# Copyright 2021 Huawei Technologies Co., Ltd
#
# 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 os
import sys
import torch
from collections import OrderedDict
from torch import nn
from torch.autograd import Variable

sys.path.append('./utils')
from utils.model import RandPointCNN
from utils.util_funcs import knn_indices_func_gpu, knn_indices_func_cpu
from utils.util_layers import Dense


AbbPointCNN = lambda a, b, c, d, e: RandPointCNN(a, b, 3, c, d, e, knn_indices_func_gpu)

NUM_CLASS = 40


class Classifier(nn.Module):

    def __init__(self):
        super(Classifier, self).__init__()

        self.pcnn1 = AbbPointCNN(3, 32, 8, 1, -1)
        self.pcnn2 = nn.Sequential(
            AbbPointCNN(32, 64, 8, 2, -1),
            AbbPointCNN(64, 96, 8, 4, -1),
            AbbPointCNN(96, 128, 12, 4, 120),
            AbbPointCNN(128, 160, 12, 6, 120)
        )

        self.fcn = nn.Sequential(
            Dense(160, 128),
            Dense(128, 64, drop_rate=0.5),
            Dense(64, NUM_CLASS, with_bn=False, activation=None)
        )

    def forward(self, x):
        x = self.pcnn1(x)
        x = self.pcnn2(x)[1]  # grab features

        logits = self.fcn(x)
        logits_mean = torch.mean(logits, dim=1)
        return logits_mean


def proc_nodes_module(checkpoint):
    new_state_dict = OrderedDict()
    for k, v in checkpoint.items():
        if "module." in k:
            name = k.replace("module.", "")
        else:
            name = k
        new_state_dict[name] = v
    return new_state_dict


def pth2onnx(input_path, output_path):
    model = Classifier()
    checkpoint = torch.load(input_path, map_location='cpu')
    checkpoint = proc_nodes_module(checkpoint)
    model.load_state_dict(checkpoint)
    model.eval()

    input_names = ["P_sampled", "P_patched"]
    output_names = ["out"]
    input_0 = torch.randn((1, 1024, 3), dtype=torch.float32)
    input_1 = torch.randn((1, 1024, 3), dtype=torch.float32)
    dummy_input = [input_0, input_1]
    torch.onnx.export(model, dummy_input, output_path, input_names=input_names,
                      output_names=output_names, opset_version=11)


if __name__ == "__main__":
    input_file = sys.argv[1]
    output_file = sys.argv[2]
    pth2onnx(input_file, output_file)