def __init__(self, backbone_name, num_classes, platform="Ascend"):
self.backbone_name = backbone_name
backbone = backbones.__dict__[self.backbone_name](platform=platform)
out_channels = backbone.get_out_channels()
head = heads.CommonHead(num_classes=num_classes,
out_channels=out_channels)
super(Resnet, self).__init__(backbone, head)
default_recurisive_init(self)
for cell in self.cells_and_names():
if isinstance(cell, nn.Conv2d):
cell.weight.set_data(
init.initializer(
KaimingNormal(a=math.sqrt(5),
mode='fan_out',
nonlinearity='relu'), cell.weight.shape,
cell.weight.dtype))
elif isinstance(cell, nn.BatchNorm2d):
cell.gamma.set_data(init.initializer('ones', cell.gamma.shape))
cell.beta.set_data(init.initializer('zeros', cell.beta.shape))
# Zero-initialize the last BN in each residual branch,
# so that the residual branch starts with zeros, and each residual block behaves like an identity.
# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
for cell in self.cells_and_names():
if isinstance(cell, backbones.resnet.Bottleneck):
cell.bn3.gamma.set_data(
init.initializer('zeros', cell.bn3.gamma.shape))
elif isinstance(cell, backbones.resnet.BasicBlock):
cell.bn2.gamma.set_data(
init.initializer('zeros', cell.bn2.gamma.shape))
def __init__(self, num_classes):
super(DenseNet121, self).__init__()
self.backbone = _densenet121()
out_channels = self.backbone.get_out_channels()
self.head = CommonHead(num_classes, out_channels)
default_recurisive_init(self)
for _, cell in self.cells_and_names():
if isinstance(cell, nn.Conv2d):
cell.weight.set_data(
init.initializer(
KaimingNormal(a=math.sqrt(5),
mode='fan_out',
nonlinearity='relu'), cell.weight.shape,
cell.weight.dtype))
elif isinstance(cell, nn.BatchNorm2d):
cell.gamma.set_data(init.initializer('ones', cell.gamma.shape))
cell.beta.set_data(init.initializer('zeros', cell.beta.shape))
elif isinstance(cell, nn.Dense):
cell.bias.set_data(init.initializer('zeros', cell.bias.shape))
