fit for dygraph

metrics/coco.py

@@ -16,6 +16,8 @@ from __future__ import absolute_import
 
 import sys
 import json
+from pycocotools.cocoeval import COCOeval
+from pycocotools.coco import COCO
 
 from .metric import Metric
 
@@ -32,22 +34,21 @@ OUTFILE = './bbox.json'
 
 class COCOMetric(Metric):
     """
-    Base class for metric, encapsulates metric logic and APIs
+    Metrci for MS-COCO dataset, only support update with batch
+    size as 1.
 
-    Usage:
-    m = SomeMetric()
-    for prediction, label in ...:
-        m.update(prediction, label)
-    m.accumulate()
+    Args:
+        anno_path(str): path to COCO annotation json file
+        with_background(bool): whether load category id with
+                               background as 0, default True
     """
 
     def __init__(self, anno_path, with_background=True, **kwargs):
         super(COCOMetric, self).__init__(**kwargs)
-        self.states['bbox'] = []
         self.anno_path = anno_path
         self.with_background = with_background
+        self.bbox_results = []
 
-        from pycocotools.coco import COCO
         self.coco_gt = COCO(anno_path)
         cat_ids = self.coco_gt.getCatIds()
 	self.clsid2catid = dict(
@@ -56,39 +57,40 @@ class COCOMetric(Metric):
 
     def update(self, preds, *args, **kwargs):
         im_ids, bboxes = preds
-        if bboxes[0].shape[1] != 6:
+        assert im_ids.shape[0] == 1, \
+            "COCOMetric can only update with batch size = 1"
+        if bboxes.shape[1] != 6:
             # no bbox detected in this batch
             return
 
-        idx = 0
-        bboxes, lods = bboxes
-        for i, (im_id, lod) in enumerate(zip(im_ids, lods[0])):
-            im_id = int(im_id)
-            for i in range(lod):
-                dt = bboxes[idx]
-                clsid, score, xmin, ymin, xmax, ymax = dt.tolist()
-                catid = (self.clsid2catid[int(clsid)])
-
-		w = xmax - xmin + 1
-		h = ymax - ymin + 1
-                bbox = [xmin, ymin, w, h]
-                coco_res = {
-                    'image_id': im_id,
-                    'category_id': catid,
-                    'bbox': bbox,
-                    'score': score
-                }
-                self.states['bbox'].append(coco_res)
-                idx += 1
+        im_id = int(im_ids)
+        for i in range(bboxes.shape[0]):
+            dt = bboxes[i, :]
+            clsid, score, xmin, ymin, xmax, ymax = dt.tolist()
+            catid = (self.clsid2catid[int(clsid)])
+
+            w = xmax - xmin + 1
+            h = ymax - ymin + 1
+            bbox = [xmin, ymin, w, h]
+            coco_res = {
+                'image_id': im_id,
+                'category_id': catid,
+                'bbox': bbox,
+                'score': score
+            }
+            self.bbox_results.append(coco_res)
+
+    def reset(self):
+        self.bbox_results = []
 
     def accumulate(self):
-	if len(self.states['bbox']) == 0:
+	if len(self.bbox_results) == 0:
 	    logger.warning("The number of valid bbox detected is zero.\n \
 		Please use reasonable model and check input data.\n \
 		stop COCOMetric accumulate!")
 	    return [0.0]
 	with open(OUTFILE, 'w') as f:
-	    json.dump(self.states['bbox'], f)
+	    json.dump(self.bbox_results, f)
 
 	map_stats = self.cocoapi_eval(OUTFILE, 'bbox', coco_gt=self.coco_gt)
 	# flush coco evaluation result
@@ -98,10 +100,8 @@ class COCOMetric(Metric):
 
     def cocoapi_eval(self, jsonfile, style, coco_gt=None, anno_file=None):
 	assert coco_gt != None or anno_file != None
-	from pycocotools.cocoeval import COCOeval
 
 	if coco_gt == None:
-	    from pycocotools.coco import COCO
 	    coco_gt = COCO(anno_file)
 	logger.info("Start evaluate...")
 	coco_dt = coco_gt.loadRes(jsonfile)

metrics/metric.py

@@ -17,6 +17,11 @@ from __future__ import absolute_import
 import six
 import abc
 
+import logging
+FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
+logging.basicConfig(level=logging.INFO, format=FORMAT)
+logger = logging.getLogger(__name__)
+
 __all__ = ['Metric']
 
 
@@ -32,15 +37,12 @@ class Metric(object):
     m.accumulate()
     """
 
-    def __init__(self, **kwargs):
-        self.reset()
-
+    @abc.abstractmethod
     def reset(self):
         """
         Reset states and result
         """
-        self.states = {}
-        self.result = None
+        raise NotImplementedError("function 'reset' not implemented in {}.".format(self.__class__.__name__))
 
     @abc.abstractmethod
     def update(self, *args, **kwargs):

model.py

@@ -285,17 +285,11 @@ class StaticGraphAdapter(object):
             compiled_prog, feed=feed,
             fetch_list=fetch_list,
             return_numpy=False)
-        # rets = [(np.array(v), v.recursive_sequence_lengths()) if v.lod() for v in rets]
-        np_rets = []
-        for ret in rets:
-            seq_len = ret.recursive_sequence_lengths()
-            if len(seq_len) == 0:
-                np_rets.append(np.array(ret))
-            else:
-                np_rets.append((np.array(ret), seq_len))
-        outputs = np_rets[:num_output]
-        labels = np_rets[num_output:num_output+num_label]
-        losses = np_rets[num_output+num_label:]
+        # LoDTensor cannot be fetch as numpy directly
+        rets = [np.array(v) for v in rets]
+        outputs = rets[:num_output]
+        labels = rets[num_output:num_output+num_label]
+        losses = rets[num_output+num_label:]
         if self.mode == 'test':
             return outputs
         elif self.mode == 'eval':
@@ -443,6 +437,8 @@ class DynamicGraphAdapter(object):
         labels = to_list(labels)
         outputs = self.model.forward(*[to_variable(x) for x in inputs])
         losses = self.model._loss_function(outputs[0], labels)
+        for metric in self.model._metrics:
+            metric.update([to_numpy(o) for o in outputs[1:]], labels)
         return [to_numpy(o) for o in to_list(outputs[0])], \
             [to_numpy(l) for l in losses]
 

yolov3.py

@@ -515,7 +515,7 @@ def main():
                 coco2017(FLAGS.data, 'val'),
                 process_num=8,
                 buffer_size=4 * batch_size),
-            batch_size=batch_size),
+            batch_size=1),
         process_num=2, buffer_size=4)
 
     if not os.path.exists('yolo_checkpoints'):
@@ -536,14 +536,15 @@ def main():
                       metrics=COCOMetric(anno_path, with_background=False))
 
         for e in range(epoch):
-            logger.info("======== train epoch {} ========".format(e))
-            run(model, train_loader)
-            model.save('yolo_checkpoints/{:02d}'.format(e))
+            # logger.info("======== train epoch {} ========".format(e))
+            # run(model, train_loader)
+            # model.save('yolo_checkpoints/{:02d}'.format(e))
             logger.info("======== eval epoch {} ========".format(e))
             run(model, val_loader, mode='eval')
             # should be called in fit()
             for metric in model._metrics:
                 metric.accumulate()
+                metric.reset()
 
 
 if __name__ == '__main__':