def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None, bn_momentum=0.1, bn_eps=1e-05): super(Bottleneck, self).__init__() self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) self.bn1 = SynchronizedBatchNorm2d(planes, momentum=bn_momentum, eps=bn_eps) self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=dilation, dilation=dilation, bias=False) self.bn2 = SynchronizedBatchNorm2d(planes, momentum=bn_momentum, eps=bn_eps) self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False) self.bn3 = SynchronizedBatchNorm2d(planes * self.expansion, momentum=bn_momentum, eps=bn_eps) self.relu = nn.ReLU(inplace=True) self.downsample = downsample self.stride = stride
def __init__(self, class_num, bn_momentum=0.1): super(Decoder, self).__init__() self.conv1 = nn.Conv2d(256, 48, kernel_size=1, bias=False) self.bn1 = SynchronizedBatchNorm2d(48, momentum=bn_momentum) self.relu = nn.ReLU() # self.conv2 = SeparableConv2d(304, 256, kernel_size=3) # self.conv3 = SeparableConv2d(256, 256, kernel_size=3) self.conv2 = nn.Conv2d(304, 256, kernel_size=3, padding=1, bias=False) self.bn2 = SynchronizedBatchNorm2d(256, momentum=bn_momentum) self.dropout2 = nn.Dropout(0.5) self.conv3 = nn.Conv2d(256, 256, kernel_size=3, padding=1, bias=False) self.bn3 = SynchronizedBatchNorm2d(256, momentum=bn_momentum) self.dropout3 = nn.Dropout(0.1) self.conv4 = nn.Conv2d(256, class_num, kernel_size=1) self._init_weight()
def __init__(self, bn_momentum=0.1, output_stride=16): super(Encoder, self).__init__() self.ASPP = AsppModule(bn_momentum=bn_momentum, output_stride=output_stride) self.relu = nn.ReLU() self.conv1 = nn.Conv2d(1280, 256, 1, bias=False) self.bn1 = SynchronizedBatchNorm2d(256, momentum=bn_momentum) self.dropout = nn.Dropout(0.5) self.__init_weight()
def __init__(self, block, layers, bn_momentum=0.1, pretrained=False, output_stride=16): if output_stride == 16: dilations = [1, 1, 1, 2] strides = [1, 2, 2, 1] elif output_stride == 8: dilations = [1, 1, 2, 4] strides = [1, 2, 1, 1] else: raise Warning("output_stride must be 8 or 16!") self.inplanes = 64 super(ResNet, self).__init__() self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = SynchronizedBatchNorm2d(64, momentum=bn_momentum) self.relu = nn.ReLU(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(block, 64, layers[0], stride=strides[0], dilation=dilations[0], bn_momentum=bn_momentum) self.layer2 = self._make_layer(block, 128, layers[1], stride=strides[1], dilation=dilations[1], bn_momentum=bn_momentum) self.layer3 = self._make_layer(block, 256, layers[2], stride=strides[2], dilation=dilations[2], bn_momentum=bn_momentum) self.layer4 = self._make_layer(block, 512, layers[3], stride=strides[3], dilation=dilations[3], bn_momentum=bn_momentum) self._init_weight() if pretrained: self._load_pretrained_model()
def __init__(self, bn_momentum=0.1, output_stride=16, bn_eps=1e-05): super(AsppModule, self).__init__() # output_stride choice if output_stride == 16: atrous_rates = [0, 6, 12, 18] elif output_stride == 8: atrous_rates = 2 * [0, 12, 24, 36] else: raise Warning("output_stride must be 8 or 16!") # atrous_spatial_pyramid_pooling part self._atrous_convolution1 = _AsppConv(2048, 256, 1, 1, bn_momentum=bn_momentum, bn_eps=bn_eps) self._atrous_convolution2 = _AsppConv(2048, 256, 3, 1, padding=atrous_rates[1], dilation=atrous_rates[1], bn_momentum=bn_momentum, bn_eps=bn_eps) self._atrous_convolution3 = _AsppConv(2048, 256, 3, 1, padding=atrous_rates[2], dilation=atrous_rates[2], bn_momentum=bn_momentum, bn_eps=bn_eps) self._atrous_convolution4 = _AsppConv(2048, 256, 3, 1, padding=atrous_rates[3], dilation=atrous_rates[3], bn_momentum=bn_momentum, bn_eps=bn_eps) #image_pooling part self._image_pool = nn.Sequential( nn.AdaptiveAvgPool2d((1, 1)), nn.Conv2d(2048, 256, kernel_size=1, bias=False), SynchronizedBatchNorm2d(256, momentum=bn_momentum, eps=bn_eps), nn.ReLU()) self.__init_weight()
def _AsppConv(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, bn_momentum=0.1): asppconv = nn.Sequential( nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, dilation, bias=False), SynchronizedBatchNorm2d(out_channels, momentum=bn_momentum), nn.ReLU()) return asppconv
def _make_layer(self, block, planes, blocks, stride=1, dilation=1, bn_momentum=0.1, bn_eps=1e-05): 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, momentum=bn_momentum, eps=bn_eps), ) layers = [] layers.append( block(self.inplanes, planes, stride, dilation, downsample, bn_momentum=bn_momentum, bn_eps=bn_eps)) self.inplanes = planes * block.expansion for i in range(1, blocks): layers.append( block(self.inplanes, planes, dilation=dilation, bn_momentum=bn_momentum, bn_eps=bn_eps)) return nn.Sequential(*layers)