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model_cls.py
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import os
import sys
import copy
import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
def gram_schmidt(vectors):
"""Gram-Schmidt process. Modified from https://stackoverflow.com/questions/48119473.
Parameters
----------
vectors: 2D tensor - [v1, v2, ...]
"""
basis = (vectors[0:1, :] / torch.norm(vectors[0:1, :]))
for i in range(1, vectors.size(0)):
v = vectors[i:i + 1, :]
w = v - torch.matmul(torch.matmul(v, basis.T), basis)
basis = torch.cat([basis, w / torch.norm(w)], dim=0)
return basis
def knn(x, k):
inner = -2 * torch.matmul(x.transpose(2, 1), x)
xx = torch.sum(x ** 2, dim=1, keepdim=True)
pairwise_distance = -xx - inner - xx.transpose(2, 1)
idx = pairwise_distance.topk(k=k, dim=-1)[1] # (batch_size, num_points, k)
return idx
def get_graph_feature1(x, k=20, idx=None):
batch_size = x.size(0)
num_points = x.size(2)
x = x.view(batch_size, -1, num_points)
if idx is None:
idx = knn(x, k=k) # (batch_size, num_points, k)
device = x.device
idx_base = torch.arange(0, batch_size, device=device).view(-1, 1, 1) * num_points
idx = idx + idx_base
idx = idx.view(-1)
_, num_dims, _ = x.size()
x = x.transpose(2,
1).contiguous() # (batch_size, num_points, num_dims) -> (batch_size*num_points, num_dims) # batch_size * num_points * k + range(0, batch_size*num_points)
feature = x.view(batch_size * num_points, -1)[idx, :]
feature = feature.view(batch_size, num_points, k, num_dims)
x = x.view(batch_size, num_points, 1, num_dims).repeat(1, 1, k, 1)
delta = feature - x
r = torch.abs(delta).max(dim=2, keepdim=True)[0]
feature = torch.cat((delta / (r + 0.000001), x), dim=3).permute(0, 3, 1, 2).contiguous()
return feature, idx
def get_graph_feature(x, k=20, idx=None):
batch_size = x.size(0)
num_points = x.size(2)
x = x.view(batch_size, -1, num_points)
if idx is None:
idx = knn(x, k=k) # (batch_size, num_points, k)
device = x.device
idx_base = torch.arange(0, batch_size, device=device).view(-1, 1, 1) * num_points
idx = idx + idx_base
idx = idx.view(-1)
_, num_dims, _ = x.size()
x = x.transpose(2,
1).contiguous() # (batch_size, num_points, num_dims) -> (batch_size*num_points, num_dims) # batch_size * num_points * k + range(0, batch_size*num_points)
feature = x.view(batch_size * num_points, -1)[idx, :]
feature = feature.view(batch_size, num_points, k, num_dims)
x = x.view(batch_size, num_points, 1, num_dims).repeat(1, 1, k, 1)
feature = torch.cat((feature - x, x), dim=3).permute(0, 3, 1, 2).contiguous()
return feature, idx
class SAKS(nn.Module):
def __init__(self, in_channels, out_channels, feat_channels, rank=8):
super(SAKS, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.feat_channels = feat_channels
self.conv0 = nn.Sequential()
basis = nn.init.orthogonal_(torch.empty(rank, out_channels * in_channels))
self.basis = nn.Parameter(basis, requires_grad=True)
mu = nn.init.kaiming_normal_(torch.empty(out_channels, in_channels), nonlinearity='relu').view(
out_channels * in_channels)
self.mu = nn.Parameter(mu, requires_grad=True)
self.bn0 = nn.BatchNorm2d(rank)
self.bn1 = nn.BatchNorm2d(out_channels)
self.leaky_relu = nn.LeakyReLU(negative_slope=0.2)
self.noise = nn.Sequential()
self.leaky_relu = nn.LeakyReLU(negative_slope=0.2)
def forward(self, x, y):
# x: (bs, in_channels, num_points, k), y: (bs, feat_channels, num_points, k)
batch_size, n_dims, num_points, k = x.size()
pass
return x, corr_loss
class Net(nn.Module):
def __init__(self, args, output_channels=40):
super(Net, self).__init__()
self.args = args
self.k = args.k
self.bn1 = nn.BatchNorm2d(64)
self.bn2 = nn.BatchNorm2d(64)
self.bn3 = nn.BatchNorm2d(128)
self.bn4 = nn.BatchNorm2d(256)
self.bn5 = nn.BatchNorm1d(args.emb_dims)
self.conv3 = nn.Sequential(nn.Conv2d(64 * 2, 128, kernel_size=1, bias=False),
self.bn3,
nn.LeakyReLU(negative_slope=0.2))
self.conv4 = nn.Sequential(nn.Conv2d(128 * 2, 256, kernel_size=1, bias=False),
self.bn4,
nn.LeakyReLU(negative_slope=0.2))
self.conv5 = nn.Sequential(nn.Conv1d(512, args.emb_dims, kernel_size=1, bias=False),
self.bn5,
nn.LeakyReLU(negative_slope=0.2))
self.linear1 = nn.Linear(args.emb_dims * 2, 512, bias=False)
self.bn6 = nn.BatchNorm1d(512)
self.dp1 = nn.Dropout(p=args.dropout)
self.linear2 = nn.Linear(512, 256)
self.bn7 = nn.BatchNorm1d(256)
self.dp2 = nn.Dropout(p=args.dropout)
self.linear3 = nn.Linear(256, output_channels)
self.saks1 = SAKS(6, 64, 6)
self.saks2 = SAKS(6, 64, 64 * 2)
def forward(self, x):
batch_size = x.size(0)
points = x
x, idx = get_graph_feature(x, k=self.k)
p, _ = get_graph_feature(points, k=self.k, idx=idx)
x, corr_loss1 = self.saks1(p, x)
x1 = x.max(dim=-1, keepdim=False)[0]
x, idx = get_graph_feature(x1, k=self.k)
p, _ = get_graph_feature(points, k=self.k, idx=idx)
x, corr_loss2 = self.saks2(p, x)
x2 = x.max(dim=-1, keepdim=False)[0]
x, _ = get_graph_feature(x2, k=self.k)
x = self.conv3(x)
x3 = x.max(dim=-1, keepdim=False)[0]
x, _ = get_graph_feature(x3, k=self.k)
x = self.conv4(x)
x4 = x.max(dim=-1, keepdim=False)[0]
x = torch.cat((x1, x2, x3, x4), dim=1)
x = self.conv5(x)
x1 = F.adaptive_max_pool1d(x, 1).view(batch_size, -1)
x2 = F.adaptive_avg_pool1d(x, 1).view(batch_size, -1)
x = torch.cat((x1, x2), 1)
x = F.leaky_relu(self.bn6(self.linear1(x)), negative_slope=0.2)
x = self.dp1(x)
x = F.leaky_relu(self.bn7(self.linear2(x)), negative_slope=0.2)
x = self.dp2(x)
x = self.linear3(x)
return x, corr_loss1 + corr_loss2