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SDNet3_test.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
from PIL import Image
parser = argparse.ArgumentParser(description="Deep Multi-Patch Hierarchical Network")
parser.add_argument("-e","--epochs",type = int, default = 3000)
parser.add_argument("-se","--start_epoch",type = int, default = 0)
parser.add_argument("-b","--batchsize",type = int, default = 2)
parser.add_argument("-c","--cropsize",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 = "SDNet3"
SAMPLE_DIR = "test_samples"
EXPDIR = "SDNet3_test_res"
LEARNING_RATE = args.learning_rate
EPOCHS = args.epochs
GPU = args.gpu
BATCH_SIZE = args.batchsize
CROP_SIZE = args.cropsize
def save_images(images, name):
filename = './test_results/' + EXPDIR + "/" + name
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, math.sqrt(0.5 / 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 = {}
decoder = {}
encoder_optim = {}
decoder_optim = {}
encoder_scheduler = {}
decoder_scheduler = {}
for s in ['s1', 's2', 's3']:
encoder[s] = {}
decoder[s] = {}
encoder_optim[s] = {}
decoder_optim[s] = {}
encoder_scheduler[s] = {}
decoder_scheduler[s] = {}
for lv in ['lv1', 'lv2', 'lv3']:
encoder[s][lv] = models.Encoder()
decoder[s][lv] = models.Decoder()
encoder[s][lv].apply(weight_init).cuda(GPU)
decoder[s][lv].apply(weight_init).cuda(GPU)
encoder_optim[s][lv] = torch.optim.Adam(encoder[s][lv].parameters(),lr=LEARNING_RATE)
encoder_scheduler[s][lv] = StepLR(encoder_optim[s][lv],step_size=1000,gamma=0.1)
decoder_optim[s][lv] = torch.optim.Adam(decoder[s][lv].parameters(),lr=LEARNING_RATE)
decoder_scheduler[s][lv] = StepLR(decoder_optim[s][lv],step_size=1000,gamma=0.1)
if os.path.exists(str('./checkpoints/' + METHOD + "/encoder_" + s + "_" + lv + ".pkl")):
encoder[s][lv].load_state_dict(torch.load(str('./checkpoints/' + METHOD + "/encoder_" + s + "_" + lv + ".pkl")))
print("load encoder_" + s + "_" + lv + " successfully!")
if os.path.exists(str('./checkpoints/' + METHOD + "/decoder_" + s + "_" + lv + ".pkl")):
decoder[s][lv].load_state_dict(torch.load(str('./checkpoints/' + METHOD + "/decoder_" + s + "_" + lv + ".pkl")))
print("load decoder_" + s + "_" + lv + " successfully!")
if os.path.exists('./test_results/' + EXPDIR) == False:
os.system('mkdir ./test_results/' + EXPDIR)
iteration = 0.0
test_time = 0.0
for images_name in os.listdir(SAMPLE_DIR):
with torch.no_grad():
images = {}
feature = {}
residual = {}
for s in ['s1', 's2', 's3']:
feature[s] = {}
residual[s] = {}
images['lv1'] = transforms.ToTensor()(Image.open(SAMPLE_DIR + '/' + images_name).convert('RGB'))
images['lv1'] = Variable(images['lv1'] - 0.5).unsqueeze(0).cuda(GPU)
start = time.time()
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]
s = 's1'
feature[s]['lv3_1'] = encoder[s]['lv3'](images['lv3_1'])
feature[s]['lv3_2'] = encoder[s]['lv3'](images['lv3_2'])
feature[s]['lv3_3'] = encoder[s]['lv3'](images['lv3_3'])
feature[s]['lv3_4'] = encoder[s]['lv3'](images['lv3_4'])
feature[s]['lv3_top'] = torch.cat((feature[s]['lv3_1'], feature[s]['lv3_2']), 3)
feature[s]['lv3_bot'] = torch.cat((feature[s]['lv3_3'], feature[s]['lv3_4']), 3)
residual[s]['lv3_top'] = decoder[s]['lv3'](feature[s]['lv3_top'])
residual[s]['lv3_bot'] = decoder[s]['lv3'](feature[s]['lv3_bot'])
feature[s]['lv2_1'] = encoder[s]['lv2'](images['lv2_1'] + residual[s]['lv3_top']) + feature[s]['lv3_top']
feature[s]['lv2_2'] = encoder[s]['lv2'](images['lv2_2'] + residual[s]['lv3_bot']) + feature[s]['lv3_bot']
feature[s]['lv2'] = torch.cat((feature[s]['lv2_1'], feature[s]['lv2_2']), 2)
residual[s]['lv2'] = decoder[s]['lv2'](feature[s]['lv2'])
feature[s]['lv1'] = encoder[s]['lv1'](images['lv1'] + residual[s]['lv2']) + feature[s]['lv2']
residual[s]['lv1'] = decoder[s]['lv1'](feature[s]['lv1'])
s = 's2'
ps = 's1'
feature[s]['lv3_1'] = encoder[s]['lv3'](residual[ps]['lv1'][:,:,0:int(H/2),0:int(W/2)])
feature[s]['lv3_2'] = encoder[s]['lv3'](residual[ps]['lv1'][:,:,0:int(H/2),int(W/2):W])
feature[s]['lv3_3'] = encoder[s]['lv3'](residual[ps]['lv1'][:,:,int(H/2):H,0:int(W/2)])
feature[s]['lv3_4'] = encoder[s]['lv3'](residual[ps]['lv1'][:,:,int(H/2):H,int(W/2):W])
feature[s]['lv3_top'] = torch.cat((feature[s]['lv3_1'], feature[s]['lv3_2']), 3) + feature[ps]['lv3_top']
feature[s]['lv3_bot'] = torch.cat((feature[s]['lv3_3'], feature[s]['lv3_4']), 3) + feature[ps]['lv3_bot']
residual[s]['lv3_top'] = decoder[s]['lv3'](feature[s]['lv3_top'])
residual[s]['lv3_bot'] = decoder[s]['lv3'](feature[s]['lv3_bot'])
feature[s]['lv2_1'] = encoder[s]['lv2'](residual[ps]['lv1'][:,:,0:int(H/2),:] + residual[s]['lv3_top']) + feature[s]['lv3_top'] + feature[ps]['lv2_1']
feature[s]['lv2_2'] = encoder[s]['lv2'](residual[ps]['lv1'][:,:,int(H/2):H,:] + residual[s]['lv3_bot']) + feature[s]['lv3_bot'] + feature[ps]['lv2_2']
feature[s]['lv2'] = torch.cat((feature[s]['lv2_1'], feature[s]['lv2_2']), 2)
residual[s]['lv2'] = decoder[s]['lv2'](feature[s]['lv2']) + residual['s1']['lv1']
feature[s]['lv1'] = encoder[s]['lv1'](residual[ps]['lv1'] + residual[s]['lv2']) + feature[s]['lv2'] + feature[ps]['lv1']
residual[s]['lv1'] = decoder[s]['lv1'](feature[s]['lv1'])
s = 's3'
ps = 's2'
feature[s]['lv3_1'] = encoder[s]['lv3'](residual[ps]['lv1'][:,:,0:int(H/2),0:int(W/2)])
feature[s]['lv3_2'] = encoder[s]['lv3'](residual[ps]['lv1'][:,:,0:int(H/2),int(W/2):W])
feature[s]['lv3_3'] = encoder[s]['lv3'](residual[ps]['lv1'][:,:,int(H/2):H,0:int(W/2)])
feature[s]['lv3_4'] = encoder[s]['lv3'](residual[ps]['lv1'][:,:,int(H/2):H,int(W/2):W])
feature[s]['lv3_top'] = torch.cat((feature[s]['lv3_1'], feature[s]['lv3_2']), 3) + feature[ps]['lv3_top']
feature[s]['lv3_bot'] = torch.cat((feature[s]['lv3_3'], feature[s]['lv3_4']), 3) + feature[ps]['lv3_bot']
residual[s]['lv3_top'] = decoder[s]['lv3'](feature[s]['lv3_top'])
residual[s]['lv3_bot'] = decoder[s]['lv3'](feature[s]['lv3_bot'])
feature[s]['lv2_1'] = encoder[s]['lv2'](residual[ps]['lv1'][:,:,0:int(H/2),:] + residual[s]['lv3_top']) + feature[s]['lv3_top'] + feature[ps]['lv2_1']
feature[s]['lv2_2'] = encoder[s]['lv2'](residual[ps]['lv1'][:,:,int(H/2):H,:] + residual[s]['lv3_bot']) + feature[s]['lv3_bot'] + feature[ps]['lv2_2']
feature[s]['lv2'] = torch.cat((feature[s]['lv2_1'], feature[s]['lv2_2']), 2)
residual[s]['lv2'] = decoder[s]['lv2'](feature[s]['lv2']) + residual['s1']['lv1']
feature[s]['lv1'] = encoder[s]['lv1'](residual[ps]['lv1'] + residual[s]['lv2']) + feature[s]['lv2'] + feature[ps]['lv1']
residual[s]['lv1'] = decoder[s]['lv1'](feature[s]['lv1'])
deblurred_image = residual[s]['lv1']
stop = time.time()
test_time += stop-start
print('RunTime:%.4f'%(stop-start), ' Average Runtime:%.4f'%(test_time/(iteration+1)))
save_images(deblurred_image.data + 0.5, images_name)
iteration += 1
if __name__ == '__main__':
main()