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import os
import cv2
import torch
import argparse
import numpy as np
import torch.nn as nn
from models.TMC import ETMC
from models import image
#Set random seed for reproducibility.
torch.manual_seed(42)
# Define the audio_args dictionary
audio_args = {
'nb_samp': 64600,
'first_conv': 1024,
'in_channels': 1,
'filts': [20, [20, 20], [20, 128], [128, 128]],
'blocks': [2, 4],
'nb_fc_node': 1024,
'gru_node': 1024,
'nb_gru_layer': 3,
}
def get_args(parser):
parser.add_argument("--batch_size", type=int, default=8)
parser.add_argument("--data_dir", type=str, default="datasets/train/fakeavceleb*")
parser.add_argument("--LOAD_SIZE", type=int, default=256)
parser.add_argument("--FINE_SIZE", type=int, default=224)
parser.add_argument("--dropout", type=float, default=0.2)
parser.add_argument("--gradient_accumulation_steps", type=int, default=1)
parser.add_argument("--hidden", nargs="*", type=int, default=[])
parser.add_argument("--hidden_sz", type=int, default=768)
parser.add_argument("--img_embed_pool_type", type=str, default="avg", choices=["max", "avg"])
parser.add_argument("--img_hidden_sz", type=int, default=1024)
parser.add_argument("--include_bn", type=int, default=True)
parser.add_argument("--lr", type=float, default=1e-4)
parser.add_argument("--lr_factor", type=float, default=0.3)
parser.add_argument("--lr_patience", type=int, default=10)
parser.add_argument("--max_epochs", type=int, default=500)
parser.add_argument("--n_workers", type=int, default=12)
parser.add_argument("--name", type=str, default="MMDF")
parser.add_argument("--num_image_embeds", type=int, default=1)
parser.add_argument("--patience", type=int, default=20)
parser.add_argument("--savedir", type=str, default="./savepath/")
parser.add_argument("--seed", type=int, default=1)
parser.add_argument("--n_classes", type=int, default=2)
parser.add_argument("--annealing_epoch", type=int, default=10)
parser.add_argument("--device", type=str, default='cpu')
parser.add_argument("--pretrained_image_encoder", type=bool, default = False)
parser.add_argument("--freeze_image_encoder", type=bool, default = False)
parser.add_argument("--pretrained_audio_encoder", type = bool, default=False)
parser.add_argument("--freeze_audio_encoder", type = bool, default = False)
parser.add_argument("--augment_dataset", type = bool, default = True)
for key, value in audio_args.items():
parser.add_argument(f"--{key}", type=type(value), default=value)
def model_summary(args):
'''Prints the model summary.'''
model = ETMC(args)
for name, layer in model.named_modules():
print(name, layer)
def load_multimodal_model(args):
'''Load multimodal model'''
model = ETMC(args)
ckpt = torch.load('checkpoints/model_best.pt', map_location = torch.device('cpu'))
model.load_state_dict(ckpt,strict = False)
model.eval()
return model
def load_img_modality_model(args):
'''Loads image modality model.'''
rgb_encoder = image.ImageEncoder(args)
ckpt = torch.load('checkpoints/model_best.pt', map_location = torch.device('cpu'))
rgb_encoder.load_state_dict(ckpt,strict = False)
rgb_encoder.eval()
return rgb_encoder
def load_spec_modality_model(args):
spec_encoder = image.RawNet(args)
ckpt = torch.load('checkpoints/model_best.pt', map_location = torch.device('cpu'))
spec_encoder.load_state_dict(ckpt,strict = False)
spec_encoder.eval()
return spec_encoder
#Load models.
parser = argparse.ArgumentParser(description="Train Models")
get_args(parser)
args, remaining_args = parser.parse_known_args()
assert remaining_args == [], remaining_args
multimodal = load_multimodal_model(args)
spec_model = load_spec_modality_model(args)
img_model = load_img_modality_model(args)
def preprocess_img(face):
face = face / 255
face = cv2.resize(face, (256, 256))
face = face.transpose(2, 0, 1) #(W, H, C) -> (C, W, H)
face_pt = torch.unsqueeze(torch.Tensor(face), dim = 0)
return face_pt
def preprocess_audio(audio_file):
audio_pt = torch.unsqueeze(torch.Tensor(audio_file), dim = 0)
return audio_pt
def deepfakes_spec_predict(input_audio):
x, _ = input_audio
audio = preprocess_audio(x)
spec_grads = spec_model.forward(audio)
multimodal_grads = multimodal.spec_depth[0].forward(spec_grads)
out = nn.Softmax()(multimodal_grads)
max = torch.argmax(out, dim = -1) #Index of the max value in the tensor.
max_value = out[max] #Actual value of the tensor.
max_value = np.argmax(out[max].detach().numpy())
if max_value > 0.5:
preds = round(100 - (max_value*100), 3)
text2 = f"The audio is REAL."
else:
preds = round(max_value*100, 3)
text2 = f"The audio is FAKE."
return text2
def deepfakes_image_predict(input_image):
face = preprocess_img(input_image)
img_grads = img_model.forward(face)
multimodal_grads = multimodal.clf_rgb[0].forward(img_grads)
out = nn.Softmax()(multimodal_grads)
max = torch.argmax(out, dim=-1) #Index of the max value in the tensor.
max = max.cpu().detach().numpy()
max_value = out[max] #Actual value of the tensor.
max_value = np.argmax(out[max].detach().numpy())
if max_value > 0.5:
preds = round(100 - (max_value*100), 3)
text2 = f"The image is REAL."
else:
preds = round(max_value*100, 3)
text2 = f"The image is FAKE."
return text2
def preprocess_video(input_video, n_frames = 5):
v_cap = cv2.VideoCapture(input_video)
v_len = int(v_cap.get(cv2.CAP_PROP_FRAME_COUNT))
# Pick 'n_frames' evenly spaced frames to sample
if n_frames is None:
sample = np.arange(0, v_len)
else:
sample = np.linspace(0, v_len - 1, n_frames).astype(int)
#Loop through frames.
frames = []
for j in range(v_len):
success = v_cap.grab()
if j in sample:
# Load frame
success, frame = v_cap.retrieve()
if not success:
continue
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame = preprocess_img(frame)
frames.append(frame)
v_cap.release()
return frames
def deepfakes_video_predict(input_video):
'''Perform inference on a video.'''
video_frames = preprocess_video(input_video)
real_grads = []
fake_grads = []
for face in video_frames:
img_grads = img_model.forward(face)
multimodal_grads = multimodal.clf_rgb[0].forward(img_grads)
out = nn.Softmax()(multimodal_grads)
real_grads.append(out.cpu().detach().numpy()[0])
print(f"Video out tensor shape is: {out.shape}, {out}")
fake_grads.append(out.cpu().detach().numpy()[0])
real_grads_mean = np.mean(real_grads)
fake_grads_mean = np.mean(fake_grads)
if real_grads_mean > fake_grads_mean:
res = round(real_grads_mean * 100, 3)
text = f"The video is REAL."
else:
res = round(100 - (real_grads_mean * 100), 3)
text = f"The video is FAKE."
return text
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