Merge pull request #483 from hellock/master

Update evaluation hooks

mmdet/core/evaluation/eval_hooks.py

 import os
 import os.path as osp
-import shutil
-import time
 
 import mmcv
 import numpy as np
 import torch
+import torch.distributed as dist
 from mmcv.runner import Hook, obj_from_dict
 from mmcv.parallel import scatter, collate
 from pycocotools.cocoeval import COCOeval
@@ -29,42 +28,13 @@ class DistEvalHook(Hook):
                 'dataset must be a Dataset object or a dict, not {}'.format(
                     type(dataset)))
         self.interval = interval
-        self.lock_dir = None
-
-    def _barrier(self, rank, world_size):
-        """Due to some issues with `torch.distributed.barrier()`, we have to
-        implement this ugly barrier function.
-        """
-        if rank == 0:
-            for i in range(1, world_size):
-                tmp = osp.join(self.lock_dir, '{}.pkl'.format(i))
-                while not (osp.exists(tmp)):
-                    time.sleep(1)
-            for i in range(1, world_size):
-                tmp = osp.join(self.lock_dir, '{}.pkl'.format(i))
-                os.remove(tmp)
-        else:
-            tmp = osp.join(self.lock_dir, '{}.pkl'.format(rank))
-            mmcv.dump([], tmp)
-            while osp.exists(tmp):
-                time.sleep(1)
-
-    def before_run(self, runner):
-        self.lock_dir = osp.join(runner.work_dir, '.lock_map_hook')
-        if runner.rank == 0:
-            if osp.exists(self.lock_dir):
-                shutil.rmtree(self.lock_dir)
-            mmcv.mkdir_or_exist(self.lock_dir)
-
-    def after_run(self, runner):
-        if runner.rank == 0:
-            shutil.rmtree(self.lock_dir)
 
     def after_train_epoch(self, runner):
         if not self.every_n_epochs(runner, self.interval):
             return
         runner.model.eval()
         results = [None for _ in range(len(self.dataset))]
+        if runner.rank == 0:
             prog_bar = mmcv.ProgressBar(len(self.dataset))
         for idx in range(runner.rank, len(self.dataset), runner.world_size):
             data = self.dataset[idx]
@@ -79,12 +49,13 @@ class DistEvalHook(Hook):
             results[idx] = result
 
             batch_size = runner.world_size
+            if runner.rank == 0:
                 for _ in range(batch_size):
                     prog_bar.update()
 
         if runner.rank == 0:
             print('\n')
-            self._barrier(runner.rank, runner.world_size)
+            dist.barrier()
             for i in range(1, runner.world_size):
                 tmp_file = osp.join(runner.work_dir, 'temp_{}.pkl'.format(i))
                 tmp_results = mmcv.load(tmp_file)
@@ -96,8 +67,8 @@ class DistEvalHook(Hook):
             tmp_file = osp.join(runner.work_dir,
                                 'temp_{}.pkl'.format(runner.rank))
             mmcv.dump(results, tmp_file)
-            self._barrier(runner.rank, runner.world_size)
-        self._barrier(runner.rank, runner.world_size)
+            dist.barrier()
+        dist.barrier()
 
     def evaluate(self):
         raise NotImplementedError
@@ -179,7 +150,13 @@ class CocoDistEvalmAPHook(DistEvalHook):
             cocoEval.evaluate()
             cocoEval.accumulate()
             cocoEval.summarize()
-            field = '{}_mAP'.format(res_type)
-            runner.log_buffer.output[field] = cocoEval.stats[0]
+            metrics = ['mAP', 'mAP_50', 'mAP_75', 'mAP_s', 'mAP_m', 'mAP_l']
+            for i in range(len(metrics)):
+                key = '{}_{}'.format(res_type, metrics[i])
+                val = float('{:.3f}'.format(cocoEval.stats[i]))
+                runner.log_buffer.output[key] = val
+            runner.log_buffer.output['{}_mAP_copypaste'.format(res_type)] = (
+                '{ap[0]:.3f} {ap[1]:.3f} {ap[2]:.3f} {ap[3]:.3f} '
+                '{ap[4]:.3f} {ap[5]:.3f}').format(ap=cocoEval.stats[:6])
         runner.log_buffer.ready = True
         os.remove(tmp_file)