Spaces:
Runtime error
Runtime error
File size: 3,712 Bytes
bb5cd12 |
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 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 |
from torchvision import transforms as T
import torch.nn.functional as F
from PIL import ImageOps
import PIL
import random
def pad_to_size(x, size=256):
delta_w = size - x.size[0]
delta_h = size - x.size[1]
padding = (
delta_w // 2,
delta_h // 2,
delta_w - (delta_w // 2),
delta_h - (delta_h // 2),
)
new_im = ImageOps.expand(x, padding)
return new_im
def pad_to_size_tensor(x, size=256):
offset_dim_1 = size - x.shape[1]
offset_dim_2 = size - x.shape[2]
padding_dim_1 = max(offset_dim_1 // 2, 0)
padding_dim_2 = max(offset_dim_2 // 2, 0)
if offset_dim_1 % 2 == 0:
pad_tuple_1 = (padding_dim_1, padding_dim_1)
else:
pad_tuple_1 = (padding_dim_1 + 1, padding_dim_1)
if offset_dim_2 % 2 == 0:
pad_tuple_2 = (padding_dim_2, padding_dim_2)
else:
pad_tuple_2 = (padding_dim_2 + 1, padding_dim_2)
padded = F.pad(x, pad=(*pad_tuple_2, *pad_tuple_1, 0, 0))
return padded
class RandCropResize(object):
"""
Randomly crops, then randomly resizes, then randomly crops again, an image. Mirroring the augmentations from https://arxiv.org/abs/2102.12092
"""
def __init__(self, target_size):
self.target_size = target_size
def __call__(self, img):
img = pad_to_size(img, self.target_size)
d_min = min(img.size)
img = T.RandomCrop(size=d_min)(img)
t_min = min(d_min, round(9 / 8 * self.target_size))
t_max = min(d_min, round(12 / 8 * self.target_size))
t = random.randint(t_min, t_max + 1)
img = T.Resize(t)(img)
if min(img.size) < 256:
img = T.Resize(256)(img)
return T.RandomCrop(size=self.target_size)(img)
def get_transforms(
image_size, encoder_name, input_resolution=None, use_extra_transforms=False
):
if "clip" in encoder_name:
assert input_resolution is not None
return clip_preprocess(input_resolution)
base_transforms = [
T.Lambda(lambda img: img.convert("RGB") if img.mode != "RGB" else img),
RandCropResize(image_size),
T.RandomHorizontalFlip(p=0.5),
]
if use_extra_transforms:
extra_transforms = [T.ColorJitter(0.1, 0.1, 0.1, 0.05)]
base_transforms += extra_transforms
base_transforms += [
T.ToTensor(),
maybe_add_batch_dim,
]
base_transforms = T.Compose(base_transforms)
return base_transforms
def maybe_add_batch_dim(t):
if t.ndim == 3:
return t.unsqueeze(0)
else:
return t
def pad_img(desired_size):
def fn(im):
old_size = im.size # old_size[0] is in (width, height) format
ratio = float(desired_size) / max(old_size)
new_size = tuple([int(x * ratio) for x in old_size])
im = im.resize(new_size, PIL.Image.ANTIALIAS)
# create a new image and paste the resized on it
new_im = PIL.Image.new("RGB", (desired_size, desired_size))
new_im.paste(
im, ((desired_size - new_size[0]) // 2, (desired_size - new_size[1]) // 2)
)
return new_im
return fn
def crop_or_pad(n_px, pad=False):
if pad:
return pad_img(n_px)
else:
return T.CenterCrop(n_px)
def clip_preprocess(n_px, use_pad=False):
return T.Compose(
[
T.Resize(n_px, interpolation=T.InterpolationMode.BICUBIC),
crop_or_pad(n_px, pad=use_pad),
lambda image: image.convert("RGB"),
T.ToTensor(),
maybe_add_batch_dim,
T.Normalize(
(0.48145466, 0.4578275, 0.40821073),
(0.26862954, 0.26130258, 0.27577711),
),
]
)
|