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benchmark.py
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import os
import argparse
import pandas as pd
from model.pred_func import *
from sklearn.metrics import classification_report, accuracy_score
from tqdm import tqdm
from datetime import datetime
def eval(
vae_name, ed_name, root_dir="sample_prediction_data", num_frames=15, net=None, fp16=False, ignored=True
):
model = load_genconvit(net, fp16, ed=ed_name, vae=vae_name)
results = pd.DataFrame(columns=['Split', 'Label', 'Filename', 'Prediction', 'Confidence'])
for split in [ "valid_vid", "test_vid", "train_vid"]:
data_dir = os.path.join(root_dir, split)
print(f"showing results for {split}")
y_labels = []
y_preds = []
for correct_label in ["fake", "real"]:
label_dir = os.path.join(data_dir, correct_label)
correct_label = correct_label.upper()
for filename in tqdm(os.listdir(label_dir), total=len(os.listdir(label_dir))):
curr_vid = os.path.join(label_dir, filename)
try:
if is_video(curr_vid):
y_pred, y_val = predict(
curr_vid,
model,
fp16,
num_frames,
net,
)
if ignored and y_val != 0.5:
y_preds.append(real_or_fake(y_pred))
y_labels.append(correct_label)
new_row = pd.DataFrame({
'Split': [split],
'Label': [correct_label],
'Filename': [filename],
'Prediction': [real_or_fake(y_pred)],
'Confidence': [y_val]
})
elif not ignored:
y_preds.append(real_or_fake(y_pred))
y_labels.append(correct_label)
new_row = pd.DataFrame({
'Split': [split],
'Label': [correct_label],
'Filename': [filename],
'Prediction': [real_or_fake(y_pred)],
'Confidence': [y_val]
})
results = pd.concat([results, new_row], ignore_index=True)
else:
print(f"Invalid video file: {curr_vid}. Please provide a valid video file.")
except Exception as e:
print(f"An error occurred in processing video {split}/{correct_label}/{curr_vid}: {str(e)}")
calculate_metrics(y_true=y_labels, y_pred=y_preds)
current_time = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
results.to_csv(f'result/video_prediction_results_{current_time}.csv', index=False)
def eval_all(
vae_name, ed_name, root_dir="sample_prediction_data", num_frames=15, net=None, fp16=False
):
model = load_genconvit(net, fp16, ed=ed_name, vae=vae_name)
results = pd.DataFrame(columns=['Label', 'Filename', 'Prediction', 'Confidence'])
y_labels = []
y_preds = []
for correct_label in ["fake", "real"]:
label_dir = os.path.join(root_dir, correct_label)
correct_label = correct_label.upper()
for filename in tqdm(os.listdir(label_dir), total=len(os.listdir(label_dir))):
curr_vid = os.path.join(label_dir, filename)
try:
if is_video(curr_vid):
y_pred, y_val = predict(
curr_vid,
model,
fp16,
num_frames,
net,
)
# if y_val != 0.5:
y_preds.append(real_or_fake(y_pred))
y_labels.append(correct_label)
new_row = pd.DataFrame({
'Label': [correct_label],
'Filename': [filename],
'Prediction': [real_or_fake(y_pred)],
'Confidence': [y_val]
})
results = pd.concat([results, new_row], ignore_index=True)
# else:
# print(f"y_val = 0.5 for processing video {correct_label}/{curr_vid}")
else:
print(f"Invalid video file: {curr_vid}. Please provide a valid video file.")
except Exception as e:
print(f"An error occurred in processing video {correct_label}/{curr_vid}: {str(e)}")
calculate_metrics(y_true=y_labels, y_pred=y_preds)
current_time = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
results.to_csv(f'result/video_prediction_results_{current_time}.csv', index=False)
def predict(
vid,
model,
fp16,
num_frames,
net
):
df = df_face(vid, num_frames, net) # extract face from the frames
if fp16:
df.half()
y, y_val = (
pred_vid(df, model)
if len(df) >= 1
else (torch.tensor(0).item(), torch.tensor(0.5).item())
)
return y, y_val
def gen_parser():
parser = argparse.ArgumentParser("GenConViT benchmark test")
parser.add_argument("--f", type=int, help="number of frames to process for prediction")
parser.add_argument("--d", type=str, help="data path")
parser.add_argument("--n", type=str, choices=['ed', 'vae'], help="network ed or vae")
parser.add_argument("--fp16", action='store_true', help="half precision support")
parser.add_argument("--vae", type=str, help="load pretrained vae model")
parser.add_argument("--ed", type=str, help="load pretrained ed model")
parser.add_argument("--eval_all", action='store_true', help="evaluation on all data")
parser.add_argument("--include_unkown", action='store_true', help="bool flag to test if ignore the case y_val = 0.5")
args = parser.parse_args()
is_eval_all = True if args.eval_all else False
num_frames = args.f if args.f else 15
data_dir = args.d if args.d else "data"
net = args.n if args.n else "genconvit"
fp16 = args.fp16
vae_name = args.vae if args.vae else "genconvit_vae_inference"
ed_name = args.ed if args.ed else "genconvit_ed_inference"
ignored = False if args.include_unkown else True
return data_dir, num_frames, net, fp16, vae_name, ed_name, is_eval_all, ignored
def calculate_metrics(y_true, y_pred):
print(classification_report(y_true, y_pred, target_names=['FAKE', 'REAL']))
print("Accuracy:", accuracy_score(y_true, y_pred))
def main():
data_dir, num_frames, net, fp16, vae_name, ed_name, is_eval_all, ignored = gen_parser()
if is_eval_all:
eval_all(vae_name, ed_name, root_dir=data_dir, num_frames=num_frames, net=net, fp16=fp16, ignored=ignored)
else:
eval(vae_name, ed_name, root_dir=data_dir, num_frames=num_frames, net=net, fp16=fp16, ignored=ignored)
if __name__ == "__main__":
main()