def __init__(self, in_planes, out_planes, stride, dropRate=0.0):
super(BasicBlock, self).__init__()
self.bn1 = custom.BN_Class(in_planes)
self.relu1 = nn.ReLU(inplace=True)
self.conv1 = custom.Con2d_Class(in_planes,
out_planes,
kernel_size=3,
stride=stride,
padding=1,
bias=False)
self.bn2 = custom.BN_Class(out_planes)
self.relu2 = nn.ReLU(inplace=True)
self.conv2 = custom.Con2d_Class(out_planes,
out_planes,
kernel_size=3,
stride=1,
padding=1,
bias=False)
self.droprate = dropRate
self.equalInOut = (in_planes == out_planes)
self.convShortcut = (not self.equalInOut) and custom.Con2d_Class(
in_planes,
out_planes,
kernel_size=1,
stride=stride,
padding=0,
bias=False) or None
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = custom.BN_Class(planes)
self.relu = nn.ReLU(inplace=False)
self.conv2 = conv3x3(planes, planes)
self.bn2 = custom.BN_Class(planes)
self.downsample = downsample
self.stride = stride
def __init__(self, block, layers, num_classes=1000):
self.inplanes = 64
super(ResNet, self).__init__()
self.conv1 = custom.Con2d_Class(3,
64,
kernel_size=7,
stride=2,
padding=3,
bias=False)
self.bn1 = custom.BN_Class(64)
self.relu = nn.ReLU(inplace=False)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
self.avgpool = nn.AvgPool2d(7, stride=1)
self.classifier = custom.Linear_Class(512 * block.expansion,
num_classes)
for m in self.modules():
if isinstance(m, custom.Con2d_Class):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, custom.BN_Class):
m.weight.data.fill_(1)
m.bias.data.zero_()
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = custom.Con2d_Class(inplanes,
planes,
kernel_size=1,
bias=False)
self.bn1 = custom.BN_Class(planes)
self.conv2 = custom.Con2d_Class(planes,
planes,
kernel_size=3,
stride=stride,
padding=1,
bias=False)
self.bn2 = custom.BN_Class(planes)
self.conv3 = custom.Con2d_Class(planes,
planes * 4,
kernel_size=1,
bias=False)
self.bn3 = custom.BN_Class(planes * 4)
self.relu = nn.ReLU(inplace=False)
self.downsample = downsample
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(
custom.Con2d_Class(self.inplanes,
planes * block.expansion,
kernel_size=1,
stride=stride,
bias=False),
custom.BN_Class(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def __init__(self, depth, num_classes=1000, block_name='BasicBlock'):
super(ResNet, self).__init__()
# Model type specifies number of layers for CIFAR-10 model
if block_name.lower() == 'basicblock':
assert (
depth - 2
) % 6 == 0, 'When use basicblock, depth should be 6n+2, e.g. 20, 32, 44, 56, 110, 1202'
n = (depth - 2) // 6
block = BasicBlock
elif block_name.lower() == 'bottleneck':
assert (
depth - 2
) % 9 == 0, 'When use bottleneck, depth should be 9n+2, e.g. 20, 29, 47, 56, 110, 1199'
n = (depth - 2) // 9
block = Bottleneck
else:
raise ValueError('block_name shoule be Basicblock or Bottleneck')
self.inplanes = 16
self.conv1 = custom.Con2d_Class(3,
16,
kernel_size=3,
padding=1,
bias=False)
self.bn1 = custom.BN_Class(16)
self.relu = nn.ReLU(inplace=False)
self.layer1 = self._make_layer(block, 16, n)
self.layer2 = self._make_layer(block, 32, n, stride=2)
self.layer3 = self._make_layer(block, 64, n, stride=2)
self.avgpool = nn.AvgPool2d(8)
self.classifier = custom.Linear_Class(64 * block.expansion,
num_classes)
for m in self.modules():
if isinstance(m, custom.Linear_Class) or isinstance(
m, custom.Con2d_Class):
init.kaiming_normal_(m.weight)
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, custom.BN_Class):
m.weight.data.fill_(1)
if m.bias is not None:
m.bias.data.zero_()
def __init__(self, depth, num_classes, widen_factor=1, dropRate=0.0):
super(WideResNet, self).__init__()
nChannels = [
16, 16 * widen_factor, 32 * widen_factor, 64 * widen_factor
]
assert (depth - 4) % 6 == 0, 'depth should be 6n+4'
n = (depth - 4) // 6
block = BasicBlock
# 1st conv before any network block
self.conv1 = custom.Con2d_Class(3,
nChannels[0],
kernel_size=3,
stride=1,
padding=1,
bias=False)
# 1st block
self.block1 = NetworkBlock(n, nChannels[0], nChannels[1], block, 1,
dropRate)
# 2nd block
self.block2 = NetworkBlock(n, nChannels[1], nChannels[2], block, 2,
dropRate)
# 3rd block
self.block3 = NetworkBlock(n, nChannels[2], nChannels[3], block, 2,
dropRate)
# global average pooling and classifier
self.bn1 = custom.BN_Class(nChannels[3])
self.relu = nn.ReLU(inplace=True)
self.classifier = custom.Linear_Class(nChannels[3], num_classes)
self.nChannels = nChannels[3]
for m in self.modules():
if isinstance(m, custom.Con2d_Class):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, custom.BN_Class):
m.weight.data.fill_(1)
m.bias.data.zero_()
elif isinstance(m, custom.Linear_Class):
if m.bias is not None:
m.bias.data.zero_()