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