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432 lines
15 KiB
432 lines
15 KiB
11 months ago
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# -*- coding: utf-8 -*-
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"""
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Created on Fri Aug 20 15:36:00 2021
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@author: Liangchao Zhu
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"""
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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class View(nn.Module):
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def __init__(self, shape):
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super(View, self).__init__()
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self.shape = shape
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def forward(self, input):
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return input.view(self.shape)
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def module_size(module):
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assert isinstance(module, torch.nn.Module)
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n_params, n_conv_layers = 0, 0
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for name, param in module.named_parameters():
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if 'conv' in name:
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n_conv_layers += 1
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n_params += param.numel()
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return n_params, n_conv_layers
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class G_CNN3d(nn.Module):
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def __init__(self, input_dims=256, dense_channels=32, nelx=20,nely=20,nelz=20):
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super(G_CNN3d, self).__init__()
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self.features = nn.Sequential()
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# resizes = (1, 2, 1, 2, 1, 2, 1)
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# conv_filters = (128, 64, 32, 32, 16, 16, 1)
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resizes = (1, 2, 1, 2, 1)
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conv_filters = (128, 64, 32, 16, 1)
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# 多加几层conv
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# conv_filters = (128, 64, 1)
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# 5, 10, 20, 40
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fx = nelx//4
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fy = nely//4
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fz = nelz//4
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dense_filters = fx*fy*fz * dense_channels
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self.features.add_module('In_dense', nn.Linear(input_dims, dense_filters))
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self.features.add_module('reshape', View((-1, dense_channels, fx, fy, fz))) # bs,32,5,5,5
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pre_filter = dense_channels
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i = 1
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# for filters in conv_filters:
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for resize in resizes:
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filters = conv_filters[i-1]
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self.features.add_module('Block%d_Act' % i, nn.Tanh())
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if resize == 2:
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self.features.add_module('Block%d_Up' % i, nn.Upsample(scale_factor=2, mode='trilinear'))
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self.features.add_module('Block%d_BN' % i, nn.BatchNorm3d(pre_filter))
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self.features.add_module('Block%d_conv' % i, nn.Conv3d(pre_filter, filters, kernel_size=5, stride=1, padding=2,
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bias=True, padding_mode='replicate'))
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# bias?
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pre_filter = filters
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i += 1
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# self.input = nn.Linear(input_dims, dense_filters)
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# self.Trans = View((-1, dense_channels, 5, 5, 5))
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print('# params {}, # conv layers {}'.format(
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*self.model_size))
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def forward(self, x):
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return F.sigmoid(self.features(x))
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@property
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def model_size(self):
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return module_size(self)
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class generator_NAND_rand(nn.Module):
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def __init__(self, z_dim, point_dim, gf_dim):
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super(generator_NAND_rand, self).__init__()
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self.z_dim = z_dim
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self.point_dim = point_dim
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self.gf_dim = gf_dim
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self.linear_1 = nn.Linear(self.z_dim+self.point_dim, self.gf_dim*8, bias=True)
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self.linear_2 = nn.Linear(self.gf_dim*8, self.gf_dim*8, bias=True)
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self.linear_3 = nn.Linear(self.gf_dim*8, self.gf_dim*8, bias=True)
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self.linear_4 = nn.Linear(self.gf_dim*8, self.gf_dim*4, bias=True)
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self.linear_5 = nn.Linear(self.gf_dim*4, self.gf_dim*2, bias=True)
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self.linear_6 = nn.Linear(self.gf_dim*2, self.gf_dim*1, bias=True)
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self.linear_7 = nn.Linear(self.gf_dim*1, 1, bias=True)
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def forward(self, points):
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# zs = z.view(-1,1,self.z_dim).repeat(1,points.size()[1],1)
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# pointz = torch.cat([points,zs],2)
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pointz = points
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l1 = self.linear_1(pointz)
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l1 = F.leaky_relu(l1, negative_slope=0.02, inplace=True)
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l2 = self.linear_2(l1)
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l2 = F.leaky_relu(l2, negative_slope=0.02, inplace=True)
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l3 = self.linear_3(l2)
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l3 = F.leaky_relu(l3, negative_slope=0.02, inplace=True)
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l4 = self.linear_4(l3)
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l4 = F.leaky_relu(l4, negative_slope=0.02, inplace=True)
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l5 = self.linear_5(l4)
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l5 = F.leaky_relu(l5, negative_slope=0.02, inplace=True)
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l6 = self.linear_6(l5)
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l6 = F.leaky_relu(l6, negative_slope=0.02, inplace=True)
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l7 = self.linear_7(l6)
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# print("--------- l7: ",l7.min().item(),l7.max().item())
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#l7 = torch.clamp(l7, min=0, max=1)
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# l7 = torch.max(torch.min(l7, l7*0.01+0.99), l7*0.01)
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l7 = torch.sigmoid(l7)
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# l7 = (torch.tanh(l7)+1)/2
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return l7
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class generator_NAND(nn.Module):
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def __init__(self, z_dim, point_dim, gf_dim):
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super(generator_NAND, self).__init__()
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self.z_dim = z_dim
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self.point_dim = point_dim
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self.gf_dim = gf_dim
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self.linear_1 = nn.Linear(self.z_dim+self.point_dim, self.gf_dim*8, bias=True)
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self.linear_2 = nn.Linear(self.gf_dim*8, self.gf_dim*8, bias=True)
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self.linear_3 = nn.Linear(self.gf_dim*8, self.gf_dim*8, bias=True)
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self.linear_4 = nn.Linear(self.gf_dim*8, self.gf_dim*4, bias=True)
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self.linear_5 = nn.Linear(self.gf_dim*4, self.gf_dim*2, bias=True)
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self.linear_6 = nn.Linear(self.gf_dim*2, self.gf_dim*1, bias=True)
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self.linear_7 = nn.Linear(self.gf_dim*1, 1, bias=True)
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nn.init.normal_(self.linear_1.weight, mean=0.0, std=0.02)
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nn.init.constant_(self.linear_1.bias,0)
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nn.init.normal_(self.linear_2.weight, mean=0.0, std=0.02)
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nn.init.constant_(self.linear_2.bias,0)
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nn.init.normal_(self.linear_3.weight, mean=0.0, std=0.02)
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nn.init.constant_(self.linear_3.bias,0)
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nn.init.normal_(self.linear_4.weight, mean=0.0, std=0.02)
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nn.init.constant_(self.linear_4.bias,0)
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nn.init.normal_(self.linear_5.weight, mean=0.0, std=0.02)
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nn.init.constant_(self.linear_5.bias,0)
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nn.init.normal_(self.linear_6.weight, mean=0.0, std=0.02)
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nn.init.constant_(self.linear_6.bias,0)
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nn.init.normal_(self.linear_7.weight, mean=1e-5, std=0.02)
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nn.init.constant_(self.linear_7.bias,0)
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def forward(self, points):
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# zs = z.view(-1,1,self.z_dim).repeat(1,points.size()[1],1)
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# pointz = torch.cat([points,zs],2)
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pointz = points
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l1 = self.linear_1(pointz)
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l1 = F.leaky_relu(l1, negative_slope=0.02, inplace=True)
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l2 = self.linear_2(l1)
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l2 = F.leaky_relu(l2, negative_slope=0.02, inplace=True)
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l3 = self.linear_3(l2)
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l3 = F.leaky_relu(l3, negative_slope=0.02, inplace=True)
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l4 = self.linear_4(l3)
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l4 = F.leaky_relu(l4, negative_slope=0.02, inplace=True)
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l5 = self.linear_5(l4)
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l5 = F.leaky_relu(l5, negative_slope=0.02, inplace=True)
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l6 = self.linear_6(l5)
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l6 = F.leaky_relu(l6, negative_slope=0.02, inplace=True)
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l7 = self.linear_7(l6)
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print("--------- l7: ",l7.min().item(),l7.max().item())
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#l7 = torch.clamp(l7, min=0, max=1)
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# l7 = torch.max(torch.min(l7, l7*0.01+0.99), l7*0.01)
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# l7 = torch.sigmoid(l7)
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# l7 = (torch.tanh(l7)+1)/2
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return l7
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class generator_NAND_U(nn.Module):
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def __init__(self, z_dim, point_dim, gf_dim):
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super(generator_NAND_U, self).__init__()
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self.z_dim = z_dim
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self.point_dim = point_dim
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self.gf_dim = gf_dim
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self.linear_1 = nn.Linear(self.z_dim+self.point_dim, self.gf_dim*8, bias=True)
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self.linear_2 = nn.Linear(self.gf_dim*8, self.gf_dim*8, bias=True)
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self.linear_3 = nn.Linear(self.gf_dim*8, self.gf_dim*8, bias=True)
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self.linear_4 = nn.Linear(self.gf_dim*8, self.gf_dim*4, bias=True)
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self.linear_5 = nn.Linear(self.gf_dim*4, self.gf_dim*2, bias=True)
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self.linear_6 = nn.Linear(self.gf_dim*2, self.gf_dim*1, bias=True)
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self.linear_7 = nn.Linear(self.gf_dim*1, 3, bias=True)
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# nn.init.normal_(self.linear_1.weight, mean=0.0, std=0.02)
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# nn.init.constant_(self.linear_1.bias,0)
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# nn.init.normal_(self.linear_2.weight, mean=0.0, std=0.02)
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# nn.init.constant_(self.linear_2.bias,0)
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# nn.init.normal_(self.linear_3.weight, mean=0.0, std=0.02)
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# nn.init.constant_(self.linear_3.bias,0)
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# nn.init.normal_(self.linear_4.weight, mean=0.0, std=0.02)
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# nn.init.constant_(self.linear_4.bias,0)
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# nn.init.normal_(self.linear_5.weight, mean=0.0, std=0.02)
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# nn.init.constant_(self.linear_5.bias,0)
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# nn.init.normal_(self.linear_6.weight, mean=0.0, std=0.02)
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# nn.init.constant_(self.linear_6.bias,0)
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# nn.init.normal_(self.linear_7.weight, mean=1e-5, std=0.02)
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# nn.init.constant_(self.linear_7.bias,0)
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def forward(self, points):
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# zs = z.view(-1,1,self.z_dim).repeat(1,points.size()[1],1)
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# pointz = torch.cat([points,zs],2)
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pointz = points
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l1 = self.linear_1(pointz)
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l1 = F.leaky_relu(l1, negative_slope=0.02, inplace=True)
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l2 = self.linear_2(l1)
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l2 = F.leaky_relu(l2, negative_slope=0.02, inplace=True)
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l3 = self.linear_3(l2)
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l3 = F.leaky_relu(l3, negative_slope=0.02, inplace=True)
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l4 = self.linear_4(l3)
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l4 = F.leaky_relu(l4, negative_slope=0.02, inplace=True)
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l5 = self.linear_5(l4)
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l5 = F.leaky_relu(l5, negative_slope=0.02, inplace=True)
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l6 = self.linear_6(l5)
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l6 = F.leaky_relu(l6, negative_slope=0.02, inplace=True)
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l7 = self.linear_7(l6)
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#l7 = torch.clamp(l7, min=0, max=1)
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# l7 = torch.max(torch.min(l7, l7*0.01+0.99), l7*0.01)
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# l7 = torch.sigmoid(l7)
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return l7
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class generator_SAND(nn.Module):
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def __init__(self, z_dim, point_dim, gf_dim):
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super(generator_SAND, self).__init__()
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self.z_dim = z_dim
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self.point_dim = point_dim
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self.gf_dim = gf_dim
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self.linear_1 = nn.Linear(self.z_dim+self.point_dim, self.gf_dim*8, bias=True)
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self.linear_2 = nn.Linear(self.gf_dim*8, self.gf_dim*8, bias=True)
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self.linear_3 = nn.Linear(self.gf_dim*8, self.gf_dim*8, bias=True)
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self.linear_4 = nn.Linear(self.gf_dim*8, self.gf_dim*4, bias=True)
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self.linear_5 = nn.Linear(self.gf_dim*4, self.gf_dim*2, bias=True)
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self.linear_6 = nn.Linear(self.gf_dim*2, self.gf_dim*1, bias=True)
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self.linear_7 = nn.Linear(self.gf_dim*1, 4, bias=True)
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nn.init.normal_(self.linear_1.weight, mean=0.0, std=0.02)
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nn.init.constant_(self.linear_1.bias,0)
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nn.init.normal_(self.linear_2.weight, mean=0.0, std=0.02)
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nn.init.constant_(self.linear_2.bias,0)
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nn.init.normal_(self.linear_3.weight, mean=0.0, std=0.02)
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nn.init.constant_(self.linear_3.bias,0)
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nn.init.normal_(self.linear_4.weight, mean=0.0, std=0.02)
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nn.init.constant_(self.linear_4.bias,0)
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nn.init.normal_(self.linear_5.weight, mean=0.0, std=0.02)
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nn.init.constant_(self.linear_5.bias,0)
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nn.init.normal_(self.linear_6.weight, mean=0.0, std=0.02)
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nn.init.constant_(self.linear_6.bias,0)
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nn.init.normal_(self.linear_7.weight, mean=1e-5, std=0.02)
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nn.init.constant_(self.linear_7.bias,0)
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def forward(self, points):
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# zs = z.view(-1,1,self.z_dim).repeat(1,points.size()[1],1)
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# pointz = torch.cat([points,zs],2)
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pointz = points
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l1 = self.linear_1(pointz)
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l1 = F.leaky_relu(l1, negative_slope=0.02, inplace=True)
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l2 = self.linear_2(l1)
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l2 = F.leaky_relu(l2, negative_slope=0.02, inplace=True)
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l3 = self.linear_3(l2)
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l3 = F.leaky_relu(l3, negative_slope=0.02, inplace=True)
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l4 = self.linear_4(l3)
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l4 = F.leaky_relu(l4, negative_slope=0.02, inplace=True)
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l5 = self.linear_5(l4)
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l5 = F.leaky_relu(l5, negative_slope=0.02, inplace=True)
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l6 = self.linear_6(l5)
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|
l6 = F.leaky_relu(l6, negative_slope=0.02, inplace=True)
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||
|
|
|
||
|
|
l7 = self.linear_7(l6)
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||
|
|
|
||
|
|
#l7 = torch.clamp(l7, min=0, max=1)
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||
|
|
# l7 = torch.max(torch.min(l7, l7*0.01+0.99), l7*0.01)
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||
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|
l7[:,0] = torch.sigmoid(l7[:,0])
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return l7
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class generator_SAND_rand(nn.Module):
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|
def __init__(self, z_dim, point_dim, gf_dim):
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|
super(generator_SAND_rand, self).__init__()
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|
|
self.z_dim = z_dim
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|
|
self.point_dim = point_dim
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|
self.gf_dim = gf_dim
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||
|
|
self.linear_1 = nn.Linear(self.z_dim+self.point_dim, self.gf_dim*8, bias=True)
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||
|
|
self.linear_2 = nn.Linear(self.gf_dim*8, self.gf_dim*8, bias=True)
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||
|
|
self.linear_3 = nn.Linear(self.gf_dim*8, self.gf_dim*8, bias=True)
|
||
|
|
self.linear_4 = nn.Linear(self.gf_dim*8, self.gf_dim*4, bias=True)
|
||
|
|
self.linear_5 = nn.Linear(self.gf_dim*4, self.gf_dim*2, bias=True)
|
||
|
|
self.linear_6 = nn.Linear(self.gf_dim*2, self.gf_dim*1, bias=True)
|
||
|
|
self.linear_7 = nn.Linear(self.gf_dim*1, 4, bias=True)
|
||
|
|
|
||
|
|
def forward(self, points):
|
||
|
|
# zs = z.view(-1,1,self.z_dim).repeat(1,points.size()[1],1)
|
||
|
|
# pointz = torch.cat([points,zs],2)
|
||
|
|
pointz = points
|
||
|
|
l1 = self.linear_1(pointz)
|
||
|
|
l1 = F.leaky_relu(l1, inplace=True)
|
||
|
|
|
||
|
|
l2 = self.linear_2(l1)
|
||
|
|
l2 = F.leaky_relu(l2, inplace=True)
|
||
|
|
|
||
|
|
l3 = self.linear_3(l2)
|
||
|
|
l3 = F.leaky_relu(l3, inplace=True)
|
||
|
|
|
||
|
|
l4 = self.linear_4(l3)
|
||
|
|
l4 = F.leaky_relu(l4, inplace=True)
|
||
|
|
|
||
|
|
l5 = self.linear_5(l4)
|
||
|
|
l5 = F.leaky_relu(l5, inplace=True)
|
||
|
|
|
||
|
|
l6 = self.linear_6(l5)
|
||
|
|
l6 = F.leaky_relu(l6, inplace=True)
|
||
|
|
|
||
|
|
l7 = self.linear_7(l6)
|
||
|
|
|
||
|
|
#l7 = torch.clamp(l7, min=0, max=1)
|
||
|
|
# l7 = torch.max(torch.min(l7, l7*0.01+0.99), l7*0.01)
|
||
|
|
l7[:,0] = torch.sigmoid(l7[:,0])
|
||
|
|
|
||
|
|
return l7
|
||
|
|
|
||
|
|
class generator_SAND_seperate(nn.Module):
|
||
|
|
def __init__(self, z_dim, point_dim, gf_dim):
|
||
|
|
super(generator_SAND_seperate, self).__init__()
|
||
|
|
self.z_dim = z_dim
|
||
|
|
self.point_dim = point_dim
|
||
|
|
self.gf_dim = gf_dim
|
||
|
|
self.linear_1 = nn.Linear(self.z_dim+self.point_dim, self.gf_dim*8, bias=True)
|
||
|
|
self.linear_2 = nn.Linear(self.gf_dim*8, self.gf_dim*8, bias=True)
|
||
|
|
self.linear_3 = nn.Linear(self.gf_dim*8, self.gf_dim*8, bias=True)
|
||
|
|
self.linear_4 = nn.Linear(self.gf_dim*8, self.gf_dim*4, bias=True)
|
||
|
|
self.linear_5 = nn.Linear(self.gf_dim*4, self.gf_dim*2, bias=True)
|
||
|
|
self.linear_6 = nn.Linear(self.gf_dim*2, self.gf_dim*1, bias=True)
|
||
|
|
self.linear_7 = nn.Linear(self.gf_dim*1, 1, bias=True)
|
||
|
|
nn.init.normal_(self.linear_1.weight, mean=0.0, std=0.02)
|
||
|
|
nn.init.constant_(self.linear_1.bias,0)
|
||
|
|
nn.init.normal_(self.linear_2.weight, mean=0.0, std=0.02)
|
||
|
|
nn.init.constant_(self.linear_2.bias,0)
|
||
|
|
nn.init.normal_(self.linear_3.weight, mean=0.0, std=0.02)
|
||
|
|
nn.init.constant_(self.linear_3.bias,0)
|
||
|
|
nn.init.normal_(self.linear_4.weight, mean=0.0, std=0.02)
|
||
|
|
nn.init.constant_(self.linear_4.bias,0)
|
||
|
|
nn.init.normal_(self.linear_5.weight, mean=0.0, std=0.02)
|
||
|
|
nn.init.constant_(self.linear_5.bias,0)
|
||
|
|
nn.init.normal_(self.linear_6.weight, mean=0.0, std=0.02)
|
||
|
|
nn.init.constant_(self.linear_6.bias,0)
|
||
|
|
nn.init.normal_(self.linear_7.weight, mean=1e-5, std=0.02)
|
||
|
|
nn.init.constant_(self.linear_7.bias,0)
|
||
|
|
|
||
|
|
self.linear_1b = nn.Linear(self.z_dim+self.point_dim, self.gf_dim*8, bias=True)
|
||
|
|
self.linear_2b = nn.Linear(self.gf_dim*8, self.gf_dim*8, bias=True)
|
||
|
|
self.linear_3b = nn.Linear(self.gf_dim*8, self.gf_dim*8, bias=True)
|
||
|
|
self.linear_4b = nn.Linear(self.gf_dim*8, self.gf_dim*4, bias=True)
|
||
|
|
self.linear_5b = nn.Linear(self.gf_dim*4, self.gf_dim*2, bias=True)
|
||
|
|
self.linear_6b = nn.Linear(self.gf_dim*2, self.gf_dim*1, bias=True)
|
||
|
|
self.linear_7b = nn.Linear(self.gf_dim*1, 3, bias=True)
|
||
|
|
|
||
|
|
|
||
|
|
def forward(self, pointz):
|
||
|
|
# zs = z.view(-1,1,self.z_dim).repeat(1,points.size()[1],1)
|
||
|
|
# pointz = torch.cat([points,zs],2)
|
||
|
|
# pointz = points
|
||
|
|
l1 = self.linear_1(pointz)
|
||
|
|
l1 = F.leaky_relu(l1, negative_slope=0.02, inplace=True)
|
||
|
|
|
||
|
|
l2 = self.linear_2(l1)
|
||
|
|
l2 = F.leaky_relu(l2, negative_slope=0.02, inplace=True)
|
||
|
|
|
||
|
|
l3 = self.linear_3(l2)
|
||
|
|
l3 = F.leaky_relu(l3, negative_slope=0.02, inplace=True)
|
||
|
|
|
||
|
|
l4 = self.linear_4(l3)
|
||
|
|
l4 = F.leaky_relu(l4, negative_slope=0.02, inplace=True)
|
||
|
|
|
||
|
|
l5 = self.linear_5(l4)
|
||
|
|
l5 = F.leaky_relu(l5, negative_slope=0.02, inplace=True)
|
||
|
|
|
||
|
|
l6 = self.linear_6(l5)
|
||
|
|
l6 = F.leaky_relu(l6, negative_slope=0.02, inplace=True)
|
||
|
|
|
||
|
|
l7 = self.linear_7(l6)
|
||
|
|
|
||
|
|
#l7 = torch.clamp(l7, min=0, max=1)
|
||
|
|
# l7 = torch.max(torch.min(l7, l7*0.01+0.99), l7*0.01)
|
||
|
|
l7[:,0] = torch.sigmoid(l7[:,0])
|
||
|
|
|
||
|
|
|
||
|
|
l1b = self.linear_1b(pointz)
|
||
|
|
l1b = F.leaky_relu(l1b, inplace=True)
|
||
|
|
|
||
|
|
l2b = self.linear_2b(l1b)
|
||
|
|
l2b = F.leaky_relu(l2b, inplace=True)
|
||
|
|
|
||
|
|
l3b = self.linear_3b(l2b)
|
||
|
|
l3b = F.leaky_relu(l3b, inplace=True)
|
||
|
|
|
||
|
|
l4b = self.linear_4b(l3b)
|
||
|
|
l4b = F.leaky_relu(l4b, inplace=True)
|
||
|
|
|
||
|
|
l5b = self.linear_5b(l4b)
|
||
|
|
l5b = F.leaky_relu(l5b, inplace=True)
|
||
|
|
|
||
|
|
l6b = self.linear_6b(l5b)
|
||
|
|
l6b = F.leaky_relu(l6b, inplace=True)
|
||
|
|
|
||
|
|
l7b = self.linear_7b(l6b)
|
||
|
|
|
||
|
|
return torch.cat((l7,l7b),1)
|