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from abc import abstractmethod
import torch
import torch.nn as nn
import torch.nn.functional as F
def make_independent_token(conf, attn_feat):
token_type = conf.get("type", "FixedViewIndependentToken")
if token_type == "FixedViewIndependentToken":
return FixedViewIndependentToken(attn_feat)
elif token_type == "DataViewIndependentToken":
return DataViewIndependentToken(attn_feat)
elif token_type == "NeuRayIndependentToken":
return NeuRayIndependentToken(att_feat=attn_feat, **conf["args"])
else:
raise NotImplementedError("Unsupported Token type")
class BaseIndependentToken(nn.Module):
def __init__(self, attn_feat: int) -> None:
super().__init__()
self.attn_feat = attn_feat
self.require_bottleneck_feats = False
@abstractmethod
def forward(self, view_dependent_tokens: torch.Tensor, **kwargs) -> torch.Tensor:
pass
class FixedViewIndependentToken(BaseIndependentToken):
def __init__(self, attn_feat: int) -> None:
super().__init__(attn_feat)
self.require_bottleneck_feats = False
self.readout_token = nn.Parameter(torch.rand(1, 1, attn_feat), requires_grad=True)
def forward(self, view_dependent_tokens: torch.Tensor, **kwargs) -> torch.Tensor:
return self.readout_token.expand(view_dependent_tokens.shape[0], -1, -1) ### (n_pts, 1, 16)
def weights_init(m):
if isinstance(m, nn.Linear):
nn.init.kaiming_normal_(m.weight.data)
if m.bias is not None:
nn.init.zeros_(m.bias.data)
@torch.jit.script
def fused_mean_variance(x, weight):
mean = torch.sum(x * weight, dim=-2, keepdim=True)
var = torch.sum(weight * (x - mean) ** 2, dim=-2, keepdim=True)
return mean, var
class DataViewIndependentToken(BaseIndependentToken):
def __init__(self, attn_feat: int) -> None:
super().__init__(attn_feat)
self.require_bottleneck_feats = False
self.eps = 1.0e-9
self.layer = nn.Linear(2 * attn_feat, attn_feat, bias=True)
# def forward(self, view_dependent_tokens: torch.Tensor, invalid_mask: torch.Tensor) -> torch.Tensor:
def forward(self, view_dependent_tokens: torch.Tensor, **kwargs) -> torch.Tensor:
mask = 1 - kwargs["invalid_features"].float()
# mask = 1 - invalid_mask
weights = mask / (torch.sum(mask, dim=-1, keepdim=True) + 1e-8)
mean, var = fused_mean_variance(view_dependent_tokens, weights.unsqueeze(-1))
# num_valid_tokens = torch.sum((1 - invalid_mask), dim=-1, keepdim=True) + self.eps
# mean = torch.sum(view_dependent_tokens * (1 - invalid_mask).unsqueeze(-1), dim=-2) / num_valid_tokens
# var = torch.sum((view_dependent_tokens - mean)**2 * (1 - invalid_mask).unsqueeze(-1), dim=-2) / num_valid_tokens
return nn.ELU()(self.layer(torch.cat([mean, var], dim=-1)))
class NeuRayIndependentToken(BaseIndependentToken):
def __init__(
self,
n_points_per_ray: int,
# neuray_in_dim: int = 32,
in_feat_ch: int = 32,
n_samples: int = 64,
att_feat: int = 16,
d_model: int = 103,
rbs: int = 2048,
**kwargs
):
super().__init__(att_feat)
self.n_points_per_ray = n_points_per_ray
self.require_bottleneck_feats = True
# self.args = args
self.anti_alias_pooling = False
if self.anti_alias_pooling:
self.s = nn.Parameter(torch.tensor(0.2), requires_grad=True)
activation_func = nn.ELU(
inplace=True
) ## (+): Mean Outputs Closer to Zero: want activations with mean outputs closer to zero. ## nn.LeakyReLU: (+): faster convergence, When the distribution of the negative values in your dataset is meaningful and shouldn't be discarded.
self.n_samples = n_samples
self.ray_dir_fc = nn.Sequential(
nn.Linear(4, 16), ## defualt: 4
activation_func,
nn.Linear(16, in_feat_ch), ## default: in_feat_ch + 3
activation_func,
)
self.base_fc = nn.Sequential(
nn.Linear((in_feat_ch) * 5 + att_feat, 64), ## default: ((in_feat_ch+3)*5+neuray_in_dim, 64)
activation_func,
nn.Linear(64, 32),
activation_func,
)
self.vis_fc = nn.Sequential(
nn.Linear(32, 32),
activation_func,
nn.Linear(32, 33),
activation_func,
)
self.vis_fc2 = nn.Sequential(nn.Linear(32, 32), activation_func, nn.Linear(32, 1), nn.Sigmoid())
self.geometry_fc = nn.Sequential(
nn.Linear(32 * 2 + 1, att_feat * 2), ## default: (32*2+1, 64)
activation_func,
nn.Linear(att_feat * 2, att_feat),
activation_func,
)
# self.ray_attention = MultiHeadAttention(nhead, att_feat, 4, 4) ## default: (4, 16, 4, 4)
self.out_geometry_fc = nn.Sequential(nn.Linear(16, 16), activation_func, nn.Linear(16, 1), nn.ReLU())
self.rgb_fc = nn.Sequential(
nn.Linear(32 + 1 + 4, 16), activation_func, nn.Linear(16, 8), activation_func, nn.Linear(8, 1)
)
self.neuray_fc = nn.Sequential(
nn.Linear(
att_feat,
8,
),
activation_func,
nn.Linear(8, 1),
)
self.img_feat2low = nn.Sequential(
nn.Linear(rbs, rbs // 4), ## TODO: replace this hard coded with the flexible
activation_func,
# nn.Linear(rbs // 4, d_model),
nn.Linear(rbs // 4, in_feat_ch),
)
# self.pos_encoding = self.posenc(d_hid=16, n_samples=self.n_samples)
self.base_fc.apply(weights_init)
self.vis_fc2.apply(weights_init)
self.vis_fc.apply(weights_init)
# self.geometry_fc.apply(weights_init)
self.rgb_fc.apply(weights_init)
self.neuray_fc.apply(weights_init)
def forward(self, view_dependent_tokens, bottleneck_feats, ray_diff, invalid_features, **kwargs):
"""ibrnet dim e.g. [6, 64, 8, 35]
:param rgb_feat: rgbs and image features [n_rays, n_samples, n_views, n_feat] == img_feat
:param neuray_feat: rgbs and image features [n_rays, n_samples, n_views, n_feat] == viz_feat
:param ray_diff: ray direction difference [n_rays, n_samples, n_views, 4], first 3 channels are directions, ## tensor encodes information about how rays in the novel view differ from rays in the source views
last channel is inner product
:param mask: mask for whether each projection is valid or not. [n_rays, n_samples, n_views, 1]
:return: rgb and density output, [n_rays, n_samples, 4]
"""
"""ibrnet dim e.g. [6, 64, 8, 35]
:param view_dependent_tokens: (B*n_pts, n_views, C) = (B*num_rays*point_per_ray, n_views, C)
:param bottleneck_features: (B*n_pts, n_views, C_bottleneck) = (B*num_rays*point_per_ray, n_views, C)
:param ray_diff: (B*n_pts, n_views, 4) = (B*num_rays*point_per_ray, n_views, 4)
:param invalid_features: (B*n_pts, n_views) = (B*num_rays*point_per_ray, n_views)
:return: rgb and density output, [n_rays, n_samples, 4]
"""
view_dependent_tokens = view_dependent_tokens.reshape(
(-1, self.n_points_per_ray) + view_dependent_tokens.shape[-2:]
) # (B*num_rays, point_per_ray, n_views, C)
bottleneck_feats = bottleneck_feats.reshape(
(-1, self.n_points_per_ray) + bottleneck_feats.shape[-2:]
) # (B*num_rays, point_per_ray, n_views, C_bottleneck)
ray_diff = ray_diff.reshape(
(-1, self.n_points_per_ray) + ray_diff.shape[-2:]
) # (B*num_rays, point_per_ray, n_views, 4)
invalid_features = invalid_features.reshape(
(-1, self.n_points_per_ray) + invalid_features.shape[-1:]
) # (B*num_rays, point_per_ray, n_views)
## Assumption: rgb_feat already contains image feature + dir_feat / this can be implemented further
mask = ~invalid_features.unsqueeze(-1)
num_views = bottleneck_feats.shape[2]
direction_feat = self.ray_dir_fc(ray_diff)
# rgb_in = rgb_feat[..., :3] ## no used in both original code and necessary code here
bottleneck_feats = self.img_feat2low(bottleneck_feats)
bottleneck_feats = bottleneck_feats + direction_feat
if self.anti_alias_pooling:
_, dot_prod = torch.split(ray_diff, [3, 1], dim=-1)
exp_dot_prod = torch.exp(torch.abs(self.s) * (dot_prod - 1))
weight = (exp_dot_prod - torch.min(exp_dot_prod, dim=2, keepdim=True)[0]) * mask
weight = weight / (
torch.sum(weight, dim=2, keepdim=True) + 1e-8
) # means it will trust the one more with more consistent view point
else:
weight = mask / (torch.sum(mask, dim=2, keepdim=True) + 1e-8)
# neuray layer 0 ## == feature aggregation networks (M) above pipeline from fig. 19
weight0 = torch.sigmoid(self.neuray_fc(view_dependent_tokens)) * weight # [rn,dn,rfn,f]
mean0, var0 = fused_mean_variance(bottleneck_feats, weight0) # [n_rays, n_samples, 1, n_feat] ## 2nd one
mean1, var1 = fused_mean_variance(bottleneck_feats, weight) # [n_rays, n_samples, 1, n_feat] ## 1st one
globalfeat = torch.cat([mean0, var0, mean1, var1], dim=-1) # [n_rays, n_samples, 1, 2*n_feat]
x = torch.cat(
[globalfeat.expand(-1, -1, num_views, -1), bottleneck_feats, view_dependent_tokens], dim=-1
) # [n_rays, n_samples, n_views, 3*n_feat]
x = self.base_fc(x) ## after concat it gives input for net A
x_vis = self.vis_fc(x * weight)
x_res, vis = torch.split(x_vis, [x_vis.shape[-1] - 1, 1], dim=-1)
vis = F.sigmoid(vis) * mask
x = x + x_res
vis = self.vis_fc2(x * vis) * mask ## above one from Network A from Fig. 19
weight = vis / (
torch.sum(vis, dim=2, keepdim=True) + 1e-8
) ## normalized: weighed mean and var ## weight == buttom from net A [N, K, 32]
mean, var = fused_mean_variance(x, weight)
globalfeat = torch.cat(
[mean.squeeze(2), var.squeeze(2), weight.mean(dim=2)], dim=-1
) # [n_rays, n_samples, 32*2+1]
globalfeat = self.geometry_fc(globalfeat) # [n_rays, n_samples, att_feat] ## MLP for input transformer
# num_valid_obs = torch.sum(mask, dim=2)
# num_valid_obs = num_valid_obs > torch.mean(num_valid_obs, dtype=float) ## making boolean
return globalfeat.flatten(0, 1).unsqueeze(-2) # (B*num_rays*point_per_ray, 1, C)
# return globalfeat, num_valid_obs
# class IBRNetWithNeuRay(nn.Module):
# def __init__(
# self, neuray_in_dim=32, in_feat_ch=32, n_samples=64, att_feat=16, d_model=103, rbs=2048, nhead=4, **kwargs
# ):
# super().__init__()
# # self.args = args
# self.anti_alias_pooling = False
# if self.anti_alias_pooling:
# self.s = nn.Parameter(torch.tensor(0.2), requires_grad=True)
# activation_func = nn.ELU(
# inplace=True
# ) ## (+): Mean Outputs Closer to Zero: want activations with mean outputs closer to zero. ## nn.LeakyReLU: (+): faster convergence, When the distribution of the negative values in your dataset is meaningful and shouldn't be discarded.
# self.n_samples = n_samples
# self.ray_dir_fc = nn.Sequential(
# nn.Linear(4, 16), ## defualt: 4
# activation_func,
# nn.Linear(16, in_feat_ch), ## default: in_feat_ch + 3
# activation_func,
# )
# self.base_fc = nn.Sequential(
# nn.Linear((in_feat_ch) * 5 + neuray_in_dim, 64), ## default: ((in_feat_ch+3)*5+neuray_in_dim, 64)
# activation_func,
# nn.Linear(64, 32),
# activation_func,
# )
# self.vis_fc = nn.Sequential(
# nn.Linear(32, 32),
# activation_func,
# nn.Linear(32, 33),
# activation_func,
# )
# self.vis_fc2 = nn.Sequential(nn.Linear(32, 32), activation_func, nn.Linear(32, 1), nn.Sigmoid())
# self.geometry_fc = nn.Sequential(
# nn.Linear(32 * 2 + 1, att_feat * 2), ## default: (32*2+1, 64)
# activation_func,
# nn.Linear(att_feat * 2, att_feat),
# activation_func,
# )
# # self.ray_attention = MultiHeadAttention(nhead, att_feat, 4, 4) ## default: (4, 16, 4, 4)
# self.out_geometry_fc = nn.Sequential(nn.Linear(16, 16), activation_func, nn.Linear(16, 1), nn.ReLU())
# self.rgb_fc = nn.Sequential(
# nn.Linear(32 + 1 + 4, 16), activation_func, nn.Linear(16, 8), activation_func, nn.Linear(8, 1)
# )
# self.neuray_fc = nn.Sequential(
# nn.Linear(
# neuray_in_dim,
# 8,
# ),
# activation_func,
# nn.Linear(8, 1),
# )
# self.img_feat2low = nn.Sequential(
# nn.Linear(rbs, rbs // 4), ## TODO: replace this hard coded with the flexible
# activation_func,
# nn.Linear(rbs // 4, d_model),
# )
# # self.pos_encoding = self.posenc(d_hid=16, n_samples=self.n_samples)
# self.base_fc.apply(weights_init)
# self.vis_fc2.apply(weights_init)
# self.vis_fc.apply(weights_init)
# # self.geometry_fc.apply(weights_init)
# self.rgb_fc.apply(weights_init)
# self.neuray_fc.apply(weights_init)
# def forward(self, rgb_feat, neuray_feat, ray_diff, mask):
# """ibrnet dim e.g. [6, 64, 8, 35]
# :param rgb_feat: rgbs and image features [n_rays, n_samples, n_views, n_feat] == img_feat
# :param neuray_feat: rgbs and image features [n_rays, n_samples, n_views, n_feat] == viz_feat
# :param ray_diff: ray direction difference [n_rays, n_samples, n_views, 4], first 3 channels are directions, ## tensor encodes information about how rays in the novel view differ from rays in the source views
# last channel is inner product
# :param mask: mask for whether each projection is valid or not. [n_rays, n_samples, n_views, 1]
# :return: rgb and density output, [n_rays, n_samples, 4]
# """
# ## Assumption: rgb_feat already contains image feature + dir_feat / this can be implemented further
# num_views = rgb_feat.shape[2]
# direction_feat = self.ray_dir_fc(ray_diff)
# # rgb_in = rgb_feat[..., :3] ## no used in both original code and necessary code here
# rgb_feat = self.img_feat2low(rgb_feat)
# rgb_feat = rgb_feat + direction_feat
# if self.anti_alias_pooling:
# _, dot_prod = torch.split(ray_diff, [3, 1], dim=-1)
# exp_dot_prod = torch.exp(torch.abs(self.s) * (dot_prod - 1))
# weight = (exp_dot_prod - torch.min(exp_dot_prod, dim=2, keepdim=True)[0]) * mask
# weight = weight / (
# torch.sum(weight, dim=2, keepdim=True) + 1e-8
# ) # means it will trust the one more with more consistent view point
# else:
# weight = mask / (torch.sum(mask, dim=2, keepdim=True) + 1e-8)
# # neuray layer 0 ## == feature aggregation networks (M) above pipeline from fig. 19
# weight0 = torch.sigmoid(self.neuray_fc(neuray_feat)) * weight # [rn,dn,rfn,f]
# mean0, var0 = fused_mean_variance(rgb_feat, weight0) # [n_rays, n_samples, 1, n_feat] ## 2nd one
# mean1, var1 = fused_mean_variance(rgb_feat, weight) # [n_rays, n_samples, 1, n_feat] ## 1st one
# globalfeat = torch.cat([mean0, var0, mean1, var1], dim=-1) # [n_rays, n_samples, 1, 2*n_feat]
# x = torch.cat(
# [globalfeat.expand(-1, -1, num_views, -1), rgb_feat, neuray_feat], dim=-1
# ) # [n_rays, n_samples, n_views, 3*n_feat]
# x = self.base_fc(x) ## after concat it gives input for net A
# x_vis = self.vis_fc(x * weight)
# x_res, vis = torch.split(x_vis, [x_vis.shape[-1] - 1, 1], dim=-1)
# vis = F.sigmoid(vis) * mask
# x = x + x_res
# vis = self.vis_fc2(x * vis) * mask ## above one from Network A from Fig. 19
# weight = vis / (
# torch.sum(vis, dim=2, keepdim=True) + 1e-8
# ) ## normalized: weighed mean and var ## weight == buttom from net A [N, K, 32]
# mean, var = fused_mean_variance(x, weight)
# globalfeat = torch.cat(
# [mean.squeeze(2), var.squeeze(2), weight.mean(dim=2)], dim=-1
# ) # [n_rays, n_samples, 32*2+1]
# globalfeat = self.geometry_fc(globalfeat) # [n_rays, n_samples, att_feat] ## MLP for input transformer
# # num_valid_obs = torch.sum(mask, dim=2)
# # num_valid_obs = num_valid_obs > torch.mean(num_valid_obs, dtype=float) ## making boolean
# return globalfeat
# # return globalfeat, num_valid_obs
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