TSN测试是什么样的

是TSN网络的模型，具体代码如下：

import paddle.fluid as fluid
import numpy as np
from paddle.fluid.layer_helper import LayerHelper
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear

class ConvBNLayer(fluid.dygraph.Layer):
    def __init__(self,
                 name_scope,
                 num_channels,
                 num_filters,
                 filter_size,
                 stride=1,
                 groups=1,
                 act=None):
        super(ConvBNLayer, self).__init__(name_scope)

        self._conv = Conv2D(
            num_channels=num_channels,
            num_filters=num_filters,
            filter_size=filter_size,
            stride=stride,
            padding=(filter_size - 1) // 2,
            groups=groups,
            act=None,
            bias_attr=False)

        self._batch_norm = BatchNorm(num_filters, act=None)

    def forward(self, inputs):
        y = self._conv(inputs)
        y = self._batch_norm(y)

        return y

class BottleneckBlock(fluid.dygraph.Layer):
    def __init__(self,
                 name_scope,
                 num_channels,
                 num_filters,
                 stride,
                 shortcut=True):
        super(BottleneckBlock, self).__init__(name_scope)

        self.conv0 = ConvBNLayer(
            self.full_name(),
            num_channels=num_channels,
            num_filters=num_filters,
            filter_size=1,
            act='relu')
        self.conv1 = ConvBNLayer(
            self.full_name(),
            num_channels=num_filters,
            num_filters=num_filters,
            filter_size=3,
            stride=stride,
            act='relu')
        self.conv2 = ConvBNLayer(
            self.full_name(),
            num_channels=num_filters,
            num_filters=num_filters * 4,
            filter_size=1,
            act=None)

        if not shortcut:
            self.short = ConvBNLayer(
                self.full_name(),
                num_channels=num_channels,
                num_filters=num_filters * 4,
                filter_size=1,
                stride=stride)

        self.shortcut = shortcut

        self._num_channels_out = num_filters * 4

    def forward(self, inputs):
        y = self.conv0(inputs)
        conv1 = self.conv1(y)
        conv2 = self.conv2(conv1)

        if self.shortcut:
            short = inputs
        else:
            short = self.short(inputs)

        y = fluid.layers.elementwise_add(x=short, y=conv2)

        layer_helper = LayerHelper(self.full_name(), act='relu')
        return layer_helper.append_activation(y)

class TSNResNet(fluid.dygraph.Layer):
    #定义网络结构，代码补齐
    def __init__(self, name_scope, layers=50, class_dim=102, seg_num=10, weight_devay=None):
        super(TSNResNet, self).__init__(name_scope)

        self.laters = layers
        self.seg_num = seg_num
        supported_layers = [50, 101, 152]
        assert layers in supported_layers, \
             "supported layers are {} but input layer is {}".format(supported_layers, layers)

        if layers == 50:
            depth = [3, 4, 6, 3]
        elif layers == 101:
            depth = [3, 4, 23, 3]
        elif layers == 152:
            depth = [3, 8, 36, 3]
        num_filters = [64, 128, 256, 512]

        self.conv = ConvBNLayer(
            self.full_name(),
            num_channels =3,
            num_filters =64,
            filter_size =7,
            stride =2,
            act='relu')
        self.pool2d_max = Pool2D(
            pool_size =3,
            pool_stride =2,
            pool_padding =1,
            pool_type ='max')

        self.bottleneck_block_list = []
        num_channels = 64
        for block in range(len(depth)):
            shortcut = False
            for i in range(depth[block]):
                bottleneck_block = self.add_sublayer(
                    'bb_%d_%d' % (block, i),
                    BottleneckBlock(
                        self.full_name(),
                        num_channels=num_channels,
                        num_filters=num_filters[block],
                        stride=2 if i==0 and block != 0 else 1,
                        shortcut= shortcut))
                num_channels = bottleneck_block._num_channels_out
                self.bottleneck_block_list.append(bottleneck_block)
                shortcut = True

        self.pool2d_avg = Pool2D(pool_size=7, pool_type='avg', global_pooling=True)

        import math
        stdv = 1.0 / math.sqrt(2048 * 1.0)

        self.out = Linear(input_dim=num_channels,
                          output_dim=class_dim,
                          act='softmax',
                          param_attr=fluid.param_attr.ParamAttr(
                              initializer=fluid.initializer.Uniform(-stdv, stdv)))

    def forward(self, inputs, label=None):
        out = fluid.layers.reshape(inputs, [-1, inputs.shape[2], inputs.shape[3], inputs.shape[4]])
        y = self.conv(out)
        y = self.pool2d_max(y)
        for bottleneck_block in self.bottleneck_block_list:
            y = bottleneck_block(y)
        y = self.pool2d_avg(y)
        out = fluid.layers.reshape(x=y, shape=[-1, self.seg_num, y.shape[1]])

        out = fluid.layers.reduce_mean(out, dim=1)
        y = self.out(out)

        if label is None:
            acc = fluid.layers.accuracy(input=y, label=label)
            return y, acc
        else:
            return y

if __name__ == '__main__':
    with fluid.dygraph.guard():
        network = TSNResNet('resnet', 50)
        img = np.zeros([1, 10, 3, 224, 224]).astype('float32')
        img = fluid.dygraph.to_variable(img)
        outs = network(img).numpy()
        print(outs)