dpn.py

"""
Dual Path Networks in PyTorch.

Credits: https://github.com/kuangliu/pytorch-cifar/blob/master/models/dpn.py
"""
from typing import Tuple

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

from super_gradients.training.models import BaseClassifier


class Bottleneck(nn.Module):
    def __init__(self, last_planes, in_planes, out_planes, dense_depth, stride, first_layer):
        super(Bottleneck, self).__init__()
        self.out_planes = out_planes
        self.dense_depth = dense_depth

        self.conv1 = nn.Conv2d(last_planes, in_planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(in_planes)
        self.conv2 = nn.Conv2d(in_planes, in_planes, kernel_size=3, stride=stride, padding=1, groups=32, bias=False)
        self.bn2 = nn.BatchNorm2d(in_planes)
        self.conv3 = nn.Conv2d(in_planes, out_planes + dense_depth, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(out_planes + dense_depth)

        self.shortcut = nn.Sequential()
        if first_layer:
            self.shortcut = nn.Sequential(
                nn.Conv2d(last_planes, out_planes + dense_depth, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(out_planes + dense_depth)
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = F.relu(self.bn2(self.conv2(out)))
        out = self.bn3(self.conv3(out))
        x = self.shortcut(x)
        d = self.out_planes
        out = torch.cat([x[:, :d, :, :] + out[:, :d, :, :], x[:, d:, :, :], out[:, d:, :, :]], 1)
        out = F.relu(out)
        return out


class DPN(BaseClassifier):
    def __init__(
        self,
        in_planes: Tuple[int, int, int, int],
        out_planes: Tuple[int, int, int, int],
        num_blocks: Tuple[int, int, int, int],
        dense_depth: Tuple[int, int, int, int],
    ):
        super(DPN, self).__init__()

        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.last_planes = 64
        self.layer1 = self._make_layer(in_planes[0], out_planes[0], num_blocks[0], dense_depth[0], stride=1)
        self.layer2 = self._make_layer(in_planes[1], out_planes[1], num_blocks[1], dense_depth[1], stride=2)
        self.layer3 = self._make_layer(in_planes[2], out_planes[2], num_blocks[2], dense_depth[2], stride=2)
        self.layer4 = self._make_layer(in_planes[3], out_planes[3], num_blocks[3], dense_depth[3], stride=2)
        self.linear = nn.Linear(out_planes[3] + (num_blocks[3] + 1) * dense_depth[3], 10)

    def _make_layer(self, in_planes, out_planes, num_blocks, dense_depth, stride):
        strides = [stride] + [1] * (num_blocks - 1)
        layers = []
        for i, stride in enumerate(strides):
            layers.append(Bottleneck(self.last_planes, in_planes, out_planes, dense_depth, stride, i == 0))
            self.last_planes = out_planes + (i + 2) * dense_depth
        return nn.Sequential(*layers)

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = F.avg_pool2d(out, 4)
        out = out.view(out.size(0), -1)
        out = self.linear(out)
        return out


def DPN26():
    return DPN(in_planes=(96, 192, 384, 768), out_planes=(256, 512, 1024, 2048), num_blocks=(2, 2, 2, 2), dense_depth=(16, 32, 24, 128))


def DPN92():
    return DPN(in_planes=(96, 192, 384, 768), out_planes=(256, 512, 1024, 2048), num_blocks=(3, 4, 20, 3), dense_depth=(16, 32, 24, 128))


def test():
    net = DPN92()
    x = torch.randn(1, 3, 32, 32)
    y = net(x)
    print(y)


# test()