test_conv3d_op.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 unittest
import numpy as np

import paddle.fluid.core as core
from op_test import OpTest
import paddle.fluid as fluid
import paddle


def conv3d_forward_naive(input,
                         filter,
                         group,
                         conv_param,
                         padding_algorithm='EXPLICIT',
                         data_format="NCDHW"):

    if padding_algorithm not in ["SAME", "VALID", "EXPLICIT"]:
        raise ValueError("Unknown Attr(padding_algorithm): '%s'. "
                         "It can only be 'SAME' or 'VALID'." %
                         str(padding_algorithm))

    if data_format not in ["NCDHW", "NDHWC"]:
        raise ValueError("Unknown Attr(data_format): '%s' ."
                         "It can only be 'NCDHW' or 'NDHWC'." %
                         str(data_format))

    channel_last = (data_format == "NDHWC")
    if channel_last:
        input = np.transpose(input, [0, 4, 1, 2, 3])

    in_n, in_c, in_d, in_h, in_w = input.shape

    f_n, f_c, f_d, f_h, f_w = filter.shape
    out_n = in_n
    out_c = f_n
    assert f_c * group == in_c
    assert np.mod(out_c, group) == 0
    sub_out_c = out_c // group
    sub_f_n = f_n // group

    stride, pad, dilation = conv_param['stride'], conv_param['pad'], conv_param[
        'dilations']

    # update pad and dilation
    def _get_padding_with_SAME(input_shape, pool_size, pool_stride):
        padding = []
        for input_size, filter_size, stride_size in zip(input_shape, pool_size,
                                                        pool_stride):
            out_size = int((input_size + stride_size - 1) / stride_size)
            pad_sum = np.max(
                ((out_size - 1) * stride_size + filter_size - input_size, 0))
            pad_0 = int(pad_sum / 2)
            pad_1 = int(pad_sum - pad_0)
            padding.append(pad_0)
            padding.append(pad_1)
        return padding

    ksize = filter.shape[2:5]
    if padding_algorithm == "VALID":
        pad = [0, 0, 0, 0, 0, 0]
    elif padding_algorithm == "SAME":
        dilation = [1, 1, 1]
        input_data_shape = input.shape[2:5]
        pad = _get_padding_with_SAME(input_data_shape, ksize, stride)

    pad_d_0, pad_d_1 = pad[0], pad[0]
    pad_h_0, pad_h_1 = pad[1], pad[1]
    pad_w_0, pad_w_1 = pad[2], pad[2]
    if len(pad) == 6:
        pad_d_0, pad_d_1 = pad[0], pad[1]
        pad_h_0, pad_h_1 = pad[2], pad[3]
        pad_w_0, pad_w_1 = pad[4], pad[5]

    out_d = 1 + (in_d + pad_d_0 + pad_d_1 - (dilation[0] *
                                             (f_d - 1) + 1)) // stride[0]
    out_h = 1 + (in_h + pad_h_0 + pad_h_1 - (dilation[1] *
                                             (f_h - 1) + 1)) // stride[1]
    out_w = 1 + (in_w + pad_w_0 + pad_w_1 - (dilation[2] *
                                             (f_w - 1) + 1)) // stride[2]

    out = np.zeros((in_n, out_c, out_d, out_h, out_w))

    d_bolck_d = (dilation[0] * (f_d - 1) + 1)
    d_bolck_h = (dilation[1] * (f_h - 1) + 1)
    d_bolck_w = (dilation[2] * (f_w - 1) + 1)

    input_pad = np.pad(input, ((0, 0), (0, 0), (pad_d_0, pad_d_1),
                               (pad_h_0, pad_h_1), (pad_w_0, pad_w_1)),
                       mode='constant',
                       constant_values=0)

    filter_dilation = np.zeros((f_n, f_c, d_bolck_d, d_bolck_h, d_bolck_w))
    filter_dilation[:, :, 0:d_bolck_d:dilation[0], 0:d_bolck_h:dilation[1],
                    0:d_bolck_w:dilation[2]] = filter

    for d in range(out_d):
        for i in range(out_h):
            for j in range(out_w):
                for g in range(group):
                    input_pad_masked = \
                        input_pad[:, g * f_c:(g + 1) * f_c,
                        d * stride[0]:d * stride[0] + d_bolck_d,
                        i * stride[1]:i * stride[1] + d_bolck_h,
                        j * stride[2]:j * stride[2] + d_bolck_w]

                    f_sub = filter_dilation[g * sub_f_n:(g + 1) *
                                            sub_f_n, :, :, :, :]
                    for k in range(sub_out_c):
                        out[:, g * sub_out_c + k, d, i, j] = \
                            np.sum(input_pad_masked * f_sub[k, :, :, :, :],
                                   axis=(1, 2, 3, 4))
    if channel_last:
        out = np.transpose(out, [0, 2, 3, 4, 1])
    return out


def create_test_cudnn_class(parent):

    @unittest.skipIf(not core.is_compiled_with_cuda(),
                     "core is not compiled with CUDA")
    class TestCUDNNCase(parent):

        def init_kernel_type(self):
            self.use_cudnn = True
            self.dtype = np.float32 if core.is_compiled_with_rocm(
            ) else np.float64

    cls_name = "{0}_{1}".format(parent.__name__, "CUDNN")
    TestCUDNNCase.__name__ = cls_name
    globals()[cls_name] = TestCUDNNCase


def create_test_padding_SAME_class(parent):

    class TestPaddingSMAECase(parent):

        def init_paddings(self):
            self.pad = [0, 0, 0]
            self.padding_algorithm = "SAME"

    cls_name = "{0}_{1}".format(parent.__name__, "PaddingSAMEOp")
    TestPaddingSMAECase.__name__ = cls_name
    globals()[cls_name] = TestPaddingSMAECase


def create_test_padding_VALID_class(parent):

    class TestPaddingVALIDCase(parent):

        def init_paddings(self):
            self.pad = [1, 1, 1]
            self.padding_algorithm = "VALID"

    cls_name = "{0}_{1}".format(parent.__name__, "PaddingVALIDOp")
    TestPaddingVALIDCase.__name__ = cls_name
    globals()[cls_name] = TestPaddingVALIDCase


def create_test_cudnn_padding_SAME_class(parent):

    @unittest.skipIf(not core.is_compiled_with_cuda(),
                     "core is not compiled with CUDA")
    class TestCUDNNPaddingSMAECase(parent):

        def init_kernel_type(self):
            self.use_cudnn = True
            self.dtype = np.float32 if core.is_compiled_with_rocm(
            ) else np.float64

        def init_paddings(self):
            self.pad = [1, 1, 1]
            self.padding_algorithm = "SAME"

    cls_name = "{0}_{1}".format(parent.__name__, "CudnnPaddingSAMEOp")
    TestCUDNNPaddingSMAECase.__name__ = cls_name
    globals()[cls_name] = TestCUDNNPaddingSMAECase


def create_test_cudnn_padding_VALID_class(parent):

    @unittest.skipIf(not core.is_compiled_with_cuda(),
                     "core is not compiled with CUDA")
    class TestCUDNNPaddingVALIDCase(parent):

        def init_kernel_type(self):
            self.use_cudnn = True
            self.dtype = np.float32 if core.is_compiled_with_rocm(
            ) else np.float64

        def init_paddings(self):
            self.pad = [1, 1, 1]
            self.padding_algorithm = "VALID"

    cls_name = "{0}_{1}".format(parent.__name__, "CudnnPaddingVALIDOp")
    TestCUDNNPaddingVALIDCase.__name__ = cls_name
    globals()[cls_name] = TestCUDNNPaddingVALIDCase


def create_test_channel_last_class(parent):

    class TestChannelLastCase(parent):

        def init_data_format(self):
            self.data_format = "NDHWC"

        def init_test_case_2(self):
            N, C, D, H, W = self.input_size
            self.input_size = [N, D, H, W, C]

    cls_name = "{0}_{1}".format(parent.__name__, "ChannelLast")
    TestChannelLastCase.__name__ = cls_name
    globals()[cls_name] = TestChannelLastCase


def create_test_cudnn_channel_last_class(parent):

    @unittest.skipIf(not core.is_compiled_with_cuda(),
                     "core is not compiled with CUDA")
    class TestCudnnChannelLastCase(parent):

        def init_kernel_type(self):
            self.use_cudnn = True
            self.dtype = np.float32 if core.is_compiled_with_rocm(
            ) else np.float64

        def init_data_format(self):
            self.data_format = "NDHWC"

        def init_test_case_2(self):
            N, C, D, H, W = self.input_size
            self.input_size = [N, D, H, W, C]

    cls_name = "{0}_{1}".format(parent.__name__, "CudnnChannelLast")
    TestCudnnChannelLastCase.__name__ = cls_name
    globals()[cls_name] = TestCudnnChannelLastCase


class TestConv3DOp(OpTest):

    def setUp(self):
        self.op_type = "conv3d"
        self.use_cudnn = False
        self.use_mkldnn = False
        self.data_format = "AnyLayout"
        self.dtype = np.float64
        self.init_kernel_type()
        self.init_group()
        self.init_dilation()
        self.init_test_case()

        conv3d_param = {
            'stride': self.stride,
            'pad': self.pad,
            'dilations': self.dilations
        }

        input = np.random.random(self.input_size).astype(self.dtype)
        filter = np.random.random(self.filter_size).astype(self.dtype)
        output = conv3d_forward_naive(
            input,
            filter,
            self.groups,
            conv3d_param,
        ).astype(self.dtype)

        self.inputs = {
            'Input': OpTest.np_dtype_to_fluid_dtype(input),
            'Filter': OpTest.np_dtype_to_fluid_dtype(filter)
        }
        self.attrs = {
            'strides': self.stride,
            'paddings': self.pad,
            'groups': self.groups,
            'dilations': self.dilations,
            'use_cudnn': self.use_cudnn,
            'use_mkldnn': self.use_mkldnn,
            'data_format': self.data_format
        }
        self.outputs = {'Output': output}

    def has_cudnn(self):
        return core.is_compiled_with_cuda() and self.use_cudnn

    def test_check_output(self):
        # TODO(wangzhongpu): support mkldnn op in dygraph mode
        place = core.CUDAPlace(0) if self.has_cudnn() else core.CPUPlace()
        self.check_output_with_place(place,
                                     atol=1e-5,
                                     check_dygraph=(self.use_mkldnn == False))

    def test_check_grad(self):
        if self.dtype == np.float16:
            return
        place = core.CUDAPlace(0) if self.has_cudnn() else core.CPUPlace()
        # TODO(wangzhongpu): support mkldnn op in dygraph mode
        self.check_grad_with_place(place, {'Input', 'Filter'},
                                   'Output',
                                   max_relative_error=0.03,
                                   check_dygraph=(self.use_mkldnn == False))

    def test_check_grad_no_filter(self):
        if self.dtype == np.float16:
            return
        place = core.CUDAPlace(0) if self.has_cudnn() else core.CPUPlace()
        # TODO(wangzhongpu): support mkldnn op in dygraph mode
        self.check_grad_with_place(place, ['Input'],
                                   'Output',
                                   max_relative_error=0.03,
                                   no_grad_set=set(['Filter']),
                                   check_dygraph=(self.use_mkldnn == False))

    def test_check_grad_no_input(self):
        if self.dtype == np.float16:
            return
        place = core.CUDAPlace(0) if self.has_cudnn() else core.CPUPlace()
        # TODO(wangzhongpu): support mkldnn op in dygraph mode
        self.check_grad_with_place(place, ['Filter'],
                                   'Output',
                                   max_relative_error=0.03,
                                   no_grad_set=set(['Input']),
                                   check_dygraph=(self.use_mkldnn == False))

    def init_test_case(self):
        self.pad = [0, 0, 0]
        self.stride = [1, 1, 1]
        self.input_size = [2, 3, 4, 4, 4]  # NCDHW
        assert np.mod(self.input_size[1], self.groups) == 0
        f_c = self.input_size[1] // self.groups
        self.filter_size = [6, f_c, 3, 3, 3]

    def init_test_case_2(self):
        pass

    def init_dilation(self):
        self.dilations = [1, 1, 1]

    def init_group(self):
        self.groups = 1

    def init_kernel_type(self):
        pass


class TestCase1(TestConv3DOp):

    def init_test_case(self):
        self.pad = [1, 1, 1]
        self.stride = [1, 1, 1]
        self.input_size = [2, 3, 4, 4, 4]  # NCDHW
        assert np.mod(self.input_size[1], self.groups) == 0
        f_c = self.input_size[1] // self.groups
        self.filter_size = [6, f_c, 3, 3, 3]


class TestWithGroup1(TestConv3DOp):

    def init_group(self):
        self.groups = 3


class TestWithGroup2(TestCase1):

    def init_group(self):
        self.groups = 3


class TestWith1x1(TestConv3DOp):

    def init_test_case(self):
        self.pad = [0, 0, 0]
        self.stride = [1, 1, 1]
        self.input_size = [2, 3, 4, 4, 4]
        assert np.mod(self.input_size[1], self.groups) == 0
        f_c = self.input_size[1] // self.groups
        self.filter_size = [120, f_c, 1, 1, 1]

    def init_dilation(self):
        self.dilations = [1, 1, 1]

    def init_group(self):
        self.groups = 3


class TestWithInput1x1Filter1x1(TestConv3DOp):

    def init_test_case(self):
        self.pad = [0, 0, 0]
        self.stride = [1, 1, 1]
        self.input_size = [40, 3, 1, 1, 1]
        assert np.mod(self.input_size[1], self.groups) == 0
        f_c = self.input_size[1] // self.groups
        self.filter_size = [120, f_c, 1, 1, 1]

    def init_dilation(self):
        self.dilations = [1, 1, 1]

    def init_group(self):
        self.groups = 3


class TestWithDilation(TestConv3DOp):

    def init_test_case(self):
        self.pad = [0, 0, 0]
        self.stride = [1, 1, 1]
        self.input_size = [2, 3, 6, 6, 6]
        assert np.mod(self.input_size[1], self.groups) == 0
        f_c = self.input_size[1] // self.groups
        self.filter_size = [24, f_c, 2, 2, 2]

    def init_dilation(self):
        self.dilations = [2, 2, 2]

    def init_group(self):
        self.groups = 3


#---------------- Conv3DCUDNN ----------------


@unittest.skipIf(not core.is_compiled_with_cuda(),
                 "core is not compiled with CUDA")
class TestCUDNN(TestConv3DOp):

    def init_kernel_type(self):
        self.use_cudnn = True
        self.dtype = np.float32 if core.is_compiled_with_rocm() else np.float64


@unittest.skipIf(not core.is_compiled_with_cuda(),
                 "core is not compiled with CUDA")
class TestFP16CUDNN(TestConv3DOp):

    def init_kernel_type(self):
        self.use_cudnn = True
        self.dtype = np.float16

    def test_check_output(self):
        if core.is_compiled_with_cuda():
            place = core.CUDAPlace(0)
            if core.is_float16_supported(place):
                self.check_output_with_place(place, atol=2e-2)


@unittest.skipIf(not core.is_compiled_with_cuda(),
                 "core is not compiled with CUDA")
class TestWithGroup1CUDNN(TestWithGroup1):

    def init_kernel_type(self):
        self.use_cudnn = True
        self.dtype = np.float32 if core.is_compiled_with_rocm() else np.float64


@unittest.skipIf(not core.is_compiled_with_cuda(),
                 "core is not compiled with CUDA")
class TestFP16WithGroup1CUDNN(TestWithGroup1):

    def init_kernel_type(self):
        self.use_cudnn = True
        self.dtype = np.float16

    def test_check_output(self):
        if core.is_compiled_with_cuda():
            place = core.CUDAPlace(0)
            if core.is_float16_supported(place):
                self.check_output_with_place(place, atol=2e-2)


@unittest.skipIf(not core.is_compiled_with_cuda(),
                 "core is not compiled with CUDA")
class TestWithGroup2CUDNN(TestWithGroup2):

    def init_kernel_type(self):
        self.use_cudnn = True
        self.dtype = np.float32 if core.is_compiled_with_rocm() else np.float64


@unittest.skipIf(not core.is_compiled_with_cuda(),
                 "core is not compiled with CUDA")
class TestFP16WithGroup2CUDNN(TestWithGroup2):

    def init_kernel_type(self):
        self.use_cudnn = True
        self.dtype = np.float16

    def test_check_output(self):
        if core.is_compiled_with_cuda():
            place = core.CUDAPlace(0)
            if core.is_float16_supported(place):
                self.check_output_with_place(place, atol=2e-2)


@unittest.skipIf(not core.is_compiled_with_cuda(),
                 "core is not compiled with CUDA")
class TestWith1x1CUDNN(TestWith1x1):

    def init_kernel_type(self):
        self.use_cudnn = True
        self.dtype = np.float32 if core.is_compiled_with_rocm() else np.float64


@unittest.skipIf(not core.is_compiled_with_cuda(),
                 "core is not compiled with CUDA")
class TestFP16With1x1CUDNN(TestWith1x1):

    def init_kernel_type(self):
        self.use_cudnn = True
        self.dtype = np.float16

    def test_check_output(self):
        if core.is_compiled_with_cuda():
            place = core.CUDAPlace(0)
            if core.is_float16_supported(place):
                self.check_output_with_place(place, atol=2e-2)


@unittest.skipIf(not core.is_compiled_with_cuda(),
                 "core is not compiled with CUDA")
class TestWithInput1x1Filter1x1CUDNN(TestWithInput1x1Filter1x1):

    def init_kernel_type(self):
        self.use_cudnn = True
        self.dtype = np.float32 if core.is_compiled_with_rocm() else np.float64


@unittest.skipIf(not core.is_compiled_with_cuda(),
                 "core is not compiled with CUDA")
class TestFP16WithInput1x1Filter1x1CUDNN(TestWithInput1x1Filter1x1):

    def init_kernel_type(self):
        self.use_cudnn = True
        self.dtype = np.float16

    def test_check_output(self):
        if core.is_compiled_with_cuda():
            place = core.CUDAPlace(0)
            if core.is_float16_supported(place):
                self.check_output_with_place(place, atol=2e-2)


class TestCUDNNExhaustiveSearch(TestCUDNN):

    def init_kernel_type(self):
        self.use_cudnn = True
        self.exhaustive_search = True
        self.dtype = np.float32 if core.is_compiled_with_rocm() else np.float64


# ---- test asymmetric padding ----


class TestConv3DOp_2(OpTest):

    def setUp(self):
        self.op_type = "conv3d"
        self.use_cudnn = False
        self.use_mkldnn = False
        self.data_format = "NCDHW"
        self.dtype = np.float64
        self.init_kernel_type()
        self.init_group()
        self.init_dilation()
        self.init_data_format()
        self.init_test_case()
        self.init_paddings()

        self.init_test_case_2()

        conv3d_param = {
            'stride': self.stride,
            'pad': self.pad,
            'dilations': self.dilations
        }

        input = np.random.random(self.input_size).astype(self.dtype)
        filter = np.random.random(self.filter_size).astype(self.dtype)
        output = conv3d_forward_naive(input, filter, self.groups, conv3d_param,
                                      self.padding_algorithm,
                                      self.data_format).astype(self.dtype)

        self.inputs = {
            'Input': OpTest.np_dtype_to_fluid_dtype(input),
            'Filter': OpTest.np_dtype_to_fluid_dtype(filter)
        }
        self.attrs = {
            'strides': self.stride,
            'paddings': self.pad,
            'padding_algorithm': self.padding_algorithm,
            'groups': self.groups,
            'dilations': self.dilations,
            'use_cudnn': self.use_cudnn,
            'use_mkldnn': self.use_mkldnn,
            'data_format': self.data_format
        }
        self.outputs = {'Output': output}

    def has_cudnn(self):
        return core.is_compiled_with_cuda() and self.use_cudnn

    def test_check_output(self):
        place = core.CUDAPlace(0) if self.has_cudnn() else core.CPUPlace()
        self.check_output_with_place(place, atol=1e-5)

    def test_check_grad(self):
        if self.dtype == np.float16:
            return
        place = core.CUDAPlace(0) if self.has_cudnn() else core.CPUPlace()
        self.check_grad_with_place(place, {'Input', 'Filter'},
                                   'Output',
                                   max_relative_error=0.03)

    def test_check_grad_no_filter(self):
        if self.dtype == np.float16:
            return
        place = core.CUDAPlace(0) if self.has_cudnn() else core.CPUPlace()
        self.check_grad_with_place(place, ['Input'],
                                   'Output',
                                   max_relative_error=0.03,
                                   no_grad_set=set(['Filter']))

    def test_check_grad_no_input(self):
        if self.dtype == np.float16:
            return
        place = core.CUDAPlace(0) if self.has_cudnn() else core.CPUPlace()
        self.check_grad_with_place(place, ['Filter'],
                                   'Output',
                                   max_relative_error=0.03,
                                   no_grad_set=set(['Input']))

    def init_test_case(self):
        self.stride = [1, 1, 1]
        self.input_size = [2, 3, 4, 4, 4]  # NCDHW
        assert np.mod(self.input_size[1], self.groups) == 0
        f_c = self.input_size[1] // self.groups
        self.filter_size = [6, f_c, 3, 3, 3]

    def init_test_case_2(self):
        pass

    def init_dilation(self):
        self.dilations = [1, 1, 1]

    def init_group(self):
        self.groups = 1

    def init_kernel_type(self):
        pass

    def init_paddings(self):
        self.pad = [0, 0, 0]
        self.padding_algorithm = "EXPLICIT"

    def init_data_format(self):
        self.data_format = "NCDHW"


class TestConv3DOp_AsyPadding(TestConv3DOp_2):

    def init_test_case(self):
        self.stride = [1, 1, 2]
        self.input_size = [2, 3, 4, 4, 4]  # NCDHW
        assert np.mod(self.input_size[1], self.groups) == 0
        f_c = self.input_size[1] // self.groups
        self.filter_size = [6, f_c, 3, 3, 3]

    def init_paddings(self):
        self.pad = [1, 0, 1, 0, 0, 2]
        self.padding_algorithm = "EXPLICIT"


class TestConv3DOp_DiffDataInDiffDim(TestConv3DOp_2):

    def init_test_case(self):
        self.stride = [1, 1, 2]
        self.input_size = [2, 3, 4, 5, 5]  # NCDHW
        assert np.mod(self.input_size[1], self.groups) == 0
        f_c = self.input_size[1] // self.groups
        self.filter_size = [6, f_c, 3, 4, 3]

    def init_paddings(self):
        self.pad = [1, 0, 1, 0, 0, 2]
        self.padding_algorithm = "EXPLICIT"


create_test_padding_SAME_class(TestConv3DOp_DiffDataInDiffDim)
create_test_padding_VALID_class(TestConv3DOp_DiffDataInDiffDim)
create_test_channel_last_class(TestConv3DOp_DiffDataInDiffDim)


class TestCase1_AsyPadding(TestConv3DOp_2):

    def init_test_case(self):
        self.stride = [1, 1, 1]
        self.input_size = [2, 3, 4, 4, 4]  # NCDHW
        assert np.mod(self.input_size[1], self.groups) == 0
        f_c = self.input_size[1] // self.groups
        self.filter_size = [6, f_c, 3, 3, 3]

    def init_paddings(self):
        self.pad = [0, 0, 1, 0, 0, 2]
        self.padding_algorithm = "EXPLICIT"


class TestWithGroup1_AsyPadding(TestConv3DOp_2):

    def init_group(self):
        self.groups = 3

    def init_paddings(self):
        self.pad = [1, 1, 1, 0, 0, 2]
        self.padding_algorithm = "EXPLICIT"


class TestWithGroup2_AsyPadding(TestConv3DOp_2):

    def init_test_case(self):
        self.stride = [1, 1, 1]
        self.input_size = [2, 3, 4, 4, 4]  # NCDHW
        assert np.mod(self.input_size[1], self.groups) == 0
        f_c = self.input_size[1] // self.groups
        self.filter_size = [6, f_c, 3, 3, 3]

    def init_group(self):
        self.groups = 3

    def init_paddings(self):
        self.pad = [1, 1, 0, 1, 0, 2]
        self.padding_algorithm = "EXPLICIT"


class TestWith1x1_AsyPadding(TestConv3DOp_2):

    def init_test_case(self):
        self.stride = [1, 1, 1]
        self.input_size = [2, 3, 4, 4, 4]
        assert np.mod(self.input_size[1], self.groups) == 0
        f_c = self.input_size[1] // self.groups
        self.filter_size = [120, f_c, 1, 1, 1]

    def init_dilation(self):
        self.dilations = [1, 1, 1]

    def init_group(self):
        self.groups = 3

    def init_paddings(self):
        self.pad = [0, 0, 1, 0, 0, 2]
        self.padding_algorithm = "EXPLICIT"


class TestWithDilation_AsyPadding(TestConv3DOp_2):

    def init_test_case(self):
        self.stride = [1, 1, 1]
        self.input_size = [2, 3, 6, 6, 6]
        assert np.mod(self.input_size[1], self.groups) == 0
        f_c = self.input_size[1] // self.groups
        self.filter_size = [24, f_c, 2, 2, 2]

    def init_dilation(self):
        self.dilations = [2, 2, 2]

    def init_group(self):
        self.groups = 3

    def init_paddings(self):
        self.pad = [0, 0, 1, 0, 1, 0]
        self.padding_algorithm = "EXPLICIT"


create_test_cudnn_class(TestConv3DOp_AsyPadding)
create_test_cudnn_class(TestWithGroup1_AsyPadding)
create_test_cudnn_class(TestWithGroup2_AsyPadding)
create_test_cudnn_class(TestWith1x1_AsyPadding)
create_test_cudnn_class(TestWithDilation_AsyPadding)

create_test_padding_SAME_class(TestConv3DOp_AsyPadding)
create_test_padding_SAME_class(TestWithGroup1_AsyPadding)
create_test_padding_SAME_class(TestWith1x1_AsyPadding)

create_test_padding_VALID_class(TestConv3DOp_AsyPadding)
create_test_padding_VALID_class(TestWithGroup1_AsyPadding)
create_test_padding_VALID_class(TestWith1x1_AsyPadding)

create_test_cudnn_padding_SAME_class(TestConv3DOp_AsyPadding)
create_test_cudnn_padding_SAME_class(TestWithGroup1_AsyPadding)
create_test_cudnn_padding_SAME_class(TestWith1x1_AsyPadding)

create_test_cudnn_padding_VALID_class(TestConv3DOp_AsyPadding)
create_test_cudnn_padding_VALID_class(TestWithGroup1_AsyPadding)
create_test_cudnn_padding_VALID_class(TestWith1x1_AsyPadding)

create_test_channel_last_class(TestConv3DOp_AsyPadding)
create_test_channel_last_class(TestWithGroup1_AsyPadding)
create_test_channel_last_class(TestWith1x1_AsyPadding)

create_test_channel_last_class(TestConv3DOp_AsyPadding)
create_test_channel_last_class(TestWithGroup1_AsyPadding)
create_test_channel_last_class(TestWith1x1_AsyPadding)

create_test_cudnn_channel_last_class(TestConv3DOp_AsyPadding)
create_test_cudnn_channel_last_class(TestWithGroup1_AsyPadding)
create_test_cudnn_channel_last_class(TestWith1x1_AsyPadding)

create_test_cudnn_channel_last_class(TestConv3DOp_AsyPadding)
create_test_cudnn_channel_last_class(TestWithGroup1_AsyPadding)
create_test_cudnn_channel_last_class(TestWith1x1_AsyPadding)

# FIXME(typhoonzero): find a way to determine if
# using cudnn > 6 in python
# class TestWithDilationCUDNN(TestWithDilation):
#     def init_op_type(self):
#         self.op_type = "conv3d"


# --------- test python API ---------------
class TestConv3DAPI(unittest.TestCase):

    def test_api(self):

        input_NDHWC = fluid.layers.data(name="input_NDHWC",
                                        shape=[2, 5, 5, 5, 3],
                                        append_batch_size=False,
                                        dtype="float32")

        input_NCDHW = fluid.layers.data(name="input_NCDHW",
                                        shape=[2, 3, 5, 5, 3],
                                        append_batch_size=False,
                                        dtype="float32")

        fluid.layers.conv3d(input=input_NDHWC,
                            num_filters=3,
                            filter_size=[3, 3, 3],
                            stride=[1, 1, 1],
                            padding=0,
                            dilation=[1, 1, 1],
                            groups=1,
                            data_format="NCDHW")

        fluid.layers.conv3d(input=input_NCDHW,
                            num_filters=3,
                            filter_size=[3, 3, 3],
                            stride=[1, 1, 1],
                            padding=[1, 2, 1, 0, 1, 0],
                            dilation=[1, 1, 1],
                            groups=1,
                            data_format="NCDHW")

        fluid.layers.conv3d(input=input_NCDHW,
                            num_filters=3,
                            filter_size=[3, 3, 3],
                            stride=[1, 1, 1],
                            padding=[[0, 0], [0, 0], [1, 1], [1, 1], [1, 1]],
                            dilation=[1, 1, 1],
                            groups=1,
                            data_format="NCDHW")

        fluid.layers.conv3d(input=input_NDHWC,
                            num_filters=3,
                            filter_size=[3, 3, 3],
                            stride=[1, 1, 1],
                            padding=[[0, 0], [1, 1], [1, 1], [1, 1], [0, 0]],
                            dilation=[1, 1, 1],
                            groups=1,
                            data_format="NDHWC")

        fluid.layers.conv3d(input=input_NCDHW,
                            num_filters=3,
                            filter_size=[3, 3, 3],
                            stride=[1, 1, 1],
                            padding="SAME",
                            dilation=[1, 1, 1],
                            groups=1,
                            data_format="NCDHW")

        fluid.layers.conv3d(input=input_NCDHW,
                            num_filters=3,
                            filter_size=[3, 3, 3],
                            stride=[1, 1, 1],
                            padding="VALID",
                            dilation=[1, 1, 1],
                            groups=1,
                            data_format="NCDHW")


class TestConv3DAPI_Error(unittest.TestCase):

    def test_api(self):
        input = fluid.layers.data(name="input",
                                  shape=[2, 5, 5, 5, 4],
                                  append_batch_size=False,
                                  dtype="float32")

        # ValueError: cudnn
        def run_1():
            fluid.layers.conv3d(input=input,
                                num_filters=3,
                                filter_size=3,
                                stride=1,
                                padding=0,
                                dilation=1,
                                groups=1,
                                use_cudnn=[0],
                                data_format="NCDHW")

        self.assertRaises(ValueError, run_1)

        # ValueError: data_format
        def run_2():
            fluid.layers.conv3d(input=input,
                                num_filters=3,
                                filter_size=[3, 3, 3],
                                stride=[1, 1, 1],
                                padding=0,
                                dilation=[1, 1, 1],
                                groups=1,
                                use_cudnn=False,
                                data_format="NCHWC")

        self.assertRaises(ValueError, run_2)

        # ValueError: padding
        def run_3():
            fluid.layers.conv3d(input=input,
                                num_filters=3,
                                filter_size=3,
                                stride=1,
                                padding="SAMEE",
                                dilation=1,
                                groups=1,
                                use_cudnn=False,
                                data_format="NCDHW")

        self.assertRaises(ValueError, run_3)

        def run_4():
            fluid.layers.conv3d(input=input,
                                num_filters=3,
                                filter_size=3,
                                stride=1,
                                padding=[[0, 1], [0, 0], [0, 1], [0, 1], [0,
                                                                          1]],
                                dilation=1,
                                groups=1,
                                use_cudnn=False,
                                data_format="NCDHW")

        self.assertRaises(ValueError, run_4)

        def run_5():
            fluid.layers.conv3d(input=input,
                                num_filters=3,
                                filter_size=0,
                                stride=0,
                                padding=[[0, 1], [0, 1], [0, 1], [0, 1], [0,
                                                                          1]],
                                dilation=1,
                                groups=1,
                                use_cudnn=False,
                                data_format="NDHWC")

        self.assertRaises(ValueError, run_5)

        # ValueError: channel dimmention
        x = fluid.layers.data(name="x",
                              shape=[2, 5, 5, 5, -1],
                              append_batch_size=False,
                              dtype="float32")

        def run_6():
            fluid.layers.conv3d(input=x,
                                num_filters=3,
                                filter_size=3,
                                stride=1,
                                padding=0,
                                dilation=1,
                                groups=1,
                                use_cudnn=False,
                                data_format="NDHWC")

        self.assertRaises(ValueError, run_6)

        # ValueError: groups
        def run_7():
            fluid.layers.conv3d(input=input,
                                num_filters=3,
                                filter_size=3,
                                stride=1,
                                padding=0,
                                dilation=1,
                                groups=3,
                                use_cudnn=False,
                                data_format="NDHWC")

        self.assertRaises(ValueError, run_7)

        # ValueError: filter num
        def run_8():
            fluid.layers.conv3d(input=input,
                                num_filters=0,
                                filter_size=0,
                                stride=0,
                                padding=0,
                                dilation=0,
                                groups=1,
                                use_cudnn=False,
                                data_format="NDHWC")

        self.assertRaises(ValueError, run_8)


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