File size: 16,753 Bytes
9e14838 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 | # -*- 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)
|