Spaces:
Build error
Build error
File size: 3,520 Bytes
dfc786f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 |
import torch.nn as nn
import torch
from torch.nn import functional as F
from torchvision import models
class ContextualModule(nn.Module):
def __init__(self, features, out_features=512, sizes=(1, 2, 3, 6)):
super(ContextualModule, self).__init__()
self.scales = []
self.scales = nn.ModuleList([self._make_scale(features, size) for size in sizes])
self.bottleneck = nn.Conv2d(features * 2, out_features, kernel_size=1)
self.relu = nn.ReLU()
self.weight_net = nn.Conv2d(features,features,kernel_size=1)
def __make_weight(self,feature,scale_feature):
weight_feature = feature - scale_feature
return F.sigmoid(self.weight_net(weight_feature))
def _make_scale(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)
multi_scales = [F.upsample(input=stage(feats), size=(h, w), mode='bilinear') for stage in self.scales]
weights = [self.__make_weight(feats,scale_feature) for scale_feature in multi_scales]
overall_features = [(multi_scales[0]*weights[0]+multi_scales[1]*weights[1]+multi_scales[2]*weights[2]+multi_scales[3]*weights[3])/(weights[0]+weights[1]+weights[2]+weights[3])]+ [feats]
bottle = self.bottleneck(torch.cat(overall_features, 1))
return self.relu(bottle)
class CANNet(nn.Module):
def __init__(self, load_weights=False):
super(CANNet, self).__init__()
self.seen = 0
self.context = ContextualModule(512, 512)
self.frontend_feat = [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512]
self.backend_feat = [512, 512, 512,256,128,64]
self.frontend = make_layers(self.frontend_feat)
self.backend = make_layers(self.backend_feat,in_channels = 512,batch_norm=True, dilation = True)
self.output_layer = nn.Conv2d(64, 1, kernel_size=1)
if not load_weights:
mod = models.vgg16(pretrained = True)
self._initialize_weights()
for i in range(len(self.frontend.state_dict().items())):
list(self.frontend.state_dict().items())[i][1].data[:] = list(mod.state_dict().items())[i][1].data[:]
def forward(self,x):
x = self.frontend(x)
x = self.context(x)
x = self.backend(x)
x = self.output_layer(x)
return x
def _initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.normal_(m.weight, std=0.01)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def make_layers(cfg, in_channels = 3,batch_norm=False,dilation = False):
if dilation:
d_rate = 2
else:
d_rate = 1
layers = []
for v in cfg:
if v == 'M':
layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
else:
conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=d_rate,dilation = d_rate)
if batch_norm:
layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
else:
layers += [conv2d, nn.ReLU(inplace=True)]
in_channels = v
return nn.Sequential(*layers)
|