Update train.py (#2290)

* Update train.py

* Update train.py

* Update train.py

* Update train.py

* Create train.py

train.py

@@ -146,8 +146,7 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
 
         # Results
         if ckpt.get('training_results') is not None:
-            with open(results_file, 'w') as file:
-                file.write(ckpt['training_results'])  # write results.txt
+            results_file.write_text(ckpt['training_results'])  # write results.txt
 
         # Epochs
         start_epoch = ckpt['epoch'] + 1
@@ -354,7 +353,7 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
 
             # Write
             with open(results_file, 'a') as f:
-                f.write(s + '%10.4g' * 7 % results + '\n')  # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls)
+                f.write(s + '%10.4g' * 7 % results + '\n')  # append metrics, val_loss
             if len(opt.name) and opt.bucket:
                 os.system('gsutil cp %s gs://%s/results/results%s.txt' % (results_file, opt.bucket, opt.name))
 
@@ -375,15 +374,13 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
                 best_fitness = fi
 
             # Save model
-            save = (not opt.nosave) or (final_epoch and not opt.evolve)
-            if save:
-                with open(results_file, 'r') as f:  # create checkpoint
-                    ckpt = {'epoch': epoch,
-                            'best_fitness': best_fitness,
-                            'training_results': f.read(),
-                            'model': ema.ema,
-                            'optimizer': None if final_epoch else optimizer.state_dict(),
-                            'wandb_id': wandb_run.id if wandb else None}
+            if (not opt.nosave) or (final_epoch and not opt.evolve):  # if save
+                ckpt = {'epoch': epoch,
+                        'best_fitness': best_fitness,
+                        'training_results': results_file.read_text(),
+                        'model': ema.ema,
+                        'optimizer': None if final_epoch else optimizer.state_dict(),
+                        'wandb_id': wandb_run.id if wandb else None}
 
                 # Save last, best and delete
                 torch.save(ckpt, last)
@@ -396,9 +393,9 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
     if rank in [-1, 0]:
         # Strip optimizers
         final = best if best.exists() else last  # final model
-        for f in [last, best]:
+        for f in last, best:
             if f.exists():
-                strip_optimizer(f)  # strip optimizers
+                strip_optimizer(f)
         if opt.bucket:
             os.system(f'gsutil cp {final} gs://{opt.bucket}/weights')  # upload
 
@@ -415,17 +412,17 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
         # Test best.pt
         logger.info('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600))
         if opt.data.endswith('coco.yaml') and nc == 80:  # if COCO
-            for conf, iou, save_json in ([0.25, 0.45, False], [0.001, 0.65, True]):  # speed, mAP tests
+            for m in (last, best) if best.exists() else (last):  # speed, mAP tests
                 results, _, _ = test.test(opt.data,
                                           batch_size=batch_size * 2,
                                           imgsz=imgsz_test,
-                                          conf_thres=conf,
-                                          iou_thres=iou,
-                                          model=attempt_load(final, device).half(),
+                                          conf_thres=0.001,
+                                          iou_thres=0.7,
+                                          model=attempt_load(m, device).half(),
                                           single_cls=opt.single_cls,
                                           dataloader=testloader,
                                           save_dir=save_dir,
-                                          save_json=save_json,
+                                          save_json=True,
                                           plots=False)
 
     else: