Spaces:
Build error
Build error
File size: 8,799 Bytes
d51072c |
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 |
import torch
import torch.nn as nn
import torch.nn.functional as F
from sync_batchnorm import SynchronizedBatchNorm2d as BatchNorm2d
from sync_batchnorm import SynchronizedBatchNorm3d as BatchNorm3d
import einops
from modules.util import UpBlock2d, DownBlock2d
def make_coordinate_grid(spatial_size, type):
d, h, w = spatial_size
x = torch.arange(w).type(type)
y = torch.arange(h).type(type)
z = torch.arange(d).type(type)
x = (2 * (x / (w - 1)) - 1)
y = (2 * (y / (h - 1)) - 1)
z = (2 * (z / (d - 1)) - 1)
yy = y.view(1, -1, 1).repeat(d, 1, w)
xx = x.view(1, 1, -1).repeat(d, h, 1)
zz = z.view(-1, 1, 1).repeat(1, h, w)
meshed = torch.cat([xx.unsqueeze_(3), yy.unsqueeze_(3), zz.unsqueeze_(3)], 3)
return meshed
def kp2gaussian_3d(kp, spatial_size, kp_variance):
"""
Transform a keypoint into gaussian like representation
"""
# mean = kp['value']
mean = kp
coordinate_grid = make_coordinate_grid(spatial_size, mean.type())
number_of_leading_dimensions = len(mean.shape) - 1
shape = (1,) * number_of_leading_dimensions + coordinate_grid.shape
coordinate_grid = coordinate_grid.view(*shape)
repeats = mean.shape[:number_of_leading_dimensions] + (1, 1, 1, 1)
coordinate_grid = coordinate_grid.repeat(*repeats)
# Preprocess kp shape
shape = mean.shape[:number_of_leading_dimensions] + (1, 1, 1, 3)
mean = mean.view(*shape)
mean_sub = (coordinate_grid - mean)
out = torch.exp(-0.5 * (mean_sub ** 2).sum(-1) / kp_variance)
return out
class ResBlock3d(nn.Module):
"""
Res block, preserve spatial resolution.
"""
def __init__(self, in_features, kernel_size, padding):
super(ResBlock3d, self).__init__()
self.conv1 = nn.Conv3d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size,
padding=padding)
self.conv2 = nn.Conv3d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size,
padding=padding)
self.norm1 = BatchNorm3d(in_features, affine=True)
self.norm2 = BatchNorm3d(in_features, affine=True)
def forward(self, x):
out = self.norm1(x)
out = F.relu(out)
out = self.conv1(out)
out = self.norm2(out)
out = F.relu(out)
out = self.conv2(out)
out += x
return out
class rgb_predictor(nn.Module):
def __init__(self, in_channels, simpled_channel=128, floor_num=8):
super(rgb_predictor, self).__init__()
self.floor_num = floor_num
self.down_conv = nn.Conv2d(in_channels=in_channels, out_channels=simpled_channel, kernel_size=3, padding=1)
def forward(self, feature):
"""
Args:
feature: warp feature: bs * c * h * w
Returns:
rgb: bs * h * w * floor_num * e
"""
feature = self.down_conv(feature)
feature = einops.rearrange(feature, 'b (c f) h w -> b c f h w', f=self.floor_num)
feature = einops.rearrange(feature, 'b c f h w -> b h w f c')
return feature
class sigma_predictor(nn.Module):
def __init__(self, in_channels, simpled_channel=128, floor_num=8):
super(sigma_predictor, self).__init__()
self.floor_num = floor_num
self.down_conv = nn.Conv2d(in_channels=in_channels, out_channels=simpled_channel, kernel_size=3, padding=1)
self.res_conv3d = nn.Sequential(
ResBlock3d(16, 3, 1),
nn.BatchNorm3d(16),
ResBlock3d(16, 3, 1),
nn.BatchNorm3d(16),
ResBlock3d(16, 3, 1),
nn.BatchNorm3d(16)
)
def forward(self, feature):
"""
Args:
feature: bs * h * w * floor * c, the output of rgb predictor
Returns:
sigma: bs * h * w * floor * encode
point: bs * 5023 * 3
"""
heatmap = self.down_conv(feature)
heatmap = einops.rearrange(heatmap, "b (c f) h w -> b c f h w", f=self.floor_num)
heatmap = self.res_conv3d(heatmap)
sigma = einops.rearrange(heatmap, "b c f h w -> b h w f c")
point_dict = {'sigma_map': heatmap}
# point_pred = einops.rearrange(point_pred, 'b p n -> b n p')
return sigma, point_dict
class MultiHeadNeRFModel(torch.nn.Module):
def __init__(self, hidden_size=128, num_encoding_rgb=16, num_encoding_sigma=16):
super(MultiHeadNeRFModel, self).__init__()
# self.xyz_encoding_dims = 1 + 1 * 2 * num_encoding_functions + num_encoding_rgb
self.xyz_encoding_dims = num_encoding_sigma
self.viewdir_encoding_dims = num_encoding_rgb
# Input layer (default: 16 -> 128)
self.layer1 = torch.nn.Linear(self.xyz_encoding_dims, hidden_size)
# Layer 2 (default: 128 -> 128)
self.layer2 = torch.nn.Linear(hidden_size, hidden_size)
# Layer 3_1 (default: 128 -> 1): Predicts radiance ("sigma")
self.layer3_1 = torch.nn.Linear(hidden_size, 1)
# Layer 3_2 (default: 128 -> 32): Predicts a feature vector (used for color)
self.layer3_2 = torch.nn.Linear(hidden_size, hidden_size // 4)
self.layer3_3 = torch.nn.Linear(self.viewdir_encoding_dims, hidden_size)
# Layer 4 (default: 32 + 128 -> 128)
self.layer4 = torch.nn.Linear(
hidden_size // 4 + hidden_size, hidden_size
)
# Layer 5 (default: 128 -> 128)
self.layer5 = torch.nn.Linear(hidden_size, hidden_size)
# Layer 6 (default: 128 -> 256): Predicts RGB color
self.layer6 = torch.nn.Linear(hidden_size, 256)
# Short hand for torch.nn.functional.relu
self.relu = torch.nn.functional.relu
def forward(self, rgb_in, sigma_in):
"""
Args:
x: rgb pred result of Perdict3D
view: result of LightPredict
Returns:
"""
bs, h, w, floor_num, _ = rgb_in.size()
# x = torch.cat((x, point3D), dim=-1)
out = self.relu(self.layer1(sigma_in))
out = self.relu(self.layer2(out))
sigma = self.layer3_1(out)
feat_sigma = self.relu(self.layer3_2(out))
feat_rgb = self.relu(self.layer3_3(rgb_in))
x = torch.cat((feat_sigma, feat_rgb), dim=-1)
x = self.relu(self.layer4(x))
x = self.relu(self.layer5(x))
x = self.layer6(x)
return x, sigma
def volume_render(rgb_pred, sigma_pred):
"""
Args:
rgb_pred: result of Nerf, [bs, h, w, floor, rgb_channel]
sigma_pred: result of Nerf, [bs, h, w, floor, sigma_channel]
Returns:
"""
_, _, _, floor, _ = sigma_pred.size()
c = 0
T = 0
for i in range(floor):
sigma_mid = torch.nn.functional.relu(sigma_pred[:, :, :, i, :])
T = T + (-sigma_mid)
c = c + torch.exp(T) * (1 - torch.exp(-sigma_mid)) * rgb_pred[:, :, :, i, :]
c = einops.rearrange(c, 'b h w c -> b c h w')
return c
class RenderModel(nn.Module):
def __init__(self, in_channels, simpled_channel_rgb, simpled_channel_sigma, floor_num, hidden_size):
super(RenderModel, self).__init__()
self.rgb_predict = rgb_predictor(in_channels=in_channels, simpled_channel=simpled_channel_rgb,
floor_num=floor_num)
self.sigma_predict = sigma_predictor(in_channels=in_channels, simpled_channel=simpled_channel_sigma,
floor_num=floor_num)
num_encoding_rgb, num_encoding_sigma = simpled_channel_rgb // floor_num, simpled_channel_sigma // floor_num
self.nerf_module = MultiHeadNeRFModel(hidden_size=hidden_size, num_encoding_rgb=num_encoding_rgb,
num_encoding_sigma=num_encoding_sigma)
self.mini_decoder = nn.Sequential(
UpBlock2d(256, 64, kernel_size=3, padding=1),
nn.ReLU(),
UpBlock2d(64, 3, kernel_size=3, padding=1),
nn.Sigmoid()
)
def forward(self, feature):
rgb_in = self.rgb_predict(feature)
# sigma_in, point_dict = self.sigma_predict(feature.detach())
sigma_in, point_dict = self.sigma_predict(feature)
rgb_out, sigma_out = self.nerf_module(rgb_in, sigma_in)
render_result = volume_render(rgb_out, sigma_out)
render_result = torch.sigmoid(render_result)
mini_pred = self.mini_decoder(render_result)
out_dict = {'render': render_result, 'mini_pred': mini_pred, 'point_pred': point_dict}
return out_dict
|