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import torch.nn as nn
import torch
import torch.nn.functional as F
from . import misc
# from knn_cuda import KNN
# knn = KNN(k=4, transpose_mode=False)
class DGCNN(nn.Module):
def __init__(self, encoder_channel, output_channel):
super().__init__()
'''
K has to be 16
'''
self.input_trans = nn.Conv1d(encoder_channel, 128, 1)
self.layer1 = nn.Sequential(nn.Conv2d(256, 256, kernel_size=1, bias=False),
nn.GroupNorm(4, 256),
nn.LeakyReLU(negative_slope=0.2)
)
self.layer2 = nn.Sequential(nn.Conv2d(512, 512, kernel_size=1, bias=False),
nn.GroupNorm(4, 512),
nn.LeakyReLU(negative_slope=0.2)
)
self.layer3 = nn.Sequential(nn.Conv2d(1024, 512, kernel_size=1, bias=False),
nn.GroupNorm(4, 512),
nn.LeakyReLU(negative_slope=0.2)
)
self.layer4 = nn.Sequential(nn.Conv2d(1024, 1024, kernel_size=1, bias=False),
nn.GroupNorm(4, 1024),
nn.LeakyReLU(negative_slope=0.2)
)
self.layer5 = nn.Sequential(nn.Conv1d(2304, output_channel, kernel_size=1, bias=False),
nn.GroupNorm(4, output_channel),
nn.LeakyReLU(negative_slope=0.2)
)
@staticmethod
def get_graph_feature(coor_q, x_q, coor_k, x_k):
# coor: bs, 3, np, x: bs, c, np
k = 4
batch_size = x_k.size(0)
num_points_k = x_k.size(2)
num_points_q = x_q.size(2)
with torch.no_grad():
_, idx = knn(coor_k, coor_q) # bs k np
assert idx.shape[1] == k
idx_base = torch.arange(0, batch_size, device=x_q.device).view(-1, 1, 1) * num_points_k
idx = idx + idx_base
idx = idx.view(-1)
num_dims = x_k.size(1)
x_k = x_k.transpose(2, 1).contiguous()
feature = x_k.view(batch_size * num_points_k, -1)[idx, :]
feature = feature.view(batch_size, k, num_points_q, num_dims).permute(0, 3, 2, 1).contiguous()
x_q = x_q.view(batch_size, num_dims, num_points_q, 1).expand(-1, -1, -1, k)
feature = torch.cat((feature - x_q, x_q), dim=1)
return feature
def forward(self, f, coor):
# f: B G C
# coor: B G 3
# bs 3 N bs C N
feature_list = []
coor = coor.transpose(1, 2).contiguous() # B 3 N
f = f.transpose(1, 2).contiguous() # B C N
f = self.input_trans(f) # B 128 N
f = self.get_graph_feature(coor, f, coor, f) # B 256 N k
f = self.layer1(f) # B 256 N k
f = f.max(dim=-1, keepdim=False)[0] # B 256 N
feature_list.append(f)
f = self.get_graph_feature(coor, f, coor, f) # B 512 N k
f = self.layer2(f) # B 512 N k
f = f.max(dim=-1, keepdim=False)[0] # B 512 N
feature_list.append(f)
f = self.get_graph_feature(coor, f, coor, f) # B 1024 N k
f = self.layer3(f) # B 512 N k
f = f.max(dim=-1, keepdim=False)[0] # B 512 N
feature_list.append(f)
f = self.get_graph_feature(coor, f, coor, f) # B 1024 N k
f = self.layer4(f) # B 1024 N k
f = f.max(dim=-1, keepdim=False)[0] # B 1024 N
feature_list.append(f)
f = torch.cat(feature_list, dim=1) # B 2304 N
f = self.layer5(f) # B C' N
f = f.transpose(-1, -2)
return f
### ref https://github.com/Strawberry-Eat-Mango/PCT_Pytorch/blob/main/util.py ###
def knn_point(nsample, xyz, new_xyz):
"""
Input:
nsample: max sample number in local region
xyz: all points, [B, N, C]
new_xyz: query points, [B, S, C]
Return:
group_idx: grouped points index, [B, S, nsample]
"""
sqrdists = square_distance(new_xyz, xyz)
_, group_idx = torch.topk(sqrdists, nsample, dim=-1, largest=False, sorted=False)
return group_idx
def square_distance(src, dst):
"""
Calculate Euclid distance between each two points.
src^T * dst = xn * xm + yn * ym + zn * zm；
sum(src^2, dim=-1) = xn*xn + yn*yn + zn*zn;
sum(dst^2, dim=-1) = xm*xm + ym*ym + zm*zm;
dist = (xn - xm)^2 + (yn - ym)^2 + (zn - zm)^2
= sum(src**2,dim=-1)+sum(dst**2,dim=-1)-2*src^T*dst
Input:
src: source points, [B, N, C]
dst: target points, [B, M, C]
Output:
dist: per-point square distance, [B, N, M]
"""
B, N, _ = src.shape
_, M, _ = dst.shape
dist = -2 * torch.matmul(src, dst.permute(0, 2, 1))
dist += torch.sum(src ** 2, -1).view(B, N, 1)
dist += torch.sum(dst ** 2, -1).view(B, 1, M)
return dist
class Group(nn.Module):
def __init__(self, num_group, group_size):
super().__init__()
self.num_group = num_group
self.group_size = group_size
# self.knn = KNN(k=self.group_size, transpose_mode=True)
def forward(self, xyz):
'''
input: B N 3
---------------------------
output: B G M 3
center : B G 3
'''
B, N, C = xyz.shape
if C > 3:
data = xyz
xyz = data[:,:,:3]
rgb = data[:, :, 3:]
batch_size, num_points, _ = xyz.shape
# fps the centers out
center = misc.fps(xyz, self.num_group) # B G 3
# knn to get the neighborhood
# _, idx = self.knn(xyz, center) # B G M
idx = knn_point(self.group_size, xyz, center) # B G M
assert idx.size(1) == self.num_group
assert idx.size(2) == self.group_size
idx_base = torch.arange(0, batch_size, device=xyz.device).view(-1, 1, 1) * num_points
idx = idx + idx_base
idx = idx.view(-1)
neighborhood_xyz = xyz.view(batch_size * num_points, -1)[idx, :]
neighborhood_xyz = neighborhood_xyz.view(batch_size, self.num_group, self.group_size, 3).contiguous()
if C > 3:
neighborhood_rgb = rgb.view(batch_size * num_points, -1)[idx, :]
neighborhood_rgb = neighborhood_rgb.view(batch_size, self.num_group, self.group_size, -1).contiguous()
# normalize xyz
neighborhood_xyz = neighborhood_xyz - center.unsqueeze(2)
if C > 3:
neighborhood = torch.cat((neighborhood_xyz, neighborhood_rgb), dim=-1)
else:
neighborhood = neighborhood_xyz
return neighborhood, center
class Encoder(nn.Module):
def __init__(self, encoder_channel, point_input_dims=3):
super().__init__()
self.encoder_channel = encoder_channel
self.point_input_dims = point_input_dims
self.first_conv = nn.Sequential(
nn.Conv1d(self.point_input_dims, 128, 1),
nn.BatchNorm1d(128),
nn.ReLU(inplace=True),
nn.Conv1d(128, 256, 1)
)
self.second_conv = nn.Sequential(
nn.Conv1d(512, 512, 1),
nn.BatchNorm1d(512),
nn.ReLU(inplace=True),
nn.Conv1d(512, self.encoder_channel, 1)
)
def forward(self, point_groups):
'''
point_groups : B G N 3
-----------------
feature_global : B G C
'''
bs, g, n, c = point_groups.shape
point_groups = point_groups.reshape(bs * g, n, c)
# encoder
feature = self.first_conv(point_groups.transpose(2, 1)) # BG 256 n
feature_global = torch.max(feature, dim=2, keepdim=True)[0] # BG 256 1
feature = torch.cat([feature_global.expand(-1, -1, n), feature], dim=1) # BG 512 n
feature = self.second_conv(feature) # BG 1024 n
feature_global = torch.max(feature, dim=2, keepdim=False)[0] # BG 1024
return feature_global.reshape(bs, g, self.encoder_channel)
class Decoder(nn.Module):
def __init__(self, encoder_channel, num_fine):
super().__init__()
self.num_fine = num_fine
self.grid_size = 2
self.num_coarse = self.num_fine // 4
assert num_fine % 4 == 0
self.mlp = nn.Sequential(
nn.Linear(encoder_channel, 1024),
nn.ReLU(inplace=True),
nn.Linear(1024, 1024),
nn.ReLU(inplace=True),
nn.Linear(1024, 3 * self.num_coarse)
)
self.final_conv = nn.Sequential(
nn.Conv1d(encoder_channel + 3 + 2, 512, 1),
nn.BatchNorm1d(512),
nn.ReLU(inplace=True),
nn.Conv1d(512, 512, 1),
nn.BatchNorm1d(512),
nn.ReLU(inplace=True),
nn.Conv1d(512, 3, 1)
)
a = torch.linspace(-0.05, 0.05, steps=self.grid_size, dtype=torch.float).view(1, self.grid_size).expand(
self.grid_size, self.grid_size).reshape(1, -1)
b = torch.linspace(-0.05, 0.05, steps=self.grid_size, dtype=torch.float).view(self.grid_size, 1).expand(
self.grid_size, self.grid_size).reshape(1, -1)
self.folding_seed = torch.cat([a, b], dim=0).view(1, 2, self.grid_size ** 2) # 1 2 S
def forward(self, feature_global):
'''
feature_global : B G C
-------
coarse : B G M 3
fine : B G N 3
'''
bs, g, c = feature_global.shape
feature_global = feature_global.reshape(bs * g, c)
coarse = self.mlp(feature_global).reshape(bs * g, self.num_coarse, 3) # BG M 3
point_feat = coarse.unsqueeze(2).expand(-1, -1, self.grid_size ** 2, -1) # BG (M) S 3
point_feat = point_feat.reshape(bs * g, self.num_fine, 3).transpose(2, 1) # BG 3 N
seed = self.folding_seed.unsqueeze(2).expand(bs * g, -1, self.num_coarse, -1) # BG 2 M (S)
seed = seed.reshape(bs * g, -1, self.num_fine).to(feature_global.device) # BG 2 N
feature_global = feature_global.unsqueeze(2).expand(-1, -1, self.num_fine) # BG 1024 N
feat = torch.cat([feature_global, seed, point_feat], dim=1) # BG C N
center = coarse.unsqueeze(2).expand(-1, -1, self.grid_size ** 2, -1) # BG (M) S 3
center = center.reshape(bs * g, self.num_fine, 3).transpose(2, 1) # BG 3 N
fine = self.final_conv(feat) + center # BG 3 N
fine = fine.reshape(bs, g, 3, self.num_fine).transpose(-1, -2)
coarse = coarse.reshape(bs, g, self.num_coarse, 3)
return coarse, fine
class DiscreteVAE(nn.Module):
def __init__(self, config, **kwargs):
super().__init__()
self.group_size = config.group_size
self.num_group = config.num_group
self.encoder_dims = config.encoder_dims
self.tokens_dims = config.tokens_dims
self.decoder_dims = config.decoder_dims
self.num_tokens = config.num_tokens
self.group_divider = Group(num_group=self.num_group, group_size=self.group_size)
self.encoder = Encoder(encoder_channel=self.encoder_dims)
self.dgcnn_1 = DGCNN(encoder_channel=self.encoder_dims, output_channel=self.num_tokens)
self.codebook = nn.Parameter(torch.randn(self.num_tokens, self.tokens_dims))
self.dgcnn_2 = DGCNN(encoder_channel=self.tokens_dims, output_channel=self.decoder_dims)
self.decoder = Decoder(encoder_channel=self.decoder_dims, num_fine=self.group_size)
# self.build_loss_func()
# def build_loss_func(self):
# self.loss_func_cdl1 = ChamferDistanceL1().cuda()
# self.loss_func_cdl2 = ChamferDistanceL2().cuda()
# self.loss_func_emd = emd().cuda()
def recon_loss(self, ret, gt):
whole_coarse, whole_fine, coarse, fine, group_gt, _ = ret
bs, g, _, _ = coarse.shape
coarse = coarse.reshape(bs * g, -1, 3).contiguous()
fine = fine.reshape(bs * g, -1, 3).contiguous()
group_gt = group_gt.reshape(bs * g, -1, 3).contiguous()
loss_coarse_block = self.loss_func_cdl1(coarse, group_gt)
loss_fine_block = self.loss_func_cdl1(fine, group_gt)
loss_recon = loss_coarse_block + loss_fine_block
return loss_recon
def get_loss(self, ret, gt):
# reconstruction loss
loss_recon = self.recon_loss(ret, gt)
# kl divergence
logits = ret[-1] # B G N
softmax = F.softmax(logits, dim=-1)
mean_softmax = softmax.mean(dim=1)
log_qy = torch.log(mean_softmax)
log_uniform = torch.log(torch.tensor([1. / self.num_tokens], device=gt.device))
loss_klv = F.kl_div(log_qy, log_uniform.expand(log_qy.size(0), log_qy.size(1)), None, None, 'batchmean',
log_target=True)
return loss_recon, loss_klv
def forward(self, inp, temperature=1., hard=False, **kwargs):
neighborhood, center = self.group_divider(inp)
logits = self.encoder(neighborhood) # B G C
logits = self.dgcnn_1(logits, center) # B G N
soft_one_hot = F.gumbel_softmax(logits, tau=temperature, dim=2, hard=hard) # B G N
sampled = torch.einsum('b g n, n c -> b g c', soft_one_hot, self.codebook) # B G C
feature = self.dgcnn_2(sampled, center)
coarse, fine = self.decoder(feature)
with torch.no_grad():
whole_fine = (fine + center.unsqueeze(2)).reshape(inp.size(0), -1, 3)
whole_coarse = (coarse + center.unsqueeze(2)).reshape(inp.size(0), -1, 3)
assert fine.size(2) == self.group_size
ret = (whole_coarse, whole_fine, coarse, fine, neighborhood, logits)
return ret |