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。
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：
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。
如下。
我要评论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')
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
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