ahangchen

"""Script to illustrate usage of tf.estimator.Estimator in TF v1.5"""
import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data as mnist_data
from tensorflow.contrib import slim

# Show debugging output
tf.logging.set_verbosity(tf.logging.DEBUG)

# Set default flags for the output directories

ahangchen

#RT:RightTop
#LB:LeftBottom
def IOU(rectangle A, rectangleB):
    xs = [A.RT.x, A.LB.x, B.RT.x, B.LB.x]
    ys = [A.RT.y, A.LB.y, B.RT.y, B.LB.y]
    s_x = sorted(xs)
    s_y = sorted(ys)
    if s_x[1] in [A.LB.x, B.LB.x] or s_y[1] in [A.LB.y, B.LB.y]:
        return 0

ahangchen

function mpii_convert_json(  )
% convert mpii annotations .mat file to .json


%% load annotation file
fprintf('Load annotations... ')
data = load('/media/HDD2/Datasets/Human_Pose/mpii/mpii_human_pose_v1_u12_2/mpii_human_pose_v1_u12_1.mat');
fprintf('Done.\n')
%% open file
fprintf('Open file mpii_human_pose_annotations.json\n')

ahangchen

def label2xml(list_path, category_id, ANN_DIR='data/Annotations/', IMG_DIR='data/images/', SET_DIR='data/ImageSets/',
              train=True):
    set_file_list = [SET_DIR + 'train.txt', SET_DIR + 'minival.txt', SET_DIR + 'testdev.txt', SET_DIR + 'test.txt']
    TYPEPREFIX = 'train' if train else 'val'
    lines = read_lines(list_path)
    line_i = 0
    last_name = ''
    E = None
    img_annotation = None

ahangchen

def map_rank_faster_eval(query_info, test_info, result_argsort):
    # about 10% lower than matlab result
    # for evaluate rank1 and map
    match = []
    junk = []

    for q_index, (qp, qc) in enumerate(query_info):
        tmp_match = []
        tmp_junk = []
        for t_index in range(len(test_info)):

ahangchen

from keras.layers.merge import Concatenate
from keras.layers.core import Lambda
from keras.models import Model

import tensorflow as tf

def make_parallel(model, gpu_count):
    def get_slice(data, idx, parts):
        shape = tf.shape(data)
        size = tf.concat([ shape[:1] // parts, shape[1:] ],axis=0)