Delete lowlight_test.py

lowlight_test.py

@@ -1,62 +0,0 @@
-import torch
-import torch.nn as nn
-import torchvision
-import torch.backends.cudnn as cudnn
-import torch.optim
-import os
-import sys
-import argparse
-import time
-import dataloader
-import model
-import numpy as np
-from torchvision import transforms
-from PIL import Image
-import glob
-import time
-
-
- 
-def lowlight(image_path):
-	os.environ['CUDA_VISIBLE_DEVICES']='0'
-	data_lowlight = Image.open(image_path)
-
- 
-
-	data_lowlight = (np.asarray(data_lowlight)/255.0)
-
-
-	data_lowlight = torch.from_numpy(data_lowlight).float()
-	data_lowlight = data_lowlight.permute(2,0,1)
-	data_lowlight = data_lowlight.cuda().unsqueeze(0)
-
-	DCE_net = model.enhance_net_nopool().cuda()
-	DCE_net.load_state_dict(torch.load('snapshots/Epoch99.pth'))
-	start = time.time()
-	_,enhanced_image,_ = DCE_net(data_lowlight)
-
-	end_time = (time.time() - start)
-	print(end_time)
-	image_path = image_path.replace('test_data','result')
-	result_path = image_path
-	if not os.path.exists(image_path.replace('/'+image_path.split("/")[-1],'')):
-		os.makedirs(image_path.replace('/'+image_path.split("/")[-1],''))
-
-	torchvision.utils.save_image(enhanced_image, result_path)
-
-if __name__ == '__main__':
-# test_images
-	with torch.no_grad():
-		filePath = 'data/test_data/'
-	
-		file_list = os.listdir(filePath)
-
-		for file_name in file_list:
-			test_list = glob.glob(filePath+file_name+"/*") 
-			for image in test_list:
-				# image = image
-				print(image)
-				lowlight(image)
-
-		
-