# Copyright 2019-2021 Canaan Inc.
#
# 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.
# pylint: disable=invalid-name, unused-argument, import-outside-toplevel

import pytest
import torch
from onnx_test_runner import OnnxTestRunner


def _make_module(in_shape, out_channel, kernel_size):

    class ReshapeModule(torch.nn.Module):
        def __init__(self):
            super(ReshapeModule, self).__init__()
            self.conv2d = torch.nn.Conv2d(in_shape[1], out_channel, kernel_size)
            self.n = in_shape[0]
            self.c = out_channel
            self.h = in_shape[2] - kernel_size + 1
            self.w = in_shape[3] - kernel_size + 1

        def forward(self, x):
            x = self.conv2d(x)
            x = torch.reshape(x, (self.n, self.c * self.h * self.w))
            x = torch.reshape(x, (self.n, self.c, self.h * self.w))
            x = torch.reshape(x, (self.n, self.c * self.h, self.w))
            x = torch.reshape(x, (self.n * self.c, self.h * self.w))
            x = torch.reshape(x, (self.n * self.c, self.h, self.w))
            x = torch.reshape(x, (self.n * self.c * self.h, self.w))
            x = torch.reshape(x, (-1, self.w))
            return x

    return ReshapeModule()


in_shapes = [
    [1, 4, 60, 72],
    [1, 3, 224, 224]
]

out_channels = [
    1,
    3,
    16
]

kernel_sizes = [
    1,
    3,
    5
]


@pytest.mark.parametrize('in_shape', in_shapes)
@pytest.mark.parametrize('out_channel', out_channels)
@pytest.mark.parametrize('kernel_size', kernel_sizes)
def test_reshape(in_shape, out_channel, kernel_size, request):
    module = _make_module(in_shape, out_channel, kernel_size)

    runner = OnnxTestRunner(request.node.name)
    model_file = runner.from_torch(module, in_shape)
    runner.run(model_file)


if __name__ == "__main__":
    pytest.main(['-vv', 'test_reshape.py'])

# Copyright 2019-2021 Canaan Inc.
#
# 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.
# pylint: disable=invalid-name, unused-argument, import-outside-toplevel

import pytest
import torch
from onnx_test_runner import OnnxTestRunner


def _make_module(in_shape, out_channel, kernel_size):

    class ReshapeModule(torch.nn.Module):
        def __init__(self):
            super(ReshapeModule, self).__init__()
            self.conv2d = torch.nn.Conv2d(in_shape[1], out_channel, kernel_size)
            self.n = in_shape[0]
            self.c = out_channel
            self.h = in_shape[2] - kernel_size + 1
            self.w = in_shape[3] - kernel_size + 1

        def forward(self, x):
            x = self.conv2d(x)
            x = torch.reshape(x, (self.n, self.c * self.h * self.w))
            x = torch.reshape(x, (self.n, self.c, self.h * self.w))
            x = torch.reshape(x, (self.n, self.c * self.h, self.w))
            x = torch.reshape(x, (self.n * self.c, self.h * self.w))
            x = torch.reshape(x, (self.n * self.c, self.h, self.w))
            x = torch.reshape(x, (self.n * self.c * self.h, self.w))
            x = torch.reshape(x, (-1, self.w))
            return x

    return ReshapeModule()


in_shapes = [
    [1, 4, 60, 72],
    [1, 3, 224, 224]
]

out_channels = [
    1,
    3,
    16
]

kernel_sizes = [
    1,
    3,
    5
]


@pytest.mark.parametrize('in_shape', in_shapes)
@pytest.mark.parametrize('out_channel', out_channels)
@pytest.mark.parametrize('kernel_size', kernel_sizes)
def test_reshape(in_shape, out_channel, kernel_size, request):
    module = _make_module(in_shape, out_channel, kernel_size)

    runner = OnnxTestRunner(request.node.name)
    model_file = runner.from_torch(module, in_shape)
    runner.run(model_file)


if __name__ == "__main__":
    pytest.main(['-vv', 'test_reshape.py'])

# Copyright 2019-2021 Canaan Inc. # # 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. # pylint: disable=invalid-name, unused-argument, import-outside-toplevel