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from sklearn import metrics
from sklearn.metrics import auc
from sklearn.metrics import accuracy_score
from sklearn.metrics import f1_score
import os
import cv2
import numpy as np
import torch
from torch import nn, einsum
from sklearn.metrics import plot_confusion_matrix
from utils import get_method, check_correct, resize, shuffle_dataset, get_n_params
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
from cross_efficient_vit import CrossEfficientViT
from utils import transform_frame
import glob
from os import cpu_count
import json
from multiprocessing.pool import Pool
from progress.bar import Bar
import pandas as pd
from tqdm import tqdm
from multiprocessing import Manager
from utils import custom_round, custom_video_round
from albumentations import Compose, RandomBrightnessContrast, \
HorizontalFlip, FancyPCA, HueSaturationValue, OneOf, ToGray, \
ShiftScaleRotate, ImageCompression, PadIfNeeded, GaussNoise, GaussianBlur, Rotate
from transforms.albu import IsotropicResize
import yaml
import argparse
#########################
####### CONSTANTS #######
#########################
MODELS_DIR = "models"
BASE_DIR = "../../deep_fakes"
DATA_DIR = os.path.join(BASE_DIR, "dataset")
TEST_DIR = os.path.join(DATA_DIR, "test_set")
OUTPUT_DIR = os.path.join(MODELS_DIR, "tests")
TEST_LABELS_PATH = os.path.join(BASE_DIR, "dataset/dfdc_test_labels.csv")
#########################
####### UTILITIES #######
#########################
def save_confusion_matrix(confusion_matrix):
fig, ax = plt.subplots()
im = ax.imshow(confusion_matrix, cmap="Blues")
threshold = im.norm(confusion_matrix.max())/2.
textcolors=("black", "white")
ax.set_xticks(np.arange(2))
ax.set_yticks(np.arange(2))
ax.set_xticklabels(["original", "fake"])
ax.set_yticklabels(["original", "fake"])
ax.tick_params(top=True, bottom=False, labeltop=True, labelbottom=False)
for i in range(2):
for j in range(2):
text = ax.text(j, i, confusion_matrix[i, j], ha="center", va="center",
fontsize=12, color=textcolors[int(im.norm(confusion_matrix[i, j]) > threshold)])
fig.tight_layout()
plt.savefig(os.path.join(OUTPUT_DIR, "confusion.jpg"))
def save_roc_curves(correct_labels, preds, model_name, accuracy, loss, f1):
plt.figure(1)
plt.plot([0, 1], [0, 1], 'k--')
fpr, tpr, th = metrics.roc_curve(correct_labels, preds)
model_auc = auc(fpr, tpr)
plt.plot(fpr, tpr, label="Model_"+ model_name + ' (area = {:.3f})'.format(model_auc))
plt.xlabel('False positive rate')
plt.ylabel('True positive rate')
plt.title('ROC curve')
plt.legend(loc='best')
plt.savefig(os.path.join(OUTPUT_DIR, model_name + "_" + opt.dataset + "_acc" + str(accuracy*100) + "_loss"+str(loss)+"_f1"+str(f1)+".jpg"))
plt.clf()
def read_frames(video_path, videos):
# Get the video label based on dataset selected
method = get_method(video_path, DATA_DIR)
if "Original" in video_path:
label = 0.
elif method == "DFDC":
test_df = pd.DataFrame(pd.read_csv(TEST_LABELS_PATH))
video_folder_name = os.path.basename(video_path)
video_key = video_folder_name + ".mp4"
label = test_df.loc[test_df['filename'] == video_key]['label'].values[0]
else:
label = 1.
# Calculate the interval to extract the frames
frames_number = len(os.listdir(video_path))
frames_interval = int(frames_number / opt.frames_per_video)
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(0,3): # Consider up to 3 faces per video
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][:opt.frames_per_video]
# Select N frames from the collected ones
video = {}
for key in frames_paths_dict.keys():
for index, frame_image in enumerate(frames_paths_dict[key]):
transform = create_base_transform(config['model']['image-size'])
image = transform(image=cv2.imread(os.path.join(video_path, frame_image)))['image']
if len(image) > 0:
if key in video:
video[key].append(image)
else:
video[key] = [image]
videos.append((video, label, video_path))
def create_base_transform(size):
return Compose([
IsotropicResize(max_side=size, interpolation_down=cv2.INTER_AREA, interpolation_up=cv2.INTER_CUBIC),
PadIfNeeded(min_height=size, min_width=size, border_mode=cv2.BORDER_CONSTANT),
])
#########################
####### MODEL #######
#########################
# Main body
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--workers', default=10, type=int,
help='Number of data loader workers.')
parser.add_argument('--model_path', default='', type=str, metavar='PATH',
help='Path to model checkpoint (default: none).')
parser.add_argument('--dataset', type=str, default='DFDC',
help="Which dataset to use (Deepfakes|Face2Face|FaceShifter|FaceSwap|NeuralTextures|DFDC)")
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('--frames_per_video', type=int, default=30,
help="How many equidistant frames for each video (default: 30)")
parser.add_argument('--batch_size', type=int, default=32,
help="Batch size (default: 32)")
opt = parser.parse_args()
print(opt)
with open(opt.config, 'r') as ymlfile:
config = yaml.safe_load(ymlfile)
if os.path.exists(opt.model_path):
model = CrossEfficientViT(config=config)
model.load_state_dict(torch.load(opt.model_path))
model.eval()
model = model.cuda()
else:
print("No model found.")
exit()
model_name = os.path.basename(opt.model_path)
#########################
####### EXECUTION #######
#########################
OUTPUT_DIR = os.path.join(OUTPUT_DIR, opt.dataset)
if not os.path.exists(OUTPUT_DIR):
os.makedirs(OUTPUT_DIR)
NUM_CLASSES = 1
preds = []
mgr = Manager()
paths = []
videos = mgr.list()
if opt.dataset != "DFDC":
folders = ["Original", opt.dataset]
else:
folders = [opt.dataset]
# Read all videos paths
for folder in folders:
method_folder = os.path.join(TEST_DIR, folder)
for index, video_folder in enumerate(os.listdir(method_folder)):
paths.append(os.path.join(method_folder, video_folder))
# Read faces
with Pool(processes=cpu_count()-1) as p:
with tqdm(total=len(paths)) as pbar:
for v in p.imap_unordered(partial(read_frames, videos=videos),paths):
pbar.update()
video_names = np.asarray([row[2] for row in videos])
correct_test_labels = np.asarray([row[1] for row in videos])
videos = np.asarray([row[0] for row in videos])
preds = []
# Perform prediction
bar = Bar('Predicting', max=len(videos))
f = open(opt.dataset + "_" + model_name + "_labels.txt", "w+")
for index, video in enumerate(videos):
video_faces_preds = []
video_name = video_names[index]
f.write(video_name)
for key in video:
faces_preds = []
video_faces = video[key]
for i in range(0, len(video_faces), opt.batch_size):
faces = video_faces[i:i+opt.batch_size]
faces = torch.tensor(np.asarray(faces))
if faces.shape[0] == 0:
continue
faces = np.transpose(faces, (0, 3, 1, 2))
faces = faces.cuda().float()
pred = model(faces)
scaled_pred = []
for idx, p in enumerate(pred):
scaled_pred.append(torch.sigmoid(p))
faces_preds.extend(scaled_pred)
current_faces_pred = sum(faces_preds)/len(faces_preds)
face_pred = current_faces_pred.cpu().detach().numpy()[0]
f.write(" " + str(face_pred))
video_faces_preds.append(face_pred)
bar.next()
if len(video_faces_preds) > 1:
video_pred = custom_video_round(video_faces_preds)
else:
video_pred = video_faces_preds[0]
preds.append([video_pred])
f.write(" --> " + str(video_pred) + "(CORRECT: " + str(correct_test_labels[index]) + ")" +"\n")
f.close()
bar.finish()
#########################
####### METRICS #######
#########################
loss_fn = torch.nn.BCEWithLogitsLoss()
tensor_labels = torch.tensor([[float(label)] for label in correct_test_labels])
tensor_preds = torch.tensor(preds)
loss = loss_fn(tensor_preds, tensor_labels).numpy()
#accuracy = accuracy_score(np.asarray(preds).round(), correct_test_labels) # Classic way
accuracy = accuracy_score(custom_round(np.asarray(preds)), correct_test_labels) # Custom way
f1 = f1_score(correct_test_labels, custom_round(np.asarray(preds)))
print(model_name, "Test Accuracy:", accuracy, "Loss:", loss, "F1", f1)
save_roc_curves(correct_test_labels, preds, model_name, accuracy, loss, f1)
save_confusion_matrix(metrics.confusion_matrix(correct_test_labels,custom_round(np.asarray(preds))))
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