/
evonorm2d.py
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/
evonorm2d.py
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import torch
import torch.nn as nn
class SwishImplementation(torch.autograd.Function):
@staticmethod
def forward(ctx, i):
ctx.save_for_backward(i)
return i * torch.sigmoid(i)
@staticmethod
def backward(ctx, grad_output):
sigmoid_i = torch.sigmoid(ctx.saved_variables[0])
return grad_output * (
sigmoid_i * (1 + ctx.saved_variables[0] * (1 - sigmoid_i))
)
class MemoryEfficientSwish(nn.Module):
def forward(self, x):
return SwishImplementation.apply(x)
def instance_std(x, eps=1e-5):
var = torch.var(x, dim=(2, 3), keepdim=True).expand_as(x)
if torch.isnan(var).any():
var = torch.zeros(var.shape)
return torch.sqrt(var + eps)
def group_std(x, groups=32, eps=1e-5):
N, C, H, W = x.size()
x = torch.reshape(x, (N, groups, C // groups, H, W))
var = torch.var(x, dim=(2, 3, 4), keepdim=True).expand_as(x)
return torch.reshape(torch.sqrt(var + eps), (N, C, H, W))
class EvoNorm2D(nn.Module):
def __init__(
self,
input,
non_linear=True,
version="S0",
efficient=False,
affine=True,
momentum=0.9,
eps=1e-5,
groups=32,
training=True,
):
super(EvoNorm2D, self).__init__()
self.non_linear = non_linear
self.version = version
self.training = training
self.momentum = momentum
self.efficient = efficient
if self.version == "S0":
self.swish = MemoryEfficientSwish()
self.groups = groups
self.eps = eps
if self.version not in ["B0", "S0"]:
raise ValueError("Invalid EvoNorm version")
self.insize = input
self.affine = affine
if self.affine:
self.gamma = nn.Parameter(torch.ones(1, self.insize, 1, 1))
self.beta = nn.Parameter(torch.zeros(1, self.insize, 1, 1))
if self.non_linear:
self.v = nn.Parameter(torch.ones(1, self.insize, 1, 1))
else:
self.register_parameter("gamma", None)
self.register_parameter("beta", None)
self.register_buffer("v", None)
self.register_buffer("running_var", torch.ones(1, self.insize, 1, 1))
self.reset_parameters()
def reset_parameters(self):
self.running_var.fill_(1)
def _check_input_dim(self, x):
if x.dim() != 4:
raise ValueError("expected 4D input (got {}D input)".format(x.dim()))
def forward(self, x):
self._check_input_dim(x)
if self.version == "S0":
if self.non_linear:
if not self.efficient:
num = x * torch.sigmoid(
self.v * x
) # Original Swish Implementation, however memory intensive.
else:
num = self.swish(
x
) # Experimental Memory Efficient Variant of Swish
return (
num / group_std(x, groups=self.groups, eps=self.eps) * self.gamma
+ self.beta
)
else:
return x * self.gamma + self.beta
if self.version == "B0":
if self.training:
var = torch.var(x, dim=(0, 2, 3), unbiased=False, keepdim=True)
self.running_var.mul_(self.momentum)
self.running_var.add_((1 - self.momentum) * var)
else:
var = self.running_var
if self.non_linear:
den = torch.max(
(var + self.eps).sqrt(), self.v * x + instance_std(x, eps=self.eps)
)
return x / den * self.gamma + self.beta
else:
return x * self.gamma + self.beta