From 1b1f305babc3c91d0761814306df0004620be309 Mon Sep 17 00:00:00 2001 From: Yang Yu Date: Wed, 31 Jan 2018 13:07:19 +0800 Subject: [PATCH] Make image_classification as a normal python unittest --- .../paddle/v2/fluid/tests/book/CMakeLists.txt | 4 +- .../book/test_image_classification_train.py | 143 +++++++++++------- 2 files changed, 89 insertions(+), 58 deletions(-) diff --git a/python/paddle/v2/fluid/tests/book/CMakeLists.txt b/python/paddle/v2/fluid/tests/book/CMakeLists.txt index dda02c03f..a870478db 100644 --- a/python/paddle/v2/fluid/tests/book/CMakeLists.txt +++ b/python/paddle/v2/fluid/tests/book/CMakeLists.txt @@ -1,9 +1,7 @@ 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) diff --git a/python/paddle/v2/fluid/tests/book/test_image_classification_train.py b/python/paddle/v2/fluid/tests/book/test_image_classification_train.py index 30582a21d..a4168d16d 100644 --- a/python/paddle/v2/fluid/tests/book/test_image_classification_train.py +++ b/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 vgg16_bn_drop(input): 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() -- GitLab