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import torch
import numpy as np
import argparse
from tqdm import tqdm
import math
import yaml
from utils import check_correct, aggregate_attentions, save_attention_plots, count_parameters, slowfast_input_transform
from torch.optim.lr_scheduler import LambdaLR
from datetime import datetime, timedelta
from statistics import mean
import tensorflow as tf
import collections
import os
import json
from sklearn import metrics
from sklearn.metrics import f1_score
from itertools import chain
import random
from einops import rearrange, reduce
import pandas as pd
from os import cpu_count
from multiprocessing.pool import Pool
from functools import partial
from multiprocessing import Manager
from progress.bar import ChargingBar
from torch.optim import lr_scheduler
from deepfakes_dataset import DeepFakesDataset
from models.size_invariant_timesformer import SizeInvariantTimeSformer
from models.efficientnet.efficientnet_pytorch import EfficientNet
from torch.utils.tensorboard import SummaryWriter
import torch_optimizer as optim
from timm.scheduler.cosine_lr import CosineLRScheduler
from models.baseline import Baseline
from models.xception import xception
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--test_list_file', default="../../datasets/ForgeryNet/faces/test.csv", type=str,
help='Test List txt file path)')
parser.add_argument('--data_path', default="../../datasets/ForgeryNet/faces", type=str,
help='Path to the dataset converted into identities.')
parser.add_argument('--video_path', default="../../datasets/ForgeryNet/videos", type=str,
help='Path to the dataset original videos (.mp4 files).')
parser.add_argument('--deepfake_methods', nargs='*', required=False,
help="For ForgeryNet dataset, filter some deepfake methods for partial training.")
parser.add_argument('--workers', default=8, type=int,
help='Number of data loader workers.')
parser.add_argument('--random_state', default=42, type=int,
help='Random state value')
parser.add_argument('--model_weights', type=str,
help='Model weights.')
parser.add_argument('--extractor_model', type=int, default=0,
help="Which model use for features extraction (0: EfficientNet; 1: XceptionNet).")
parser.add_argument('--extractor_weights', default='ImageNet', type=str,
help='Path to extractor weights or "imagenet".')
parser.add_argument('--gpu_id', default=0, type=int,
help='ID of GPU to be used.')
parser.add_argument('--max_videos', type=int, default=-1,
help="Maximum number of videos to use for training (default: all).")
parser.add_argument('--only_multiidentity', default=False, action="store_true",
help='Use only multiidentity videos.')
parser.add_argument('--config', type=str,
help="Which configuration to use. See into 'config' folder.")
parser.add_argument('--model', type=int,
help="Which model to use. (0: Baseline | 1: Size Invariant TimeSformer | 2: SlowFast).")
parser.add_argument('--identities_ordering', type=int, default = 0,
help="Which ordering rule to use. (0: Size-based | 1: Frequency-based | 2: Random).")
parser.add_argument('--save_attentions', default=False, action="store_true",
help='Save attentions plots.')
opt = parser.parse_args()
print(opt)
with open(opt.config, 'r') as ymlfile:
config = yaml.safe_load(ymlfile)
os.environ["CUDA_VISIBLE_DEVICES"] = "0,1"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Check for integrity
if config['model']['num-frames'] != 8 and config['model']['num-frames'] != 16:
raise Exception("Invalid number of frames.")
# Setup CUDA settings
torch.backends.cudnn.deterministic = True
random.seed(opt.random_state)
torch.manual_seed(opt.random_state)
torch.cuda.manual_seed(opt.random_state)
np.random.seed(opt.random_state)
# Load required weights for feature extractor
if opt.model != 2:
if opt.extractor_model == 0: # EfficientNet-B0
if opt.extractor_weights.lower() == 'imagenet':
features_extractor = EfficientNet.from_pretrained('efficientnet-b0')
else:
features_extractor = EfficientNet.from_name('efficientnet-b0')
features_extractor.load_matching_state_dict(torch.load(opt.extractor_weights, map_location=torch.device('cpu')))
print("Custom features extractor weights loaded.")
else: # XceptionNet
if opt.extractor_weights.lower() == 'pretrained':
features_extractor = xception(num_classes=1, pretrain_path="weights/ckpt_iter.pth.tar")
else:
features_extractor = xception(num_classes=1, pretrain_path=opt.extractor_weights)
else:
features_extractor = None
# Init the required model
if opt.model == 0:
model = Baseline(config=config)
num_patches = None
elif opt.model == 1:
model = SizeInvariantTimeSformer(config=config, require_attention=True)
num_patches = config['model']['num-patches']
elif opt.model == 2:
torch.hub._validate_not_a_forked_repo=lambda a,b,c: True
model = torch.hub.load('facebookresearch/pytorchvideo', 'slowfast_r50', pretrained=True)
output_layer = torch.nn.Linear(2304 , 1)
model.blocks[6].proj = output_layer
num_patches = None
if features_extractor != None and opt.gpu_id == -1:
features_extractor = torch.nn.DataParallel(features_extractor)
if opt.gpu_id == -1:
model = torch.nn.DataParallel(model)
if os.path.exists(opt.model_weights):
model.load_state_dict(torch.load(opt.model_weights))
else:
raise Exception("No checkpoint loaded for the model.")
loss_fn = torch.nn.BCEWithLogitsLoss()
# Move into GPU
if features_extractor != None:
features_extractor = features_extractor.to(device)
features_extractor.eval()
print("Extractor Parameters: ", count_parameters(features_extractor))
print("Model Parameters: ", count_parameters(model))
model = model.to(device)
model.eval()
# Read all the paths and initialize data loaders for train and validation
paths = []
col_names = ["video", "label", "8_cls"]
df_test = pd.read_csv(opt.test_list_file, sep=' ', names=col_names)
df_test = df_test.sample(frac=1, random_state=opt.random_state).reset_index(drop=True)
# Filter out deepfake methods if requested for ForgeryNet
if opt.deepfake_methods is not None and len(opt.deepfake_methods) > 0:
opt.deepfake_methods = [int(method) for method in opt.deepfake_methods]
indexes_to_drop = []
for index, row in df_test.iterrows():
if row['8_cls'] not in opt.deepfake_methods:
indexes_to_drop.append(index)
df_test.drop(df_test.index[indexes_to_drop], inplace=True)
# Filter out non-multi-identity videos if requested
if opt.only_multiidentity:
indexes_to_drop = []
for index, row in df_test.iterrows():
video_path = os.path.join(opt.data_path, row['video'])
folders = os.listdir(video_path)
if len(folders) < 2:
indexes_to_drop.append(index)
else:
counter = 0
for folder in folders:
if os.path.isdir(os.path.join(opt.data_path, row['video'], folder)):
counter += 1
if counter < 2:
indexes_to_drop.append(index)
df_test.drop(df_test.index[indexes_to_drop], inplace=True)
# Split videos and labels and reduce to the required number of videos
test_videos = df_test['video'].tolist()
test_labels = df_test['label'].tolist()
multiclass_labels = df_test['8_cls'].tolist()
class_counter = collections.Counter(multiclass_labels)
if opt.max_videos > -1:
test_videos = test_videos[:opt.max_videos]
test_labels = test_labels[:opt.max_videos]
test_samples = len(test_videos)
# Create the data loaders
test_dataset = DeepFakesDataset(test_videos, test_labels, multiclass_labels = multiclass_labels, image_size=config['model']['image-size'], data_path=opt.data_path, video_path=opt.video_path, num_frames=config['model']['num-frames'], num_patches=num_patches, max_identities=config['model']['max-identities'], enable_identity_attention=config['model']['enable-identity-attention'], identities_ordering = opt.identities_ordering, mode='test')
test_dl = torch.utils.data.DataLoader(test_dataset, batch_size=config['test']['bs'], shuffle=False, 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)
# Print some useful statistics
print("Test videos:", test_samples)
print("__TEST STATS__")
test_counters = collections.Counter(test_labels)
print(test_counters)
# Init variables
total_test_loss = 0
test_correct = 0
test_positive = 0
test_negative = 0
test_counter = 0
multiclass_errors = dict.fromkeys([i for i in range(9)])
for key in multiclass_errors:
multiclass_errors[key] = [0, class_counter[key]]
bar = ChargingBar('PREDICT', max=(len(test_dl)))
preds = []
videos_errors = []
# Test loop
for index, (videos, size_embeddings, masks, identities_masks, positions, tokens_per_identity, labels, multiclass_labels, video_ids) in enumerate(test_dl):
b, f, h, w, c = videos.shape
labels = labels.unsqueeze(1).float()
identities_masks = identities_masks.to(device)
masks = masks.to(device)
positions = positions.to(device)
with torch.no_grad():
if opt.model != 2: # Use the features extractor
videos = rearrange(videos, "b f h w c -> (b f) c h w")
videos = videos.to(device)
features = features_extractor(videos)
if opt.model == 0:
test_pred = model(features)
test_pred = torch.mean(test_pred.reshape(-1, config["model"]["num-frames"]), axis=1).unsqueeze(1)
elif opt.model == 1:
features = rearrange(features, '(b f) c h w -> b f c h w', b = b, f = f)
test_pred, attentions = model(features, mask=masks, size_embedding=size_embeddings, identities_mask=identities_masks, positions=positions)
if opt.save_attentions:
identity_names = [row[0] for row in tokens_per_identity]
frames_per_identity = [int(row[1] / config["model"]["num-patches"]) for row in tokens_per_identity]
aggregated_attentions, identity_attentions = aggregate_attentions(attentions, config['model']['heads'], config['model']['num-frames'], frames_per_identity)
save_attention_plots(aggregated_attentions, identity_names, frames_per_identity, config['model']['num-frames'], video_ids[0])
elif opt.model == 2:
videos = rearrange(videos, 'b f h w c -> b c f h w')
videos = slowfast_input_transform(videos)
videos = [torch.cat([v[None, ...].to(device) for v in videos[0]]), torch.cat([v[None, ...].to(device) for v in videos[1]])]
test_pred = model(videos)
if opt.model != 2:
videos = videos.cpu()
else:
videos = [torch.cat([v[None, ...].cpu() for v in videos[0]]), torch.cat([v[None, ...].cpu() for v in videos[1]])]
test_pred = test_pred.cpu()
test_loss = loss_fn(test_pred, labels)
total_test_loss += round(test_loss.item(), 2)
corrects, positive_class, negative_class, multiclass_errors, batch_errors = check_correct(test_pred, labels, multiclass_labels, multiclass_errors, video_ids)
videos_errors.extend(batch_errors)
test_correct += corrects
test_positive += positive_class
test_counter += 1
test_negative += negative_class
preds.extend(test_pred)
bar.next()
preds = [torch.sigmoid(torch.tensor(pred)) for pred in preds]
fpr, tpr, th = metrics.roc_curve(test_labels, preds)
auc = metrics.auc(fpr, tpr)
f1 = f1_score(test_labels, [round(pred.item()) for pred in preds])
bar.finish()
total_test_loss /= test_counter
test_correct /= test_samples
print("Videos errors", videos_errors)
print("Class errors", multiclass_errors)
print(str(opt.model_weights) + " test loss:" +
str(total_test_loss) + " f1 score: " + str(f1) + " test accuracy:" + str(test_correct) + " test_0s:" + str(test_negative) + "/" + str(test_counters[0]) + " test_1s:" + str(test_positive) + "/" + str(test_counters[1]) + " AUC " + str(auc))
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