225 lines
8.7 KiB
Python
225 lines
8.7 KiB
Python
import torch
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import numpy as np
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import torch.nn.functional as F
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from torch import optim
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from torch import nn
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from models.flow import get_point_cnf
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from models.flow import get_latent_cnf
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from utils import truncated_normal, reduce_tensor, standard_normal_logprob
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class Encoder(nn.Module):
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def __init__(self, zdim, input_dim=3, use_deterministic_encoder=False):
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super(Encoder, self).__init__()
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self.use_deterministic_encoder = use_deterministic_encoder
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self.zdim = zdim
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self.conv1 = nn.Conv1d(input_dim, 128, 1)
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self.conv2 = nn.Conv1d(128, 128, 1)
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self.conv3 = nn.Conv1d(128, 256, 1)
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self.conv4 = nn.Conv1d(256, 512, 1)
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self.bn1 = nn.BatchNorm1d(128)
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self.bn2 = nn.BatchNorm1d(128)
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self.bn3 = nn.BatchNorm1d(256)
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self.bn4 = nn.BatchNorm1d(512)
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if self.use_deterministic_encoder:
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self.fc1 = nn.Linear(512, 256)
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self.fc2 = nn.Linear(256, 128)
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self.fc_bn1 = nn.BatchNorm1d(256)
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self.fc_bn2 = nn.BatchNorm1d(128)
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self.fc3 = nn.Linear(128, zdim)
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else:
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# Mapping to [c], cmean
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self.fc1_m = nn.Linear(512, 256)
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self.fc2_m = nn.Linear(256, 128)
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self.fc3_m = nn.Linear(128, zdim)
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self.fc_bn1_m = nn.BatchNorm1d(256)
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self.fc_bn2_m = nn.BatchNorm1d(128)
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# Mapping to [c], cmean
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self.fc1_v = nn.Linear(512, 256)
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self.fc2_v = nn.Linear(256, 128)
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self.fc3_v = nn.Linear(128, zdim)
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self.fc_bn1_v = nn.BatchNorm1d(256)
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self.fc_bn2_v = nn.BatchNorm1d(128)
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def forward(self, x):
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x = x.transpose(1, 2)
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x = F.relu(self.bn1(self.conv1(x)))
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x = F.relu(self.bn2(self.conv2(x)))
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x = F.relu(self.bn3(self.conv3(x)))
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x = self.bn4(self.conv4(x))
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x = torch.max(x, 2, keepdim=True)[0]
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x = x.view(-1, 512)
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if self.use_deterministic_encoder:
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ms = F.relu(self.fc_bn1(self.fc1(x)))
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ms = F.relu(self.fc_bn2(self.fc2(ms)))
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ms = self.fc3(ms)
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m, v = ms, 0
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else:
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m = F.relu(self.fc_bn1_m(self.fc1_m(x)))
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m = F.relu(self.fc_bn2_m(self.fc2_m(m)))
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m = self.fc3_m(m)
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v = F.relu(self.fc_bn1_v(self.fc1_v(x)))
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v = F.relu(self.fc_bn2_v(self.fc2_v(v)))
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v = self.fc3_v(v)
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return m, v
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# Model
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class PointFlow(nn.Module):
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def __init__(self, args):
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super(PointFlow, self).__init__()
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self.input_dim = args.input_dim
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self.zdim = args.zdim
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self.use_latent_flow = args.use_latent_flow
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self.use_deterministic_encoder = args.use_deterministic_encoder
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self.prior_weight = args.prior_weight
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self.recon_weight = args.recon_weight
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self.entropy_weight = args.entropy_weight
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self.distributed = args.distributed
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self.truncate_std = None
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self.encoder = Encoder(
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zdim=args.zdim, input_dim=args.input_dim,
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use_deterministic_encoder=args.use_deterministic_encoder)
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self.point_cnf = get_point_cnf(args)
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self.latent_cnf = get_latent_cnf(args) if args.use_latent_flow else nn.Sequential()
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@staticmethod
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def sample_gaussian(size, truncate_std=None, gpu=None):
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y = torch.randn(*size).float()
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y = y if gpu is None else y.cuda(gpu)
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if truncate_std is not None:
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truncated_normal(y, mean=0, std=1, trunc_std=truncate_std)
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return y
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@staticmethod
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def reparameterize_gaussian(mean, logvar):
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std = torch.exp(0.5 * logvar)
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eps = torch.randn(std.size()).to(mean)
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return mean + std * eps
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@staticmethod
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def gaussian_entropy(logvar):
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const = 0.5 * float(logvar.size(1)) * (1. + np.log(np.pi * 2))
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ent = 0.5 * logvar.sum(dim=1, keepdim=False) + const
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return ent
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def multi_gpu_wrapper(self, f):
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self.encoder = f(self.encoder)
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self.point_cnf = f(self.point_cnf)
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self.latent_cnf = f(self.latent_cnf)
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def make_optimizer(self, args):
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def _get_opt_(params):
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if args.optimizer == 'adam':
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optimizer = optim.Adam(params, lr=args.lr, betas=(args.beta1, args.beta2),
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weight_decay=args.weight_decay)
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elif args.optimizer == 'sgd':
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optimizer = torch.optim.SGD(params, lr=args.lr, momentum=args.momentum)
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else:
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assert 0, "args.optimizer should be either 'adam' or 'sgd'"
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return optimizer
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opt = _get_opt_(list(self.encoder.parameters()) + list(self.point_cnf.parameters())
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+ list(list(self.latent_cnf.parameters())))
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return opt
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def forward(self, x, opt, step, writer=None):
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opt.zero_grad()
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batch_size = x.size(0)
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num_points = x.size(1)
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z_mu, z_sigma = self.encoder(x)
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if self.use_deterministic_encoder:
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z = z_mu + 0 * z_sigma
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else:
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z = self.reparameterize_gaussian(z_mu, z_sigma)
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# Compute H[Q(z|X)]
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if self.use_deterministic_encoder:
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entropy = torch.zeros(batch_size).to(z)
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else:
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entropy = self.gaussian_entropy(z_sigma)
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# Compute the prior probability P(z)
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if self.use_latent_flow:
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w, delta_log_pw = self.latent_cnf(z, None, torch.zeros(batch_size, 1).to(z))
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log_pw = standard_normal_logprob(w).view(batch_size, -1).sum(1, keepdim=True)
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delta_log_pw = delta_log_pw.view(batch_size, 1)
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log_pz = log_pw - delta_log_pw
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else:
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log_pz = torch.zeros(batch_size, 1).to(z)
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# Compute the reconstruction likelihood P(X|z)
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z_new = z.view(*z.size())
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z_new = z_new + (log_pz * 0.).mean()
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y, delta_log_py = self.point_cnf(x, z_new, torch.zeros(batch_size, num_points, 1).to(x))
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log_py = standard_normal_logprob(y).view(batch_size, -1).sum(1, keepdim=True)
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delta_log_py = delta_log_py.view(batch_size, num_points, 1).sum(1)
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log_px = log_py - delta_log_py
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# Loss
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entropy_loss = -entropy.mean() * self.entropy_weight
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recon_loss = -log_px.mean() * self.recon_weight
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prior_loss = -log_pz.mean() * self.prior_weight
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loss = entropy_loss + prior_loss + recon_loss
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loss.backward()
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opt.step()
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# LOGGING (after the training)
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if self.distributed:
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entropy_log = reduce_tensor(entropy.mean())
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recon = reduce_tensor(-log_px.mean())
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prior = reduce_tensor(-log_pz.mean())
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else:
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entropy_log = entropy.mean()
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recon = -log_px.mean()
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prior = -log_pz.mean()
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recon_nats = recon / float(x.size(1) * x.size(2))
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prior_nats = prior / float(self.zdim)
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if writer is not None:
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writer.add_scalar('train/entropy', entropy_log, step)
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writer.add_scalar('train/prior', prior, step)
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writer.add_scalar('train/prior(nats)', prior_nats, step)
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writer.add_scalar('train/recon', recon, step)
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writer.add_scalar('train/recon(nats)', recon_nats, step)
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return {
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'entropy': entropy_log.cpu().detach().item()
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if not isinstance(entropy_log, float) else entropy_log,
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'prior_nats': prior_nats,
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'recon_nats': recon_nats,
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}
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def encode(self, x):
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z_mu, z_sigma = self.encoder(x)
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if self.use_deterministic_encoder:
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return z_mu
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else:
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return self.reparameterize_gaussian(z_mu, z_sigma)
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def decode(self, z, num_points, truncate_std=None):
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# transform points from the prior to a point cloud, conditioned on a shape code
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y = self.sample_gaussian((z.size(0), num_points, self.input_dim), truncate_std)
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x = self.point_cnf(y, z, reverse=True).view(*y.size())
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return y, x
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def sample(self, batch_size, num_points, truncate_std=None, truncate_std_latent=None, gpu=None):
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assert self.use_latent_flow, "Sampling requires `self.use_latent_flow` to be True."
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# Generate the shape code from the prior
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w = self.sample_gaussian((batch_size, self.zdim), truncate_std_latent, gpu=gpu)
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z = self.latent_cnf(w, None, reverse=True).view(*w.size())
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# Sample points conditioned on the shape code
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y = self.sample_gaussian((batch_size, num_points, self.input_dim), truncate_std, gpu=gpu)
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x = self.point_cnf(y, z, reverse=True).view(*y.size())
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return z, x
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def reconstruct(self, x, num_points=None, truncate_std=None):
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num_points = x.size(1) if num_points is None else num_points
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z = self.encode(x)
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_, x = self.decode(z, num_points, truncate_std)
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return x
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