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architecture_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 model2 as 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 = 2600)
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_Spatial_skip"
SAMPLE_DIR = "test_samples"
EXPDIR = "DMPHN_1_2_4_8_test_res"
LEARNING_RATE = args.learning_rate
EPOCHS = args.epochs
GPU = args.gpu
BATCH_SIZE = args.batchsize
IMAGE_SIZE = args.imagesize
def save_images(images, name):
filename = './test_results/' + 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, 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().apply(weight_init).cuda(GPU)
encoder_lv2 = models.Encoder().apply(weight_init).cuda(GPU)
encoder_lv3 = models.Encoder().apply(weight_init).cuda(GPU)
encoder_lv4 = models.Encoder().apply(weight_init).cuda(GPU)
decoder_lv1 = models.Decoder().apply(weight_init).cuda(GPU)
decoder_lv2 = models.Decoder().apply(weight_init).cuda(GPU)
decoder_lv3 = models.Decoder().apply(weight_init).cuda(GPU)
decoder_lv4 = models.Decoder().apply(weight_init).cuda(GPU)
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('./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_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]
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)
deblur_image_4 = decoder_lv4(feature_lv4)
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)
deblur_image_3 = decoder_lv3(feature_lv3)
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)
deblur_image_2 = decoder_lv2(feature_lv2)
feature_lv1 = encoder_lv1(images_lv1 + residual_lv2) + feature_lv2
deblur_image = decoder_lv1(feature_lv1)
#save_images(deblur_image.data + 0.5, images_name)
#save_images(deblur_image_2.data + 0.5, images_name)
#save_images(deblur_image_3.data + 0.5, images_name)
save_images(deblur_image_4.data + 0.5, images_name)
iteration += 1
if __name__ == '__main__':
main()