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import os
import pickle
import random
import shutil
import typing as tp
import numpy as np
import torch
import torchvision.transforms as T
import wandb
from PIL import Image
from joblib import Parallel, delayed
from torch.utils.data import DataLoader, TensorDataset
from torchmetrics.image.fid import FrechetInceptionDistance
from tqdm.auto import tqdm
from models.Encoders import ClipModel
def image_grid(imgs, rows, cols):
assert len(imgs) == rows * cols
w, h = imgs[0].size
grid = Image.new('RGB', size=(cols * w, rows * h))
for i, img in enumerate(imgs):
grid.paste(img, box=(i % cols * w, i // cols * h))
return grid
class WandbLogger:
def __init__(self, name='base-name', project='HairFast'):
self.name = name
self.project = project
def start_logging(self):
wandb.login(key=os.environ['WANDB_KEY'].strip(), relogin=True)
wandb.init(
project=self.project,
name=self.name
)
self.wandb = wandb
self.run_dir = self.wandb.run.dir
self.train_step = 0
def log(self, scalar_name: str, scalar: tp.Any):
self.wandb.log({scalar_name: scalar}, step=self.train_step, commit=False)
def log_scalars(self, scalars: dict):
self.wandb.log(scalars, step=self.train_step, commit=False)
def next_step(self):
self.train_step += 1
def save(self, file_path, save_online=True):
file = os.path.basename(file_path)
new_path = os.path.join(self.run_dir, file)
shutil.copy2(file_path, new_path)
if save_online:
self.wandb.save(new_path)
def __del__(self):
self.wandb.finish()
def toggle_grad(model, flag=True):
for p in model.parameters():
p.requires_grad = flag
class _LegacyUnpickler(pickle.Unpickler):
def find_class(self, module, name):
if module == 'dnnlib.tflib.network' and name == 'Network':
return _TFNetworkStub
module = module.replace('torch_utils', 'models.stylegan2.torch_utils')
module = module.replace('dnnlib', 'models.stylegan2.dnnlib')
return super().find_class(module, name)
def seed_everything(seed: int = 1729) -> None:
random.seed(seed)
os.environ["PYTHONHASHSEED"] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True
def load_images_to_torch(paths, imgs=None, use_tqdm=True):
transform = T.PILToTensor()
tensor = []
for path in paths:
if imgs is None:
pbar = sorted(os.listdir(path))
else:
pbar = imgs
if use_tqdm:
pbar = tqdm(pbar)
for img_name in pbar:
if '.jpg' in img_name or '.png' in img_name:
img_path = os.path.join(path, img_name)
img = Image.open(img_path).resize((299, 299), resample=Image.LANCZOS)
tensor.append(transform(img))
try:
return torch.stack(tensor)
except:
print(paths, imgs)
return torch.tensor([], dtype=torch.uint8)
def parallel_load_images(paths, imgs):
assert imgs is not None
if not isinstance(paths, list):
paths = [paths]
list_torch_images = Parallel(n_jobs=-1)(delayed(load_images_to_torch)(
paths, [i], use_tqdm=False
) for i in tqdm(imgs))
return torch.cat(list_torch_images)
def get_fid_calc(instance='fid.pkl', dataset_path='', device=torch.device('cuda')):
if os.path.isfile(instance):
with open(instance, 'rb') as f:
fid = pickle.load(f)
else:
fid = FrechetInceptionDistance(feature=ClipModel(), reset_real_features=False, normalize=True)
fid.to(device).eval()
imgs_file = []
for file in os.listdir(dataset_path):
if 'flip' not in file and os.path.splitext(file)[1] in ['.png', '.jpg']:
imgs_file.append(file)
tensor_images = parallel_load_images([dataset_path], imgs_file).float().div(255)
real_dataloader = DataLoader(TensorDataset(tensor_images), batch_size=128)
with torch.inference_mode():
for batch in tqdm(real_dataloader):
batch = batch[0].to(device)
fid.update(batch, real=True)
with open(instance, 'wb') as f:
pickle.dump(fid.cpu(), f)
fid.to(device).eval()
@torch.inference_mode()
def compute_fid_datasets(images):
nonlocal fid, device
fid.reset()
fake_dataloader = DataLoader(TensorDataset(images), batch_size=128)
for batch in tqdm(fake_dataloader):
batch = batch[0].to(device)
fid.update(batch, real=False)
return fid.compute()
return compute_fid_datasets
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