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import torch.nn as nn
import torch.nn.functional as F
import os
import copy
import math
import numpy as np
from torch_geometric.data import Data, DataLoader
from torch_geometric.nn import (
GCNConv,
GATConv,
global_mean_pool,
global_max_pool,
JumpingKnowledge,
)
from torch.nn import Sequential, Linear, ReLU, BatchNorm1d, Dropout
from torch_geometric.nn import BatchNorm
def knn(x, k):
"""
Computes the k-nearest neighbors for each point in x.
Args:
x (torch.Tensor): The input tensor of shape (batch_size, num_dims, num_points).
k (int): The number of nearest neighbors to find.
Returns:
torch.Tensor: Indices of the k-nearest neighbors for each point, shape (batch_size, num_points, k).
"""
# Calculate pairwise distance, shape (batch_size, num_points, num_points)
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)
# Retrieve the indices of the k nearest neighbors
idx = pairwise_distance.topk(k=k, dim=-1)[1]
return idx
def get_graph_feature(x, k=20, idx=None):
"""
Constructs local graph features for each point by finding its k-nearest neighbors and
concatenating the relative position vectors.
Args:
x (torch.Tensor): The input tensor of shape (batch_size, num_dims, num_points).
k (int): The number of neighbors to consider for graph construction.
idx (torch.Tensor, optional): Precomputed k-nearest neighbor indices.
Returns:
torch.Tensor: The constructed graph features of shape (batch_size, 2*num_dims, num_points, k).
"""
batch_size = x.size(0)
num_points = x.size(2)
x = x.view(batch_size, -1, num_points)
# Compute k-nearest neighbors if not provided
if idx is None:
idx = knn(x, k=k)
# Prepare indices for gathering
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()
# Gather neighbors for each point to construct local regions
feature = x.view(batch_size * num_points, -1)[idx, :]
feature = feature.view(batch_size, num_points, k, num_dims)
# Expand x to match the dimensions for broadcasting subtraction
x = x.view(batch_size, num_points, 1, num_dims).repeat(1, 1, k, 1)
# Concatenate the original point features with the relative positions to form the graph features
feature = torch.cat((feature - x, x), dim=3).permute(0, 3, 1, 2).contiguous()
return feature
# class Model(nn.Module):
# """
# Deep Graph Convolutional Neural Network for Regression Tasks (RegDGCNN) for processing 3D point cloud data.
# This network architecture extracts hierarchical features from point clouds using graph-based convolutions,
# enabling effective learning of spatial structures.
# """
# def __init__(self, args, device):
# """
# Initializes the RegDGCNN model with specified configurations.
# Args:
# args (dict): Configuration parameters including 'k' for the number of neighbors, 'emb_dims' for embedding
# dimensions, and 'dropout' rate.
# output_channels (int): Number of output channels (e.g., for drag prediction, this is 1).
# """
# super(Model, self).__init__()
# self.__name__ = 'RegDGCNN'
# self.args = args
# self.k = 20 # Number of nearest neighbors
# # Batch normalization layers to stabilize and accelerate training
# self.bn1 = nn.BatchNorm2d(args.n_hidden)
# self.bn2 = nn.BatchNorm2d(args.n_hidden * 2)
# self.bn3 = nn.BatchNorm2d(args.n_hidden * 2)
# self.bn4 = nn.BatchNorm2d(args.n_hidden * 4)
# self.bn5 = nn.BatchNorm1d(args.emb_dims)
# # EdgeConv layers: Convolutional layers leveraging local neighborhood information
# self.conv1 = nn.Sequential(nn.Conv2d(2*(args.fun_dim + args.space_dim), args.n_hidden, kernel_size=1, bias=False),
# self.bn1,
# nn.LeakyReLU(negative_slope=0.2))
# self.conv2 = nn.Sequential(nn.Conv2d(args.n_hidden * 2, args.n_hidden * 2, kernel_size=1, bias=False),
# self.bn2,
# nn.LeakyReLU(negative_slope=0.2))
# self.conv3 = nn.Sequential(nn.Conv2d(args.n_hidden * 4, args.n_hidden * 2, kernel_size=1, bias=False),
# self.bn3,
# nn.LeakyReLU(negative_slope=0.2))
# self.conv4 = nn.Sequential(nn.Conv2d(args.n_hidden * 4, args.n_hidden * 4, kernel_size=1, bias=False),
# self.bn4,
# nn.LeakyReLU(negative_slope=0.2))
# self.conv5 = nn.Sequential(nn.Conv1d(args.n_hidden * 9, args.emb_dims, kernel_size=1, bias=False),
# self.bn5,
# nn.LeakyReLU(negative_slope=0.2))
# # Fully connected layers to interpret the extracted features and make predictions
# self.linear1 = nn.Linear(args.emb_dims*2, 128, bias=False)
# self.bn6 = nn.LayerNorm(128)
# self.dp1 = nn.Dropout(p=args.dropout)
# self.linear2 = nn.Linear(128, 64)
# self.bn7 = nn.LayerNorm(64)
# self.dp2 = nn.Dropout(p=args.dropout)
# self.linear3 = nn.Linear(64, 32)
# self.bn8 = nn.LayerNorm(32)
# self.dp3 = nn.Dropout(p=args.dropout)
# self.linear4 = nn.Linear(32, 16)
# self.bn9 = nn.LayerNorm(16)
# self.dp4 = nn.Dropout(p=args.dropout)
# self.linear5 = nn.Linear(16, args.out_dim) # The final output layer
# def _make_norm_layer(self, channels):
# """创建归一化层,支持batch size=1"""
# return nn.GroupNorm(num_groups=min(32, channels), num_channels=channels)
# def forward(self, x, fx, T=None, geo=None):
# """
# Forward pass of the model to process input data and predict outputs.
# Args:
# x (torch.Tensor): Input tensor representing a batch of point clouds.
# Returns:
# torch.Tensor: Model predictions for the input batch.
# """
# batch_size = x.size(0)
# # 兼容 batch_size = 1 输入：去除 batch 维度
# x = torch.cat([x, fx], dim=-1)
# x = x.permute(0, 2, 1)
# print(f"x:{x.shape}")
# # Extract graph features and apply EdgeConv blocks
# x = get_graph_feature(x, k=self.k) # (batch_size, 3, num_points) -> (batch_size, 3*2, num_points, k)
# print(f"x:{x.shape}")
# x = self.conv1(x) # (batch_size, 3*2, num_points, k) -> (batch_size, 256, num_points, k)
# # Global max pooling
# x1 = x.max(dim=-1, keepdim=False)[0] # (batch_size, 64, num_points, k) -> (batch_size, 64, num_points)
# # Repeat the process for subsequent EdgeConv blocks
# x = get_graph_feature(x1, k=self.k) # (batch_size, 256, num_points) -> (batch_size, 256*2, num_points, k)
# x = self.conv2(x) # (batch_size, 256*2, num_points, k) -> (batch_size, 512, num_points, k)
# x2 = x.max(dim=-1, keepdim=False)[0] # (batch_size, 512, num_points, k) -> (batch_size, 512, num_points)
# x = get_graph_feature(x2, k=self.k) # (batch_size, 512, num_points) -> (batch_size, 512*2, num_points, k)
# x = self.conv3(x) # (batch_size, 512*2, num_points, k) -> (batch_size, 512, num_points, k)
# x3 = x.max(dim=-1, keepdim=False)[0] # (batch_size, 512, num_points, k) -> (batch_size, 512, num_points)
# x = get_graph_feature(x3, k=self.k) # (batch_size, 512, num_points) -> (batch_size, 512*2, num_points, k)
# x = self.conv4(x) # (batch_size, 512*2, num_points, k) -> (batch_size, 1024, num_points, k)
# x4 = x.max(dim=-1, keepdim=False)[0] # (batch_size, 1024, num_points, k) -> (batch_size, 1024, num_points)
# # Concatenate features from all EdgeConv blocks
# x = torch.cat((x1, x2, x3, x4), dim=1) # (batch_size, 256+512+512+1024, num_points)
# # Apply the final convolutional block
# x = self.conv5(x) # (batch_size, 256+512+512+1024, num_points) -> (batch_size, emb_dims, num_points)
# # Combine global max and average pooling features
# # (batch_size, emb_dims, num_points) -> (batch_size, emb_dims)
# 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)
# x = torch.cat((x1, x2), 1) # (batch_size, emb_dims*2)
# print(f"x:{x.shape}")
# # Process features through fully connected layers with dropout and batch normalization
# x = F.leaky_relu(self.bn6(self.linear1(x)), negative_slope=0.2) # (batch_size, emb_dims*2) -> (batch_size, 128)
# x = self.dp1(x)
# x = F.leaky_relu(self.bn7(self.linear2(x)), negative_slope=0.2) # (batch_size, 128) -> (batch_size, 64)
# x = self.dp2(x)
# x = F.leaky_relu(self.bn8(self.linear3(x)), negative_slope=0.2) # (batch_size, 64) -> (batch_size, 32)
# x = self.dp3(x)
# x = F.leaky_relu(self.bn9(self.linear4(x)), negative_slope=0.2) # (batch_size, 32) -> (batch_size, 16)
# x = self.dp4(x)
# # Final linear layer to produce the output
# x = self.linear5(x) # (batch_size, 16) -> (batch_size, 1)
# print(f"x:{x.shape}")
# x = x.permute(0, 2, 1)
# return x
class Model(nn.Module):
"""
Deep Graph Convolutional Neural Network for Regression Tasks (RegDGCNN) for processing 3D point cloud data.
This network architecture extracts hierarchical features from point clouds using graph-based convolutions,
enabling effective learning of spatial structures.
"""
def __init__(self, args, device):
"""
Initializes the RegDGCNN model with specified configurations.
Args:
args (dict): Configuration parameters including 'k' for the number of neighbors, 'emb_dims' for embedding
dimensions, and 'dropout' rate.
output_channels (int): Number of output channels (e.g., for drag prediction, this is 1).
"""
super(Model, self).__init__()
self.__name__ = "RegDGCNN"
self.args = args
self.k = 40 # Number of nearest neighbors
# Batch normalization layers to stabilize and accelerate training
self.bn1 = nn.BatchNorm2d(args.n_hidden)
self.bn2 = nn.BatchNorm2d(args.n_hidden * 2)
self.bn3 = nn.BatchNorm2d(args.n_hidden * 2)
self.bn4 = nn.BatchNorm2d(args.n_hidden * 4)
self.bn5 = nn.BatchNorm1d(args.emb_dims)
# EdgeConv layers: Convolutional layers leveraging local neighborhood information
self.conv1 = nn.Sequential(
nn.Conv2d(
2 * (args.fun_dim + args.space_dim),
args.n_hidden,
kernel_size=1,
bias=False,
),
self.bn1,
nn.LeakyReLU(negative_slope=0.2),
)
self.conv2 = nn.Sequential(
nn.Conv2d(args.n_hidden * 2, args.n_hidden * 2, kernel_size=1, bias=False),
self.bn2,
nn.LeakyReLU(negative_slope=0.2),
)
self.conv3 = nn.Sequential(
nn.Conv2d(args.n_hidden * 4, args.n_hidden * 2, kernel_size=1, bias=False),
self.bn3,
nn.LeakyReLU(negative_slope=0.2),
)
self.conv4 = nn.Sequential(
nn.Conv2d(args.n_hidden * 4, args.n_hidden * 4, kernel_size=1, bias=False),
self.bn4,
nn.LeakyReLU(negative_slope=0.2),
)
self.conv5 = nn.Sequential(
nn.Conv1d(args.n_hidden * 9, args.emb_dims, kernel_size=1, bias=False),
self.bn5,
nn.LeakyReLU(negative_slope=0.2),
)
# Fully connected layers to interpret the extracted features and make predictions
self.point_pred = nn.Sequential(
nn.Linear(args.emb_dims, 64), nn.ReLU(), nn.Linear(64, args.out_dim)
)
def _make_norm_layer(self, channels):
return nn.GroupNorm(num_groups=min(32, channels), num_channels=channels)
def forward(self, x, fx, T=None, geo=None):
"""
Forward pass of the model to process input data and predict outputs.
Args:
x (torch.Tensor): Input tensor representing a batch of point clouds.
Returns:
torch.Tensor: Model predictions for the input batch.
"""
batch_size = x.size(0)
# 兼容 batch_size = 1 输入：去除 batch 维度
x = torch.cat([x, fx], dim=-1)
x = x.permute(0, 2, 1)
# Extract graph features and apply EdgeConv blocks
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, 256, num_points, k)
# Global max pooling
x1 = x.max(dim=-1, keepdim=False)[
0
] # (batch_size, 64, num_points, k) -> (batch_size, 64, num_points)
# Repeat the process for subsequent EdgeConv blocks
x = get_graph_feature(
x1, k=self.k
) # (batch_size, 256, num_points) -> (batch_size, 256*2, num_points, k)
x = self.conv2(
x
) # (batch_size, 256*2, num_points, k) -> (batch_size, 512, num_points, k)
x2 = x.max(dim=-1, keepdim=False)[
0
] # (batch_size, 512, num_points, k) -> (batch_size, 512, num_points)
x = get_graph_feature(
x2, k=self.k
) # (batch_size, 512, num_points) -> (batch_size, 512*2, num_points, k)
x = self.conv3(
x
) # (batch_size, 512*2, num_points, k) -> (batch_size, 512, num_points, k)
x3 = x.max(dim=-1, keepdim=False)[
0
] # (batch_size, 512, num_points, k) -> (batch_size, 512, num_points)
x = get_graph_feature(
x3, k=self.k
) # (batch_size, 512, num_points) -> (batch_size, 512*2, num_points, k)
x = self.conv4(
x
) # (batch_size, 512*2, num_points, k) -> (batch_size, 1024, num_points, k)
x4 = x.max(dim=-1, keepdim=False)[
0
] # (batch_size, 1024, num_points, k) -> (batch_size, 1024, num_points)
# Concatenate features from all EdgeConv blocks
x = torch.cat(
(x1, x2, x3, x4), dim=1
) # (batch_size, 256+512+512+1024, num_points)
# Apply the final convolutional block
x = self.conv5(
x
) # (batch_size, 256+512+512+1024, num_points) -> (batch_size, emb_dims, num_points)
x = x.permute(0, 2, 1) # (batch_size, num_points, emb_dims)
out = self.point_pred(x) # 点级预测层，输出 (batch_size, num_points, out_dim)
return out
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