skip cost when inference

benchmark/paddle/image/googlenet.py

@@ -6,8 +6,15 @@ width = 224
 num_class = 1000
 batch_size = get_config_arg('batch_size', int, 128)
 use_gpu = get_config_arg('use_gpu', bool, True)
-
-args = {'height': height, 'width': width, 'color': True, 'num_class': num_class}
+is_infer = get_config_arg("is_infer", bool, False)
+
+args = {
+    'height': height,
+    'width': width,
+    'color': True,
+    'num_class': num_class,
+    'is_infer': is_infer
+}
 define_py_data_sources2(
     "train.list", "test.list", module="provider", obj="process", args=args)
 
@@ -146,7 +153,6 @@ def inception(name, input, channels, \
     return cat
 
 
-lab = data_layer(name="label", size=1000)
 data = data_layer(name="input", size=3 * height * width)
 
 # stage 1
@@ -224,6 +230,10 @@ pool5 = img_pool_layer(
 dropout = dropout_layer(name="dropout", input=pool5, dropout_rate=0.4)
 out3 = fc_layer(
     name="output3", input=dropout, size=1000, act=SoftmaxActivation())
-loss3 = cross_entropy(name='loss3', input=out3, label=lab)
 
+if is_infer:
+    outputs(out3)
+else:
+    lab = data_layer(name="label", size=num_class)
+    loss3 = cross_entropy(name='loss3', input=out3, label=lab)
     outputs(loss3)

benchmark/paddle/image/provider.py

@@ -13,7 +13,10 @@ def initHook(settings, height, width, color, num_class, **kwargs):
         settings.data_size = settings.height * settings.width * 3
     else:
         settings.data_size = settings.height * settings.width
-
+    settings.is_infer = kwargs.get('is_infer', False)
+    if settings.is_infer:
+        settings.slots = [dense_vector(settings.data_size)]
+    else:
         settings.slots = [dense_vector(settings.data_size), integer_value(1)]
 
 
@@ -22,5 +25,8 @@ def initHook(settings, height, width, color, num_class, **kwargs):
 def process(settings, file_list):
     for i in xrange(1024):
         img = np.random.rand(1, settings.data_size).reshape(-1, 1).flatten()
+        if settings.is_infer:
+            yield img.astype('float32')
+        else:
             lab = random.randint(0, settings.num_class - 1)
             yield img.astype('float32'), int(lab)

benchmark/paddle/image/resnet.py

@@ -6,9 +6,15 @@ width = 224
 num_class = 1000
 batch_size = get_config_arg('batch_size', int, 64)
 layer_num = get_config_arg("layer_num", int, 50)
-is_test = get_config_arg("is_test", bool, False)
-
-args = {'height': height, 'width': width, 'color': True, 'num_class': num_class}
+is_infer = get_config_arg("is_infer", bool, False)
+
+args = {
+    'height': height,
+    'width': width,
+    'color': True,
+    'num_class': num_class,
+    'is_infer': is_infer
+}
 define_py_data_sources2(
     "train.list", "test.list", module="provider", obj="process", args=args)
 
@@ -45,7 +51,10 @@ def conv_bn_layer(name,
         act=LinearActivation(),
         bias_attr=False)
     return batch_norm_layer(
-        name=name + "_bn", input=tmp, act=active_type, use_global_stats=is_test)
+        name=name + "_bn",
+        input=tmp,
+        act=active_type,
+        use_global_stats=is_infer)
 
 
 def bottleneck_block(name, input, num_filters1, num_filters2):
@@ -207,7 +216,9 @@ elif layer_num == 152:
 else:
     print("Wrong layer number.")
 
+if is_infer:
+    outputs(resnet)
+else:
     lbl = data_layer(name="label", size=num_class)
     loss = cross_entropy(name='loss', input=resnet, label=lbl)
-inputs(img, lbl)
     outputs(loss)

benchmark/paddle/image/vgg.py

@@ -6,8 +6,15 @@ width = 224
 num_class = 1000
 batch_size = get_config_arg('batch_size', int, 64)
 layer_num = get_config_arg('layer_num', int, 19)
+is_infer = get_config_arg("is_infer", bool, False)
 
-args = {'height': height, 'width': width, 'color': True, 'num_class': num_class}
+args = {
+    'height': height,
+    'width': width,
+    'color': True,
+    'num_class': num_class,
+    'is_infer': is_infer
+}
 define_py_data_sources2(
     "train.list", "test.list", module="provider", obj="process", args=args)
 
@@ -98,6 +105,9 @@ elif layer_num == 19:
 else:
     print("Wrong layer number.")
 
+if is_infer:
+    outputs(vgg)
+else:
     lab = data_layer('label', num_class)
     loss = cross_entropy(input=vgg, label=lab)
     outputs(loss)