resnext101_wsl.py

import paddle
import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear

__all__ = ["ResNeXt101_32x8d_wsl",
            "ResNeXt101_wsl_32x16d_wsl",
            "ResNeXt101_wsl_32x32d_wsl",
            "ResNeXt101_wsl_32x48d_wsl"]

class ConvBNLayer(fluid.dygraph.Layer):
    def __init__(self, 
                input_channels, 
                output_channels,
                filter_size,
                stride=1,
                groups=1,
                act=None, 
                name=None):
        super(ConvBNLayer, self).__init__()
        if "downsample" in name:
            conv_name = name + ".0"
        else:
            conv_name = name 
        self._conv = Conv2D(num_channels=input_channels,
                            num_filters=output_channels,
                            filter_size=filter_size,
                            stride=stride,
                            padding=(filter_size-1)//2,
                            groups=groups,
                            act=None,
                            param_attr=ParamAttr(name=conv_name + ".weight"),
                            bias_attr=False)
        if "downsample" in name:
            bn_name = name[:9] + "downsample.1"
        else:
            if "conv1" == name:
                bn_name = "bn" + name[-1]
            else:
                bn_name = (name[:10] if name[7:9].isdigit() else name[:9]) + "bn" + name[-1]
        self._bn = BatchNorm(num_channels=output_channels,
                            act=act,
                            param_attr=ParamAttr(name=bn_name + ".weight"),
                            bias_attr=ParamAttr(name=bn_name + ".bias"),
                            moving_mean_name=bn_name + ".running_mean",
                            moving_variance_name=bn_name + ".running_var")

    def forward(self, inputs):
        x = self._conv(inputs)
        x = self._bn(x)
        return x

class ShortCut(fluid.dygraph.Layer):
    def __init__(self, input_channels, output_channels, stride, name=None):
        super(ShortCut, self).__init__()

        self.input_channels = input_channels
        self.output_channels = output_channels
        self.stride = stride
        if input_channels!=output_channels or stride!=1:
            self._conv = ConvBNLayer(
                input_channels, output_channels, filter_size=1, stride=stride, name=name)

    def forward(self, inputs):
        if self.input_channels!= self.output_channels or self.stride!=1:
            return self._conv(inputs)
        return inputs 

class BottleneckBlock(fluid.dygraph.Layer):
    def __init__(self, input_channels, output_channels, stride, cardinality, width, name):
        super(BottleneckBlock, self).__init__()

        self._conv0 = ConvBNLayer(
            input_channels, output_channels, filter_size=1, act="relu", name=name + ".conv1")
        self._conv1 = ConvBNLayer(
            output_channels, output_channels, filter_size=3, act="relu", stride=stride, groups=cardinality, name=name + ".conv2")
        self._conv2 = ConvBNLayer(
            output_channels, output_channels//(width//8), filter_size=1, act=None, name=name + ".conv3")
        self._short = ShortCut(
            input_channels, output_channels//(width//8), stride=stride, name=name + ".downsample")

    def forward(self, inputs):
        x = self._conv0(inputs)
        x = self._conv1(x)
        x = self._conv2(x)
        y = self._short(inputs)
        return fluid.layers.elementwise_add(x, y, act="relu")

class ResNeXt101WSL(fluid.dygraph.Layer):
    def __init__(self, layers=101, cardinality=32, width=48, class_dim=1000):
        super(ResNeXt101WSL, self).__init__()

        self.class_dim = class_dim

        self.layers = layers
        self.cardinality = cardinality
        self.width = width
        self.scale = width//8

        self.depth = [3, 4, 23, 3]
        self.base_width = cardinality * width
        num_filters = [self.base_width*i for i in [1,2,4,8]] #[256, 512, 1024, 2048]
        self._conv_stem = ConvBNLayer(
            3, 64, 7, stride=2, act="relu", name="conv1")
        self._pool = Pool2D(pool_size=3,
                            pool_stride=2,
                            pool_padding=1,
                            pool_type="max")

        self._conv1_0 = BottleneckBlock(
            64, num_filters[0], stride=1, cardinality=self.cardinality, width=self.width, name="layer1.0")
        self._conv1_1 = BottleneckBlock(
            num_filters[0]//(width//8), num_filters[0], stride=1, cardinality=self.cardinality, width=self.width, name="layer1.1")
        self._conv1_2 = BottleneckBlock(
            num_filters[0]//(width//8), num_filters[0], stride=1, cardinality=self.cardinality, width=self.width, name="layer1.2")

        self._conv2_0 = BottleneckBlock(
            num_filters[0]//(width//8), num_filters[1], stride=2, cardinality=self.cardinality, width=self.width, name="layer2.0")
        self._conv2_1 = BottleneckBlock(
            num_filters[1]//(width//8), num_filters[1], stride=1, cardinality=self.cardinality, width=self.width, name="layer2.1")
        self._conv2_2 = BottleneckBlock(
            num_filters[1]//(width//8), num_filters[1], stride=1, cardinality=self.cardinality, width=self.width, name="layer2.2")
        self._conv2_3 = BottleneckBlock(
            num_filters[1]//(width//8), num_filters[1], stride=1, cardinality=self.cardinality, width=self.width, name="layer2.3")

        self._conv3_0 = BottleneckBlock(
            num_filters[1]//(width//8), num_filters[2], stride=2, cardinality=self.cardinality, width=self.width, name="layer3.0")
        self._conv3_1 = BottleneckBlock(
            num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.1")
        self._conv3_2 = BottleneckBlock(
            num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.2")
        self._conv3_3 = BottleneckBlock(
            num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.3")
        self._conv3_4 = BottleneckBlock(
            num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.4")
        self._conv3_5 = BottleneckBlock(
            num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.5")
        self._conv3_6 = BottleneckBlock(
            num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.6")
        self._conv3_7 = BottleneckBlock(
            num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.7")
        self._conv3_8 = BottleneckBlock(
            num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.8")
        self._conv3_9 = BottleneckBlock(
            num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.9")
        self._conv3_10 = BottleneckBlock(
            num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.10")
        self._conv3_11 = BottleneckBlock(
            num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.11")
        self._conv3_12 = BottleneckBlock(
            num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.12")
        self._conv3_13 = BottleneckBlock(
            num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.13")
        self._conv3_14 = BottleneckBlock(
            num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.14")
        self._conv3_15 = BottleneckBlock(
            num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.15")
        self._conv3_16 = BottleneckBlock(
            num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.16")
        self._conv3_17 = BottleneckBlock(
            num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.17")
        self._conv3_18 = BottleneckBlock(
            num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.18")
        self._conv3_19 = BottleneckBlock(
            num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.19")
        self._conv3_20 = BottleneckBlock(
            num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.20")
        self._conv3_21 = BottleneckBlock(
            num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.21")
        self._conv3_22 = BottleneckBlock(
            num_filters[2]//(width//8), num_filters[2], stride=1, cardinality=self.cardinality, width=self.width, name="layer3.22")

        self._conv4_0 = BottleneckBlock(
            num_filters[2]//(width//8), num_filters[3], stride=2, cardinality=self.cardinality, width=self.width, name="layer4.0")
        self._conv4_1 = BottleneckBlock(
            num_filters[3]//(width//8), num_filters[3], stride=1, cardinality=self.cardinality, width=self.width, name="layer4.1")
        self._conv4_2 = BottleneckBlock(
            num_filters[3]//(width//8), num_filters[3], stride=1, cardinality=self.cardinality, width=self.width, name="layer4.2")

        self._avg_pool = Pool2D(pool_type="avg", global_pooling=True)
        self._out = Linear(input_dim=num_filters[3]//(width//8),
                        output_dim=class_dim,
                        param_attr=ParamAttr(name="fc.weight"),
                        bias_attr=ParamAttr(name="fc.bias"))

    def forward(self, inputs):
        x = self._conv_stem(inputs)
        x = self._pool(x)

        x = self._conv1_0(x)
        x = self._conv1_1(x)
        x = self._conv1_2(x)

        x = self._conv2_0(x)
        x = self._conv2_1(x)
        x = self._conv2_2(x)
        x = self._conv2_3(x)

        x = self._conv3_0(x)
        x = self._conv3_1(x)
        x = self._conv3_2(x)
        x = self._conv3_3(x)
        x = self._conv3_4(x)
        x = self._conv3_5(x)
        x = self._conv3_6(x)
        x = self._conv3_7(x)
        x = self._conv3_8(x)
        x = self._conv3_9(x)
        x = self._conv3_10(x)
        x = self._conv3_11(x)
        x = self._conv3_12(x)
        x = self._conv3_13(x)
        x = self._conv3_14(x)
        x = self._conv3_15(x)
        x = self._conv3_16(x)
        x = self._conv3_17(x)
        x = self._conv3_18(x)
        x = self._conv3_19(x)
        x = self._conv3_20(x)
        x = self._conv3_21(x)
        x = self._conv3_22(x)

        x = self._conv4_0(x)
        x = self._conv4_1(x)
        x = self._conv4_2(x)

        x = self._avg_pool(x)
        x = fluid.layers.squeeze(x, axes=[2, 3])
        x = self._out(x)
        return x

def ResNeXt101_32x8d_wsl(**args):
    model = ResNeXt101WSL(cardinality=32, width=8, **args)
    return model 

def ResNeXt101_32x16d_wsl(**args):
    model = ResNeXt101WSL(cardinality=32, width=16, **args)
    return model 

def ResNeXt101_32x32d_wsl(**args):
    model = ResNeXt101WSL(cardinality=32, width=32, **args)
    return model 

def ResNeXt101_32x48d_wsl(**args):
    model = ResNeXt101WSL(cardinality=32, width=48, **args)
    return model