论文实践学习 - Look into Person: Self-supervised Structure-sensitive Learning

AIHGF

发布于 2019-02-18 10:30:54

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发布于 2019-02-18 10:30:54

文章被收录于专栏：AIUAI

Look into Person: Self-supervised Structure-sensitive Learning

Code-Caffe

Paper

LIP Dataset - 百度云

LIP Dataset - Google Drive

attention+ssl.caffemodel - Google Drive

摘要——基于提供的训练模型进行测试，模型是基于 Attention Model - Attention to Scale: Scale-aware Semantic Image Segmentation，并提出 Self-supervised Structure-sensitive Loss 进行训练. 【论文阅读理解 - Look into Person: Self-supervised Structure-sensitive Learning】

方法框架：

网络结构：

主体网络采用的是 Deeplabv2- Attention，训练网络：

新增Caffe Layers：

type: MaskCreate name: fc8_mask_1st cpp: mask_create_layer.cpp
type: PoseEvaluate name: label_pose_1st, fc8_pose_1st cpp: pose_evaluate_layer.cpp
type: PoseCreate name: label_heatmap, predict_heatmap cpp: pose_create_layer.cpp
type: HeatmapError name: heatmap_error cpp: heatmap_error_layer.cpp

部署网络：

简单测试示例 - demo.py

#!/usr/bin/env python
from PIL import Image
import numpy as np
import cv2
import matplotlib.pyplot as plt
import sys

caffe_root = './caffe_ssl/'
sys.path.insert(0, caffe_root + 'python')
import caffe

caffe.set_device(0)
caffe.set_mode_gpu()
# caffe.set_mode_cpu()

##
deploy  = 'model/deploy.prototxt'
weights = 'model/attention+ssl.caffemodel'
net = caffe.Net(deploy, weights, caffe.TEST)

mean = [104.008, 116.669, 122.675]

imgfile = './images/19.jpg'
# img = np.array(Image.open(imgfile), dtype=np.float32)
img = cv2.imread(imgfile, 1)
img = img.astype(np.float32)
img -= mean

img = cv2.resize(img, (513, 513), interpolation=cv2.INTER_LINEAR)

data = img.transpose((2, 0, 1))
net.blobs['data'].data[0, ...] = data

out = net.forward()
prediction = net.blobs['fc8_mask'].data[0, ...][0]
# output = net.blobs['fc8_interp'].data[0, ...]
# prediction = np.argmax(output.transpose([1, 2, 0]), axis=2)
plt.imshow(prediction)
plt.show()

print 'Done.'