#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
@Author: Yue Wang
@Contact: yuewangx@mit.edu
@File: model.py
@Time: 2018/10/13 6:35 PM
"""


import os
import sys
import copy
import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F


def normalize_point_clouds(pcs):
    B,N,C = pcs.shape
    shift = torch.mean(pcs,dim=1).unsqueeze(1)
    scale = torch.std(pcs.view(B,N*C),dim=1).unsqueeze(1).unsqueeze(1)
    newpcs = (pcs - shift) / scale
    return newpcs,shift,scale


def knn(x, k):
    inner = -2*torch.matmul(x.transpose(2, 1), x)
    xx = torch.sum(x**2, dim=1, keepdim=True)
    pairwise_distance = -xx - inner - xx.transpose(2, 1)
    idx = pairwise_distance.topk(k=k, dim=-1)[1]   # (batch_size, num_points, k)
    return idx


def get_graph_feature(x, k=20, idx=None):
    batch_size = x.size(0)
    num_points = x.size(2)
    x = x.view(batch_size, -1, num_points)
    if idx is None:
        idx = knn(x, k=k)   # (batch_size, num_points, k)

    device = x.device

    idx_base = torch.arange(0, batch_size, device=device).view(-1, 1, 1)*num_points

    idx = idx + idx_base

    idx = idx.view(-1)
 
    _, num_dims, _ = x.size()

    x = x.transpose(2, 1).contiguous()   # (batch_size, num_points, num_dims)  -> (batch_size*num_points, num_dims) #   batch_size * num_points * k + range(0, batch_size*num_points)
    feature = x.view(batch_size*num_points, -1)[idx, :]
    feature = feature.view(batch_size, num_points, k, num_dims) 
    x = x.view(batch_size, num_points, 1, num_dims).repeat(1, 1, k, 1)
    
    feature = torch.cat((feature-x, x), dim=3).permute(0, 3, 1, 2).contiguous()

    return feature


class DGCNN_main(nn.Module):
    def __init__(self, num_classes=40, embedding=512, k=32):
        super(DGCNN_main, self).__init__()
        self.k = k
        self.bn1 = nn.BatchNorm2d(64)
        self.bn2 = nn.BatchNorm2d(64)
        self.bn3 = nn.BatchNorm2d(128)
        self.bn4 = nn.BatchNorm2d(256)
        self.bn5 = nn.BatchNorm1d(embedding)

        self.conv1 = nn.Sequential(nn.Conv2d(6, 64, kernel_size=1, bias=False),
                                   self.bn1,
                                   nn.LeakyReLU(negative_slope=0.2))
        self.conv2 = nn.Sequential(nn.Conv2d(64*2, 64, kernel_size=1, bias=False),
                                   self.bn2,
                                   nn.LeakyReLU(negative_slope=0.2))
        self.conv3 = nn.Sequential(nn.Conv2d(64*2, 128, kernel_size=1, bias=False),
                                   self.bn3,
                                   nn.LeakyReLU(negative_slope=0.2))
        self.conv4 = nn.Sequential(nn.Conv2d(128*2, 256, kernel_size=1, bias=False),
                                   self.bn4,
                                   nn.LeakyReLU(negative_slope=0.2))
        self.conv5 = nn.Sequential(nn.Conv1d(512, embedding, kernel_size=1, bias=False),
                                   self.bn5,
                                   nn.LeakyReLU(negative_slope=0.2))
        self.linear1 = nn.Linear(embedding*2, 512, bias=False)
        self.bn6 = nn.BatchNorm1d(512)
        self.dp1 = nn.Dropout(0.4)
        self.linear2 = nn.Linear(512, 256)
        self.bn7 = nn.BatchNorm1d(256)
        self.dp2 = nn.Dropout(0.4)
        self.linear3 = nn.Linear(256, num_classes)

    def forward(self, x):

        batch_size = x.size(0)

        x = get_graph_feature(x, k=self.k)
        x = self.conv1(x)
        x1 = x.max(dim=-1, keepdim=False)[0]

        x = get_graph_feature(x1, k=self.k)
        x = self.conv2(x)
        x2 = x.max(dim=-1, keepdim=False)[0]

        x = get_graph_feature(x2, k=self.k)
        x = self.conv3(x)
        x3 = x.max(dim=-1, keepdim=False)[0]

        x = get_graph_feature(x3, k=self.k)
        x = self.conv4(x)
        x4 = x.max(dim=-1, keepdim=False)[0]

        x = torch.cat((x1, x2, x3, x4), dim=1)

        x = self.conv5(x)
        x1 = F.adaptive_max_pool1d(x, 1).view(batch_size, -1)
        x2 = F.adaptive_avg_pool1d(x, 1).view(batch_size, -1)
        x = torch.cat((x1, x2), 1)

        x = F.leaky_relu(self.bn6(self.linear1(x)), negative_slope=0.2)
        x = self.dp1(x)
        x = F.leaky_relu(self.bn7(self.linear2(x)), negative_slope=0.2)
        x = self.dp2(x)
        x = self.linear3(x)
        return x

def DGCNN(num_classes=40, **kwargs) -> DGCNN_main:
    return DGCNN_main(num_classes=num_classes)


if __name__ == '__main__':
    import os
    import torch
    os.environ["CUDA_VISIBLE_DEVICES"] = '0'
    input = torch.randn((8,3,1024))
    label = torch.randn(8,16)
    model = DGCNN(num_classes=40)
    output= model(input)
    print(output)
    print(output.size())
