forked from mrq/DL-Art-School
70 lines
2.4 KiB
Python
70 lines
2.4 KiB
Python
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import torch
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from torch import nn as nn
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from models.modules import thops
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from models.modules.FlowStep import FlowStep
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from models.modules.flow import Conv2dZeros, GaussianDiag
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from utils.util import opt_get
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class Split2d(nn.Module):
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def __init__(self, num_channels, logs_eps=0, cond_channels=0, position=None, consume_ratio=0.5, opt=None):
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super().__init__()
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self.num_channels_consume = int(round(num_channels * consume_ratio))
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self.num_channels_pass = num_channels - self.num_channels_consume
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self.conv = Conv2dZeros(in_channels=self.num_channels_pass + cond_channels,
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out_channels=self.num_channels_consume * 2)
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self.logs_eps = logs_eps
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self.position = position
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self.opt = opt
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def split2d_prior(self, z, ft):
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if ft is not None:
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z = torch.cat([z, ft], dim=1)
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h = self.conv(z)
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return thops.split_feature(h, "cross")
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def exp_eps(self, logs):
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return torch.exp(logs) + self.logs_eps
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def forward(self, input, logdet=0., reverse=False, eps_std=None, eps=None, ft=None, y_onehot=None):
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if not reverse:
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# self.input = input
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z1, z2 = self.split_ratio(input)
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mean, logs = self.split2d_prior(z1, ft)
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eps = (z2 - mean) / self.exp_eps(logs)
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logdet = logdet + self.get_logdet(logs, mean, z2)
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# print(logs.shape, mean.shape, z2.shape)
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# self.eps = eps
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# print('split, enc eps:', eps)
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return z1, logdet, eps
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else:
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z1 = input
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mean, logs = self.split2d_prior(z1, ft)
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if eps is None:
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#print("WARNING: eps is None, generating eps untested functionality!")
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eps = GaussianDiag.sample_eps(mean.shape, eps_std)
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eps = eps.to(mean.device)
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z2 = mean + self.exp_eps(logs) * eps
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z = thops.cat_feature(z1, z2)
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logdet = logdet - self.get_logdet(logs, mean, z2)
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return z, logdet
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# return z, logdet, eps
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def get_logdet(self, logs, mean, z2):
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logdet_diff = GaussianDiag.logp(mean, logs, z2)
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# print("Split2D: logdet diff", logdet_diff.item())
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return logdet_diff
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def split_ratio(self, input):
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z1, z2 = input[:, :self.num_channels_pass, ...], input[:, self.num_channels_pass:, ...]
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return z1, z2
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