def __init__(self, num_class=150, fc_dim=4096, use_softmax=False, pool_scales=(1, 2, 3, 6)): super(PPMBilinearDeepsup, self).__init__() self.use_softmax = use_softmax self.ppm = [] for scale in pool_scales: self.ppm.append( nn.Sequential( nn.AdaptiveAvgPool2d(scale), nn.Conv2d(fc_dim, 512, kernel_size=1, bias=False), SynchronizedBatchNorm2d(512), nn.ReLU(inplace=True))) self.ppm = nn.ModuleList(self.ppm) #self.cbr_deepsup = conv3x3_bn_relu(fc_dim // 2, fc_dim // 4, 1) self.aspp_last = nn.Sequential( nn.Conv2d(fc_dim + len(pool_scales) * 512, 512, kernel_size=3, padding=1, bias=False), SynchronizedBatchNorm2d(512), nn.ReLU(inplace=True), nn.Dropout2d(0.1)) #self.conv_last_deepsup = nn.Conv2d(fc_dim // 4, num_class, 1, 1, 0) #self.dropout_deepsup = nn.Dropout2d(0.1) for m in self.modules(): if isinstance(m, nn.Conv2d): our_kaiming_normal_(m.weight, 0.1) if m.bias is not None: constant_(m.bias, 0) elif isinstance(m, nn.BatchNorm2d): constant_(m.weight, 1) constant_(m.bias, 0)
def __init__(self, block, layers, num_classes=1000): self.inplanes = 128 super(ResNet, self).__init__() self.conv1 = conv3x3(3, 64, stride=2) self.bn1 = SynchronizedBatchNorm2d(64) self.relu1 = nn.ReLU(inplace=True) self.conv2 = conv3x3(64, 64) self.bn2 = SynchronizedBatchNorm2d(64) self.relu2 = nn.ReLU(inplace=True) self.conv3 = conv3x3(64, 128) self.bn3 = SynchronizedBatchNorm2d(128) self.relu3 = nn.ReLU(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(block, 64, layers[0]) self.layer2 = self._make_layer(block, 128, layers[1], stride=2) self.layer3 = self._make_layer(block, 256, layers[2], stride=2) self.layer4 = self._make_layer(block, 512, layers[3], stride=2) self.avgpool = nn.AvgPool2d(7, stride=1) self.fc = nn.Linear(512 * block.expansion, num_classes) for m in self.modules(): if isinstance(m, nn.Conv2d): n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) elif isinstance(m, SynchronizedBatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_()
def __init__(self, num_class=150, fc_dim=4096, use_softmax=False, pool_scales=(1, 2, 3, 6)): super(PPMBilinear, self).__init__() self.use_softmax = use_softmax self.ppm = [] for scale in pool_scales: self.ppm.append( nn.Sequential( nn.AdaptiveAvgPool2d(scale), nn.Conv2d(fc_dim, 512, kernel_size=1, bias=False), SynchronizedBatchNorm2d(512), nn.ReLU(inplace=True))) self.ppm = nn.ModuleList(self.ppm) self.aspp_conv = nn.Sequential( nn.Conv2d(fc_dim + len(pool_scales) * 512, 512, kernel_size=3, padding=1, bias=False), SynchronizedBatchNorm2d(512), nn.ReLU(inplace=True)) self.conv_last = nn.Sequential( nn.Dropout2d(0.1), nn.Conv2d(512, num_class, kernel_size=1))
def __init__(self, inplanes, planes, stride=1, downsample=None): super(BasicBlock, self).__init__() self.conv1 = conv3x3(inplanes, planes, stride) self.bn1 = SynchronizedBatchNorm2d(planes) self.relu = nn.ReLU(inplace=True) self.conv2 = conv3x3(planes, planes) self.bn2 = SynchronizedBatchNorm2d(planes) self.downsample = downsample self.stride = stride
def __init__(self): super(Generalized_SEMSEG, self).__init__() builder = semseg_heads.ModelBuilder() self.encoder = builder.build_encoder( arch=cfg.SEM.ARCH_ENCODER, fc_dim=cfg.SEM.FC_DIM, weights=cfg.RESNETS.IMAGENET_PRETRAINED_WEIGHTS) #define shape weights self.decoder = builder.build_decoder(arch=cfg.SEM.DECODER_TYPE, fc_dim=cfg.SEM.FC_DIM, num_class=cfg.MODEL.NUM_CLASSES, use_softmax=not self.training, weights='') ##define semseg loss self.conv_last_semseg = nn.Sequential( nn.Conv2d(cfg.SEM.DIM * 2, cfg.MODEL.NUM_CLASSES, kernel_size=1)) self.conv_last_semseg.apply(self._init_weights_kaiming) self.crit = nn.NLLLoss(ignore_index=255) self.deep_sup_scale = cfg.SEM.DEEP_SUB_SCALE if len(cfg.SEM.DOWNSAMPLE) > 1: self.semseg_deepsup = nn.Sequential( nn.Conv2d(cfg.SEM.FC_DIM // 2, cfg.SEM.FC_DIM // 4, kernel_size=3, stride=1, padding=1, bias=None), SynchronizedBatchNorm2d(cfg.SEM.FC_DIM // 4), nn.ReLU(inplace=True), nn.Dropout2d(0.1), nn.Conv2d(cfg.SEM.FC_DIM // 4, cfg.MODEL.NUM_CLASSES, kernel_size=1)) #self.semseg_deepsup.apply(self._init_weights_kaiming) if len(cfg.SEM.DOWNSAMPLE) > 1: self.disp_deepsup = nn.Sequential( nn.Conv2d(cfg.SEM.FC_DIM // 2, cfg.SEM.FC_DIM // 4, kernel_size=3, stride=1, padding=1, bias=None), SynchronizedBatchNorm2d(cfg.SEM.FC_DIM // 4), nn.ReLU(inplace=True), nn.Dropout2d(0.1), nn.Conv2d(cfg.SEM.FC_DIM // 4, 1, kernel_size=3, stride=1, padding=1, bias=None))
def __init__(self, inplanes, planes, stride=1, downsample=None): super(Bottleneck, self).__init__() self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) self.bn1 = SynchronizedBatchNorm2d(planes) self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) self.bn2 = SynchronizedBatchNorm2d(planes) self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) self.bn3 = SynchronizedBatchNorm2d(planes * 4) self.relu = nn.ReLU(inplace=True) self.downsample = downsample self.stride = stride
def __init__(self, num_class=150, fc_dim=4096, use_softmax=False, pool_scales=(1, 2, 3, 6), fpn_inplanes=(256, 512, 1024, 2048), fpn_dim=256): super(UPerNet, self).__init__() self.use_softmax = use_softmax # PPM Module self.ppm_pooling = [] self.ppm_conv = [] for scale in pool_scales: self.ppm_pooling.append(nn.AdaptiveAvgPool2d(scale)) self.ppm_conv.append( nn.Sequential( nn.Conv2d(fc_dim, 512, kernel_size=1, bias=False), SynchronizedBatchNorm2d(512), nn.ReLU(inplace=True))) self.ppm_pooling = nn.ModuleList(self.ppm_pooling) self.ppm_conv = nn.ModuleList(self.ppm_conv) self.ppm_last_conv = conv3x3_bn_relu(fc_dim + len(pool_scales) * 512, fpn_dim, 1) # FPN Module self.fpn_in = [] for fpn_inplane in fpn_inplanes[:-1]: # skip the top layer self.fpn_in.append( nn.Sequential( nn.Conv2d(fpn_inplane, fpn_dim, kernel_size=1, bias=False), SynchronizedBatchNorm2d(fpn_dim), nn.ReLU(inplace=True))) self.fpn_in = nn.ModuleList(self.fpn_in) self.fpn_out = [] for i in range(len(fpn_inplanes) - 1): # skip the top layer self.fpn_out.append( nn.Sequential(conv3x3_bn_relu(fpn_dim, fpn_dim, 1), )) self.fpn_out = nn.ModuleList(self.fpn_out) self.conv_last = nn.Sequential( conv3x3_bn_relu(len(fpn_inplanes) * fpn_dim, fpn_dim, 1))
def __init__(self, inplanes, planes, stride=1, downsample=None): super(Bottleneck_GE, self).__init__() self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) self.bn1 = SynchronizedBatchNorm2d(planes) self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) self.bn2 = SynchronizedBatchNorm2d(planes) self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) self.bn3 = SynchronizedBatchNorm2d(planes * 4) self.relu = nn.ReLU(inplace=True) self.downsample = downsample self.stride = stride self.Sigmoid = nn.Sigmoid() ge_list = [] if cfg.SEM.USE_GE_BLOCK: for i in range(3): ge_list.append(self._conv(inplanes, inplanes, groups=inplanes)) self.ge_model_list = nn.ModuleList(ge_list)
def _make_layer(self, block, planes, blocks, stride=1): downsample = None if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False), SynchronizedBatchNorm2d(planes * block.expansion), ) layers = [] layers.append(block(self.inplanes, planes, stride, downsample)) self.inplanes = planes * block.expansion for i in range(1, blocks): layers.append(block(self.inplanes, planes)) return nn.Sequential(*layers)
def conv3x3_bn_relu(in_planes, out_planes, stride=1): return nn.Sequential( conv3x3(in_planes, out_planes, stride), SynchronizedBatchNorm2d(out_planes), nn.ReLU(inplace=True), )