composer.algorithms.progressive_resizing.progressive_resizing#

Core Progressive Resizing classes and functions.

Functions

resize_batch

Resize inputs and optionally outputs by cropping or interpolating.

Classes

ProgressiveResizing

Resize inputs and optionally outputs by cropping or interpolating.
class composer.algorithms.progressive_resizing.progressive_resizing.ProgressiveResizing(mode='resize', initial_scale=0.5, finetune_fraction=0.2, delay_fraction=0.5, size_increment=4, resize_targets=False, input_key=0, target_key=1)[source]#

Bases: composer.core.algorithm.Algorithm

Resize inputs and optionally outputs by cropping or interpolating.

Apply Fastaiโ€™s progressive resizing data augmentation to speed up training.

Progressive resizing initially reduces input resolution to speed up early training. Throughout training, the downsampling factor is gradually increased, yielding larger inputs up to the original input size. A final finetuning period is then run to finetune the model using the full-sized inputs.

Example

from composer.algorithms import ProgressiveResizing
from composer.trainer import Trainer
progressive_resizing_algorithm = ProgressiveResizing(
                                    mode='resize',
                                    initial_scale=1.0,
                                    finetune_fraction=0.2,
                                    delay_fraction=0.2,
                                    size_increment=32,
                                    resize_targets=False
                                )
trainer = Trainer(
    model=model,
    train_dataloader=train_dataloader,
    eval_dataloader=eval_dataloader,
    max_duration="1ep",
    algorithms=[progressive_resizing_algorithm],
    optimizers=[optimizer]
)
Parameters

  • mode (str, optional) โ€“ Type of scaling to perform. Value must be one of 'crop' or 'resize'. 'crop' performs a random crop, whereas 'resize' performs a bilinear interpolation. Default: 'resize'.

  • initial_scale (float, optional) โ€“ Initial scale factor used to shrink the inputs. Must be a value in between 0 and 1. Default: 0.5.

  • finetune_fraction (float, optional) โ€“ Fraction of training to reserve for finetuning on the full-sized inputs. Must be a value in between 0 and 1. Default: 0.2.

  • delay_fraction (float, optional) โ€“ Fraction of training before resizing ramp begins. Must be a value in between 0 and 1. Default: 0.5.

  • size_increment (int, optional) โ€“ Align sizes to a multiple of this number. Default: 4.

  • resize_targets (bool, optional) โ€“ If True, resize targets also. Default: False.

  • input_key (str | int | Tuple[Callable, Callable] | Any, optional) โ€“ A key that indexes to the input from the batch. Can also be a pair of get and set functions, where the getter is assumed to be first in the pair. The default is 0, which corresponds to any sequence, where the first element is the input. Default: 0.

  • target_key (str | int | Tuple[Callable, Callable] | Any, optional) โ€“ A key that indexes to the target from the batch. Can also be a pair of get and set functions, where the getter is assumed to be first in the pair. The default is 1, which corresponds to any sequence, where the second element is the target. Default: 1.

composer.algorithms.progressive_resizing.progressive_resizing.resize_batch(input, target, scale_factor, mode='resize', resize_targets=False)[source]#

Resize inputs and optionally outputs by cropping or interpolating.

Parameters

  • input (Tensor) โ€“ input tensor of shape (N, C, H, W). Resizing will be done along dimensions H and W using the constant factor scale_factor.

  • target (Tensor) โ€“ output tensor of shape (N, H, W) or (N, C, H, W) that will also be resized if resize_targets is True,

  • scale_factor (float) โ€“ scaling coefficient for the height and width of the input/output tensor. 1.0 keeps the original size.

  • mode (str, optional) โ€“ type of scaling to perform. Value must be one of 'crop' or 'resize'. 'crop' performs a random crop, whereas 'resize' performs a nearest neighbor interpolation. Default: "resize".

  • resize_targets (bool, optional) โ€“ whether to resize the targets, y. Default: False.

Returns

  • X_sized โ€“ resized input tensor of shape (N, C, H * scale_factor, W * scale_factor).

  • y_sized โ€“ if resized_targets is True, resized output tensor of shape (N, H * scale_factor, W * scale_factor) or (N, C, H * scale_factor, W * scale_factor). Depending on the input y. Otherwise returns original y.

Example

from composer.algorithms.progressive_resizing import resize_batch
X_resized, y_resized = resize_batch(X_example,
                                    y_example,
                                    scale_factor=0.5,
                                    mode='resize',
                                    resize_targets=False)