Folder Image Loader API¶
The following API can be used to load a folder of images to create a representative dataset for PTQ calibration
- class model_compression_toolkit.FolderImageLoader(folder, preprocessing, batch_size, file_types=FILETYPES)¶
Class for images loading, processing and retrieving.
Initialize a FolderImageLoader object.
- Parameters
folder (str) – Path of folder with images to load. The path have to be exist, and have to contain at
image. (least one) –
preprocessing (List[Callable]) – List of functions to use when processing the images before retrieving them.
batch_size (int) – Number of images to retrieve each sample.
file_types (List[str]) – Files types to scan in the folder. Default list is
FILETYPES
Examples
Instantiate a FolderImageLoader using a directory of images, that returns 10 images randomly each time it is sampled:
>>> image_data_loader = FolderImageLoader('path/to/images/directory', preprocessing=[], batch_size=10) >>> images = image_data_loader.sample()
To preprocess the images before retrieving them, a list of preprocessing methods can be passed:
>>> image_data_loader = FolderImageLoader('path/to/images/directory', preprocessing=[lambda x: (x-127.5)/127.5], batch_size=10)
For the FolderImageLoader to scan only specific files extensions, a list of extensions can be passed:
>>> image_data_loader = FolderImageLoader('path/to/images/directory', preprocessing=[], batch_size=10, file_types=['png'])
- sample()¶
Returns: A sample of batch_size images from the folder the FolderImageLoader scanned.
Default file types to scan¶
- model_compression_toolkit.common.data_loader.FILETYPES = ['jpeg', 'jpg', 'bmp', 'png']¶