# Copyright (c) Facebook, Inc. and its affiliates.

from collections import OrderedDict

import torch
import torch.nn as nn
from inplace_abn import ABN


class DenseModule(nn.Module):
    def __init__(
        self, in_channels, growth, layers, bottleneck_factor=4, norm_act=ABN, dilation=1
    ):
        super(DenseModule, self).__init__()
        self.in_channels = in_channels
        self.growth = growth
        self.layers = layers

        self.convs1 = nn.ModuleList()
        self.convs3 = nn.ModuleList()
        for i in range(self.layers):
            self.convs1.append(
                nn.Sequential(
                    OrderedDict(
                        [
                            ("bn", norm_act(in_channels)),
                            (
                                "conv",
                                nn.Conv2d(
                                    in_channels,
                                    self.growth * bottleneck_factor,
                                    1,
                                    bias=False,
                                ),
                            ),
                        ]
                    )
                )
            )
            self.convs3.append(
                nn.Sequential(
                    OrderedDict(
                        [
                            ("bn", norm_act(self.growth * bottleneck_factor)),
                            (
                                "conv",
                                nn.Conv2d(
                                    self.growth * bottleneck_factor,
                                    self.growth,
                                    3,
                                    padding=dilation,
                                    bias=False,
                                    dilation=dilation,
                                ),
                            ),
                        ]
                    )
                )
            )
            in_channels += self.growth

    @property
    def out_channels(self):
        return self.in_channels + self.growth * self.layers

    def forward(self, x):
        inputs = [x]
        for i in range(self.layers):
            x = torch.cat(inputs, dim=1)
            x = self.convs1[i](x)
            x = self.convs3[i](x)
            inputs += [x]

        return torch.cat(inputs, dim=1)

# Copyright (c) Facebook, Inc. and its affiliates.

from collections import OrderedDict

import torch
import torch.nn as nn
from inplace_abn import ABN


class DenseModule(nn.Module):
    def __init__(
        self, in_channels, growth, layers, bottleneck_factor=4, norm_act=ABN, dilation=1
    ):
        super(DenseModule, self).__init__()
        self.in_channels = in_channels
        self.growth = growth
        self.layers = layers

        self.convs1 = nn.ModuleList()
        self.convs3 = nn.ModuleList()
        for i in range(self.layers):
            self.convs1.append(
                nn.Sequential(
                    OrderedDict(
                        [
                            ("bn", norm_act(in_channels)),
                            (
                                "conv",
                                nn.Conv2d(
                                    in_channels,
                                    self.growth * bottleneck_factor,
                                    1,
                                    bias=False,
                                ),
                            ),
                        ]
                    )
                )
            )
            self.convs3.append(
                nn.Sequential(
                    OrderedDict(
                        [
                            ("bn", norm_act(self.growth * bottleneck_factor)),
                            (
                                "conv",
                                nn.Conv2d(
                                    self.growth * bottleneck_factor,
                                    self.growth,
                                    3,
                                    padding=dilation,
                                    bias=False,
                                    dilation=dilation,
                                ),
                            ),
                        ]
                    )
                )
            )
            in_channels += self.growth

    @property
    def out_channels(self):
        return self.in_channels + self.growth * self.layers

    def forward(self, x):
        inputs = [x]
        for i in range(self.layers):
            x = torch.cat(inputs, dim=1)
            x = self.convs1[i](x)
            x = self.convs3[i](x)
            inputs += [x]

        return torch.cat(inputs, dim=1)

# Copyright (c) Facebook, Inc. and its affiliates.