import os
import copy
import numpy as np
import math
import torch
import torch.nn as nn
def readtextfile(filename):
with open(filename) as f:
content = f.readlines()
f.close()
return content
def writetextfile(data, filename):
with open(filename, 'w') as f:
f.writelines(data)
f.close()
def delete_file(filename):
if os.path.isfile(filename) == True:
os.remove(filename)
def eformat(f, prec, exp_digits):
s = "%.*e"%(prec, f)
mantissa, exp = s.split('e')
# add 1 to digits as 1 is taken by sign +/-
return "%se%+0*d"%(mantissa, exp_digits+1, int(exp))
def saveargs(args):
path = args.logs
if os.path.isdir(path) == False:
os.makedirs(path)
with open(os.path.join(path,'args.txt'), 'w') as f:
for arg in vars(args):
f.write(arg+' '+str(getattr(args,arg))+'\n')
def init_params(net):
for m in net.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal(m.weight, mode='fan_out')
if m.bias:
nn.init.constant(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant(m.weight, 1)
nn.init.constant(m.bias, 0)
elif isinstance(m, nn.Linear):
nn.init.normal(m.weight, std=1e-3)
if m.bias:
nn.init.constant(m.bias, 0)
def weights_init(m):
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
class Counter: #not used currently
def __init__(self):
self.mask_size = 0
def update(self, size):
self.mask_size += size
def get_total(self):
return self.mask_size
def act_fn(act):
if act == 'relu':
act_ = nn.ReLU(inplace=False)
elif act == 'lrelu':
act_ = nn.LeakyReLU(inplace=True)
elif act == 'prelu':
act_ = nn.PReLU()
elif act == 'rrelu':
act_ = nn.RReLU(inplace=True)
elif act == 'elu':
act_ = nn.ELU(inplace=True)
elif act == 'selu':
act_ = nn.SELU(inplace=True)
elif act == 'tanh':
act_ = nn.Tanh()
elif act == 'sigmoid':
act_ = nn.Sigmoid()
else:
print('\n\nActivation function {} is not supported/understood\n\n'.format(act))
act_ = None
return act_
def print_values(x, noise, y, unique_masks, n=2):
np.set_printoptions(precision=5, linewidth=200, threshold=1000000, suppress=True)
print('\nimage: {} image0, channel0 {}'.format(list(x.unsqueeze(2).size()), x.unsqueeze(2).data[0, 0, 0, 0, :n].cpu().numpy()))
print('image: {} image0, channel1 {}'.format(list(x.unsqueeze(2).size()), x.unsqueeze(2).data[0, 1, 0, 0, :n].cpu().numpy()))
print('\nimage: {} image1, channel0 {}'.format(list(x.unsqueeze(2).size()), x.unsqueeze(2).data[1, 0, 0, 0, :n].cpu().numpy()))
print('image: {} image1, channel1 {}'.format(list(x.unsqueeze(2).size()), x.unsqueeze(2).data[1, 1, 0, 0, :n].cpu().numpy()))
if noise is not None:
print('\nnoise {} channel0, mask0: {}'.format(list(noise.size()), noise.data[0, 0, 0, 0, :n].cpu().numpy()))
print('noise {} channel0, mask1: {}'.format(list(noise.size()), noise.data[0, 0, 1, 0, :n].cpu().numpy()))
if unique_masks:
print('\nnoise {} channel1, mask0: {}'.format(list(noise.size()), noise.data[0, 1, 0, 0, :n].cpu().numpy()))
print('noise {} channel1, mask1: {}'.format(list(noise.size()), noise.data[0, 1, 1, 0, :n].cpu().numpy()))
print('\nmasks: {} image0, channel0, mask0: {}'.format(list(y.size()), y.data[0, 0, 0, 0, :n].cpu().numpy()))
print('masks: {} image0, channel0, mask1: {}'.format(list(y.size()), y.data[0, 0, 1, 0, :n].cpu().numpy()))
print('masks: {} image0, channel1, mask0: {}'.format(list(y.size()), y.data[0, 1, 0, 0, :n].cpu().numpy()))
print('masks: {} image0, channel1, mask1: {}'.format(list(y.size()), y.data[0, 1, 1, 0, :n].cpu().numpy()))
print('\nmasks: {} image1, channel0, mask0: {}'.format(list(y.size()), y.data[1, 0, 0, 0, :n].cpu().numpy()))
print('masks: {} image1, channel0, mask1: {}'.format(list(y.size()), y.data[1, 0, 1, 0, :n].cpu().numpy()))
print('masks: {} image1, channel1, mask0: {}'.format(list(y.size()), y.data[1, 1, 0, 0, :n].cpu().numpy()))
print('masks: {} image1, channel1, mask1: {}'.format(list(y.size()), y.data[1, 1, 1, 0, :n].cpu().numpy()))
