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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.init as init
- import torch.nn.functional as F
-
- 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_feature(x, k=20, idx=None, dim9=False):
- batch_size = x.size(0)
- num_points = x.size(2)
- x = x.view(batch_size, -1, num_points)
- if idx is None:
- if dim9 == False:
- idx = knn(x, k=k) # (batch_size, num_points, k)
- else:
- idx = knn(x[:, 6:], k=k)
- #device = torch.device('cuda')
-
- idx_base = torch.arange(0, batch_size).view(-1, 1, 1).cuda() * 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 # (batch_size, 2*num_dims, num_points, k)
-
-
-
-
-
- class DGCNN_cls(nn.Module):
- def __init__(self, output_channels=40):
- super(DGCNN_cls, self).__init__()
- self.k = 20
-
- self.bn1 = nn.BatchNorm2d(64)
- self.bn2 = nn.BatchNorm2d(64)
- self.bn3 = nn.BatchNorm2d(128)
- self.bn4 = nn.BatchNorm2d(256)
- self.bn5 = nn.BatchNorm1d(1024)
-
- self.conv1 = nn.Sequential(nn.Conv2d(6, 64, kernel_size=1, bias=False),
- self.bn1,
- nn.LeakyReLU(negative_slope=0.2))
- self.conv2 = nn.Sequential(nn.Conv2d(64 * 2, 64, kernel_size=1, bias=False),
- self.bn2,
- nn.LeakyReLU(negative_slope=0.2))
- 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, 1024, kernel_size=1, bias=False),
- self.bn5,
- nn.LeakyReLU(negative_slope=0.2))
- self.linear1 = nn.Linear(1024 * 2, 512, bias=False)
- self.bn6 = nn.BatchNorm1d(512)
- self.dp1 = nn.Dropout(p=0.5)
- self.linear2 = nn.Linear(512, 256)
- self.bn7 = nn.BatchNorm1d(256)
- self.dp2 = nn.Dropout(p=0.5)
- self.linear3 = nn.Linear(256, output_channels)
-
- def forward(self, x):
- batch_size = x.size(0)
- x = get_graph_feature(x, k=self.k) # (batch_size, 3, num_points) -> (batch_size, 3*2, num_points, k)
- x = self.conv1(x) # (batch_size, 3*2, num_points, k) -> (batch_size, 64, num_points, k)
- x1 = x.max(dim=-1, keepdim=False)[0] # (batch_size, 64, num_points, k) -> (batch_size, 64, num_points)
-
- x = get_graph_feature(x1, k=self.k) # (batch_size, 64, num_points) -> (batch_size, 64*2, num_points, k)
- x = self.conv2(x) # (batch_size, 64*2, num_points, k) -> (batch_size, 64, num_points, k)
- x2 = x.max(dim=-1, keepdim=False)[0] # (batch_size, 64, num_points, k) -> (batch_size, 64, num_points)
-
- x = get_graph_feature(x2, k=self.k) # (batch_size, 64, num_points) -> (batch_size, 64*2, num_points, k)
- x = self.conv3(x) # (batch_size, 64*2, num_points, k) -> (batch_size, 128, num_points, k)
- x3 = x.max(dim=-1, keepdim=False)[0] # (batch_size, 128, num_points, k) -> (batch_size, 128, num_points)
-
- x = get_graph_feature(x3, k=self.k) # (batch_size, 128, num_points) -> (batch_size, 128*2, num_points, k)
- x = self.conv4(x) # (batch_size, 128*2, num_points, k) -> (batch_size, 256, num_points, k)
- x4 = x.max(dim=-1, keepdim=False)[0] # (batch_size, 256, num_points, k) -> (batch_size, 256, num_points)
-
- x = torch.cat((x1, x2, x3, x4), dim=1) # (batch_size, 64+64+128+256, num_points)
-
- x = self.conv5(x) # (batch_size, 64+64+128+256, num_points) -> (batch_size, emb_dims, num_points)
- #xbeforepooling = x
- x1 = F.adaptive_max_pool1d(x, 1).view(batch_size,
- -1) # (batch_size, emb_dims, num_points) -> (batch_size, emb_dims)
- x2 = F.adaptive_avg_pool1d(x, 1).view(batch_size,
- -1) # (batch_size, emb_dims, num_points) -> (batch_size, emb_dims)
- x = torch.cat((x1, x2), 1) # (batch_size, emb_dims*2)
- catx = x
-
- x = F.leaky_relu(self.bn6(self.linear1(x)), negative_slope=0.2) # (batch_size, emb_dims*2) -> (batch_size, 512)
- x = self.dp1(x)
- x = F.leaky_relu(self.bn7(self.linear2(x)), negative_slope=0.2) # (batch_size, 512) -> (batch_size, 256)
- x = self.dp2(x)
- x = self.linear3(x) # (batch_size, 256) -> (batch_size, output_channels)
-
- return x,x1,catx
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