复现：Pyramid Feature Attention Network for Saliency detection

引言：复现Pyramid Feature Attention Network for Saliency detection。该文发表于CVPR2019，有两种开源实现，基于pytorch和基于keras，本文都进行了尝试，其中主要使用Keras版本。

1.环境

条件所限，我使用的机器为Jetson Xavier。根据项目安装的包的版本如下：

• tensorboard 1.15.0
• tensorboardX 2.0
• tensorflow-estimator 1.15.1
• tensorflow-gpu 1.15.0+nv20.1.tf1
• numpy 1.17.0
• Keras 2.1.1

xavier安装的opencv3.4.3

不过该环境会有很多的warning，可能和作者使用的还不一样。

2.训练与测试

如下。

from keras import callbacks, optimizers
import tensorflow as tf
import os
from keras.layers import Input
from model import VGG16
from data import getTrainGenerator
from utils import *
from edge_hold_loss import *
import math

# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
# os.environ["CUDA_VISIBLE_DEVICES"] = "0"

def lr_scheduler(epoch):
    drop = 0.5
    epoch_drop = epochs/8.
    lr = base_lr * math.pow(drop, math.floor((1+epoch)/epoch_drop))
    print('lr: %f' % lr)
    return lr

if __name__ == '__main__':
    import argparse
    parser = argparse.ArgumentParser(description='Train model your dataset')
    parser.add_argument('--train_file',default='train_pair.txt',help='your train file', type=str)
    parser.add_argument('--model_weights',default='model/vgg16_no_top.h5',help='your model weights', type=str)

    args = parser.parse_args()
    model_name = args.model_weights
    '''
    the from of 'train_pair.txt' is 
    img_path1 gt_path1\n
    img_path2 gt_path2\n 
    '''
    train_path = args.train_file
    
    print("train_file", train_path)
    print("model_weights", model_name)
    
    target_size = (256,256)
    batch_size = 15
    base_lr = 1e-2
    epochs = 50

    f = open(train_path, 'rb') # encoding='unicode_escape'
    trainlist = f.readlines()
    f.close()
    steps_per_epoch = len(trainlist)/batch_size

    optimizer = optimizers.SGD(lr=base_lr, momentum=0.9, decay=0)
    # optimizer = optimizers.Adam(lr=base_lr)
    loss = EdgeHoldLoss

    metrics = [acc,pre,rec]
    dropout = True
    with_CPFE = True
    with_CA = True
    with_SA = True
    log = './PFA.csv'
    tb_log = './tensorboard-logs/PFA'
    model_save = 'model/PFA_'
    model_save_period = 5

    if target_size[0 ] % 32 != 0 or target_size[1] % 32 != 0:
        raise ValueError('Image height and wight must be a multiple of 32')
    print(type(target_size[0]),' ', type(target_size[1]))
    print(target_size[0], ' ', target_size[1])

    traingen = getTrainGenerator(train_path, target_size, batch_size, israndom=True)

    model_input = Input(shape=(target_size[0],target_size[1],3))
    model = VGG16(model_input,dropout=dropout, with_CPFE=with_CPFE, with_CA=with_CA, with_SA=with_SA)
    for i,layer in enumerate(model.layers):
        print(i,layer.name)
    model.load_weights(model_name,by_name=True)

    tb = callbacks.TensorBoard(log_dir=tb_log)
    lr_decay = callbacks.LearningRateScheduler(schedule=lr_scheduler)
    es = callbacks.EarlyStopping(monitor='loss', patience=3, verbose=0, mode='auto')
    modelcheck = callbacks.ModelCheckpoint(model_save+'{epoch:05d}.h5', monitor='loss', verbose=1,
        save_best_only=False, save_weights_only=True, mode='auto', period=model_save_period)
    callbacks = [lr_decay,modelcheck,tb]

    model.compile(optimizer=optimizer,loss=loss,metrics=metrics)
    model.fit_generator(traingen, steps_per_epoch=steps_per_epoch,
                        epochs=epochs,verbose=1,callbacks=callbacks)

用于预测测试的test文本如下：

import cv2

import numpy as np
import os
from keras.layers import Input
from model import VGG16
import matplotlib.pyplot as plt

def padding(x):
    h,w,c = x.shape
    size = max(h,w)
    paddingh = (size-h)//2
    paddingw = (size-w)//2
    temp_x = np.zeros((size,size,c))
    temp_x[paddingh:h+paddingh,paddingw:w+paddingw,:] = x
    return temp_x

def load_image(path):
    x = cv2.imread(path)
    sh = x.shape
    x = np.array(x, dtype=np.float32)
    x = x[..., ::-1]
    # Zero-center by mean pixel
    x[..., 0] -= 103.939
    x[..., 1] -= 116.779
    x[..., 2] -= 123.68
    x = padding(x)
    x = cv2.resize(x, target_size, interpolation=cv2.INTER_LINEAR)
    x = np.expand_dims(x,0)
    return x,sh

def cut(pridict,shape):
    h,w,c = shape
    size = max(h, w)
    pridict = cv2.resize(pridict, (size,size))
    paddingh = (size - h) // 2
    paddingw = (size - w) // 2
    return pridict[paddingh:h + paddingh, paddingw:w + paddingw]

def sigmoid(x):
    return 1/(1 + np.exp(-x))

def getres(pridict,shape):
    pridict = sigmoid(pridict)*255
    pridict = np.array(pridict, dtype=np.uint8)
    pridict = np.squeeze(pridict)
    pridict = cut(pridict, shape)
    return pridict

def laplace_edge(x):
    laplace = np.array([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]])
    edge = cv2.filter2D(x/255.,-1,laplace)
    edge = np.maximum(np.tanh(edge),0)
    edge = edge * 255
    edge = np.array(edge, dtype=np.uint8)
    return edge


if __name__ == '__main__':
    os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
    os.environ["CUDA_VISIBLE_DEVICES"] = "0"
    
    model_name = 'model/PFA_00050.h5'

    target_size = (256,256)

    dropout = False
    with_CPFE = True
    with_CA = True
    with_SA = True

    if target_size[0 ] % 32 != 0 or target_size[1] % 32 != 0:
        raise ValueError('Image height and wight must be a multiple of 32')

    model_input = Input(shape=(target_size[0],target_size[1],3))
    model = VGG16(model_input,dropout=dropout, with_CPFE=with_CPFE, with_CA=with_CA, with_SA=with_SA)
    model.load_weights(model_name,by_name=True)

    for layer in model.layers:
        layer.trainable = False

    # image_path = 'image/3.jpg'
    image_path = '/home/bafs/SODDatasets/CSSD/images/69015.jpg'
    img, shape = load_image(image_path)
    img = np.array(img, dtype=np.float32)
    sa = model.predict(img)
    sa = getres(sa, shape)
    plt.title('saliency')
    plt.subplot(131)
    plt.imshow(cv2.imread(image_path))
    plt.subplot(132)
    plt.imshow(sa, cmap='gray')
    plt.subplot(133)
    edge = laplace_edge(sa)
    plt.imshow(edge, cmap='gray')
    plt.show()
    plt.savefig('./res/res.jpg')

3.结果

初步测试，使用CSDD作为训练集合，训练50个epochs，使用上述test.py文件进行预测。结果还是很不错的，如下：

后续：在仔细阅读一下该文献，做分享。

本文地址：https://blog.csdn.net/wbzhang233/article/details/107423752