enable training alexnet benchmark

benchmark/paddle/image/alexnet.py

@@ -6,6 +6,7 @@ height = 227
 width = 227
 num_class = 1000
 batch_size = get_config_arg('batch_size', int, 128)
+use_mkldnn = get_config_arg('use_mkldnn', bool, False)
 
 args = {'height': height, 'width': width, 'color': True, 'num_class': num_class}
 define_py_data_sources2(
@@ -31,7 +32,12 @@ net = img_pool_layer(input=net, pool_size=3, stride=2)
 
 # conv2
 net = img_conv_layer(
-    input=net, filter_size=5, num_filters=256, stride=1, padding=2, groups=1)
+    input=net,
+    filter_size=5,
+    num_filters=256,
+    stride=1,
+    padding=2,
+    groups=2 if use_mkldnn else 1)
 net = img_cmrnorm_layer(input=net, size=5, scale=0.0001, power=0.75)
 net = img_pool_layer(input=net, pool_size=3, stride=2)
 
@@ -40,11 +46,21 @@ net = img_conv_layer(
     input=net, filter_size=3, num_filters=384, stride=1, padding=1)
 # conv4
 net = img_conv_layer(
-    input=net, filter_size=3, num_filters=384, stride=1, padding=1, groups=1)
+    input=net,
+    filter_size=3,
+    num_filters=384,
+    stride=1,
+    padding=1,
+    groups=2 if use_mkldnn else 1)
 
 # conv5
 net = img_conv_layer(
-    input=net, filter_size=3, num_filters=256, stride=1, padding=1, groups=1)
+    input=net,
+    filter_size=3,
+    num_filters=256,
+    stride=1,
+    padding=1,
+    groups=2 if use_mkldnn else 1)
 net = img_pool_layer(input=net, pool_size=3, stride=2)
 
 net = fc_layer(

benchmark/paddle/image/run_mkl_train.sh

@@ -47,5 +47,6 @@ for use_mkldnn in True False; do
     train vgg 19 $batchsize $use_mkldnn
     train resnet 50 $batchsize $use_mkldnn
     train googlenet v1 $batchsize $use_mkldnn
+    train alexnet group2 $batchsize $use_mkldnn
   done
 done

benchmark/paddle/image/run_openblas_train.sh

@@ -36,4 +36,5 @@ for batchsize in 64 128 256; do
   train vgg 19 $batchsize
   train resnet 50 $batchsize
   train googlenet v1 $batchsize
+  train alexnet group2 $batchsize $use_mkldnn
 done