File size: 2,514 Bytes
b910c09 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 | 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)}"
# resize shorter size to self.size
img = self.resizer(img)
_, orig_h, orig_w = img.shape
# random crop
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
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