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from torch.utils.data import DataLoader, TensorDataset, Dataset
from einops import rearrange, repeat
from torch import nn, einsum
import torch.nn as nn
import torch.nn.functional as F
from random import random, randint, choice
from vit_pytorch import ViT
import numpy as np
import os
import json
from multiprocessing.pool import Pool
from functools import partial
from multiprocessing import Manager
from progress.bar import ChargingBar
from cross_efficient_vit import CrossEfficientViT
import uuid
from torch.utils.data import DataLoader, TensorDataset, Dataset
from sklearn.metrics import accuracy_score
import cv2
from transforms.albu import IsotropicResize
import glob
import pandas as pd
from tqdm import tqdm
from utils import get_method, check_correct, resize, shuffle_dataset, get_n_params
from sklearn.utils.class_weight import compute_class_weight
from torch.optim import lr_scheduler
import collections
from deepfakes_dataset import DeepFakesDataset
import math
import yaml
import argparse
BASE_DIR = '../../deep_fakes/'
DATA_DIR = os.path.join(BASE_DIR, "dataset")
TRAINING_DIR = os.path.join(DATA_DIR, "training_set")
VALIDATION_DIR = os.path.join(DATA_DIR, "validation_set")
TEST_DIR = os.path.join(DATA_DIR, "test_set")
MODELS_PATH = "models"
METADATA_PATH = os.path.join(BASE_DIR, "data/metadata") # Folder containing all training metadata for DFDC dataset
VALIDATION_LABELS_PATH = os.path.join(DATA_DIR, "dfdc_val_labels.csv")
def read_frames(video_path, train_dataset, validation_dataset):
# Get the video label based on dataset selected
method = get_method(video_path, DATA_DIR)
if TRAINING_DIR in video_path:
if "Original" in video_path:
label = 0.
elif "DFDC" in video_path:
for json_path in glob.glob(os.path.join(METADATA_PATH, "*.json")):
with open(json_path, "r") as f:
metadata = json.load(f)
video_folder_name = os.path.basename(video_path)
video_key = video_folder_name + ".mp4"
if video_key in metadata.keys():
item = metadata[video_key]
label = item.get("label", None)
if label == "FAKE":
label = 1.
else:
label = 0.
break
else:
label = None
else:
label = 1.
if label == None:
print("NOT FOUND", video_path)
else:
if "Original" in video_path:
label = 0.
elif "DFDC" in video_path:
val_df = pd.DataFrame(pd.read_csv(VALIDATION_LABELS_PATH))
video_folder_name = os.path.basename(video_path)
video_key = video_folder_name + ".mp4"
label = val_df.loc[val_df['filename'] == video_key]['label'].values[0]
else:
label = 1.
# Calculate the interval to extract the frames
frames_number = len(os.listdir(video_path))
if label == 0:
min_video_frames = max(int(config['training']['frames-per-video'] * config['training']['rebalancing-real']),1) # Compensate unbalancing
else:
min_video_frames = max(int(config['training']['frames-per-video'] * config['training']['rebalancing-fake']),1)
if VALIDATION_DIR in video_path:
min_video_frames = int(max(min_video_frames/8, 2))
frames_interval = int(frames_number / min_video_frames)
frames_paths = os.listdir(video_path)
frames_paths_dict = {}
# Group the faces with the same index, reduce probabiity to skip some faces in the same video
for path in frames_paths:
for i in range(0,1):
if "_" + str(i) in path:
if i not in frames_paths_dict.keys():
frames_paths_dict[i] = [path]
else:
frames_paths_dict[i].append(path)
# Select only the frames at a certain interval
if frames_interval > 0:
for key in frames_paths_dict.keys():
if len(frames_paths_dict) > frames_interval:
frames_paths_dict[key] = frames_paths_dict[key][::frames_interval]
frames_paths_dict[key] = frames_paths_dict[key][:min_video_frames]
# Select N frames from the collected ones
for key in frames_paths_dict.keys():
for index, frame_image in enumerate(frames_paths_dict[key]):
#image = transform(np.asarray(cv2.imread(os.path.join(video_path, frame_image))))
image = cv2.imread(os.path.join(video_path, frame_image))
if image is not None:
if TRAINING_DIR in video_path:
train_dataset.append((image, label))
else:
validation_dataset.append((image, label))
# Main body
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--num_epochs', default=300, type=int,
help='Number of training epochs.')
parser.add_argument('--workers', default=10, type=int,
help='Number of data loader workers.')
parser.add_argument('--resume', default='', type=str, metavar='PATH',
help='Path to latest checkpoint (default: none).')
parser.add_argument('--dataset', type=str, default='All',
help="Which dataset to use (Deepfakes|Face2Face|FaceShifter|FaceSwap|NeuralTextures|All)")
parser.add_argument('--max_videos', type=int, default=-1,
help="Maximum number of videos to use for training (default: all).")
parser.add_argument('--config', type=str,
help="Which configuration to use. See into 'config' folder.")
parser.add_argument('--efficient_net', type=int, default=0,
help="Which EfficientNet version to use (0 or 7, default: 0)")
parser.add_argument('--patience', type=int, default=5,
help="How many epochs wait before stopping for validation loss not improving.")
opt = parser.parse_args()
print(opt)
with open(opt.config, 'r') as ymlfile:
config = yaml.safe_load(ymlfile)
model = CrossEfficientViT(config=config)
model.train()
optimizer = torch.optim.SGD(model.parameters(), lr=config['training']['lr'], weight_decay=config['training']['weight-decay'])
scheduler = lr_scheduler.StepLR(optimizer, step_size=config['training']['step-size'], gamma=config['training']['gamma'])
starting_epoch = 0
if os.path.exists(opt.resume):
model.load_state_dict(torch.load(opt.resume))
starting_epoch = int(opt.resume.split("checkpoint")[1].split("_")[0]) + 1
else:
print("No checkpoint loaded.")
print("Model Parameters:", get_n_params(model))
#READ DATASET
if opt.dataset != "All":
folders = ["Original", opt.dataset]
else:
folders = ["Original", "DFDC", "Deepfakes", "Face2Face", "FaceShifter", "FaceSwap", "NeuralTextures"]
sets = [TRAINING_DIR, VALIDATION_DIR]
paths = []
for dataset in sets:
for folder in folders:
subfolder = os.path.join(dataset, folder)
for index, video_folder_name in enumerate(os.listdir(subfolder)):
if index == opt.max_videos:
break
if os.path.isdir(os.path.join(subfolder, video_folder_name)):
paths.append(os.path.join(subfolder, video_folder_name))
mgr = Manager()
train_dataset = mgr.list()
validation_dataset = mgr.list()
with Pool(processes=10) as p:
with tqdm(total=len(paths)) as pbar:
for v in p.imap_unordered(partial(read_frames, train_dataset=train_dataset, validation_dataset=validation_dataset),paths):
pbar.update()
train_samples = len(train_dataset)
train_dataset = shuffle_dataset(train_dataset)
validation_samples = len(validation_dataset)
validation_dataset = shuffle_dataset(validation_dataset)
# Print some useful statistics
print("Train images:", len(train_dataset), "Validation images:", len(validation_dataset))
print("__TRAINING STATS__")
train_counters = collections.Counter(image[1] for image in train_dataset)
print(train_counters)
class_weights = train_counters[0] / train_counters[1]
print("Weights", class_weights)
print("__VALIDATION STATS__")
val_counters = collections.Counter(image[1] for image in validation_dataset)
print(val_counters)
print("___________________")
loss_fn = torch.nn.BCEWithLogitsLoss(pos_weight=torch.tensor([class_weights]))
# Create the data loaders
validation_labels = np.asarray([row[1] for row in validation_dataset])
labels = np.asarray([row[1] for row in train_dataset])
train_dataset = DeepFakesDataset(np.asarray([row[0] for row in train_dataset]), labels, config['model']['image-size'])
dl = torch.utils.data.DataLoader(train_dataset, batch_size=config['training']['bs'], shuffle=True, sampler=None,
batch_sampler=None, num_workers=opt.workers, collate_fn=None,
pin_memory=False, drop_last=False, timeout=0,
worker_init_fn=None, prefetch_factor=2,
persistent_workers=False)
del train_dataset
validation_dataset = DeepFakesDataset(np.asarray([row[0] for row in validation_dataset]), validation_labels, config['model']['image-size'], mode='validation')
val_dl = torch.utils.data.DataLoader(validation_dataset, batch_size=config['training']['bs'], shuffle=True, sampler=None,
batch_sampler=None, num_workers=opt.workers, collate_fn=None,
pin_memory=False, drop_last=False, timeout=0,
worker_init_fn=None, prefetch_factor=2,
persistent_workers=False)
del validation_dataset
model = model.cuda()
counter = 0
not_improved_loss = 0
previous_loss = math.inf
for t in range(starting_epoch, opt.num_epochs + 1):
if not_improved_loss == opt.patience:
break
counter = 0
total_loss = 0
total_val_loss = 0
bar = ChargingBar('EPOCH #' + str(t), max=(len(dl)*config['training']['bs'])+len(val_dl))
train_correct = 0
positive = 0
negative = 0
for index, (images, labels) in enumerate(dl):
images = np.transpose(images, (0, 3, 1, 2))
labels = labels.unsqueeze(1)
images = images.cuda()
y_pred = model(images)
y_pred = y_pred.cpu()
loss = loss_fn(y_pred, labels)
corrects, positive_class, negative_class = check_correct(y_pred, labels)
train_correct += corrects
positive += positive_class
negative += negative_class
optimizer.zero_grad()
loss.backward()
optimizer.step()
counter += 1
total_loss += round(loss.item(), 2)
for i in range(config['training']['bs']):
bar.next()
if index%1200 == 0:
print("\nLoss: ", total_loss/counter, "Accuracy: ",train_correct/(counter*config['training']['bs']) ,"Train 0s: ", negative, "Train 1s:", positive)
val_counter = 0
val_correct = 0
val_positive = 0
val_negative = 0
train_correct /= train_samples
total_loss /= counter
for index, (val_images, val_labels) in enumerate(val_dl):
val_images = np.transpose(val_images, (0, 3, 1, 2))
val_images = val_images.cuda()
val_labels = val_labels.unsqueeze(1)
val_pred = model(val_images)
val_pred = val_pred.cpu()
val_loss = loss_fn(val_pred, val_labels)
total_val_loss += round(val_loss.item(), 2)
corrects, positive_class, negative_class = check_correct(val_pred, val_labels)
val_correct += corrects
val_positive += positive_class
val_negative += negative_class
val_counter += 1
bar.next()
scheduler.step()
bar.finish()
total_val_loss /= val_counter
val_correct /= validation_samples
if previous_loss <= total_val_loss:
print("Validation loss did not improved")
not_improved_loss += 1
else:
not_improved_loss = 0
previous_loss = total_val_loss
print("#" + str(t) + "/" + str(opt.num_epochs) + " loss:" +
str(total_loss) + " accuracy:" + str(train_correct) +" val_loss:" + str(total_val_loss) + " val_accuracy:" + str(val_correct) + " val_0s:" + str(val_negative) + "/" + str(np.count_nonzero(validation_labels == 0)) + " val_1s:" + str(val_positive) + "/" + str(np.count_nonzero(validation_labels == 1)))
if not os.path.exists(MODELS_PATH):
os.makedirs(MODELS_PATH)
torch.save(model.state_dict(), os.path.join(MODELS_PATH, "efficientnet_checkpoint" + str(t) + "_" + opt.dataset))
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