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dpn.py
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dpn.py
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"""
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()