提交 1b1f305b 编写于 作者: Y Yang Yu

Make image_classification as a normal python unittest

上级 c091dbdf
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 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)
list(REMOVE_ITEM TEST_OPS test_recognize_digits)
py_test(test_recognize_digits_mlp_cpu
SRCS test_recognize_digits.py
ARGS mlp)
......
......@@ -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):
......@@ -89,48 +89,49 @@ def vgg16_bn_drop(input):
return fc2
classdim = 10
data_shape = [3, 32, 32]
def main(net_type, use_cuda):
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:
net_type = sys.argv[1]
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":
if net_type == "vgg":
print("train vgg net")
net = vgg16_bn_drop(images)
elif net_type == "resnet":
elif net_type == "resnet":
print("train resnet")
net = resnet_cifar10(images, 32)
else:
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)
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)
optimizer = fluid.optimizer.Adam(learning_rate=0.001)
optimizer.minimize(avg_cost)
accuracy = fluid.evaluator.Accuracy(input=predict, label=label)
accuracy = fluid.evaluator.Accuracy(input=predict, label=label)
BATCH_SIZE = 128
PASS_NUM = 1
BATCH_SIZE = 128
PASS_NUM = 1
train_reader = paddle.batch(
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())
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())
for pass_id in range(PASS_NUM):
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(),
......@@ -139,6 +140,38 @@ for pass_id in range(PASS_NUM):
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)
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()
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