test_conv3d_op.py

import unittest
import numpy as np
from op_test import OpTest


class TestConv3dOp(OpTest):
    def setUp(self):
        self.init_groups()
        self.op_type = "conv3d"
        batch_size = 2
        input_channels = 3
        input_depth = 5
        input_height = 5
        input_width = 5
        output_channels = 6
        filter_depth = 3
        filter_height = 3
        filter_width = 3
        stride = 1
        padding = 0
        output_depth = (input_depth - filter_depth + 2 * padding) / stride + 1
        output_height = (input_height - filter_height + 2 * padding
                         ) / stride + 1
        output_width = (input_width - filter_width + 2 * padding) / stride + 1
        input = np.random.random((batch_size, input_channels, input_depth,
                                  input_height, input_width)).astype("float32")

        filter = np.random.random(
            (output_channels, input_channels / self.groups, filter_depth,
             filter_height, filter_width)).astype("float32")
        output = np.ndarray((batch_size, output_channels, output_depth,
                             output_height, output_width))

        self.inputs = {'Input': input, 'Filter': filter}
        self.attrs = {
            'strides': [1, 1, 1],
            'paddings': [0, 0, 0],
            'groups': self.groups
        }

        output_group_channels = output_channels / self.groups
        input_group_channels = input_channels / self.groups
        for batchid in xrange(batch_size):
            for group in xrange(self.groups):
                for outchannelid in range(group * output_group_channels,
                                          (group + 1) * output_group_channels):
                    for deepid in xrange(output_depth):
                        for rowid in xrange(output_height):
                            for colid in xrange(output_width):
                                start_d = (deepid * stride) - padding
                                start_h = (rowid * stride) - padding
                                start_w = (colid * stride) - padding
                                output_value = 0.0
                                for inchannelid in range(
                                        group * input_group_channels,
                                    (group + 1) * input_group_channels):
                                    for fdeepid in xrange(filter_depth):
                                        for frowid in xrange(filter_height):
                                            for fcolid in xrange(filter_width):
                                                input_value = 0.0
                                                indeepid = start_d + fdeepid
                                                inrowid = start_h + frowid
                                                incolid = start_w + fcolid
                                                if ((indeepid >= 0 and
                                                     indeepid < input_depth) and
                                                    (inrowid >= 0 and
                                                     inrowid < input_height) and
                                                    (incolid >= 0 and
                                                     incolid < input_width)):

                                                    input_value = input[
                                                        batchid][inchannelid][
                                                            indeepid][inrowid][
                                                                incolid]
                                                filter_value = filter[
                                                    outchannelid][
                                                        inchannelid %
                                                        input_group_channels][
                                                            fdeepid][frowid][
                                                                fcolid]
                                                output_value += input_value * filter_value
                                output[batchid][outchannelid][deepid][rowid][
                                    colid] = output_value

        self.outputs = {'Output': output}

    def test_check_output(self):
        self.check_output()

    def test_check_grad(self):
        self.check_grad(
            set(['Input', 'Filter']), 'Output', max_relative_error=0.05)

    def test_check_grad_no_filter(self):
        self.check_grad(
            ['Input'],
            'Output',
            max_relative_error=0.05,
            no_grad_set=set(['Filter']))

    def test_check_grad_no_input(self):
        self.check_grad(
            ['Filter'],
            'Output',
            max_relative_error=0.05,
            no_grad_set=set(['Input']))

    def init_groups(self):
        self.groups = 1


class TestWithGroup(TestConv3dOp):
    def init_groups(self):
        self.groups = 3


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