Change imports

__init__.py

@@ -0,0 +1,2 @@
+from .modeling_vatrpp import VATrPP
+from .configuration_vatrpp import VATrPPConfig

data/dataset.py

@@ -1,7 +1,6 @@
 import random
 from collections import defaultdict
 
-import matplotlib.pyplot as plt
 import torch
 from torch.utils.data import Dataset
 import torchvision.transforms as transforms

modeling_vatrpp.py

@@ -6,9 +6,9 @@ import cv2
 import numpy as np
 import torch
 
-from data.dataset import FolderDataset
-from models.model import VATr
-from util.vision import detect_text_bounds
+from .data.dataset import FolderDataset
+from .models.model import VATr
+from .models.util.vision import detect_text_bounds
 from torchvision.transforms.functional import to_pil_image
 
 

models/BigGAN_networks.py

@@ -9,14 +9,14 @@ import torch.nn as nn
 import torch.nn.functional as F
 import random
 
-from util.augmentations import ProgressiveWordCrop, CycleWordCrop, StaticWordCrop, RandomWordCrop
+from .util.augmentations import ProgressiveWordCrop, CycleWordCrop, StaticWordCrop, RandomWordCrop
 from . import BigGAN_layers as layers
 from .networks import init_weights
 import torchvision
 # Attention is passed in in the format '32_64' to mean applying an attention
 # block at both resolution 32x32 and 64x64. Just '64' will apply at 64x64.
 
-from models.blocks import Conv2dBlock, ResBlocks
+from .blocks import Conv2dBlock, ResBlocks
 
 
 # Discriminator architecture, same paradigm as G's above

models/model.py

@@ -4,19 +4,19 @@ from torch.nn.utils import clip_grad_norm_
 import sys
 import torchvision.models as models
 
-from models.inception import InceptionV3
-from models.transformer import *
-from util.augmentations import OCRAugment
-from util.misc import SmoothedValue
-from util.text import get_generator, AugmentedGenerator
+from .inception import InceptionV3
+from .transformer import *
+from .util.augmentations import OCRAugment
+from .util.misc import SmoothedValue
+from .util.text import get_generator, AugmentedGenerator
 from .BigGAN_networks import *
 from .OCR_network import *
-from models.blocks import Conv2dBlock, ResBlocks
-from util.util import loss_hinge_dis, loss_hinge_gen, make_one_hot
+from .blocks import Conv2dBlock, ResBlocks
+from .util.util import loss_hinge_dis, loss_hinge_gen, make_one_hot
 
-import models.config as config
+from . import config
 from .positional_encodings import PositionalEncoding1D
-from models.unifont_module import UnifontModule
+from .unifont_module import UnifontModule
 from PIL import Image
 
 

models/networks.py

@@ -3,7 +3,7 @@ import torch.nn as nn
 from torch.nn import init
 import functools
 from torch.optim import lr_scheduler
-from util.util import to_device, load_network
+from .util.util import to_device, load_network
 
 ###############################################################################
 # Helper Functions

{util → models/util}/augmentations.py

@@ -11,7 +11,7 @@ import torchvision.transforms
 import torchvision.transforms.functional as F
 from matplotlib import pyplot as plt
 
-from data.dataset import CollectionTextDataset, TextDataset
+# from data.dataset import CollectionTextDataset, TextDataset
 
 
 def to_opencv(batch: torch.Tensor):