from __future__ import absolute_import

import torch
import torch.nn as nn


def init_weights(m):
    if isinstance(m, nn.Linear):
        nn.init.kaiming_normal_(m.weight)



class Linear(nn.Module):
    def __init__(self, linear_size, p_dropout=0.5):
        super(Linear, self).__init__()
        self.l_size = linear_size

        self.relu = nn.ReLU(inplace=True)
        self.dropout = nn.Dropout(p_dropout)

        self.w1 = nn.Linear(self.l_size, self.l_size)
        self.batch_norm1 = nn.BatchNorm1d(self.l_size)

        self.w2 = nn.Linear(self.l_size, self.l_size)
        self.batch_norm2 = nn.BatchNorm1d(self.l_size)

    def forward(self, x):
        y = self.w1(x)
        y = self.batch_norm1(y)
        y = self.relu(y)
        y = self.dropout(y)

        y = self.w2(y)
        y = self.batch_norm2(y)
        y = self.relu(y)
        y = self.dropout(y)

        out = x + y

        return out


class LinearModel(nn.Module):
    def __init__(self, input_size, output_size, linear_size=1024, num_stage=2, p_dropout=0.5):
        super(LinearModel, self).__init__()

        self.linear_size = linear_size
        self.p_dropout = p_dropout
        self.num_stage = num_stage

        # 2d joints
        self.input_size = input_size  # 16 * 2
        # 3d joints
        self.output_size = output_size  # 16 * 3

        # process input to linear size
        self.w1 = nn.Linear(self.input_size, self.linear_size)
        self.batch_norm1 = nn.BatchNorm1d(self.linear_size)

        self.linear_stages = []
        for l in range(num_stage):
            self.linear_stages.append(Linear(self.linear_size, self.p_dropout))
        self.linear_stages = nn.ModuleList(self.linear_stages)

        # post processing
        self.w2 = nn.Linear(self.linear_size, self.output_size)

        self.relu = nn.ReLU(inplace=True)
        self.dropout = nn.Dropout(self.p_dropout)

    def forward(self, x):
        # pre-processing
        y = self.w1(x)
        y = self.batch_norm1(y)
        y = self.relu(y)
        y = self.dropout(y)

        # linear layers
        for i in range(self.num_stage):
            y = self.linear_stages[i](y)

        y = self.w2(y)

        return y

if __name__ == '__main__':
    import time

    print('start')
    model = LinearModel(input_size=34, output_size=51, linear_size=1024, num_stage=2, p_dropout=0.25)
    input = torch.zeros(2, 34)
    t0 = time.time()
    out = model(input)
    print(time.time() - t0)
    print(out.shape)