parallel_dygraph_mnist.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 numpy as np
from test_dist_base import TestParallelDyGraphRunnerBase, runtime_main

import paddle
import paddle.fluid as fluid
from paddle.fluid.dygraph.base import to_variable


class SimpleImgConvPool(fluid.dygraph.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().__init__()

        self._conv2d = paddle.nn.Conv2D(
            in_channels=num_channels,
            out_channels=num_filters,
            kernel_size=filter_size,
            stride=conv_stride,
            padding=conv_padding,
            dilation=conv_dilation,
            groups=conv_groups,
            weight_attr=None,
            bias_attr=None,
        )

        self._pool2d = paddle.nn.MaxPool2D(
            kernel_size=pool_size,
            stride=pool_stride,
            padding=pool_padding,
        )

    def forward(self, inputs):
        x = self._conv2d(inputs)
        x = self._pool2d(x)
        return x


class MNIST(fluid.dygraph.Layer):
    def __init__(self):
        super().__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"
        )

        self.pool_2_shape = 50 * 4 * 4
        SIZE = 10
        scale = (2.0 / (self.pool_2_shape**2 * SIZE)) ** 0.5
        self._fc = paddle.nn.Linear(
            self.pool_2_shape,
            10,
            weight_attr=paddle.ParamAttr(
                initializer=paddle.nn.initializer.Normal(mean=0.0, std=scale)
            ),
        )
        self.act = paddle.nn.Softmax()

    def forward(self, inputs, label):
        x = self._simple_img_conv_pool_1(inputs)
        x = self._simple_img_conv_pool_2(x)
        x = paddle.reshape(x, shape=[-1, self.pool_2_shape])
        cost = self._fc(x)
        loss = paddle.nn.functional.cross_entropy(
            self.act(cost), label, reduction='none', use_softmax=False
        )
        avg_loss = paddle.mean(loss)
        return avg_loss


class TestMnist(TestParallelDyGraphRunnerBase):
    def get_model(self):
        model = MNIST()
        train_reader = paddle.batch(
            paddle.dataset.mnist.train(), batch_size=2, drop_last=True
        )
        opt = paddle.optimizer.Adam(
            learning_rate=1e-3, parameters=model.parameters()
        )
        return model, train_reader, opt

    def run_one_loop(self, model, opt, data):
        batch_size = len(data)
        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(batch_size, 1)
        )
        img = to_variable(dy_x_data)
        label = to_variable(y_data)
        label.stop_gradient = True

        avg_loss = model(img, label)

        return avg_loss


if __name__ == "__main__":
    runtime_main(TestMnist)