test_conv3d_transpose_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.

from __future__ import print_function

import unittest
import numpy as np

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


def conv3dtranspose_forward_naive(input_, filter_, attrs):
    padding_algorithm = attrs['padding_algorithm']
    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 attrs['data_format'] == 'NHWC':
        input_ = np.transpose(input_, [0, 4, 1, 2, 3])
    in_n, in_c, in_d, in_h, in_w = input_.shape
    f_c, f_out_c, f_d, f_h, f_w = filter_.shape
    groups = attrs['groups']
    assert in_c == f_c
    out_c = f_out_c * groups
    sub_in_c = in_c // groups

    stride, pad, dilations = attrs['strides'], attrs['paddings'], attrs[
        'dilations']

    def _get_padding_with_SAME(input_shape, kernel_size, kernel_stride):
        padding = []
        for input_size, filter_size, stride_size in zip(
                input_shape, kernel_size, kernel_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":
        dilations = [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]

    d_bolck_d = dilations[0] * (f_d - 1) + 1
    d_bolck_h = dilations[1] * (f_h - 1) + 1
    d_bolck_w = dilations[2] * (f_w - 1) + 1
    out_d = (in_d - 1) * stride[0] + d_bolck_d
    out_h = (in_h - 1) * stride[1] + d_bolck_h
    out_w = (in_w - 1) * stride[2] + d_bolck_w
    out = np.zeros((in_n, out_c, out_d, out_h, out_w))

    for n in range(in_n):
        for d in range(in_d):
            for i in range(in_h):
                for j in range(in_w):
                    for g in range(groups):
                        input_masked = input_[n, g * sub_in_c:(g + 1
                                                               ) * sub_in_c, d,
                                              i, j]  # (c)
                        input_masked = np.reshape(input_masked,
                                                  (sub_in_c, 1, 1, 1))
                        input_masked = np.tile(input_masked, (1, f_d, f_h, f_w))

                        for k in range(f_out_c):
                            tmp_out = np.sum(input_masked * filter_[
                                g * sub_in_c:(g + 1) * sub_in_c, k, :, :, :],
                                             axis=0)
                            d1, d2 = d * stride[0], d * stride[0] + d_bolck_d
                            i1, i2 = i * stride[1], i * stride[1] + d_bolck_h
                            j1, j2 = j * stride[2], j * stride[2] + d_bolck_w
                            out[n, g * f_out_c + k, d1:d2:dilations[0], i1:i2:
                                dilations[1], j1:j2:dilations[2]] += tmp_out

    out = out[:, :, pad_d_0:out_d - pad_d_1, pad_h_0:out_h - pad_h_1, pad_w_0:
              out_w - pad_w_1]
    if attrs['data_format'] == 'NHWC':
        out = np.transpose(out, [0, 2, 3, 4, 1])
    return out


class TestConv3dTransposeOp(OpTest):
    def setUp(self):
        # init as conv transpose
        self.use_cudnn = False
        self.data_format = 'NCHW'
        self.pad = [0, 0, 0]
        self.padding_algorithm = "EXPLICIT"
        self.init_op_type()
        self.init_test_case()

        input_ = np.random.random(self.input_size).astype("float32")
        filter_ = np.random.random(self.filter_size).astype("float32")

        self.inputs = {'Input': input_, 'Filter': filter_}
        self.attrs = {
            'strides': self.stride,
            'paddings': self.pad,
            'padding_algorithm': self.padding_algorithm,
            'dilations': self.dilations,
            'groups': self.groups,
            'use_cudnn': self.use_cudnn,
            'data_format': self.data_format
        }

        output = conv3dtranspose_forward_naive(input_, filter_,
                                               self.attrs).astype("float32")

        self.outputs = {'Output': output}

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

    def test_check_grad(self):
        if self.use_cudnn:
            place = core.CUDAPlace(0)
            self.check_grad_with_place(
                place,
                set(['Input', 'Filter']),
                'Output',
                max_relative_error=0.03)
        else:
            self.check_grad(
                set(['Input', 'Filter']), 'Output', max_relative_error=0.03)

    def test_check_grad_no_filter(self):
        if self.use_cudnn:
            place = core.CUDAPlace(0)
            self.check_grad_with_place(
                place, ['Input'],
                'Output',
                max_relative_error=0.03,
                no_grad_set=set(['Filter']))
        else:
            self.check_grad(
                ['Input'],
                'Output',
                max_relative_error=0.03,
                no_grad_set=set(['Filter']))

    def test_check_grad_no_input(self):
        if self.use_cudnn:
            place = core.CUDAPlace(0)
            self.check_grad_with_place(
                place, ['Filter'],
                'Output',
                max_relative_error=0.03,
                no_grad_set=set(['Input']))
        else:
            self.check_grad(
                ['Filter'],
                'Output',
                max_relative_error=0.03,
                no_grad_set=set(['Input']))

    def init_test_case(self):
        self.pad = [0, 0, 0]
        self.stride = [1, 1, 1]
        self.dilations = [1, 1, 1]
        self.groups = 1
        self.input_size = [2, 3, 5, 5, 5]  # NCDHW
        f_c = self.input_size[1]
        self.filter_size = [f_c, 6, 3, 3, 3]

    def init_op_type(self):
        self.op_type = "conv3d_transpose"


class TestWithSymmetricPad(TestConv3dTransposeOp):
    def init_test_case(self):
        self.pad = [1, 1, 1]
        self.stride = [1, 1, 1]
        self.dilations = [1, 1, 1]
        self.groups = 1
        self.input_size = [2, 3, 5, 5, 5]  # NCDHW
        f_c = self.input_size[1]
        self.filter_size = [f_c, 6, 3, 3, 3]


class TestWithAsymmetricPad(TestConv3dTransposeOp):
    def init_test_case(self):
        self.pad = [1, 0, 1, 0, 1, 2]
        self.stride = [1, 1, 1]
        self.dilations = [1, 1, 1]
        self.groups = 1
        self.input_size = [2, 3, 5, 5, 5]  # NCDHW
        f_c = self.input_size[1]
        self.filter_size = [f_c, 6, 3, 3, 3]


class TestWithSAMEPad(TestConv3dTransposeOp):
    def init_test_case(self):
        self.stride = [1, 1, 2]
        self.dilations = [1, 2, 1]
        self.groups = 1
        self.input_size = [2, 3, 5, 5, 6]  # NCDHW
        f_c = self.input_size[1]
        self.filter_size = [f_c, 6, 3, 3, 4]
        self.padding_algorithm = 'SAME'


class TestWithVALIDPad(TestConv3dTransposeOp):
    def init_test_case(self):
        self.stride = [2, 1, 1]
        self.dilations = [1, 1, 1]
        self.groups = 1
        self.input_size = [2, 3, 5, 5, 5]  # NCDHW
        f_c = self.input_size[1]
        self.filter_size = [f_c, 6, 3, 4, 3]
        self.padding_algorithm = 'VALID'


class TestWithGroups(TestConv3dTransposeOp):
    def init_test_case(self):
        self.pad = [1, 1, 1]
        self.stride = [1, 1, 1]
        self.dilations = [1, 1, 1]
        self.groups = 2
        self.input_size = [2, 4, 5, 5, 5]  # NCHW
        f_c = self.input_size[1]
        self.filter_size = [f_c, 3, 3, 3, 3]


class TestWithStride(TestConv3dTransposeOp):
    def init_test_case(self):
        self.pad = [1, 1, 1]
        self.stride = [2, 2, 2]
        self.dilations = [1, 1, 1]
        self.groups = 1
        self.input_size = [2, 3, 5, 5, 5]  # NCDHW
        f_c = self.input_size[1]
        self.filter_size = [f_c, 6, 3, 3, 3]


class TestWithDilation(TestConv3dTransposeOp):
    def init_test_case(self):
        self.pad = [1, 1, 1]
        self.stride = [1, 1, 1]
        self.dilations = [2, 2, 2]
        self.groups = 1
        self.input_size = [2, 3, 5, 5, 5]  # NCDHW
        f_c = self.input_size[1]
        self.filter_size = [f_c, 6, 3, 3, 3]


class Test_NHWC(TestConv3dTransposeOp):
    def init_test_case(self):
        self.pad = [0, 0, 0]
        self.stride = [1, 1, 1]
        self.dilations = [1, 1, 1]
        self.groups = 1
        self.input_size = [2, 5, 5, 5, 3]  # NDHWC
        f_c = self.input_size[-1]
        self.filter_size = [f_c, 6, 3, 3, 3]
        self.data_format = 'NHWC'


class TestWithSymmetricPad_NHWC(TestConv3dTransposeOp):
    def init_test_case(self):
        self.pad = [1, 1, 1]
        self.stride = [1, 1, 1]
        self.dilations = [1, 1, 1]
        self.groups = 1
        self.input_size = [2, 5, 5, 5, 3]  # NDHWC
        f_c = self.input_size[-1]
        self.filter_size = [f_c, 6, 3, 3, 3]
        self.data_format = 'NHWC'


class TestWithAsymmetricPad_NHWC(TestConv3dTransposeOp):
    def init_test_case(self):
        self.pad = [1, 0, 1, 0, 1, 2]
        self.stride = [1, 1, 1]
        self.dilations = [1, 1, 1]
        self.groups = 1
        self.input_size = [2, 5, 5, 5, 3]  # NDHWC
        f_c = self.input_size[-1]
        self.filter_size = [f_c, 6, 3, 3, 3]
        self.data_format = 'NHWC'


class TestWithGroups_NHWC(TestConv3dTransposeOp):
    def init_test_case(self):
        self.pad = [1, 1, 1]
        self.stride = [1, 1, 1]
        self.dilations = [1, 1, 1]
        self.groups = 2
        self.input_size = [2, 5, 5, 5, 4]  # NDHWC
        f_c = self.input_size[-1]
        self.filter_size = [f_c, 3, 3, 3, 3]
        self.data_format = 'NHWC'


class TestWithStride_NHWC(TestConv3dTransposeOp):
    def init_test_case(self):
        self.pad = [1, 1, 1]
        self.stride = [2, 2, 2]
        self.dilations = [1, 1, 1]
        self.groups = 1
        self.input_size = [2, 5, 5, 5, 3]  # NCDHW
        f_c = self.input_size[-1]
        self.filter_size = [f_c, 6, 3, 3, 3]
        self.data_format = 'NHWC'


class TestWithDilation_NHWC(TestConv3dTransposeOp):
    def init_test_case(self):
        self.pad = [1, 1, 1]
        self.stride = [1, 1, 1]
        self.dilations = [2, 2, 2]
        self.groups = 1
        self.input_size = [2, 5, 5, 5, 3]  # NCDHW
        f_c = self.input_size[-1]
        self.filter_size = [f_c, 6, 3, 3, 3]
        self.data_format = 'NHWC'


# ------------ test_cudnn ------------
@unittest.skipIf(not core.is_compiled_with_cuda(),
                 "core is not compiled with CUDA")
class TestCUDNN(TestConv3dTransposeOp):
    def init_op_type(self):
        self.use_cudnn = True
        self.op_type = "conv3d_transpose"


@unittest.skipIf(not core.is_compiled_with_cuda(),
                 "core is not compiled with CUDA")
class TestCUDNNWithSymmetricPad(TestWithSymmetricPad):
    def init_test_case(self):
        self.pad = [1, 1, 1]
        self.stride = [1, 1, 1]
        self.dilations = [1, 1, 1]
        self.groups = 1
        self.input_size = [2, 3, 5, 5, 5]  # NCDHW
        f_c = self.input_size[1]
        self.filter_size = [f_c, 6, 3, 3, 3]

    def init_op_type(self):
        self.use_cudnn = True
        self.op_type = "conv3d_transpose"


@unittest.skipIf(not core.is_compiled_with_cuda(),
                 "core is not compiled with CUDA")
class TestCUDNNWithAsymmetricPad(TestWithAsymmetricPad):
    def init_test_case(self):
        self.pad = [1, 1, 1, 0, 0, 2]
        self.stride = [1, 1, 1]
        self.dilations = [1, 1, 1]
        self.groups = 1
        self.input_size = [2, 3, 4, 4, 4]  # NCDHW
        f_c = self.input_size[1]
        self.filter_size = [f_c, 6, 3, 3, 3]

    def init_op_type(self):
        self.use_cudnn = True
        self.op_type = "conv3d_transpose"


@unittest.skipIf(not core.is_compiled_with_cuda(),
                 "core is not compiled with CUDA")
class TestCUDNNWithSAMEPad(TestWithSAMEPad):
    def init_test_case(self):
        self.stride = [1, 1, 2]
        self.dilations = [1, 2, 1]
        self.groups = 1
        self.input_size = [2, 3, 5, 5, 5]  # NCDHW
        f_c = self.input_size[1]
        self.filter_size = [f_c, 6, 3, 4, 3]
        self.padding_algorithm = 'SAME'

    def init_op_type(self):
        self.use_cudnn = True
        self.op_type = "conv3d_transpose"


@unittest.skipIf(not core.is_compiled_with_cuda(),
                 "core is not compiled with CUDA")
class TestCUDNNWithVALIDPad(TestWithVALIDPad):
    def init_test_case(self):
        self.stride = [1, 1, 1]
        self.dilations = [1, 1, 1]
        self.groups = 1
        self.input_size = [2, 3, 5, 5, 5]  # NCDHW
        f_c = self.input_size[1]
        self.filter_size = [f_c, 6, 3, 3, 3]
        self.padding_algorithm = 'VALID'

    def init_op_type(self):
        self.use_cudnn = True
        self.op_type = "conv3d_transpose"


@unittest.skipIf(not core.is_compiled_with_cuda(),
                 "core is not compiled with CUDA")
class TestCUDNNWithStride(TestWithStride):
    def init_test_case(self):
        self.pad = [1, 1, 1]
        self.stride = [2, 2, 2]
        self.dilations = [1, 1, 1]
        self.groups = 1
        self.input_size = [2, 3, 5, 5, 5]  # NCDHW
        f_c = self.input_size[1]
        self.filter_size = [f_c, 6, 3, 3, 3]

    def init_op_type(self):
        self.use_cudnn = True
        self.op_type = "conv3d_transpose"


@unittest.skipIf(not core.is_compiled_with_cuda(),
                 "core is not compiled with CUDA")
class TestCUDNNWithGroups(TestWithGroups):
    def init_test_case(self):
        self.pad = [1, 1, 1]
        self.stride = [1, 1, 1]
        self.dilations = [1, 1, 1]
        self.groups = 2
        self.input_size = [2, 4, 5, 5, 5]  # NCHW
        f_c = self.input_size[1]
        self.filter_size = [f_c, 3, 3, 3, 3]

    def init_op_type(self):
        self.use_cudnn = True
        self.op_type = "conv3d_transpose"


# Please Don't remove the following code.
# Currently, CI use cudnn V5.0 which not support dilation conv.
# class TestCUDNNWithDilation(TestWithDilation):
#     def init_test_case(self):
#         self.pad = [1, 1, 1]
#         self.stride = [2, 2, 2]
#         self.dilations = [2, 2, 2]
#         self.input_size = [2, 3, 5, 5, 5]  # NCDHW
#         f_c = self.input_size[1]
#         self.filter_size = [f_c, 6, 3, 3, 3]
#
#     def init_op_type(self):
#         self.op_type = "conv3d_transpose"


@unittest.skipIf(not core.is_compiled_with_cuda(),
                 "core is not compiled with CUDA")
class TestCUDNN_NHWC(TestConv3dTransposeOp):
    def init_test_case(self):
        self.pad = [0, 0, 0]
        self.stride = [1, 1, 1]
        self.dilations = [1, 1, 1]
        self.groups = 1
        self.input_size = [2, 5, 5, 5, 3]  # NDHWC
        f_c = self.input_size[-1]
        self.filter_size = [f_c, 6, 3, 3, 3]
        self.data_format = 'NHWC'

    def init_op_type(self):
        self.use_cudnn = True
        self.op_type = "conv3d_transpose"


@unittest.skipIf(not core.is_compiled_with_cuda(),
                 "core is not compiled with CUDA")
class TestCUDNNWithSymmetricPad_NHWC(TestWithSymmetricPad):
    def init_test_case(self):
        self.pad = [1, 1, 1]
        self.stride = [1, 1, 1]
        self.dilations = [1, 1, 1]
        self.groups = 1
        self.input_size = [2, 5, 5, 5, 3]  # NDHWC
        f_c = self.input_size[-1]
        self.filter_size = [f_c, 6, 3, 3, 3]
        self.data_format = 'NHWC'

    def init_op_type(self):
        self.use_cudnn = True
        self.op_type = "conv3d_transpose"


@unittest.skipIf(not core.is_compiled_with_cuda(),
                 "core is not compiled with CUDA")
class TestCUDNNWithAsymmetricPad_NHWC(TestWithAsymmetricPad):
    def init_test_case(self):
        self.pad = [1, 0, 1, 0, 0, 2]
        self.stride = [1, 1, 1]
        self.dilations = [1, 1, 1]
        self.groups = 1
        self.input_size = [2, 5, 5, 5, 3]  # NDHWC
        f_c = self.input_size[-1]
        self.filter_size = [f_c, 6, 3, 3, 3]
        self.data_format = 'NHWC'

    def init_op_type(self):
        self.use_cudnn = True
        self.op_type = "conv3d_transpose"


@unittest.skipIf(not core.is_compiled_with_cuda(),
                 "core is not compiled with CUDA")
class TestCUDNNWithStride_NHWC(TestWithStride):
    def init_test_case(self):
        self.pad = [1, 1, 1]
        self.stride = [2, 2, 2]
        self.dilations = [1, 1, 1]
        self.groups = 1
        self.input_size = [2, 5, 5, 5, 3]  # NCDHW
        f_c = self.input_size[-1]
        self.filter_size = [f_c, 6, 3, 3, 3]
        self.data_format = 'NHWC'

    def init_op_type(self):
        self.use_cudnn = True
        self.op_type = "conv3d_transpose"


@unittest.skipIf(not core.is_compiled_with_cuda(),
                 "core is not compiled with CUDA")
class TestCUDNNWithGroups_NHWC(TestWithGroups):
    def init_test_case(self):
        self.pad = [1, 1, 1]
        self.stride = [1, 1, 1]
        self.dilations = [1, 1, 1]
        self.groups = 2
        self.input_size = [2, 5, 5, 5, 4]  # NCHW
        f_c = self.input_size[-1]
        self.filter_size = [f_c, 3, 3, 3, 3]
        self.data_format = 'NHWC'

    def init_op_type(self):
        self.use_cudnn = True
        self.op_type = "conv3d_transpose"


class TestConv3dTransposeAPI(unittest.TestCase):
    def test_case1(self):
        data1 = fluid.layers.data(
            name='data1', shape=[3, 5, 5, 5], dtype='float32')
        data2 = fluid.layers.data(
            name='data2', shape=[5, 5, 5, 3], dtype='float32')

        out1 = fluid.layers.conv3d_transpose(
            input=data1,
            groups=1,
            num_filters=6,
            filter_size=3,
            data_format='NCDHW')
        out2 = fluid.layers.conv3d_transpose(
            input=data2,
            groups=1,
            num_filters=6,
            filter_size=3,
            data_format='NDHWC')
        out3 = fluid.layers.conv3d_transpose(
            input=data1,
            groups=1,
            num_filters=6,
            filter_size=3,
            padding=[[0, 0], [0, 0], [1, 1], [0, 0], [1, 1]],
            data_format='NCDHW')
        out4 = fluid.layers.conv3d_transpose(
            input=data2,
            groups=3,
            num_filters=6,
            filter_size=3,
            padding=[[0, 0], [0, 0], [1, 1], [1, 2], [0, 0]],
            data_format='NDHWC')
        out5 = fluid.layers.conv3d_transpose(
            input=data2,
            groups=1,
            num_filters=6,
            filter_size=3,
            padding='SAME',
            data_format='NCDHW')
        out6 = fluid.layers.conv3d_transpose(
            input=data2,
            groups=1,
            num_filters=6,
            filter_size=3,
            padding='VALID',
            data_format='NDHWC')
        out7 = fluid.layers.conv3d_transpose(
            input=data2,
            groups=1,
            num_filters=6,
            output_size=[7, 7, 7],
            padding=[0, 0, 0],
            data_format='NDHWC')

        data1_np = np.random.random((2, 3, 5, 5, 5)).astype("float32")
        data2_np = np.random.random((2, 5, 5, 5, 3)).astype("float32")

        if core.is_compiled_with_cuda():
            place = core.CUDAPlace(0)
        else:
            place = core.CPUPlace()
        exe = fluid.Executor(place)
        exe.run(fluid.default_startup_program())
        results = exe.run(
            fluid.default_main_program(),
            feed={"data1": data1_np,
                  "data2": data2_np},
            fetch_list=[out1, out2, out3, out4, out5, out6, out7],
            return_numpy=True)
        self.assertIsNotNone(results[0])
        self.assertIsNotNone(results[1])
        self.assertIsNotNone(results[2])
        self.assertIsNotNone(results[3])
        self.assertIsNotNone(results[4])
        self.assertIsNotNone(results[5])
        self.assertIsNotNone(results[6])


class TestConv3dTransposeOpException(unittest.TestCase):
    def test_exception(self):
        data = fluid.layers.data(
            name='data', shape=[3, 5, 5, 5], dtype="float32")

        def attr_data_format():
            out = fluid.layers.conv2d_transpose(
                input=data,
                groups=1,
                num_filters=6,
                filter_size=3,
                data_format="NCDW")

        self.assertRaises(ValueError, attr_data_format)

        def attr_padding_str():
            out = fluid.layers.conv2d_transpose(
                input=data,
                groups=1,
                num_filters=6,
                filter_size=3,
                padding='Vald')

        self.assertRaises(ValueError, attr_padding_str)

        def attr_padding_list():
            out = fluid.layers.conv2d_transpose(
                input=data,
                groups=1,
                num_filters=6,
                filter_size=3,
                padding=[[1, 1], [1, 1], [0, 0], [0, 0], [1, 1]])

        self.assertRaises(ValueError, attr_padding_list)

        def attr_padding_with_data_format():
            out = fluid.layers.conv2d_transpose(
                input=data,
                groups=1,
                num_filters=6,
                filter_size=3,
                padding=[[1, 1], [0, 0], [0, 0], [1, 0], [1, 1]],
                data_format='NDHWC')

        self.assertRaises(ValueError, attr_padding_with_data_format)


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