From a424ab499e291a14d587b578054376e082d15060 Mon Sep 17 00:00:00 2001 From: minqiyang Date: Mon, 11 Mar 2019 18:52:50 +0800 Subject: [PATCH] Change CMakeFiles test=develop --- .../fluid/tests/unittests/CMakeLists.txt | 4 +- .../tests/unittests/test_imperative_mnist.py | 132 ++++++------------ 2 files changed, 41 insertions(+), 95 deletions(-) diff --git a/python/paddle/fluid/tests/unittests/CMakeLists.txt b/python/paddle/fluid/tests/unittests/CMakeLists.txt index a1cf5fad1..562866cf6 100644 --- a/python/paddle/fluid/tests/unittests/CMakeLists.txt +++ b/python/paddle/fluid/tests/unittests/CMakeLists.txt @@ -76,7 +76,7 @@ list(REMOVE_ITEM TEST_OPS test_image_classification_resnet) list(REMOVE_ITEM TEST_OPS test_bilinear_interp_op) list(REMOVE_ITEM TEST_OPS test_nearest_interp_op) list(REMOVE_ITEM TEST_OPS test_imperative_resnet) -list(REMOVE_ITEM TEST_OPS test_imperative_optimizer) +list(REMOVE_ITEM TEST_OPS test_imperative_mnist) list(REMOVE_ITEM TEST_OPS test_ir_memory_optimize_transformer) foreach(TEST_OP ${TEST_OPS}) py_test_modules(${TEST_OP} MODULES ${TEST_OP}) @@ -87,7 +87,7 @@ py_test_modules(test_bilinear_interp_op MODULES test_bilinear_interp_op SERIAL) py_test_modules(test_nearest_interp_op MODULES test_nearest_interp_op SERIAL) py_test_modules(test_imperative_resnet MODULES test_imperative_resnet ENVS FLAGS_cudnn_deterministic=1) -py_test_modules(test_imperative_optimizer MODULES test_imperative_optimizer ENVS +py_test_modules(test_imperative_mnist MODULES test_imperative_mnist ENVS FLAGS_cudnn_deterministic=1) if(WITH_DISTRIBUTE) py_test_modules(test_dist_train MODULES test_dist_train SERIAL) diff --git a/python/paddle/fluid/tests/unittests/test_imperative_mnist.py b/python/paddle/fluid/tests/unittests/test_imperative_mnist.py index d0a5a8831..d82132436 100644 --- a/python/paddle/fluid/tests/unittests/test_imperative_mnist.py +++ b/python/paddle/fluid/tests/unittests/test_imperative_mnist.py @@ -12,6 +12,8 @@ # See the License for the specific language governing permissions and # limitations under the License. +from __future__ import print_function + import contextlib import unittest import numpy as np @@ -21,112 +23,56 @@ import paddle import paddle.fluid as fluid from paddle.fluid import core from paddle.fluid.optimizer import SGDOptimizer -from paddle.fluid.imperative.nn import Conv2D, Pool2D, FC +from paddle.fluid.imperative.nn import FC from paddle.fluid.imperative.base import to_variable from test_imperative_base import new_program_scope -class SimpleImgConvPool(fluid.imperative.Layer): - def __init__(self, - num_channels, - num_filters, - filter_size, - pool_size, - pool_stride, - pool_padding=0, - pool_type='max', - global_pooling=False, - conv_stride=1, - conv_padding=0, - conv_dilation=1, - conv_groups=1, - act=None, - use_cudnn=False, - param_attr=None, - bias_attr=None): - super(SimpleImgConvPool, self).__init__() - - self._conv2d = Conv2D( - num_channels=num_channels, - num_filters=num_filters, - filter_size=filter_size, - stride=conv_stride, - padding=conv_padding, - dilation=conv_dilation, - groups=conv_groups, - param_attr=None, - bias_attr=None, - use_cudnn=use_cudnn) - - self._pool2d = Pool2D( - pool_size=pool_size, - pool_type=pool_type, - pool_stride=pool_stride, - pool_padding=pool_padding, - global_pooling=global_pooling, - use_cudnn=use_cudnn) - - def forward(self, inputs): - x = self._conv2d(inputs) - x = self._pool2d(x) - return x - - -class MNIST(fluid.imperative.Layer): +class MLP(fluid.imperative.Layer): def __init__(self, param_attr=None, bias_attr=None): - super(MNIST, self).__init__() - - self._simple_img_conv_pool_1 = SimpleImgConvPool( - 1, 20, 5, 2, 2, act="relu") + self._fc1 = FC(10) + self._fc2 = FC(10) - self._simple_img_conv_pool_2 = SimpleImgConvPool( - 20, 50, 5, 2, 2, act="relu") + def forward(self, inputs): + y = self._fc1(inputs) + y = self._fc2(y) + return y - pool_2_shape = 50 * 8 * 8 - SIZE = 10 - scale = (2.0 / (pool_2_shape**2 * SIZE))**0.5 - self._fc = FC(10, - param_attr=fluid.param_attr.ParamAttr( - initializer=fluid.initializer.NormalInitializer( - loc=0.0, scale=scale))) - def forward(self, inputs): - x = self._simple_img_conv_pool_1(inputs) - x = self._simple_img_conv_pool_2(x) - x = self._fc(x) - return x +class TestImperativeOptimizerBase(unittest.TestCase): + def setUp(self): + self.batch_num = 2 + def get_optimizer(self): + self.optimizer = SGDOptimizer(learning_rate=1e-3) -class TestImperativeMnist(unittest.TestCase): - def test_mnist_cpu_float32(self): + def test_optimizer_float32(self): seed = 90 - with fluid.imperative.guard(): fluid.default_startup_program().random_seed = seed fluid.default_main_program().random_seed = seed - mnist = MNIST() - sgd = SGDOptimizer(learning_rate=1e-3) + mlp = MLP() + self.get_optimizer() train_reader = paddle.batch( - paddle.dataset.mnist.train(), batch_size=128) + paddle.dataset.mnist.train(), batch_size=128, drop_last=True) dy_param_init_value = {} for batch_id, data in enumerate(train_reader()): - if batch_id >= 2: + if batch_id >= self.batch_num: break - x_data = np.array( + dy_x_data = np.array( [x[0].reshape(1, 28, 28) for x in data]).astype('float32') y_data = np.array([x[1] for x in data]).astype('int64').reshape( 128, 1) - img = to_variable(x_data) + img = to_variable(dy_x_data) label = to_variable(y_data) label._stop_gradient = True - cost = mnist(img) - loss = fluid.layers.cross_entropy(cost, label) - avg_loss = fluid.layers.mean(loss) + cost = mlp(img) + avg_loss = fluid.layers.reduce_mean(cost) dy_out = avg_loss._numpy() if batch_id == 0: @@ -135,7 +81,8 @@ class TestImperativeMnist(unittest.TestCase): dy_param_init_value[param.name] = param._numpy() avg_loss._backward() - sgd.minimize(avg_loss) + self.optimizer.minimize(avg_loss) + mlp.clear_gradients() dy_param_value = {} for param in fluid.default_main_program().global_block( ).all_parameters(): @@ -149,23 +96,21 @@ class TestImperativeMnist(unittest.TestCase): ) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0)) mnist = MNIST() - sgd = SGDOptimizer(learning_rate=1e-3) + self.get_optimizer() train_reader = paddle.batch( - paddle.dataset.mnist.train(), batch_size=128) + paddle.dataset.mnist.train(), batch_size=128, drop_last=True) img = fluid.layers.data( name='pixel', shape=[1, 28, 28], dtype='float32') label = fluid.layers.data(name='label', shape=[1], dtype='int64') cost = mnist(img) - loss = fluid.layers.cross_entropy(cost, label) - avg_loss = fluid.layers.mean(loss) - sgd.minimize(avg_loss) + avg_loss = fluid.layers.reduce_mean(cost) + self.optimizer.minimize(avg_loss) # initialize params and fetch them static_param_init_value = {} static_param_name_list = [] - for param in fluid.default_startup_program().global_block( - ).all_parameters(): + for param in mnist.parameters(): static_param_name_list.append(param.name) out = exe.run(fluid.default_startup_program(), @@ -175,10 +120,10 @@ class TestImperativeMnist(unittest.TestCase): static_param_init_value[static_param_name_list[i]] = out[i] for batch_id, data in enumerate(train_reader()): - if batch_id >= 2: + if batch_id >= self.batch_num: break - x_data = np.array( + static_x_data = np.array( [x[0].reshape(1, 28, 28) for x in data]).astype('float32') y_data = np.array([x[1] for x in data]).astype('int64').reshape( [128, 1]) @@ -186,7 +131,7 @@ class TestImperativeMnist(unittest.TestCase): fetch_list = [avg_loss.name] fetch_list.extend(static_param_name_list) out = exe.run(fluid.default_main_program(), - feed={"pixel": x_data, + feed={"pixel": static_x_data, "label": y_data}, fetch_list=fetch_list) @@ -196,11 +141,12 @@ class TestImperativeMnist(unittest.TestCase): static_param_value[static_param_name_list[i - 1]] = out[i] for key, value in six.iteritems(static_param_init_value): - self.assertTrue( - np.allclose(value.all(), dy_param_init_value[key].all())) - self.assertTrue(np.allclose(static_out.all(), dy_out.all())) + self.assertTrue(np.allclose(value, dy_param_init_value[key])) + + self.assertTrue(np.allclose(static_out, dy_out)) + for key, value in six.iteritems(static_param_value): - self.assertTrue(np.allclose(value.all(), dy_param_value[key].all())) + self.assertTrue(np.allclose(value, dy_param_value[key], atol=1e-5)) if __name__ == '__main__': -- GitLab