resnet.py

import paddle.fluid as fluid
import math

class DistResNet():
    def __init__(self, is_train=True):

        self.is_train = is_train
        self.weight_decay = 1e-4

    def infer(self, input, class_dim=10):
    
        depth = [3, 3, 3, 3]
        num_filters = [16, 16, 32, 64]

        conv = self.conv_bn_layer(
            input=input, num_filters=16, filter_size=3, act='elu')
        conv = fluid.layers.pool2d(
            input=conv,
            pool_size=3,
            pool_stride=2,
            pool_padding=1,
            pool_type='max')

        for block in range(len(depth)):
            for i in range(depth[block]):
                conv = self.bottleneck_block(
                    input=conv,
                    num_filters=num_filters[block],
                    stride=2 if i == 0 and block != 0 else 1)
                conv = fluid.layers.batch_norm(input=conv, act='elu')
        print(conv.shape)
        pool = fluid.layers.pool2d(
            input=conv, pool_size=4, pool_type='avg', global_pooling=True)
        stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
        out = fluid.layers.fc(input=pool,
                              size=class_dim,
                              act="softmax",
                              param_attr=fluid.param_attr.ParamAttr(
                                  initializer=fluid.initializer.Uniform(-stdv,
                                                                        stdv),
                                  regularizer=fluid.regularizer.L2Decay(self.weight_decay)),
                              bias_attr=fluid.ParamAttr(
                                  regularizer=fluid.regularizer.L2Decay(self.weight_decay))
                              )
        return out

    def conv_bn_layer(self,
                      input,
                      num_filters,
                      filter_size,
                      stride=1,
                      groups=1,
                      act=None,
                      bn_init_value=1.0):
        conv = fluid.layers.conv2d(
            input=input,
            num_filters=num_filters,
            filter_size=filter_size,
            stride=stride,
            padding=(filter_size - 1) // 2,
            groups=groups,
            act=None,
            bias_attr=False,
            param_attr=fluid.ParamAttr(regularizer=fluid.regularizer.L2Decay(self.weight_decay)))
        return fluid.layers.batch_norm(
                input=conv, act=act, is_test=not self.is_train,
                param_attr=fluid.ParamAttr(
                    initializer=fluid.initializer.Constant(bn_init_value),
                    regularizer=None))

    def shortcut(self, input, ch_out, stride):
        ch_in = input.shape[1]
        if ch_in != ch_out or stride != 1:
            return self.conv_bn_layer(input, ch_out, 1, stride)
        else:
            return input

    def bottleneck_block(self, input, num_filters, stride):
        conv0 = self.conv_bn_layer(
            input=input, num_filters=num_filters, filter_size=1, act='elu')
        conv1 = self.conv_bn_layer(
            input=conv0,
            num_filters=num_filters,
            filter_size=3,
            stride=stride,
            act='elu')
        conv2 = self.conv_bn_layer(
            input=conv1, num_filters=num_filters * 4, filter_size=1, act=None, bn_init_value=0.0)

        short = self.shortcut(input, num_filters * 4, stride)

        return fluid.layers.elementwise_add(x=short, y=conv2, act='elu')