def __init__(self, num_classes, builder: ConvBuilder, deps): super(VCNet, self).__init__() self.stem = _create_vgg_stem(builder=builder, deps=deps) self.flatten = builder.Flatten() self.linear1 = builder.IntermediateLinear(in_features=deps[12], out_features=512) self.relu = builder.ReLU() self.linear2 = builder.Linear(in_features=512, out_features=num_classes)
def __init__(self, builder:ConvBuilder, deps): super(LeNet5BN, self).__init__() self.bd = builder stem = builder.Sequential() stem.add_module('conv1', builder.Conv2dBNReLU(in_channels=1, out_channels=deps[0], kernel_size=5)) stem.add_module('maxpool1', builder.Maxpool2d(kernel_size=2)) stem.add_module('conv2', builder.Conv2dBNReLU(in_channels=deps[0], out_channels=deps[1], kernel_size=5)) stem.add_module('maxpool2', builder.Maxpool2d(kernel_size=2)) self.stem = stem self.flatten = builder.Flatten() self.linear1 = builder.IntermediateLinear(in_features=deps[1] * 16, out_features=500) self.relu1 = builder.ReLU() self.linear2 = builder.Linear(in_features=500, out_features=10)
def __init__(self, num_classes, builder: ConvBuilder, deps): super(VANet, self).__init__() sq = builder.Sequential() sq.add_module( 'conv1', builder.Conv2dBNReLU(in_channels=3, out_channels=deps[0], kernel_size=3, stride=1, padding=1)) sq.add_module( 'conv2', builder.Conv2dBNReLU(in_channels=deps[0], out_channels=deps[1], kernel_size=3, stride=1, padding=1)) sq.add_module('maxpool1', builder.Maxpool2d(kernel_size=2)) sq.add_module( 'conv3', builder.Conv2dBNReLU(in_channels=deps[1], out_channels=deps[2], kernel_size=3, stride=1, padding=1)) sq.add_module( 'conv4', builder.Conv2dBNReLU(in_channels=deps[2], out_channels=deps[3], kernel_size=3, stride=1, padding=1)) sq.add_module('maxpool2', builder.Maxpool2d(kernel_size=2)) sq.add_module( 'conv5', builder.Conv2dBNReLU(in_channels=deps[3], out_channels=deps[4], kernel_size=3, stride=1, padding=1)) sq.add_module( 'conv6', builder.Conv2dBNReLU(in_channels=deps[4], out_channels=deps[5], kernel_size=3, stride=1, padding=1)) sq.add_module( 'conv7', builder.Conv2dBNReLU(in_channels=deps[5], out_channels=deps[6], kernel_size=3, stride=1, padding=1)) sq.add_module('maxpool3', builder.Maxpool2d(kernel_size=2)) sq.add_module( 'conv8', builder.Conv2dBNReLU(in_channels=deps[6], out_channels=deps[7], kernel_size=3, stride=1, padding=1)) sq.add_module( 'conv9', builder.Conv2dBNReLU(in_channels=deps[7], out_channels=deps[8], kernel_size=3, stride=1, padding=1)) sq.add_module( 'conv10', builder.Conv2dBNReLU(in_channels=deps[8], out_channels=deps[9], kernel_size=3, stride=1, padding=1)) sq.add_module('maxpool4', builder.Maxpool2d(kernel_size=2)) sq.add_module( 'conv11', builder.Conv2dBNReLU(in_channels=deps[9], out_channels=deps[10], kernel_size=3, stride=1, padding=1)) sq.add_module( 'conv12', builder.Conv2dBNReLU(in_channels=deps[10], out_channels=deps[11], kernel_size=3, stride=1, padding=1)) sq.add_module( 'conv13', builder.Conv2dBNReLU(in_channels=deps[11], out_channels=deps[12], kernel_size=3, stride=1, padding=1)) sq.add_module('maxpool5', builder.Maxpool2d(kernel_size=2)) self.stem = sq self.flatten = builder.Flatten() self.linear1 = builder.IntermediateLinear(in_features=deps[12], out_features=512) self.relu = builder.ReLU() self.linear2 = builder.Linear(in_features=512, out_features=num_classes)