Python imageNetObj示例

示例#1

文件： train_imagenet_vgg.py 项目： slundqui/DeepGAP

from dataObj.image import imageNetObj
from tf.VGGGap import VGGGap
import numpy as np
import pdb

#Paths to list of filenames
trainImageList = "/home/slundquist/mountData/datasets/imagenet/train_cls.txt"
testImageList = "/home/slundquist/mountData/datasets/imagenet/val_cls.txt"

trainImagePrefix = "/shared/imageNet/CLS_LOC/ILSVRC2015/Data/CLS-LOC/train/"
testImagePrefix =  "/shared/imageNet/CLS_LOC/ILSVRC2015/Data/CLS-LOC/val/"

clsMeta = "/shared/imageNet/devkit/data/meta_clsloc.mat"

#Get object from which tensorflow will pull data from
trainDataObj = imageNetObj(trainImageList, trainImagePrefix, clsMeta, useClassDir = True, resizeMethod="crop", normStd=False)
testDataObj = imageNetObj(testImageList, testImagePrefix, clsMeta, useClassDir = False, resizeMethod="crop", normStd=False)

params = {
    #Base output directory
    'outDir':          "/home/slundquist/mountData/DeepGAP/",
    #Inner run directory
    'runDir':          "/imagenet_vgg/",
    'tfDir':           "/tfout",
    #Save parameters
    'ckptDir':         "/checkpoints/",
    'saveFile':        "/save-model",
    'savePeriod':      10, #In terms of displayPeriod
    #output plots directory
    'plotDir':         "plots/",
    'plotPeriod':      10, #With respect to displayPeriod

示例#2

文件： imagenet_time_demo.py 项目： slundqui/TFSparseCode

import matplotlib
matplotlib.use('Agg')
from dataObj.image import imageNetObj
from tf.ista_time import ISTA_Time
#from plot.roc import makeRocCurve
import pdb

trainImageLists =  "/shared/imageNet/vid2015_128x64/imageNetVID_2015_list.txt"
randImageSeed = None
#Get object from which tensorflow will pull data from
trainDataObj = imageNetObj(trainImageLists, resizeMethod="crop", shuffle=True, seed=randImageSeed)

#ISTA params
params = {
    #Base output directory
    'outDir':          "/home/slundquist/mountData/tfLCA/",
    #Inner run directory
    'runDir':          "/imagenetTime_demo/",
    'tfDir':           "/tfout",
    #Save parameters
    'ckptDir':         "/checkpoints/",
    'saveFile':        "/save-model",
    'savePeriod':      100, #In terms of displayPeriod
    #output plots directory
    'plotDir':         "plots/",
    'plotPeriod':      100, #With respect to displayPeriod
    #Progress step
    'progress':        10,
    #Controls how often to write out to tensorboard
    'writeStep':       10, #300,
    #Threshold

示例#3

import matplotlib
matplotlib.use('Agg')
from dataObj.image import imageNetObj
from tf.ista_time import ISTA_Time
#from plot.roc import makeRocCurve
import pdb

trainImageLists =  "/shared/imageNet/vid2015_128x64/imageNetVID_2015_list.txt"
randImageSeed = None
#Get object from which tensorflow will pull data from
trainDataObj = imageNetObj(trainImageLists, resizeMethod="crop", shuffle=True, seed=randImageSeed)

#ISTA params
params = {
    #Base output directory
    'outDir':          "/home/slundquist/mountData/tfLCA/",
    #Inner run directory
    'runDir':          "/imagenetTime/",
    'tfDir':           "/tfout",
    #Save parameters
    'ckptDir':         "/checkpoints/",
    'saveFile':        "/save-model",
    'savePeriod':      100, #In terms of displayPeriod
    #output plots directory
    'plotDir':         "plots/",
    'plotPeriod':      100, #With respect to displayPeriod
    #Progress step
    'progress':        10,
    #Controls how often to write out to tensorboard
    'writeStep':       100, #300,
    #Threshold

示例#4

文件： imagenet_train.py 项目： slundqui/TFSparseCode

import matplotlib
matplotlib.use('Agg')
from dataObj.image import imageNetObj
from tf.ista import ISTA
#from plot.roc import makeRocCurve
import pdb

#Input vgg file for preloaded weights
trainImageLists =  "/shared/imageNet/vid2015_128x64/imageNetVID_2015_list.txt"
#Get object from which tensorflow will pull data from
trainDataObj = imageNetObj(trainImageLists, resizeMethod="pad")

#ISTA params
params = {
    #Base output directory
    'outDir': "/home/slundquist/mountData/tfLCA/",
    #Inner run directory
    'runDir': "/imagenet/",
    'tfDir': "/tfout",
    'ckptDir': '/checkpoints/',
    'saveFile': '/save-model',
    #Flag for loading weights from checkpoint
    'load': True,
    'loadFile': "/home/slundquist/mountData/tfLCA/saved/imagenet.ckpt",
    'numIterations': 1000000,
    'displayPeriod': 300,
    'savePeriod': 10, #In terms of displayPeriod
    #output plots directory
    'plotDir': "plots/",
    'plotPeriod': 20, #With respect to displayPeriod
    #Progress step (also controls how often to save and write out to tensorboard)

示例#5

import numpy as np
import pdb

#Paths to list of filenames
trainImageList = "/home/slundquist/mountData/datasets/imagenet/train_cls.txt"
testImageList = "/home/slundquist/mountData/datasets/imagenet/val_cls.txt"

trainImagePrefix = "/nh/compneuro/Data/imageNet/CLS_LOC/ILSVRC2015/Data/CLS-LOC/train/"
testImagePrefix = "/nh/compneuro/Data/imageNet/CLS_LOC/ILSVRC2015/Data/CLS-LOC/val/"

clsMeta = "/nh/compneuro/Data/imageNet/devkit/data/meta_clsloc.mat"

#Get object from which tensorflow will pull data from
trainDataObj = imageNetObj(trainImageList,
                           trainImagePrefix,
                           clsMeta,
                           useClassDir=True,
                           resizeMethod="crop")
testDataObj = imageNetObj(testImageList,
                          testImagePrefix,
                          clsMeta,
                          useClassDir=False,
                          resizeMethod="crop")

params = {
    #Base output directory
    'outDir': "/home/slundquist/mountData/DeepGAP/",
    #Inner run directory
    'runDir': "/imagenet/",
    'tfDir': "/tfout",
    #Save parameters

示例#6

import matplotlib
matplotlib.use('Agg')
from dataObj.image import imageNetObj
from tf.ista import ISTA
#from plot.roc import makeRocCurve
import pdb

#Input vgg file for preloaded weights
trainImageLists = "/shared/imageNet/vid2015_128x64/imageNetVID_2015_list.txt"
#Get object from which tensorflow will pull data from
trainDataObj = imageNetObj(trainImageLists, resizeMethod="pad")

#ISTA params
params = {
    #Base output directory
    'outDir': "/home/slundquist/mountData/tfLCA/",
    #Inner run directory
    'runDir': "/imagenet/",
    'tfDir': "/tfout",
    'ckptDir': '/checkpoints/',
    'saveFile': '/save-model',
    #Flag for loading weights from checkpoint
    'load': True,
    'loadFile': "/home/slundquist/mountData/tfLCA/saved/imagenet.ckpt",
    'numIterations': 1000000,
    'displayPeriod': 300,
    'savePeriod': 10,  #In terms of displayPeriod
    #output plots directory
    'plotDir': "plots/",
    'plotPeriod': 20,  #With respect to displayPeriod
    #Progress step (also controls how often to save and write out to tensorboard)