add save only in eval/train test=develop (#2604)

ppdet/engine/trainer.py

@@ -125,6 +125,8 @@ class Trainer(object):
             bias = self.cfg['bias'] if 'bias' in self.cfg else 0
             output_eval = self.cfg['output_eval'] \
                 if 'output_eval' in self.cfg else None
+            save_prediction_only = self.cfg['save_prediction_only'] \
+                if 'save_prediction_only' in self.cfg else False
 
             # pass clsid2catid info to metric instance to avoid multiple loading
             # annotation file
@@ -145,7 +147,8 @@ class Trainer(object):
                     clsid2catid=clsid2catid,
                     classwise=classwise,
                     output_eval=output_eval,
-                    bias=bias)
+                    bias=bias,
+                    save_prediction_only=save_prediction_only)
             ]
         elif self.cfg.metric == 'VOC':
             self._metrics = [

ppdet/metrics/metrics.py

@@ -69,6 +69,7 @@ class COCOMetric(Metric):
         self.output_eval = kwargs.get('output_eval', None)
         # TODO: bias should be unified
         self.bias = kwargs.get('bias', 0)
+        self.save_prediction_only = kwargs.get('save_prediction_only', False)
         self.reset()
 
     def reset(self):
@@ -104,13 +105,17 @@ class COCOMetric(Metric):
                 json.dump(self.results['bbox'], f)
                 logger.info('The bbox result is saved to bbox.json.')
 
-            bbox_stats = cocoapi_eval(
-                output,
-                'bbox',
-                anno_file=self.anno_file,
-                classwise=self.classwise)
-            self.eval_results['bbox'] = bbox_stats
-            sys.stdout.flush()
+            if self.save_prediction_only:
+                logger.info('The bbox result is saved to {} and do not '
+                            'evaluate the mAP.'.format(output))
+            else:
+                bbox_stats = cocoapi_eval(
+                    output,
+                    'bbox',
+                    anno_file=self.anno_file,
+                    classwise=self.classwise)
+                self.eval_results['bbox'] = bbox_stats
+                sys.stdout.flush()
 
         if len(self.results['mask']) > 0:
             output = "mask.json"
@@ -120,13 +125,17 @@ class COCOMetric(Metric):
                 json.dump(self.results['mask'], f)
                 logger.info('The mask result is saved to mask.json.')
 
-            seg_stats = cocoapi_eval(
-                output,
-                'segm',
-                anno_file=self.anno_file,
-                classwise=self.classwise)
-            self.eval_results['mask'] = seg_stats
-            sys.stdout.flush()
+            if self.save_prediction_only:
+                logger.info('The mask result is saved to {} and do not '
+                            'evaluate the mAP.'.format(output))
+            else:
+                seg_stats = cocoapi_eval(
+                    output,
+                    'segm',
+                    anno_file=self.anno_file,
+                    classwise=self.classwise)
+                self.eval_results['mask'] = seg_stats
+                sys.stdout.flush()
 
         if len(self.results['segm']) > 0:
             output = "segm.json"
@@ -136,13 +145,17 @@ class COCOMetric(Metric):
                 json.dump(self.results['segm'], f)
                 logger.info('The segm result is saved to segm.json.')
 
-            seg_stats = cocoapi_eval(
-                output,
-                'segm',
-                anno_file=self.anno_file,
-                classwise=self.classwise)
-            self.eval_results['mask'] = seg_stats
-            sys.stdout.flush()
+            if self.save_prediction_only:
+                logger.info('The segm result is saved to {} and do not '
+                            'evaluate the mAP.'.format(output))
+            else:
+                seg_stats = cocoapi_eval(
+                    output,
+                    'segm',
+                    anno_file=self.anno_file,
+                    classwise=self.classwise)
+                self.eval_results['mask'] = seg_stats
+                sys.stdout.flush()
 
     def log(self):
         pass

tools/eval.py

@@ -66,6 +66,12 @@ def parse_args():
         action="store_true",
         help="whether per-category AP and draw P-R Curve or not.")
 
+    parser.add_argument(
+        '--save_prediction_only',
+        action='store_true',
+        default=False,
+        help='Whether to save the evaluation results only')
+
     args = parser.parse_args()
     return args
 
@@ -85,7 +91,7 @@ def run(FLAGS, cfg):
     # init parallel environment if nranks > 1
     init_parallel_env()
 
-    # build trainer 
+    # build trainer
     trainer = Trainer(cfg, mode='eval')
 
     # load weights
@@ -102,6 +108,7 @@ def main():
     cfg['bias'] = 1 if FLAGS.bias else 0
     cfg['classwise'] = True if FLAGS.classwise else False
     cfg['output_eval'] = FLAGS.output_eval
+    cfg['save_prediction_only'] = FLAGS.save_prediction_only
     merge_config(FLAGS.opt)
 
     place = paddle.set_device('gpu' if cfg.use_gpu else 'cpu')

tools/train.py

@@ -75,6 +75,11 @@ def parse_args():
         type=str,
         default="vdl_log_dir/scalar",
         help='VisualDL logging directory for scalar.')
+    parser.add_argument(
+        '--save_prediction_only',
+        action='store_true',
+        default=False,
+        help='Whether to save the evaluation results only')
     args = parser.parse_args()
     return args
 
@@ -110,6 +115,7 @@ def main():
     cfg['fleet'] = FLAGS.fleet
     cfg['use_vdl'] = FLAGS.use_vdl
     cfg['vdl_log_dir'] = FLAGS.vdl_log_dir
+    cfg['save_prediction_only'] = FLAGS.save_prediction_only
     merge_config(FLAGS.opt)
 
     place = paddle.set_device('gpu' if cfg.use_gpu else 'cpu')