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| 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) | |