Source code for super_gradients.training.models.dpn

'''
Dual Path Networks in PyTorch.

Credits: https://github.com/kuangliu/pytorch-cifar/blob/master/models/dpn.py
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
from super_gradients.training.models.sg_module import SgModule


[docs]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) )
[docs] 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
[docs]class DPN(SgModule): def __init__(self, cfg): super(DPN, self).__init__() in_planes, out_planes = cfg['in_planes'], cfg['out_planes'] num_blocks, dense_depth = cfg['num_blocks'], cfg['dense_depth'] 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)
[docs] 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
[docs]def DPN26(): cfg = { 'in_planes': (96, 192, 384, 768), 'out_planes': (256, 512, 1024, 2048), 'num_blocks': (2, 2, 2, 2), 'dense_depth': (16, 32, 24, 128) } return DPN(cfg)
[docs]def DPN92(): cfg = { 'in_planes': (96, 192, 384, 768), 'out_planes': (256, 512, 1024, 2048), 'num_blocks': (3, 4, 20, 3), 'dense_depth': (16, 32, 24, 128) } return DPN(cfg)
[docs]def test(): net = DPN92() x = torch.randn(1, 3, 32, 32) y = net(x) print(y)
# test()