Linter edits & add transform var

test_patch.py

@@ -82,14 +82,14 @@ def __init__(self, cfg: edict) -> None:
 
  @staticmethod
  def calc_asr(
- boxes,
- boxes_pred,
- class_list: List[str],
- lo_area: float = 20**2,
- hi_area: float = 67**2,
- cls_id: Optional[int] = None,
- class_agnostic: bool = False,
- recompute_asr_all: bool = False) -> Tuple[float, float, float, float]:
+  boxes,
+  boxes_pred,
+  class_list: List[str],
+  lo_area: float = 20**2,
+  hi_area: float = 67**2,
+  cls_id: Optional[int] = None,
+  class_agnostic: bool = False,
+  recompute_asr_all: bool = False) -> Tuple[float, float, float, float]:
  """
  Calculate attack success rate (How many bounding boxes were hidden from the detector)
  for all predictions and for different bbox areas.
@@ -175,9 +175,9 @@ def draw_bbox_on_pil_image(bbox: np.ndarray, padded_img_pil: Image, class_list:
 
  annotator = Annotator(padded_img_np, line_width=1, example=str(label_2_class))
  for *xyxy, conf, cls in bbox:
- c = int(cls) # integer class
- label = f'{label_2_class[c]} {conf:.2f}'
- annotator.box_label(xyxy, label, color=colors(c, True))
+ cls_int = int(cls) # integer class
+ label = f'{label_2_class[cls_int]} {conf:.2f}'
+ annotator.box_label(xyxy, label, color=colors(cls_int, True))
  return Image.fromarray(padded_img_np)
 
  def _create_coco_image_annot(self, file_path: Path, width: int, height: int, image_id: int) -> dict:
@@ -220,7 +220,7 @@ def test(self,
  Returns:
  dict of patch and noise coco_map and asr results
  """
- t0 = time.time()
+ t_0 = time.time()
 
  patch_size = self.cfg.patch_size
  model_in_sz = self.cfg.model_in_sz
@@ -283,6 +283,9 @@ def test(self,
  all_noise_preds = []
  det_boxes = dropped_boxes = 0
 
+ # apply rotation, location shift, brightness, contrast transforms for patch
+ apply_patch_transforms = True
+
  #### iterate through all images ####
  box_id = 0
  transforms_resize = transforms.Resize(model_in_sz)
@@ -380,10 +383,10 @@ def test(self,
  # transform patch and add it to image
  adv_batch_t = self.patch_transformer(
  adv_patch, lab_fake_batch, model_in_sz,
- use_mul_add_gau=False,
- do_transforms=False,
- do_rotate=False, 
- rand_loc=False)
+ use_mul_add_gau=apply_patch_transforms,
+ do_transforms=apply_patch_transforms,
+ do_rotate=apply_patch_transforms,
+ rand_loc=apply_patch_transforms)
  p_tensor_batch = self.patch_applier(img_fake_batch, adv_batch_t)
 
  properpatchedname = img_name + ".jpg"
@@ -441,10 +444,10 @@ def test(self,
  random_patch = torch.rand(adv_patch_cpu.size()).to(self.dev)
  adv_batch_t = self.patch_transformer(
  random_patch, lab_fake_batch, model_in_sz,
- use_mul_add_gau=False,
- do_transforms=False,
- do_rotate=False, 
- rand_loc=False)
+ use_mul_add_gau=apply_patch_transforms,
+ do_transforms=apply_patch_transforms,
+ do_rotate=apply_patch_transforms,
+ rand_loc=apply_patch_transforms)
  p_tensor_batch = self.patch_applier(img_fake_batch, adv_batch_t)
 
  randompatchedname = img_name + ".jpg"
@@ -508,8 +511,10 @@ def test(self,
  noise_confusion_matrix = ConfusionMatrix(len(self.cfg.class_list))
  noise_confusion_matrix.process_batch(all_noise_preds, all_labels)
 
- patch_confusion_matrix.plot(save_dir=self.cfg.savedir, names=self.cfg.class_list, save_name="conf_matrix_patch.png")
- noise_confusion_matrix.plot(save_dir=self.cfg.savedir, names=self.cfg.class_list, save_name="conf_matrix_noise.png")
+ patch_confusion_matrix.plot(save_dir=self.cfg.savedir, names=self.cfg.class_list,
+ save_name="conf_matrix_patch.png")
+ noise_confusion_matrix.plot(save_dir=self.cfg.savedir, names=self.cfg.class_list,
+ save_name="conf_matrix_noise.png")
 
  # add all required fields for a reference GT clean annotation
  clean_gt_results_json = {"annotations": clean_gt_results,
@@ -585,27 +590,28 @@ def test(self,
  ffmpeg_combine_three_vids(clean_vid, random_vid, patch_vid, osp.join(video_dir, "clean_random_patch.mp4"))
 
  if min_pixel_area:
- dperc = 100*dropped_boxes/det_boxes
+ dperc = 100 * dropped_boxes / det_boxes
  drop_str = f" Det Boxes: {det_boxes} | Dropped Boxes: {dropped_boxes} | Dropped: {dperc:.2f}% @gt{min_pixel_area} px\n"
  print(drop_str)
  with open(clean_txt_path, 'a', encoding="utf-8") as fwriter:
  fwriter.write(drop_str)
- tf = time.time()
- print(f" Time to complete evaluation = {tf - t0} seconds")
+ t_f = time.time()
+ print(f" Time to complete evaluation = {t_f - t_0} seconds")
  return {"patch": metrics_patch, "noise": metrics_noise}
 
 
 def main():
- parser = get_argparser(desc="Test patches on a directory with images. Params from argparse take precedence over those from config")
+ parser = get_argparser(
+ desc="Test patches on a directory with images. Params from argparse take precedence over those from config")
  parser.add_argument('--dev', type=str,
  dest="device", default=None, required=False,
- help='Device to use (i.e. cpu, cuda:0, cuda:1). If absent, use "device" from cfg json (default: %(default)s)')
+ help='Device to use (i.e. cpu, cuda:0). If None, use "device" in cfg json (default: %(default)s)')
  parser.add_argument('--ts', '--target_size_frac', type=float, nargs='+',
  dest="target_size_frac", default=[0.3], required=False,
- help='Patch target_size_frac of the bounding box area. Providing two values sets a range. (default: %(default)s)')
+ help='Patch target_size_frac of the bbox area. Providing two values sets a range. (default: %(default)s)')
  parser.add_argument('-w', '--weights', type=str,
  dest="weights", default=None, required=False,
- help='Path to yolov5 model wt file. If absent, use "weights_file" model path from cfg json (default: %(default)s)')
+ help='Path to yolov5 model wt. If None, use "weights_file" path in cfg json (default: %(default)s)')
  parser.add_argument('-p', '--patchfile', type=str,
  dest="patchfile", default=None, required=True,
  help='Path to patch image file for testing (default: %(default)s)')
@@ -632,13 +638,13 @@ def main():
  help='Save the confusion matrix plots, PR, P & R curves')
  parser.add_argument('--class-agnostic',
  dest="class_agnostic", action='store_true',
- help='All classes are teated the same. Use when only evaluating for obj det & not classification')
+ help='All classes are teated the same. Use when only evaluating for obj det & not clsf')
  parser.add_argument('--target-class', type=int,
  dest="target_class", default=None, required=False,
  help='Target specific class with id for misclassification test (default: %(default)s)')
  parser.add_argument('--min-pixel-area', type=int,
  dest="min_pixel_area", default=None, required=False,
- help='all bboxes having area < this are filtered out during testing. if None, use all boxes (default: %(default)s)')
+ help='all bboxes having area < this are filtered in test. if None, use all bboxes (default: %(default)s)')
 
  args = parser.parse_args()
  cfg = load_config_object(args.config)
@@ -652,7 +658,7 @@ def main():
  if not isinstance(args.target_size_frac, float) and len(args.target_size_frac) != 2:
  raise ValueError("target_size_frac can only have one or two values")
  if args.savevideo and not args.saveimg:
- raise ValueError(f"To save videos, images must also be saved pass both --save-img & --save-vid flags")
+ raise ValueError("To save videos, images must also be saved pass both --save-img & --save-vid flags")
  savename = f'{time.strftime("%Y%m%d-%H%M%S")}_' + cfg.patch_name
  if args.class_agnostic and args.target_class is not None:
  print(f"""{BColors.WARNING}WARNING:{BColors.ENDC} target_class and class_agnostic are both set.
@@ -666,8 +672,8 @@ def main():
 
  print(f"{BColors.OKBLUE} Test Arguments: {args} {BColors.ENDC}")
  tester = PatchTester(cfg)
- tester.test(conf_thresh=args.conf_thresh, save_txt=args.savetxt, save_image=args.saveimg, 
- class_agnostic=args.class_agnostic, cls_id=args.target_class, min_pixel_area=args.min_pixel_area, 
+ tester.test(conf_thresh=args.conf_thresh, save_txt=args.savetxt, save_image=args.saveimg,
+ class_agnostic=args.class_agnostic, cls_id=args.target_class, min_pixel_area=args.min_pixel_area,
  save_plots=args.saveplots, save_video=args.savevideo)