args = init_args()
# manualSeed = random.randint(1, 10000) # fix seed
manualSeed = 10
random.seed(manualSeed)
np.random.seed(manualSeed)
torch.manual_seed(manualSeed)
cudnn.benchmark = True
# alphabet = alphabet = utils.to_alphabet("H:/DL-DATASET/BaiduTextR/train.list")
# store model path
if not os.path.exists('./expr'):
os.mkdir('./expr')
# read train set
# dataset = baiduDataset("H:/DL-DATASET/BaiduTextR/train_images/train_images", "H:/DL-DATASET/BaiduTextR/train.list", params.alphabet, True)
dataset = baiduDataset(
"H:/DL-DATASET/360M/images",
"E:/08-Github-resources/00-MY-GitHub-Entries/crnn_chinese_characters_rec-master/crnn_chinese_characters_rec-master/label/train.txt",
params.alphabet, False, (params.imgW, params.imgH))
val_dataset = baiduDataset(
"H:/DL-DATASET/360M/images",
"E:/08-Github-resources/00-MY-GitHub-Entries/crnn_chinese_characters_rec-master/crnn_chinese_characters_rec-master/label/test.txt",
params.alphabet, False, (params.imgW, params.imgH))
# dataset = baiduDataset("/media/hane/DL-DATASET/360M/images", "E:/08-Github-resources/00-MY-GitHub-Entries/crnn_chinese_characters_rec-master/crnn_chinese_characters_rec-master/label/train.txt", params.alphabet, False)
# val_dataset = baiduDataset("/media/hane/DL-DATASET/360M/images", "E:/08-Github-resources/00-MY-GitHub-Entries/crnn_chinese_characters_rec-master/crnn_chinese_characters_rec-master/label/test.txt", params.alphabet, False)
train_loader = DataLoader(dataset,
batch_size=params.batchSize,
shuffle=True,
num_workers=params.workers)
# shuffle=True, just for time consuming.
val_loader = DataLoader(val_dataset,
batch_size=params.val_batchSize,
# device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
device = torch.device("cpu")
# alphabet = alphabet = utils.to_alphabet("H:/DL-DATASET/BaiduTextR/train.list")
# store model path
if not os.path.exists(params.experiment):
os.mkdir(params.experiment)
# read train set
# dataset = baiduDataset("H:/DL-DATASET/BaiduTextR/train_images/train_images", "H:/DL-DATASET/BaiduTextR/train.list", params.alphabet, True)
# dataset = baiduDataset("H:/DL-DATASET/360M/images", "E:/08-Github-resources/00-MY-GitHub-Entries/crnn_chinese_characters_rec-master/crnn_chinese_characters_rec-master/label/train.txt", params.alphabet, False, (params.imgW, params.imgH))
# val_dataset = baiduDataset("H:/DL-DATASET/360M/images", "E:/08-Github-resources/00-MY-GitHub-Entries/crnn_chinese_characters_rec-master/crnn_chinese_characters_rec-master/label/test.txt", params.alphabet, False, (params.imgW, params.imgH))
# dataset = baiduDataset("/media/hane/DL-DATASET/360M/images", "E:/08-Github-resources/00-MY-GitHub-Entries/crnn_chinese_characters_rec-master/crnn_chinese_characters_rec-master/label/train.txt", params.alphabet, False)
# val_dataset = baiduDataset("/media/hane/DL-DATASET/360M/images", "E:/08-Github-resources/00-MY-GitHub-Entries/crnn_chinese_characters_rec-master/crnn_chinese_characters_rec-master/label/test.txt", params.alphabet, False)
dataset = baiduDataset(
"/uuz/song/datasets/ocr/train_items",
"/home/song/workplace/OCR/ocr_idcard/label/train/train_label_{}.txt".
format(params.experiment), params.alphabet, False,
(params.imgW, params.imgH))
val_dataset = baiduDataset(
"/uuz/song/datasets/ocr/train_items",
"/home/song/workplace/OCR/ocr_idcard/label/val/val_label_{}.txt".
format(params.experiment), params.alphabet, False,
(params.imgW, params.imgH))
# /home/song/workplace/OCR/ocr_idcard/label/train/train_label_birth_d.txt
train_loader = DataLoader(dataset,
batch_size=params.batchSize,
shuffle=True,
num_workers=params.workers)
# shuffle=True, just for time consuming.
val_loader = DataLoader(val_dataset,
batch_size=params.val_batchSize,
# args = init_args()
# manualSeed = random.randint(1, 10000) #fix seed
manualSeed = 10
random.seed(manualSeed)
np.random.seed(manualSeed)
torch.manual_seed(manualSeed)
cudnn.benchmark = True
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# alphabet = alphabet = utils.to_alphabet("H:/DL-DATASET/BaiduTextR/train.list")
# store model path
if not os.path.exists('./expr'):
os.mkdir('./expr')
# read train set
# dataset = baiduDataset("H:/DL-DATASET/BaiduTextR/train_images/train_images", "H:/DL-DATASET/BaiduTextR/train.list", params.alphabet, True)
dataset = baiduDataset("data/vehicle/Image", "data/vehicle/Main/train.txt",
params.alphabet, False, (params.imgW, params.imgH))
val_dataset = baiduDataset("data/vehicle/Image",
"data/vehicle/Main/val.txt", params.alphabet,
False, (params.imgW, params.imgH))
# val_dataset = baiduDataset("H:/DL-DATASET/360M/images", "E:/08-Github-resources/00-MY-GitHub-Entries/crnn_chinese_characters_rec-master/crnn_chinese_characters_rec-master/label/test.txt", params.alphabet, False, (params.imgW, params.imgH))
# dataset = baiduDataset("/media/hane/DL-DATASET/360M/images", "E:/08-Github-resources/00-MY-GitHub-Entries/crnn_chinese_characters_rec-master/crnn_chinese_characters_rec-master/label/train.txt", params.alphabet, False)
# val_dataset = baiduDataset("/media/hane/DL-DATASET/360M/images", "E:/08-Github-resources/00-MY-GitHub-Entries/crnn_chinese_characters_rec-master/crnn_chinese_characters_rec-master/label/test.txt", params.alphabet, False)
train_loader = DataLoader(dataset,
batch_size=params.batchSize,
shuffle=True,
num_workers=params.workers)
# shuffle=True, just for time consuming.
val_loader = DataLoader(val_dataset,
batch_size=params.val_batchSize,
shuffle=True,
# args = init_args()
# manualSeed = random.randint(1, 10000) #fix seed
os.environ["CUDA_VISIBLE_DEVICES"] = "7"
manualSeed = 10
random.seed(manualSeed)
np.random.seed(manualSeed)
torch.manual_seed(manualSeed)
cudnn.benchmark = True
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# store model path
if not os.path.exists('./expr'):
os.mkdir('./expr')
# read train set
dataset = baiduDataset(
"./data_chinese_tra/images_add_fake",
"./data_chinese_tra/label_add_fake/train_add_fake.txt",
params.alphabet, False, (params.imgW, params.imgH))
val_dataset = baiduDataset(
"./data_chinese_tra/images_add_fake",
"./data_chinese_tra/label_add_fake/val_add_fake.txt", params.alphabet,
False, (params.imgW, params.imgH))
train_loader = DataLoader(dataset,
batch_size=params.batchSize,
shuffle=True,
num_workers=params.workers)
# shuffle=True, just for time consuming.
val_loader = DataLoader(val_dataset,
batch_size=params.val_batchSize,
shuffle=True,
num_workers=params.workers)
