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DMPHN_1_2_4_8.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torch.optim.lr_scheduler import StepLR
import numpy as np
import os
import math
import argparse
import random
import models
import torchvision
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, datasets
from datasets import GoProDataset
import time
parser = argparse.ArgumentParser(description="Deep Multi-Patch Hierarchical Network")
parser.add_argument("-e","--epochs",type = int, default = 4000)
parser.add_argument("-se","--start_epoch",type = int, default = 0)
parser.add_argument("-b","--batchsize",type = int, default = 2)
parser.add_argument("-s","--imagesize",type = int, default = 256)
parser.add_argument("-l","--learning_rate", type = float, default = 0.0001)
parser.add_argument("-g","--gpu",type=int, default=0)
args = parser.parse_args()
#Hyper Parameters
METHOD = "DMPHN_1_2_4_8"
LEARNING_RATE = args.learning_rate
EPOCHS = args.epochs
GPU = args.gpu
BATCH_SIZE = args.batchsize
IMAGE_SIZE = args.imagesize
def save_deblur_images(images, iteration, epoch):
filename = './checkpoints/' + METHOD + "/epoch" + str(epoch) + "/" + "Iter_" + str(iteration) + "_deblur.png"
torchvision.utils.save_image(images, filename)
def weight_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0.0, 0.5*math.sqrt(2. / n))
if m.bias is not None:
m.bias.data.zero_()
elif classname.find('BatchNorm') != -1:
m.weight.data.fill_(1)
m.bias.data.zero_()
elif classname.find('Linear') != -1:
n = m.weight.size(1)
m.weight.data.normal_(0, 0.01)
m.bias.data = torch.ones(m.bias.data.size())
def main():
print("init data folders")
encoder_lv1 = models.Encoder()
encoder_lv2 = models.Encoder()
encoder_lv3 = models.Encoder()
encoder_lv4 = models.Encoder()
decoder_lv1 = models.Decoder()
decoder_lv2 = models.Decoder()
decoder_lv3 = models.Decoder()
decoder_lv4 = models.Decoder()
encoder_lv1.apply(weight_init).cuda(GPU)
encoder_lv2.apply(weight_init).cuda(GPU)
encoder_lv3.apply(weight_init).cuda(GPU)
encoder_lv4.apply(weight_init).cuda(GPU)
decoder_lv1.apply(weight_init).cuda(GPU)
decoder_lv2.apply(weight_init).cuda(GPU)
decoder_lv3.apply(weight_init).cuda(GPU)
decoder_lv4.apply(weight_init).cuda(GPU)
encoder_lv1_optim = torch.optim.Adam(encoder_lv1.parameters(),lr=LEARNING_RATE)
encoder_lv1_scheduler = StepLR(encoder_lv1_optim,step_size=1000,gamma=0.1)
encoder_lv2_optim = torch.optim.Adam(encoder_lv2.parameters(),lr=LEARNING_RATE)
encoder_lv2_scheduler = StepLR(encoder_lv2_optim,step_size=1000,gamma=0.1)
encoder_lv3_optim = torch.optim.Adam(encoder_lv3.parameters(),lr=LEARNING_RATE)
encoder_lv3_scheduler = StepLR(encoder_lv3_optim,step_size=1000,gamma=0.1)
encoder_lv4_optim = torch.optim.Adam(encoder_lv4.parameters(),lr=LEARNING_RATE)
encoder_lv4_scheduler = StepLR(encoder_lv4_optim,step_size=1000,gamma=0.1)
decoder_lv1_optim = torch.optim.Adam(decoder_lv1.parameters(),lr=LEARNING_RATE)
decoder_lv1_scheduler = StepLR(decoder_lv1_optim,step_size=1000,gamma=0.1)
decoder_lv2_optim = torch.optim.Adam(decoder_lv2.parameters(),lr=LEARNING_RATE)
decoder_lv2_scheduler = StepLR(decoder_lv2_optim,step_size=1000,gamma=0.1)
decoder_lv3_optim = torch.optim.Adam(decoder_lv3.parameters(),lr=LEARNING_RATE)
decoder_lv3_scheduler = StepLR(decoder_lv3_optim,step_size=1000,gamma=0.1)
decoder_lv4_optim = torch.optim.Adam(decoder_lv4.parameters(),lr=LEARNING_RATE)
decoder_lv4_scheduler = StepLR(decoder_lv4_optim,step_size=1000,gamma=0.1)
if os.path.exists(str('./checkpoints/' + METHOD + "/encoder_lv1.pkl")):
encoder_lv1.load_state_dict(torch.load(str('./checkpoints/' + METHOD + "/encoder_lv1.pkl")))
print("load encoder_lv1 success")
if os.path.exists(str('./checkpoints/' + METHOD + "/encoder_lv2.pkl")):
encoder_lv2.load_state_dict(torch.load(str('./checkpoints/' + METHOD + "/encoder_lv2.pkl")))
print("load encoder_lv2 success")
if os.path.exists(str('./checkpoints/' + METHOD + "/encoder_lv3.pkl")):
encoder_lv3.load_state_dict(torch.load(str('./checkpoints/' + METHOD + "/encoder_lv3.pkl")))
print("load encoder_lv3 success")
if os.path.exists(str('./checkpoints/' + METHOD + "/encoder_lv4.pkl")):
encoder_lv4.load_state_dict(torch.load(str('./checkpoints/' + METHOD + "/encoder_lv4.pkl")))
print("load encoder_lv4 success")
if os.path.exists(str('./checkpoints/' + METHOD + "/decoder_lv1.pkl")):
decoder_lv1.load_state_dict(torch.load(str('./checkpoints/' + METHOD + "/decoder_lv1.pkl")))
print("load encoder_lv1 success")
if os.path.exists(str('./checkpoints/' + METHOD + "/decoder_lv2.pkl")):
decoder_lv2.load_state_dict(torch.load(str('./checkpoints/' + METHOD + "/decoder_lv2.pkl")))
print("load decoder_lv2 success")
if os.path.exists(str('./checkpoints/' + METHOD + "/decoder_lv3.pkl")):
decoder_lv3.load_state_dict(torch.load(str('./checkpoints/' + METHOD + "/decoder_lv3.pkl")))
print("load decoder_lv3 success")
if os.path.exists(str('./checkpoints/' + METHOD + "/decoder_lv4.pkl")):
decoder_lv4.load_state_dict(torch.load(str('./checkpoints/' + METHOD + "/decoder_lv4.pkl")))
print("load decoder_lv4 success")
if os.path.exists('./checkpoints/' + METHOD) == False:
os.system('mkdir ./checkpoints/' + METHOD)
for epoch in range(args.start_epoch, EPOCHS):
encoder_lv1_scheduler.step(epoch)
encoder_lv2_scheduler.step(epoch)
encoder_lv3_scheduler.step(epoch)
encoder_lv4_scheduler.step(epoch)
decoder_lv1_scheduler.step(epoch)
decoder_lv2_scheduler.step(epoch)
decoder_lv3_scheduler.step(epoch)
decoder_lv4_scheduler.step(epoch)
print("Training...")
train_dataset = GoProDataset(
blur_image_files = './datas/GoPro/train_blur_file.txt',
sharp_image_files = './datas/GoPro/train_sharp_file.txt',
root_dir = './datas/GoPro',
crop = True,
crop_size = IMAGE_SIZE,
transform = transforms.Compose([
transforms.ToTensor()
]))
train_dataloader = DataLoader(train_dataset, batch_size = BATCH_SIZE, shuffle=True)
start = 0
for iteration, images in enumerate(train_dataloader):
mse = nn.MSELoss().cuda(GPU)
gt = Variable(images['sharp_image'] - 0.5).cuda(GPU)
H = gt.size(2)
W = gt.size(3)
images_lv1 = Variable(images['blur_image'] - 0.5).cuda(GPU)
images_lv2_1 = images_lv1[:,:,0:int(H/2),:]
images_lv2_2 = images_lv1[:,:,int(H/2):H,:]
images_lv3_1 = images_lv2_1[:,:,:,0:int(W/2)]
images_lv3_2 = images_lv2_1[:,:,:,int(W/2):W]
images_lv3_3 = images_lv2_2[:,:,:,0:int(W/2)]
images_lv3_4 = images_lv2_2[:,:,:,int(W/2):W]
images_lv4_1 = images_lv3_1[:,:,0:int(H/4),:]
images_lv4_2 = images_lv3_1[:,:,int(H/4):int(H/2),:]
images_lv4_3 = images_lv3_2[:,:,0:int(H/4),:]
images_lv4_4 = images_lv3_2[:,:,int(H/4):int(H/2),:]
images_lv4_5 = images_lv3_3[:,:,0:int(H/4),:]
images_lv4_6 = images_lv3_3[:,:,int(H/4):int(H/2),:]
images_lv4_7 = images_lv3_4[:,:,0:int(H/4),:]
images_lv4_8 = images_lv3_4[:,:,int(H/4):int(H/2),:]
feature_lv4_1 = encoder_lv4(images_lv4_1)
feature_lv4_2 = encoder_lv4(images_lv4_2)
feature_lv4_3 = encoder_lv4(images_lv4_3)
feature_lv4_4 = encoder_lv4(images_lv4_4)
feature_lv4_5 = encoder_lv4(images_lv4_5)
feature_lv4_6 = encoder_lv4(images_lv4_6)
feature_lv4_7 = encoder_lv4(images_lv4_7)
feature_lv4_8 = encoder_lv4(images_lv4_8)
feature_lv4_top_left = torch.cat((feature_lv4_1, feature_lv4_2), 2)
feature_lv4_top_right = torch.cat((feature_lv4_3, feature_lv4_4), 2)
feature_lv4_bot_left = torch.cat((feature_lv4_5, feature_lv4_6), 2)
feature_lv4_bot_right = torch.cat((feature_lv4_7, feature_lv4_8), 2)
feature_lv4_top = torch.cat((feature_lv4_top_left, feature_lv4_top_right), 3)
feature_lv4_bot = torch.cat((feature_lv4_bot_left, feature_lv4_bot_right), 3)
feature_lv4 = torch.cat((feature_lv4_top, feature_lv4_bot), 2)
residual_lv4_top_left = decoder_lv4(feature_lv4_top_left)
residual_lv4_top_right = decoder_lv4(feature_lv4_top_right)
residual_lv4_bot_left = decoder_lv4(feature_lv4_bot_left)
residual_lv4_bot_right = decoder_lv4(feature_lv4_bot_right)
feature_lv3_1 = encoder_lv3(images_lv3_1 + residual_lv4_top_left)
feature_lv3_2 = encoder_lv3(images_lv3_2 + residual_lv4_top_right)
feature_lv3_3 = encoder_lv3(images_lv3_3 + residual_lv4_bot_left)
feature_lv3_4 = encoder_lv3(images_lv3_4 + residual_lv4_bot_right)
feature_lv3_top = torch.cat((feature_lv3_1, feature_lv3_2), 3) + feature_lv4_top
feature_lv3_bot = torch.cat((feature_lv3_3, feature_lv3_4), 3) + feature_lv4_bot
feature_lv3 = torch.cat((feature_lv3_top, feature_lv3_bot), 2)
residual_lv3_top = decoder_lv3(feature_lv3_top)
residual_lv3_bot = decoder_lv3(feature_lv3_bot)
feature_lv2_1 = encoder_lv2(images_lv2_1 + residual_lv3_top)
feature_lv2_2 = encoder_lv2(images_lv2_2 + residual_lv3_bot)
feature_lv2 = torch.cat((feature_lv2_1, feature_lv2_2), 2) + feature_lv3
residual_lv2 = decoder_lv2(feature_lv2)
feature_lv1 = encoder_lv1(images_lv1 + residual_lv2) + feature_lv2
deblur_image = decoder_lv1(feature_lv1)
loss = mse(deblur_image, gt)
encoder_lv1.zero_grad()
encoder_lv2.zero_grad()
encoder_lv3.zero_grad()
encoder_lv4.zero_grad()
decoder_lv1.zero_grad()
decoder_lv2.zero_grad()
decoder_lv3.zero_grad()
decoder_lv4.zero_grad()
loss.backward()
encoder_lv1_optim.step()
encoder_lv2_optim.step()
encoder_lv3_optim.step()
encoder_lv4_optim.step()
decoder_lv1_optim.step()
decoder_lv2_optim.step()
decoder_lv3_optim.step()
decoder_lv4_optim.step()
if (iteration+1)%10 == 0:
stop = time.time()
print("epoch:", epoch, "iteration:", iteration+1, "loss:%.4f"%loss.item(), 'time:%.4f'%(stop-start))
start = time.time()
if (epoch)%100==0:
if os.path.exists('./checkpoints/' + METHOD + '/epoch' + str(epoch)) == False:
os.system('mkdir ./checkpoints/' + METHOD + '/epoch' + str(epoch))
print("Testing...")
test_dataset = GoProDataset(
blur_image_files = './datas/GoPro/test_blur_file.txt',
sharp_image_files = './datas/GoPro/test_sharp_file.txt',
root_dir = './datas/GoPro',
transform = transforms.Compose([
transforms.ToTensor()
]))
test_dataloader = DataLoader(test_dataset, batch_size = 1, shuffle=False)
test_time = 0
for iteration, images in enumerate(test_dataloader):
with torch.no_grad():
start = time.time()
images_lv1 = Variable(images['blur_image'] - 0.5).cuda(GPU)
H = images_lv1.size(2)
W = images_lv1.size(3)
images_lv2_1 = images_lv1[:,:,0:int(H/2),:]
images_lv2_2 = images_lv1[:,:,int(H/2):H,:]
images_lv3_1 = images_lv2_1[:,:,:,0:int(W/2)]
images_lv3_2 = images_lv2_1[:,:,:,int(W/2):W]
images_lv3_3 = images_lv2_2[:,:,:,0:int(W/2)]
images_lv3_4 = images_lv2_2[:,:,:,int(W/2):W]
images_lv4_1 = images_lv3_1[:,:,0:int(H/4),:]
images_lv4_2 = images_lv3_1[:,:,int(H/4):int(H/2),:]
images_lv4_3 = images_lv3_2[:,:,0:int(H/4),:]
images_lv4_4 = images_lv3_2[:,:,int(H/4):int(H/2),:]
images_lv4_5 = images_lv3_3[:,:,0:int(H/4),:]
images_lv4_6 = images_lv3_3[:,:,int(H/4):int(H/2),:]
images_lv4_7 = images_lv3_4[:,:,0:int(H/4),:]
images_lv4_8 = images_lv3_4[:,:,int(H/4):int(H/2),:]
feature_lv4_1 = encoder_lv4(images_lv4_1)
feature_lv4_2 = encoder_lv4(images_lv4_2)
feature_lv4_3 = encoder_lv4(images_lv4_3)
feature_lv4_4 = encoder_lv4(images_lv4_4)
feature_lv4_5 = encoder_lv4(images_lv4_5)
feature_lv4_6 = encoder_lv4(images_lv4_6)
feature_lv4_7 = encoder_lv4(images_lv4_7)
feature_lv4_8 = encoder_lv4(images_lv4_8)
feature_lv4_top_left = torch.cat((feature_lv4_1, feature_lv4_2), 2)
feature_lv4_top_right = torch.cat((feature_lv4_3, feature_lv4_4), 2)
feature_lv4_bot_left = torch.cat((feature_lv4_5, feature_lv4_6), 2)
feature_lv4_bot_right = torch.cat((feature_lv4_7, feature_lv4_8), 2)
feature_lv4_top = torch.cat((feature_lv4_top_left, feature_lv4_top_right), 3)
feature_lv4_bot = torch.cat((feature_lv4_bot_left, feature_lv4_bot_right), 3)
residual_lv4_top_left = decoder_lv4(feature_lv4_top_left)
residual_lv4_top_right = decoder_lv4(feature_lv4_top_right)
residual_lv4_bot_left = decoder_lv4(feature_lv4_bot_left)
residual_lv4_bot_right = decoder_lv4(feature_lv4_bot_right)
feature_lv3_1 = encoder_lv3(images_lv3_1 + residual_lv4_top_left)
feature_lv3_2 = encoder_lv3(images_lv3_2 + residual_lv4_top_right)
feature_lv3_3 = encoder_lv3(images_lv3_3 + residual_lv4_bot_left)
feature_lv3_4 = encoder_lv3(images_lv3_4 + residual_lv4_bot_right)
feature_lv3_top = torch.cat((feature_lv3_1, feature_lv3_2), 3) + feature_lv4_top
feature_lv3_bot = torch.cat((feature_lv3_3, feature_lv3_4), 3) + feature_lv4_bot
residual_lv3_top = decoder_lv3(feature_lv3_top)
residual_lv3_bot = decoder_lv3(feature_lv3_bot)
feature_lv2_1 = encoder_lv2(images_lv2_1 + residual_lv3_top)
feature_lv2_2 = encoder_lv2(images_lv2_2 + residual_lv3_bot)
feature_lv2 = torch.cat((feature_lv2_1, feature_lv2_2), 2) + torch.cat((feature_lv3_top, feature_lv3_bot), 2)
residual_lv2 = decoder_lv2(feature_lv2)
feature_lv1 = encoder_lv1(images_lv1 + residual_lv2) + feature_lv2
deblur_image = decoder_lv1(feature_lv1)
stop = time.time()
test_time += stop - start
print('RunTime:%.4f'%(stop-start), ' Average Runtime:%.4f'%(test_time/(iteration+1)))
save_deblur_images(deblur_image.data + 0.5, iteration, epoch)
torch.save(encoder_lv1.state_dict(),str('./checkpoints/' + METHOD + "/encoder_lv1.pkl"))
torch.save(encoder_lv2.state_dict(),str('./checkpoints/' + METHOD + "/encoder_lv2.pkl"))
torch.save(encoder_lv3.state_dict(),str('./checkpoints/' + METHOD + "/encoder_lv3.pkl"))
torch.save(encoder_lv4.state_dict(),str('./checkpoints/' + METHOD + "/encoder_lv4.pkl"))
torch.save(decoder_lv1.state_dict(),str('./checkpoints/' + METHOD + "/decoder_lv1.pkl"))
torch.save(decoder_lv2.state_dict(),str('./checkpoints/' + METHOD + "/decoder_lv2.pkl"))
torch.save(decoder_lv3.state_dict(),str('./checkpoints/' + METHOD + "/decoder_lv3.pkl"))
torch.save(decoder_lv4.state_dict(),str('./checkpoints/' + METHOD + "/decoder_lv4.pkl"))
if __name__ == '__main__':
main()