def gen_traffic(self, url, page_source, response_headers):
if self.browser == 'chrome':
request = HttpRequest(method='GET',
url=url,
headers=Traffic_generator.DEFAULT_HEADER,
body='')
if response_headers is None:
response_headers = {}
response = HttpResponse(code='200',
reason='OK',
headers=response_headers,
data=page_source)
return (request, response)
# pickled error when phantomjs,the headers must be str
elif self.browser == 'phantomjs':
request = HttpRequest(method='GET',
url=url,
headers=dict2str(
Traffic_generator.DEFAULT_HEADER),
body='')
if response_headers is None:
response_headers = {}
response = HttpResponse(code='200',
reason='OK',
headers=dict2str(response_headers),
data=page_source)
return (request, response)
def init(f):
global LOG_FOUT
global MODEL
global MODEL_CONF
global TRAIN_FILES
global TEST_FILES
# IMPORT network module
module = importlib.import_module(f['model'])
MODEL = getattr(module, f['model'])
MODEL_CONF = getattr(module, f['model'] + '_conf')
MODEL_FILE = os.path.join(BASE_DIR, 'models', f['model'] + '.py')
# MAKE log directory
if not os.path.exists(f['log_dir']):
os.mkdir(f['log_dir'])
os.system('cp %s %s' % (MODEL_FILE, f['log_dir'])) # bkp of model def
# CREATE log file
LOG_FOUT = open(os.path.join(f['log_dir'], 'log_train.txt'), 'a')
LOG_FOUT.write(dict2str(f))
# GET dataset files' list
TRAIN_FILES = provider.getDataFiles(
os.path.join(f['dataset_path'], 'train_files.txt'))
TEST_FILES = provider.getDataFiles(
os.path.join(f['dataset_path'], 'test_files.txt'))
def init(f):
global LOG_FOUT
global DUMP_FILES
global TEST_FILES
# MAKE log directory
if not os.path.exists(f['visual_dir']):
os.mkdir(f['visual_dir'])
# CREATE log file
LOG_FOUT = open(os.path.join(f['visual_dir'], 'log_visualization.txt'),
'w')
LOG_FOUT.write(dict2str(f))
DUMP_FILES = provider.getDataFiles( \
os.path.join(BASE_DIR, os.path.join(f['dump_dir'], 'list_evaluate.txt')))
TEST_FILES = provider.getDataFiles( \
os.path.join(BASE_DIR, os.path.join(f['dataset_path'], 'test_files.txt')))
def init(f):
global LOG_FOUT
global MODEL
global MODEL_CONF
global TEST_FILES
global FLIST_FOUT
# IMPORT network module
sys.path.append(f['model_path'])
module = importlib.import_module(f['model'])
MODEL = getattr(module, f['model'])
MODEL_CONF = getattr(module, f['model'] + '_conf')
# MAKE log directory
if not os.path.exists(f['dump_dir']):
os.mkdir(f['dump_dir'])
# CREATE log file
LOG_FOUT = open(os.path.join(f['dump_dir'], 'log_evaluate.txt'), 'w')
LOG_FOUT.write(dict2str(f))
FLIST_FOUT = open(os.path.join(f['dump_dir'], 'list_evaluate.txt'), 'w')
TEST_FILES = provider.getDataFiles( \
os.path.join(BASE_DIR, os.path.join(f['dataset_path'], 'test_files.txt')))
def main(config):
device = torch.device(config['device'])
##### Setup Dirs #####
experiment_dir = config['path']['experiments'] + config['name']
util.mkdir_and_rename(
experiment_dir) # rename experiment folder if exists
util.mkdirs((experiment_dir+'/sr_images', experiment_dir+'/lr_images'))
##### Setup Logger #####
logger = util.Logger('test', experiment_dir, 'test_' + config['name'])
##### print Experiment Config
logger.log(util.dict2str(config))
###### Load Dataset #####
testing_data_loader = dataset.get_test_sets(config['dataset'], logger)
trainer = create_model(config, logger)
trainer.print_network_params(logger)
total_avg_psnr = 0.0
total_avg_ssim = 0.0
for name, test_set in testing_data_loader.items():
logger.log('Testing Dataset {:s}'.format(name))
valid_start_time = time.time()
avg_psnr = 0.0
avg_ssim = 0.0
idx = 0
for i, batch in enumerate(test_set):
idx += 1
img_name = batch[2][0][batch[2][0].rindex('/')+1:]
# print(img_name)
img_name = img_name[:img_name.index('.')]
img_dir_sr = experiment_dir+'/sr_images'
img_dir_lr = experiment_dir+'/lr_images'
util.mkdir(img_dir_sr)
infer_time = trainer.test(batch)
visuals = trainer.get_current_visuals()
lr_img = util.tensor2img(visuals['LR'])
sr_img = util.tensor2img(visuals['SR']) # uint8
gt_img = util.tensor2img(visuals['HR']) # uint8
save_sr_img_path = os.path.join(img_dir_sr, '{:s}.png'.format(img_name))
save_lr_img_path = os.path.join(img_dir_lr, '{:s}.png'.format(img_name))
util.save_img(lr_img, save_lr_img_path)
util.save_img(sr_img, save_sr_img_path)
crop_size = config['dataset']['scale']
psnr, ssim = util.calc_metrics(sr_img, gt_img, crop_size)
#logger.log('[ Image: {:s} PSNR: {:.4f} SSIM: {:.4f} Inference Time: {:.8f}]'.format(img_name, psnr, ssim, infer_time))
avg_psnr += psnr
avg_ssim += ssim
avg_psnr = avg_psnr / idx
avg_ssim = avg_ssim / idx
valid_t = time.time() - valid_start_time
logger.log('[ Set: {:s} Time:{:.3f}] PSNR: {:.2f} SSIM {:.4f}'.format(name, valid_t, avg_psnr, avg_ssim))
iter_start_time = time.time()
total_avg_ssim += avg_ssim
total_avg_psnr += avg_psnr
total_avg_ssim /= len(testing_data_loader)
total_avg_psnr /= len(testing_data_loader)
logger.log('[ Total Average of Sets: PSNR: {:.2f} SSIM {:.4f}'.format(total_avg_psnr, total_avg_ssim))