test_conv2d_api.py

#   Copyright (c) 2021 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

paddle.enable_static()
import paddle.fluid as fluid
import paddle.fluid.core as core


class TestConv2DAPI(unittest.TestCase):
    def test_api(self):

        input_NHWC = paddle.static.data(
            name="input_NHWC",
            shape=[2, 5, 5, 3],
            dtype="float32",
        )

        input_NCHW = paddle.static.data(
            name="input_NCHW",
            shape=[2, 3, 5, 5],
            dtype="float32",
        )

        paddle.static.nn.conv2d(
            input=input_NHWC,
            num_filters=3,
            filter_size=[3, 3],
            stride=[1, 1],
            padding=0,
            dilation=[1, 1],
            groups=1,
            data_format="NCHW",
        )

        paddle.static.nn.conv2d(
            input=input_NCHW,
            num_filters=3,
            filter_size=[3, 3],
            stride=[1, 1],
            padding=[1, 2, 1, 0],
            dilation=[1, 1],
            groups=1,
            data_format="NCHW",
        )

        paddle.static.nn.conv2d(
            input=input_NCHW,
            num_filters=3,
            filter_size=[3, 3],
            stride=[1, 1],
            padding=[[0, 0], [0, 0], [1, 1], [1, 1]],
            dilation=[1, 1],
            groups=1,
            data_format="NCHW",
        )

        paddle.static.nn.conv2d(
            input=input_NHWC,
            num_filters=3,
            filter_size=[3, 3],
            stride=[1, 1],
            padding=[[0, 0], [1, 1], [1, 1], [0, 0]],
            dilation=[1, 1],
            groups=1,
            data_format="NHWC",
        )

        paddle.static.nn.conv2d(
            input=input_NCHW,
            num_filters=3,
            filter_size=[3, 3],
            stride=[1, 1],
            padding="SAME",
            dilation=[1, 1],
            groups=1,
            data_format="NCHW",
        )

        paddle.static.nn.conv2d(
            input=input_NCHW,
            num_filters=3,
            filter_size=[3, 3],
            stride=[1, 1],
            padding="VALID",
            dilation=[1, 1],
            groups=1,
            data_format="NCHW",
        )

    def test_depthwise_conv2d(self):
        x_var = paddle.uniform((2, 8, 8, 4), dtype='float32', min=-1.0, max=1.0)
        conv = paddle.nn.Conv2D(
            in_channels=4,
            out_channels=4,
            kernel_size=(3, 3),
            groups=4,
            data_format='NHWC',
        )
        y_var = conv(x_var)


class TestConv2DAPI_Error(unittest.TestCase):
    def test_api(self):
        input = paddle.static.data(
            name="input",
            shape=[2, 5, 5, 5],
            dtype="float32",
        )

        # ValueError: cudnn
        def run_1():
            paddle.static.nn.conv2d(
                input=input,
                num_filters=3,
                filter_size=[3, 3],
                stride=[1, 1],
                padding=0,
                dilation=[1, 1],
                groups=1,
                use_cudnn=[0],
                data_format="NCHW",
            )

        self.assertRaises(ValueError, run_1)

        # ValueError: data_format
        def run_2():
            paddle.static.nn.conv2d(
                input=input,
                num_filters=3,
                filter_size=[3, 3],
                stride=[1, 1],
                padding=0,
                dilation=[1, 1],
                groups=1,
                use_cudnn=False,
                data_format="NCHWC",
            )

        self.assertRaises(ValueError, run_2)

        # ValueError: padding
        def run_3():
            paddle.static.nn.conv2d(
                input=input,
                num_filters=3,
                filter_size=[3, 3],
                stride=[1, 1],
                padding="SAMEE",
                dilation=[1, 1],
                groups=1,
                use_cudnn=False,
                data_format="NCHW",
            )

        self.assertRaises(ValueError, run_3)

        def run_4():
            paddle.static.nn.conv2d(
                input=input,
                num_filters=3,
                filter_size=[3, 3],
                stride=[1, 1],
                padding=[[0, 1], [0, 1], [0, 1], [0, 1]],
                dilation=[1, 1],
                groups=1,
                use_cudnn=False,
                data_format="NCHW",
            )

        self.assertRaises(ValueError, run_4)

        def run_5():
            paddle.static.nn.conv2d(
                input=input,
                num_filters=3,
                filter_size=[3, 3],
                stride=[1, 1],
                padding=[[0, 1], [0, 1], [0, 1], [0, 1]],
                dilation=[1, 1],
                groups=1,
                use_cudnn=False,
                data_format="NHWC",
            )

        self.assertRaises(ValueError, run_5)

        # ValueError: channel dimmention
        x = paddle.static.data(
            name="x",
            shape=[2, 5, 5, -1],
            dtype="float32",
        )

        def run_6():
            paddle.static.nn.conv2d(
                input=x,
                num_filters=3,
                filter_size=[3, 3],
                stride=[1, 1],
                padding=0,
                dilation=[1, 1],
                groups=1,
                use_cudnn=False,
                data_format="NHWC",
            )

        self.assertRaises(ValueError, run_6)

        # ValueError: groups
        def run_7():
            paddle.static.nn.conv2d(
                input=input,
                num_filters=3,
                filter_size=[3, 3],
                stride=[1, 1],
                padding=0,
                dilation=[1, 1],
                groups=3,
                use_cudnn=False,
                data_format="NHWC",
            )

        self.assertRaises(ValueError, run_7)

        # ValueError: filter num
        def run_8():
            paddle.static.nn.conv2d(
                input=input,
                num_filters=0,
                filter_size=0,
                stride=0,
                padding=0,
                dilation=0,
                groups=1,
                use_cudnn=False,
                data_format="NCHW",
            )

        self.assertRaises(ValueError, run_8)

        # ValueError: groups
        def run_9():
            paddle.static.nn.conv2d(
                input=input,
                num_filters=0,
                filter_size=0,
                stride=0,
                padding=0,
                dilation=0,
                groups=0,
                use_cudnn=False,
                data_format="NCHW",
            )

        self.assertRaises(ValueError, run_9)

        # ValueError: stride
        def run_10():
            paddle.static.nn.conv2d(
                input=input,
                num_filters=1,
                filter_size=1,
                stride=0,
                padding=0,
                dilation=0,
                groups=1,
                use_cudnn=False,
                data_format="NCHW",
            )

        self.assertRaises(ValueError, run_10)

    def test_api_with_error_input(self):
        input = paddle.static.data(
            name="error_input",
            shape=[1],
            dtype="float32",
        )

        # ValueError: cudnn
        def run_1():
            paddle.static.nn.conv2d(
                input=input,
                num_filters=0,
                filter_size=0,
                stride=0,
                padding=0,
                dilation=0,
                groups=0,
                use_cudnn=False,
                data_format="NCHW",
            )

        self.assertRaises(ValueError, run_1)


# --------- test environment variable ------
@unittest.skipIf(
    not (core.is_compiled_with_cuda() or core.is_compiled_with_rocm()),
    "core is not compiled with CUDA or ROCM",
)
class TestConv2DEnviron(unittest.TestCase):
    def run1(self, place):
        with fluid.program_guard(fluid.Program(), fluid.Program()):
            inputs = paddle.static.data(
                shape=[2, 3, 5, 5],
                name="inputs",
                dtype="float32",
            )
            result = paddle.static.nn.conv2d(
                input=inputs,
                num_filters=4,
                filter_size=[3, 3],
                stride=[1, 1],
                padding=0,
                dilation=[1, 1],
                groups=1,
                data_format="NCHW",
            )
            exe = fluid.Executor(place)
            exe.run(fluid.default_startup_program())
            fetches = exe.run(
                fluid.default_main_program(),
                feed={"inputs": self.input_np},
                fetch_list=[result],
            )

    def run2(self, place):
        with fluid.dygraph.guard(place):
            inputs = fluid.dygraph.to_variable(self.input_np)
            conv = paddle.nn.Conv2D(
                in_channels=3,
                out_channels=4,
                kernel_size=(3, 3),
                data_format="NCHW",
            )
            result = conv(inputs)

    def run_all(self, place):
        self.run1(place)
        self.run2(place)

    def test_environ(self):
        self.input_np = np.random.random([2, 3, 5, 5]).astype("float32")
        for place in [paddle.CPUPlace(), paddle.CUDAPlace(0)]:
            fluid.set_flags({'FLAGS_conv2d_disable_cudnn': False})
            self.run_all(place)
            fluid.set_flags({'FLAGS_conv2d_disable_cudnn': True})
            self.run_all(place)


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