# Tencent is pleased to support the open source community by making ncnn available.
#
# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved.
#
# Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
# in compliance with the License. You may obtain a copy of the License at
#
# https://opensource.org/licenses/BSD-3-Clause
#
# 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 torch
import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()

        self.bn_0 = nn.BatchNorm2d(num_features=32)
        self.bn_1 = nn.BatchNorm2d(num_features=32, eps=1e-1, affine=False)
        self.bn_2 = nn.BatchNorm2d(num_features=11, affine=True)

    def forward(self, x, y):
        x = self.bn_0(x)
        x = self.bn_1(x)

        y = self.bn_2(y)

        return x, y

def test():
    net = Model()
    net.eval()

    torch.manual_seed(0)
    x = torch.rand(1, 32, 12, 64)
    y = torch.rand(1, 11, 1, 1)

    a0, a1 = net(x, y)

    # export onnx
    torch.onnx.export(net, (x, y), "test_nn_BatchNorm2d.onnx")

    # onnx to pnnx
    import os
    os.system("../../src/pnnx test_nn_BatchNorm2d.onnx inputshape=[1,32,12,64],[1,11,1,1]")

    # pnnx inference
    import test_nn_BatchNorm2d_pnnx
    b0, b1 = test_nn_BatchNorm2d_pnnx.test_inference()

    return torch.equal(a0, b0) and torch.equal(a1, b1)

if __name__ == "__main__":
    if test():
        exit(0)
    else:
        exit(1)
