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train.py
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train.py
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#!/usr/bin/env python
import torch
import torch.nn
import torch.optim
import math
import numpy as np
from model import *
import config as c
from tensorboardX import SummaryWriter
import datasets
import viz
import modules.Unet_common as common
import warnings
warnings.filterwarnings("ignore")
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
def gauss_noise(shape):
noise = torch.zeros(shape).cuda()
for i in range(noise.shape[0]):
noise[i] = torch.randn(noise[i].shape).cuda()
return noise
def guide_loss(output, bicubic_image):
loss_fn = torch.nn.MSELoss(reduce=True, size_average=False)
loss = loss_fn(output, bicubic_image)
return loss.to(device)
def reconstruction_loss(rev_input, input):
loss_fn = torch.nn.MSELoss(reduce=True, size_average=False)
loss = loss_fn(rev_input, input)
return loss.to(device)
def low_frequency_loss(ll_input, gt_input):
loss_fn = torch.nn.MSELoss(reduce=True, size_average=False)
loss = loss_fn(ll_input, gt_input)
return loss.to(device)
# 网络参数数量
def get_parameter_number(net):
total_num = sum(p.numel() for p in net.parameters())
trainable_num = sum(p.numel() for p in net.parameters() if p.requires_grad)
return {'Total': total_num, 'Trainable': trainable_num}
def computePSNR(origin,pred):
origin = np.array(origin)
origin = origin.astype(np.float32)
pred = np.array(pred)
pred = pred.astype(np.float32)
mse = np.mean((origin/1.0 - pred/1.0) ** 2 )
if mse < 1.0e-10:
return 100
return 10 * math.log10(255.0**2/mse)
def load(name):
state_dicts = torch.load(name)
network_state_dict = {k: v for k, v in state_dicts['net'].items() if 'tmp_var' not in k}
net.load_state_dict(network_state_dict)
try:
optim.load_state_dict(state_dicts['opt'])
except:
print('Cannot load optimizer for some reason or other')
#####################
# Model initialize: #
#####################
net = Model()
net.cuda()
init_model(net)
net = torch.nn.DataParallel(net, device_ids=c.device_ids)
para = get_parameter_number(net)
print(para)
params_trainable = (list(filter(lambda p: p.requires_grad, net.parameters())))
optim = torch.optim.Adam(params_trainable, lr=c.lr, betas=c.betas, eps=1e-6, weight_decay=c.weight_decay)
weight_scheduler = torch.optim.lr_scheduler.StepLR(optim, c.weight_step, gamma=c.gamma)
dwt = common.DWT()
iwt = common.IWT()
if c.tain_next:
load(c.MODEL_PATH + c.suffix)
try:
writer = SummaryWriter(comment='hinet', filename_suffix="steg")
for i_epoch in range(c.epochs):
i_epoch = i_epoch + c.trained_epoch + 1
loss_history = []
#################
# train: #
#################
for i_batch, data in enumerate(datasets.trainloader):
data = data.to(device)
cover = data[data.shape[0] // 2:]
secret = data[:data.shape[0] // 2]
cover_input = dwt(cover)
secret_input = dwt(secret)
input_img = torch.cat((cover_input, secret_input), 1)
#################
# forward: #
#################
output = net(input_img)
output_steg = output.narrow(1, 0, 4 * c.channels_in)
output_z = output.narrow(1, 4 * c.channels_in, output.shape[1] - 4 * c.channels_in)
steg_img = iwt(output_steg)
#################
# backward: #
#################
output_z_guass = gauss_noise(output_z.shape)
output_rev = torch.cat((output_steg, output_z_guass), 1)
output_image = net(output_rev, rev=True)
secret_rev = output_image.narrow(1, 4 * c.channels_in, output_image.shape[1] - 4 * c.channels_in)
secret_rev = iwt(secret_rev)
#################
# loss: #
#################
g_loss = guide_loss(steg_img.cuda(), cover.cuda())
r_loss = reconstruction_loss(secret_rev, secret)
steg_low = output_steg.narrow(1, 0, c.channels_in)
cover_low = cover_input.narrow(1, 0, c.channels_in)
l_loss = low_frequency_loss(steg_low, cover_low)
total_loss = c.lamda_reconstruction * r_loss + c.lamda_guide * g_loss + c.lamda_low_frequency * l_loss
total_loss.backward()
optim.step()
optim.zero_grad()
loss_history.append([total_loss.item(), 0.])
epoch_losses = np.mean(np.array(loss_history), axis=0)
epoch_losses[1] = np.log10(optim.param_groups[0]['lr'])
#################
# val: #
#################
if i_epoch % c.val_freq == 0:
with torch.no_grad():
psnr_s = []
psnr_c = []
net.eval()
for x in datasets.testloader:
x = x.to(device)
cover = x[x.shape[0] // 2:, :, :, :]
secret = x[:x.shape[0] // 2, :, :, :]
cover_input = dwt(cover)
secret_input = dwt(secret)
input_img = torch.cat((cover_input, secret_input), 1)
#################
# forward: #
#################
output = net(input_img)
output_steg = output.narrow(1, 0, 4 * c.channels_in)
steg = iwt(output_steg)
output_z = output.narrow(1, 4 * c.channels_in, output.shape[1] - 4 * c.channels_in)
output_z = gauss_noise(output_z.shape)
#################
# backward: #
#################
output_steg = output_steg.cuda()
output_rev = torch.cat((output_steg, output_z), 1)
output_image = net(output_rev, rev=True)
secret_rev = output_image.narrow(1, 4 * c.channels_in, output_image.shape[1] - 4 * c.channels_in)
secret_rev = iwt(secret_rev)
secret_rev = secret_rev.cpu().numpy().squeeze() * 255
np.clip(secret_rev, 0, 255)
secret = secret.cpu().numpy().squeeze() * 255
np.clip(secret, 0, 255)
cover = cover.cpu().numpy().squeeze() * 255
np.clip(cover, 0, 255)
steg = steg.cpu().numpy().squeeze() * 255
np.clip(steg, 0, 255)
psnr_temp = computePSNR(secret_rev, secret)
psnr_s.append(psnr_temp)
psnr_temp_c = computePSNR(cover, steg)
psnr_c.append(psnr_temp_c)
writer.add_scalars("PSNR_S", {"average psnr": np.mean(psnr_s)}, i_epoch)
writer.add_scalars("PSNR_C", {"average psnr": np.mean(psnr_c)}, i_epoch)
viz.show_loss(epoch_losses)
writer.add_scalars("Train", {"Train_Loss": epoch_losses[0]}, i_epoch)
if i_epoch > 0 and (i_epoch % c.SAVE_freq) == 0:
torch.save({'opt': optim.state_dict(),
'net': net.state_dict()}, c.MODEL_PATH + 'model_checkpoint_%.5i' % i_epoch + '.pt')
weight_scheduler.step()
torch.save({'opt': optim.state_dict(),
'net': net.state_dict()}, c.MODEL_PATH + 'model' + '.pt')
writer.close()
except:
if c.checkpoint_on_error:
torch.save({'opt': optim.state_dict(),
'net': net.state_dict()}, c.MODEL_PATH + 'model_ABORT' + '.pt')
raise
finally:
viz.signal_stop()