"""
 Copyright (c) 2019 Intel Corporation
 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 torch
from torch import nn
from examples.torch.common.sample_config import SampleConfig

from examples.torch.object_detection.layers.modules.ssd_head import MultiOutputSequential, SSDDetectionOutput
from nncf.torch.checkpoint_loading import load_state


def conv_bn(inp, oup, kernel, stride, padding):
    return nn.Sequential(
        nn.Conv2d(inp, oup, kernel, stride, padding, bias=False),
        nn.BatchNorm2d(oup),
        nn.ReLU(inplace=True)
    )


def conv_dw(inp, oup, stride):
    return nn.Sequential(
        nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False),
        nn.BatchNorm2d(inp),
        nn.ReLU(inplace=True),

        nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
        nn.BatchNorm2d(oup),
        nn.ReLU(inplace=True),
    )


def mobilenet(start_input_channels=3):
    model = MultiOutputSequential(
        [11, 13],
        [
            conv_bn(start_input_channels, 32, 3, 2, 1),
            conv_dw(32, 64, 1),
            conv_dw(64, 128, 2),
            conv_dw(128, 128, 1),
            conv_dw(128, 256, 2),
            conv_dw(256, 256, 1),
            conv_dw(256, 512, 2),
            conv_dw(512, 512, 1),
            conv_dw(512, 512, 1),
            conv_dw(512, 512, 1),
            conv_dw(512, 512, 1),
            conv_dw(512, 512, 1),
            conv_dw(512, 1024, 2),
            conv_dw(1024, 1024, 1)
        ]
    )
    return model


def extra_layers(start_input_channels):
    return MultiOutputSequential(
        [1, 3, 5, 7],
        [
            conv_bn(start_input_channels, 256, 1, 1, 0),
            conv_bn(256, 512, 3, 2, 1),
            conv_bn(512, 128, 1, 1, 0),
            conv_bn(128, 256, 3, 2, 1),
            conv_bn(256, 128, 1, 1, 0),
            conv_bn(128, 256, 3, 2, 1),
            conv_bn(256, 64, 1, 1, 0),
            conv_bn(64, 128, 3, 2, 1)
        ]
    )


class MobileNetSSD(nn.Module):
    def __init__(self, num_classes, cfg):
        super().__init__()
        self.cfg = cfg
        self.num_classes = num_classes

        self.basenet = mobilenet()
        self.extras = extra_layers(1024)

        NUM_INPUT_FEATURES = [512, 1024, 512, 256, 256, 128]
        self.detection_head = SSDDetectionOutput(NUM_INPUT_FEATURES, num_classes, cfg)

    def forward(self, x):
        img_tensor = x[0].clone().unsqueeze(0)

        sources, x = self.basenet(x)
        extra_sources, x = self.extras(x)

        return self.detection_head(sources + extra_sources, img_tensor)


def build_ssd_mobilenet(cfg, size, num_classes, config):
    if size != 300:
        raise ValueError("Only Mobilenet-SSD with input size 300 is supported")
    mobilenet_ssd = MobileNetSSD(num_classes, cfg)

    if config.basenet and (config.resuming_checkpoint_path is None) and (config.weights is None):
        print('Loading base network...')
        basenet_weights = torch.load(config.basenet)['state_dict']
        new_weights = {}
        for wn, wv in basenet_weights.items():
            wn = wn.replace('model.', '')
            new_weights[wn] = wv

        load_state(mobilenet_ssd.basenet, new_weights, is_resume=False)
    return mobilenet_ssd


def ssd_mobilenet():
    ssd_params = SampleConfig({
        "variance": [0.1, 0.1, 0.2, 0.2],
        "max_sizes": [60, 111, 162, 213, 264, 315],
        "min_sizes": [30, 60, 111, 162, 213, 264],
        "steps": [16, 32, 64, 100, 150, 300],
        "aspect_ratios": [[2], [2, 3], [2, 3], [2, 3], [2], [2]],
        "clip": False,
        "flip": True,
        "top_k": 200
    })

    return MobileNetSSD(21, ssd_params)

torch.nn.ReLU torch.nn.Conv2d torch.nn.BatchNorm2d torch.load