87 lines
3.0 KiB
Python
87 lines
3.0 KiB
Python
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import sys
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sys.path.append("..")
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import math
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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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from torch.nn import Parameter
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import utils
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from metrics import calculate_kl as KL_DIV
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from ..misc import ModuleWrapper
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class BBBConv2d(ModuleWrapper):
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def __init__(self, in_channels, out_channels, kernel_size, stride=1,
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padding=0, dilation=1, bias=True, priors=None):
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super(BBBConv2d, self).__init__()
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.kernel_size = (kernel_size, kernel_size)
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self.stride = stride
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self.padding = padding
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self.dilation = dilation
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self.groups = 1
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self.use_bias = bias
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self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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if priors is None:
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priors = {
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'prior_mu': 0,
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'prior_sigma': 0.1,
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'posterior_mu_initial': (0, 0.1),
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'posterior_rho_initial': (-3, 0.1),
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}
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self.prior_mu = priors['prior_mu']
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self.prior_sigma = priors['prior_sigma']
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self.posterior_mu_initial = priors['posterior_mu_initial']
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self.posterior_rho_initial = priors['posterior_rho_initial']
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self.W_mu = Parameter(torch.Tensor(out_channels, in_channels, *self.kernel_size))
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self.W_rho = Parameter(torch.Tensor(out_channels, in_channels, *self.kernel_size))
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if self.use_bias:
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self.bias_mu = Parameter(torch.Tensor(out_channels))
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self.bias_rho = Parameter(torch.Tensor(out_channels))
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else:
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self.register_parameter('bias_mu', None)
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self.register_parameter('bias_rho', None)
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self.reset_parameters()
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def reset_parameters(self):
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self.W_mu.data.normal_(*self.posterior_mu_initial)
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self.W_rho.data.normal_(*self.posterior_rho_initial)
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if self.use_bias:
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self.bias_mu.data.normal_(*self.posterior_mu_initial)
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self.bias_rho.data.normal_(*self.posterior_rho_initial)
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def forward(self, x, sample=True):
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self.W_sigma = torch.log1p(torch.exp(self.W_rho))
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if self.use_bias:
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self.bias_sigma = torch.log1p(torch.exp(self.bias_rho))
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bias_var = self.bias_sigma ** 2
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else:
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self.bias_sigma = bias_var = None
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act_mu = F.conv2d(
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x, self.W_mu, self.bias_mu, self.stride, self.padding, self.dilation, self.groups)
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act_var = 1e-16 + F.conv2d(
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x ** 2, self.W_sigma ** 2, bias_var, self.stride, self.padding, self.dilation, self.groups)
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act_std = torch.sqrt(act_var)
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if self.training or sample:
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eps = torch.empty(act_mu.size()).normal_(0, 1).to(self.device)
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return act_mu + act_std * eps
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else:
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return act_mu
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def kl_loss(self):
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kl = KL_DIV(self.prior_mu, self.prior_sigma, self.W_mu, self.W_sigma)
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if self.use_bias:
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kl += KL_DIV(self.prior_mu, self.prior_sigma, self.bias_mu, self.bias_sigma)
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return kl
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