import torch
from torch import nn
from torch.nn import functional as F
import net.extractors as extractors
class PSPModule(nn.Module):
def __init__(self, features, out_features=1024, sizes=(1, 2, 3, 6)):
super().__init__()
self.stages = []
self.stages = nn.ModuleList([self._make_stage(features, size) for size in sizes])
self.bottleneck = nn.Conv2d(features * (len(sizes) + 1), out_features, kernel_size=1)
self.relu = nn.ReLU()
def _make_stage(self, features, size):
prior = nn.AdaptiveAvgPool2d(output_size=(size, size))
conv = nn.Conv2d(features, features, kernel_size=1, bias=False)
return nn.Sequential(prior, conv)
def forward(self, feats):
h, w = feats.size(2), feats.size(3)
priors = [F.interpolate(input=stage(feats), size=(h, w), mode='bilinear', align_corners=False) for stage in self.stages] + [feats]
bottle = self.bottleneck(torch.cat(priors, 1))
return self.relu(bottle)
class PSPUpsample(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_channels, out_channels, 3, padding=1),
nn.BatchNorm2d(out_channels),
nn.PReLU()
)
def forward(self, x):
h, w = 2 * x.size(2), 2 * x.size(3)
p = F.interpolate(input=x, size=(h, w), mode='bilinear', align_corners=False)
return self.conv(p)
class PSPNet(nn.Module):
def __init__(self, n_classes=20, sizes=(1, 2, 3, 6), psp_size=2048, deep_features_size=1024, backend='resnet34',
pretrained=True):
super().__init__()
self.feats = getattr(extractors, backend)(pretrained)
self.psp = PSPModule(psp_size, 1024, sizes)
self.drop_1 = nn.Dropout2d(p=0.3)
self.up_1 = PSPUpsample(1024, 256)
self.up_2 = PSPUpsample(256, 64)
self.up_3 = PSPUpsample(64, 64)
self.drop_2 = nn.Dropout2d(p=0.15)
self.final = nn.Sequential(
nn.Conv2d(64, n_classes, kernel_size=1),
nn.LogSoftmax(dim=1)
)
self.classifier = nn.Sequential(
nn.Linear(deep_features_size, 256),
nn.ReLU(),
nn.Linear(256, n_classes)
)
def forward(self, x):
f, class_f = self.feats(x)
p = self.psp(f)
p = self.drop_1(p)
p = self.up_1(p)
p = self.drop_2(p)
p = self.up_2(p)
p = self.drop_2(p)
p = self.up_3(p)
p = self.drop_2(p)
auxiliary = F.adaptive_max_pool2d(input=class_f, output_size=(1, 1)).view(-1, class_f.size(1))
return self.final(p), self.classifier(auxiliary)
