Helper functions to get data in a DataLoaders in the vision application and higher class ImageDataLoaders
The main classes defined in this module are ImageDataLoaders and SegmentationDataLoaders, so you probably want to jump to their definitions. They provide factory methods that are a great way to quickly get your data ready for training, see the vision tutorial for examples.
def get_grid( n:int, # Number of axes in the returned grid nrows:int=None, # Number of rows in the returned grid, defaulting to `int(math.sqrt(n))` ncols:int=None, # Number of columns in the returned grid, defaulting to `ceil(n/rows)` figsize:tuple=None, # Width, height in inches of the returned figure double:bool=False, # Whether to double the number of columns and `n` title:str=None, # If passed, title set to the figure return_fig:bool=False, # Whether to return the figure created by `subplots` flatten:bool=True, # Whether to flatten the matplot axes such that they can be iterated over with a single loop*, imsize:int=3, # Size (in inches) of images that will be displayed in the returned figure suptitle:str=None, # Title to be set to returned figure sharex:"bool | Literal['none', 'all', 'row', 'col']"=False, sharey:"bool | Literal['none', 'all', 'row', 'col']"=False, squeeze:bool=True, width_ratios:Sequence[float] |None=None, height_ratios:Sequence[float] |None=None, subplot_kw:dict[str, Any] |None=None, gridspec_kw:dict[str, Any] |None=None)->(<class'matplotlib.figure.Figure'>, <class'matplotlib.axes._axes.Axes'>): # Returns just `axs` by default, and (`fig`, `axs`) if `return_fig` is set to True
Return a grid of n axes, rows by cols
This is used by the type-dispatched versions of show_batch and show_results for the vision application. The default figsize is (cols*imsize, rows*imsize+0.6). imsize is passed down to subplots. suptitle, sharex, sharey, squeeze, subplot_kw and gridspec_kw are all passed down to plt.subplots. If return_fig is True, returns fig,axs, otherwise just axs.
def clip_remove_empty( bbox:fastai.vision.core.TensorBBox, # Coordinates of bounding boxes label:fastai.torch_core.TensorMultiCategory, # Labels of the bounding boxes):
Clip bounding boxes with image border and remove empty boxes along with corresponding labels
def bb_pad( samples:list, # List of 3-tuples like (image, bounding_boxes, labels) pad_idx:int=0, # Label that will be used to pad each list of labels):
Function that collects samples of labelled bboxes and adds padding with pad_idx.
def MaskBlock( codes:list=None, # Vocab labels for segmentation masks):
A TransformBlock for segmentation masks, potentially with codes
TransformBlock??
class TransformBlock():"A basic wrapper that links defaults transforms for the data block API"def__init__(self, type_tfms:list=None, # One or more `Transform`s item_tfms:list=None, # `ItemTransform`s, applied on an item batch_tfms:list=None, # `Transform`s or `RandTransform`s, applied by batch dl_type:TfmdDL=None, # Task specific `TfmdDL`, defaults to `TfmdDL` dls_kwargs:dict=None, # Additional arguments to be passed to `DataLoaders` ):self.type_tfms = L(type_tfms)self.item_tfms = ToTensor + L(item_tfms)self.batch_tfms = L(batch_tfms)self.dl_type,self.dls_kwargs = dl_type,({} if dls_kwargs isNoneelse dls_kwargs)
def ImageDataLoaders(*loaders, # `DataLoader` objects to wrap path:str| pathlib.Path='.', # Path to store export objects device:NoneType=None, # Device to put `DataLoaders`):
Basic wrapper around several DataLoaders with factory methods for computer vision problems
This class should not be used directly, one of the factory methods should be preferred instead. All those factory methods accept as arguments:
item_tfms: one or several transforms applied to the items before batching them
batch_tfms: one or several transforms applied to the batches once they are formed
bs: the batch size
val_bs: the batch size for the validation DataLoader (defaults to bs)
shuffle_train: if we shuffle the training DataLoader or not
device: the PyTorch device to use (defaults to default_device())
def from_folder( path, # Path to put in `DataLoaders` train:str='train', valid:str='valid', valid_pct:NoneType=None, seed:NoneType=None, vocab:NoneType=None, item_tfms:NoneType=None, batch_tfms:NoneType=None, img_cls:fastcore.meta.BypassNewMeta=PILImage, *, bs:int=64, # Size of batch val_bs:int=None, # Size of batch for validation `DataLoader` shuffle:bool=True, # Whether to shuffle data device:NoneType=None, # Device to put `DataLoaders`):
Create from imagenet style dataset in path with train and valid subfolders (or provide valid_pct)
If valid_pct is provided, a random split is performed (with an optional seed) by setting aside that percentage of the data for the validation set (instead of looking at the grandparents folder). If a vocab is passed, only the folders with names in vocab are kept.
def from_path_func( path, # Path to put in `DataLoaders` fnames, label_func, valid_pct:float=0.2, seed:NoneType=None, item_tfms:NoneType=None, batch_tfms:NoneType=None, img_cls:fastcore.meta.BypassNewMeta=PILImage, *, bs:int=64, # Size of batch val_bs:int=None, # Size of batch for validation `DataLoader` shuffle:bool=True, # Whether to shuffle data device:NoneType=None, # Device to put `DataLoaders`):
Create from list of fnames in paths with label_func
The validation set is a random subset of valid_pct, optionally created with seed for reproducibility.
Here is how to create the same DataLoaders on the MNIST dataset as the previous example with a label_func:
Here is another example on the pets dataset. Here filenames are all in an “images” folder and their names have the form class_name_123.jpg. One way to properly label them is thus to throw away everything after the last _:
def from_path_re( path, # Path to put in `DataLoaders` fnames, pat, *, valid_pct:float=0.2, seed:NoneType=None, item_tfms:NoneType=None, batch_tfms:NoneType=None, img_cls:fastcore.meta.BypassNewMeta=PILImage, bs:int=64, # Size of batch val_bs:int=None, # Size of batch for validation `DataLoader` shuffle:bool=True, # Whether to shuffle data device:NoneType=None, # Device to put `DataLoaders`):
Create from list of fnames in paths with re expression pat
The validation set is a random subset of valid_pct, optionally created with seed for reproducibility.
Here is how to create the same DataLoaders on the MNIST dataset as the previous example (you will need to change the initial two / by a on Windows):
pat =r'/([^/]*)/\d+.png$'dls = ImageDataLoaders.from_path_re(path, fnames, pat)
def from_name_func( path:str| pathlib.Path, # Set the default path to a directory that a `Learner` can use to save files like models fnames:list, # A list of `os.Pathlike`'s to individual image files label_func:Callable, # A function that receives a string (the file name) and outputs a label*, valid_pct:float=0.2, seed:NoneType=None, item_tfms:NoneType=None, batch_tfms:NoneType=None, img_cls:fastcore.meta.BypassNewMeta=PILImage, bs:int=64, # Size of batch val_bs:int=None, # Size of batch for validation `DataLoader` shuffle:bool=True, # Whether to shuffle data device:NoneType=None, # Device to put `DataLoaders`)->fastai.data.core.DataLoaders:
Create from the name attrs of fnames in paths with label_func
The validation set is a random subset of valid_pct, optionally created with seed for reproducibility. This method does the same as ImageDataLoaders.from_path_func except label_func is applied to the name of each filenames, and not the full path.
def from_name_re( path, # Path to put in `DataLoaders` fnames, pat, *, bs:int=64, # Size of batch val_bs:int=None, # Size of batch for validation `DataLoader` shuffle:bool=True, # Whether to shuffle data device:NoneType=None, # Device to put `DataLoaders`):
Create from the name attrs of fnames in paths with re expression pat
The validation set is a random subset of valid_pct, optionally created with seed for reproducibility. This method does the same as ImageDataLoaders.from_path_re except pat is applied to the name of each filenames, and not the full path.
def from_df( df, path:str='.', # Path to put in `DataLoaders` valid_pct:float=0.2, seed:NoneType=None, fn_col:int=0, folder:NoneType=None, suff:str='', label_col:int=1, label_delim:NoneType=None, y_block:NoneType=None, valid_col:NoneType=None, item_tfms:NoneType=None, batch_tfms:NoneType=None, img_cls:fastcore.meta.BypassNewMeta=PILImage, *, bs:int=64, # Size of batch val_bs:int=None, # Size of batch for validation `DataLoader` shuffle:bool=True, # Whether to shuffle data device:NoneType=None, # Device to put `DataLoaders`):
Create from df using fn_col and label_col
The validation set is a random subset of valid_pct, optionally created with seed for reproducibility. Alternatively, if your df contains a valid_col, give its name or its index to that argument (the column should have True for the elements going to the validation set).
You can add an additional folder to the filenames in df if they should not be concatenated directly to path. If they do not contain the proper extensions, you can add suff. If your label column contains multiple labels on each row, you can use label_delim to warn the library you have a multi-label problem.
y_block should be passed when the task automatically picked by the library is wrong, you should then give CategoryBlock, MultiCategoryBlock or RegressionBlock. For more advanced uses, you should use the data block API.
The tiny mnist example from before also contains a version in a dataframe:
def from_lists( path, # Path to put in `DataLoaders` fnames, labels, valid_pct:float=0.2, seed:int=None, y_block:NoneType=None, item_tfms:NoneType=None, batch_tfms:NoneType=None, img_cls:fastcore.meta.BypassNewMeta=PILImage, *, bs:int=64, # Size of batch val_bs:int=None, # Size of batch for validation `DataLoader` shuffle:bool=True, # Whether to shuffle data device:NoneType=None, # Device to put `DataLoaders`):
Create from list of fnames and labels in path
The validation set is a random subset of valid_pct, optionally created with seed for reproducibility. y_block can be passed to specify the type of the targets.
path = untar_data(URLs.PETS)fnames = get_image_files(path/"images")labels = ['_'.join(x.name.split('_')[:-1]) for x in fnames]dls = ImageDataLoaders.from_lists(path, fnames, labels)
def SegmentationDataLoaders(*loaders, # `DataLoader` objects to wrap path:str| pathlib.Path='.', # Path to store export objects device:NoneType=None, # Device to put `DataLoaders`):
Basic wrapper around several DataLoaders with factory methods for segmentation problems
def from_label_func( path, # Path to put in `DataLoaders` fnames, label_func, valid_pct:float=0.2, seed:NoneType=None, codes:NoneType=None, item_tfms:NoneType=None, batch_tfms:NoneType=None, img_cls:fastcore.meta.BypassNewMeta=PILImage, *, bs:int=64, # Size of batch val_bs:int=None, # Size of batch for validation `DataLoader` shuffle:bool=True, # Whether to shuffle data device:NoneType=None, # Device to put `DataLoaders`):
Create from list of fnames in paths with label_func.
The validation set is a random subset of valid_pct, optionally created with seed for reproducibility. codes contain the mapping index to label.