Spaces:
Running on Zero
Running on Zero
File size: 7,360 Bytes
1d998f6 | 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 | import torch
import torch.nn as nn
import torch.nn.functional as nnf
import addict
class SafeDict(addict.Dict):
def __missing__(self, name):
if object.__getattribute__(self, '__frozen'):
raise AttributeError(name)
return self.__class__(__parent=self, __key=name)
def __setitem__(self, name, value):
is_frozen = (hasattr(self, '__frozen') and
object.__getattribute__(self, '__frozen'))
assert not is_frozen, f"Cannot set field '{name}': frozen dict."
super().__setitem__(name, value)
try:
p = object.__getattribute__(self, '__parent')
key = object.__getattribute__(self, '__key')
except AttributeError:
p = None
key = None
if p is not None:
p[key] = self
object.__delattr__(self, '__parent')
object.__delattr__(self, '__key')
class TransformInput(nn.Module):
def __init__(self, wo_grad=True):
super().__init__()
self.wo_grad = wo_grad
def forward(self, image, intrinsics, c2w, patch_size=None):
# pose stage model requires grad
grad_context = torch.no_grad if self.wo_grad else torch.enable_grad
with grad_context():
return self._forward(image, intrinsics, c2w, patch_size)
def _forward(self, image, intrinsics, c2w, patch_size=None):
"""transform input image before feeding into transformer
Args:
data_batch: SafeDict
patch_size: int, optional
Returns:
image: [b, v, c, h, w]
ray_o: [b, v, 3, h, w]
ray_d: [b, v, 3, h, w]
"""
# image, fxfycxcy, c2w, index = (
# data_batch.image,
# data_batch.fxfycxcy,
# data_batch.c2w,
# data_batch.index,
# )
assert image.dim() == 5, f"image dim should be 5, but got {image.dim()}"
# assert (
# fxfycxcy.dim() == 3
# ), f"fxfycxcy dim should be 3, but got {fxfycxcy.dim()}"
# assert c2w.dim() == 4, f"c2w dim should be 4, but got {c2w.dim()}"
fxfycxcy = torch.stack(
[intrinsics[..., 0,0], intrinsics[..., 1,1],
intrinsics[..., 0,2], intrinsics[..., 1,2]], dim=-1
)
b, v, c, h, w = image.size()
image = image.reshape(b * v, c, h * w)
fxfycxcy = fxfycxcy.reshape(b * v, 4)
c2w = c2w.reshape(b * v, 4, 4)
y, x = torch.meshgrid(torch.arange(h), torch.arange(w), indexing="ij")
y, x = y.to(image.device), x.to(image.device)
# [h, w]
y_norm, x_norm = (y + 0.5) / h * 2 - 1, (x + 0.5) / w * 2 - 1
# [b, v, 2, h, w]
xy_norm = torch.stack([x_norm, y_norm], dim=0)[None, None, :, :, :].expand(
b, v, -1, -1, -1
)
x = x[None, :, :].expand(b * v, -1, -1).reshape(b * v, -1)
y = y[None, :, :].expand(b * v, -1, -1).reshape(b * v, -1)
x = (x + 0.5 - fxfycxcy[:, 2:3]) / fxfycxcy[:, 0:1]
y = (y + 0.5 - fxfycxcy[:, 3:4]) / fxfycxcy[:, 1:2]
z = torch.ones_like(x)
ray_d = torch.stack([x, y, z], dim=2) # [b*v, h*w, 3]
ray_d_cam = ray_d.clone()
ray_d = torch.bmm(ray_d, c2w[:, :3, :3].transpose(1, 2)) # [b*v, h*w, 3]
ray_d = ray_d / torch.norm(ray_d, dim=2, keepdim=True) # [b*v, h*w, 3]
ray_d_cam = ray_d_cam / torch.norm(ray_d_cam, dim=2, keepdim=True)
ray_o = c2w[:, :3, 3][:, None, :].expand_as(ray_d) # [b*v, h*w, 3]
# fetch color, ray_o, ray_d for all patch centers
ray_color_patch, ray_o_patch, ray_d_patch = None, None, None
ray_xy_norm_patch, proj_mat = None, None
if patch_size is not None:
start_patch_center = patch_size / 2.0
y, x = torch.meshgrid(
torch.arange(h // patch_size) * patch_size + start_patch_center,
torch.arange(w // patch_size) * patch_size + start_patch_center,
indexing="ij",
)
y, x = y.to(image.device), x.to(image.device)
x = x[None, :, :].expand(b * v, -1, -1).reshape(b * v, -1)
y = y[None, :, :].expand(b * v, -1, -1).reshape(b * v, -1)
ray_xy_norm_patch = torch.stack(
[x / w, y / h], dim=2
) # use [0,1] for patch center
K_norm = (
torch.eye(3, device=image.device).unsqueeze(0).repeat(b * v, 1, 1)
) # [b*v, 3, 3]
K_norm[:, 0, 0] = fxfycxcy[:, 0] / w
K_norm[:, 1, 1] = fxfycxcy[:, 1] / h
K_norm[:, 0, 2] = fxfycxcy[:, 2] / w
K_norm[:, 1, 2] = fxfycxcy[:, 3] / h
w2c = torch.inverse(c2w) # [b*v, 4, 4]
proj_mat = torch.bmm(K_norm, w2c[:, :3, :4]) # [b*v, 3, 4]
proj_mat = proj_mat.reshape(b * v, 12)
proj_mat = proj_mat / (proj_mat.norm(dim=1, keepdim=True) + 1e-6)
proj_mat = proj_mat.reshape(b * v, 3, 4)
proj_mat = proj_mat * proj_mat[:, 0:1, 0:1].sign()
# fetch colors for all patch centers
ray_color_patch = (
nnf.grid_sample(
image.reshape(b * v, c, h, w),
torch.stack([x / w * 2.0 - 1.0, y / h * 2.0 - 1.0], dim=2).reshape(
b * v, -1, 1, 2
),
align_corners=False,
)
.squeeze(-1)
.permute(0, 2, 1)
).contiguous() # [b*v, h'*w', c]
x = (x - fxfycxcy[:, 2:3]) / fxfycxcy[:, 0:1]
y = (y - fxfycxcy[:, 3:4]) / fxfycxcy[:, 1:2]
z = torch.ones_like(x)
ray_d_patch = torch.stack([x, y, z], dim=2) # [b*v, h'*w', 3]
ray_d_patch = torch.bmm(
ray_d_patch, c2w[:, :3, :3].transpose(1, 2)
) # [b*v, h'*w', 3]
ray_d_patch = ray_d_patch / torch.norm(
ray_d_patch, dim=2, keepdim=True
) # [b*v, h'*w', 3]
ray_o_patch = c2w[:, :3, 3][:, None, :].expand_as(
ray_d_patch
) # [b*v, h'*w', 3]
n_patch = ray_color_patch.size(1)
ray_color_patch = ray_color_patch.reshape(b, v, n_patch, c)
ray_o_patch = ray_o_patch.reshape(b, v, n_patch, 3)
ray_d_patch = ray_d_patch.reshape(b, v, n_patch, 3)
ray_xy_norm_patch = ray_xy_norm_patch.reshape(b, v, n_patch, 2)
proj_mat = proj_mat.reshape(b, v, 3, 4)
ray_o = ray_o.reshape(b, v, h, w, 3).permute(0, 1, 4, 2, 3)
ray_d = ray_d.reshape(b, v, h, w, 3).permute(0, 1, 4, 2, 3)
ray_d_cam = ray_d_cam.reshape(b, v, h, w, 3).permute(0, 1, 4, 2, 3)
image = image.reshape(b, v, c, h, w)
fxfycxcy = fxfycxcy.reshape(b, v, 4)
c2w = c2w.reshape(b, v, 4, 4)
ret = SafeDict(
image=image,
ray_o=ray_o,
ray_d=ray_d,
ray_d_cam=ray_d_cam,
fxfycxcy=fxfycxcy,
c2w=c2w,
# index=index,
xy_norm=xy_norm,
ray_color_patch=ray_color_patch,
ray_o_patch=ray_o_patch,
ray_d_patch=ray_d_patch,
ray_xy_norm_patch=ray_xy_norm_patch,
proj_mat=proj_mat,
)
return ret
|