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--- |
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title: vessl.Image |
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version: EN |
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--- |
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Use the `vessl.Image` class to log image data. This takes the image data and saves it as a local PNG file in the `vessl-media/image` directory with randomly generated names. |
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| Parameter | Description | |
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| --------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | |
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| `data` | <p>Supported types - <code>PIL Image</code>: the <code>Image</code> module of Pillow</p><p> - <code>torch.Tensor</code>: a PyTorch tensor </p><p> - <code>numpy.ndarray</code>: a NumPy array </p><p> - <code>str</code>: the image path</p> | |
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| `caption` | Label of the given image | |
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### `PIL Image` |
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```python |
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import vessl |
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from PIL import Image |
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my_PIL_image = Image.open('my-image.png') |
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vessl.Image( |
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data=my_PIL_image, |
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caption='my-caption', |
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) |
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``` |
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### `torch.Tensor` |
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```python |
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import vessl |
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import torch |
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vessl.Image() |
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test_loader = torch.utils.data.DataLoader( |
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test_dataset, batch_size=10, shuffle=True) |
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for data, target in test_loader: |
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vessl.Image( |
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data=data[0], |
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caption=f'Target:{target[0]}', |
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) |
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``` |
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### `numpy.ndarray` |
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```python |
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import vessl |
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import numpy as np |
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my_np_image = np.array([[0,1,1,0],[1,0,0,1],[0,1,1,0]]) |
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vessl.Image( |
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data= my_np_image, |
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caption='my-caption', |
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) |
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``` |
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### `str` |
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```python |
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import vessl |
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my_image_path = 'my-image.png' |
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vessl.Image( |
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data=my_image_path, |
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caption='my-caption', |
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) |
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``` |
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