layers.py

import torch
import torch.nn as nn
import torch.nn.functional as F


__all__ = ['LambdaLayer', 'ScaledStdConv2d', 'HardBinaryScaledStdConv2d', 'LearnableBias','BinaryActivation', 'HardBinaryConv']

def get_weight(module):
    std, mean = torch.std_mean(module.weight, dim=[1, 2, 3], keepdim=True, unbiased=False)
    weight = (module.weight - mean) / (std + module.eps)
    return weight

# Calculate symmetric padding for a convolution
def get_padding(kernel_size: int, stride: int = 1, dilation: int = 1, **_) -> int:
    padding = ((stride - 1) + dilation * (kernel_size - 1)) // 2
    return padding

class LambdaLayer(nn.Module):
    def __init__(self, lambd):
        super(LambdaLayer, self).__init__()
        self.lambd = lambd

    def forward(self, x):
        return self.lambd(x)


class ScaledStdConv2d(nn.Conv2d):
    """Conv2d layer with Scaled Weight Standardization.

    Paper: `Characterizing signal propagation to close the performance gap in unnormalized ResNets` -
        https://arxiv.org/abs/2101.08692

    NOTE: the operations used in this impl differ slightly from the DeepMind Haiku impl. The impact is minor.
    """

    def __init__(
            self, in_channels, out_channels, kernel_size, stride=1, padding=None, dilation=1, groups=1,
            bias=False, gamma=1.0, eps=1e-5, use_layernorm=False):
        if padding is None:
            padding = get_padding(kernel_size, stride, dilation)
        super().__init__(
            in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation,
            groups=groups, bias=bias)
        self.gain = nn.Parameter(torch.ones(self.out_channels, 1, 1, 1))
        self.scale = gamma * self.weight[0].numel() ** -0.5  # gamma * 1 / sqrt(fan-in)
        self.eps = eps ** 2 if use_layernorm else eps
        self.use_layernorm = use_layernorm  # experimental, slightly faster/less GPU memory to hijack LN kernel

    def get_weight(self):
        if self.use_layernorm:
            weight = self.scale * F.layer_norm(self.weight, self.weight.shape[1:], eps=self.eps)
        else:
            std, mean = torch.std_mean(self.weight, dim=[1, 2, 3], keepdim=True, unbiased=False)
            weight = self.scale * (self.weight - mean) / (std + self.eps)
        return self.gain * weight

    def forward(self, x):
        return F.conv2d(x, self.get_weight(), self.bias, self.stride, self.padding, self.dilation, self.groups)

class HardBinaryScaledStdConv2d(nn.Module):

    def __init__(self, in_chn, out_chn, kernel_size=3, stride=1, padding=1, gamma=1.0, eps=1e-5, use_layernorm=False):
        super(HardBinaryScaledStdConv2d, self).__init__()
        self.stride = stride
        self.padding = padding
        self.shape = (out_chn, in_chn, kernel_size, kernel_size)
        self.weight = nn.Parameter(torch.rand(self.shape) * 0.001, requires_grad=True)

        self.gain = nn.Parameter(torch.ones(out_chn, 1, 1, 1))
        self.scale = gamma * self.weight[0].numel() ** -0.5
        self.eps = eps ** 2 if use_layernorm else eps
        self.use_layernorm = use_layernorm

    def get_weight(self):
        if self.use_layernorm:
            weight = self.scale * F.layer_norm(self.weight, self.weight.shape[1:], eps=self.eps)
        else:
            std, mean = torch.std_mean(self.weight, dim=[1, 2, 3], keepdim=True, unbiased=False)
            weight = self.scale * (self.weight - mean) / (std + self.eps)

        scaling_factor = torch.mean(torch.mean(torch.mean(abs(weight),dim=3,keepdim=True),dim=2,keepdim=True),dim=1,keepdim=True)
        scaling_factor = scaling_factor.detach()
        binary_weights_no_grad = scaling_factor * torch.sign(weight)
        cliped_weights = torch.clamp(weight, -1.0, 1.0)
        binary_weights = binary_weights_no_grad.detach() - cliped_weights.detach() + cliped_weights

        return self.gain * binary_weights

    def forward(self, x):

        return F.conv2d(x, self.get_weight(), stride=self.stride, padding=self.padding)

class LearnableBias(nn.Module):
    def __init__(self, out_chn):
        super(LearnableBias, self).__init__()
        self.bias = nn.Parameter(torch.zeros(1,out_chn,1,1), requires_grad=True)

    def forward(self, x):
        out = x + self.bias.expand_as(x)
        return out

class BinaryActivation(nn.Module):
    def __init__(self):
        super(BinaryActivation, self).__init__()

    def forward(self, x):
        out_forward = torch.sign(x)
        #out_e1 = (x^2 + 2*x)
        #out_e2 = (-x^2 + 2*x)
        out_e_total = 0
        mask1 = x < -1
        mask2 = x < 0
        mask3 = x < 1
        out1 = (-1) * mask1.type(torch.float32) + (x*x + 2*x) * (1-mask1.type(torch.float32))
        out2 = out1 * mask2.type(torch.float32) + (-x*x + 2*x) * (1-mask2.type(torch.float32))
        out3 = out2 * mask3.type(torch.float32) + 1 * (1- mask3.type(torch.float32))
        out = out_forward.detach() - out3.detach() + out3

        return out

class HardBinaryConv(nn.Module):
    def __init__(self, in_chn, out_chn, kernel_size=3, stride=1, padding=1):
        super(HardBinaryConv, self).__init__()
        self.stride = stride
        self.padding = padding
        self.number_of_weights = in_chn * out_chn * kernel_size * kernel_size
        self.shape = (out_chn, in_chn, kernel_size, kernel_size)
        #self.weight = nn.Parameter(torch.rand((self.number_of_weights,1)) * 0.001, requires_grad=True)
        self.weight = nn.Parameter(torch.rand((self.shape)) * 0.001, requires_grad=True)

    def forward(self, x):
        real_weights = self.weight
        scaling_factor = torch.mean(torch.mean(torch.mean(abs(real_weights),dim=3,keepdim=True),dim=2,keepdim=True),dim=1,keepdim=True)
        scaling_factor = scaling_factor.detach()
        binary_weights_no_grad = scaling_factor * torch.sign(real_weights)
        cliped_weights = torch.clamp(real_weights, -1.0, 1.0)
        binary_weights = binary_weights_no_grad.detach() - cliped_weights.detach() + cliped_weights
        y = F.conv2d(x, binary_weights, stride=self.stride, padding=self.padding)

        return y