Source code for ffcv.transforms.random_resized_crop

Random resized crop, similar to torchvision.transforms.RandomResizedCrop
from dataclasses import replace
from .utils import fast_crop
import numpy as np
from typing import Callable, Optional, Tuple
from ..pipeline.allocation_query import AllocationQuery
from ..pipeline.operation import Operation
from ..pipeline.state import State
from ..pipeline.compiler import Compiler

[docs]class RandomResizedCrop(Operation): """Crop a random portion of image with random aspect ratio and resize it to a given size. Chances are you do not want to use this augmentation and instead want to include RRC as part of the decoder, by using the :cla:`~ffcv.fields.rgb_image.ResizedCropRGBImageDecoder` class. Parameters ---------- scale : Tuple[float, float] Lower and upper bounds for the ratio of random area of the crop. ratio : Tuple[float, float] Lower and upper bounds for random aspect ratio of the crop. size : int Side length of the output. """ def __init__(self, scale: Tuple[float, float], ratio: Tuple[float, float], size: int): super().__init__() self.scale = scale self.ratio = ratio self.size = size
[docs] def generate_code(self) -> Callable: scale, ratio = self.scale, self.ratio if isinstance(scale, tuple): scale = np.array(scale) if isinstance(ratio, tuple): ratio = np.array(ratio) my_range = Compiler.get_iterator() def random_resized_crop(images, dst): for idx in my_range(images.shape[0]): i, j, h, w = fast_crop.get_random_crop(images[idx].shape[0], images[idx].shape[1], scale, ratio) fast_crop.resize_crop(images[idx], i, i + h, j, j + w, dst[idx]) return dst random_resized_crop.is_parallel = True return random_resized_crop
[docs] def declare_state_and_memory(self, previous_state: State) -> Tuple[State, Optional[AllocationQuery]]: return replace(previous_state, jit_mode=True, shape=(self.size, self.size, 3)), \ AllocationQuery((self.size, self.size, 3), dtype=previous_state.dtype)