test_pool2d_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
from __future__ import division

import unittest
import numpy as np

import paddle.fluid.core as core
from op_test import OpTest
import paddle.fluid as fluid
from paddle.fluid import Program, program_guard


def adaptive_start_index(index, input_size, output_size):
    return int(np.floor(index * input_size / output_size))


def adaptive_end_index(index, input_size, output_size):
    return int(np.ceil((index + 1) * input_size / output_size))


def max_pool2D_forward_naive(x,
                             ksize,
                             strides,
                             paddings,
                             global_pool=0,
                             ceil_mode=False,
                             exclusive=True,
                             adaptive=False,
                             data_type=np.float64):
    if data_type == np.float64 and core.is_compiled_with_rocm():
        data_type = np.float32
    N, C, H, W = x.shape
    if global_pool == 1:
        ksize = [H, W]
    if adaptive:
        H_out, W_out = ksize
    else:
        H_out = (H - ksize[0] + 2 * paddings[0] + strides[0] - 1
                 ) // strides[0] + 1 if ceil_mode else (
                     H - ksize[0] + 2 * paddings[0]) // strides[0] + 1
        W_out = (W - ksize[1] + 2 * paddings[1] + strides[1] - 1
                 ) // strides[1] + 1 if ceil_mode else (
                     W - ksize[1] + 2 * paddings[1]) // strides[1] + 1
    out = np.zeros((N, C, H_out, W_out))
    for i in range(H_out):
        for j in range(W_out):
            if adaptive:
                r_start = adaptive_start_index(i, H, ksize[0])
                r_end = adaptive_end_index(i, H, ksize[0])
                c_start = adaptive_start_index(j, W, ksize[1])
                c_end = adaptive_end_index(j, W, ksize[1])
            else:
                r_start = np.max((i * strides[0] - paddings[0], 0))
                r_end = np.min((i * strides[0] + ksize[0] - paddings[0], H))
                c_start = np.max((j * strides[1] - paddings[1], 0))
                c_end = np.min((j * strides[1] + ksize[1] - paddings[1], W))
            x_masked = x[:, :, r_start:r_end, c_start:c_end]

            out[:, :, i, j] = np.max(x_masked, axis=(2, 3))
    return out


def avg_pool2D_forward_naive(x,
                             ksize,
                             strides,
                             paddings,
                             global_pool=0,
                             ceil_mode=False,
                             exclusive=True,
                             adaptive=False,
                             data_type=np.float64):
    if data_type == np.float64 and core.is_compiled_with_rocm():
        data_type = np.float32
    N, C, H, W = x.shape
    if global_pool == 1:
        ksize = [H, W]
    if adaptive:
        H_out, W_out = ksize
    else:
        H_out = (H - ksize[0] + 2 * paddings[0] + strides[0] - 1
                 ) // strides[0] + 1 if ceil_mode else (
                     H - ksize[0] + 2 * paddings[0]) // strides[0] + 1
        W_out = (W - ksize[1] + 2 * paddings[1] + strides[1] - 1
                 ) // strides[1] + 1 if ceil_mode else (
                     W - ksize[1] + 2 * paddings[1]) // strides[1] + 1
    out = np.zeros((N, C, H_out, W_out))
    for i in range(H_out):
        for j in range(W_out):
            if adaptive:
                r_start = adaptive_start_index(i, H, ksize[0])
                r_end = adaptive_end_index(i, H, ksize[0])
                c_start = adaptive_start_index(j, W, ksize[1])
                c_end = adaptive_end_index(j, W, ksize[1])
            else:
                r_start = i * strides[0] - paddings[0]
                r_end = i * strides[0] + ksize[0] - paddings[0]
                c_start = j * strides[1] - paddings[1]
                c_end = j * strides[1] + ksize[1] - paddings[1]
                field_size = (r_end - r_start) * (c_end - c_start)
                r_start = np.max((r_start, 0))
                r_end = np.min((r_end, H))
                c_start = np.max((c_start, 0))
                c_end = np.min((c_end, W))

            x_masked = x[:, :, r_start:r_end, c_start:c_end]

            if (exclusive or adaptive):
                field_size = (r_end - r_start) * (c_end - c_start)

            if data_type == np.int8 or data_type == np.uint8:
                out[:, :, i, j] = (np.rint(
                    np.sum(x_masked, axis=(2, 3)) /
                    field_size)).astype(data_type)
            else:
                out[:, :, i, j] = (np.sum(x_masked, axis=(2, 3)) /
                                   field_size).astype(data_type)
    return out


def pool2D_forward_naive(x,
                         ksize,
                         strides,
                         paddings,
                         global_pool=0,
                         ceil_mode=False,
                         exclusive=True,
                         adaptive=False,
                         data_format='NCHW',
                         pool_type="max",
                         padding_algorithm="EXPLICIT"):

    # update paddings
    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

    if isinstance(padding_algorithm, str):
        padding_algorithm = padding_algorithm.upper()
        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 padding_algorithm == "VALID":
            paddings = [0, 0, 0, 0]
            if ceil_mode != False:
                raise ValueError(
                    "When Attr(pool_padding) is \"VALID\", Attr(ceil_mode)"
                    " must be False. "
                    "Received ceil_mode: True.")
        elif padding_algorithm == "SAME":
            input_data_shape = []
            if data_format == "NCHW":
                input_data_shape = x.shape[2:4]
            elif data_format == "NHWC":
                input_data_shape = x.shape[1:3]
            paddings = _get_padding_with_SAME(input_data_shape, ksize, strides)

    assert len(paddings) == 2 or len(paddings) == 4
    is_sys = True if len(paddings) == 2 else False

    N = x.shape[0]
    C, H, W = [x.shape[1], x.shape[2], x.shape[3]] if data_format == 'NCHW' \
        else [x.shape[3], x.shape[1], x.shape[2]]

    if global_pool == 1:
        ksize = [H, W]
        paddings = [0 for _ in range(len(paddings))]

    pad_h_up = paddings[0] if is_sys else paddings[0]
    pad_h_down = paddings[0] if is_sys else paddings[1]
    pad_w_left = paddings[1] if is_sys else paddings[2]
    pad_w_right = paddings[1] if is_sys else paddings[3]

    if adaptive:
        H_out, W_out = ksize
    else:
        H_out = (H - ksize[0] + pad_h_up + pad_h_down + strides[0] - 1) // strides[0] + 1 \
            if ceil_mode else (H - ksize[0] + pad_h_up + pad_h_down) // strides[0] + 1
        W_out = (W - ksize[1] + pad_w_left + pad_w_right + strides[1] - 1) // strides[1] + 1 \
            if ceil_mode else (W - ksize[1] + pad_w_left + pad_w_right) // strides[1] + 1

    out = np.zeros((N, C, H_out, W_out)) if data_format=='NCHW' \
        else np.zeros((N, H_out, W_out, C))
    for i in range(H_out):
        if adaptive:
            in_h_start = adaptive_start_index(i, H, ksize[0])
            in_h_end = adaptive_end_index(i, H, ksize[0])
        else:
            in_h_start = np.max((i * strides[0] - pad_h_up, 0))
            in_h_end = np.min((i * strides[0] + ksize[0] - pad_h_up, H))

        for j in range(W_out):
            if adaptive:
                in_w_start = adaptive_start_index(j, W, ksize[1])
                in_w_end = adaptive_end_index(j, W, ksize[1])
            else:
                in_h_start = i * strides[0] - pad_h_up
                in_w_start = j * strides[1] - pad_w_left
                in_h_end = i * strides[0] + ksize[0] - pad_h_up
                in_w_end = j * strides[1] + ksize[1] - pad_w_left

                field_size = (in_h_end - in_h_start) * (in_w_end - in_w_start)
                in_h_start = np.max((in_h_start, 0))
                in_w_start = np.max((in_w_start, 0))
                in_h_end = np.min((in_h_end, H))
                in_w_end = np.min((in_w_end, W))

            if data_format == 'NCHW':
                x_masked = x[:, :, in_h_start:in_h_end, in_w_start:in_w_end]
                if pool_type == 'avg':
                    if (exclusive or adaptive):
                        field_size = (in_h_end - in_h_start) * (
                            in_w_end - in_w_start)

#                         if (exclusive or adaptive) else (ksize[0] * ksize[1])
                    out[:, :, i, j] = np.sum(x_masked, axis=(2, 3)) / field_size
                elif pool_type == 'max':
                    out[:, :, i, j] = np.max(x_masked, axis=(2, 3))
            elif data_format == 'NHWC':
                x_masked = x[:, in_h_start:in_h_end, in_w_start:in_w_end, :]
                if pool_type == 'avg':
                    if (exclusive or adaptive):
                        field_size = (in_h_end - in_h_start) * (
                            in_w_end - in_w_start)
                    out[:, i, j, :] = np.sum(x_masked, axis=(1, 2)) / field_size
                elif pool_type == 'max':
                    out[:, i, j, :] = np.max(x_masked, axis=(1, 2))
    return out


class TestPool2D_Op(OpTest):
    def setUp(self):
        self.op_type = "pool2d"
        self.use_cudnn = False
        self.init_kernel_type()
        self.use_mkldnn = False
        self.init_data_type()
        self.init_test_case()
        self.padding_algorithm = "EXPLICIT"
        self.init_paddings()
        self.init_global_pool()
        self.init_kernel_type()
        self.init_pool_type()
        self.init_ceil_mode()
        self.init_exclusive()
        self.init_adaptive()
        self.init_data_format()
        self.init_shape()

        input = np.random.random(self.shape).astype(self.dtype)
        output = pool2D_forward_naive(
            input, self.ksize, self.strides, self.paddings, self.global_pool,
            self.ceil_mode, self.exclusive, self.adaptive, self.data_format,
            self.pool_type, self.padding_algorithm).astype(self.dtype)
        self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(input)}

        self.attrs = {
            'strides': self.strides,
            'paddings': self.paddings,
            'ksize': self.ksize,
            'pooling_type': self.pool_type,
            'global_pooling': self.global_pool,
            'use_cudnn': self.use_cudnn,
            'use_mkldnn': self.use_mkldnn,
            'ceil_mode': self.ceil_mode,
            'data_format': self.data_format,
            'exclusive': self.exclusive,
            'adaptive': self.adaptive,
            "padding_algorithm": self.padding_algorithm,
        }

        self.outputs = {'Out': 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
        if self.has_cudnn():
            place = core.CUDAPlace(0)
            self.check_output_with_place(
                place, atol=1e-5, check_dygraph=(self.use_mkldnn == False))
        else:
            self.check_output(check_dygraph=(self.use_mkldnn == False))

    def test_check_grad(self):
        if self.dtype == np.float16:
            return
        # TODO(wangzhongpu): support mkldnn op in dygraph mode
        if self.has_cudnn() and self.pool_type != "max":
            place = core.CUDAPlace(0)
            self.check_grad_with_place(
                place,
                set(['X']),
                'Out',
                max_relative_error=0.07,
                check_dygraph=(self.use_mkldnn == False))
        elif self.pool_type != "max":
            self.check_grad(
                set(['X']),
                'Out',
                max_relative_error=0.07,
                check_dygraph=(self.use_mkldnn == False))

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

    def init_shape(self):
        self.shape = [2, 3, 5, 5]

    def init_test_case(self):
        self.ksize = [3, 3]
        self.strides = [1, 1]

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

    def init_kernel_type(self):
        self.use_cudnn = False

    def init_data_type(self):
        self.dtype = np.float32 if core.is_compiled_with_rocm() else np.float64

    def init_pool_type(self):
        self.pool_type = "avg"
        self.pool2D_forward_naive = avg_pool2D_forward_naive

    def init_global_pool(self):
        self.global_pool = True

    def init_ceil_mode(self):
        self.ceil_mode = False

    def init_exclusive(self):
        self.exclusive = True

    def init_adaptive(self):
        self.adaptive = False


class TestCase1(TestPool2D_Op):
    def init_test_case(self):
        self.ksize = [3, 3]
        self.strides = [1, 1]

    def init_paddings(self):
        self.paddings = [0, 0]

    def init_pool_type(self):
        self.pool_type = "avg"
        self.pool2D_forward_naive = avg_pool2D_forward_naive

    def init_global_pool(self):
        self.global_pool = False

    def init_shape(self):
        self.shape = [2, 3, 7, 7]


class TestCase2(TestPool2D_Op):
    def init_test_case(self):
        self.ksize = [3, 3]
        self.strides = [1, 1]

    def init_paddings(self):
        self.paddings = [1, 1]

    def init_pool_type(self):
        self.pool_type = "avg"
        self.pool2D_forward_naive = avg_pool2D_forward_naive

    def init_global_pool(self):
        self.global_pool = False

    def init_shape(self):
        self.shape = [2, 3, 7, 7]


class TestCase3(TestPool2D_Op):
    def init_pool_type(self):
        self.pool_type = "max"
        self.pool2D_forward_naive = max_pool2D_forward_naive


class TestCase4(TestCase1):
    def init_pool_type(self):
        self.pool_type = "max"
        self.pool2D_forward_naive = max_pool2D_forward_naive


class TestCase5(TestCase2):
    def init_pool_type(self):
        self.pool_type = "max"
        self.pool2D_forward_naive = max_pool2D_forward_naive


#--------------------test pool2d cudnn--------------------


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

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


create_test_cudnn_class(TestPool2D_Op)
create_test_cudnn_class(TestCase1)
create_test_cudnn_class(TestCase2)
create_test_cudnn_class(TestCase3)
create_test_cudnn_class(TestCase4)
create_test_cudnn_class(TestCase5)

#--------------------test pool2d cudnn_fp16--------------------


def create_test_cudnn_fp16_class(parent, check_grad=True):
    @unittest.skipIf(not core.is_compiled_with_cuda(),
                     "core is not compiled with CUDA")
    class TestCUDNNFp16Case(parent):
        def init_kernel_type(self):
            self.use_cudnn = True
            self.dtype = np.float16

        def test_check_output(self):
            # TODO(wangzhongpu): support mkldnn op in dygraph mode
            if core.is_compiled_with_cuda():
                place = core.CUDAPlace(0)
                if core.is_float16_supported(place):
                    self.check_output_with_place(
                        place,
                        atol=1e-3,
                        check_dygraph=(self.use_mkldnn == False))

        def test_check_grad(self):
            # TODO(wangzhongpu): support mkldnn op in dygraph mode
            place = core.CUDAPlace(0)
            if core.is_float16_supported(
                    place) and self.pool_type != "max" and check_grad:
                self.check_grad_with_place(
                    place,
                    set(['X']),
                    'Out',
                    max_relative_error=0.07,
                    check_dygraph=(self.use_mkldnn == False))

    cls_name = "{0}_{1}".format(parent.__name__, "CUDNNFp16Op")
    TestCUDNNFp16Case.__name__ = cls_name
    globals()[cls_name] = TestCUDNNFp16Case


def create_test_fp16_class(parent, check_grad=True):
    @unittest.skipIf(not core.is_compiled_with_cuda(),
                     "core is not compiled with CUDA")
    class TestFp16Case(parent):
        def init_kernel_type(self):
            self.use_cudnn = False
            self.dtype = np.float16

        def test_check_output(self):
            # TODO(wangzhongpu): support mkldnn op in dygraph mode
            if core.is_compiled_with_cuda():
                place = core.CUDAPlace(0)
                if core.is_float16_supported(place):
                    self.check_output_with_place(
                        place,
                        atol=1e-3,
                        check_dygraph=(self.use_mkldnn == False))

        def test_check_grad(self):
            # TODO(wangzhongpu): support mkldnn op in dygraph mode
            place = core.CUDAPlace(0)
            if core.is_float16_supported(
                    place) and self.pool_type != "max" and check_grad:
                self.check_grad_with_place(
                    place,
                    set(['X']),
                    'Out',
                    max_relative_error=0.07,
                    check_dygraph=(self.use_mkldnn == False))

    cls_name = "{0}_{1}".format(parent.__name__, "Fp16Op")
    TestFp16Case.__name__ = cls_name
    globals()[cls_name] = TestFp16Case


create_test_cudnn_fp16_class(TestPool2D_Op)
create_test_cudnn_fp16_class(TestCase1, check_grad=False)
create_test_cudnn_fp16_class(TestCase2)
create_test_cudnn_fp16_class(TestCase3)
create_test_cudnn_fp16_class(TestCase4)
create_test_cudnn_fp16_class(TestCase5)

create_test_fp16_class(TestPool2D_Op)
create_test_fp16_class(TestCase1, check_grad=False)
create_test_fp16_class(TestCase2)
create_test_fp16_class(TestCase3)
create_test_fp16_class(TestCase4)
create_test_fp16_class(TestCase5)

#--------------------test pool2d use ceil mode--------------------


def create_test_cudnn_use_ceil_class(parent):
    @unittest.skipIf(not core.is_compiled_with_cuda(),
                     "core is not compiled with CUDA")
    class TestPool2DUseCeilCase(parent):
        def init_kernel_type(self):
            self.use_cudnn = True

        def init_ceil_mode(self):
            self.ceil_mode = True

    cls_name = "{0}_{1}".format(parent.__name__, "CUDNNOpCeilMode")
    TestPool2DUseCeilCase.__name__ = cls_name
    globals()[cls_name] = TestPool2DUseCeilCase


create_test_cudnn_use_ceil_class(TestPool2D_Op)
create_test_cudnn_use_ceil_class(TestCase1)


def create_test_use_ceil_class(parent):
    class TestPool2DUseCeilCase(parent):
        def init_ceil_mode(self):
            self.ceil_mode = True

    cls_name = "{0}_{1}".format(parent.__name__, "CeilModeCast")
    TestPool2DUseCeilCase.__name__ = cls_name
    globals()[cls_name] = TestPool2DUseCeilCase


create_test_use_ceil_class(TestCase1)
create_test_use_ceil_class(TestCase2)


class TestAvgInclude(TestCase2):
    def init_exclusive(self):
        self.exclusive = False


class TestCUDNNAvgInclude(TestCase2):
    def init_kernel_type(self):
        self.use_cudnn = True

    def init_exclusive(self):
        self.exclusive = False


class TestAvgPoolAdaptive(TestCase1):
    def init_adaptive(self):
        self.adaptive = True


class TestAvgPoolAdaptiveAsyOutSize(TestCase1):
    def init_adaptive(self):
        self.adaptive = True

    def init_shape(self):
        self.shape = [8, 3, 6, 6]

    def init_test_case(self):
        self.ksize = [2, 3]
        self.strides = [1, 1]
        self.paddings = [0, 0, 0, 0]


#-------test pool2d with asymmetric padding-----


class TestPool2D_AsyPadding(TestPool2D_Op):
    def init_test_case(self):
        self.ksize = [3, 3]
        self.strides = [1, 1]
        self.paddings = [1, 0, 1, 2]

    def init_shape(self):
        self.shape = [2, 3, 5, 5]


class TestCase1_AsyPadding(TestCase1):
    def init_test_case(self):
        self.ksize = [3, 3]
        self.strides = [1, 1]
        self.paddings = [1, 0, 1, 0]

    def init_shape(self):
        self.shape = [2, 3, 7, 7]


class TestCase2_AsyPadding(TestCase2):
    def init_test_case(self):
        self.ksize = [3, 3]
        self.strides = [1, 1]
        self.paddings = [1, 2, 1, 2]

    def init_shape(self):
        self.shape = [2, 3, 7, 7]


class TestCase3_AsyPadding(TestCase3):
    def init_test_case(self):
        self.ksize = [3, 3]
        self.strides = [1, 1]
        self.paddings = [1, 0, 1, 2]

    def init_shape(self):
        self.shape = [2, 3, 5, 5]


class TestCase4_AsyPadding(TestCase4):
    def init_test_case(self):
        self.ksize = [3, 3]
        self.strides = [1, 1]
        self.paddings = [1, 0, 1, 0]

    def init_shape(self):
        self.shape = [2, 3, 7, 7]


class TestCase5_AsyPadding((TestCase5)):
    def init_test_case(self):
        self.ksize = [3, 3]
        self.strides = [1, 1]
        self.paddings = [2, 2, 1, 2]

    def init_shape(self):
        self.shape = [2, 3, 7, 7]


create_test_cudnn_class(TestPool2D_AsyPadding)
create_test_cudnn_class(TestCase1_AsyPadding)
create_test_cudnn_class(TestCase2_AsyPadding)
create_test_cudnn_class(TestCase3_AsyPadding)
create_test_cudnn_class(TestCase4_AsyPadding)
create_test_cudnn_class(TestCase5_AsyPadding)

create_test_cudnn_fp16_class(TestPool2D_AsyPadding)
create_test_cudnn_fp16_class(TestCase1_AsyPadding, check_grad=False)
create_test_cudnn_fp16_class(TestCase2_AsyPadding)
create_test_cudnn_fp16_class(TestCase3_AsyPadding)
create_test_cudnn_fp16_class(TestCase4_AsyPadding)
create_test_cudnn_fp16_class(TestCase5_AsyPadding)

create_test_cudnn_use_ceil_class(TestPool2D_AsyPadding)
create_test_cudnn_use_ceil_class(TestCase1_AsyPadding)

create_test_use_ceil_class(TestCase1_AsyPadding)
create_test_use_ceil_class(TestCase2_AsyPadding)


class TestAvgInclude_AsyPadding(TestCase2):
    def init_exclusive(self):
        self.exclusive = False

    def init_test_case(self):
        self.ksize = [3, 3]
        self.strides = [1, 1]
        self.paddings = [1, 2, 1, 2]

    def init_shape(self):
        self.shape = [2, 3, 7, 7]


class TestCUDNNAvgInclude_AsyPadding(TestCase2):
    def init_kernel_type(self):
        self.use_cudnn = True

    def init_exclusive(self):
        self.exclusive = False

    def init_test_case(self):
        self.ksize = [3, 3]
        self.strides = [1, 1]
        self.paddings = [2, 1, 1, 1]

    def init_shape(self):
        self.shape = [2, 3, 7, 7]


class TestAvgPoolAdaptive_AsyPadding(TestCase1):
    def init_adaptive(self):
        self.adaptive = True

    def init_test_case(self):
        self.ksize = [3, 3]
        self.strides = [1, 1]
        self.paddings = [1, 1, 0, 2]

    def init_shape(self):
        self.shape = [2, 3, 7, 7]


#----------- test channel_last --------------
class TestPool2D_channel_last(TestPool2D_Op):
    def init_data_format(self):
        self.data_format = "NHWC"

    def init_shape(self):
        self.shape = [2, 5, 5, 3]


class TestCase1_channel_last(TestCase1):
    def init_data_format(self):
        self.data_format = "NHWC"

    def init_shape(self):
        self.shape = [2, 7, 7, 3]


class TestCase2_channel_last(TestCase2):
    def init_data_format(self):
        self.data_format = "NHWC"

    def init_shape(self):
        self.shape = [2, 7, 7, 3]


class TestCase3_channel_last(TestCase3):
    def init_data_format(self):
        self.data_format = "NHWC"

    def init_shape(self):
        self.shape = [2, 5, 5, 3]


class TestCase4_channel_last(TestCase4):
    def init_data_format(self):
        self.data_format = "NHWC"

    def init_shape(self):
        self.shape = [2, 7, 7, 3]


class TestCase5_channel_last(TestCase5):
    def init_data_format(self):
        self.data_format = "NHWC"

    def init_shape(self):
        self.shape = [2, 7, 7, 3]


create_test_cudnn_class(TestPool2D_channel_last)
create_test_cudnn_class(TestCase1_channel_last)
create_test_cudnn_class(TestCase2_channel_last)
create_test_cudnn_class(TestCase3_channel_last)
create_test_cudnn_class(TestCase4_channel_last)
create_test_cudnn_class(TestCase5_channel_last)

create_test_cudnn_fp16_class(TestPool2D_channel_last)
create_test_cudnn_fp16_class(TestCase1_channel_last, check_grad=False)
create_test_cudnn_fp16_class(TestCase2_channel_last)
create_test_cudnn_fp16_class(TestCase3_channel_last)
create_test_cudnn_fp16_class(TestCase4_channel_last)
create_test_cudnn_fp16_class(TestCase5_channel_last)

create_test_cudnn_use_ceil_class(TestPool2D_channel_last)
create_test_cudnn_use_ceil_class(TestCase1_channel_last)

create_test_use_ceil_class(TestCase1_channel_last)
create_test_use_ceil_class(TestCase2_channel_last)


class TestCase5_Max(TestCase2):
    def init_pool_type(self):
        self.pool_type = "max"

    def test_check_grad(self):
        if self.dtype == np.float16:
            return
        if self.has_cudnn() and self.pool_type == "max":
            place = core.CUDAPlace(0)
            self.check_grad_with_place(
                place, set(['X']), 'Out', max_relative_error=1.00)
        elif self.pool_type == "max":
            self.check_grad(set(['X']), 'Out', max_relative_error=1.00)


class TestCase5_channel_last_Max(TestCase5_Max):
    def init_data_format(self):
        self.data_format = "NHWC"

    def init_shape(self):
        self.shape = [2, 7, 7, 3]


create_test_cudnn_class(TestCase5_Max)
create_test_cudnn_class(TestCase5_channel_last_Max)


class TestAvgInclude_channel_last(TestCase2_channel_last):
    def init_exclusive(self):
        self.exclusive = False


class TestCUDNNAvgInclude_channel_last(TestCase2_channel_last):
    def init_kernel_type(self):
        self.use_cudnn = True

    def init_exclusive(self):
        self.exclusive = False


class TestAvgPoolAdaptive_channel_last(TestCase1_channel_last):
    def init_adaptive(self):
        self.adaptive = True


class TestPool2D_AsyPadding_channel_last(TestPool2D_AsyPadding):
    def init_data_format(self):
        self.data_format = "NHWC"

    def init_shape(self):
        self.shape = [2, 5, 5, 3]


class TestCase1_AsyPadding_channel_last(TestCase1_AsyPadding):
    def init_data_format(self):
        self.data_format = "NHWC"

    def init_shape(self):
        self.shape = [2, 7, 7, 3]


class TestCase2_AsyPadding_channel_last(TestCase2_AsyPadding):
    def init_data_format(self):
        self.data_format = "NHWC"

    def init_shape(self):
        self.shape = [2, 7, 7, 3]


class TestCase3_AsyPadding_channel_last(TestCase3_AsyPadding):
    def init_data_format(self):
        self.data_format = "NHWC"

    def init_shape(self):
        self.shape = [2, 5, 5, 3]


class TestCase4_AsyPadding_channel_last(TestCase4_AsyPadding):
    def init_data_format(self):
        self.data_format = "NHWC"

    def init_shape(self):
        self.shape = [2, 7, 7, 3]


class TestCase5_AsyPadding_channel_last(TestCase5_AsyPadding):
    def init_data_format(self):
        self.data_format = "NHWC"

    def init_shape(self):
        self.shape = [2, 7, 7, 3]


create_test_cudnn_class(TestPool2D_AsyPadding_channel_last)
create_test_cudnn_class(TestCase1_AsyPadding_channel_last)
create_test_cudnn_class(TestCase2_AsyPadding_channel_last)
create_test_cudnn_class(TestCase3_AsyPadding_channel_last)
create_test_cudnn_class(TestCase4_AsyPadding_channel_last)
create_test_cudnn_class(TestCase5_AsyPadding_channel_last)

create_test_cudnn_fp16_class(TestPool2D_AsyPadding_channel_last)
create_test_cudnn_fp16_class(
    TestCase1_AsyPadding_channel_last, check_grad=False)
create_test_cudnn_fp16_class(TestCase2_AsyPadding_channel_last)
create_test_cudnn_fp16_class(TestCase3_AsyPadding_channel_last)
create_test_cudnn_fp16_class(TestCase4_AsyPadding_channel_last)
create_test_cudnn_fp16_class(TestCase5_AsyPadding_channel_last)

create_test_cudnn_use_ceil_class(TestPool2D_AsyPadding_channel_last)
create_test_cudnn_use_ceil_class(TestCase1_AsyPadding_channel_last)

create_test_use_ceil_class(TestCase1_AsyPadding_channel_last)
create_test_use_ceil_class(TestCase2_AsyPadding_channel_last)


class TestAvgInclude_AsyPadding_channel_last(TestAvgInclude_AsyPadding):
    def init_data_format(self):
        self.data_format = "NHWC"

    def init_shape(self):
        self.shape = [2, 7, 7, 3]


class TestCUDNNAvgInclude_AsyPadding_channel_last(
        TestCUDNNAvgInclude_AsyPadding):
    def init_data_format(self):
        self.data_format = "NHWC"

    def init_shape(self):
        self.shape = [2, 7, 7, 3]


class TestAvgPoolAdaptive_AsyPadding_channel_last(
        TestAvgPoolAdaptive_AsyPadding):
    def init_data_format(self):
        self.data_format = "NHWC"

    def init_shape(self):
        self.shape = [2, 7, 7, 3]


# test paddings: SAME VALID


def create_test_padding_SAME_class(parent):
    class TestPaddingSMAECase(parent):
        def init_paddings(self):
            self.paddings = [0, 0]
            self.padding_algorithm = "SAME"

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


create_test_padding_SAME_class(TestPool2D_Op)
create_test_padding_SAME_class(TestCase1)
create_test_padding_SAME_class(TestCase2)
create_test_padding_SAME_class(TestCase3)
create_test_padding_SAME_class(TestCase4)
create_test_padding_SAME_class(TestCase5)

create_test_padding_SAME_class(TestPool2D_channel_last)
create_test_padding_SAME_class(TestCase1_channel_last)
create_test_padding_SAME_class(TestCase2_channel_last)
create_test_padding_SAME_class(TestCase3_channel_last)
create_test_padding_SAME_class(TestCase4_channel_last)
create_test_padding_SAME_class(TestCase5_channel_last)


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

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

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


create_test_cudnn_padding_SAME_class(TestPool2D_Op)
create_test_cudnn_padding_SAME_class(TestCase1)
create_test_cudnn_padding_SAME_class(TestCase2)
create_test_cudnn_padding_SAME_class(TestCase3)
create_test_cudnn_padding_SAME_class(TestCase4)
create_test_cudnn_padding_SAME_class(TestCase5)

create_test_cudnn_padding_SAME_class(TestPool2D_channel_last)
create_test_cudnn_padding_SAME_class(TestCase1_channel_last)
create_test_cudnn_padding_SAME_class(TestCase2_channel_last)
create_test_cudnn_padding_SAME_class(TestCase3_channel_last)
create_test_cudnn_padding_SAME_class(TestCase4_channel_last)
create_test_cudnn_padding_SAME_class(TestCase5_channel_last)


def create_test_padding_VALID_class(parent):
    class TestPaddingVALIDCase(parent):
        def init_paddings(self):
            self.paddings = [1, 1]
            self.padding_algorithm = "VALID"

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


create_test_padding_VALID_class(TestPool2D_Op)
create_test_padding_VALID_class(TestCase1)
create_test_padding_VALID_class(TestCase2)
create_test_padding_VALID_class(TestCase3)
create_test_padding_VALID_class(TestCase4)
create_test_padding_VALID_class(TestCase5)

create_test_padding_VALID_class(TestPool2D_channel_last)
create_test_padding_VALID_class(TestCase1_channel_last)
create_test_padding_VALID_class(TestCase2_channel_last)
create_test_padding_VALID_class(TestCase3_channel_last)
create_test_padding_VALID_class(TestCase4_channel_last)
create_test_padding_VALID_class(TestCase5_channel_last)


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

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

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


create_test_cudnn_padding_VALID_class(TestPool2D_Op)
create_test_cudnn_padding_VALID_class(TestCase1)
create_test_cudnn_padding_VALID_class(TestCase2)
create_test_cudnn_padding_VALID_class(TestCase3)
create_test_cudnn_padding_VALID_class(TestCase4)
create_test_cudnn_padding_VALID_class(TestCase5)

create_test_cudnn_padding_VALID_class(TestPool2D_channel_last)
create_test_cudnn_padding_VALID_class(TestCase1_channel_last)
create_test_cudnn_padding_VALID_class(TestCase2_channel_last)
create_test_cudnn_padding_VALID_class(TestCase3_channel_last)
create_test_cudnn_padding_VALID_class(TestCase4_channel_last)
create_test_cudnn_padding_VALID_class(TestCase5_channel_last)


class TestCase1_strides(TestCase1):
    def init_test_case(self):
        self.ksize = [3, 3]
        self.strides = [1, 2]

    def init_shape(self):
        self.shape = [2, 3, 4, 5]


create_test_cudnn_class(TestCase1_strides)
create_test_padding_SAME_class(TestCase1_strides)
create_test_cudnn_padding_SAME_class(TestCase1_strides)


# ----- test API
class TestPool2DAPI(unittest.TestCase):
    def test_api(self):
        x_NHWC = np.random.random([2, 5, 5, 3]).astype("float32")
        x_NCHW = np.random.random([2, 3, 5, 5]).astype("float32")

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

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

        input_NHWC_negetive = fluid.layers.data(
            name="input_NHWC_negetive",
            shape=[2, -1, 5, 3],
            append_batch_size=False,
            dtype="float32")

        input_NCHW_negetive = fluid.layers.data(
            name="input_NCHW_negetive",
            shape=[2, 3, -1, -1],
            append_batch_size=False,
            dtype="float32")

        ksize = [3, 3]
        out_1 = fluid.layers.pool2d(
            input=input_NHWC,
            pool_size=ksize,
            pool_type="max",
            pool_padding=[1, 1],
            use_cudnn=False,
            data_format="NHWC")

        out_2 = fluid.layers.pool2d(
            input=input_NHWC,
            pool_size=ksize,
            pool_type="avg",
            pool_padding=[[0, 0], [1, 1], [1, 1], [0, 0]],
            use_cudnn=False,
            data_format="NHWC")

        out_3 = fluid.layers.pool2d(
            input=input_NCHW,
            pool_size=ksize,
            pool_type="avg",
            pool_padding=[[0, 0], [0, 0], [1, 1], [1, 1]],
            use_cudnn=False,
            data_format="NCHW")

        out_4 = fluid.layers.pool2d(
            input=input_NCHW,
            pool_size=ksize,
            pool_type="avg",
            pool_padding=[1, 2, 1, 0],
            use_cudnn=False,
            data_format="NCHW")
        # test VALID
        out_5 = fluid.layers.pool2d(
            input=input_NCHW,
            pool_size=ksize,
            pool_type="avg",
            pool_padding="VALID",
            use_cudnn=False,
            data_format="NCHW")

        out_6 = fluid.layers.pool2d(
            input=input_NHWC,
            pool_size=ksize,
            pool_type="max",
            pool_padding="VALID",
            use_cudnn=False,
            data_format="NHWC")

        # test SAME
        out_7 = fluid.layers.pool2d(
            input=input_NCHW,
            pool_size=[4, 4],
            pool_type="avg",
            pool_padding="SAME",
            use_cudnn=False,
            data_format="NCHW")

        out_8 = fluid.layers.pool2d(
            input=input_NHWC,
            pool_size=[4, 4],
            pool_type="max",
            pool_padding="SAME",
            use_cudnn=False,
            data_format="NHWC")

        # test negetive
        out_9 = fluid.layers.pool2d(
            input=input_NHWC_negetive,
            pool_size=ksize,
            pool_type="avg",
            pool_padding=[0, 0],
            use_cudnn=False,
            data_format="NHWC")
        assert out_9.shape == (2, -1, 3, 3)

        out_10 = fluid.layers.pool2d(
            input=input_NCHW_negetive,
            pool_size=ksize,
            pool_type="avg",
            pool_padding=[0, 0],
            use_cudnn=False,
            data_format="NCHW")
        assert out_10.shape == (2, 3, -1, -1)

        exe = fluid.Executor(place=fluid.CPUPlace())
        [res_1, res_2, res_3, res_4, res_5, res_6, res_7, res_8] = exe.run(
            fluid.default_main_program(),
            feed={
                "input_NHWC": x_NHWC,
                "input_NCHW": x_NCHW,
                "input_NHWC_negetive": x_NHWC,
                "input_NCHW_negetive": x_NCHW
            },
            fetch_list=[
                out_1, out_2, out_3, out_4, out_5, out_6, out_7, out_8
            ])

        assert np.allclose(
            res_1,
            pool2D_forward_naive(
                x=x_NHWC,
                ksize=ksize,
                pool_type="max",
                strides=[1, 1],
                paddings=[1, 1],
                data_format="NHWC"))

        assert np.allclose(
            res_2,
            pool2D_forward_naive(
                x=x_NHWC,
                ksize=ksize,
                pool_type="avg",
                strides=[1, 1],
                paddings=[1, 1, 1, 1],
                data_format="NHWC"))
        assert np.allclose(
            res_3,
            pool2D_forward_naive(
                x=x_NCHW,
                ksize=ksize,
                pool_type="avg",
                strides=[1, 1],
                paddings=[1, 1, 1, 1],
                data_format="NCHW"),
            rtol=0.07,
            atol=1e-05)

        assert np.allclose(
            res_4,
            pool2D_forward_naive(
                x=x_NCHW,
                ksize=ksize,
                pool_type="avg",
                strides=[1, 1],
                paddings=[1, 2, 1, 0],
                data_format="NCHW"),
            rtol=0.07,
            atol=1e-05)

        # VALID
        assert np.allclose(
            res_5,
            pool2D_forward_naive(
                x=x_NCHW,
                ksize=ksize,
                pool_type="avg",
                strides=[1, 1],
                paddings=[10, 20],  # any ele is ok
                padding_algorithm="VALID",
                data_format="NCHW"),
            rtol=0.07,
            atol=1e-05)
        assert np.allclose(
            res_6,
            pool2D_forward_naive(
                x=x_NHWC,
                ksize=ksize,
                pool_type="max",
                strides=[1, 1],
                paddings=[10, 20],
                padding_algorithm="VALID",
                data_format="NHWC"))
        # SAME
        assert np.allclose(
            res_7,
            pool2D_forward_naive(
                x=x_NCHW,
                ksize=[4, 4],
                pool_type="avg",
                strides=[1, 1],
                paddings=[10, 20],
                padding_algorithm="SAME",
                data_format="NCHW"),
            rtol=0.07,
            atol=1e-05)

        assert np.allclose(
            res_8,
            pool2D_forward_naive(
                x=x_NHWC,
                ksize=[4, 4],
                pool_type="max",
                strides=[1, 1],
                paddings=[10, 20],
                padding_algorithm="SAME",
                data_format="NHWC"))


class TestPool2DAPI_Error(unittest.TestCase):
    def test_api(self):
        input_NHWC = fluid.layers.data(
            name="input_NHWC",
            shape=[2, 5, 5, 3],
            append_batch_size=False,
            dtype="float32")
        ksize = [3, 3]

        # cudnn type error
        def run_1():
            out_1 = fluid.layers.pool2d(
                input=input_NHWC,
                pool_size=ksize,
                pool_type="max",
                pool_padding=[1, 1],
                use_cudnn=[0],
                data_format="NHWC")

        self.assertRaises(TypeError, run_1)

        # data_format value error
        def run_2():
            out_2 = fluid.layers.pool2d(
                input=input_NHWC,
                pool_size=ksize,
                pool_type="max",
                pool_padding=[1, 1],
                use_cudnn=False,
                data_format="NHWCC")

        self.assertRaises(ValueError, run_2)

        # padding str value error
        def run_3():
            out_3 = fluid.layers.pool2d(
                input=input_NHWC,
                pool_size=ksize,
                pool_type="max",
                pool_padding="VALIDSAME",
                use_cudnn=False,
                data_format="NHWC")

        self.assertRaises(ValueError, run_3)

        # padding str valid and ceil_mode value error
        def run_4():
            out_4 = fluid.layers.pool2d(
                input=input_NHWC,
                pool_size=ksize,
                pool_type="max",
                pool_padding="VALID",
                use_cudnn=False,
                ceil_mode=True,
                data_format="NHWC")

        self.assertRaises(ValueError, run_4)

        # padding with 8 ele. value error
        def run_5():
            out_5 = fluid.layers.pool2d(
                input=input_NHWC,
                pool_size=ksize,
                pool_type="max",
                pool_padding=[[1, 1], [0, 0], [0, 0], [1, 1]],
                use_cudnn=False,
                data_format="NHWC")

        self.assertRaises(ValueError, run_5)


class TestDygraphPool2DAPIError(unittest.TestCase):
    def test_errors(self):
        with program_guard(Program(), Program()):
            # the input of Pool2D must be Variable.
            data1 = np.random.random((3, 32, 32, 5)).astype('float32')
            pool2d = fluid.dygraph.Pool2D(
                pool_size=2,
                pool_type='max',
                pool_stride=1,
                global_pooling=False)
            self.assertRaises(TypeError, pool2d, data1)

            # the input dtype of Pool2D must be uint8 or int8 or float16 or float32 or float64
            # uint8 and int8 only can be set on mkldnn
            # float16 only can be set on GPU place
            data2 = fluid.layers.data(
                name='x1', shape=[3, 32, 32, 5], dtype="int32")
            self.assertRaises(TypeError, pool2d, data2)

    def test_data_format_error(self):
        with program_guard(Program(), Program()):
            # the data_format must be 'NCHW' or 'NHWC'
            data1 = np.random.random((3, 32, 32, 5)).astype('float32')
            self.assertRaises(
                ValueError,
                fluid.dygraph.Pool2D,
                pool_size=2,
                pool_type='max',
                pool_stride=1,
                global_pooling=False,
                data_format='NWHC')


class TestDygraphPool2DAPI(unittest.TestCase):
    def test_nhwc(self):
        with fluid.dygraph.guard():
            data = np.random.random((3, 32, 32, 5)).astype('float32')
            x = fluid.dygraph.to_variable(data)
            pool2d = fluid.dygraph.Pool2D(
                pool_size=2,
                pool_type='max',
                pool_stride=1,
                pool_padding=[0, 0],
                global_pooling=False,
                data_format='NHWC')
            out1 = pool2d(x)
            out2 = pool2D_forward_naive(
                data, [2, 2], [1, 1],
                paddings=[0, 0],
                pool_type='max',
                data_format='NHWC')
            self.assertTrue(np.allclose(out1.numpy(), out2))

    def test_lower_case(self):
        with fluid.dygraph.guard():
            data = np.random.random((3, 32, 32, 5)).astype('float32')
            x = fluid.dygraph.to_variable(data)
            pool2d = fluid.dygraph.Pool2D(
                pool_size=2,
                pool_type='max',
                pool_stride=1,
                pool_padding=[0, 0],
                global_pooling=False,
                data_format='nhwc')
            out1 = pool2d(x)
            out2 = pool2D_forward_naive(
                data, [2, 2], [1, 1],
                paddings=[0, 0],
                pool_type='max',
                data_format='NHWC')
            self.assertTrue(np.allclose(out1.numpy(), out2))

    def test_upper_case(self):
        with fluid.dygraph.guard():
            data = np.random.random((3, 32, 32, 5)).astype('float32')
            x = fluid.dygraph.to_variable(data)
            pool2d = fluid.dygraph.Pool2D(
                pool_size=2,
                pool_type='MAX',
                pool_stride=1,
                pool_padding=[0, 0],
                global_pooling=False,
                data_format='nhwc')
            out1 = pool2d(x)
            out2 = pool2D_forward_naive(
                data, [2, 2], [1, 1],
                paddings=[0, 0],
                pool_type='max',
                data_format='NHWC')
            self.assertTrue(np.allclose(out1.numpy(), out2))


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