test_image_classification_layer.py

import unittest

import paddle.v2.fluid.layers as layers
import paddle.v2.fluid.nets as nets
from paddle.v2.fluid.framework import Program


def conv_block(input,
               num_filter,
               groups,
               dropouts,
               main_program=None,
               startup_program=None):
    return nets.img_conv_group(
        input=input,
        pool_size=2,
        pool_stride=2,
        conv_num_filter=[num_filter] * groups,
        conv_filter_size=3,
        conv_act='relu',
        conv_with_batchnorm=True,
        conv_batchnorm_drop_rate=dropouts,
        pool_type='max',
        main_program=main_program,
        startup_program=startup_program)


class TestLayer(unittest.TestCase):
    def test_batch_norm_layer(self):
        main_program = Program()
        startup_program = Program()
        images = layers.data(
            name='pixel',
            shape=[3, 48, 48],
            data_type='float32',
            main_program=main_program)
        layers.batch_norm(
            input=images,
            main_program=main_program,
            startup_program=startup_program)

        # print str(main_program)

    def test_dropout_layer(self):
        main_program = Program()
        startup_program = Program()
        images = layers.data(
            name='pixel',
            shape=[3, 48, 48],
            data_type='float32',
            main_program=main_program)
        layers.dropout(
            x=images,
            dropout_prob=0.5,
            main_program=main_program,
            startup_program=startup_program)

        # print str(main_program)

    def test_img_conv_group(self):
        main_program = Program()
        startup_program = Program()

        images = layers.data(
            name='pixel',
            shape=[3, 48, 48],
            data_type='float32',
            main_program=main_program,
            startup_program=startup_program)
        conv1 = conv_block(images, 64, 2, [0.3, 0], main_program,
                           startup_program)
        conv2 = conv_block(conv1, 256, 3, [0.4, 0.4, 0], main_program,
                           startup_program)

        # print str(main_program)

    def test_elementwise_add_with_act(self):
        main_program = Program()
        startup_program = Program()
        image1 = layers.data(
            name='pixel1',
            shape=[3, 48, 48],
            data_type='float32',
            main_program=main_program,
            startup_program=startup_program)
        image2 = layers.data(
            name='pixel2',
            shape=[3, 48, 48],
            data_type='float32',
            main_program=main_program,
            startup_program=startup_program)
        out = layers.elementwise_add(
            x=image1,
            y=image2,
            act='relu',
            main_program=main_program,
            startup_program=startup_program)
        # print(main_program)


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