from torch import nn
import torch.nn.functional as F


class RpnHead(nn.Module):
    def __init__(self, anchor_len, in_channels=128):
        super(RpnHead, self).__init__()
        self.anchor_len = anchor_len
        self.drop = nn.Dropout3d(p=0.5, inplace=False)
        self.conv = nn.Sequential(nn.Conv3d(in_channels, 64, kernel_size=1),
                                  nn.ReLU())
        self.logits = nn.Conv3d(64, 1 * self.anchor_len, kernel_size=1)
        self.deltas = nn.Conv3d(64, 6 * self.anchor_len, kernel_size=1)

    def forward(self, f):
        # out = self.drop(f)
        out = self.conv(f)

        logits = self.logits(out)
        deltas = self.deltas(out)

        size = logits.size()
        logits = logits.view(logits.size(0), logits.size(1), -1)
        logits = logits.transpose(1, 2).contiguous().view(size[0], size[2], size[3], size[4], self.anchor_len, 1)
        logits = logits.view(logits.size(0), -1, 1)

        size = deltas.size()
        deltas = deltas.view(deltas.size(0), deltas.size(1), -1)
        deltas = deltas.transpose(1, 2).contiguous().view(size[0], size[2], size[3], size[4], self.anchor_len, 6)
        deltas = deltas.view(deltas.size(0), -1, 6)

        return logits, deltas