def __init__(self,
inplanes,
planes,
stride=1,
downsample=None,
norm_type=None):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = ModuleHelper.BatchNorm2d(norm_type=norm_type)(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = ModuleHelper.BatchNorm2d(norm_type=norm_type)(planes)
self.downsample = downsample
self.stride = stride
def _make_layer(self, block, planes, blocks, stride=1, norm_type=None):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes,
planes * block.expansion,
kernel_size=1,
stride=stride,
bias=False),
ModuleHelper.BatchNorm2d(norm_type=norm_type)(planes *
block.expansion),
)
layers = []
layers.append(
block(self.inplanes,
planes,
stride,
downsample,
norm_type=norm_type))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes, norm_type=norm_type))
return nn.Sequential(*layers)
def __init__(self,
inplanes,
planes,
stride=1,
downsample=None,
norm_type=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = ModuleHelper.BatchNorm2d(norm_type=norm_type)(planes)
self.conv2 = nn.Conv2d(planes,
planes,
kernel_size=3,
stride=stride,
padding=1,
bias=False)
self.bn2 = ModuleHelper.BatchNorm2d(norm_type=norm_type)(planes)
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
self.bn3 = ModuleHelper.BatchNorm2d(norm_type=norm_type)(planes * 4)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def deepbase_resnet101(num_classes=1000,
pretrained=None,
norm_type='batchnorm',
**kwargs):
"""Constructs a ResNet-101 model.
Args:
pretrained (bool): If True, returns a model pre-trained on Places
"""
model = ResNet(Bottleneck, [3, 4, 23, 3],
num_classes=num_classes,
deep_base=True,
norm_type=norm_type)
model = ModuleHelper.load_model(model, pretrained=pretrained)
return model
def resnet34(num_classes=1000,
pretrained=None,
norm_type='batchnorm',
**kwargs):
"""Constructs a ResNet-34 model.
Args:
pretrained (bool): If True, returns a model pre-trained on Places
"""
model = ResNet(BasicBlock, [3, 4, 6, 3],
num_classes=num_classes,
deep_base=False,
norm_type=norm_type)
model = ModuleHelper.load_model(model, pretrained=pretrained)
return model
def resnet50(num_classes=1000,
pretrained=None,
norm_type='batchnorm',
**kwargs):
# def resnet50(num_classes=1000, pretrained=None, norm_type='encsync_batchnorm', **kwargs):
"""Constructs a ResNet-50 model.
Args:
pretrained (bool): If True, returns a model pre-trained on Places
# """
# print("entered")
# input()
model = ResNet(Bottleneck, [3, 4, 6, 3],
num_classes=num_classes,
deep_base=False,
norm_type=norm_type)
model = ModuleHelper.load_model(model, pretrained=pretrained)
return model
def __init__(self,
block,
layers,
num_classes=1000,
deep_base=False,
norm_type=None):
super(ResNet, self).__init__()
self.inplanes = 128 if deep_base else 64
if deep_base:
self.prefix = nn.Sequential(
OrderedDict([
('conv1',
nn.Conv2d(3,
64,
kernel_size=3,
stride=2,
padding=1,
bias=False)),
('bn1', ModuleHelper.BatchNorm2d(norm_type=norm_type)(64)),
('relu1', nn.ReLU(inplace=False)),
('conv2',
nn.Conv2d(64,
64,
kernel_size=3,
stride=1,
padding=1,
bias=False)),
('bn2', ModuleHelper.BatchNorm2d(norm_type=norm_type)(64)),
('relu2', nn.ReLU(inplace=False)),
('conv3',
nn.Conv2d(64,
128,
kernel_size=3,
stride=1,
padding=1,
bias=False)),
('bn3', ModuleHelper.BatchNorm2d(norm_type=norm_type)(
self.inplanes)), ('relu3', nn.ReLU(inplace=False))
]))
else:
self.prefix = nn.Sequential(
OrderedDict([('conv1',
nn.Conv2d(3,
64,
kernel_size=7,
stride=2,
padding=3,
bias=False)),
('bn1',
ModuleHelper.BatchNorm2d(norm_type=norm_type)(
self.inplanes)),
('relu', nn.ReLU(inplace=False))]))
self.maxpool = nn.MaxPool2d(kernel_size=3,
stride=2,
padding=1,
ceil_mode=False) # change.
self.layer1 = self._make_layer(block,
64,
layers[0],
norm_type=norm_type)
self.layer2 = self._make_layer(block,
128,
layers[1],
stride=2,
norm_type=norm_type)
self.layer3 = self._make_layer(block,
256,
layers[2],
stride=2,
norm_type=norm_type)
self.layer4 = self._make_layer(block,
512,
layers[3],
stride=2,
norm_type=norm_type)
self.avgpool = nn.AvgPool2d(7, stride=1)
self.fc = nn.Linear(512 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(
m,
ModuleHelper.BatchNorm2d(norm_type=norm_type,
ret_cls=True)):
m.weight.data.fill_(1)
m.bias.data.zero_()
