polish googlenet

tools/program.py

@@ -157,7 +157,11 @@ def create_loss(out,
         return loss(out, target)
 
 
-def create_metric(out, feeds, topk=5, classes_num=1000,
+def create_metric(out,
+                  feeds,
+                  architecture,
+                  topk=5,
+                  classes_num=1000,
                   use_distillation=False):
     """
     Create measures of model accuracy, such as top1 and top5
@@ -171,16 +175,22 @@ def create_metric(out, feeds, topk=5, classes_num=1000,
     Returns:
         fetchs(dict): dict of measures
     """
-    # just need student label to get metrics
-    if use_distillation:
-        out = out[1]
+    if architecture["name"] == "GoogLeNet":
+        assert len(out) == 3, "GoogLeNet should have 3 outputs"
+        softmax_out = out[0]
+    else:
+        # just need student label to get metrics
+        if use_distillation:
+            out = out[1]
+        softmax_out = fluid.layers.softmax(out, use_cudnn=False)
+
     fetchs = OrderedDict()
-    label = feeds['label']
-    softmax_out = fluid.layers.softmax(out, use_cudnn=False)
-    top1 = fluid.layers.accuracy(softmax_out, label=label, k=1)
+    # set top1 to fetchs
+    top1 = fluid.layers.accuracy(softmax_out, label=feeds['label'], k=1)
     fetchs['top1'] = (top1, AverageMeter('top1', '.4f', need_avg=True))
+    # set topk to fetchs
     k = min(topk, classes_num)
-    topk = fluid.layers.accuracy(softmax_out, label=label, k=k)
+    topk = fluid.layers.accuracy(softmax_out, label=feeds['label'], k=k)
     topk_name = 'top{}'.format(k)
     fetchs[topk_name] = (topk, AverageMeter(topk_name, '.4f', need_avg=True))
 
@@ -201,7 +211,8 @@ def create_fetchs(out,
 
     Args:
         out(variable): model output variable
-        feeds(dict): dict of model input variables(included label)
+        feeds(dict): dict of model input variables.
+            If use mix_up, it will not include label.
         architecture(dict): architecture information,
             name(such as ResNet50) is needed
         topk(int): usually top5
@@ -217,7 +228,8 @@ def create_fetchs(out,
                        use_distillation)
     fetchs['loss'] = (loss, AverageMeter('loss', '7.4f', need_avg=True))
     if not use_mix:
-        metric = create_metric(out, feeds, topk, classes_num, use_distillation)
+        metric = create_metric(out, feeds, architecture, topk, classes_num,
+                               use_distillation)
         fetchs.update(metric)
 
     return fetchs