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SDNet2_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 = "SDNet2"
SAMPLE_DIR = "test_samples"
EXPDIR = "SDNet2_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']:
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']:
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'])
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()