Refactor YOLOv5 code for readability, maintainability, and efficiency

export.py

@@ -449,6 +449,7 @@ def transform_fn(data_item):
  Quantization transform function.
 
  Extracts and preprocess input data from dataloader item for quantization.
+
  Parameters:
  data_item: Tuple with data item produced by DataLoader during iteration
  Returns:

utils/augmentations.py

@@ -156,7 +156,6 @@ def random_perspective(
 ):
  # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(0.1, 0.1), scale=(0.9, 1.1), shear=(-10, 10))
  # targets = [cls, xyxy]
-
  """Applies random perspective transformation to an image, modifying the image and corresponding labels."""
  height = im.shape[0] + border[0] * 2 # shape(h,w,c)
  width = im.shape[1] + border[1] * 2

utils/callbacks.py

@@ -64,7 +64,6 @@ def run(self, hook, *args, thread=False, **kwargs):
  thread: (boolean) Run callbacks in daemon thread
  kwargs: Keyword Arguments to receive from YOLOv5
  """
-
  assert hook in self._callbacks, f"hook '{hook}' not found in callbacks {self._callbacks}"
  for logger in self._callbacks[hook]:
  if thread:

utils/dataloaders.py

@@ -1104,7 +1104,8 @@ def extract_boxes(path=DATASETS_DIR / "coco128"):
 def autosplit(path=DATASETS_DIR / "coco128/images", weights=(0.9, 0.1, 0.0), annotated_only=False):
  """Autosplit a dataset into train/val/test splits and save path/autosplit_*.txt files
  Usage: from utils.dataloaders import *; autosplit()
- Arguments
+
+ Arguments:
  path: Path to images directory
  weights: Train, val, test weights (list, tuple)
  annotated_only: Only use images with an annotated txt file
@@ -1183,7 +1184,7 @@ class HUBDatasetStats:
  """
  Class for generating HUB dataset JSON and `-hub` dataset directory.
 
- Arguments
+ Arguments:
  path: Path to data.yaml or data.zip (with data.yaml inside data.zip)
  autodownload: Attempt to download dataset if not found locally
 
@@ -1314,7 +1315,7 @@ class ClassificationDataset(torchvision.datasets.ImageFolder):
  """
  YOLOv5 Classification Dataset.
 
- Arguments
+ Arguments:
  root: Dataset path
  transform: torchvision transforms, used by default
  album_transform: Albumentations transforms, used if installed

utils/general.py

@@ -518,7 +518,6 @@ def check_font(font=FONT, progress=False):
 
 def check_dataset(data, autodownload=True):
  """Validates and/or auto-downloads a dataset, returning its configuration as a dictionary."""
-
  # Download (optional)
  extract_dir = ""
  if isinstance(data, (str, Path)) and (is_zipfile(data) or is_tarfile(data)):
@@ -1023,7 +1022,6 @@ def non_max_suppression(
  Returns:
  list of detections, on (n,6) tensor per image [xyxy, conf, cls]
  """
-
  # Checks
  assert 0 <= conf_thres <= 1, f"Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0"
  assert 0 <= iou_thres <= 1, f"Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0"

utils/loggers/__init__.py

@@ -350,7 +350,8 @@ class GenericLogger:
  """
  YOLOv5 General purpose logger for non-task specific logging
  Usage: from utils.loggers import GenericLogger; logger = GenericLogger(...)
- Arguments
+
+ Arguments:
  opt: Run arguments
  console_logger: Console logger
  include: loggers to include

utils/loggers/clearml/clearml_utils.py

@@ -80,7 +80,7 @@ def __init__(self, opt, hyp):
  - Initialize ClearML Task, this object will capture the experiment
  - Upload dataset version to ClearML Data if opt.upload_dataset is True
 
- arguments:
+ Arguments:
  opt (namespace) -- Commandline arguments for this run
  hyp (dict) -- Hyperparameters for this run
 
@@ -133,7 +133,7 @@ def log_scalars(self, metrics, epoch):
  """
  Log scalars/metrics to ClearML.
 
- arguments:
+ Arguments:
  metrics (dict) Metrics in dict format: {"metrics/mAP": 0.8, ...}
  epoch (int) iteration number for the current set of metrics
  """
@@ -145,7 +145,7 @@ def log_model(self, model_path, model_name, epoch=0):
  """
  Log model weights to ClearML.
 
- arguments:
+ Arguments:
  model_path (PosixPath or str) Path to the model weights
  model_name (str) Name of the model visible in ClearML
  epoch (int) Iteration / epoch of the model weights
@@ -158,7 +158,7 @@ def log_summary(self, metrics):
  """
  Log final metrics to a summary table.
 
- arguments:
+ Arguments:
  metrics (dict) Metrics in dict format: {"metrics/mAP": 0.8, ...}
  """
  for k, v in metrics.items():
@@ -168,7 +168,7 @@ def log_plot(self, title, plot_path):
  """
  Log image as plot in the plot section of ClearML.
 
- arguments:
+ Arguments:
  title (str) Title of the plot
  plot_path (PosixPath or str) Path to the saved image file
  """
@@ -183,7 +183,7 @@ def log_debug_samples(self, files, title="Debug Samples"):
  """
  Log files (images) as debug samples in the ClearML task.
 
- arguments:
+ Arguments:
  files (List(PosixPath)) a list of file paths in PosixPath format
  title (str) A title that groups together images with the same values
  """
@@ -199,7 +199,7 @@ def log_image_with_boxes(self, image_path, boxes, class_names, image, conf_thres
  """
  Draw the bounding boxes on a single image and report the result as a ClearML debug sample.
 
- arguments:
+ Arguments:
  image_path (PosixPath) the path the original image file
  boxes (list): list of scaled predictions in the format - [xmin, ymin, xmax, ymax, confidence, class]
  class_names (dict): dict containing mapping of class int to class name

utils/loggers/wandb/wandb_utils.py

@@ -49,7 +49,7 @@ def __init__(self, opt, run_id=None, job_type="Training"):
  - Upload dataset if opt.upload_dataset is True
  - Setup training processes if job_type is 'Training'
 
- arguments:
+ Arguments:
  opt (namespace) -- Commandline arguments for this run
  run_id (str) -- Run ID of W&B run to be resumed
  job_type (str) -- To set the job_type for this run
@@ -90,7 +90,7 @@ def setup_training(self, opt):
  - Update data_dict, to contain info of previous run if resumed and the paths of dataset artifact if downloaded
  - Setup log_dict, initialize bbox_interval
 
- arguments:
+ Arguments:
  opt (namespace) -- commandline arguments for this run
 
  """
@@ -120,7 +120,7 @@ def log_model(self, path, opt, epoch, fitness_score, best_model=False):
  """
  Log the model checkpoint as W&B artifact.
 
- arguments:
+ Arguments:
  path (Path) -- Path of directory containing the checkpoints
  opt (namespace) -- Command line arguments for this run
  epoch (int) -- Current epoch number
@@ -159,7 +159,7 @@ def log(self, log_dict):
  """
  Save the metrics to the logging dictionary.
 
- arguments:
+ Arguments:
  log_dict (Dict) -- metrics/media to be logged in current step
  """
  if self.wandb_run:
@@ -170,7 +170,7 @@ def end_epoch(self):
  """
  Commit the log_dict, model artifacts and Tables to W&B and flush the log_dict.
 
- arguments:
+ Arguments:
  best_result (boolean): Boolean representing if the result of this evaluation is best or not
  """
  if self.wandb_run:
@@ -197,7 +197,7 @@ def finish_run(self):
 
 @contextmanager
 def all_logging_disabled(highest_level=logging.CRITICAL):
- """source - https://gist.github.com/simon-weber/7853144
+ """Source - https://gist.github.com/simon-weber/7853144
  A context manager that will prevent any logging messages triggered during the body from being processed.
  :param highest_level: the maximum logging level in use.
  This would only need to be changed if a custom level greater than CRITICAL is defined.

utils/metrics.py

@@ -41,7 +41,6 @@ def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir=".", names
  # Returns
  The average precision as computed in py-faster-rcnn.
  """
-
  # Sort by objectness
  i = np.argsort(-conf)
  tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]
@@ -103,7 +102,6 @@ def compute_ap(recall, precision):
  # Returns
  Average precision, precision curve, recall curve
  """
-
  # Append sentinel values to beginning and end
  mrec = np.concatenate(([0.0], recall, [1.0]))
  mpre = np.concatenate(([1.0], precision, [0.0]))
@@ -137,6 +135,7 @@ def process_batch(self, detections, labels):
  Return intersection-over-union (Jaccard index) of boxes.
 
  Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
+
  Arguments:
  detections (Array[N, 6]), x1, y1, x2, y2, conf, class
  labels (Array[M, 5]), class, x1, y1, x2, y2
@@ -233,7 +232,6 @@ def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7
 
  Input shapes are box1(1,4) to box2(n,4).
  """
-
  # Get the coordinates of bounding boxes
  if xywh: # transform from xywh to xyxy
  (x1, y1, w1, h1), (x2, y2, w2, h2) = box1.chunk(4, -1), box2.chunk(4, -1)
@@ -279,14 +277,15 @@ def box_iou(box1, box2, eps=1e-7):
  Return intersection-over-union (Jaccard index) of boxes.
 
  Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
+
  Arguments:
  box1 (Tensor[N, 4])
  box2 (Tensor[M, 4])
+
  Returns:
  iou (Tensor[N, M]): the NxM matrix containing the pairwise
  IoU values for every element in boxes1 and boxes2
  """
-
  # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
  (a1, a2), (b1, b2) = box1.unsqueeze(1).chunk(2, 2), box2.unsqueeze(0).chunk(2, 2)
  inter = (torch.min(a2, b2) - torch.max(a1, b1)).clamp(0).prod(2)
@@ -304,7 +303,6 @@ def bbox_ioa(box1, box2, eps=1e-7):
  box2: np.array of shape(nx4)
  returns: np.array of shape(n)
  """
-
  # Get the coordinates of bounding boxes
  b1_x1, b1_y1, b1_x2, b1_y2 = box1
  b2_x1, b2_y1, b2_x2, b2_y2 = box2.T

utils/segment/augmentations.py

@@ -29,7 +29,6 @@ def random_perspective(
 ):
  # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10))
  # targets = [cls, xyxy]
-
  """Applies random perspective, rotation, scale, shear, and translation augmentations to an image and targets."""
  height = im.shape[0] + border[0] * 2 # shape(h,w,c)
  width = im.shape[1] + border[1] * 2

utils/segment/general.py

@@ -14,7 +14,6 @@ def crop_mask(masks, boxes):
  - masks should be a size [n, h, w] tensor of masks
  - boxes should be a size [n, 4] tensor of bbox coords in relative point form
  """
-
  n, h, w = masks.shape
  x1, y1, x2, y2 = torch.chunk(boxes[:, :, None], 4, 1) # x1 shape(1,1,n)
  r = torch.arange(w, device=masks.device, dtype=x1.dtype)[None, None, :] # rows shape(1,w,1)
@@ -33,7 +32,6 @@ def process_mask_upsample(protos, masks_in, bboxes, shape):
 
  return: h, w, n
  """
-
  c, mh, mw = protos.shape # CHW
  masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw)
  masks = F.interpolate(masks[None], shape, mode="bilinear", align_corners=False)[0] # CHW
@@ -51,7 +49,6 @@ def process_mask(protos, masks_in, bboxes, shape, upsample=False):
 
  return: h, w, n
  """
-
  c, mh, mw = protos.shape # CHW
  ih, iw = shape
  masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw) # CHW

utils/triton.py

@@ -17,10 +17,9 @@ class TritonRemoteModel:
 
  def __init__(self, url: str):
  """
- Keyword arguments:
+ Keyword Arguments:
  url: Fully qualified address of the Triton server - for e.g. grpc://localhost:8000
  """
-
  parsed_url = urlparse(url)
  if parsed_url.scheme == "grpc":
  from tritonclient.grpc import InferenceServerClient, InferInput