Daemon thread plotting (#1561)

* Daemon thread plotting

* remove process_batch

* plot after print

test.py

@@ -3,6 +3,7 @@ import glob
 import json
 import os
 from pathlib import Path
+from threading import Thread
 
 import numpy as np
 import torch
@@ -206,10 +207,10 @@ def test(data,
 
         # Plot images
         if plots and batch_i < 3:
-            f = save_dir / f'test_batch{batch_i}_labels.jpg'  # filename
-            plot_images(img, targets, paths, f, names)  # labels
-            f = save_dir / f'test_batch{batch_i}_pred.jpg'
-            plot_images(img, output_to_target(output), paths, f, names)  # predictions
+            f = save_dir / f'test_batch{batch_i}_labels.jpg'  # labels
+            Thread(target=plot_images, args=(img, targets, paths, f, names), daemon=True).start()
+            f = save_dir / f'test_batch{batch_i}_pred.jpg'  # predictions
+            Thread(target=plot_images, args=(img, output_to_target(output), paths, f, names), daemon=True).start()
 
     # Compute statistics
     stats = [np.concatenate(x, 0) for x in zip(*stats)]  # to numpy
@@ -221,13 +222,6 @@ def test(data,
     else:
         nt = torch.zeros(1)
 
-    # Plots
-    if plots:
-        confusion_matrix.plot(save_dir=save_dir, names=list(names.values()))
-        if wandb and wandb.run:
-            wandb.log({"Images": wandb_images})
-            wandb.log({"Validation": [wandb.Image(str(f), caption=f.name) for f in sorted(save_dir.glob('test*.jpg'))]})
-
     # Print results
     pf = '%20s' + '%12.3g' * 6  # print format
     print(pf % ('all', seen, nt.sum(), mp, mr, map50, map))
@@ -242,6 +236,13 @@ def test(data,
     if not training:
         print('Speed: %.1f/%.1f/%.1f ms inference/NMS/total per %gx%g image at batch-size %g' % t)
 
+    # Plots
+    if plots:
+        confusion_matrix.plot(save_dir=save_dir, names=list(names.values()))
+        if wandb and wandb.run:
+            wandb.log({"Images": wandb_images})
+            wandb.log({"Validation": [wandb.Image(str(f), caption=f.name) for f in sorted(save_dir.glob('test*.jpg'))]})
+
     # Save JSON
     if save_json and len(jdict):
         w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else ''  # weights