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import os
import pickle
import random
from pathlib import Path
from typing import Dict
from typing import List
import torch
from loguru import logger
from PIL import Image
from torch.utils.data import Dataset
from torchvision import transforms
from configs.mode import FaceSwapMode
from configs.train_config import TrainConfig
class ManyToManyTrainDataset(Dataset):
def __init__(self, dataset_root: str, dataset_index: str, same_rate=0.5):
"""
Many-to-many 训练数据集构建
Parameters:
-----------
dataset_root: str, 数据集根目录
dataset_index: str, 数据集index文件路径
same_rate: float, 每个batch里面相同人脸所占的比例
"""
super(ManyToManyTrainDataset, self).__init__()
self.transform = transforms.Compose(
[
transforms.Resize((256, 256)),
transforms.CenterCrop((256, 256)),
transforms.ToTensor(),
]
)
self.data_root = Path(dataset_root)
with open(dataset_index, "rb") as f:
self.file_index = pickle.load(f, encoding="bytes")
self.same_rate = same_rate
self.id_list: List[str] = list(self.file_index.keys())
# 所有id都遍历一遍，视为一个epoch
self.length = len(self.id_list)
self.image_num = sum([len(v) for v in self.file_index.values()])
self.mask_dir = "mask" if TrainConfig().mouth_mask else "mask_no_mouth"
logger.info(f"dataset contains {self.length} ids and {self.image_num} images")
logger.info(f"will use mask mode: {self.mask_dir}")
def __len__(self):
return self.length
def __getitem__(self, index):
source_id_index = index
source_file = random.choice(self.file_index[self.id_list[source_id_index]])
if random.random() < self.same_rate:
# 在相同id的文件列表中选择
target_file = random.choice(self.file_index[self.id_list[source_id_index]])
same = torch.ones(1)
else:
# 在不同id的文件列表中选择
target_id_index = random.choice(list(set(range(self.length)) - set([source_id_index])))
target_file = random.choice(self.file_index[self.id_list[target_id_index]])
same = torch.zeros(1)
source_file = self.data_root / Path(source_file)
target_file = self.data_root / Path(target_file)
target_mask_file = target_file.parent.parent.parent / self.mask_dir / target_file.parent.stem / target_file.name
target_img = Image.open(target_file.as_posix()).convert("RGB")
source_img = Image.open(source_file.as_posix()).convert("RGB")
target_mask = Image.open(target_mask_file.as_posix()).convert("RGB")
source_img = self.transform(source_img)
target_img = self.transform(target_img)
target_mask = self.transform(target_mask)[0, :, :].unsqueeze(0)
return {
"source_image": source_img,
"target_image": target_img,
"target_mask": target_mask,
"same": same,
# "source_img_name": source_file.as_posix(),
# "target_img_name": target_file.as_posix(),
# "target_mask_name": target_mask_file.as_posix(),
}
class OneToManyTrainDataset(Dataset):
def __init__(self, dataset_root: str, dataset_index: str, source_name: str, same_rate=0.5):
"""
One-to-many 训练数据集构建
Parameters:
-----------
dataset_root: str, 数据集根目录
dataset_index: str, 数据集index文件路径
source_name: str, source face id的名称, one-to-many里面的one
same_rate: float, 每个batch里面相同人脸所占的比例
"""
super(OneToManyTrainDataset, self).__init__()
self.transform = transforms.Compose(
[
transforms.Resize((256, 256)),
transforms.CenterCrop((256, 256)),
transforms.ToTensor(),
]
)
self.data_root = Path(dataset_root)
with open(dataset_index, "rb") as f:
self.file_index = pickle.load(f, encoding="bytes")
self.same_rate = same_rate
self.source_name = source_name
self.id_list: List[str] = list(self.file_index.keys())
try:
self.source_id_index: int = self.id_list.index(self.source_name)
except Exception:
raise Exception(f"{self.source_name} not in dataset dir")
# 所有id都遍历一遍，视为一个epoch
self.length = len(self.id_list)
self.image_num = sum([len(v) for v in self.file_index.values()])
self.mask_dir = "mask" if TrainConfig().mouth_mask else "mask_no_mouth"
logger.info(f"dataset contains {self.length} ids and {self.image_num} images")
logger.info(f"will use mask mode: {self.mask_dir}")
def __len__(self):
return self.length
def __getitem__(self, index):
target_id_index = index
target_file = random.choice(self.file_index[self.id_list[target_id_index]])
if random.random() < self.same_rate:
# 在相同id的文件列表中选择
source_file = random.choice(self.file_index[self.id_list[target_id_index]])
same = torch.ones(1)
else:
# 直接选择source name中的图片
source_file = random.choice(self.file_index[self.source_name])
# 如果和target同个id
if self.source_id_index == target_id_index:
same = torch.ones(1)
else:
same = torch.zeros(1)
source_file = self.data_root / Path(source_file)
target_file = self.data_root / Path(target_file)
target_mask_file = target_file.parent.parent.parent / self.mask_dir / target_file.parent.stem / target_file.name
target_img = Image.open(target_file.as_posix()).convert("RGB")
source_img = Image.open(source_file.as_posix()).convert("RGB")
target_mask = Image.open(target_mask_file.as_posix()).convert("RGB")
source_img = self.transform(source_img)
target_img = self.transform(target_img)
target_mask = self.transform(target_mask)[0, :, :].unsqueeze(0)
return {
"source_image": source_img,
"target_image": target_img,
"target_mask": target_mask,
"same": same,
# "source_img_name": source_file.as_posix(),
# "target_img_name": target_file.as_posix(),
# "target_mask_name": target_mask_file.as_posix(),
}
class TrainDatasetDataLoader:
"""Wrapper class of Dataset class that performs multi-threaded data loading"""
def __init__(self):
"""Initialize this class"""
opt = TrainConfig()
if opt.mode is FaceSwapMode.MANY_TO_MANY:
self.dataset = ManyToManyTrainDataset(opt.dataset_root, opt.dataset_index, opt.same_rate)
elif opt.mode is FaceSwapMode.ONE_TO_MANY:
logger.info(f"In one-to-many mode, source face is {opt.source_name}")
self.dataset = OneToManyTrainDataset(opt.dataset_root, opt.dataset_index, opt.source_name, opt.same_rate)
else:
raise NotImplementedError
logger.info(f"dataset {type(self.dataset).__name__} created")
if opt.use_ddp:
self.train_sampler = torch.utils.data.distributed.DistributedSampler(self.dataset, shuffle=True)
self.dataloader = torch.utils.data.DataLoader(
self.dataset,
batch_size=opt.batch_size,
num_workers=int(opt.num_threads),
drop_last=True,
sampler=self.train_sampler,
pin_memory=True,
)
else:
self.dataloader = torch.utils.data.DataLoader(
self.dataset,
batch_size=opt.batch_size,
shuffle=True,
num_workers=int(opt.num_threads),
drop_last=True,
pin_memory=True,
)
def load_data(self):
return self
def __len__(self):
"""Return the number of data in the dataset"""
return len(self.dataset)
def __iter__(self):
"""Return a batch of data"""
for data in self.dataloader:
yield data
if __name__ == "__main__":
dataloader = TrainDatasetDataLoader()
for idx, data in enumerate(dataloader):
# print(data["source_img_name"])
# print(data["target_img_name"])
# print(data["target_mask_name"])
print(data["same"])
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