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gcn3d.py
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gcn3d.py
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"""
@Author: Zhi-Hao Lin
@Contact: r08942062@ntu.edu.tw
@Time: 2020/03/06
@Document: Basic operation/blocks of 3D-GCN
"""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
def get_neighbor_index(vertices: "(bs, vertice_num, 3)", neighbor_num: int):
"""
Return: (bs, vertice_num, neighbor_num)
"""
bs, v, _ = vertices.size()
device = vertices.device
inner = torch.bmm(vertices, vertices.transpose(1, 2)) #(bs, v, v)
quadratic = torch.sum(vertices**2, dim= 2) #(bs, v)
distance = inner * (-2) + quadratic.unsqueeze(1) + quadratic.unsqueeze(2)
neighbor_index = torch.topk(distance, k= neighbor_num + 1, dim= -1, largest= False)[1]
neighbor_index = neighbor_index[:, :, 1:]
return neighbor_index
def get_nearest_index(target: "(bs, v1, 3)", source: "(bs, v2, 3)"):
"""
Return: (bs, v1, 1)
"""
inner = torch.bmm(target, source.transpose(1, 2)) #(bs, v1, v2)
s_norm_2 = torch.sum(source ** 2, dim= 2) #(bs, v2)
t_norm_2 = torch.sum(target ** 2, dim= 2) #(bs, v1)
d_norm_2 = s_norm_2.unsqueeze(1) + t_norm_2.unsqueeze(2) - 2 * inner
nearest_index = torch.topk(d_norm_2, k= 1, dim= -1, largest= False)[1]
return nearest_index
def indexing_neighbor(tensor: "(bs, vertice_num, dim)", index: "(bs, vertice_num, neighbor_num)" ):
"""
Return: (bs, vertice_num, neighbor_num, dim)
"""
bs, v, n = index.size()
id_0 = torch.arange(bs).view(-1, 1, 1)
tensor_indexed = tensor[id_0, index]
return tensor_indexed
def get_neighbor_direction_norm(vertices: "(bs, vertice_num, 3)", neighbor_index: "(bs, vertice_num, neighbor_num)"):
"""
Return: (bs, vertice_num, neighobr_num, 3)
"""
neighbors = indexing_neighbor(vertices, neighbor_index) # (bs, v, n, 3)
neighbor_direction = neighbors - vertices.unsqueeze(2)
neighbor_direction_norm = F.normalize(neighbor_direction, dim= -1)
return neighbor_direction_norm
class Conv_surface(nn.Module):
"""Extract structure feafure from surface, independent from vertice coordinates"""
def __init__(self, kernel_num, support_num):
super().__init__()
self.kernel_num = kernel_num
self.support_num = support_num
self.relu = nn.ReLU(inplace= True)
self.directions = nn.Parameter(torch.FloatTensor(3, support_num * kernel_num))
self.initialize()
def initialize(self):
stdv = 1. / math.sqrt(self.support_num * self.kernel_num)
self.directions.data.uniform_(-stdv, stdv)
def forward(self,
neighbor_index: "(bs, vertice_num, neighbor_num)",
vertices: "(bs, vertice_num, 3)"):
"""
Return vertices with local feature: (bs, vertice_num, kernel_num)
"""
bs, vertice_num, neighbor_num = neighbor_index.size()
neighbor_direction_norm = get_neighbor_direction_norm(vertices, neighbor_index)
support_direction_norm = F.normalize(self.directions, dim= 0) #(3, s * k)
theta = neighbor_direction_norm @ support_direction_norm # (bs, vertice_num, neighbor_num, s*k)
theta = self.relu(theta)
theta = theta.contiguous().view(bs, vertice_num, neighbor_num, self.support_num, self.kernel_num)
theta = torch.max(theta, dim= 2)[0] # (bs, vertice_num, support_num, kernel_num)
feature = torch.sum(theta, dim= 2) # (bs, vertice_num, kernel_num)
return feature
class Conv_layer(nn.Module):
def __init__(self, in_channel, out_channel, support_num):
super().__init__()
# arguments:
self.in_channel = in_channel
self.out_channel = out_channel
self.support_num = support_num
# parameters:
self.relu = nn.ReLU(inplace= True)
self.weights = nn.Parameter(torch.FloatTensor(in_channel, (support_num + 1) * out_channel))
self.bias = nn.Parameter(torch.FloatTensor((support_num + 1) * out_channel))
self.directions = nn.Parameter(torch.FloatTensor(3, support_num * out_channel))
self.initialize()
def initialize(self):
stdv = 1. / math.sqrt(self.out_channel * (self.support_num + 1))
self.weights.data.uniform_(-stdv, stdv)
self.bias.data.uniform_(-stdv, stdv)
self.directions.data.uniform_(-stdv, stdv)
def forward(self,
neighbor_index: "(bs, vertice_num, neighbor_index)",
vertices: "(bs, vertice_num, 3)",
feature_map: "(bs, vertice_num, in_channel)"):
"""
Return: output feature map: (bs, vertice_num, out_channel)
"""
bs, vertice_num, neighbor_num = neighbor_index.size()
neighbor_direction_norm = get_neighbor_direction_norm(vertices, neighbor_index)
support_direction_norm = F.normalize(self.directions, dim= 0)
theta = neighbor_direction_norm @ support_direction_norm # (bs, vertice_num, neighbor_num, support_num * out_channel)
theta = self.relu(theta)
theta = theta.contiguous().view(bs, vertice_num, neighbor_num, -1)
# (bs, vertice_num, neighbor_num, support_num * out_channel)
feature_out = feature_map @ self.weights + self.bias # (bs, vertice_num, (support_num + 1) * out_channel)
feature_center = feature_out[:, :, :self.out_channel] # (bs, vertice_num, out_channel)
feature_support = feature_out[:, :, self.out_channel:] #(bs, vertice_num, support_num * out_channel)
# Fuse together - max among product
feature_support = indexing_neighbor(feature_support, neighbor_index) # (bs, vertice_num, neighbor_num, support_num * out_channel)
activation_support = theta * feature_support # (bs, vertice_num, neighbor_num, support_num * out_channel)
activation_support = activation_support.view(bs,vertice_num, neighbor_num, self.support_num, self.out_channel)
activation_support = torch.max(activation_support, dim= 2)[0] # (bs, vertice_num, support_num, out_channel)
activation_support = torch.sum(activation_support, dim= 2) # (bs, vertice_num, out_channel)
feature_fuse = feature_center + activation_support # (bs, vertice_num, out_channel)
return feature_fuse
class Pool_layer(nn.Module):
def __init__(self, pooling_rate: int= 4, neighbor_num: int= 4):
super().__init__()
self.pooling_rate = pooling_rate
self.neighbor_num = neighbor_num
def forward(self,
vertices: "(bs, vertice_num, 3)",
feature_map: "(bs, vertice_num, channel_num)"):
"""
Return:
vertices_pool: (bs, pool_vertice_num, 3),
feature_map_pool: (bs, pool_vertice_num, channel_num)
"""
bs, vertice_num, _ = vertices.size()
neighbor_index = get_neighbor_index(vertices, self.neighbor_num)
neighbor_feature = indexing_neighbor(feature_map, neighbor_index) #(bs, vertice_num, neighbor_num, channel_num)
pooled_feature = torch.max(neighbor_feature, dim= 2)[0] #(bs, vertice_num, channel_num)
pool_num = int(vertice_num / self.pooling_rate)
sample_idx = torch.randperm(vertice_num)[:pool_num]
vertices_pool = vertices[:, sample_idx, :] # (bs, pool_num, 3)
feature_map_pool = pooled_feature[:, sample_idx, :] #(bs, pool_num, channel_num)
return vertices_pool, feature_map_pool
def test():
import time
bs = 8
v = 1024
dim = 3
n = 20
vertices = torch.randn(bs, v, dim)
neighbor_index = get_neighbor_index(vertices, n)
s = 3
conv_1 = Conv_surface(kernel_num= 32, support_num= s)
conv_2 = Conv_layer(in_channel= 32, out_channel= 64, support_num= s)
pool = Pool_layer(pooling_rate= 4, neighbor_num= 4)
print("Input size: {}".format(vertices.size()))
start = time.time()
f1 = conv_1(neighbor_index, vertices)
print("\n[1] Time: {}".format(time.time() - start))
print("[1] Out shape: {}".format(f1.size()))
start = time.time()
f2 = conv_2(neighbor_index, vertices, f1)
print("\n[2] Time: {}".format(time.time() - start))
print("[2] Out shape: {}".format(f2.size()))
start = time.time()
v_pool, f_pool = pool(vertices, f2)
print("\n[3] Time: {}".format(time.time() - start))
print("[3] v shape: {}, f shape: {}".format(v_pool.size(), f_pool.size()))
if __name__ == "__main__":
test()