from __future__ import print_function

import contextlib
import numpy as np
import six

import paddle
import paddle.fluid as fluid
from paddle.fluid.optimizer import SGDOptimizer
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, FC
from paddle.fluid.dygraph.base import to_variable


class SimpleImgConvPool(fluid.dygraph.Layer):
    """
    Conv Pool Layer
    """
    def __init__(self,
                 name_scope,
                 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__(name_scope)

        self._conv2d = Conv2D(
            self.full_name(),
            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(
            self.full_name(),
            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.dygraph.Layer):
    """
    MNIST model
    """
    def __init__(self, name_scope):
        super(MNIST, self).__init__(name_scope)

        self._simple_img_conv_pool_1 = SimpleImgConvPool(
            self.full_name(), 1, 20, 5, 2, 2, act="relu")

        self._simple_img_conv_pool_2 = SimpleImgConvPool(
            self.full_name(), 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(self.full_name(),
                      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


def train_mnist():
    seed = 90
    epoch_num = 10

    with fluid.dygraph.guard():
        fluid.default_startup_program().random_seed = seed
        fluid.default_main_program().random_seed = seed

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

        for epoch in range(epoch_num):
            for batch_id, data in enumerate(train_reader()):
                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

                cost = mnist(img)
                loss = fluid.layers.cross_entropy(cost, label)
                avg_loss = fluid.layers.mean(loss)
                avg_loss.backward()
                sgd.minimize(avg_loss)
                mnist.clear_gradients()

                dy_out = avg_loss.numpy()
                print("batch id %d, loss %f" % (batch_id, dy_out))


if __name__ == '__main__':
    train_mnist()