| import torch |
| import torchvision |
| import random |
| from jaxtyping import Float |
|
|
| from jutils import instantiate_from_config |
|
|
|
|
| class ResizeCropWithMetaInfo: |
| def __init__(self, size: int = 256, antialias: bool = True, img_key: str = "image", meta_key: str = "img_meta"): |
| self.size = int(size) |
| self.resizer = torchvision.transforms.Resize(size=self.size, antialias=antialias) |
| self.img_key = img_key |
| self.meta_key = meta_key |
|
|
| def resize_crop_image(self, img: Float[torch.Tensor, "c h w"]): |
| """ |
| Args: |
| img: (c, h, w) torch tensor in [-1, 1] |
| """ |
| assert img.ndim == 3, f"Expected (C,H,W), got {tuple(img.shape)}" |
|
|
| |
| img = self.resizer(img) |
| _, orig_h, orig_w = img.shape |
|
|
| |
| top, left = 0, 0 |
| if orig_h > self.size: |
| top = random.randint(0, orig_h - self.size) |
| if orig_w > self.size: |
| left = random.randint(0, orig_w - self.size) |
| img_cropped = img[:, top : top + self.size, left : left + self.size] |
|
|
| img_meta = dict(orig_h=orig_h, orig_w=orig_w, top=top, left=left) |
|
|
| return img_cropped, img_meta |
|
|
| def __call__(self, sample: dict): |
| img = sample[self.img_key] |
| img_cropped, img_meta = self.resize_crop_image(img) |
| sample[self.img_key] = img_cropped |
| sample[self.meta_key] = img_meta |
| return sample |
|
|
|
|
| class CaptionSampler: |
| def __init__(self, txt_sampling_cfg: dict, out_txt_key: str = "txt"): |
| self.out_txt_key = out_txt_key |
| self.text_sampling_cfg = txt_sampling_cfg |
| self.total_ratio = sum(self.text_sampling_cfg.values()) |
| self.txt_keys = list(self.text_sampling_cfg.keys()) |
| self.txt_probs = [self.text_sampling_cfg[k] / self.total_ratio for k in self.txt_keys] |
|
|
| def __call__(self, sample: dict): |
| txt_key = random.choices(self.txt_keys, weights=self.txt_probs, k=1)[0] |
| caption = sample[txt_key] |
| if isinstance(caption, bytes): |
| caption = caption.decode() |
| sample[self.out_txt_key] = caption |
| return sample |
|
|
|
|
| |
|
|
|
|
| class TransformComposer: |
| def __init__(self, transforms): |
| self.transforms = [instantiate_from_config(t) for t in transforms] |
|
|
| def __call__(self, sample): |
| for t in self.transforms: |
| sample = t(sample) |
| return sample |
|
|
