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 __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):
"""Constructs a ResNet-50 model.
Args:
pretrained (bool): If True, returns a model pre-trained on Places
"""
model = ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, deep_base=False, norm_type=norm_type,
width_multiplier=kwargs["width_multiplier"])
model = ModuleHelper.load_model(model, pretrained=pretrained)
return model
def __init__(self, block, layers, width_multiplier=1.0, num_classes=1000, deep_base=False, norm_type=None):
super(ResNet, self).__init__()
self.inplanes = 128 if deep_base else int(64 * width_multiplier)
self.width_multiplier = width_multiplier
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, self.inplanes, 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, int(64 * width_multiplier), layers[0], norm_type=norm_type)
print(self.layer1)
self.layer2 = self._make_layer(block, int(128 * width_multiplier), layers[1], stride=2, norm_type=norm_type)
self.layer3 = self._make_layer(block, int(256 * width_multiplier), layers[2], stride=2, norm_type=norm_type)
self.layer4 = self._make_layer(block, int(512 * width_multiplier), layers[3], stride=2, norm_type=norm_type)
self.avgpool = nn.AvgPool2d(7, stride=1)
self.fc = nn.Linear(int(512 * block.expansion * width_multiplier), 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_()
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)(int(planes * block.expansion * self.width_multiplier)),
)
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)