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349 lines (271 loc) · 13.9 KB
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Mar 19 10:30:02 2019
@author: Sebastien M. Popoff
Based on https://openreview.net/forum?id=H1T2hmZAb
"""
import torch
from torch.nn import Module, Parameter, init, Sequential
from torch.nn import Conv2d, Linear, BatchNorm1d, BatchNorm2d
from torch.nn import ConvTranspose2d
from complexFunctions import complex_relu, complex_max_pool2d
from complexFunctions import complex_dropout, complex_dropout2d
class ComplexSequential(Sequential):
def forward(self, input_r, input_t):
for module in self._modules.values():
input_r, input_t = module(input_r, input_t)
return input_r, input_t
class ComplexDropout(Module):
def __init__(self,p=0.5, inplace=False):
super(ComplexDropout,self).__init__()
self.p = p
self.inplace = inplace
def forward(self,input_r,input_i):
return complex_dropout(input_r,input_i,self.p,self.inplace)
class ComplexDropout2d(Module):
def __init__(self,p=0.5, inplace=False):
super(ComplexDropout2d,self).__init__()
self.p = p
self.inplace = inplace
def forward(self,input_r,input_i):
return complex_dropout2d(input_r,input_i,self.p,self.inplace)
class ComplexMaxPool2d(Module):
def __init__(self,kernel_size, stride= None, padding = 0,
dilation = 1, return_indices = False, ceil_mode = False):
super(ComplexMaxPool2d,self).__init__()
self.kernel_size = kernel_size
self.stride = stride
self.padding = padding
self.dilation = dilation
self.ceil_mode = ceil_mode
self.return_indices = return_indices
def forward(self,input_r,input_i):
return complex_max_pool2d(input_r,input_i,kernel_size = self.kernel_size,
stride = self.stride, padding = self.padding,
dilation = self.dilation, ceil_mode = self.ceil_mode,
return_indices = self.return_indices)
class ComplexReLU(Module):
def forward(self,input_r,input_i):
return complex_relu(input_r,input_i)
class ComplexConvTranspose2d(Module):
def __init__(self,in_channels, out_channels, kernel_size, stride=1, padding=0,
output_padding=0, groups=1, bias=True, dilation=1, padding_mode='zeros'):
super(ComplexConvTranspose2d, self).__init__()
self.conv_tran_r = ConvTranspose2d(in_channels, out_channels, kernel_size, stride, padding,
output_padding, groups, bias, dilation, padding_mode)
self.conv_tran_i = ConvTranspose2d(in_channels, out_channels, kernel_size, stride, padding,
output_padding, groups, bias, dilation, padding_mode)
def forward(self,input_r,input_i):
return self.conv_tran_r(input_r)-self.conv_tran_i(input_i), \
self.conv_tran_r(input_i)+self.conv_tran_i(input_r)
class ComplexConv2d(Module):
def __init__(self,in_channels, out_channels, kernel_size=3, stride=1, padding = 0,
dilation=1, groups=1, bias=True):
super(ComplexConv2d, self).__init__()
self.conv_r = Conv2d(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias)
self.conv_i = Conv2d(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias)
def forward(self,input_r, input_i):
# assert(input_r.size() == input_i.size())
return self.conv_r(input_r)-self.conv_i(input_i), \
self.conv_r(input_i)+self.conv_i(input_r)
class ComplexLinear(Module):
def __init__(self, in_features, out_features):
super(ComplexLinear, self).__init__()
self.fc_r = Linear(in_features, out_features)
self.fc_i = Linear(in_features, out_features)
def forward(self,input_r, input_i):
return self.fc_r(input_r)-self.fc_i(input_i), \
self.fc_r(input_i)+self.fc_i(input_r)
class NaiveComplexBatchNorm1d(Module):
'''
Naive approach to complex batch norm, perform batch norm independently on real and imaginary part.
'''
def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True, \
track_running_stats=True):
super(NaiveComplexBatchNorm1d, self).__init__()
self.bn_r = BatchNorm1d(num_features, eps, momentum, affine, track_running_stats)
self.bn_i = BatchNorm1d(num_features, eps, momentum, affine, track_running_stats)
def forward(self,input_r, input_i):
return self.bn_r(input_r), self.bn_i(input_i)
class NaiveComplexBatchNorm2d(Module):
'''
Naive approach to complex batch norm, perform batch norm independently on real and imaginary part.
'''
def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True, \
track_running_stats=True):
super(NaiveComplexBatchNorm2d, self).__init__()
self.bn_r = BatchNorm2d(num_features, eps, momentum, affine, track_running_stats)
self.bn_i = BatchNorm2d(num_features, eps, momentum, affine, track_running_stats)
def forward(self,input_r, input_i):
return self.bn_r(input_r), self.bn_i(input_i)
class NaiveComplexBatchNorm1d(Module):
'''
Naive approach to complex batch norm, perform batch norm independently on real and imaginary part.
'''
def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True, \
track_running_stats=True):
super(NaiveComplexBatchNorm1d, self).__init__()
self.bn_r = BatchNorm1d(num_features, eps, momentum, affine, track_running_stats)
self.bn_i = BatchNorm1d(num_features, eps, momentum, affine, track_running_stats)
def forward(self,input_r, input_i):
return self.bn_r(input_r), self.bn_i(input_i)
class _ComplexBatchNorm(Module):
def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True,
track_running_stats=True):
super(_ComplexBatchNorm, self).__init__()
self.num_features = num_features
self.eps = eps
self.momentum = momentum
self.affine = affine
self.track_running_stats = track_running_stats
if self.affine:
self.weight = Parameter(torch.Tensor(num_features,3))
self.bias = Parameter(torch.Tensor(num_features,2))
else:
self.register_parameter('weight', None)
self.register_parameter('bias', None)
if self.track_running_stats:
self.register_buffer('running_mean', torch.zeros(num_features,2))
self.register_buffer('running_covar', torch.zeros(num_features,3))
self.running_covar[:,0] = 1.4142135623730951
self.running_covar[:,1] = 1.4142135623730951
self.register_buffer('num_batches_tracked', torch.tensor(0, dtype=torch.long))
else:
self.register_parameter('running_mean', None)
self.register_parameter('running_covar', None)
self.register_parameter('num_batches_tracked', None)
self.reset_parameters()
def reset_running_stats(self):
if self.track_running_stats:
self.running_mean.zero_()
self.running_covar.zero_()
self.running_covar[:,0] = 1.4142135623730951
self.running_covar[:,1] = 1.4142135623730951
self.num_batches_tracked.zero_()
def reset_parameters(self):
self.reset_running_stats()
if self.affine:
init.constant_(self.weight[:,:2],1.4142135623730951)
init.zeros_(self.weight[:,2])
init.zeros_(self.bias)
class ComplexBatchNorm2d(_ComplexBatchNorm):
def forward(self, input_r, input_i):
assert(input_r.size() == input_i.size())
assert(len(input_r.shape) == 4)
exponential_average_factor = 0.0
if self.training and self.track_running_stats:
if self.num_batches_tracked is not None:
self.num_batches_tracked += 1
if self.momentum is None: # use cumulative moving average
exponential_average_factor = 1.0 / float(self.num_batches_tracked)
else: # use exponential moving average
exponential_average_factor = self.momentum
if self.training:
# calculate mean of real and imaginary part
mean_r = input_r.mean([0, 2, 3])
mean_i = input_i.mean([0, 2, 3])
mean = torch.stack((mean_r,mean_i),dim=1)
# update running mean
with torch.no_grad():
self.running_mean = exponential_average_factor * mean\
+ (1 - exponential_average_factor) * self.running_mean
input_r = input_r-mean_r[None, :, None, None]
input_i = input_i-mean_i[None, :, None, None]
# Elements of the covariance matrix (biased for train)
n = input_r.numel() / input_r.size(1)
Crr = 1./n*input_r.pow(2).sum(dim=[0,2,3])+self.eps
Cii = 1./n*input_i.pow(2).sum(dim=[0,2,3])+self.eps
Cri = (input_r.mul(input_i)).mean(dim=[0,2,3])
with torch.no_grad():
self.running_covar[:,0] = exponential_average_factor * Crr * n / (n - 1)\
+ (1 - exponential_average_factor) * self.running_covar[:,0]
self.running_covar[:,1] = exponential_average_factor * Cii * n / (n - 1)\
+ (1 - exponential_average_factor) * self.running_covar[:,1]
self.running_covar[:,2] = exponential_average_factor * Cri * n / (n - 1)\
+ (1 - exponential_average_factor) * self.running_covar[:,2]
else:
mean = self.running_mean
Crr = self.running_covar[:,0]+self.eps
Cii = self.running_covar[:,1]+self.eps
Cri = self.running_covar[:,2]#+self.eps
input_r = input_r-mean[None,:,0,None,None]
input_i = input_i-mean[None,:,1,None,None]
# calculate the inverse square root the covariance matrix
det = Crr*Cii-Cri.pow(2)
s = torch.sqrt(det)
t = torch.sqrt(Cii+Crr + 2 * s)
inverse_st = 1.0 / (s * t)
Rrr = (Cii + s) * inverse_st
Rii = (Crr + s) * inverse_st
Rri = -Cri * inverse_st
input_r, input_i = Rrr[None,:,None,None]*input_r+Rri[None,:,None,None]*input_i, \
Rii[None,:,None,None]*input_i+Rri[None,:,None,None]*input_r
if self.affine:
input_r, input_i = self.weight[None,:,0,None,None]*input_r+self.weight[None,:,2,None,None]*input_i+\
self.bias[None,:,0,None,None], \
self.weight[None,:,2,None,None]*input_r+self.weight[None,:,1,None,None]*input_i+\
self.bias[None,:,1,None,None]
return input_r, input_i
class ComplexBatchNorm1d(_ComplexBatchNorm):
def forward(self, input_r, input_i):
assert(input_r.size() == input_i.size())
assert(len(input_r.shape) == 2)
#self._check_input_dim(input)
exponential_average_factor = 0.0
if self.training and self.track_running_stats:
if self.num_batches_tracked is not None:
self.num_batches_tracked += 1
if self.momentum is None: # use cumulative moving average
exponential_average_factor = 1.0 / float(self.num_batches_tracked)
else: # use exponential moving average
exponential_average_factor = self.momentum
if self.training:
# calculate mean of real and imaginary part
mean_r = input_r.mean(dim=0)
mean_i = input_i.mean(dim=0)
mean = torch.stack((mean_r,mean_i),dim=1)
# update running mean
with torch.no_grad():
self.running_mean = exponential_average_factor * mean\
+ (1 - exponential_average_factor) * self.running_mean
# zero mean values
input_r = input_r-mean_r[None, :]
input_i = input_i-mean_i[None, :]
# Elements of the covariance matrix (biased for train)
n = input_r.numel() / input_r.size(1)
Crr = input_r.var(dim=0,unbiased=False)+self.eps
Cii = input_i.var(dim=0,unbiased=False)+self.eps
Cri = (input_r.mul(input_i)).mean(dim=0)
with torch.no_grad():
self.running_covar[:,0] = exponential_average_factor * Crr * n / (n - 1)\
+ (1 - exponential_average_factor) * self.running_covar[:,0]
self.running_covar[:,1] = exponential_average_factor * Cii * n / (n - 1)\
+ (1 - exponential_average_factor) * self.running_covar[:,1]
self.running_covar[:,2] = exponential_average_factor * Cri * n / (n - 1)\
+ (1 - exponential_average_factor) * self.running_covar[:,2]
else:
mean = self.running_mean
Crr = self.running_covar[:,0]+self.eps
Cii = self.running_covar[:,1]+self.eps
Cri = self.running_covar[:,2]
# zero mean values
input_r = input_r-mean[None,:,0]
input_i = input_i-mean[None,:,1]
# calculate the inverse square root the covariance matrix
det = Crr*Cii-Cri.pow(2)
s = torch.sqrt(det)
t = torch.sqrt(Cii+Crr + 2 * s)
inverse_st = 1.0 / (s * t)
Rrr = (Cii + s) * inverse_st
Rii = (Crr + s) * inverse_st
Rri = -Cri * inverse_st
input_r, input_i = Rrr[None,:]*input_r+Rri[None,:]*input_i, \
Rii[None,:]*input_i+Rri[None,:]*input_r
if self.affine:
input_r, input_i = self.weight[None,:,0]*input_r+self.weight[None,:,2]*input_i+\
self.bias[None,:,0], \
self.weight[None,:,2]*input_r+self.weight[None,:,1]*input_i+\
self.bias[None,:,1]
del Crr, Cri, Cii, Rrr, Rii, Rri, det, s, t
return input_r, input_i