src/augmentations.py

from typing import List, Optional, Tuple, Union

import numpy as np
import torch
from PIL import Image
from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from torchvision import transforms


class GroupResize:
    def __init__(self, size: int = 256) -> None:
        self.transform = transforms.Resize(size)

    def __call__(
        self, img_tuple: Tuple[torch.Tensor, torch.Tensor]
    ) -> Tuple[List[torch.Tensor], torch.Tensor]:
        img_group, label = img_tuple
        return [self.transform(img) for img in img_group], label


class GroupNormalize:
    def __init__(self, mean: List[float], std: List[float]) -> None:
        self.mean = mean
        self.std = std

    def __call__(
        self, tensor_tuple: Tuple[torch.Tensor, torch.Tensor]
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        tensor, label = tensor_tuple
        rep_mean = self.mean * (tensor.size()[0] // len(self.mean))
        rep_std = self.std * (tensor.size()[0] // len(self.std))

        for t, m, s in zip(tensor, rep_mean, rep_std):
            t.sub_(m).div_(s)

        return tensor, label


class GroupCenterCrop:
    def __init__(self, size: int) -> None:
        self.worker = transforms.CenterCrop(size)

    def __call__(
        self, img_tuple: Tuple[torch.Tensor, torch.Tensor]
    ) -> Tuple[List[torch.Tensor], torch.Tensor]:
        img_group, label = img_tuple
        return [self.worker(img) for img in img_group], label


class Stack:
    def __init__(self, roll: Optional[bool] = False) -> None:
        self.roll = roll

    def __call__(self, img_tuple: Tuple[torch.Tensor, torch.Tensor]):
        img_group, label = img_tuple

        if img_group[0].mode == "L":
            return (
                np.concatenate([np.expand_dims(x, 2) for x in img_group], axis=2),
                label,
            )
        elif img_group[0].mode == "RGB":
            if self.roll:
                return (
                    np.concatenate(
                        [np.array(x)[:, :, ::-1] for x in img_group], axis=2
                    ),
                    label,
                )
            else:
                return np.concatenate(img_group, axis=2), label


class ToTorchFormatTensor:
    def __init__(self, div: Optional[bool] = True) -> None:
        self.div = div

    def __call__(
        self, pic_tuple: Tuple[Union[np.ndarray, torch.Tensor], torch.Tensor]
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        pic, label = pic_tuple

        if isinstance(pic, np.ndarray):
            # handle numpy array
            img = torch.from_numpy(pic).permute(2, 0, 1).contiguous()
        elif isinstance(pic, Image.Image):
            # handle PIL Image
            img = torch.ByteTensor(torch.ByteStorage.from_buffer(pic.tobytes()))
            img = img.view(pic.size[1], pic.size[0], len(pic.mode))
            # put it from HWC to CHW format
            # yikes, this transpose takes 80% of the loading time/CPU
            img = img.transpose(0, 1).transpose(0, 2).contiguous()
        else:
            raise TypeError(
                f"Unsupported type {type(pic)} must be np.ndarray or torch.Tensor"
            )
        return img.float().div(255.0) if self.div else img.float(), label


class TubeMaskingGenerator:
    def __init__(self, input_size: Tuple[int, int, int], mask_ratio: float) -> None:
        self.frames, self.height, self.width = input_size
        self.num_patches_per_frame = self.height * self.width
        self.total_patches = self.frames * self.num_patches_per_frame
        self.num_masks_per_frame = int(mask_ratio * self.num_patches_per_frame)
        self.total_masks = self.frames * self.num_masks_per_frame

    def __call__(self):
        mask_per_frame = np.hstack(
            [
                np.zeros(self.num_patches_per_frame - self.num_masks_per_frame),
                np.ones(self.num_masks_per_frame),
            ]
        )
        np.random.shuffle(mask_per_frame)
        mask = np.tile(mask_per_frame, (self.frames, 1)).flatten()
        return mask


def get_videomae_transform(input_size: int = 224) -> "transforms.Compose":
    return transforms.Compose(
        [
            GroupResize(size=384),
            GroupCenterCrop(input_size),
            Stack(roll=False),
            ToTorchFormatTensor(div=True),
            GroupNormalize(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
        ]
    )