resnet.py

import os
import time
import sys
import paddle.fluid as fluid
import math


class TSN_ResNet():
    def __init__(self, layers=50, seg_num=7):
        self.layers = layers
        self.seg_num = seg_num

    def conv_bn_layer(self,
                      input,
                      num_filters,
                      filter_size,
                      stride=1,
                      groups=1,
                      act=None):
        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)
        return fluid.layers.batch_norm(input=conv, act=act)

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

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

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

    def net(self, input, class_dim=101):
        layers = self.layers
        seg_num = self.seg_num
        supported_layers = [50, 101, 152]
        if layers not in supported_layers:
            print("supported layers are", supported_layers, \
                  "but input layer is ", layers)
            exit()

        # reshape input
        channels = input.shape[2]
        short_size = input.shape[3]
        input = fluid.layers.reshape(
            x=input, shape=[-1, channels, short_size, short_size])

        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]

        conv = self.conv_bn_layer(
            input=input, num_filters=64, filter_size=7, stride=2, act='relu')
        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)
        pool = fluid.layers.pool2d(
            input=conv, pool_size=7, pool_type='avg', global_pooling=True)

        feature = fluid.layers.reshape(
            x=pool, shape=[-1, seg_num, pool.shape[1]])
        out = fluid.layers.reduce_mean(feature, dim=1)

        stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
        out = fluid.layers.fc(input=out,
                              size=class_dim,
                              act='softmax',
                              param_attr=fluid.param_attr.ParamAttr(
                                  initializer=fluid.initializer.Uniform(-stdv,
                                                                        stdv)))
        return out