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import numpy as np
import torch.nn.functional as F
import torch.nn as nn
import torch
import models.common.model.multi_view_head as DFT
class ScaledDotProductAttention(nn.Module):
"""Scaled Dot-Product Attention"""
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
# self.dropout = nn.Dropout(attn_dropout)
def forward(self, q, k, v, mask=None):
attn = torch.matmul(q / self.temperature, k.transpose(2, 3))
if mask is not None:
attn = attn.masked_fill(mask == 0, -1e9)
# attn = attn * mask
attn = F.softmax(attn, dim=-1)
# attn = self.dropout(F.softmax(attn, dim=-1))
output = torch.matmul(attn, v)
return output, attn
class PositionwiseFeedForward(nn.Module):
"""A two-feed-forward-layer module"""
def __init__(self, d_in, d_hid, dropout=0.1):
super().__init__()
self.w_1 = nn.Linear(d_in, d_hid) # position-wise
self.w_2 = nn.Linear(d_hid, d_in) # position-wise
self.layer_norm = nn.LayerNorm(d_in, eps=1e-6)
# self.dropout = nn.Dropout(dropout)
def forward(self, x):
residual = x
x = self.w_2(F.relu(self.w_1(x)))
# x = self.dropout(x)
x += residual
x = self.layer_norm(x)
return x
class MultiHeadAttention(nn.Module):
"""Multi-Head Attention module"""
def __init__(self, n_head, d_model, d_k, d_v, dropout=0.1):
super().__init__()
self.n_head = n_head
self.d_k = d_k
self.d_v = d_v
self.w_qs = nn.Linear(d_model, n_head * d_k, bias=False)
self.w_ks = nn.Linear(d_model, n_head * d_k, bias=False)
self.w_vs = nn.Linear(d_model, n_head * d_v, bias=False)
self.fc = nn.Linear(n_head * d_v, d_model, bias=False)
self.attention = ScaledDotProductAttention(temperature=d_k**0.5)
# self.dropout = nn.Dropout(dropout)
self.layer_norm = nn.LayerNorm(d_model, eps=1e-6)
def forward(self, q, k, v, mask=None):
d_k, d_v, n_head = self.d_k, self.d_v, self.n_head
sz_b, len_q, len_k, len_v = q.size(0), q.size(1), k.size(1), v.size(1)
residual = q
# Pass through the pre-attention projection: b x lq x (n*dv)
# Separate different heads: b x lq x n x dv
q = self.w_qs(q).view(sz_b, len_q, n_head, d_k)
k = self.w_ks(k).view(sz_b, len_k, n_head, d_k)
v = self.w_vs(v).view(sz_b, len_v, n_head, d_v)
# Transpose for attention dot product: b x n x lq x dv
q, k, v = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2)
if mask is not None:
mask = mask.unsqueeze(1) # For head axis broadcasting.
q, attn = self.attention(q, k, v, mask=mask)
# Transpose to move the head dimension back: b x lq x n x dv
# Combine the last two dimensions to concatenate all the heads together: b x lq x (n*dv)
q = q.transpose(1, 2).contiguous().view(sz_b, len_q, -1)
# q = self.dropout(self.fc(q))
q = self.fc(q)
q += residual
q = self.layer_norm(q)
return q, attn
# default tensorflow initialization of linear layers
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 IBRNet(nn.Module):
# def __init__(self, in_feat_ch=32, n_samples=64, **kwargs):
# super(IBRNet, self).__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)
# self.n_samples = n_samples
# self.ray_dir_fc = nn.Sequential(nn.Linear(4, 16),
# activation_func,
# nn.Linear(16, in_feat_ch + 3),
# activation_func)
# self.base_fc = nn.Sequential(nn.Linear((in_feat_ch+3)*3, 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, 64),
# activation_func,
# nn.Linear(64, 16),
# activation_func)
# self.ray_attention = MultiHeadAttention(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.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)
# def posenc(self, d_hid, n_samples):
# def get_position_angle_vec(position):
# return [position / np.power(10000, 2 * (hid_j // 2) / d_hid) for hid_j in range(d_hid)]
# sinusoid_table = np.array([get_position_angle_vec(pos_i) for pos_i in range(n_samples)])
# sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i
# sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1
# sinusoid_table = torch.from_numpy(sinusoid_table).to("cuda:{}".format(self.args.local_rank)).float().unsqueeze(0)
# return sinusoid_table
# def forward(self, rgb_feat, ray_diff, mask):
# '''
# :param rgb_feat: featumre map from encoder from BTS. rgbs and image features [n_rays, n_samples, n_views, n_feat]
# :param ray_diff: ray direction difference [n_rays, n_samples, n_views, 4], first 3 channels are directions,
# 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]
# '''
# num_views = rgb_feat.shape[2]
# direction_feat = self.ray_dir_fc(ray_diff)
# rgb_in = rgb_feat[..., :3]
# rgb_feat = rgb_feat + direction_feat ## element-wise summation
# 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: ## from view dirs
# weight = mask / (torch.sum(mask, dim=2, keepdim=True) + 1e-8)
# # compute mean and variance across different views for each point
# mean, var = fused_mean_variance(rgb_feat, weight) # [n_rays, n_samples, 1, n_feat]
# globalfeat = torch.cat([mean, var], dim=-1) # [n_rays, n_samples, 1, 2*n_feat]
# x = torch.cat([globalfeat.expand(-1, -1, num_views, -1), rgb_feat], dim=-1) # [n_rays, n_samples, n_views, 3*n_feat]
# x = self.base_fc(x)
# 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
# weight = vis / (torch.sum(vis, dim=2, keepdim=True) + 1e-8) ## weight vector
# 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, 16]
# num_valid_obs = torch.sum(mask, dim=2)
# globalfeat = globalfeat + self.pos_encoding
# globalfeat, _ = self.ray_attention(globalfeat, globalfeat, globalfeat,
# mask=(num_valid_obs > 1).float()) # [n_rays, n_samples, 16]
# sigma = self.out_geometry_fc(globalfeat) # [n_rays, n_samples, 1]
# sigma_out = sigma.masked_fill(num_valid_obs < 1, 0.) # set the sigma of invalid point to zero
# # rgb computation
# x = torch.cat([x, vis, ray_diff], dim=-1)
# x = self.rgb_fc(x)
# x = x.masked_fill(mask == 0, -1e9)
# blending_weights_valid = F.softmax(x, dim=2) # color blending
# rgb_out = torch.sum(rgb_in*blending_weights_valid, dim=2)
# out = torch.cat([rgb_out, sigma_out], dim=-1)
# return out
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 change_pos_encoding(self,n_samples):
# self.pos_encoding = self.posenc(16, n_samples=n_samples)
# def posenc(self, d_hid, n_samples):
# def get_position_angle_vec(position):
# return [position / np.power(10000, 2 * (hid_j // 2) / d_hid) for hid_j in range(d_hid)]
#
# sinusoid_table = np.array([get_position_angle_vec(pos_i) for pos_i in range(n_samples)])
# sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i
# sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1
# sinusoid_table = torch.from_numpy(sinusoid_table).to("cuda:{}".format(0)).float().unsqueeze(0)
# return sinusoid_table
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
# globalfeat = globalfeat + self.pos_encoding
# globalfeat, _ = self.ray_attention(globalfeat, globalfeat, globalfeat, ## This should be replaced by DFT
# mask=(num_valid_obs > 1).float()) # [n_rays, n_samples, 16]
# sigma = self.out_geometry_fc(globalfeat) # [n_rays, n_samples, 1]
# sigma_out = sigma.masked_fill(num_valid_obs < 1, 0.) # set the sigma of invalid point to zero
# return sigma
return globalfeat, num_valid_obs ### [M, 64, att_feat], [M, 64, 1] ## Note: gfeat is already masked out above
# rgb computation
# x = torch.cat([x, vis, ray_diff], dim=-1)
# x = self.rgb_fc(x)
# x = x.masked_fill(mask == 0, -1e9)
# blending_weights_valid = F.softmax(x, dim=2) # color blending
# rgb_out = torch.sum(rgb_in*blending_weights_valid, dim=2)
# out = torch.cat([rgb_out, sigma_out], dim=-1)
# return out
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