def __init__(self, builder: ConvBuilder, deps):
super(LeNet5, self).__init__()
self.bd = builder
stem = builder.Sequential()
stem.add_module(
'conv1',
builder.Conv2d(in_channels=1,
out_channels=LENET5_DEPS[0],
kernel_size=5,
bias=True))
stem.add_module('maxpool1', builder.Maxpool2d(kernel_size=2))
stem.add_module(
'conv2',
builder.Conv2d(in_channels=LENET5_DEPS[0],
out_channels=LENET5_DEPS[1],
kernel_size=5,
bias=True))
stem.add_module('maxpool2', builder.Maxpool2d(kernel_size=2))
self.stem = stem
self.flatten = builder.Flatten()
self.linear1 = builder.Linear(in_features=LENET5_DEPS[1] * 16,
out_features=LENET5_DEPS[2])
self.relu1 = builder.ReLU()
self.linear2 = builder.Linear(in_features=LENET5_DEPS[2],
out_features=10)
def __init__(self, block_counts, num_classes, builder:ConvBuilder, deps, use_dropout):
super(WRNCifarNet, self).__init__()
self.bd = builder
converted_deps = wrn_convert_flattened_deps(deps)
print('the converted deps is ', converted_deps)
self.conv1 = builder.Conv2d(in_channels=3, out_channels=converted_deps[0], kernel_size=3, stride=1, padding=1, bias=False)
self.stage1 = self._build_wrn_stage(num_blocks=block_counts[0], stage_input_channels=converted_deps[0],
stage_deps=converted_deps[1], downsample=False, use_dropout=use_dropout)
self.stage2 = self._build_wrn_stage(num_blocks=block_counts[1], stage_input_channels=converted_deps[1][-1][1],
stage_deps=converted_deps[2], downsample=True, use_dropout=use_dropout)
self.stage3 = self._build_wrn_stage(num_blocks=block_counts[2], stage_input_channels=converted_deps[2][-1][1],
stage_deps=converted_deps[3], downsample=True, use_dropout=use_dropout)
self.last_bn = builder.BatchNorm2d(num_features=converted_deps[3][-1][1])
self.linear = builder.Linear(in_features=converted_deps[3][-1][1], out_features=num_classes)