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bipointnet_cls.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
from basic import BiLinear
offset_map = {
1024: -3.2041,
2048: -3.4025,
4096: -3.5836
}
class Conv1d(nn.Module):
def __init__(self, inplane, outplane, Linear):
super().__init__()
self.lin = Linear(inplane, outplane)
def forward(self, x):
B, C, N = x.shape
x = x.permute(0, 2, 1).contiguous().view(-1, C)
x = self.lin(x).view(B, N, -1).permute(0, 2, 1).contiguous()
return x
class EmaMaxPool(nn.Module):
def __init__(self, kernel_size, affine=True, Linear=BiLinear, use_bn=True):
super(EmaMaxPool, self).__init__()
self.kernel_size = kernel_size
self.bn3 = nn.BatchNorm1d(1024, affine=affine)
self.use_bn = use_bn
def forward(self, x):
batchsize, D, N = x.size()
if self.use_bn:
x = torch.max(x, 2, keepdim=True)[0] + offset_map[N]
else:
x = torch.max(x, 2, keepdim=True)[0] - 0.3
return x
class BiPointNetCls(nn.Module):
def __init__(self, output_classes, input_dims=3, conv1_dim=64,
use_transform=True, Linear=BiLinear):
super(BiPointNetCls, self).__init__()
self.input_dims = input_dims
self.conv1 = nn.ModuleList()
self.conv1.append(Conv1d(input_dims, conv1_dim, Linear=Linear))
self.conv1.append(Conv1d(conv1_dim, conv1_dim, Linear=Linear))
self.conv1.append(Conv1d(conv1_dim, conv1_dim, Linear=Linear))
self.bn1 = nn.ModuleList()
self.bn1.append(nn.BatchNorm1d(conv1_dim))
self.bn1.append(nn.BatchNorm1d(conv1_dim))
self.bn1.append(nn.BatchNorm1d(conv1_dim))
self.conv2 = nn.ModuleList()
self.conv2.append(Conv1d(conv1_dim, conv1_dim * 2, Linear=Linear))
self.conv2.append(Conv1d(conv1_dim * 2, conv1_dim * 16, Linear=Linear))
self.bn2 = nn.ModuleList()
self.bn2.append(nn.BatchNorm1d(conv1_dim * 2))
self.bn2.append(nn.BatchNorm1d(conv1_dim * 16))
self.maxpool = EmaMaxPool(conv1_dim * 16, Linear=Linear, use_bn=True)
self.pool_feat_len = conv1_dim * 16
self.mlp3 = nn.ModuleList()
self.mlp3.append(Linear(conv1_dim * 16, conv1_dim * 8))
self.mlp3.append(Linear(conv1_dim * 8, conv1_dim * 4))
self.bn3 = nn.ModuleList()
self.bn3.append(nn.BatchNorm1d(conv1_dim * 8))
self.bn3.append(nn.BatchNorm1d(conv1_dim * 4))
self.dropout = nn.Dropout(0.3)
self.mlp_out = Linear(conv1_dim * 4, output_classes)
self.use_transform = use_transform
if use_transform:
self.transform1 = TransformNet(input_dims)
self.trans_bn1 = nn.BatchNorm1d(input_dims)
self.transform2 = TransformNet(conv1_dim)
self.trans_bn2 = nn.BatchNorm1d(conv1_dim)
def forward(self, x):
batch_size = x.shape[0]
h = x.permute(0, 2, 1)
if self.use_transform:
trans = self.transform1(h)
h = h.transpose(2, 1)
h = torch.bmm(h, trans)
h = h.transpose(2, 1)
h = F.relu(self.trans_bn1(h))
for conv, bn in zip(self.conv1, self.bn1):
h = conv(h)
h = bn(h)
h = F.relu(h)
if self.use_transform:
trans = self.transform2(h)
h = h.transpose(2, 1)
h = torch.bmm(h, trans)
h = h.transpose(2, 1)
h = F.relu(self.trans_bn2(h))
for conv, bn in zip(self.conv2, self.bn2):
h = conv(h)
h = bn(h)
h = F.relu(h)
h = self.maxpool(h).view(-1, self.pool_feat_len)
for mlp, bn in zip(self.mlp3, self.bn3):
h = mlp(h)
h = bn(h)
h = F.relu(h)
h = self.dropout(h)
out = self.mlp_out(h)
return out
class TransformNet(nn.Module):
def __init__(self, input_dims=3, conv1_dim=64, Linear=BiLinear):
super(TransformNet, self).__init__()
self.conv = nn.ModuleList()
self.conv.append(Conv1d(input_dims, conv1_dim, Linear=Linear))
self.conv.append(Conv1d(conv1_dim, conv1_dim * 2, Linear=Linear))
self.conv.append(Conv1d(conv1_dim * 2, conv1_dim * 16, Linear=Linear))
self.bn = nn.ModuleList()
self.bn.append(nn.BatchNorm1d(conv1_dim))
self.bn.append(nn.BatchNorm1d(conv1_dim * 2))
self.bn.append(nn.BatchNorm1d(conv1_dim * 16))
# self.maxpool = nn.MaxPool1d(conv1_dim * 16)
self.maxpool = EmaMaxPool(conv1_dim * 16, Linear=Linear, use_bn=True)
self.pool_feat_len = conv1_dim * 16
self.mlp2 = nn.ModuleList()
self.mlp2.append(Linear(conv1_dim * 16, conv1_dim * 8))
self.mlp2.append(Linear(conv1_dim * 8, conv1_dim * 4))
self.bn2 = nn.ModuleList()
self.bn2.append(nn.BatchNorm1d(conv1_dim * 8))
self.bn2.append(nn.BatchNorm1d(conv1_dim * 4))
self.input_dims = input_dims
self.mlp_out = Linear(conv1_dim * 4, input_dims * input_dims)
def forward(self, h):
batch_size = h.shape[0]
for conv, bn in zip(self.conv, self.bn):
h = conv(h)
h = bn(h)
h = F.relu(h)
h = self.maxpool(h).view(-1, self.pool_feat_len)
for mlp, bn in zip(self.mlp2, self.bn2):
h = mlp(h)
h = bn(h)
h = F.relu(h)
out = self.mlp_out(h)
iden = Variable(torch.from_numpy(np.eye(self.input_dims).flatten().astype(np.float32)))
iden = iden.view(1, self.input_dims * self.input_dims).repeat(batch_size, 1)
if out.is_cuda:
iden = iden.cuda()
out = out + iden
out = out.view(-1, self.input_dims, self.input_dims)
return out