点云步态识别代码和数据 dgcnn-hdnet-mmgait-data-STPointGCN-Data

#!/usr/bin/env python # -*- coding: utf-8 -*- """ @Author: Yue Wang @Contact: yuewangx@mit.edu @File: model.py @Time: 2018/10/13 6:35 PM Modified by @Author: An Tao, Ziyi Wu @Contact: ta19@mails.tsinghua.edu.cn, dazitu616@gmail.com @Time: 2022/7/30 7:49 PM """ 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, device='cuda'): 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(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() # (batch_size, num_points, num_dims) -> (batch_size*num_points, num_dims) # batch_size * num_points * k + range(0, batch_size*num_points) x = x.transpose(2, 1).contiguous() 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(nn.Module): def __init__(self, k=2, emb_dims=256, dropout=0., output_channels=40): super(DGCNN, self).__init__() # self.args = args self.k = 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(emb_dims) 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, emb_dims, kernel_size=1, bias=False), self.bn5, nn.LeakyReLU(negative_slope=0.2)) # reduce layer self.linear1 = nn.Linear(emb_dims * 2, 512, bias=False) self.bn6 = nn.BatchNorm1d(512) self.dp1 = nn.Dropout(p=dropout) self.linear2 = nn.Linear(512, 256) self.bn7 = nn.BatchNorm1d(256) self.dp2 = nn.Dropout(p=dropout) 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) return x class PointNet(nn.Module): def __init__(self, args, output_channels=40): super(PointNet, self).__init__() self.args = args self.conv1 = nn.Conv1d(3, 64, kernel_size=1, bias=False) self.conv2 = nn.Conv1d(64, 64, kernel_size=1, bias=False) self.conv3 = nn.Conv1d(64, 64, kernel_size=1, bias=False) self.conv4 = nn.Conv1d(64, 128, kernel_size=1, bias=False) self.conv5 = nn.Conv1d(128, args.emb_dims, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm1d(64) self.bn2 = nn.BatchNorm1d(64) self.bn3 = nn.BatchNorm1d(64) self.bn4 = nn.BatchNorm1d(128) self.bn5 = nn.BatchNorm1d(args.emb_dims) self.linear1 = nn.Linear(args.emb_dims, 512, bias=False) self.bn6 = nn.BatchNorm1d(512) self.dp1 = nn.Dropout() self.linear2 = nn.Linear(512, output_channels) def forward(self, x): x = F.relu(self.bn1(self.conv1(x))) x = F.relu(self.bn2(self.conv2(x))) x = F.relu(self.bn3(self.conv3(x))) x = F.relu(self.bn4(self.conv4(x))) x = F.relu(self.bn5(self.conv5(x))) x = F.adaptive_max_pool1d(x, 1).squeeze() x = F.relu(self.bn6(self.linear1(x))) x = self.dp1(x) x = self.linear2(x) return x class DGCNN_cls(nn.Module): def __init__(self, args, output_channels=40): super(DGCNN_cls, 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.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, 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.BatchNorm1