def __init__(self, block=creat_residual_block):
super(ResNet18_M, self).__init__()
self.block = block
self.stage1 = nn.Sequential(
conv_bn_relu(3,
32,
stride=2,
kszie=3,
pad=3,
has_bn=True,
has_relu=True,
bias=False),
conv_bn_relu(32,
32,
stride=1,
kszie=3,
pad=1,
has_bn=True,
has_relu=True,
bias=False),
conv_bn_relu(32,
32,
stride=1,
kszie=3,
pad=1,
has_bn=True,
has_relu=True,
bias=False), nn.MaxPool2d(3, 2, 1, ceil_mode=False))
self.stage2 = self.__make_stage(self.block, 32, 64, 2, 1)
self.stage3 = self.__make_stage(self.block, 64, 128, 2, 2)
self.stage4 = self.__make_stage(self.block, 128, 256, 2, 2)
self.stage5 = self.__make_stage(self.block, 256, 256, 2, 2)
def __init__(self, inplanes, outplanes, stride, has_proj=False):
super(creat_residual_block, self).__init__()
self.has_proj = has_proj
if self.has_proj:
self.proj_conv = conv_bn_relu(inplanes,
outplanes,
stride=stride,
kszie=1,
pad=0,
has_bn=True,
has_relu=False,
bias=False)
self.conv1 = conv_bn_relu(inplanes,
outplanes,
stride=stride,
kszie=3,
pad=1,
has_bn=True,
has_relu=True,
bias=False)
self.conv2 = conv_bn_relu(outplanes,
outplanes,
stride=1,
kszie=3,
pad=1,
has_bn=True,
has_relu=False,
bias=False)
self.relu = nn.ReLU()
def __init__(self):
super(TinyConv, self).__init__()
self.conv1 = conv_bn_relu(3, 32, stride=2, kszie=3, pad=0)
self.pool1 = nn.MaxPool2d(3, stride=2, padding=0, ceil_mode=True)
self.conv2a = conv_bn_relu(32, 64, stride=1, kszie=1, pad=0)
self.conv2b = conv_bn_relu(64, 64, stride=2, kszie=7, pad=0, groups=64)
self.conv3a = conv_bn_relu(64, 64, stride=1, kszie=3, pad=0)
self.conv3b = conv_bn_relu(64,
64,
stride=1,
kszie=1,
pad=0,
has_relu=False)
# initialization
for m in self.modules():
if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
import scipy.stats as stats
stddev = m.stddev if hasattr(m, 'stddev') else 0.1
X = stats.truncnorm(-2, 2, scale=stddev)
values = torch.as_tensor(X.rvs(m.weight.numel()),
dtype=m.weight.dtype)
values = values.view(m.weight.size())
with torch.no_grad():
m.weight.copy_(values)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def __init__(self):
super(AlexNet, self).__init__()
self.conv1 = conv_bn_relu(3, 96, stride=2, kszie=11, pad=0)
self.pool1 = nn.MaxPool2d(3, 2, 0, ceil_mode=True)
self.conv2 = conv_bn_relu(96, 256, 1, 5, 0)
self.pool2 = nn.MaxPool2d(3, 2, 0, ceil_mode=True)
self.conv3 = conv_bn_relu(256, 384, 1, 3, 0)
self.conv4 = conv_bn_relu(384, 384, 1, 3, 0)
self.conv5 = conv_bn_relu(384, 256, 1, 3, 0, has_relu=False)
def _make_convs(self):
head_width = self._hyper_params['head_width']
# feature adjustment
self.r_z_k = conv_bn_relu(head_width,
head_width,
1,
3,
0,
has_relu=False)
self.c_z_k = conv_bn_relu(head_width,
head_width,
1,
3,
0,
has_relu=False)
self.r_x = conv_bn_relu(head_width, head_width, 1, 3, 0, has_relu=False)
self.c_x = conv_bn_relu(head_width, head_width, 1, 3, 0, has_relu=False)