test_imperative_optimizer.py

# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import contextlib
import unittest
import numpy as np
import six

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.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):
    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._simple_img_conv_pool_2 = SimpleImgConvPool(
            20, 50, 5, 2, 2, act="relu")

        pool_2_shape = 50 * 4 * 4
        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)),
                      act="softmax")

    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 TestImperativeMnist(unittest.TestCase):
    def test_mnist_float32(self):
        seed = 90
        batch_num = 100000
        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)
            train_reader = paddle.batch(
                paddle.dataset.mnist.train(), batch_size=128)

            dy_param_init_value = {}
            for batch_id, data in enumerate(train_reader()):
                if batch_id >= batch_num:
                    break

                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(dy_x_data)
                label = to_variable(y_data)
                label._stop_gradient = True

                print("forward start")

                cost = mnist(img)
                loss = fluid.layers.cross_entropy(cost, label)
                avg_loss = fluid.layers.mean(loss)
                #  dy_out = avg_loss._numpy()
                print("forward end")

                #  if batch_id == 0:
                    #  for param in fluid.default_main_program().global_block(
                    #  ).all_parameters():
                        #  dy_param_init_value[param.name] = param._numpy()

                avg_loss._backward()

                print("backward end")

                sgd.minimize(avg_loss)

                print("sgd end")

                mnist.clear_gradients()

                import gc
                for name, var in fluid.default_main_program().global_block().vars.items():
                    if not var.persistable:
                        fluid.default_main_program().global_block()._remove_var(name)
                        #  var._ivar._clear_values()
                for op in fluid.default_main_program().global_block().ops:
                    fluid.default_main_program().global_block()._remove_op(op.idx)

                assert len(gc.get_referrers(avg_loss)) == 1

                print("clear end")
                print("ivar ref ", gc.get_referrers(gc.get_referrers(avg_loss._ivar)[0])[0].__class__.__name__)
                print("ivar ref ", gc.get_referrers(gc.get_referrers(avg_loss._ivar)[1])[0].__class__.__name__)

                #  dy_param_value = {}
                #  for param in fluid.default_main_program().global_block(
                #  ).all_parameters():
                    #  dy_param_value[param.name] = param._numpy()

        #  with new_program_scope():
            #  fluid.default_startup_program().random_seed = seed
            #  fluid.default_main_program().random_seed = seed

            #  exe = fluid.Executor(fluid.CPUPlace(
            #  ) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))

            #  mnist = MNIST()
            #  sgd = SGDOptimizer(learning_rate=1e-3)
            #  train_reader = paddle.batch(
                #  paddle.dataset.mnist.train(), batch_size=128)

            #  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)

            #  # initialize params and fetch them
            #  static_param_init_value = {}
            #  static_param_name_list = []
            #  for param in fluid.default_startup_program().global_block(
            #  ).all_parameters():
                #  static_param_name_list.append(param.name)

            #  out = exe.run(fluid.default_startup_program(),
                          #  fetch_list=static_param_name_list)

            #  for i in range(len(static_param_name_list)):
                #  static_param_init_value[static_param_name_list[i]] = out[i]

            #  for batch_id, data in enumerate(train_reader()):
                #  if batch_id >= batch_num:
                    #  break

                #  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])

                #  fetch_list = [avg_loss.name]
                #  fetch_list.extend(static_param_name_list)
                #  out = exe.run(fluid.default_main_program(),
                              #  feed={"pixel": static_x_data,
                                    #  "label": y_data},
                              #  fetch_list=fetch_list)

                #  static_param_value = {}
                #  static_out = out[0]
                #  for i in range(1, len(out)):
                    #  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, 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, dy_param_value[key]))


if __name__ == '__main__':
    unittest.main()