Merge branch 'develop' of github.com:baidu/Paddle into feature/make_recognize_digits_normal_unittest

Author: reyoung

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

@@ -1,10 +1,6 @@
 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)

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

@@ -12,44 +12,74 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import numpy as np
 import paddle.v2 as paddle
 import paddle.v2.fluid as fluid
+import contextlib
+import unittest
 
-x = fluid.layers.data(name='x', shape=[13], dtype='float32')
 
-y_predict = fluid.layers.fc(input=x, size=1, act=None)
+def main(use_cuda):
+ if use_cuda and not fluid.core.is_compiled_with_cuda():
+ return
 
-y = fluid.layers.data(name='y', shape=[1], dtype='float32')
+ x = fluid.layers.data(name='x', shape=[13], dtype='float32')
 
-cost = fluid.layers.square_error_cost(input=y_predict, label=y)
-avg_cost = fluid.layers.mean(x=cost)
+ y_predict = fluid.layers.fc(input=x, size=1, act=None)
 
-sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
-sgd_optimizer.minimize(avg_cost)
+ y = fluid.layers.data(name='y', shape=[1], dtype='float32')
 
-BATCH_SIZE = 20
+ cost = fluid.layers.square_error_cost(input=y_predict, label=y)
+ avg_cost = fluid.layers.mean(x=cost)
 
-train_reader = paddle.batch(
- paddle.reader.shuffle(
- paddle.dataset.uci_housing.train(), buf_size=500),
- batch_size=BATCH_SIZE)
+ sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
+ sgd_optimizer.minimize(avg_cost)
 
-place = fluid.CPUPlace()
-feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
-exe = fluid.Executor(place)
+ BATCH_SIZE = 20
 
-exe.run(fluid.default_startup_program())
+ train_reader = paddle.batch(
+ paddle.reader.shuffle(
+ paddle.dataset.uci_housing.train(), buf_size=500),
+ batch_size=BATCH_SIZE)
 
-PASS_NUM = 100
-for pass_id in range(PASS_NUM):
- fluid.io.save_persistables(exe, "./fit_a_line.model/")
- fluid.io.load_persistables(exe, "./fit_a_line.model/")
- for data in train_reader():
- avg_loss_value, = exe.run(fluid.default_main_program(),
- feed=feeder.feed(data),
- fetch_list=[avg_cost])
- print(avg_loss_value)
- if avg_loss_value[0] < 10.0:
- exit(0) # if avg cost less than 10.0, we think our code is good.
-exit(1)
+ place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+ feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
+ exe = fluid.Executor(place)
+
+ exe.run(fluid.default_startup_program())
+
+ PASS_NUM = 100
+ for pass_id in range(PASS_NUM):
+ fluid.io.save_persistables(exe, "./fit_a_line.model/")
+ fluid.io.load_persistables(exe, "./fit_a_line.model/")
+ for data in train_reader():
+ avg_loss_value, = exe.run(fluid.default_main_program(),
+ feed=feeder.feed(data),
+ fetch_list=[avg_cost])
+ print(avg_loss_value)
+ if avg_loss_value[0] < 10.0:
+ return
+ raise AssertionError("Fit a line cost is too large, {0:2.2}".format(
+ avg_loss_value[0]))
+
+
+class TestFitALine(unittest.TestCase):
+ def test_cpu(self):
+ with self.program_scope_guard():
+ main(use_cuda=False)
+
+ def test_cuda(self):
+ with self.program_scope_guard():
+ main(use_cuda=True)
+
+ @contextlib.contextmanager
+ def program_scope_guard(self):
+ prog = fluid.Program()
+ startup_prog = fluid.Program()
+ scope = fluid.core.Scope()
+ with fluid.scope_guard(scope):
+ with fluid.program_guard(prog, startup_prog):
+ yield
+
+
+if __name__ == '__main__':
+ unittest.main()

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

@@ -14,10 +14,10 @@
 
 from __future__ import print_function
 
-import sys
-
 import paddle.v2 as paddle
 import paddle.v2.fluid as fluid
+import unittest
+import contextlib
 
 
 def resnet_cifar10(input, depth=32):
@@ -89,56 +89,89 @@ def conv_block(input, num_filter, groups, dropouts):
  return fc2
 
 
-classdim = 10
-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')
-
-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)
-avg_cost = fluid.layers.mean(x=cost)
-
-optimizer = fluid.optimizer.Adam(learning_rate=0.001)
-opts = optimizer.minimize(avg_cost)
-
-accuracy = fluid.evaluator.Accuracy(input=predict, label=label)
-
-BATCH_SIZE = 128
-PASS_NUM = 1
-
-train_reader = paddle.batch(
- paddle.reader.shuffle(
- paddle.dataset.cifar.train10(), buf_size=128 * 10),
- batch_size=BATCH_SIZE)
-
-place = fluid.CPUPlace()
-exe = fluid.Executor(place)
-feeder = fluid.DataFeeder(place=place, feed_list=[images, label])
-exe.run(fluid.default_startup_program())
-
-for pass_id in range(PASS_NUM):
- accuracy.reset(exe)
- for data in train_reader():
- loss, acc = exe.run(fluid.default_main_program(),
- feed=feeder.feed(data),
- fetch_list=[avg_cost] + accuracy.metrics)
- pass_acc = accuracy.eval(exe)
- print("loss:" + str(loss) + " acc:" + str(acc) + " pass_acc:" + str(
- pass_acc))
- # this model is slow, so if we can train two mini batch, we think it works properly.
- exit(0)
-exit(1)
+def main(net_type, use_cuda):
+ if use_cuda and not fluid.core.is_compiled_with_cuda():
+ return
+
+ classdim = 10
+ 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')
+
+ 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)
+ avg_cost = fluid.layers.mean(x=cost)
+
+ optimizer = fluid.optimizer.Adam(learning_rate=0.001)
+ optimizer.minimize(avg_cost)
+
+ accuracy = fluid.evaluator.Accuracy(input=predict, label=label)
+
+ BATCH_SIZE = 128
+ PASS_NUM = 1
+
+ train_reader = paddle.batch(
+ paddle.reader.shuffle(
+ paddle.dataset.cifar.train10(), buf_size=128 * 10),
+ batch_size=BATCH_SIZE)
+
+ place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+ exe = fluid.Executor(place)
+ feeder = fluid.DataFeeder(place=place, feed_list=[images, label])
+ exe.run(fluid.default_startup_program())
+
+ loss = 0.0
+ for pass_id in range(PASS_NUM):
+ accuracy.reset(exe)
+ for data in train_reader():
+ loss, acc = exe.run(fluid.default_main_program(),
+ feed=feeder.feed(data),
+ fetch_list=[avg_cost] + accuracy.metrics)
+ pass_acc = accuracy.eval(exe)
+ print("loss:" + str(loss) + " acc:" + str(acc) + " pass_acc:" + str(
+ pass_acc))
+ return
+
+ raise AssertionError(
+ "Image classification loss is too large, {0:2.2}".format(loss))
+
+
+class TestImageClassification(unittest.TestCase):
+ def test_vgg_cuda(self):
+ with self.scope_prog_guard():
+ main('vgg', use_cuda=True)
+
+ def test_resnet_cuda(self):
+ with self.scope_prog_guard():
+ main('resnet', use_cuda=True)
+
+ def test_vgg_cpu(self):
+ with self.scope_prog_guard():
+ main('vgg', use_cuda=False)
+
+ def test_resnet_cpu(self):
+ with self.scope_prog_guard():
+ main('resnet', use_cuda=False)
+
+ @contextlib.contextmanager
+ def scope_prog_guard(self):
+ prog = fluid.Program()
+ startup_prog = fluid.Program()
+ scope = fluid.core.Scope()
+ with fluid.scope_guard(scope):
+ with fluid.program_guard(prog, startup_prog):
+ yield
+
+
+if __name__ == '__main__':
+ unittest.main()