Daemon plot_labels() for faster start

train.py

@@ -52,7 +52,7 @@
 from utils.loggers.wandb.wandb_utils import check_wandb_resume
 from utils.loss import ComputeLoss
 from utils.metrics import fitness
-from utils.plots import plot_evolve, plot_labels
+from utils.plots import plot_evolve
 from utils.torch_utils import (EarlyStopping, ModelEMA, de_parallel, select_device, smart_DDP, smart_optimizer,
                                smart_resume, torch_distributed_zero_first)
 
@@ -215,15 +215,11 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
                                        prefix=colorstr('val: '))[0]
 
         if not resume:
-            if plots:
-                plot_labels(labels, names, save_dir)
-
-            # Anchors
             if not opt.noautoanchor:
-                check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz)
+                check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz)  # run AutoAnchor
             model.half().float()  # pre-reduce anchor precision
 
-        callbacks.run('on_pretrain_routine_end')
+        callbacks.run('on_pretrain_routine_end', labels, names, plots)
 
     # DDP mode
     if cuda and RANK != -1:

utils/callbacks.py

@@ -3,6 +3,8 @@
 Callback utils
 """
 
+import threading
+
 
 class Callbacks:
     """"
@@ -55,17 +57,20 @@ def get_registered_actions(self, hook=None):
         """
         return self._callbacks[hook] if hook else self._callbacks
 
-    def run(self, hook, *args, **kwargs):
+    def run(self, hook, *args, thread=False, **kwargs):
         """
-        Loop through the registered actions and fire all callbacks
+        Loop through the registered actions and fire all callbacks on main thread
 
         Args:
             hook: The name of the hook to check, defaults to all
             args: Arguments to receive from YOLOv5
+            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]:
-            logger['callback'](*args, **kwargs)
+            if thread:
+                threading.Thread(target=logger['callback'], args=args, kwargs=kwargs, daemon=True).start()
+            else:
+                logger['callback'](*args, **kwargs)

utils/general.py

@@ -622,7 +622,7 @@ def download_one(url, dir):
     dir.mkdir(parents=True, exist_ok=True)  # make directory
     if threads > 1:
         pool = ThreadPool(threads)
-        pool.imap(lambda x: download_one(*x), zip(url, repeat(dir)))  # multi-threaded
+        pool.imap(lambda x: download_one(*x), zip(url, repeat(dir)))  # multithreaded
         pool.close()
         pool.join()
     else:

utils/loggers/__init__.py

@@ -11,10 +11,10 @@
 import torch
 from torch.utils.tensorboard import SummaryWriter
 
-from utils.general import colorstr, cv2
+from utils.general import colorstr, cv2, threaded
 from utils.loggers.clearml.clearml_utils import ClearmlLogger
 from utils.loggers.wandb.wandb_utils import WandbLogger
-from utils.plots import plot_images, plot_results
+from utils.plots import plot_images, plot_labels, plot_results
 from utils.torch_utils import de_parallel
 
 LOGGERS = ('csv', 'tb', 'wandb', 'clearml')  # *.csv, TensorBoard, Weights & Biases, ClearML
@@ -110,13 +110,15 @@ def on_train_start(self):
         # Callback runs on train start
         pass
 
-    def on_pretrain_routine_end(self):
+    def on_pretrain_routine_end(self, labels, names, plots):
         # Callback runs on pre-train routine end
+        if plots:
+            plot_labels(labels, names, self.save_dir)
         paths = self.save_dir.glob('*labels*.jpg')  # training labels
         if self.wandb:
             self.wandb.log({"Labels": [wandb.Image(str(x), caption=x.name) for x in paths]})
-        if self.clearml:
-            pass  # ClearML saves these images automatically using hooks
+        # if self.clearml:
+        #    pass  # ClearML saves these images automatically using hooks
 
     def on_train_batch_end(self, ni, model, imgs, targets, paths, plots):
         # Callback runs on train batch end

utils/plots.py

@@ -340,7 +340,6 @@ def plot_val_study(file='', dir='', x=None):  # from utils.plots import *; plot_
 
 
 @try_except  # known issue https://github.com/ultralytics/yolov5/issues/5395
-@Timeout(30)  # known issue https://github.com/ultralytics/yolov5/issues/5611
 def plot_labels(labels, names=(), save_dir=Path('')):
     # plot dataset labels
     LOGGER.info(f"Plotting labels to {save_dir / 'labels.jpg'}... ")