# -*- coding: utf-8 -*- import argparse import os import sys import time if not os.getcwd() in sys.path: sys.path.append(os.getcwd()) import math import random from glob import glob import cv2 import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from configs.get_config import load_config from datasets import DATASETS, build_dataset from lib.core_function import AverageMeter from lib.metrics import bin_calculate_auc_ap_ar, get_acc_mesure_func from logs.logger import LOG_DIR, Logger from losses.losses import _sigmoid from models import * from natsort import natsorted from package_utils.image_utils import crop_by_margin, load_image from package_utils.tensors import masked_inputs from package_utils.transform import ( final_transform, get_affine_transform, get_center_scale, ) from package_utils.utils import save_file, vis_heatmap from PIL import Image from torch.utils.data import DataLoader from tqdm import tqdm def parse_args(args=None): arg_parser = argparse.ArgumentParser("Processing testing...") arg_parser.add_argument("--cfg", "-c", help="Config file", required=True) arg_parser.add_argument( "--image", "-i", type=str, help="Image for the single testing mode!" ) arg_parser.add_argument( "--video", "-v", type=str, help="Video for the single testing mode!" ) args = arg_parser.parse_args(args) return args if __name__ == "__main__": if sys.argv[1:] is not None: args = sys.argv[1:] else: args = sys.argv[:-1] args = parse_args(args) # Loading config file cfg = load_config(args.cfg) logger = Logger(task="testing") # Seed seed = cfg.SEED random.seed(seed) torch.manual_seed(seed) np.random.seed(seed) torch.cuda.manual_seed(seed) task = cfg.TEST.subtask flip_test = cfg.TEST.flip_test logger.info("Flip Test is used --- {}".format(flip_test)) save_preds = cfg.TEST.save_preds pred_file = cfg.TEST.pred_file if task == "test_img": assert ( args.image is not None ), "Image can not be None with single image test mode!" logger.info("Turning on single image test mode...") if task == "test_vid": assert ( args.video is not None ), "Video can not be None with single video test mode!" assert os.path.exists( args.video ), "Video path must be valid, please check the path again!" logger.info("Turning on single video test mode...") else: logger.info("Turning on evaluation mode...") if task == "eval" and cfg.DATASET.DATA.TEST.FROM_FILE: assert ( cfg.DATASET.DATA.TEST.ANNO_FILE is not None ), "Annotation file can not be None with evaluation test mode!" assert len( cfg.DATASET.DATA.TEST.ANNO_FILE ), "Annotation file can not be empty with evaluation test mode!" # assert os.access(cfg.DATASET.DATA.TEST.ANNO_FILE, os.R_OK), "Annotation file must be valid with evaluation test mode!" device_count = torch.cuda.device_count() # build and load/initiate pretrained model model = build_model(cfg.MODEL, MODELS).to(torch.float) logger.info("Loading weight ... {}".format(cfg.TEST.pretrained)) model = load_pretrained(model, cfg.TEST.pretrained) if device_count >= 1: model = nn.DataParallel(model, device_ids=cfg.TEST.gpus).cuda() else: model = model.cuda() # Define essential variables image = args.image vid = args.video test_file = cfg.TEST.test_file video_level = cfg.TEST.video_level aspect_ratio = cfg.DATASET.IMAGE_SIZE[1] * 1.0 / cfg.DATASET.IMAGE_SIZE[0] pixel_std = 200 rot = 0 transforms = final_transform(cfg.DATASET) metrics_base = cfg.METRICS_BASE acc_measure = get_acc_mesure_func(metrics_base) no_shot_preds = cfg.TEST.no_shot_preds or 1 model.eval() if image is not None and task == "test_img": img = load_image(image) img = cv2.resize(img, (317, 317)) img = img[18 : (317 - 18), 18 : (317 - 18), :] c, s = get_center_scale(img.shape[:2], aspect_ratio, pixel_std) trans = get_affine_transform(c, s, rot, cfg.DATASET.IMAGE_SIZE) input = cv2.warpAffine( img, trans, (int(cfg.DATASET.IMAGE_SIZE[0]), int(cfg.DATASET.IMAGE_SIZE[1])), flags=cv2.INTER_LINEAR, ) with torch.no_grad(): st = time.time() img_trans = transforms(input / 255).to(torch.float) img_trans = torch.unsqueeze(img_trans, 0) if device_count > 0: img_trans = img_trans.cuda(non_blocking=True) outputs = model(img_trans) hm_outputs = outputs[0]["hm"] cls_outputs = outputs[0]["cls"].sigmoid() hm_preds = _sigmoid(hm_outputs).cpu().numpy() if cfg.TEST.vis_hm: print(f"Heatmap max value --- {hm_preds.max()}") vis_heatmap(img, hm_preds[0], "output_pred.jpg") label_pred = cls_outputs.cpu().numpy() label = "Fake" if label_pred[0][-1] > cfg.TEST.threshold else "Real" logger.info("Inferencing time --- {}".format(time.time() - st)) logger.info("{} --- {}".format(label, label_pred[0][-1])) logger.info("-----------------***--------------------") if vid is not None and task == "test_vid": print(vid) img_list = [] n_frames = cfg.DATASET.DATA.SAMPLES_PER_VIDEO.NUM_FRAMES assert n_frames is not None, "Number of video frames can not be None!" # Load first n_frames inside the video img_paths = glob(f"{args.video}/*.png") img_paths = natsorted(img_paths) # correct the order of image paths img_paths = img_paths[:n_frames] for img_path in img_paths: img = Image.open(img_path) H, W = img.size img = img.crop((15, 15, W - 15, H - 15)) img_list.append(img) # Transform images transformed_imgs = torch.tensor([]).cuda() for _i in img_list: img_resize = _i.resize( (int(cfg.DATASET.IMAGE_SIZE[0]), int(cfg.DATASET.IMAGE_SIZE[1])) ) img_resize = np.array(img_resize) / 255 img_tensor = transforms(img_resize).to(torch.float) if device_count > 0: img_tensor = img_tensor.cuda(non_blocking=True) transformed_imgs = torch.cat((transformed_imgs, img_tensor.unsqueeze(0)), 0) with torch.no_grad(): st = time.time() transformed_imgs = transformed_imgs.transpose(0, 1).unsqueeze(0) outputs = model(transformed_imgs) hm_outputs = outputs[0]["hm"] cls_outputs = outputs[0]["cls"].sigmoid() temp_loc_outputs = outputs[0]["temp_loc"].sigmoid() temp_loc_preds = temp_loc_outputs.cpu().numpy() hm_preds = _sigmoid(hm_outputs).cpu().numpy() if cfg.TEST.vis_hm: print(f"Heatmap max value --- {hm_preds.max()}") print(f"Heatmap min value --- {hm_preds.min()}") vis_heatmap( img_list, hm_preds[0], "output_pred.jpg", temp_loc_preds=temp_loc_preds[0], ) label = "Fake" if temp_loc_preds[0][-1] > cfg.TEST.threshold else "Real" logger.info("Inferencing time --- {}".format(time.time() - st)) logger.info("{} --- {}".format(label, temp_loc_preds[0])) logger.info("-----------------***--------------------") if task == "eval": logger.info(f"Using metric-base {metrics_base} for evaluation!") logger.info(f"Video level evaluation mode: {video_level}") st = time.time() test_dataset = build_dataset( cfg.DATASET, DATASETS, default_args=dict(split="test", config=cfg.DATASET) ) test_dataloader = DataLoader( test_dataset, batch_size=cfg.TRAIN.batch_size * len(cfg.TRAIN.gpus), shuffle=True, num_workers=cfg.DATASET.NUM_WORKERS, ) logger.info("Dataset loading time --- {}".format(time.time() - st)) apr = cfg.TEST.apr test_dataloader = tqdm(test_dataloader, dynamic_ncols=True) with torch.no_grad(): # Make sure all tensors in same device total_preds = torch.tensor([]).cuda().to(dtype=torch.float) total_labels = torch.tensor([]).cuda().to(dtype=torch.float) vid_preds = {} vid_labels = {} # Achieving frame-level predictions to save into file pred_meta = {} for b, (inputs, labels, meta) in enumerate(test_dataloader): i_st = time.time() prev_pos_mask = None prev_hm_preds = None b_vid_ids = [vid for vid in meta["vid_id"]] if "img_path" in meta.keys(): b_data_paths = [ip for ip in meta["img_path"]] elif "vid_path" in meta.keys(): b_data_paths = [ip for ip in meta["vid_path"]] else: if save_preds: raise ValueError("There is no img or vid data for saving!") if device_count > 0: inputs = inputs.to(dtype=torch.float).cuda() labels = labels.to(dtype=torch.float).cuda() for i_shot in range(no_shot_preds): # multi-shot predictions logger.info(f"Running the {i_shot} shot of predictions") if i_shot > 0: new_inputs, pos_mask = masked_inputs( inputs=inputs, hm_preds=prev_hm_preds, prev_pos_mask=prev_pos_mask, cfg=cfg.DATASET, patch_size=16, shot=i_shot, debug=False, vid_ids=b_vid_ids, ) outputs = model(new_inputs) prev_pos_mask = pos_mask else: outputs = model(inputs) # Applying Flip test if flip_test: if inputs.dim() == 4: outputs_1 = model(inputs.flip(dims=(3,))) else: outputs_1 = model(inputs.flip(dims=(4,))) if isinstance(outputs, list): outputs = outputs[0] if flip_test: outputs_1 = outputs_1[0] # In case outputs contain a dict key if isinstance(outputs, dict): if flip_test: hm_outputs = ( (outputs["hm"] + outputs_1["hm"]) / 2 if "hm" in outputs.keys() else None ) cls_outputs = (outputs["cls"] + outputs_1["cls"]) / 2 outputs_temp_loc = ( (outputs["temp_loc"] + outputs_1["temp_loc"]) / 2 if "temp_loc" in outputs.keys() else None ) else: hm_outputs = ( outputs["hm"] if "hm" in outputs.keys() else None ) cls_outputs = outputs["cls"] outputs_temp_loc = ( outputs["temp_loc"] if "temp_loc" in outputs.keys() else None ) prev_hm_preds = hm_outputs logger.info("Inferencing time --- {}".format(time.time() - st)) # Grisping data item for b_i in range(len(b_data_paths)): if b_data_paths[b_i] not in pred_meta.keys(): pred_meta[b_data_paths[b_i]] = list( ( cls_outputs[b_i].clone().detach().item(), labels[b_i].clone().detach().item(), ) ) else: pred_meta[b_data_paths[b_i]].extend( list( ( cls_outputs[b_i].clone().detach().item(), labels[b_i].clone().detach().item(), ) ) ) if i_shot == (no_shot_preds - 1): if not video_level: total_preds = torch.cat((total_preds, cls_outputs), 0) total_labels = torch.cat((total_labels, labels), 0) else: for idx, vid_id in enumerate(b_vid_ids): if vid_id in vid_preds.keys(): vid_preds[vid_id] = torch.cat( ( vid_preds[vid_id], torch.unsqueeze(cls_outputs[idx], 0), ), 0, ) else: vid_preds[vid_id] = ( torch.unsqueeze( cls_outputs[idx].clone().detach(), 0 ) .cuda() .to(dtype=torch.float) ) vid_labels[vid_id] = ( torch.unsqueeze(labels[idx].clone().detach(), 0) .cuda() .to(dtype=torch.float) ) if video_level: for k in vid_preds.keys(): total_preds = torch.cat( (total_preds, torch.mean(vid_preds[k], 0, keepdim=True)), 0 ) total_labels = torch.cat((total_labels, vid_labels[k]), 0) acc_ = acc_measure( total_preds, targets=None, labels=total_labels, threshold=cfg.TEST.threshold, ) metrics = bin_calculate_auc_ap_ar( total_preds, total_labels, metrics_base=metrics_base, threshold=cfg.TEST.threshold, apr=apr, ) best_thr = metrics["best_thr"] if apr: auc_, ap_, ar_, mf1_ = ( metrics["auc"], metrics["ap"], metrics["ar"], metrics["mf1"], ) logger.info( f"Current ACC, AUC, AP, AR, mF1, THR for {cfg.DATASET.DATA.TEST.FAKETYPE} --- {cfg.DATASET.DATA.TEST.LABEL_FOLDER} -- \ {acc_*100} -- {auc_*100} -- {ap_*100} -- {ar_*100} -- {mf1_*100} -- {best_thr}" ) else: bacc_, auc_, p_, r_, s_, f1_, eer_ = ( metrics["bacc"], metrics["auc"], metrics["p"], metrics["r"], metrics["s"], metrics["f1"], metrics["eer"], ) logger.info( f"Current ACC, BACC, AUC, P, R, S, F1, EER, THR for {cfg.DATASET.DATA.TEST.FAKETYPE} --- {cfg.DATASET.DATA.TEST.LABEL_FOLDER} -- \ {acc_*100} -- {bacc_*100} -- {auc_*100} -- {p_*100} -- {r_*100} -- {s_*100} -- {f1_*100} -- {eer_*100} -- {best_thr}" ) if save_preds: logger.info(f"Preditions will be saved into -- {pred_file}") save_file(data=pred_meta, file_path=pred_file)