def __init__(self, block, layers, num_classes=10):
super(ResNet, self).__init__()
self.inplanes = 16
# self.proc = ['conv1', 'bn1', 'relu']
self.proc = [
'conv1', 'bn1', 'relu', 'layer1', 'layer2', 'layer3', 'avgpool',
'fc', 'bn2'
]
self.conv1 = nn.Conv2d(3,
16,
kernel_size=3,
stride=1,
padding=1,
bias=False)
self.bn1 = nn.BatchNorm2d(16)
self.relu = nn.ReLU(inplace=True)
self.layer1 = self._make_layer(block, 16, layers[0])
self.layer2 = self._make_layer(block, 32, layers[1], stride=2)
self.layer3 = self._make_layer(block, 64, layers[2], stride=2)
self.avgpool = nn.AvgPool2d(8, stride=1)
self.fc = nn.Conv2d(64 * block.expansion, num_classes, 1, bias=False)
self.bn2 = nn.BatchNorm2d(num_classes)
for m in self.modules():
if isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.proc = ['conv1', 'bn1', 'relu', 'conv2', 'bn2']
self.conv1 = conv3x3(inplanes, planes, stride=stride)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes)
self.downsample = downsample
self.stride = stride
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride=stride)
self.bn1 = nn.BatchNorm2d(planes)
self.relu1 = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes)
self.downsample = downsample
self.share1 = tqt.fold.ShareQuant()
self.share2 = tqt.fold.ShareQuant()
self.relu2 = nn.ReLU(inplace=True)
self.stride = stride
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
SEConv2d(self.inplanes,
planes * block.expansion,
kernel_size=1,
stride=stride,
bias=False), # shift
Adder2D(planes * block.expansion,
planes * block.expansion,
kernel_size=1,
stride=1), # add
nn.BatchNorm2d(planes * block.expansion))
layers = []
layers.append(
block(inplanes=self.inplanes,
planes=planes,
stride=stride,
downsample=downsample))
self.inplanes = planes * block.expansion
for _ in range(1, blocks):
layers.append(block(inplanes=self.inplanes, planes=planes))
return nn.Sequential(*layers)