test_adaptive_avg_pool2d.py

#   Copyright (c) 2020 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 os
import unittest
import numpy as np

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

from test_attribute_var import UnittestBase


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 adaptive_pool2d_forward(x,
                            output_size,
                            data_format='NCHW',
                            pool_type="avg"):

    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 (isinstance(output_size, int) or output_size == None):
        H_out = output_size
        W_out = output_size
        output_size = [H_out, W_out]
    else:
        H_out, W_out = output_size

    if output_size[0] == None:
        output_size[0] = H
        H_out = H
    if output_size[1] == None:
        output_size[1] = W
        W_out = W

    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):
        in_h_start = adaptive_start_index(i, H, output_size[0])
        in_h_end = adaptive_end_index(i, H, output_size[0])

        for j in range(W_out):
            in_w_start = adaptive_start_index(j, W, output_size[1])
            in_w_end = adaptive_end_index(j, W, output_size[1])

            if data_format == 'NCHW':
                x_masked = x[:, :, in_h_start:in_h_end, in_w_start:in_w_end]
                if pool_type == 'avg':
                    field_size = ((in_h_end - in_h_start) *
                                  (in_w_end - in_w_start))
                    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':
                    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 TestAdaptiveAvgPool2DAPI(unittest.TestCase):

    def setUp(self):
        self.x_np = np.random.random([2, 3, 7, 7]).astype("float32")
        self.res_1_np = adaptive_pool2d_forward(x=self.x_np,
                                                output_size=[3, 3],
                                                pool_type="avg")

        self.res_2_np = adaptive_pool2d_forward(x=self.x_np,
                                                output_size=5,
                                                pool_type="avg")

        self.res_3_np = adaptive_pool2d_forward(x=self.x_np,
                                                output_size=[2, 5],
                                                pool_type="avg")

        self.res_4_np = adaptive_pool2d_forward(x=self.x_np,
                                                output_size=[3, 3],
                                                pool_type="avg",
                                                data_format="NHWC")

        self.res_5_np = adaptive_pool2d_forward(x=self.x_np,
                                                output_size=[None, 3],
                                                pool_type="avg")

    def test_static_graph(self):
        for use_cuda in ([False, True]
                         if core.is_compiled_with_cuda() else [False]):
            place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace()
            paddle.enable_static()
            x = paddle.fluid.data(name="x", shape=[2, 3, 7, 7], dtype="float32")

            out_1 = paddle.nn.functional.adaptive_avg_pool2d(x=x,
                                                             output_size=[3, 3])

            out_2 = paddle.nn.functional.adaptive_avg_pool2d(x=x, output_size=5)

            out_3 = paddle.nn.functional.adaptive_avg_pool2d(x=x,
                                                             output_size=[2, 5])

            out_4 = paddle.nn.functional.adaptive_avg_pool2d(x=x,
                                                             output_size=[3, 3],
                                                             data_format="NHWC")

            out_5 = paddle.nn.functional.adaptive_avg_pool2d(
                x=x, output_size=[None, 3])

            exe = paddle.static.Executor(place=place)
            [res_1, res_2, res_3, res_4,
             res_5] = exe.run(fluid.default_main_program(),
                              feed={"x": self.x_np},
                              fetch_list=[out_1, out_2, out_3, out_4, out_5])

            assert np.allclose(res_1, self.res_1_np)

            assert np.allclose(res_2, self.res_2_np)

            assert np.allclose(res_3, self.res_3_np)

            assert np.allclose(res_4, self.res_4_np)

            assert np.allclose(res_5, self.res_5_np)

    def test_dynamic_graph(self):
        for use_cuda in ([False, True]
                         if core.is_compiled_with_cuda() else [False]):
            place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace()
            paddle.disable_static(place=place)
            x = paddle.to_tensor(self.x_np)

            out_1 = paddle.nn.functional.adaptive_avg_pool2d(x=x,
                                                             output_size=[3, 3])

            out_2 = paddle.nn.functional.adaptive_avg_pool2d(x=x, output_size=5)

            out_3 = paddle.nn.functional.adaptive_avg_pool2d(x=x,
                                                             output_size=[2, 5])

            out_4 = paddle.nn.functional.adaptive_avg_pool2d(x=x,
                                                             output_size=[3, 3],
                                                             data_format="NHWC")

            out_5 = paddle.nn.functional.adaptive_avg_pool2d(
                x=x, output_size=[None, 3])

            out_6 = paddle.nn.functional.interpolate(x=x,
                                                     mode="area",
                                                     size=[2, 5])

            assert np.allclose(out_1.numpy(), self.res_1_np)

            assert np.allclose(out_2.numpy(), self.res_2_np)

            assert np.allclose(out_3.numpy(), self.res_3_np)

            assert np.allclose(out_4.numpy(), self.res_4_np)

            assert np.allclose(out_5.numpy(), self.res_5_np)

            assert np.allclose(out_6.numpy(), self.res_3_np)


class TestAdaptiveAvgPool2DClassAPI(unittest.TestCase):

    def setUp(self):
        self.x_np = np.random.random([2, 3, 7, 7]).astype("float32")
        self.res_1_np = adaptive_pool2d_forward(x=self.x_np,
                                                output_size=[3, 3],
                                                pool_type="avg")

        self.res_2_np = adaptive_pool2d_forward(x=self.x_np,
                                                output_size=5,
                                                pool_type="avg")

        self.res_3_np = adaptive_pool2d_forward(x=self.x_np,
                                                output_size=[2, 5],
                                                pool_type="avg")

        self.res_4_np = adaptive_pool2d_forward(x=self.x_np,
                                                output_size=[3, 3],
                                                pool_type="avg",
                                                data_format="NHWC")

        self.res_5_np = adaptive_pool2d_forward(x=self.x_np,
                                                output_size=[None, 3],
                                                pool_type="avg")

    def test_static_graph(self):
        for use_cuda in ([False, True]
                         if core.is_compiled_with_cuda() else [False]):
            place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace()
            paddle.enable_static()
            x = paddle.fluid.data(name="x", shape=[2, 3, 7, 7], dtype="float32")

            adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2D(output_size=[3, 3])
            out_1 = adaptive_avg_pool(x=x)

            adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2D(output_size=5)
            out_2 = adaptive_avg_pool(x=x)

            adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2D(output_size=[2, 5])
            out_3 = adaptive_avg_pool(x=x)

            adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2D(output_size=[3, 3],
                                                            data_format="NHWC")
            out_4 = adaptive_avg_pool(x=x)

            adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2D(
                output_size=[None, 3])
            out_5 = adaptive_avg_pool(x=x)

            exe = paddle.static.Executor(place=place)
            [res_1, res_2, res_3, res_4,
             res_5] = exe.run(fluid.default_main_program(),
                              feed={"x": self.x_np},
                              fetch_list=[out_1, out_2, out_3, out_4, out_5])

            assert np.allclose(res_1, self.res_1_np)

            assert np.allclose(res_2, self.res_2_np)

            assert np.allclose(res_3, self.res_3_np)

            assert np.allclose(res_4, self.res_4_np)

            assert np.allclose(res_5, self.res_5_np)

    def test_dynamic_graph(self):
        for use_cuda in ([False, True]
                         if core.is_compiled_with_cuda() else [False]):
            place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace()
            paddle.disable_static(place=place)
            x = paddle.to_tensor(self.x_np)

            adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2D(output_size=[3, 3])
            out_1 = adaptive_avg_pool(x=x)

            adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2D(output_size=5)
            out_2 = adaptive_avg_pool(x=x)

            adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2D(output_size=[2, 5])
            out_3 = adaptive_avg_pool(x=x)

            adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2D(output_size=[3, 3],
                                                            data_format="NHWC")
            out_4 = adaptive_avg_pool(x=x)

            adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2D(
                output_size=[None, 3])
            out_5 = adaptive_avg_pool(x=x)

            assert np.allclose(out_1.numpy(), self.res_1_np)

            assert np.allclose(out_2.numpy(), self.res_2_np)

            assert np.allclose(out_3.numpy(), self.res_3_np)

            assert np.allclose(out_4.numpy(), self.res_4_np)

            assert np.allclose(out_5.numpy(), self.res_5_np)


class TestOutputSizeTensor(UnittestBase):

    def init_info(self):
        self.shapes = [[1, 3, 6, 6]]
        self.save_path = os.path.join(self.temp_dir.name, self.path_prefix())

    def test_static(self):
        paddle.enable_static()
        main_prog = Program()
        starup_prog = Program()
        with program_guard(main_prog, starup_prog):
            fc = paddle.nn.Linear(6, 6)
            x = paddle.randn(self.shapes[0])
            x.stop_gradient = False
            feat = fc(x)  # [1,3,6,6]

            out1, out2 = self.call_func(feat)

            sgd = paddle.optimizer.SGD()
            sgd.minimize(paddle.mean(out1 + out2))
            self.assertTrue(self.var_prefix() in str(main_prog))

            exe = paddle.static.Executor()
            exe.run(starup_prog)
            res = exe.run(fetch_list=[out1, out2])
            np.testing.assert_allclose(res[0], res[1])
            paddle.static.save_inference_model(self.save_path, [x],
                                               [out1, out2], exe)
            # Test for Inference Predictor
            infer_outs = self.infer_prog()
            np.testing.assert_array_equal(infer_outs[0].shape, (1, 3, 3, 3))
            np.testing.assert_allclose(infer_outs[0], infer_outs[1])

    def path_prefix(self):
        return 'pool2d_tensor'

    def var_prefix(self):
        return "Vars["

    def call_func(self, x):
        # list[Tensor]
        output_size = [paddle.assign([3]), paddle.assign([3])]
        out1 = paddle.nn.functional.adaptive_avg_pool2d(x=x, output_size=[3, 3])
        out2 = paddle.nn.functional.adaptive_avg_pool2d(x=x,
                                                        output_size=output_size)
        return out1, out2


class TestOutputSizeListTensor(TestOutputSizeTensor):

    def path_prefix(self):
        return 'pool2d_tensors'

    def call_func(self, x):
        # list[int, Tensor]
        output_size = [paddle.assign([3]), 3]
        out1 = paddle.nn.functional.adaptive_avg_pool2d(x=x, output_size=[3, 3])
        out2 = paddle.nn.functional.adaptive_avg_pool2d(x=x,
                                                        output_size=output_size)
        return out1, out2


class TestOutputSizeListTensor2(TestOutputSizeTensor):

    def path_prefix(self):
        return 'pool2d_tensor2'

    def call_func(self, x):
        # A Tensor
        output_size = paddle.assign([3, 3])
        out1 = paddle.nn.functional.adaptive_avg_pool2d(x=x, output_size=[3, 3])
        out2 = paddle.nn.functional.adaptive_avg_pool2d(x=x,
                                                        output_size=output_size)
        return out1, out2


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