def __init__(self, in_dim: int, out_dim: int):
super(StemBlock, self).__init__()
self.conv = layers.ConvBNReLU(in_dim, out_dim, 3, stride=2)
self.left = nn.Sequential(
layers.ConvBNReLU(out_dim, out_dim // 2, 1),
layers.ConvBNReLU(out_dim // 2, out_dim, 3, stride=2))
self.right = nn.MaxPool2D(kernel_size=3, stride=2, padding=1)
self.fuse = layers.ConvBNReLU(out_dim * 2, out_dim, 3)
def __init__(self, in_dim: int, mid_dim: int, num_classes: int):
super().__init__()
self.conv_3x3 = nn.Sequential(layers.ConvBNReLU(in_dim, mid_dim, 3),
nn.Dropout(0.1))
self.conv_1x1 = nn.Conv2D(mid_dim, num_classes, 1, 1)
def __init__(self, in_dim: int, out_dim: int):
super(ContextEmbeddingBlock, self).__init__()
self.gap = nn.AdaptiveAvgPool2D(1)
self.bn = layers.SyncBatchNorm(in_dim)
self.conv_1x1 = layers.ConvBNReLU(in_dim, out_dim, 1)
self.conv_3x3 = nn.Conv2D(out_dim, out_dim, 3, 1, 1)
def __init__(self, in_dim: int, out_dim: int, expand: int):
super().__init__()
expand_dim = expand * in_dim
self.conv = nn.Sequential(
layers.ConvBNReLU(in_dim, in_dim, 3),
layers.DepthwiseConvBN(in_dim, expand_dim, 3),
layers.ConvBN(expand_dim, out_dim, 1))
def __init__(self, in_channels: int):
super().__init__()
C1, C2, C3 = in_channels
self.convs = nn.Sequential(
# stage 1
layers.ConvBNReLU(3, C1, 3, stride=2),
layers.ConvBNReLU(C1, C1, 3),
# stage 2
layers.ConvBNReLU(C1, C2, 3, stride=2),
layers.ConvBNReLU(C2, C2, 3),
layers.ConvBNReLU(C2, C2, 3),
# stage 3
layers.ConvBNReLU(C2, C3, 3, stride=2),
layers.ConvBNReLU(C3, C3, 3),
layers.ConvBNReLU(C3, C3, 3),
)