File size: 7,043 Bytes
38dbec8 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 |
import dataclasses
import importlib
from dataclasses import dataclass
from typing import Any, List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.nn as nn
import torch.nn.functional as F
from jaxtyping import Float, Int, Num
from omegaconf import DictConfig, OmegaConf
from torch import Tensor
class BaseModule(nn.Module):
@dataclass
class Config:
pass
cfg: Config # add this to every subclass of BaseModule to enable static type checking
def __init__(
self, cfg: Optional[Union[dict, DictConfig]] = None, *args, **kwargs
) -> None:
super().__init__()
self.cfg = parse_structured(self.Config, cfg)
self.configure(*args, **kwargs)
def configure(self, *args, **kwargs) -> None:
raise NotImplementedError
def find_class(cls_string):
module_string = ".".join(cls_string.split(".")[:-1])
cls_name = cls_string.split(".")[-1]
module = importlib.import_module(module_string, package=None)
cls = getattr(module, cls_name)
return cls
def parse_structured(fields: Any, cfg: Optional[Union[dict, DictConfig]] = None) -> Any:
# Check if cfg.keys are in fields
cfg_ = cfg.copy()
keys = list(cfg_.keys())
field_names = {f.name for f in dataclasses.fields(fields)}
for key in keys:
# This is helpful when swapping out modules from CLI
if key not in field_names:
print(f"Ignoring {key} as it's not supported by {fields}")
cfg_.pop(key)
scfg = OmegaConf.merge(OmegaConf.structured(fields), cfg_)
return scfg
EPS_DTYPE = {
torch.float16: 1e-4,
torch.bfloat16: 1e-4,
torch.float32: 1e-7,
torch.float64: 1e-8,
}
def dot(x, y, dim=-1):
return torch.sum(x * y, dim, keepdim=True)
def reflect(x, n):
return x - 2 * dot(x, n) * n
def normalize(x, dim=-1, eps=None):
if eps is None:
eps = EPS_DTYPE[x.dtype]
return F.normalize(x, dim=dim, p=2, eps=eps)
ValidScale = Union[Tuple[float, float], Num[Tensor, "2 D"]]
def scale_tensor(
dat: Num[Tensor, "... D"], inp_scale: ValidScale, tgt_scale: ValidScale
):
if inp_scale is None:
inp_scale = (0, 1)
if tgt_scale is None:
tgt_scale = (0, 1)
if isinstance(tgt_scale, Tensor):
assert dat.shape[-1] == tgt_scale.shape[-1]
dat = (dat - inp_scale[0]) / (inp_scale[1] - inp_scale[0])
dat = dat * (tgt_scale[1] - tgt_scale[0]) + tgt_scale[0]
return dat
def dilate_fill(img, mask, iterations=10):
oldMask = mask.float()
oldImg = img
mask_kernel = torch.ones(
(1, 1, 3, 3),
dtype=oldMask.dtype,
device=oldMask.device,
)
for i in range(iterations):
newMask = torch.nn.functional.max_pool2d(oldMask, 3, 1, 1)
# Fill the extension with mean color of old valid regions
img_unfold = F.unfold(oldImg, (3, 3)).view(1, 3, 3 * 3, -1)
mask_unfold = F.unfold(oldMask, (3, 3)).view(1, 1, 3 * 3, -1)
new_mask_unfold = F.unfold(newMask, (3, 3)).view(1, 1, 3 * 3, -1)
# Average color of the valid region
mean_color = (img_unfold.sum(dim=2) / mask_unfold.sum(dim=2).clip(1)).unsqueeze(
2
)
# Extend it to the new region
fill_color = (mean_color * new_mask_unfold).view(1, 3 * 3 * 3, -1)
mask_conv = F.conv2d(
newMask, mask_kernel, padding=1
) # Get the sum for each kernel patch
newImg = F.fold(
fill_color, (img.shape[-2], img.shape[-1]), (3, 3)
) / mask_conv.clamp(1)
diffMask = newMask - oldMask
oldMask = newMask
oldImg = torch.lerp(oldImg, newImg, diffMask)
return oldImg
def float32_to_uint8_np(
x: Float[np.ndarray, "*B H W C"],
dither: bool = True,
dither_mask: Optional[Float[np.ndarray, "*B H W C"]] = None,
dither_strength: float = 1.0,
) -> Int[np.ndarray, "*B H W C"]:
if dither:
dither = (
dither_strength * np.random.rand(*x[..., :1].shape).astype(np.float32) - 0.5
)
if dither_mask is not None:
dither = dither * dither_mask
return np.clip(np.floor((256.0 * x + dither)), 0, 255).astype(np.uint8)
return np.clip(np.floor((256.0 * x)), 0, 255).astype(torch.uint8)
def convert_data(data):
if data is None:
return None
elif isinstance(data, np.ndarray):
return data
elif isinstance(data, torch.Tensor):
if data.dtype in [torch.float16, torch.bfloat16]:
data = data.float()
return data.detach().cpu().numpy()
elif isinstance(data, list):
return [convert_data(d) for d in data]
elif isinstance(data, dict):
return {k: convert_data(v) for k, v in data.items()}
else:
raise TypeError(
"Data must be in type numpy.ndarray, torch.Tensor, list or dict, getting",
type(data),
)
class ImageProcessor:
def convert_and_resize(
self,
image: Union[PIL.Image.Image, np.ndarray, torch.Tensor],
size: int,
):
if isinstance(image, PIL.Image.Image):
image = torch.from_numpy(np.array(image).astype(np.float32) / 255.0)
elif isinstance(image, np.ndarray):
if image.dtype == np.uint8:
image = torch.from_numpy(image.astype(np.float32) / 255.0)
else:
image = torch.from_numpy(image)
elif isinstance(image, torch.Tensor):
pass
batched = image.ndim >= 4
view_batch = image.ndim >= 5
if view_batch:
image = image.view(-1, *image.shape[2:])
elif not batched:
image = image[None, ...]
image = F.interpolate(
image.permute(0, 3, 1, 2),
(size, size),
mode="bilinear",
align_corners=False,
antialias=True,
).permute(0, 2, 3, 1)
if not batched:
image = image[0]
return image
def __call__(
self,
image: Union[
PIL.Image.Image,
np.ndarray,
torch.FloatTensor,
List[PIL.Image.Image],
List[np.ndarray],
List[torch.FloatTensor],
],
size: int,
) -> Any:
if isinstance(image, (np.ndarray, torch.FloatTensor)) and image.ndim == 4:
image = self.convert_and_resize(image, size)
else:
if not isinstance(image, list):
image = [image]
image = [self.convert_and_resize(im, size) for im in image]
image = torch.stack(image, dim=0)
return image
def get_intrinsic_from_fov(fov, H, W, bs=-1):
focal_length = 0.5 * H / np.tan(0.5 * fov)
intrinsic = np.identity(3, dtype=np.float32)
intrinsic[0, 0] = focal_length
intrinsic[1, 1] = focal_length
intrinsic[0, 2] = W / 2.0
intrinsic[1, 2] = H / 2.0
if bs > 0:
intrinsic = intrinsic[None].repeat(bs, axis=0)
return torch.from_numpy(intrinsic)
|