def __init__(self, in_planes, planes, stride=1, wbit=32, abit=32): super(Bottleneck, self).__init__() Conv2d = Layer.DoreafaConv2dv1(wbit, abit) self.conv1 = Conv2d(in_planes, planes, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm2d(planes) self.conv2 = Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(planes) self.conv3 = Conv2d(planes, self.expansion * planes, kernel_size=1, bias=False) self.bn3 = nn.BatchNorm2d(self.expansion * planes) self.shortcut = nn.Sequential() if stride != 1 or in_planes != self.expansion * planes: self.shortcut = nn.Sequential( Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(self.expansion * planes))
def __init__(self, inp, oup, stride, expand_ratio, wbit=32, abit=32): super(InvertedResidual, self).__init__() Conv2d = Layer.DoreafaConv2dv1(wbit, abit) self.stride = stride assert stride in [1, 2] hidden_dim = int(inp * expand_ratio) self.use_res_connect = self.stride == 1 and inp == oup if expand_ratio == 1: self.conv = nn.Sequential( # dw Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False), nn.BatchNorm2d(hidden_dim), nn.ReLU6(inplace=True), # pw-linear Conv2d(hidden_dim, oup, 1, 1, 0, bias=False), nn.BatchNorm2d(oup), ) else: self.conv = nn.Sequential( # pw Conv2d(inp, hidden_dim, 1, 1, 0, bias=False), nn.BatchNorm2d(hidden_dim), nn.ReLU6(inplace=True), # dw Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False), nn.BatchNorm2d(hidden_dim), nn.ReLU6(inplace=True), # pw-linear Conv2d(hidden_dim, oup, 1, 1, 0, bias=False), nn.BatchNorm2d(oup), )
def __init__(self, in_planes, planes, stride=1, wbit=32,abit=32,option='A'): super(BasicBlock, self).__init__() Conv2d = Layer.DoreafaConv2dv1(wbit,abit) self.conv1 = Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(planes) self.conv2 = Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(planes) self.shortcut = nn.Sequential() if stride != 1 or in_planes != planes: if option == 'A': """ For CIFAR10 ResNet paper uses option A. """ self.shortcut = LambdaLayer(lambda x: F.pad(x[:, :, ::2, ::2], (0, 0, 0, 0, planes//4, planes//4), "constant", 0)) elif option == 'B': self.shortcut = nn.Sequential( Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(self.expansion * planes) )
def __init__(self, wbit=32, abit=32, num_classes=10): super(VGG_SMALL, self).__init__() self.num_classes = num_classes Conv2d = Layer.DoreafaConv2dv1(wbit, abit) self.conv0 = Conv2d(3, 128, kernel_size=3, padding=1, bias=False) self.bn0 = nn.BatchNorm2d(128) self.conv1 = Conv2d(128, 128, kernel_size=3, padding=1, bias=False) self.pooling = nn.MaxPool2d(kernel_size=2, stride=2) self.bn1 = nn.BatchNorm2d(128) self.nonlinear = nn.ReLU(inplace=True) # self.nonlinear = nn.Hardtanh(inplace=True) self.conv2 = Conv2d(128, 256, kernel_size=3, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(256) self.conv3 = Conv2d(256, 256, kernel_size=3, padding=1, bias=False) self.bn3 = nn.BatchNorm2d(256) self.conv4 = Conv2d(256, 512, kernel_size=3, padding=1, bias=False) self.bn4 = nn.BatchNorm2d(512) self.conv5 = Conv2d(512, 512, kernel_size=3, padding=1, bias=False) self.bn5 = nn.BatchNorm2d(512) self.fc = nn.Linear(512 * 4 * 4, self.num_classes) self._initialize_weights()
def conv_1x1_bn(inp, oup, wbit, abit): Conv2d = Layer.DoreafaConv2dv1(wbit, abit) return nn.Sequential(Conv2d(inp, oup, 1, 1, 0, bias=False), nn.BatchNorm2d(oup), nn.ReLU6(inplace=True))