Convnet中由输入图片测试数据并输出结果
原作者Alex只是随机的选取训练数据中的几幅图，来预测最大概率分类。使用命令为：
python shownet.py -f /storage2/tmp/ConvNet__2011-08-24_17.56.48 --show-preds=probs
我们这里想要做的是，输入想要测试jpg图片的文件夹，然后输出测试结果。根据Alex的shownet.py改写。
运行命令行为
python show_pred.py -f ./baidu_result/batchsize_64_1-10_10/ConvNet__2013-10-04_19.40.52 --show-preds=probs
最后一行为测试的文件夹，输出为myresult.txt，格式为
- xxxx.jpg 0
- xxxx.jpg 1
在show_predict_dir函数中有个检测groundtruth与输出结果是否相同的功能，不要的话自行删除。
__author__ = 'zhyf'
import numpy as np
import sys
import getopt as opt
from util import *
from math import sqrt, ceil, floor
import os
from gpumodel import IGPUModel
import random as r
import numpy.random as nr
from convnet import ConvNet
from options import *
import Image
class ShowNetError(Exception):
pass
class ShowPredction(ConvNet):
def __init__(self, op, load_dic):
ConvNet.__init__(self, op, load_dic)
def get_gpus(self):
self.need_gpu = self.op.get_value('show_preds') or self.op.get_value('write_features')
if self.need_gpu:
ConvNet.get_gpus(self)
def init_data_providers(self):
class Dummy:
def advance_batch(self):
pass
self.train_data_provider = self.test_data_provider = Dummy()
def import_model(self):
if self.need_gpu:
ConvNet.import_model(self)
def init_model_state(self):
#ConvNet.init_model_state(self)
if self.op.get_value('show_preds'):
self.sotmax_idx = self.get_layer_idx(self.op.get_value('show_preds'), check_type='softmax')
if self.op.get_value('write_features'):
self.ftr_layer_idx = self.get_layer_idx(self.op.get_value('write_features'))
def init_model_lib(self):
if self.need_gpu:
ConvNet.init_model_lib(self)
def show_predictions(self, pic_path):
preds = n.zeros((1, 2), dtype=n.single)
data = []
file1 = file('./baidu_result/batches.meta', 'rb')
meta_cifa = cPickle.load(file1)
file1.close()
data_size = int(sqrt((meta_cifa['data_mean'].size)/3))
inputImage = Image.open(pic_path)
small_image = inputImage.resize((data_size, data_size),Image.ANTIALIAS)
try:
r, g, b = small_image.split()
reseqImage = list(r.getdata()) + list(g.getdata()) + list(b.getdata())
except:
return '1'
pixel_image = []
for pixel in reseqImage:
pixel_image.append([pixel])
data_array = np.array(pixel_image, dtype = np.float32)
data.append(n.require((data_array - meta_cifa['data_mean']), dtype=n.single, requirements='C'))
data.append(n.require((np.array([[-1]])), dtype=n.single, requirements='C'))
data.append( preds)
self.libmodel.startFeatureWriter(data, self.sotmax_idx)
self.finish_batch()
label_names = meta_cifa['label_names']
img_labels = sorted(zip(preds[0,:], label_names), key=lambda x: x[0])[-1:]
#print img_labels[0]
if img_labels[0][1] == 'exist_people':
x = '1'
elif img_labels[0][1] == 'nopeople':
x = '0'
return x
@classmethod
def get_options_parser(cls):
op = ConvNet.get_options_parser()
for option in list(op.options):
if option not in ('gpu', 'load_file', 'train_batch_range', 'test_batch_range'):
op.delete_option(option)
op.add_option("show-cost", "show_cost", StringOptionParser, "Show specified objective function", default="")
op.add_option("show-filters", "show_filters", StringOptionParser, "Show learned filters in specified layer", default="")
op.add_option("input-idx", "input_idx", IntegerOptionParser, "Input index for layer given to --show-filters", default=0)
op.add_option("cost-idx", "cost_idx", IntegerOptionParser, "Cost function return value index for --show-cost", default=0)
op.add_option("no-rgb", "no_rgb", BooleanOptionParser, "Don't combine filter channels into RGB in layer given to --show-filters", default=False)
op.add_option("yuv-to-rgb", "yuv_to_rgb", BooleanOptionParser, "Convert RGB filters to YUV in layer given to --show-filters", default=False)
op.add_option("channels", "channels", IntegerOptionParser, "Number of channels in layer given to --show-filters (fully-connected layers only)", default=0)
op.add_option("show-preds", "show_preds", StringOptionParser, "Show predictions made by given softmax on test set", default="")
op.add_option("only-errors", "only_errors", BooleanOptionParser, "Show only mistaken predictions (to be used with --show-preds)", default=False, requires=['show_preds'])
op.add_option("write-features", "write_features", StringOptionParser, "Write test data features from given layer", default="", requires=['feature-path'])
op.add_option("feature-path", "feature_path", StringOptionParser, "Write test data features to this path (to be used with --write-features)", default="")
op.options['load_file'].default = None
op.options['load_file'].value_given = False
return op
def show_predict_dir(load_path):
try:
error = 0
file_list = os.listdir(load_path)
result = []
class_file = file('./baidu_result/train-origin-pics-labels.txt', 'rb').readlines()
for item in file_list:
if item.endswith('.jpg'):
picture_number = item[0:len(item)-4]
picture_index = int(picture_number) - 1
if picture_index % 1000 == 0:
print picture_index
n = os.path.join(load_path, item)
#print item
people = model.show_predictions(n)
result.append(item + ' ' + people + '\n')
ground_truth = class_file[picture_index][10:11]
if not people == ground_truth:
error += 1
print float(error)/len(file_list)
myreslut = sorted(result, key=lambda result:result[0])
my_result = file('myresult.txt', 'wb')
my_result.writelines(myreslut)
my_result.close()
except (UnpickleError, ShowNetError, opt.GetoptError), e:
print "----------------"
print "Error:"
print e
op = ShowPredction.get_options_parser()
op, load_dic = IGPUModel.parse_options(op)
model = ShowPredction(op, load_dic)
show_predict_dir('/media/zhyf/Public/Baidu All/train-origin-pics')