Upload utils.py

utils.py

@@ -0,0 +1,59 @@
+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)