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import attr
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
logit_laplace_eps: float = 0.1
@attr.s(eq=False)
class Conv2d(nn.Module):
n_in: int = attr.ib(validator=lambda i, a, x: x >= 1)
n_out: int = attr.ib(validator=lambda i, a, x: x >= 1)
kw: int = attr.ib(validator=lambda i, a, x: x >= 1 and x % 2 == 1)
use_float16: bool = attr.ib(default=True)
device: torch.device = attr.ib(default=torch.device('cpu'))
requires_grad: bool = attr.ib(default=False)
def __attrs_post_init__(self) -> None:
super().__init__()
w = torch.empty((self.n_out, self.n_in, self.kw, self.kw), dtype=torch.float32,
device=self.device, requires_grad=self.requires_grad)
w.normal_(std=1 / math.sqrt(self.n_in * self.kw ** 2))
b = torch.zeros((self.n_out,), dtype=torch.float32, device=self.device,
requires_grad=self.requires_grad)
self.w, self.b = nn.Parameter(w), nn.Parameter(b)
def forward(self, x: torch.Tensor) -> torch.Tensor:
if self.use_float16 and 'cuda' in self.w.device.type:
if x.dtype != torch.float16:
x = x.half()
w, b = self.w.half(), self.b.half()
else:
if x.dtype != torch.float32:
x = x.float()
w, b = self.w, self.b
return F.conv2d(x, w, b, padding=(self.kw - 1) // 2)
def map_pixels(x: torch.Tensor) -> torch.Tensor:
if len(x.shape) != 4:
raise ValueError('expected input to be 4d')
if x.dtype != torch.float:
raise ValueError('expected input to have type float')
return (1 - 2 * logit_laplace_eps) * x + logit_laplace_eps
def unmap_pixels(x: torch.Tensor) -> torch.Tensor:
if len(x.shape) != 4:
raise ValueError('expected input to be 4d')
if x.dtype != torch.float:
raise ValueError('expected input to have type float')
return torch.clamp((x - logit_laplace_eps) / (1 - 2 * logit_laplace_eps), 0, 1)
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