text: from data import COCODetection, get_label_map, MEANS, COLORS #Based on https://github.com/dbolya/yolact/issues/286
  from yolact import Yolact
  from utils.augmentations import BaseTransform, FastBaseTransform, Resize
  from utils.functions import MovingAverage, ProgressBar
  from layers.box_utils import jaccard, center_size, mask_iou
  from utils import timer
  from utils.functions import SavePath
  from layers.output_utils import postprocess, undo_image_transformation
  import pycocotools
  
  from data import cfg, set_cfg, set_dataset
  
  import numpy as np
  import torch
  import torch.backends.cudnn as cudnn
  from torch.autograd import Variable
  import argparse
  import time
  import random
  import cProfile
  import pickle
  import json
  import os
  from collections import defaultdict
  from pathlib import Path
  from collections import OrderedDict
  from PIL import Image
  
  import matplotlib.pyplot as plt
  import cv2
  
  def str2bool(v):
      if v.lower() in ('yes', 'true', 't', 'y', '1'):
          return True
      elif v.lower() in ('no', 'false', 'f', 'n', '0'):
          return False
      else:
          raise argparse.ArgumentTypeError('Boolean value expected.')
  
  def parse_args(argv=None):
      parser = argparse.ArgumentParser(
          description='YOLACT COCO Evaluation')
      parser.add_argument('--trained_model',
                          default='weights/ssd300_mAP_77.43_v2.pth', type=str,
                          help='Trained state_dict file path to open. If "interrupt", this will open the interrupt file.')
      parser.add_argument('--top_k', default=5, type=int,
                          help='Further restrict the number of predictions to parse')
      parser.add_argument('--cuda', default=True, type=str2bool,
                          help='Use cuda to evaulate model')
      parser.add_argument('--fast_nms', default=True, type=str2bool,
                          help='Whether to use a faster, but not entirely correct version of NMS.')
      parser.add_argument('--cross_class_nms', default=False, type=str2bool,
                          help='Whether compute NMS cross-class or per-class.')
      parser.add_argument('--display_masks', default=True, type=str2bool,
                          help='Whether or not to display masks over bounding boxes')
      parser.add_argument('--display_bboxes', default=True, type=str2bool,
                          help='Whether or not to display bboxes around masks')
      parser.add_argument('--display_text', default=True, type=str2bool,
                          help='Whether or not to display text (class [score])')
      parser.add_argument('--display_scores', default=True, type=str2bool,
                          help='Whether or not to display scores in addition to classes')
      parser.add_argument('--display', dest='display', action='store_true',
                          help='Display qualitative results instead of quantitative ones.')
      parser.add_argument('--shuffle', dest='shuffle', action='store_true',
                          help='Shuffles the images when displaying them. Doesn\'t have much of an effect when display is off though.')
      parser.add_argument('--ap_data_file', default='results/ap_data.pkl', type=str,
                          help='In quantitative mode, the file to save detections before calculating mAP.')
      parser.add_argument('--resume', dest='resume', action='store_true',
                          help='If display not set, this resumes mAP calculations from the ap_data_file.')
      parser.add_argument('--max_images', default=-1, type=int,
                          help='The maximum number of images from the dataset to consider. Use -1 for all.')
      parser.add_argument('--output_coco_json', dest='output_coco_json', action='store_true',
                          help='If display is not set, instead of processing IoU values, this just dumps detections into the coco json file.')
      parser.add_argument('--bbox_det_file', default='results/bbox_detections.json', type=str,
                          help='The output file for coco bbox results if --coco_results is set.')
      parser.add_argument('--mask_det_file', default='results/mask_detections.json', type=str,
                          help='The output file for coco mask results if --coco_results is set.')
      parser.add_argument('--config', default=None,
                          help='The config object to use.')
      parser.add_argument('--output_web_json', dest='output_web_json', action='store_true',
                          help='If display is not set, instead of processing IoU values, this dumps detections for usage with the detections viewer web thingy.')
      parser.add_argument('--web_det_path', default='web/dets/', type=str,
                          help='If output_web_json is set, this is the path to dump detections into.')
      parser.add_argument('--no_bar', dest='no_bar', action='store_true',
                          help='Do not output the status bar. This is useful for when piping to a file.')
      parser.add_argument('--display_lincomb', default=False, type=str2bool,
                          help='If the config uses lincomb masks, output a visualization of how those masks are created.')
      parser.add_argument('--benchmark', default=False, dest='benchmark', action='store_true',
                          help='Equivalent to running display mode but without displaying an image.')
      parser.add_argument('--no_sort', default=False, dest='no_sort', action='store_true',
                          help='Do not sort images by hashed image ID.')
      parser.add_argument('--seed', default=None, type=int,
                          help='The seed to pass into random.seed. Note: this is only really for the shuffle and does not (I think) affect cuda stuff.')
      parser.add_argument('--mask_proto_debug', default=False, dest='mask_proto_debug', action='store_true',
                          help='Outputs stuff for scripts/compute_mask.py.')
      parser.add_argument('--no_crop', default=False, dest='crop', action='store_false',
                          help='Do not crop output masks with the predicted bounding box.')
      parser.add_argument('--image', default=None, type=str,
                          help='A path to an image to use for display.')
      parser.add_argument('--images', default=None, type=str,
                          help='An input folder of images and output folder to save detected images. Should be in the format input->output.')
      parser.add_argument('--video', default=None, type=str,
                          help='A path to a video to evaluate on. Passing in a number will use that index webcam.')
      parser.add_argument('--video_multiframe', default=1, type=int,
                          help='The number of frames to evaluate in parallel to make videos play at higher fps.')
      parser.add_argument('--score_threshold', default=0, type=float,
                          help='Detections with a score under this threshold will not be considered. This currently only works in display mode.')
      parser.add_argument('--dataset', default=None, type=str,
                          help='If specified, override the dataset specified in the config with this one (example: coco2017_dataset).')
      parser.add_argument('--detect', default=False, dest='detect', action='store_true',
                          help='Don\'t evauluate the mask branch at all and only do object detection. This only works for --display and --benchmark.')
      parser.add_argument('--display_fps', default=False, dest='display_fps', action='store_true',
                          help='When displaying / saving video, draw the FPS on the frame')
      parser.add_argument('--emulate_playback', default=False, dest='emulate_playback', action='store_true',
                          help='When saving a video, emulate the framerate that you\'d get running in real-time mode.')
  
      parser.set_defaults(no_bar=False, display=False, resume=False, output_coco_json=False, output_web_json=False, shuffle=False,
                          benchmark=False, no_sort=False, no_hash=False, mask_proto_debug=False, crop=True, detect=False, display_fps=False,
                          emulate_playback=False)
  
      global args
      args = parser.parse_args(argv)
  
      if args.output_web_json:
          args.output_coco_json = True
      
      if args.seed is not None:
          random.seed(args.seed)
  
  iou_thresholds = [x / 100 for x in range(50, 100, 5)]
  coco_cats = {} # Call prep_coco_cats to fill this
  coco_cats_inv = {}
  color_cache = defaultdict(lambda: {})
  
  def prep_display(dets_out, img, h, w, undo_transform=True, class_color=False, mask_alpha=0.45, fps_str=''):
      """
      Note: If undo_transform=False then im_h and im_w are allowed to be None.
      """
      if undo_transform:
          img_numpy = undo_image_transformation(img, w, h)
          img_gpu = torch.Tensor(img_numpy).cuda()
      else:
          img_gpu = img / 255.0
          h, w, _ = img.shape
      
      with timer.env('Postprocess'):
          save = cfg.rescore_bbox
          cfg.rescore_bbox = True
          t = postprocess(dets_out, w, h, visualize_lincomb = args.display_lincomb,
                                          crop_masks        = args.crop,
                                          score_threshold   = args.score_threshold)
          cfg.rescore_bbox = save
  
      with timer.env('Copy'):
          idx = t[1].argsort(0, descending=True)[:args.top_k]
          
          if cfg.eval_mask_branch:
              # Masks are drawn on the GPU, so don't copy
              masks = t[3][idx]
          classes, scores, boxes = [x[idx].cpu().numpy() for x in t[:3]]
  
      num_dets_to_consider = min(args.top_k, classes.shape[0])
      for j in range(num_dets_to_consider):
          if scores[j] < args.score_threshold:
              num_dets_to_consider = j
              break
  
      # Quick and dirty lambda for selecting the color for a particular index
      # Also keeps track of a per-gpu color cache for maximum speed
      def get_color(j, on_gpu=None):
          global color_cache
          color_idx = (classes[j] * 5 if class_color else j * 5) % len(COLORS)
          
          if on_gpu is not None and color_idx in color_cache[on_gpu]:
              return color_cache[on_gpu][color_idx]
          else:
              color = COLORS[color_idx]
              if not undo_transform:
                  # The image might come in as RGB or BRG, depending
                  color = (color[2], color[1], color[0])
              if on_gpu is not None:
                  color = torch.Tensor(color).to(on_gpu).float() / 255.
                  color_cache[on_gpu][color_idx] = color
              return color
  
      # First, draw the masks on the GPU where we can do it really fast
      # Beware: very fast but possibly unintelligible mask-drawing code ahead
      # I wish I had access to OpenGL or Vulkan but alas, I guess Pytorch tensor operations will have to suffice
      if args.display_masks and cfg.eval_mask_branch and num_dets_to_consider > 0:
          # After this, mask is of size [num_dets, h, w, 1]
          masks = masks[:num_dets_to_consider, :, :, None]
          img_gpu *= (masks.sum(dim=0) >= 1).float().expand(-1, -1, 3)
      else:
          img_gpu *= 0
      
      if args.display_fps:
              # Draw the box for the fps on the GPU
          font_face = cv2.FONT_HERSHEY_DUPLEX
          font_scale = 0.6
          font_thickness = 1
  
          text_w, text_h = cv2.getTextSize(fps_str, font_face, font_scale, font_thickness)[0]
  
          img_gpu[0:text_h+8, 0:text_w+8] *= 0.6 # 1 - Box alpha
  
  
      # Then draw the stuff that needs to be done on the cpu
      # Note, make sure this is a uint8 tensor or opencv will not anti alias text for whatever reason
      img_numpy = (img_gpu * 255).byte().cpu().numpy()
  
      if args.display_fps:
          # Draw the text on the CPU
          text_pt = (4, text_h + 2)
          text_color = [255, 255, 255]
  
          cv2.putText(img_numpy, fps_str, text_pt, font_face, font_scale, text_color, font_thickness, cv2.LINE_AA)
      
      if num_dets_to_consider == 0:
          return img_numpy
  
      if args.display_text or args.display_bboxes:
          for j in reversed(range(num_dets_to_consider)):
              x1, y1, x2, y2 = boxes[j, :]
              color = get_color(j)
              score = scores[j]
  
              if args.display_bboxes:
                  cv2.rectangle(img_numpy, (x1, y1), (x2, y2), color, 1)
  
              if args.display_text:
                  _class = cfg.dataset.class_names[classes[j]]
                  text_str = '%s: %.2f' % (_class, score) if args.display_scores else _class
  
                  font_face = cv2.FONT_HERSHEY_DUPLEX
                  font_scale = 0.6
                  font_thickness = 1
  
                  text_w, text_h = cv2.getTextSize(text_str, font_face, font_scale, font_thickness)[0]
  
                  text_pt = (x1, y1 - 3)
                  text_color = [255, 255, 255]
  
                  cv2.rectangle(img_numpy, (x1, y1), (x1 + text_w, y1 - text_h - 4), color, -1)
                  cv2.putText(img_numpy, text_str, text_pt, font_face, font_scale, text_color, font_thickness, cv2.LINE_AA)
              
      
      return img_numpy
  
  def prep_benchmark(dets_out, h, w):
      with timer.env('Postprocess'):
          t = postprocess(dets_out, w, h, crop_masks=args.crop, score_threshold=args.score_threshold)
  
      with timer.env('Copy'):
          classes, scores, boxes, masks = [x[:args.top_k] for x in t]
          if isinstance(scores, list):
              box_scores = scores[0].cpu().numpy()
              mask_scores = scores[1].cpu().numpy()
          else:
              scores = scores.cpu().numpy()
          classes = classes.cpu().numpy()
          boxes = boxes.cpu().numpy()
          masks = masks.cpu().numpy()
      
      with timer.env('Sync'):
          # Just in case
          torch.cuda.synchronize()
  
  def prep_coco_cats():
      """ Prepare inverted table for category id lookup given a coco cats object. """
      for coco_cat_id, transformed_cat_id_p1 in get_label_map().items():
          transformed_cat_id = transformed_cat_id_p1 - 1
          coco_cats[transformed_cat_id] = coco_cat_id
          coco_cats_inv[coco_cat_id] = transformed_cat_id
  
  
  def get_coco_cat(transformed_cat_id):
      """ transformed_cat_id is [0,80) as indices in cfg.dataset.class_names """
      return coco_cats[transformed_cat_id]
  
  def get_transformed_cat(coco_cat_id):
      """ transformed_cat_id is [0,80) as indices in cfg.dataset.class_names """
      return coco_cats_inv[coco_cat_id]
  
  
  class Detections:
  
      def __init__(self):
          self.bbox_data = []
          self.mask_data = []
  
      def add_bbox(self, image_id:int, category_id:int, bbox:list, score:float):
          """ Note that bbox should be a list or tuple of (x1, y1, x2, y2) """
          bbox = [bbox[0], bbox[1], bbox[2]-bbox[0], bbox[3]-bbox[1]]
  
          # Round to the nearest 10th to avoid huge file sizes, as COCO suggests
          bbox = [round(float(x)*10)/10 for x in bbox]
  
          self.bbox_data.append({
              'image_id': int(image_id),
              'category_id': get_coco_cat(int(category_id)),
              'bbox': bbox,
              'score': float(score)
          })
  
      def add_mask(self, image_id:int, category_id:int, segmentation:np.ndarray, score:float):
          """ The segmentation should be the full mask, the size of the image and with size [h, w]. """
          rle = pycocotools.mask.encode(np.asfortranarray(segmentation.astype(np.uint8)))
          rle['counts'] = rle['counts'].decode('ascii') # json.dump doesn't like bytes strings
  
          self.mask_data.append({
              'image_id': int(image_id),
              'category_id': get_coco_cat(int(category_id)),
              'segmentation': rle,
              'score': float(score)
          })
      
      def dump(self):
          dump_arguments = [
              (self.bbox_data, args.bbox_det_file),
              (self.mask_data, args.mask_det_file)
          ]
  
          for data, path in dump_arguments:
              with open(path, 'w') as f:
                  json.dump(data, f)
      
      def dump_web(self):
          """ Dumps it in the format for my web app. Warning: bad code ahead! """
          config_outs = ['preserve_aspect_ratio', 'use_prediction_module',
                          'use_yolo_regressors', 'use_prediction_matching',
                          'train_masks']
  
          output = {
              'info' : {
                  'Config': {key: getattr(cfg, key) for key in config_outs},
              }
          }
  
          image_ids = list(set([x['image_id'] for x in self.bbox_data]))
          image_ids.sort()
          image_lookup = {_id: idx for idx, _id in enumerate(image_ids)}
  
          output['images'] = [{'image_id': image_id, 'dets': []} for image_id in image_ids]
  
          # These should already be sorted by score with the way prep_metrics works.
          for bbox, mask in zip(self.bbox_data, self.mask_data):
              image_obj = output['images'][image_lookup[bbox['image_id']]]
              image_obj['dets'].append({
                  'score': bbox['score'],
                  'bbox': bbox['bbox'],
                  'category': cfg.dataset.class_names[get_transformed_cat(bbox['category_id'])],
                  'mask': mask['segmentation'],
              })
  
          with open(os.path.join(args.web_det_path, '%s.json' % cfg.name), 'w') as f:
              json.dump(output, f)
          
  
          
  
  def _mask_iou(mask1, mask2, iscrowd=False):
      with timer.env('Mask IoU'):
          ret = mask_iou(mask1, mask2, iscrowd)
      return ret.cpu()
  
  def _bbox_iou(bbox1, bbox2, iscrowd=False):
      with timer.env('BBox IoU'):
          ret = jaccard(bbox1, bbox2, iscrowd)
      return ret.cpu()
  
  def prep_metrics(ap_data, dets, img, gt, gt_masks, h, w, num_crowd, image_id, detections:Detections=None):
      """ Returns a list of APs for this image, with each element being for a class  """
      if not args.output_coco_json:
          with timer.env('Prepare gt'):
              gt_boxes = torch.Tensor(gt[:, :4])
              gt_boxes[:, [0, 2]] *= w
              gt_boxes[:, [1, 3]] *= h
              gt_classes = list(gt[:, 4].astype(int))
              gt_masks = torch.Tensor(gt_masks).view(-1, h*w)
  
              if num_crowd > 0:
                  split = lambda x: (x[-num_crowd:], x[:-num_crowd])
                  crowd_boxes  , gt_boxes   = split(gt_boxes)
                  crowd_masks  , gt_masks   = split(gt_masks)
                  crowd_classes, gt_classes = split(gt_classes)
  
      with timer.env('Postprocess'):
          classes, scores, boxes, masks = postprocess(dets, w, h, crop_masks=args.crop, score_threshold=args.score_threshold)
  
          if classes.size(0) == 0:
              return
  
          classes = list(classes.cpu().numpy().astype(int))
          if isinstance(scores, list):
              box_scores = list(scores[0].cpu().numpy().astype(float))
              mask_scores = list(scores[1].cpu().numpy().astype(float))
          else:
              scores = list(scores.cpu().numpy().astype(float))
              box_scores = scores
              mask_scores = scores
          masks = masks.view(-1, h*w).cuda()
          boxes = boxes.cuda()
  
  
      if args.output_coco_json:
          with timer.env('JSON Output'):
              boxes = boxes.cpu().numpy()
              masks = masks.view(-1, h, w).cpu().numpy()
              for i in range(masks.shape[0]):
                  # Make sure that the bounding box actually makes sense and a mask was produced
                  if (boxes[i, 3] - boxes[i, 1]) * (boxes[i, 2] - boxes[i, 0]) > 0:
                      detections.add_bbox(image_id, classes[i], boxes[i,:],   box_scores[i])
                      detections.add_mask(image_id, classes[i], masks[i,:,:], mask_scores[i])
              return
      
      with timer.env('Eval Setup'):
          num_pred = len(classes)
          num_gt   = len(gt_classes)
  
          mask_iou_cache = _mask_iou(masks, gt_masks)
          bbox_iou_cache = _bbox_iou(boxes.float(), gt_boxes.float())
  
          if num_crowd > 0:
              crowd_mask_iou_cache = _mask_iou(masks, crowd_masks, iscrowd=True)
              crowd_bbox_iou_cache = _bbox_iou(boxes.float(), crowd_boxes.float(), iscrowd=True)
          else:
              crowd_mask_iou_cache = None
              crowd_bbox_iou_cache = None
  
          box_indices = sorted(range(num_pred), key=lambda i: -box_scores[i])
          mask_indices = sorted(box_indices, key=lambda i: -mask_scores[i])
  
          iou_types = [
              ('box',  lambda i,j: bbox_iou_cache[i, j].item(),
                       lambda i,j: crowd_bbox_iou_cache[i,j].item(),
                       lambda i: box_scores[i], box_indices),
              ('mask', lambda i,j: mask_iou_cache[i, j].item(),
                       lambda i,j: crowd_mask_iou_cache[i,j].item(),
                       lambda i: mask_scores[i], mask_indices)
          ]
  
      timer.start('Main loop')
      for _class in set(classes + gt_classes):
          ap_per_iou = []
          num_gt_for_class = sum([1 for x in gt_classes if x == _class])
          
          for iouIdx in range(len(iou_thresholds)):
              iou_threshold = iou_thresholds[iouIdx]
  
              for iou_type, iou_func, crowd_func, score_func, indices in iou_types:
                  gt_used = [False] * len(gt_classes)
                  
                  ap_obj = ap_data[iou_type][iouIdx][_class]
                  ap_obj.add_gt_positives(num_gt_for_class)
  
                  for i in indices:
                      if classes[i] != _class:
                          continue
                      
                      max_iou_found = iou_threshold
                      max_match_idx = -1
                      for j in range(num_gt):
                          if gt_used[j] or gt_classes[j] != _class:
                              continue
                              
                          iou = iou_func(i, j)
  
                          if iou > max_iou_found:
                              max_iou_found = iou
                              max_match_idx = j
                      
                      if max_match_idx >= 0:
                          gt_used[max_match_idx] = True
                          ap_obj.push(score_func(i), True)
                      else:
                          # If the detection matches a crowd, we can just ignore it
                          matched_crowd = False
  
                          if num_crowd > 0:
                              for j in range(len(crowd_classes)):
                                  if crowd_classes[j] != _class:
                                      continue
                                  
                                  iou = crowd_func(i, j)
  
                                  if iou > iou_threshold:
                                      matched_crowd = True
                                      break
  
                          # All this crowd code so that we can make sure that our eval code gives the
                          # same result as COCOEval. There aren't even that many crowd annotations to
                          # begin with, but accuracy is of the utmost importance.
                          if not matched_crowd:
                              ap_obj.push(score_func(i), False)
      timer.stop('Main loop')
  
  
  class APDataObject:
      """
      Stores all the information necessary to calculate the AP for one IoU and one class.
      Note: I type annotated this because why not.
      """
  
      def __init__(self):
          self.data_points = []
          self.num_gt_positives = 0
  
      def push(self, score:float, is_true:bool):
          self.data_points.append((score, is_true))
      
      def add_gt_positives(self, num_positives:int):
          """ Call this once per image. """
          self.num_gt_positives += num_positives
  
      def is_empty(self) -> bool:
          return len(self.data_points) == 0 and self.num_gt_positives == 0
  
      def get_ap(self) -> float:
          """ Warning: result not cached. """
  
          if self.num_gt_positives == 0:
              return 0
  
          # Sort descending by score
          self.data_points.sort(key=lambda x: -x[0])
  
          precisions = []
          recalls    = []
          num_true  = 0
          num_false = 0
  
          # Compute the precision-recall curve. The x axis is recalls and the y axis precisions.
          for datum in self.data_points:
              # datum[1] is whether the detection a true or false positive
              if datum[1]: num_true += 1
              else: num_false += 1
              
              precision = num_true / (num_true + num_false)
              recall    = num_true / self.num_gt_positives
  
              precisions.append(precision)
              recalls.append(recall)
  
          # Smooth the curve by computing [max(precisions[i:]) for i in range(len(precisions))]
          # Basically, remove any temporary dips from the curve.
          # At least that's what I think, idk. COCOEval did it so I do too.
          for i in range(len(precisions)-1, 0, -1):
              if precisions[i] > precisions[i-1]:
                  precisions[i-1] = precisions[i]
  
          # Compute the integral of precision(recall) d_recall from recall=0->1 using fixed-length riemann summation with 101 bars.
          y_range = [0] * 101 # idx 0 is recall == 0.0 and idx 100 is recall == 1.00
          x_range = np.array([x / 100 for x in range(101)])
          recalls = np.array(recalls)
  
          # I realize this is weird, but all it does is find the nearest precision(x) for a given x in x_range.
          # Basically, if the closest recall we have to 0.01 is 0.009 this sets precision(0.01) = precision(0.009).
          # I approximate the integral this way, because that's how COCOEval does it.
          indices = np.searchsorted(recalls, x_range, side='left')
          for bar_idx, precision_idx in enumerate(indices):
              if precision_idx < len(precisions):
                  y_range[bar_idx] = precisions[precision_idx]
  
          # Finally compute the riemann sum to get our integral.
          # avg([precision(x) for x in 0:0.01:1])
          return sum(y_range) / len(y_range)
  
  def badhash(x):
      """
      Just a quick and dirty hash function for doing a deterministic shuffle based on image_id.
  
      Source:
      https://stackoverflow.com/questions/664014/what-integer-hash-function-are-good-that-accepts-an-integer-hash-key
      """
      x = (((x >> 16) ^ x) * 0x045d9f3b) & 0xFFFFFFFF
      x = (((x >> 16) ^ x) * 0x045d9f3b) & 0xFFFFFFFF
      x =  ((x >> 16) ^ x) & 0xFFFFFFFF
      return x
  
  def evalimage(net:Yolact, path:str, save_path:str=None):
      frame = torch.from_numpy(cv2.imread(path)).cuda().float()
      batch = FastBaseTransform()(frame.unsqueeze(0))
      preds = net(batch)
  
      img_numpy = prep_display(preds, frame, None, None, undo_transform=False)
      
      if save_path is None:
          img_numpy = img_numpy[:, :, (2, 1, 0)]
  
      if save_path is None:
          plt.imshow(img_numpy)
          plt.title(path)
          plt.show()
      else:
          cv2.imwrite(save_path, img_numpy)
  
  def evalimages(net:Yolact, input_folder:str, output_folder:str):
      if not os.path.exists(output_folder):
          os.mkdir(output_folder)
  
      print()
      for p in Path(input_folder).glob('*'): 
          path = str(p)
          name = os.path.basename(path)
          name = '.'.join(name.split('.')[:-1]) + '.png'
          out_path = os.path.join(output_folder, name)
  
          evalimage(net, path, out_path)
          print(path + ' -> ' + out_path)
      print('Done.')
  
  from multiprocessing.pool import ThreadPool
  from queue import Queue
  
  class CustomDataParallel(torch.nn.DataParallel):
      """ A Custom Data Parallel class that properly gathers lists of dictionaries. """
      def gather(self, outputs, output_device):
          # Note that I don't actually want to convert everything to the output_device
          return sum(outputs, [])
  
  def evalvideo(net:Yolact, path:str, out_path:str=None):
      # If the path is a digit, parse it as a webcam index
      is_webcam = path.isdigit()
      
      # If the input image size is constant, this make things faster (hence why we can use it in a video setting).
      cudnn.benchmark = True
      
      if is_webcam:
          vid = cv2.VideoCapture(int(path))
      else:
          vid = cv2.VideoCapture(path)
      
      if not vid.isOpened():
          print('Could not open video "%s"' % path)
          exit(-1)
  
      target_fps   = round(vid.get(cv2.CAP_PROP_FPS))
      frame_width  = round(vid.get(cv2.CAP_PROP_FRAME_WIDTH))
      frame_height = round(vid.get(cv2.CAP_PROP_FRAME_HEIGHT))
      
      if is_webcam:
          num_frames = float('inf')
      else:
          num_frames = round(vid.get(cv2.CAP_PROP_FRAME_COUNT))
  
      net = CustomDataParallel(net).cuda()
      transform = torch.nn.DataParallel(FastBaseTransform()).cuda()
      frame_times = MovingAverage(100)
      fps = 0
      frame_time_target = 1 / target_fps
      running = True
      fps_str = ''
      vid_done = False
      frames_displayed = 0
  
      if out_path is not None:
          out = cv2.VideoWriter(out_path, cv2.VideoWriter_fourcc(*"mp4v"), target_fps, (frame_width, frame_height))
  
      def cleanup_and_exit():
          print()
          pool.terminate()
          vid.release()
          if out_path is not None:
              out.release()
          cv2.destroyAllWindows()
          exit()
  
      def get_next_frame(vid):
          frames = []
          for idx in range(args.video_multiframe):
              frame = vid.read()[1]
              if frame is None:
                  return frames
              frames.append(frame)
          return frames
  
      def transform_frame(frames):
          with torch.no_grad():
              frames = [torch.from_numpy(frame).cuda().float() for frame in frames]
              return frames, transform(torch.stack(frames, 0))
  
      def eval_network(inp):
          with torch.no_grad():
              frames, imgs = inp
              num_extra = 0
              while imgs.size(0) < args.video_multiframe:
                  imgs = torch.cat([imgs, imgs[0].unsqueeze(0)], dim=0)
                  num_extra += 1
              out = net(imgs)
              if num_extra > 0:
                  out = out[:-num_extra]
              return frames, out
  
      def prep_frame(inp, fps_str):
          with torch.no_grad():
              frame, preds = inp
              return prep_display(preds, frame, None, None, undo_transform=False, class_color=True, fps_str=fps_str)
  
      frame_buffer = Queue()
      video_fps = 0
  
      # All this timing code to make sure that 
      def play_video():
          try:
              nonlocal frame_buffer, running, video_fps, is_webcam, num_frames, frames_displayed, vid_done
  
              video_frame_times = MovingAverage(100)
              frame_time_stabilizer = frame_time_target
              last_time = None
              stabilizer_step = 0.0005
              progress_bar = ProgressBar(30, num_frames)
  
              while running:
                  frame_time_start = time.time()
  
                  if not frame_buffer.empty():
                      next_time = time.time()
                      if last_time is not None:
                          video_frame_times.add(next_time - last_time)
                          video_fps = 1 / video_frame_times.get_avg()
                      if out_path is None:
                          cv2.imshow(path, frame_buffer.get())
                      else:
                          out.write(frame_buffer.get())
                      frames_displayed += 1
                      last_time = next_time
  
                      if out_path is not None:
                          if video_frame_times.get_avg() == 0:
                              fps = 0
                          else:
                              fps = 1 / video_frame_times.get_avg()
                          progress = frames_displayed / num_frames * 100
                          progress_bar.set_val(frames_displayed)
  
                          print('\rProcessing Frames  %s %6d / %6d (%5.2f%%)    %5.2f fps        '
                              % (repr(progress_bar), frames_displayed, num_frames, progress, fps), end='')
  
                  
                  # This is split because you don't want savevideo to require cv2 display functionality (see #197)
                  if out_path is None and cv2.waitKey(1) == 27:
                      # Press Escape to close
                      running = False
                  if not (frames_displayed < num_frames):
                      running = False
  
                  if not vid_done:
                      buffer_size = frame_buffer.qsize()
                      if buffer_size < args.video_multiframe:
                          frame_time_stabilizer += stabilizer_step
                      elif buffer_size > args.video_multiframe:
                          frame_time_stabilizer -= stabilizer_step
                          if frame_time_stabilizer < 0:
                              frame_time_stabilizer = 0
  
                      new_target = frame_time_stabilizer if is_webcam else max(frame_time_stabilizer, frame_time_target)
                  else:
                      new_target = frame_time_target
  
                  next_frame_target = max(2 * new_target - video_frame_times.get_avg(), 0)
                  target_time = frame_time_start + next_frame_target - 0.001 # Let's just subtract a millisecond to be safe
                  
                  if out_path is None or args.emulate_playback:
                      # This gives more accurate timing than if sleeping the whole amount at once
                      while time.time() < target_time:
                          time.sleep(0.001)
                  else:
                      # Let's not starve the main thread, now
                      time.sleep(0.001)
          except:
              # See issue #197 for why this is necessary
              import traceback
              traceback.print_exc()
  
  
      extract_frame = lambda x, i: (x[0][i] if x[1][i]['detection'] is None else x[0][i].to(x[1][i]['detection']['box'].device), [x[1][i]])
  
      # Prime the network on the first frame because I do some thread unsafe things otherwise
      print('Initializing model... ', end='')
      first_batch = eval_network(transform_frame(get_next_frame(vid)))
      print('Done.')
  
      # For each frame the sequence of functions it needs to go through to be processed (in reversed order)
      sequence = [prep_frame, eval_network, transform_frame]
      pool = ThreadPool(processes=len(sequence) + args.video_multiframe + 2)
      pool.apply_async(play_video)
      active_frames = [{'value': extract_frame(first_batch, i), 'idx': 0} for i in range(len(first_batch[0]))]
  
      print()
      if out_path is None: print('Press Escape to close.')
      try:
          while vid.isOpened() and running:
              # Hard limit on frames in buffer so we don't run out of memory >.>
              while frame_buffer.qsize() > 100:
                  time.sleep(0.001)
  
              start_time = time.time()
  
              # Start loading the next frames from the disk
              if not vid_done:
                  next_frames = pool.apply_async(get_next_frame, args=(vid,))
              else:
                  next_frames = None
              
              if not (vid_done and len(active_frames) == 0):
                  # For each frame in our active processing queue, dispatch a job
                  # for that frame using the current function in the sequence
                  for frame in active_frames:
                      _args =  [frame['value']]
                      if frame['idx'] == 0:
                          _args.append(fps_str)
                      frame['value'] = pool.apply_async(sequence[frame['idx']], args=_args)
                  
                  # For each frame whose job was the last in the sequence (i.e. for all final outputs)
                  for frame in active_frames:
                      if frame['idx'] == 0:
                          frame_buffer.put(frame['value'].get())
  
                  # Remove the finished frames from the processing queue
                  active_frames = [x for x in active_frames if x['idx'] > 0]
  
                  # Finish evaluating every frame in the processing queue and advanced their position in the sequence
                  for frame in list(reversed(active_frames)):
                      frame['value'] = frame['value'].get()
                      frame['idx'] -= 1
  
                      if frame['idx'] == 0:
                          # Split this up into individual threads for prep_frame since it doesn't support batch size
                          active_frames += [{'value': extract_frame(frame['value'], i), 'idx': 0} for i in range(1, len(frame['value'][0]))]
                          frame['value'] = extract_frame(frame['value'], 0)
                  
                  # Finish loading in the next frames and add them to the processing queue
                  if next_frames is not None:
                      frames = next_frames.get()
                      if len(frames) == 0:
                          vid_done = True
                      else:
                          active_frames.append({'value': frames, 'idx': len(sequence)-1})
  
                  # Compute FPS
                  frame_times.add(time.time() - start_time)
                  fps = args.video_multiframe / frame_times.get_avg()
              else:
                  fps = 0
              
              fps_str = 'Processing FPS: %.2f | Video Playback FPS: %.2f | Frames in Buffer: %d' % (fps, video_fps, frame_buffer.qsize())
              if not args.display_fps:
                  print('\r' + fps_str + '    ', end='')
  
      except KeyboardInterrupt:
          print('\nStopping...')
      
      cleanup_and_exit()
  
  def evaluate(net:Yolact, dataset, train_mode=False):
      net.detect.use_fast_nms = args.fast_nms
      net.detect.use_cross_class_nms = args.cross_class_nms
      cfg.mask_proto_debug = args.mask_proto_debug
  
      # TODO Currently we do not support Fast Mask Re-scroing in evalimage, evalimages, and evalvideo
      if args.image is not None:
          if ':' in args.image:
              inp, out = args.image.split(':')
              evalimage(net, inp, out)
          else:
              evalimage(net, args.image)
          return
      elif args.images is not None:
          inp, out = args.images.split(':')
          evalimages(net, inp, out)
          return
      elif args.video is not None:
          if ':' in args.video:
              inp, out = args.video.split(':')
              evalvideo(net, inp, out)
          else:
              evalvideo(net, args.video)
          return
  
      frame_times = MovingAverage()
      dataset_size = len(dataset) if args.max_images < 0 else min(args.max_images, len(dataset))
      progress_bar = ProgressBar(30, dataset_size)
  
      print()
  
      if not args.display and not args.benchmark:
          # For each class and iou, stores tuples (score, isPositive)
          # Index ap_data[type][iouIdx][classIdx]
          ap_data = {
              'box' : [[APDataObject() for _ in cfg.dataset.class_names] for _ in iou_thresholds],
              'mask': [[APDataObject() for _ in cfg.dataset.class_names] for _ in iou_thresholds]
          }
          detections = Detections()
      else:
          timer.disable('Load Data')
  
      dataset_indices = list(range(len(dataset)))
      
      if args.shuffle:
          random.shuffle(dataset_indices)
      elif not args.no_sort:
          # Do a deterministic shuffle based on the image ids
          #
          # I do this because on python 3.5 dictionary key order is *random*, while in 3.6 it's
          # the order of insertion. That means on python 3.6, the images come in the order they are in
          # in the annotations file. For some reason, the first images in the annotations file are
          # the hardest. To combat this, I use a hard-coded hash function based on the image ids
          # to shuffle the indices we use. That way, no matter what python version or how pycocotools
          # handles the data, we get the same result every time.
          hashed = [badhash(x) for x in dataset.ids]
          dataset_indices.sort(key=lambda x: hashed[x])
  
      dataset_indices = dataset_indices[:dataset_size]
  
      try:
          # Main eval loop
          for it, image_idx in enumerate(dataset_indices):
              timer.reset()
  
              with timer.env('Load Data'):
                  img, gt, gt_masks, h, w, num_crowd = dataset.pull_item(image_idx)
  
                  # Test flag, do not upvote
                  if cfg.mask_proto_debug:
                      with open('scripts/info.txt', 'w') as f:
                          f.write(str(dataset.ids[image_idx]))
                      np.save('scripts/gt.npy', gt_masks)
  
                  batch = Variable(img.unsqueeze(0))
                  if args.cuda:
                      batch = batch.cuda()
  
              with timer.env('Network Extra'):
                  preds = net(batch)
              # Perform the meat of the operation here depending on our mode.
              if args.display:
                  img_numpy = prep_display(preds, img, h, w)
              elif args.benchmark:
                  prep_benchmark(preds, h, w)
              else:
                  prep_metrics(ap_data, preds, img, gt, gt_masks, h, w, num_crowd, dataset.ids[image_idx], detections)
              
              # First couple of images take longer because we're constructing the graph.
              # Since that's technically initialization, don't include those in the FPS calculations.
              if it > 1:
                  frame_times.add(timer.total_time())
              
              if args.display:
                  if it > 1:
                      print('Avg FPS: %.4f' % (1 / frame_times.get_avg()))
                  plt.imshow(img_numpy)
                  plt.title(str(dataset.ids[image_idx]))
                  plt.show()
              elif not args.no_bar:
                  if it > 1: fps = 1 / frame_times.get_avg()
                  else: fps = 0
                  progress = (it+1) / dataset_size * 100
                  progress_bar.set_val(it+1)
                  print('\rProcessing Images  %s %6d / %6d (%5.2f%%)    %5.2f fps        '
                      % (repr(progress_bar), it+1, dataset_size, progress, fps), end='')
  
  
  
          if not args.display and not args.benchmark:
              print()
              if args.output_coco_json:
                  print('Dumping detections...')
                  if args.output_web_json:
                      detections.dump_web()
                  else:
                      detections.dump()
              else:
                  if not train_mode:
                      print('Saving data...')
                      with open(args.ap_data_file, 'wb') as f:
                          pickle.dump(ap_data, f)
  
                  return calc_map(ap_data)
          elif args.benchmark:
              print()
              print()
              print('Stats for the last frame:')
              timer.print_stats()
              avg_seconds = frame_times.get_avg()
              print('Average: %5.2f fps, %5.2f ms' % (1 / frame_times.get_avg(), 1000*avg_seconds))
  
      except KeyboardInterrupt:
          print('Stopping...')
  
  
  def calc_map(ap_data):
      print('Calculating mAP...')
      aps = [{'box': [], 'mask': []} for _ in iou_thresholds]
  
      for _class in range(len(cfg.dataset.class_names)):
          for iou_idx in range(len(iou_thresholds)):
              for iou_type in ('box', 'mask'):
                  ap_obj = ap_data[iou_type][iou_idx][_class]
  
                  if not ap_obj.is_empty():
                      aps[iou_idx][iou_type].append(ap_obj.get_ap())
  
      all_maps = {'box': OrderedDict(), 'mask': OrderedDict()}
  
      # Looking back at it, this code is really hard to read :/
      for iou_type in ('box', 'mask'):
          all_maps[iou_type]['all'] = 0 # Make this first in the ordereddict
          for i, threshold in enumerate(iou_thresholds):
              mAP = sum(aps[i][iou_type]) / len(aps[i][iou_type]) * 100 if len(aps[i][iou_type]) > 0 else 0
              all_maps[iou_type][int(threshold*100)] = mAP
          all_maps[iou_type]['all'] = (sum(all_maps[iou_type].values()) / (len(all_maps[iou_type].values())-1))
      
      print_maps(all_maps)
      
      # Put in a prettier format so we can serialize it to json during training
      all_maps = {k: {j: round(u, 2) for j, u in v.items()} for k, v in all_maps.items()}
      return all_maps
  
  def print_maps(all_maps):
      # Warning: hacky 
      make_row = lambda vals: (' %5s |' * len(vals)) % tuple(vals)
      make_sep = lambda n:  ('-------+' * n)
  
      print()
      print(make_row([''] + [('.%d ' % x if isinstance(x, int) else x + ' ') for x in all_maps['box'].keys()]))
      print(make_sep(len(all_maps['box']) + 1))
      for iou_type in ('box', 'mask'):
          print(make_row([iou_type] + ['%.2f' % x if x < 100 else '%.1f' % x for x in all_maps[iou_type].values()]))
      print(make_sep(len(all_maps['box']) + 1))
      print()
  
  
  
  if __name__ == '__main__':
      parse_args()
  
      if args.config is not None:
          set_cfg(args.config)
  
      if args.trained_model == 'interrupt':
          args.trained_model = SavePath.get_interrupt('weights/')
      elif args.trained_model == 'latest':
          args.trained_model = SavePath.get_latest('weights/', cfg.name)
  
      if args.config is None:
          model_path = SavePath.from_str(args.trained_model)
          # TODO: Bad practice? Probably want to do a name lookup instead.
          args.config = model_path.model_name + '_config'
          print('Config not specified. Parsed %s from the file name.\n' % args.config)
          set_cfg(args.config)
  
      if args.detect:
          cfg.eval_mask_branch = False
  
      if args.dataset is not None:
          set_dataset(args.dataset)
  
      with torch.no_grad():
          if not os.path.exists('results'):
              os.makedirs('results')
  
          if args.cuda:
              cudnn.fastest = True
              torch.set_default_tensor_type('torch.cuda.FloatTensor')
          else:
              torch.set_default_tensor_type('torch.FloatTensor')
  
          if args.resume and not args.display:
              with open(args.ap_data_file, 'rb') as f:
                  ap_data = pickle.load(f)
              calc_map(ap_data)
              exit()
  
          if args.image is None and args.video is None and args.images is None:
              dataset = COCODetection(cfg.dataset.valid_images, cfg.dataset.valid_info,
                                      transform=BaseTransform(), has_gt=cfg.dataset.has_gt)
              prep_coco_cats()
          else:
              dataset = None        
  
          print('Loading model...', end='')
          net = Yolact()
          net.load_weights(args.trained_model)
          net.eval()
          print(' Done.')
  
          if args.cuda:
              net = net.cuda()
  
          evaluate(net, dataset)
  
  
  