未验证 提交 2c189dca 编写于 作者: Y Yu Yang 提交者: GitHub

Merge pull request #7998 from reyoung/feature/make_image_classification_normal_unittest

Make image_classification as a normal python unittest
file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py") file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}") string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
list(REMOVE_ITEM TEST_OPS test_image_classification_train test_recognize_digits) list(REMOVE_ITEM TEST_OPS test_recognize_digits)
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)
py_test(test_recognize_digits_mlp_cpu py_test(test_recognize_digits_mlp_cpu
SRCS test_recognize_digits.py SRCS test_recognize_digits.py
ARGS mlp) ARGS mlp)
......
...@@ -14,10 +14,10 @@ ...@@ -14,10 +14,10 @@
from __future__ import print_function from __future__ import print_function
import sys
import paddle.v2 as paddle import paddle.v2 as paddle
import paddle.v2.fluid as fluid import paddle.v2.fluid as fluid
import unittest
import contextlib
def resnet_cifar10(input, depth=32): def resnet_cifar10(input, depth=32):
...@@ -89,56 +89,89 @@ def vgg16_bn_drop(input): ...@@ -89,56 +89,89 @@ def vgg16_bn_drop(input):
return fc2 return fc2
classdim = 10 def main(net_type, use_cuda):
data_shape = [3, 32, 32] if use_cuda and not fluid.core.is_compiled_with_cuda():
return
images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64') classdim = 10
data_shape = [3, 32, 32]
net_type = "vgg"
if len(sys.argv) >= 2: images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32')
net_type = sys.argv[1] label = fluid.layers.data(name='label', shape=[1], dtype='int64')
if net_type == "vgg": if net_type == "vgg":
print("train vgg net") print("train vgg net")
net = vgg16_bn_drop(images) net = vgg16_bn_drop(images)
elif net_type == "resnet": elif net_type == "resnet":
print("train resnet") print("train resnet")
net = resnet_cifar10(images, 32) net = resnet_cifar10(images, 32)
else: else:
raise ValueError("%s network is not supported" % net_type) raise ValueError("%s network is not supported" % net_type)
predict = fluid.layers.fc(input=net, size=classdim, act='softmax') predict = fluid.layers.fc(input=net, size=classdim, act='softmax')
cost = fluid.layers.cross_entropy(input=predict, label=label) cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(x=cost) avg_cost = fluid.layers.mean(x=cost)
optimizer = fluid.optimizer.Adam(learning_rate=0.001) optimizer = fluid.optimizer.Adam(learning_rate=0.001)
opts = optimizer.minimize(avg_cost) optimizer.minimize(avg_cost)
accuracy = fluid.evaluator.Accuracy(input=predict, label=label) accuracy = fluid.evaluator.Accuracy(input=predict, label=label)
BATCH_SIZE = 128 BATCH_SIZE = 128
PASS_NUM = 1 PASS_NUM = 1
train_reader = paddle.batch( train_reader = paddle.batch(
paddle.reader.shuffle( paddle.reader.shuffle(
paddle.dataset.cifar.train10(), buf_size=128 * 10), paddle.dataset.cifar.train10(), buf_size=128 * 10),
batch_size=BATCH_SIZE) batch_size=BATCH_SIZE)
place = fluid.CPUPlace() place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place) exe = fluid.Executor(place)
feeder = fluid.DataFeeder(place=place, feed_list=[images, label]) feeder = fluid.DataFeeder(place=place, feed_list=[images, label])
exe.run(fluid.default_startup_program()) exe.run(fluid.default_startup_program())
for pass_id in range(PASS_NUM): loss = 0.0
accuracy.reset(exe) for pass_id in range(PASS_NUM):
for data in train_reader(): accuracy.reset(exe)
loss, acc = exe.run(fluid.default_main_program(), for data in train_reader():
feed=feeder.feed(data), loss, acc = exe.run(fluid.default_main_program(),
fetch_list=[avg_cost] + accuracy.metrics) feed=feeder.feed(data),
pass_acc = accuracy.eval(exe) fetch_list=[avg_cost] + accuracy.metrics)
print("loss:" + str(loss) + " acc:" + str(acc) + " pass_acc:" + str( pass_acc = accuracy.eval(exe)
pass_acc)) print("loss:" + str(loss) + " acc:" + str(acc) + " pass_acc:" + str(
# this model is slow, so if we can train two mini batch, we think it works properly. pass_acc))
exit(0) return
exit(1)
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()
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