Convnetに画像テストデータを入力して出力する

原作者Alexは訓練データの中の数枚の図をランダムに選択し,最大確率分類を予測しただけである.次のコマンドを使用します.

python shownet.py -f /storage2/tmp/ConvNet__2011-08-24_17.56.48 --show-preds=probs

ここでやりたいのは、jpgピクチャをテストしたいフォルダを入力し、テスト結果を出力することです.アレックスのショーによるとpy書き換え.
コマンド動作の実行

python show_pred.py -f ./baidu_result/batchsize_64_1-10_10/ConvNet__2013-10-04_19.40.52 --show-preds=probs

最後の動作テストのフォルダはmyresultとして出力されます.txt、フォーマットは

xxxx.jpg 0

xxxx.jpg 1

show_predict_dir関数にはgroundtruthと出力結果が同じかどうかを検出する機能があり、不要なら自分で削除します.

__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 + '
')
                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')