"""
Copyright 2020 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.
"""
from __future__ import print_function, division, absolute_import
import sys
import torch
import torch.onnx
from torch import nn
sys.path.append(r"./pretrained-models.pytorch")
from pretrainedmodels.models.nasnet import NASNetALarge

pretrained_settings = {
    'nasnetalarge': {
        'imagenet': {
            'input_space': 'RGB',
            'input_size': [3, 331, 331], # resize 354
            'input_range': [0, 1],
            'mean': [0.5, 0.5, 0.5],
            'std': [0.5, 0.5, 0.5],
            'num_classes': 1000
        },
        'imagenet+background': {
            'input_space': 'RGB',
            'input_size': [3, 331, 331], # resize 354
            'input_range': [0, 1],
            'mean': [0.5, 0.5, 0.5],
            'std': [0.5, 0.5, 0.5],
            'num_classes': 1001
        }
    }
}


def nasnetalarge(localpth, num_classes=1001, pretrained='imagenet+background'):
    r"""NASNetALarge model architecture from the
    `"NASNet" <https://arxiv.org/abs/1707.07012>`_ paper.
    """
    if pretrained:
        settings = pretrained_settings['nasnetalarge'][pretrained]
        assert num_classes == settings['num_classes'], \
            "num_classes should be {}, but is {}".format(settings['num_classes'], num_classes)

        # both 'imagenet'&'imagenet+background' are loaded from same parameters
        model = NASNetALarge(num_classes=1001)
        checkpoint = torch.load(localpth)
        model.load_state_dict(checkpoint)
        if pretrained == 'imagenet':
            new_last_linear = nn.Linear(model.last_linear.in_features, 1000)
            new_last_linear.weight.data = model.last_linear.weight.data[1:]
            new_last_linear.bias.data = model.last_linear.bias.data[1:]
            model.last_linear = new_last_linear

        model.input_space = settings['input_space']
        model.input_size = settings['input_size']
        model.input_range = settings['input_range']

        model.mean = settings['mean']
        model.std = settings['std']
    else:
        model = NASNetALarge(num_classes=num_classes)
    return model


def pth2onnx(input_file, output_file):
    """
    param input_file: input pth model path
    param output_file: output onnx model path
    """
    model = nasnetalarge(localpth=input_file, num_classes=1001, pretrained='imagenet+background')
    model.eval()
    input_names = ["image"]
    output_names = ["class"]
    dynamic_axes = {'image': {0: '-1'}, 'class': {0: '-1'}}
    dummy_input = torch.randn(1, 3, 331, 331)
    torch.onnx.export(model, dummy_input, output_file,
                      input_names = input_names, dynamic_axes = dynamic_axes,
                      output_names = output_names, opset_version=11, verbose=True)


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