Merge pull request #14971 from velconia/imperative_mnist
Imperative Optimizerrevert-15207-remove_op_handle_lock_and_fix_var
Qiyang Min
6 years ago
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import contextlib
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import unittest
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import numpy as np
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import paddle.fluid as fluid
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from paddle.fluid import core
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@contextlib.contextmanager
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def new_program_scope():
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prog = fluid.Program()
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startup_prog = fluid.Program()
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scope = fluid.core.Scope()
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with fluid.scope_guard(scope):
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with fluid.program_guard(prog, startup_prog):
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yield
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import contextlib
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import unittest
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import numpy as np
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import six
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import paddle
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import paddle.fluid as fluid
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from paddle.fluid import core
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from paddle.fluid.optimizer import SGDOptimizer
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from paddle.fluid.imperative.nn import Conv2D, Pool2D, FC
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from paddle.fluid.imperative.base import to_variable
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from test_imperative_base import new_program_scope
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class SimpleImgConvPool(fluid.imperative.PyLayer):
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def __init__(self,
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num_channels,
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num_filters,
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filter_size,
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pool_size,
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pool_stride,
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pool_padding=0,
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pool_type='max',
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global_pooling=False,
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conv_stride=1,
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conv_padding=0,
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conv_dilation=1,
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conv_groups=1,
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act=None,
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use_cudnn=False,
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param_attr=None,
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bias_attr=None):
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super(SimpleImgConvPool, self).__init__()
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self._conv2d = Conv2D(
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num_channels=num_channels,
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num_filters=num_filters,
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filter_size=filter_size,
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stride=conv_stride,
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padding=conv_padding,
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dilation=conv_dilation,
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groups=conv_groups,
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param_attr=None,
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bias_attr=None,
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use_cudnn=use_cudnn)
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self._pool2d = Pool2D(
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pool_size=pool_size,
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pool_type=pool_type,
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pool_stride=pool_stride,
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pool_padding=pool_padding,
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global_pooling=global_pooling,
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use_cudnn=use_cudnn)
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def forward(self, inputs):
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x = self._conv2d(inputs)
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x = self._pool2d(x)
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return x
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class MNIST(fluid.imperative.PyLayer):
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def __init__(self, param_attr=None, bias_attr=None):
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super(MNIST, self).__init__()
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self._simple_img_conv_pool_1 = SimpleImgConvPool(
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1, 20, 5, 2, 2, act="relu")
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self._simple_img_conv_pool_2 = SimpleImgConvPool(
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20, 50, 5, 2, 2, act="relu")
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pool_2_shape = 50 * 8 * 8
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SIZE = 10
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scale = (2.0 / (pool_2_shape**2 * SIZE))**0.5
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self._fc = FC(10,
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param_attr=fluid.param_attr.ParamAttr(
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initializer=fluid.initializer.NormalInitializer(
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loc=0.0, scale=scale)))
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def forward(self, inputs):
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x = self._simple_img_conv_pool_1(inputs)
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x = self._simple_img_conv_pool_2(x)
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x = self._fc(x)
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return x
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class TestImperativeMnist(unittest.TestCase):
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def test_mnist_cpu_float32(self):
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seed = 90
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with fluid.imperative.guard():
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fluid.default_startup_program().random_seed = seed
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fluid.default_main_program().random_seed = seed
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# mnist = Conv2D(1, 20, 5)
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mnist = MNIST()
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sgd = SGDOptimizer(learning_rate=1e-3)
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train_reader = paddle.batch(
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paddle.dataset.mnist.train(), batch_size=128)
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dy_param_init_value = {}
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for batch_id, data in enumerate(train_reader()):
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if batch_id >= 2:
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break
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x_data = np.array(
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[x[0].reshape(1, 28, 28) for x in data]).astype('float32')
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y_data = np.array([x[1] for x in data]).astype('int64').reshape(
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128, 1)
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img = to_variable(x_data)
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label = to_variable(y_data)
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label._stop_gradient = True
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cost = mnist(img)
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loss = fluid.layers.reduce_mean(cost)
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dy_out = loss._numpy()
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if batch_id == 0:
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for param in fluid.default_main_program().global_block(
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).all_parameters():
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dy_param_init_value[param.name] = param._numpy()
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loss._backward()
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sgd.minimize(loss)
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dy_param_value = {}
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for param in fluid.default_main_program().global_block(
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).all_parameters():
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dy_param_value[param.name] = param._numpy()
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with new_program_scope():
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fluid.default_startup_program().random_seed = seed
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fluid.default_main_program().random_seed = seed
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exe = fluid.Executor(fluid.CPUPlace())
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# mnist = Conv2D(1, 20, 5)
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mnist = MNIST()
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sgd = SGDOptimizer(learning_rate=1e-3)
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train_reader = paddle.batch(
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paddle.dataset.mnist.train(), batch_size=128)
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img = fluid.layers.data(
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name='pixel', shape=[1, 28, 28], dtype='float32')
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label = fluid.layers.data(name='label', shape=[1], dtype='int64')
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cost = mnist(img)
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loss = fluid.layers.reduce_mean(cost)
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sgd.minimize(loss)
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# initialize params and fetch them
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static_param_init_value = {}
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static_param_name_list = []
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for param in fluid.default_startup_program().global_block(
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).all_parameters():
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static_param_name_list.append(param.name)
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out = exe.run(fluid.default_startup_program(),
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fetch_list=static_param_name_list)
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for i in range(len(static_param_name_list)):
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static_param_init_value[static_param_name_list[i]] = out[i]
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for batch_id, data in enumerate(train_reader()):
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if batch_id >= 2:
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break
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x_data = np.array(
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[x[0].reshape(1, 28, 28) for x in data]).astype('float32')
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y_data = np.array([x[1] for x in data]).astype('int64').reshape(
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[128, 1])
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fetch_list = [loss.name]
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fetch_list.extend(static_param_name_list)
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out = exe.run(fluid.default_main_program(),
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feed={"pixel": x_data,
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"label": y_data},
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fetch_list=fetch_list)
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static_param_value = {}
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static_out = out[0]
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for i in range(1, len(out)):
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static_param_value[static_param_name_list[i - 1]] = out[i]
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for key, value in six.iteritems(static_param_init_value):
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self.assertTrue(
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np.allclose(value.all(), dy_param_init_value[key].all()))
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self.assertTrue(np.allclose(static_out.all(), dy_out.all()))
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for key, value in six.iteritems(static_param_value):
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self.assertTrue(np.allclose(value.all(), dy_param_value[key].all()))
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if __name__ == '__main__':
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unittest.main()
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