COCO evolution fix (PaddlePaddle#3388)

* COCO evolution fix

* cleanup

* update print

* print fix

train.py

@@ -62,7 +62,6 @@ def train(hyp, opt, device, tb_writer=None):
     init_seeds(2 + rank)
     with open(opt.data) as f:
         data_dict = yaml.safe_load(f)  # data dict
-    is_coco = opt.data.endswith('coco.yaml')
 
     # Logging- Doing this before checking the dataset. Might update data_dict
     loggers = {'wandb': None}  # loggers dict
@@ -78,6 +77,7 @@ def train(hyp, opt, device, tb_writer=None):
     nc = 1 if opt.single_cls else int(data_dict['nc'])  # number of classes
     names = ['item'] if opt.single_cls and len(data_dict['names']) != 1 else data_dict['names']  # class names
     assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (len(names), nc, opt.data)  # check
+    is_coco = opt.data.endswith('coco.yaml') and nc == 80  # COCO dataset
 
     # Model
     pretrained = weights.endswith('.pt')
@@ -358,6 +358,7 @@ def train(hyp, opt, device, tb_writer=None):
                                                  single_cls=opt.single_cls,
                                                  dataloader=testloader,
                                                  save_dir=save_dir,
+                                                 save_json=is_coco and final_epoch,
                                                  verbose=nc < 50 and final_epoch,
                                                  plots=plots and final_epoch,
                                                  wandb_logger=wandb_logger,
@@ -409,41 +410,38 @@ def train(hyp, opt, device, tb_writer=None):
         # end epoch ----------------------------------------------------------------------------------------------------
     # end training
     if rank in [-1, 0]:
-        # Plots
+        logger.info(f'{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.\n')
         if plots:
             plot_results(save_dir=save_dir)  # save as results.png
             if wandb_logger.wandb:
                 files = ['results.png', 'confusion_matrix.png', *[f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R')]]
                 wandb_logger.log({"Results": [wandb_logger.wandb.Image(str(save_dir / f), caption=f) for f in files
                                               if (save_dir / f).exists()]})
-        # 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 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=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=True,
-                                          plots=False,
-                                          is_coco=is_coco)
-
-        # Strip optimizers
-        final = best if best.exists() else last  # final model
-        for f in last, best:
-            if f.exists():
-                strip_optimizer(f)  # strip optimizers
-        if opt.bucket:
-            os.system(f'gsutil cp {final} gs://{opt.bucket}/weights')  # upload
-        if wandb_logger.wandb and not opt.evolve:  # Log the stripped model
-            wandb_logger.wandb.log_artifact(str(final), type='model',
-                                            name='run_' + wandb_logger.wandb_run.id + '_model',
-                                            aliases=['latest', 'best', 'stripped'])
+
+        if not opt.evolve:
+            if is_coco:  # COCO dataset
+                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=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=True,
+                                              plots=False,
+                                              is_coco=is_coco)
+
+            # Strip optimizers
+            for f in last, best:
+                if f.exists():
+                    strip_optimizer(f)  # strip optimizers
+            if wandb_logger.wandb:  # Log the stripped model
+                wandb_logger.wandb.log_artifact(str(best if best.exists() else last), type='model',
+                                                name='run_' + wandb_logger.wandb_run.id + '_model',
+                                                aliases=['latest', 'best', 'stripped'])
         wandb_logger.finish_run()
     else:
         dist.destroy_process_group()