def __init__(self,
datasetFile,
textDir,
checking_folder,
lang,
client_txt,
pre_trained_gen,
pre_trained_disc,
ID,
batch_size=1):
self.generator = torch.nn.DataParallel(
gan_factory.generator_factory('gan').cuda())
self.generator.load_state_dict(torch.load(pre_trained_gen))
self.discriminator = torch.nn.DataParallel(
gan_factory.discriminator_factory('gan').cuda())
self.discriminator.load_state_dict(torch.load(pre_trained_disc))
self.checking_folder = checking_folder
self.lang = lang
self.client_txt = client_txt
self.filename = ID
self.batch_size = batch_size
cl = CorpusLoader(datasetFile=datasetFile, textDir=textDir)
self.vectorizer = cl.TrainVocab()
def __init__(self, datasetFile, imagesDir, textDir, split, arrangement,
sampling):
self.datasetFile = datasetFile
self.imagesDir = imagesDir
self.textDir = textDir
self.split = split
self.arrangement = easydict.EasyDict(arrangement)
self.sampling = easydict.EasyDict(sampling)
self.images_classes = {}
self.assign_classes()
cl = CorpusLoader(datasetFile=datasetFile, textDir=textDir)
self.vectorizer = cl.TrainVocab()