py_test and test_image_classification_train support argument (#5934)

* py_test support argument, test_image_classification_train support argument

* use REMOVE_ITEM to rm item from list in cmake

cmake/generic.cmake

@@ -459,11 +459,11 @@ function(py_test TARGET_NAME)
   if(WITH_TESTING)
     set(options STATIC static SHARED shared)
     set(oneValueArgs "")
-    set(multiValueArgs SRCS DEPS)
-    cmake_parse_arguments(py_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})  
+    set(multiValueArgs SRCS DEPS ARGS)
+    cmake_parse_arguments(py_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
     add_test(NAME ${TARGET_NAME}
              COMMAND env PYTHONPATH=${PADDLE_PYTHON_BUILD_DIR}/lib-python
-             ${PYTHON_EXECUTABLE} ${py_test_SRCS}
+             ${PYTHON_EXECUTABLE} -u ${py_test_SRCS} ${py_test_ARGS}
              WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR})
   endif()
 endfunction()

python/paddle/v2/fluid/tests/book/CMakeLists.txt

 file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
 string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
+
+list(REMOVE_ITEM TEST_OPS test_image_classification_train)
+py_test(test_image_classification_train_resnet SRCS test_image_classification_train.py ARGS resnet)
+py_test(test_image_classification_train_vgg SRCS test_image_classification_train.py ARGS vgg)
+
+# default test
 foreach(src ${TEST_OPS})
     py_test(${src} SRCS ${src}.py)
 endforeach()

python/paddle/v2/fluid/tests/book/test_image_classification_train.py

 from __future__ import print_function
+
 import numpy as np
 import paddle.v2 as paddle
 import paddle.v2.fluid as fluid
+import sys
 
 
 def resnet_cifar10(input, depth=32):
@@ -80,11 +82,18 @@ data_shape = [3, 32, 32]
 images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32')
 label = fluid.layers.data(name='label', shape=[1], dtype='int64')
 
-# Add neural network config
-# option 1. resnet
-# net = resnet_cifar10(images, 32)
-# option 2. vgg
-net = vgg16_bn_drop(images)
+net_type = "vgg"
+if len(sys.argv) >= 2:
+    net_type = sys.argv[1]
+
+if net_type == "vgg":
+    print("train vgg net")
+    net = vgg16_bn_drop(images)
+elif net_type == "resnet":
+    print("train resnet")
+    net = resnet_cifar10(images, 32)
+else:
+    raise ValueError("%s network is not supported" % net_type)
 
 predict = fluid.layers.fc(input=net, size=classdim, act='softmax')
 cost = fluid.layers.cross_entropy(input=predict, label=label)