute 'unsqueeze'

The network is GAN network and gen() represents the Generator.

unsqueeze() is for torch.tensor object. It does not exist in Numpy. Here in your code you are trying to unsqueeze a numpy numpy.ndarray: image=np.asarray(image))["image"].unsqueeze(0) — Victor Douet
– Victor Douet, Commented Jul 9, 2021 at 15:58
Please use np.expand_dims(input, axis). As Victor points out unsqueeze() is for tensors. The np eqt is expand_dims — Allohvk
– Allohvk, Commented Sep 19, 2024 at 11:11

SarthakJain · Accepted Answer · 2025-07-22 03:02:52Z

Make sure image is a tensor in the shape of [batch size, channels, height, width] before entering any Pytorch layers.

Here you have image=np.asarray(image)

I would remove this numpy conversion and keep it a torch.tensor.

Or if you really want it to be a numpy array, then right before it enters your generator make sure to use torch.from_numpy() as shown in this documentation on your numpy image before it gets unsqueezed: https://pytorch.org/docs/stable/generated/torch.from_numpy.html

This function is ofcourse an alternative if you don't want to get rid of that original conversion.

To gain a clearer insight into torch numpy convertions. You should try looking at deep learning repos (especially the data classes). Websites such as repo-rift.com can be particularly useful for this purpose. They allow you to perform text searches with queries like "Show how an opencv image loaded in numpy is converted to pytorch tensor". This can help you pinpoint how other coders do it.

Sarthak Jain

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