Sorted imports

big_sleep/big_sleep.py

@@ -1,27 +1,26 @@
 import os
-import sys
-import subprocess
+import random
+import re
 import signal
 import string
-import re
-
+import subprocess
+import sys
 from datetime import datetime
 from pathlib import Path
-import random
 
 import torch
 import torch.nn.functional as F
+import torchvision.transforms as T
+from PIL import Image
 from torch import nn
 from torch.optim import Adam
 from torchvision.utils import save_image
-import torchvision.transforms as T
-from PIL import Image
 from tqdm import tqdm, trange
 
-from big_sleep.ema import EMA
-from big_sleep.resample import resample
 from big_sleep.biggan import BigGAN
 from big_sleep.clip import load, tokenize
+from big_sleep.ema import EMA
+from big_sleep.resample import resample
 
 assert torch.cuda.is_available(), 'CUDA must be available in order to use Big Sleep'
 

big_sleep/biggan.py

@@ -1,23 +1,23 @@
 # this code is a copy from huggingface
 # with some minor modifications
 
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import math
-import json
 import copy
+import json
 import logging
+import math
 import os
 import shutil
+import sys
 import tempfile
 from functools import wraps
 from hashlib import sha256
-import sys
 from io import open
 
 import boto3
 import requests
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
 from botocore.exceptions import ClientError
 from tqdm import tqdm
 

big_sleep/cli.py

@@ -1,8 +1,11 @@
-import fire
 import random as rnd
-from big_sleep import Imagine, version
 from pathlib import Path
-from .version import __version__;
+
+import fire
+
+from big_sleep import Imagine, version
+
+from .version import __version__
 
 
 def train(

big_sleep/clip.py

@@ -1,20 +1,17 @@
-from collections import OrderedDict
-from typing import Tuple, Union
-
-import torch
-import torch.nn.functional as F
-from torch import nn
-from pathlib import Path
-
 import hashlib
 import os
 import urllib
 import warnings
-from typing import Union, List
+from collections import OrderedDict
+from pathlib import Path
+from typing import List, Tuple, Union
 
 import torch
+import torch.nn.functional as F
 from PIL import Image
-from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
+from torch import nn
+from torchvision.transforms import (CenterCrop, Compose, Normalize, Resize,
+                                    ToTensor)
 from tqdm import tqdm
 
 _MODELS = {

big_sleep/resample.py

@@ -1,7 +1,7 @@
 """Good differentiable image resampling for PyTorch."""
 
-from functools import update_wrapper
 import math
+from functools import update_wrapper
 
 import torch
 from torch.nn import functional as F

setup.py

@@ -1,5 +1,6 @@
 import sys
-from setuptools import setup, find_packages
+
+from setuptools import find_packages, setup
 
 sys.path[0:0] = ['big_sleep']
 from version import __version__

test/multi_prompt_minmax.py

@@ -1,6 +1,8 @@
-import time
 import shutil
+import time
+
 import torch
+
 from big_sleep import Imagine
 
 terminate = False