import utils from utils.wheels import * from utils.trainer import trainer_epoch from utils.DataAug import data_aug_ZSL # Make experiment result folder TimeStamp, path_folderExp, path_folderExpBestModel = utils.backup.createBackUp( ) # get all data for dataset from prepareData import * # Make dataset dataset_trn = datasetZSL( path_folderTrainImage, path_txtImageLabel, index_split=index_SeenTrn, label_enc=label_enc, data_aug=data_aug_ZSL, ) dataset_val = datasetZSL(path_folderTrainImage, path_txtImageLabel, index_split=index_SeenVal, label_enc=label_enc) dataset_tst = datasetZSL(path_folderTrainImage, path_txtImageLabel, index_split=index_UnseenTst, label_enc=label_enc) dataset_prd = datasetZSL(path_folderTestImage, path_txtImage, index_split=index_UnseenPrd, label_enc=label_enc,
from utils.wheels import * from utils.trainer import trainer_epoch from utils.DataAug import data_aug_ZSL_TestAug # Make experiment result folder TimeStamp, path_folderExp, path_folderExpBestModel = utils.backup.createBackUp( ) from prepareData import * # Make dataset # dataset_trn = datasetZSL(path_folderTrainImage, path_txtImageLabel, index_split=index_SeenTrn, label_enc=label_enc, data_aug=data_aug_ZSL,) # dataset_val = datasetZSL(path_folderTrainImage, path_txtImageLabel, index_split=index_SeenVal, label_enc=label_enc) # dataset_tst = datasetZSL(path_folderTrainImage, path_txtImageLabel, index_split=index_UnseenTst, label_enc=label_enc) dataset_prd = datasetZSL(path_folderTestImage, path_txtImage, index_split=index_UnseenPrd, label_enc=label_enc, DummyTarget=True) dataset_prd_DataAug = datasetZSL_wtDataAug(datasetZSL( path_folderTestImage, path_txtImage, index_split=index_UnseenPrd, label_enc=label_enc, DummyTarget=True, data_aug=data_aug_ZSL_TestAug, ), num_DataAug=params_numDataAug) # Make dataloader # dataloader_trn = torch.utils.data.DataLoader( # dataset=dataset_trn, batch_size=params_batch_size, shuffle=True,
from config import * import utils from utils.wheels import * from utils.trainer import trainer_epoch from utils.DataAug import data_aug_ZSL # Make experiment result folder TimeStamp, path_folderExp, path_folderExpBestModel = utils.backup.createBackUp( ) from prepareData import * # Make dataset dataset_trn = datasetZSL( path_folderTrainImage, path_txtImageLabel, label_enc=label_enc, # data_aug=data_aug_ZSL, ) lst_tsClassEmb = [ Tensor(emb).cuda() for emb in [arr_ClassNameVec, arr_ClassAttr] ] emb_size = [emb.shape[1] for emb in lst_tsClassEmb] G = G_Feat( emb_size=emb_size, dimHid=params_dimHid_G, feat_size=params_dimVisFeat, ).cuda() state_dict = torch.load(path_ptmdlWGAN_G, map_location={
from config import * import utils from utils.wheels import * from utils.trainer import trainer_epoch_fWGAN from utils.DataAug import data_aug_ZSL # Make experiment result folder TimeStamp, path_folderExp, path_folderExpBestModel = utils.backup.createBackUp( ) from prepareData import * # Make dataset dataset_trn = datasetZSL( path_folderTrainImage, path_txtImageLabel, label_enc=label_enc, # data_aug=data_aug_ZSL, ) # Make dataloader dataloader_trn = torch.utils.data.DataLoader( dataset=dataset_trn, batch_size=params_batch_size_fWGAN, shuffle=True, # sampler=WeightedRandomSampler(np.ones(len(dataset_trn)), num_samples=len(dataset_trn)//100), num_workers=params_num_workers, pin_memory=True, ) torch.cuda.set_device(params_cuda_device) # Model & Loss lst_tsClassEmb = [