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main_adaptiveCS.py
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main_adaptiveCS.py
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from __future__ import print_function
import torch
import torch.nn as nn
import numpy as np
import argparse
import cv2
import torch.optim as optim
import torch.backends.cudnn as cudnn
import torchvision.utils as vutils
import os
from torchvision import datasets, transforms
from torch.autograd import Variable
from numpy.random import randn
from torch.nn import init
import string, copy
from scipy import linalg
import math
parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
parser.add_argument('--model', help='basic | adaptiveCS | adaptiveCS_resnet',
default='adaptiveCS_resnet_wy_ifusion_ufirst') # adaptiveCS_resnet_wy_lfusion_ufirst
parser.add_argument('--dataset', help='lsun | imagenet | mnist | bsd500 | bsd500_patch', default='bsd500')
parser.add_argument('--datapath', help='path to dataset', default='/home/user/kaixu/myGitHub/CSImageNet/data/')
parser.add_argument('--batch-size', type=int, default=128, metavar='N',
help='input batch size for training (default: 64)')
parser.add_argument('--image-size', type=int, default=64, metavar='N',
help='The height / width of the input image to the network')
parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
help='input batch size for testing (default: 1000)')
parser.add_argument('--epochs', type=int, default=100, metavar='N',
help='number of epochs to train (default: 10)')
parser.add_argument('--lr', type=float, default=2e-4, metavar='LR',
help='learning rate (default: 0.01)')
parser.add_argument('--momentum', type=float, default=0.5, metavar='M',
help='SGD momentum (default: 0.5)')
parser.add_argument('--cuda', action='store_true', default=True,
help='enable CUDA training')
parser.add_argument('--ngpu', type=int, default=1,
help='number of GPUs to use')
parser.add_argument('--seed', type=int, default=1, metavar='S',
help='random seed (default: 1)')
parser.add_argument('--log-interval', type=int, default=100, metavar='N',
help='how many batches to wait before logging training status')
parser.add_argument('--layers-gan', type=int, default=3, metavar='N',
help='number of hierarchies in the GAN (default: 64)')
parser.add_argument('--gpu', type=int, default=1, metavar='N',
help='which GPU do you want to use (default: 1)')
parser.add_argument('--outf', default='./results', help='folder to output images and model checkpoints')
parser.add_argument('--w-loss', type=float, default=0.01, metavar='N.',
help='penalty for the mse and bce loss')
parser.add_argument('--stage', type=int, default=3, help='the stage under training')
parser.add_argument('--transfer', action='store_true', default=False, help='transfer weights')
parser.add_argument('--cr', type=int, default=10, help='compression ratio')
opt = parser.parse_args()
if torch.cuda.is_available() and not opt.cuda:
print("WARNING: please run with GPU")
print(opt)
if 'woy' in opt.model:
import models.lapgan_adaptiveCS_resnet_woy as lapgan
elif 'lfusion' in opt.model:
import models.lapgan_adaptiveCS_latefusion as lapgan
# elif 'adaptiveCS_resnet' in opt.model:
elif 'mnist' in opt.dataset:
import models.lapgan_adaptiveCS_resnet_mnist as lapgan
elif 'bsd500_patch' in opt.dataset:
import models.lapgan_adaptiveCS_resnet_bsd500 as lapgan
else:
import models.lapgan_adaptiveCS_resnet as lapgan
# else:
# import models.lapgan_adaptiveCS as lapgan
torch.cuda.set_device(opt.gpu)
print('Current gpu device: gpu %d' % (torch.cuda.current_device()))
if opt.seed is None:
opt.seed = np.random.randint(1, 10000)
print('Random seed: ', opt.seed)
np.random.seed(opt.seed)
torch.manual_seed(opt.seed)
if opt.cuda:
torch.cuda.manual_seed(opt.seed)
cudnn.benchmark = True
if not os.path.exists('%s/%s/cr%s/%s/stage%s/model' % (opt.outf, opt.dataset, opt.cr, opt.model, opt.stage)):
os.makedirs('%s/%s/cr%s/%s/stage%s/model' % (opt.outf, opt.dataset, opt.cr, opt.model, opt.stage))
if not os.path.exists('%s/%s/cr%s/%s/stage%s/image' % (opt.outf, opt.dataset, opt.cr, opt.model, opt.stage)):
os.makedirs('%s/%s/cr%s/%s/stage%s/image' % (opt.outf, opt.dataset, opt.cr, opt.model, opt.stage))
def weights_init_normal(m):
classname = m.__class__.__name__
# print(classname)
if classname.find('Conv') != -1:
init.uniform(m.weight.data, 0.0, 0.02)
elif classname.find('Linear') != -1:
init.uniform(m.weight.data, 0.0, 0.02)
elif classname.find('BatchNorm2d') != -1:
init.uniform(m.weight.data, 1.0, 0.02)
init.constant(m.bias.data, 0.0)
def weights_init_xavier(m):
classname = m.__class__.__name__
# print(classname)
if classname.find('Conv') != -1:
init.xavier_normal(m.weight.data, gain=1)
elif classname.find('Linear') != -1:
init.xavier_normal(m.weight.data, gain=1)
elif classname.find('BatchNorm2d') != -1:
init.uniform(m.weight.data, 1.0, 0.02)
init.constant(m.bias.data, 0.0)
def weights_init_kaiming(m):
classname = m.__class__.__name__
# print(classname)
if classname.find('Conv') != -1:
init.kaiming_normal(m.weight.data, a=0, mode='fan_in')
elif classname.find('Linear') != -1:
init.kaiming_normal(m.weight.data, a=0, mode='fan_in')
elif classname.find('BatchNorm2d') != -1:
init.uniform(m.weight.data, 1.0, 0.02)
init.constant(m.bias.data, 0.0)
def weights_init_orthogonal(m):
classname = m.__class__.__name__
print(classname)
if classname.find('Conv') != -1:
init.orthogonal(m.weight.data, gain=1)
elif classname.find('Linear') != -1:
init.orthogonal(m.weight.data, gain=1)
elif classname.find('BatchNorm2d') != -1:
init.uniform(m.weight.data, 1.0, 0.02)
init.constant(m.bias.data, 0.0)
def weights_init(net, init_type='normal'):
print('initialization method [%s]' % init_type)
if init_type == 'normal':
net.apply(weights_init_normal)
elif init_type == 'xavier':
net.apply(weights_init_xavier)
elif init_type == 'kaiming':
net.apply(weights_init_kaiming)
elif init_type == 'orthogonal':
net.apply(weights_init_orthogonal)
else:
raise NotImplementedError('initialization method [%s] is not implemented' % init_type)
def data_loader():
kwopt = {'num_workers': 2, 'pin_memory': True} if opt.cuda else {}
if opt.dataset == 'lsun':
train_dataset = datasets.LSUN(db_path=opt.datapath + 'train/', classes=['bedroom_train'],
transform=transforms.Compose([
transforms.Resize(opt.image_size),
transforms.CenterCrop(opt.image_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]))
elif opt.dataset == 'mnist':
train_dataset = datasets.MNIST('./data', train=True, download=True,
transform=transforms.Compose([
transforms.Resize(opt.image_size),
transforms.CenterCrop(opt.image_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]))
val_dataset = datasets.MNIST('./data', train=False,
transform=transforms.Compose([
transforms.Resize(opt.image_size),
transforms.CenterCrop(opt.image_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]))
elif opt.dataset == 'bsd500':
train_dataset = datasets.ImageFolder(root='/home/user/kaixu/myGitHub/datasets/BSDS500/train-aug/',
transform=transforms.Compose([
transforms.Resize(opt.image_size),
transforms.CenterCrop(opt.image_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]))
val_dataset = datasets.ImageFolder(root='/home/user/kaixu/myGitHub/datasets/SISR/val/',
transform=transforms.Compose([
transforms.Resize(opt.image_size),
transforms.CenterCrop(opt.image_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]))
elif opt.dataset == 'bsd500_patch':
train_dataset = datasets.ImageFolder(root=opt.datapath + 'train_64x64',
transform=transforms.Compose([
transforms.Resize(opt.image_size),
transforms.CenterCrop(opt.image_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]))
val_dataset = datasets.ImageFolder(root=opt.datapath + 'val_64x64',
transform=transforms.Compose([
transforms.Resize(opt.image_size),
transforms.CenterCrop(opt.image_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]))
elif opt.dataset == 'cifar10':
train_dataset = datasets.CIFAR10(root='./data', train=True, download=True,
transform=transforms.Compose([
transforms.Resize(opt.image_size),
transforms.CenterCrop(opt.image_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]))
val_dataset = datasets.CIFAR10(root='./data', train=False, download=True,
transform=transforms.Compose([
transforms.Resize(opt.image_size),
transforms.CenterCrop(opt.image_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]))
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=opt.batch_size, shuffle=True, **kwopt)
val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=opt.batch_size, shuffle=True, **kwopt)
return train_loader, val_loader
def val(epoch, stage, channels, valloader, sensing_matrix1, sensing_matrix2, sensing_matrix3, sensing_matrix4,
target, g1_input, lapnet1_gen, lapnet2_gen, lapnet3_gen, lapnet4_gen, criterion_mse, y2, y3, y4):
errD_fake_mse_total = 0
for idx, (data, _) in enumerate(valloader, 0):
if data.size(0) != opt.batch_size:
continue
data_array = data.numpy()
for i in range(opt.batch_size):
g4_target_temp = data_array[i] # 1x64x64
if stage == 1:
target[i] = torch.from_numpy(g4_target_temp[:, ::8, ::8])
elif stage == 2:
target[i] = torch.from_numpy(g4_target_temp[:, ::4, ::4]) # 3x16x16
elif stage == 3:
target[i] = torch.from_numpy(g4_target_temp[:, ::2, ::2]) # 3x32x32
elif stage == 4:
target[i] = torch.from_numpy(g4_target_temp) # 3x64x64
elif stage == 5:
target[i] = torch.from_numpy(g4_target_temp) # 3x64x64
for j in range(channels):
g1_input[i, j, :] = torch.from_numpy(sensing_matrix1[j, :, :].dot(data_array[i, j].flatten()))
y2[i, j, :] = torch.from_numpy(sensing_matrix2[j, :, :].dot(data_array[i, j].flatten()))
y3[i, j, :] = torch.from_numpy(sensing_matrix3[j, :, :].dot(data_array[i, j].flatten()))
y4[i, j, :] = torch.from_numpy(sensing_matrix4[j, :, :].dot(data_array[i, j].flatten()))
g1_input_var = Variable(g1_input, volatile=True)
if stage == 1:
output = lapnet1_gen(g1_input_var)
target_var = Variable(target, volatile=True)
elif stage == 2:
y2_var = Variable(y2)
g2_input = lapnet1_gen(g1_input_var)
output = lapnet2_gen(g2_input, y2_var)
target_var = Variable(target, volatile=True)
elif stage == 3:
y2_var = Variable(y2)
y3_var = Variable(y3)
g2_input = lapnet1_gen(g1_input_var)
g3_input = lapnet2_gen(g2_input, y2_var)
output = lapnet3_gen(g3_input, y3_var)
target_var = Variable(target, volatile=True)
elif stage == 4:
y2_var = Variable(y2)
y3_var = Variable(y3)
y4_var = Variable(y4)
g2_input = lapnet1_gen(g1_input_var)
g3_input = lapnet2_gen(g2_input, y2_var)
g4_input = lapnet3_gen(g3_input, y3_var)
output = lapnet4_gen(g4_input, y4_var)
target_var = Variable(target, volatile=True)
errD_fake_mse = criterion_mse(output, target_var)
errD_fake_mse_total += errD_fake_mse
if idx % 20 == 0:
print('Test: [%d][%d/%d] errG_mse: %.4f \n,' % (epoch, idx, len(valloader), errD_fake_mse.data[0]))
print('Test: [%d] average errG_mse: %.4f,' % (epoch, errD_fake_mse_total.data[0] / len(valloader)))
vutils.save_image(target_var.data, '%s/%s/cr%s/%s/stage%s/image/test_epoch_%03d_real.png'
% (opt.outf, opt.dataset, opt.cr, opt.model, opt.stage, epoch), normalize=True)
vutils.save_image(output.data, '%s/%s/cr%s/%s/stage%s/image/test_epoch_%03d_fake.png'
% (opt.outf, opt.dataset, opt.cr, opt.model, opt.stage, epoch), normalize=True)
def train(epochs, trainloader, valloader):
# Initialize variables
input, _ = trainloader.__iter__().__next__()
input = input.numpy()
sz_input = input.shape
cr1 = 8 * opt.cr
cr2 = 4 * opt.cr
cr3 = 2 * opt.cr
cr4 = opt.cr
channels = sz_input[1]
n = sz_input[2] ** 2
'''
m1 = int(math.ceil(float(n) / cr1))
m2 = int(math.ceil(float(n) / cr2))
m3 = int(math.ceil(float(n) / cr3))
m4 = int(math.ceil(float(n) / cr4))
'''
m1 = n / cr1
m2 = n / cr2
m3 = n / cr3
m4 = n / cr4
img_size1 = sz_input[3] / 8
img_size2 = sz_input[3] / 4
img_size3 = sz_input[3] / 2
img_size4 = sz_input[3]
if os.path.exists('sensing_matrix_cr%d.npy' % (opt.cr)):
sensing_matrix4 = np.load('sensing_matrix_cr%d.npy' % (opt.cr))
else:
sensing_matrix4 = randn(channels, m4, n)
#sensing_matrix4 = np.zeros(sensing_matrix4_unnorm.shape)
#for chan in range(channels):
# M = sensing_matrix4_unnorm[chan, :, :]
# M = np.transpose(linalg.orth(np.transpose(M)))
# sensing_matrix4[chan, :, :] = M
np.save('sensing_matrix_cr%d.npy' % (opt.cr), sensing_matrix4)
sensing_matrix3 = sensing_matrix4[:, :m3, :]
sensing_matrix2 = sensing_matrix4[:, :m2, :]
sensing_matrix1 = sensing_matrix4[:, :m1, :]
g1_input = torch.FloatTensor(opt.batch_size, channels, m1)
g2_input = torch.FloatTensor(opt.batch_size, channels, m2)
g3_input = torch.FloatTensor(opt.batch_size, channels, m3)
g4_input = torch.FloatTensor(opt.batch_size, channels, m4)
g1_target = torch.FloatTensor(opt.batch_size, channels, img_size1, img_size1)
g2_target = torch.FloatTensor(opt.batch_size, channels, img_size2, img_size2)
g3_target = torch.FloatTensor(opt.batch_size, channels, img_size3, img_size3)
g4_target = torch.FloatTensor(opt.batch_size, channels, img_size4, img_size4)
y2 = torch.FloatTensor(opt.batch_size, channels, m2)
y3 = torch.FloatTensor(opt.batch_size, channels, m3)
y4 = torch.FloatTensor(opt.batch_size, channels, m4)
label = torch.FloatTensor(opt.batch_size)
fake_label = 0.1
real_label = 0.9
# Instantiate models
lapnet1_gen = lapgan.LAPGAN_Generator_level1(channels, channels * m1, opt.ngpu)
lapnet1_disc = lapgan.LAPGAN_Discriminator_level1(channels, opt.ngpu)
lapnet2_gen = lapgan.LAPGAN_Generator_level2(channels, channels * m2, opt.ngpu)
lapnet2_disc = lapgan.LAPGAN_Discriminator_level2(channels, opt.ngpu)
lapnet3_gen = lapgan.LAPGAN_Generator_level3(channels, channels * m3, opt.ngpu)
lapnet3_disc = lapgan.LAPGAN_Discriminator_level3(channels, opt.ngpu)
lapnet4_gen = lapgan.LAPGAN_Generator_level4(channels, channels * m4, opt.ngpu)
lapnet4_disc = lapgan.LAPGAN_Discriminator_level4(channels, opt.ngpu)
# Weight initialization
weights_init(lapnet1_gen, init_type='normal'), weights_init(lapnet1_disc, init_type='normal')
weights_init(lapnet2_gen, init_type='normal'), weights_init(lapnet2_disc, init_type='normal')
weights_init(lapnet3_gen, init_type='normal'), weights_init(lapnet3_disc, init_type='normal')
weights_init(lapnet4_gen, init_type='normal'), weights_init(lapnet4_disc, init_type='normal')
print(lapnet1_gen), print(lapnet1_disc)
print(lapnet2_gen), print(lapnet2_disc)
print(lapnet3_gen), print(lapnet3_disc)
print(lapnet4_gen), print(lapnet4_disc)
optimizer_lapnet1_gen = optim.Adam(lapnet1_gen.parameters(), lr=opt.lr, betas=(0.5, 0.999))
optimizer_lapnet1_disc = optim.Adam(lapnet1_disc.parameters(), lr=opt.lr, betas=(0.5, 0.999))
optimizer_lapnet2_gen = optim.Adam(lapnet2_gen.parameters(), lr=opt.lr, betas=(0.5, 0.999))
optimizer_lapnet2_disc = optim.Adam(lapnet2_disc.parameters(), lr=opt.lr, betas=(0.5, 0.999))
optimizer_lapnet3_gen = optim.Adam(lapnet3_gen.parameters(), lr=opt.lr, betas=(0.5, 0.999))
optimizer_lapnet3_disc = optim.Adam(lapnet3_disc.parameters(), lr=opt.lr, betas=(0.5, 0.999))
optimizer_lapnet4_gen = optim.Adam(lapnet4_gen.parameters(), lr=opt.lr, betas=(0.5, 0.999))
optimizer_lapnet4_disc = optim.Adam(lapnet4_disc.parameters(), lr=opt.lr, betas=(0.5, 0.999))
criterion_mse = nn.MSELoss()
criterion_bce = nn.BCELoss()
cudnn.benchmark = True
if opt.cuda:
lapnet1_gen.cuda(), lapnet1_disc.cuda()
lapnet2_gen.cuda(), lapnet2_disc.cuda()
lapnet3_gen.cuda(), lapnet3_disc.cuda()
lapnet4_gen.cuda(), lapnet4_disc.cuda()
criterion_mse.cuda(), criterion_bce.cuda()
g1_input, g2_input, g3_input, g4_input = g1_input.cuda(), g2_input.cuda(), g3_input.cuda(), g4_input.cuda()
g1_target, g2_target, g3_target, g4_target = g1_target.cuda(), g2_target.cuda(), g3_target.cuda(), g4_target.cuda()
y2, y3, y4 = y2.cuda(), y3.cuda(), y4.cuda()
label = label.cuda()
if opt.dataset == 'bsd500_patch':
if 'wy' in opt.model and 'ifusion' in opt.model:
if opt.cr == 5:
level1_iter = 7 # 0.0264
level2_iter = 11 # 0.0157
level3_iter = 12 # 0.0090
level4_iter = 1
elif opt.cr == 10:
level1_iter = 16 # 0.0353 18 # 14
level2_iter = 16 # 0.0230 16 # 27
level3_iter = 16 # 0.0144 8 # 27
level4_iter = 21
elif opt.cr == 20:
level1_iter = 6 # 0.0450
level2_iter = 2 # 0.0389 9
level3_iter = 1 # 0.0318
level4_iter = 93
elif opt.cr == 30:
level1_iter = 5 # 0.0508
level2_iter = 0 # 0.0420 5
level3_iter = 0 # 0.0339 3
if opt.dataset == 'mnist':
if 'wy' in opt.model and 'ifusion' in opt.model:
if opt.cr == 5:
level1_iter = 94 # 0.0074
level2_iter = 36 # 0.0052
level3_iter = 88
level4_iter = 1
elif opt.cr == 10:
level1_iter = 73 # 0.0193
level2_iter = 31 # 0.0111 16 # 27
level3_iter = 78 # 0.0077 8 # 27
level4_iter = 96 # 0.0034
elif opt.cr == 20:
level1_iter = 57 # 0.0499
level2_iter = 57 # 0.0233
level3_iter = 95 # 0.0122
level4_iter = 83 # 0.0059
elif opt.cr == 30:
level1_iter = 34 # 0.0771
level2_iter = 66 # 0.0378 5
level3_iter = 99 # 0.0177 3
level4_iter = 88 # 0.0056
if opt.dataset == 'cifar10':
if 'wy' in opt.model and 'ifusion' in opt.model:
if opt.cr == 5:
level1_iter = 28 # 0.0126
level2_iter = 81 # 0.0039
level3_iter = 21 # 0.0025
level4_iter = 26 # 0.0008
elif opt.cr == 10:
level1_iter = 24 # 0.0242
level2_iter = 33 # 0.0102
level3_iter = 58 # 0.0045
level4_iter = 21 # 0.0017
elif opt.cr == 20:
level1_iter = 19 # 0.0420
level2_iter = 30 # 0.0221
level3_iter = 44 # 0.0111
level4_iter = 21 # 0.0043
elif opt.cr == 30:
level1_iter = 16 # 0.0532
level2_iter = 34 # 0.0317
level3_iter = 84 # 0.0174
level4_iter = 16 # 0.0078
elif 'wy' in opt.model and 'lfusion' in opt.model:
if opt.cr == 10:
level1_iter = 18
level2_iter = 16
level3_iter = 22
level4_iter = 99
elif 'woy' in opt.model:
if opt.cr == 10:
level1_iter = 18
level2_iter = 7
level3_iter = 5
level4_iter = 1
stage1_path = '%s/%s/cr%s/%s/stage%s/model/gen_epoch_%d.pth' % (
opt.outf, opt.dataset, opt.cr, opt.model, 1, level1_iter)
stage2_path = '%s/%s/cr%s/%s/stage%s/model/gen_epoch_%d.pth' % (
opt.outf, opt.dataset, opt.cr, opt.model, 2, level2_iter)
stage3_path = '%s/%s/cr%s/%s/stage%s/model/gen_epoch_%d.pth' % (
opt.outf, opt.dataset, opt.cr, opt.model, 3, level3_iter)
stage4_path = '%s/%s/cr%s/%s/stage%s/model/gen_epoch_%d.pth' % (
opt.outf, opt.dataset, opt.cr, opt.model, 4, level4_iter)
# if opt.stage == 1:
# if os.path.isfile(stage1_path):
# lapnet1_gen.load_state_dict(stage1_path)
if opt.stage == 2:
lapnet1_gen.load_state_dict(torch.load(stage1_path))
print('loading level1 iteration' + str(level1_iter))
elif opt.stage == 3:
lapnet1_gen.load_state_dict(torch.load(stage1_path))
lapnet2_gen.load_state_dict(torch.load(stage2_path))
print('loading level1 iteration' + str(level1_iter))
print('loading level2 iteration' + str(level2_iter))
elif opt.stage == 4:
lapnet1_gen.load_state_dict(torch.load(stage1_path))
lapnet2_gen.load_state_dict(torch.load(stage2_path))
lapnet3_gen.load_state_dict(torch.load(stage3_path))
print('loading level1 iteration' + str(level1_iter))
print('loading level2 iteration' + str(level2_iter))
print('loading level3 iteration' + str(level3_iter))
elif opt.stage == 5:
lapnet1_gen.load_state_dict(torch.load(stage1_path))
lapnet2_gen.load_state_dict(torch.load(stage2_path))
lapnet3_gen.load_state_dict(torch.load(stage3_path))
lapnet4_gen.load_state_dict(torch.load(stage4_path))
print('loading level1 iteration' + str(level1_iter))
print('loading level2 iteration' + str(level2_iter))
print('loading level3 iteration' + str(level3_iter))
print('loading level4 iteration' + str(level4_iter))
for epoch in range(epochs):
'''
if epoch < 2 :
opt.w_loss = 0.1
elif epoch < 4:
opt.w_loss = 0.01
else:
opt.w_loss = 0.001
'''
# training level 1
if opt.stage == 1 or opt.stage == 5:
for idx, (data, _) in enumerate(trainloader, 0):
if data.size(0) != opt.batch_size:
continue
lapnet1_gen.train(), lapnet1_disc.train()
data_array = data.numpy()
for i in range(opt.batch_size):
g4_target_temp = data_array[i] # 3x64x64
# g3_target_temp = g4_target_temp[:, ::2, ::2] # 3x32x32
# g2_target_temp = g3_target_temp[:, ::2, ::2] # 3x16x16
g1_target_temp = g4_target_temp[:, ::8, ::8] # 3x8x8
g1_target[i] = torch.from_numpy(g1_target_temp)
for j in range(channels):
g1_input[i, j, :] = torch.from_numpy(sensing_matrix1[j, :, :].dot(data_array[i, j].flatten()))
g1_input_var = Variable(g1_input)
g1_target_var = Variable(g1_target)
# Train disc1 with true images
lapnet1_disc.zero_grad()
d1_output = lapnet1_disc(g1_target_var)
d1_label_var = Variable(label.fill_(real_label))
errD_d1_real_bce = criterion_bce(d1_output, d1_label_var)
errD_d1_real_bce.backward()
d1_real_mean = d1_output.data.mean()
# Train disc1 with fake images
g1_output = lapnet1_gen(g1_input_var)
d1_output = lapnet1_disc(g1_output.detach())
d1_label_var = Variable(label.fill_(fake_label))
errD_d1_fake_bce = criterion_bce(d1_output, d1_label_var)
errD_d1_fake_bce.backward()
optimizer_lapnet1_disc.step()
# Train gen1 with fake images
lapnet1_gen.zero_grad()
d1_label_var = Variable(label.fill_(real_label))
d1_output = lapnet1_disc(g1_output)
errD_g1_fake_bce = criterion_bce(d1_output, d1_label_var)
errD_g1_fake_mse = criterion_mse(g1_output, g1_target_var)
errD_g1 = opt.w_loss * errD_g1_fake_bce + (1 - opt.w_loss) * errD_g1_fake_mse
errD_g1.backward()
optimizer_lapnet1_gen.step()
d1_fake_mean = d1_output.data.mean()
if idx % opt.log_interval == 0:
print('Level %d [%d/%d][%d/%d] errD_real: %.4f, errD_fake: %.4f, errG_bce: %.4f errG_mse: %.4f,'
'D(x): %.4f, D(G(z)): %.4f' % (
1, epoch, epochs, idx, len(trainloader),
errD_d1_real_bce.data[0],
errD_d1_fake_bce.data[0],
errD_g1_fake_bce.data[0],
errD_g1_fake_mse.data[0],
d1_real_mean,
d1_fake_mean))
torch.save(lapnet1_gen.state_dict(), '%s/%s/cr%s/%s/stage%s/model/gen_epoch_%d.pth'
% (opt.outf, opt.dataset, opt.cr, opt.model, 1, epoch))
torch.save(lapnet1_disc.state_dict(), '%s/%s/cr%s/%s/stage%s/model/disc_epoch_%d.pth'
% (opt.outf, opt.dataset, opt.cr, opt.model, 1, epoch))
vutils.save_image(g1_target_var.data,
'%s/%s/cr%s/%s/stage%s/image/epoch_%03d_real.png'
% (opt.outf, opt.dataset, opt.cr, opt.model, 1, epoch), normalize=True)
vutils.save_image(g1_output.data,
'%s/%s/cr%s/%s/stage%s/image/epoch_%03d_fake.png'
% (opt.outf, opt.dataset, opt.cr, opt.model, 1, epoch), normalize=True)
lapnet1_gen.eval(), lapnet1_disc.eval()
val(epoch, 1, channels, valloader, sensing_matrix1, sensing_matrix2, sensing_matrix3,
sensing_matrix4, g1_target, g1_input, lapnet1_gen, lapnet2_gen, lapnet3_gen, lapnet4_gen,
criterion_mse, y2, y3, y4)
# training level 2
# load weight of level 1
if opt.stage == 2 or opt.stage == 5:
print('loading level1 iteration' + str(level1_iter))
for idx, (data, _) in enumerate(trainloader, 0):
if data.size(0) != opt.batch_size:
continue
lapnet2_gen.train(), lapnet2_disc.train()
data_array = data.numpy()
for i in range(opt.batch_size):
g4_target_temp = data_array[i] # 3x64x64
g3_target_temp = g4_target_temp[:, ::2, ::2] # 3x32x32
g2_target_temp = g3_target_temp[:, ::2, ::2] # 3x16x16
g1_target_temp = g2_target_temp[:, ::2, ::2] # 3x8x8
g2_target[i] = torch.from_numpy(g2_target_temp)
g1_target[i] = torch.from_numpy(g1_target_temp)
for j in range(channels):
g1_input[i, j, :] = torch.from_numpy(sensing_matrix1[j, :, :].dot(data_array[i, j].flatten()))
y2[i, j, :] = torch.from_numpy(sensing_matrix2[j, :, :].dot(data_array[i, j].flatten()))
g1_input_var = Variable(g1_input)
# g2_input = lapnet1_gen(g1_input_var.detach())
g2_input = lapnet1_gen(g1_input_var)
# Train disc2 with true images
lapnet2_disc.zero_grad()
g2_target_var = Variable(g2_target)
d2_output = lapnet2_disc(g2_target_var)
d2_label_var = Variable(label.fill_(real_label))
errD_d2_real_bce = criterion_bce(d2_output, d2_label_var)
errD_d2_real_bce.backward()
d2_real_mean = d2_output.data.mean()
# Train disc2 with fake images
y2_var = Variable(y2)
g2_output = lapnet2_gen(g2_input, y2_var)
d2_output = lapnet2_disc(g2_output.detach())
d2_label_var = Variable(label.fill_(fake_label))
errD_d2_fake_bce = criterion_bce(d2_output, d2_label_var)
errD_d2_fake_bce.backward()
optimizer_lapnet2_disc.step()
# Train gen2 with fake images, disc2 is not updated
lapnet2_gen.zero_grad()
d2_label_var = Variable(label.fill_(real_label))
d2_output = lapnet2_disc(g2_output)
errD_g2_fake_bce = criterion_bce(d2_output, d2_label_var)
errD_g2_fake_mse = criterion_mse(g2_output, g2_target_var)
errD_g2 = opt.w_loss * errD_g2_fake_bce + (1 - opt.w_loss) * errD_g2_fake_mse
errD_g2.backward()
# optimizer_lapnet1_gen.step()
optimizer_lapnet2_gen.step()
d2_fake_mean = d2_output.data.mean()
if idx % opt.log_interval == 0:
g1_target_var = Variable(g1_target)
errD_g1_fake_mse = criterion_mse(g2_input, g1_target_var)
print('Level %d [%d/%d][%d/%d] errD_real: %.4f, errD_fake: %.4f, errG_bce: %.4f errG_mse: %.4f,'
'errG1_mse: %.4f, D(x): %.4f, D(G(z)): %.4f' % (
2, epoch, epochs, idx, len(trainloader),
errD_d2_real_bce.data[0],
errD_d2_fake_bce.data[0],
errD_g2_fake_bce.data[0],
errD_g2_fake_mse.data[0],
errD_g1_fake_mse.data[0],
d2_real_mean,
d2_fake_mean))
torch.save(lapnet2_gen.state_dict(), '%s/%s/cr%s/%s/stage%s/model/gen_epoch_%d.pth'
% (opt.outf, opt.dataset, opt.cr, opt.model, 2, epoch))
torch.save(lapnet2_disc.state_dict(), '%s/%s/cr%s/%s/stage%s/model/disc_epoch_%d.pth'
% (opt.outf, opt.dataset, opt.cr, opt.model, 2, epoch))
vutils.save_image(g2_target_var.data,
'%s/%s/cr%s/%s/stage%s/image/epoch_%03d_real.png'
% (opt.outf, opt.dataset, opt.cr, opt.model, 2, epoch), normalize=True)
vutils.save_image(g2_output.data,
'%s/%s/cr%s/%s/stage%s/image/epoch_%03d_fake.png'
% (opt.outf, opt.dataset, opt.cr, opt.model, 2, epoch), normalize=True)
lapnet2_gen.eval(), lapnet2_disc.eval()
val(epoch, 2, channels, valloader, sensing_matrix1, sensing_matrix2, sensing_matrix3,
sensing_matrix4, g2_target, g1_input, lapnet1_gen, lapnet2_gen, lapnet3_gen, lapnet4_gen,
criterion_mse, y2, y3, y4)
# training level 3
if opt.stage == 3 or opt.stage == 5:
'''
if epoch == 0 and opt.transfer == True:
print('transfer weidhts from level1')
lapnet3_gen.conv1 = copy.deepcopy(lapnet2_gen.conv1)
lapnet3_gen.conv2 = copy.deepcopy(lapnet2_gen.conv2)
lapnet3_gen.upsamp1 = copy.deepcopy(lapnet2_gen.upsamp1)
lapnet3_gen.upsamp2 = copy.deepcopy(lapnet2_gen.upsamp2)
lapnet3_gen.resblk1 = copy.deepcopy(lapnet2_gen.resblk1)
lapnet3_disc.main[0] = copy.deepcopy(lapnet2_disc.main[0])
lapnet3_disc.main[2] = copy.deepcopy(lapnet2_disc.main[2])
lapnet3_disc.main[4] = copy.deepcopy(lapnet2_disc.main[4])
lapnet3_disc.main[6] = copy.deepcopy(lapnet2_disc.main[5])
lapnet3_disc.main[8] = copy.deepcopy(lapnet2_disc.main[8])
'''
for idx, (data, _) in enumerate(trainloader, 0):
if data.size(0) != opt.batch_size:
continue
lapnet3_gen.train(), lapnet3_disc.train()
data_array = data.numpy()
for i in range(opt.batch_size):
g4_target_temp = data_array[i] # 1x64x64
g3_target_temp = g4_target_temp[:, ::2, ::2] # 1x32x32
g2_target_temp = g3_target_temp[:, ::2, ::2] # 3x16x16
g1_target_temp = g2_target_temp[:, ::2, ::2] # 3x8x8
g3_target[i] = torch.from_numpy(g3_target_temp)
g2_target[i] = torch.from_numpy(g2_target_temp)
g1_target[i] = torch.from_numpy(g1_target_temp)
for j in range(channels):
g1_input[i, j, :] = torch.from_numpy(sensing_matrix1[j, :, :].dot(data_array[i, j].flatten()))
y2[i, j, :] = torch.from_numpy(sensing_matrix2[j, :, :].dot(data_array[i, j].flatten()))
y3[i, j, :] = torch.from_numpy(sensing_matrix3[j, :, :].dot(data_array[i, j].flatten()))
g1_input_var = Variable(g1_input)
g2_input = lapnet1_gen(g1_input_var) # 1x8x8
y2_var = Variable(y2)
g3_input = lapnet2_gen(g2_input, y2_var) # 1x16x16
# Train disc3 with true images
lapnet3_disc.zero_grad()
g3_target_var = Variable(g3_target)
d3_output = lapnet3_disc(g3_target_var)
d3_label_var = Variable(label.fill_(real_label))
errD_d3_real_bce = criterion_bce(d3_output, d3_label_var)
errD_d3_real_bce.backward()
d3_real_mean = d3_output.data.mean()
# Train disc3 with fake images
y3_var = Variable(y3)
g3_output = lapnet3_gen(g3_input, y3_var)
d3_output = lapnet3_disc(g3_output.detach())
d3_label_var = Variable(label.fill_(fake_label))
errD_d3_fake_bce = criterion_bce(d3_output, d3_label_var)
errD_d3_fake_bce.backward()
optimizer_lapnet3_disc.step()
# Train gen3 with fake images, disc3 is not updated
lapnet3_gen.zero_grad()
d3_label_var = Variable(label.fill_(real_label))
d3_output = lapnet3_disc(g3_output)
errD_g3_fake_bce = criterion_bce(d3_output, d3_label_var)
errD_g3_fake_mse = criterion_mse(g3_output, g3_target_var)
errD_g3 = opt.w_loss * errD_g3_fake_bce + (1 - opt.w_loss) * errD_g3_fake_mse
errD_g3.backward()
optimizer_lapnet3_gen.step()
d3_fake_mean = d3_output.data.mean()
if idx % opt.log_interval == 0:
g1_target_var = Variable(g1_target)
g2_target_var = Variable(g2_target)
errD_g1_fake_mse = criterion_mse(g2_input, g1_target_var)
errD_g2_fake_mse = criterion_mse(g3_input, g2_target_var)
print('Level %d [%d/%d][%d/%d] errD_real: %.4f, errD_fake: %.4f, errG_bce: %.4f errG_mse: %.4f,'
'errG2_mse: %.4f, errG1_mse: %.4f, D(x): %.4f, D(G(z)): %.4f' % (
3, epoch, epochs, idx, len(trainloader),
errD_d3_real_bce.data[0],
errD_d3_fake_bce.data[0],
errD_g3_fake_bce.data[0],
errD_g3_fake_mse.data[0],
errD_g2_fake_mse.data[0],
errD_g1_fake_mse.data[0],
d3_real_mean,
d3_fake_mean))
torch.save(lapnet3_gen.state_dict(), '%s/%s/cr%s/%s/stage%s/model/gen_epoch_%d.pth'
% (opt.outf, opt.dataset, opt.cr, opt.model, 3, epoch))
torch.save(lapnet3_disc.state_dict(), '%s/%s/cr%s/%s/stage%s/model/disc_epoch_%d.pth'
% (opt.outf, opt.dataset, opt.cr, opt.model, 3, epoch))
vutils.save_image(g3_target_var.data,
'%s/%s/cr%s/%s/stage%s/image/epoch_%03d_real.png'
% (opt.outf, opt.dataset, opt.cr, opt.model, 3, epoch), normalize=True)
vutils.save_image(g3_output.data,
'%s/%s/cr%s/%s/stage%s/image/epoch_%03d_fake.png'
% (opt.outf, opt.dataset, opt.cr, opt.model, 3, epoch), normalize=True)
lapnet3_gen.eval(), lapnet3_disc.eval()
val(epoch, 3, channels, valloader, sensing_matrix1, sensing_matrix2, sensing_matrix3,
sensing_matrix4, g3_target, g1_input, lapnet1_gen, lapnet2_gen, lapnet3_gen, lapnet4_gen,
criterion_mse, y2, y3, y4)
# training level 4
if opt.stage == 4 or opt.stage == 5:
for idx, (data, _) in enumerate(trainloader, 0):
if data.size(0) != opt.batch_size:
continue
lapnet4_gen.train(), lapnet4_disc.train()
data_array = data.numpy()
for i in range(opt.batch_size):
g4_target_temp = data_array[i] # 1x64x64
g3_target_temp = g4_target_temp[:, ::2, ::2] # 1x32x32
g2_target_temp = g3_target_temp[:, ::2, ::2] # 1x16x16
g1_target_temp = g2_target_temp[:, ::2, ::2] # 1x8x8
g4_target[i] = torch.from_numpy(g4_target_temp)
g3_target[i] = torch.from_numpy(g3_target_temp)
g2_target[i] = torch.from_numpy(g2_target_temp)
g1_target[i] = torch.from_numpy(g1_target_temp)
for j in range(channels):
g1_input[i, j, :] = torch.from_numpy(sensing_matrix1[j, :, :].dot(data_array[i, j].flatten()))
y2[i, j, :] = torch.from_numpy(sensing_matrix2[j, :, :].dot(data_array[i, j].flatten()))
y3[i, j, :] = torch.from_numpy(sensing_matrix3[j, :, :].dot(data_array[i, j].flatten()))
y4[i, j, :] = torch.from_numpy(sensing_matrix4[j, :, :].dot(data_array[i, j].flatten()))
g1_input_var = Variable(g1_input)
g2_input = lapnet1_gen(g1_input_var) # 1x8x8
y2_var = Variable(y2)
g3_input = lapnet2_gen(g2_input, y2_var) # 1x16x16
y3_var = Variable(y3)
g4_input = lapnet3_gen(g3_input, y3_var) # 1x32x32
# Train disc4 with true images
g4_target_var = Variable(g4_target)
lapnet4_disc.zero_grad()
d4_output = lapnet4_disc(g4_target_var)
d4_label_var = Variable(label.fill_(real_label))
errD_d4_real_bce = criterion_bce(d4_output, d4_label_var)
errD_d4_real_bce.backward()
d4_real_mean = d4_output.data.mean()
# Train disc4 with fake images
y4_var = Variable(y4)
g4_output = lapnet4_gen(g4_input, y4_var)
d4_output = lapnet4_disc(g4_output.detach())
d4_label_var = Variable(label.fill_(fake_label))
errD_d4_fake_bce = criterion_bce(d4_output, d4_label_var)
errD_d4_fake_bce.backward()
optimizer_lapnet4_disc.step()
# Train gen4 with fake images, disc4 is not updated
lapnet4_gen.zero_grad()
d4_label_var = Variable(label.fill_(real_label))
d4_output = lapnet4_disc(g4_output)
errD_g4_fake_bce = criterion_bce(d4_output, d4_label_var)
errD_g4_fake_mse = criterion_mse(g4_output, g4_target_var)
errD_g4 = opt.w_loss * errD_g4_fake_bce + (1 - opt.w_loss) * errD_g4_fake_mse
errD_g4.backward()
optimizer_lapnet4_gen.step()
d4_fake_mean = d4_output.data.mean()
if idx % opt.log_interval == 0:
g1_target_var = Variable(g1_target)
g2_target_var = Variable(g2_target)
g3_target_var = Variable(g3_target)
errD_g1_fake_mse = criterion_mse(g2_input, g1_target_var)
errD_g2_fake_mse = criterion_mse(g3_input, g2_target_var)
errD_g3_fake_mse = criterion_mse(g4_input, g3_target_var)
print('Level %d [%d/%d][%d/%d] errD_real: %.4f, errD_fake: %.4f, errG_bce: %.4f errG_mse: %.4f,'
'errG3_mse: %.4f, errG2_mse: %.4f, errG1_mse: %.4f, D(x): %.4f, D(G(z)): %.4f' % (
4, epoch, epochs, idx, len(trainloader),
errD_d4_real_bce.data[0],
errD_d4_fake_bce.data[0],
errD_g4_fake_bce.data[0],
errD_g4_fake_mse.data[0],
errD_g3_fake_mse.data[0],
errD_g2_fake_mse.data[0],
errD_g1_fake_mse.data[0],
d4_real_mean,
d4_fake_mean))
torch.save(lapnet4_gen.state_dict(), '%s/%s/cr%s/%s/stage%s/model/gen_epoch_%d.pth'
% (opt.outf, opt.dataset, opt.cr, opt.model, 4, epoch))
torch.save(lapnet4_disc.state_dict(), '%s/%s/cr%s/%s/stage%s/model/disc_epoch_%d.pth'
% (opt.outf, opt.dataset, opt.cr, opt.model, 4, epoch))
vutils.save_image(g4_target_var.data,
'%s/%s/cr%s/%s/stage%s/image/epoch_%03d_real.png'
% (opt.outf, opt.dataset, opt.cr, opt.model, 4, epoch), normalize=True)
vutils.save_image(g4_output.data,
'%s/%s/cr%s/%s/stage%s/image/epoch_%03d_fake.png'
% (opt.outf, opt.dataset, opt.cr, opt.model, 4, epoch), normalize=True)
lapnet4_gen.eval(), lapnet4_disc.eval()
val(epoch, 4, channels, valloader, sensing_matrix1, sensing_matrix2, sensing_matrix3,
sensing_matrix4, g4_target, g1_input, lapnet1_gen, lapnet2_gen, lapnet3_gen, lapnet4_gen,
criterion_mse, y2, y3, y4)
# training the whole model from all the sub-models
'''
for idx, (data, _) in enumerate(trainloader, 0):
if data.size(0) != opt.batch_size:
continue
data_array = data.numpy()
for i in range(opt.batch_size):
g4_target_temp = data_array[i] # 1x64x64
g3_target_temp = g4_target_temp[:, ::2, ::2] # 1x32x32
g2_target_temp = g3_target_temp[:, ::2, ::2] # 1x16x16
g1_target_temp = g2_target_temp[:, ::2, ::2] # 1x8x8
g4_target[i] = torch.from_numpy(g4_target_temp)
g3_target[i] = torch.from_numpy(g3_target_temp)
g2_target[i] = torch.from_numpy(g2_target_temp)
g1_target[i] = torch.from_numpy(g1_target_temp)
for j in range(channels):
g1_input[i, j, :] = torch.from_numpy(sensing_matrix1.dot(data_array[i, j].flatten()))
y2[i, j, :] = torch.from_numpy(sensing_matrix2.dot(data_array[i, j].flatten()))
y3[i, j, :] = torch.from_numpy(sensing_matrix3.dot(data_array[i, j].flatten()))
y4[i, j, :] = torch.from_numpy(sensing_matrix4.dot(data_array[i, j].flatten()))
# Train lapnet_disc with true images
lapnet1_disc.zero_grad(), lapnet2_disc.zero_grad()
lapnet3_disc.zero_grad(), lapnet4_disc.zero_grad()
g1_target_var, g2_target_var, g3_target_var, g4_target_var = Variable(g1_target), \
Variable(g2_target), \
Variable(g3_target), \
Variable(g4_target)
d1_output, d2_output, d3_output, d4_output = lapnet1_disc(g1_target_var),\
lapnet2_disc(g2_target_var),\
lapnet3_disc(g3_target_var),\
lapnet4_disc(g4_target_var)
d1_label_var, d2_label_var, d3_label_var, d4_label_var = Variable(label.fill_(real_label)), \
Variable(label.fill_(real_label)), \
Variable(label.fill_(real_label)), \
Variable(label.fill_(real_label))
errD_d1_real_bce, errD_d2_real_bce, errD_d3_real_bce, errD_d4_real_bce = criterion_bce(d1_output, d1_label_var), \
criterion_bce(d2_output, d2_label_var), \
criterion_bce(d3_output, d3_label_var), \
criterion_bce(d4_output, d4_label_var)
errD_d4_real_bce.backward()
errD_d3_real_bce.backward()
errD_d2_real_bce.backward()
errD_d1_real_bce.backward()
d1_real_mean, d2_real_mean, d3_real_mean, d4_real_mean = d1_output.data.mean(), \
d2_output.data.mean(), \
d4_output.data.mean(), \
d4_output.data.mean()
# Train lapnet_disc with fake images
g1_input_var = Variable(g1_input)
g2_input = lapnet1_gen(g1_input_var) # 1x8x8
y2_var = Variable(y2)
g3_input = lapnet2_gen(g2_input, y2_var) # 1x16x16
y3_var = Variable(y3)
g4_input = lapnet3_gen(g3_input, y3_var) # 1x32x32
y4_var = Variable(y4)
g4_output = lapnet4_gen(g4_input, y4_var)
# do not update gen1, gen2, gen3, gen4
d1_output, d2_output, d3_output, d4_output = lapnet1_disc(g2_input.detach()), \
lapnet2_disc(g3_input.detach()), \
lapnet3_disc(g4_input.detach()), \
lapnet4_disc(g4_output.detach())
d1_label_var, d2_label_var, d3_label_var, d4_label_var = Variable(label.fill_(fake_label)), \
Variable(label.fill_(fake_label)), \
Variable(label.fill_(fake_label)), \
Variable(label.fill_(fake_label)),
errD_d1_fake_bce, errD_d2_fake_bce,errD_d3_fake_bce, errD_d4_fake_bce = criterion_bce(d1_output, d1_label_var), \
criterion_bce(d2_output, d2_label_var), \
criterion_bce(d3_output, d3_label_var), \
criterion_bce(d4_output, d4_label_var)
errD_d4_fake_bce.backward(), optimizer_lapnet4_disc.step()
errD_d3_fake_bce.backward(), optimizer_lapnet3_disc.step()
errD_d2_fake_bce.backward(), optimizer_lapnet2_disc.step()
errD_d1_fake_bce.backward(), optimizer_lapnet1_disc.step()