DCASE2016/4_fold_dcnn_train.py at master · DeepLJH0001/DCASE2016 · GitHub

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#coding=utf-8
import numpy as np
from keras.models import load_model
import os
import matplotlib.pyplot as plt
from PIL import Image
from dcnn_train import get_model,cnn_model
import random
from scipy import stats
from keras.callbacks import EarlyStopping,ModelCheckpoint

def get_label_dict():
    dicts = dict()
    dicts['residential_area'] = 0
    #to fix a little bug with string split
    dicts['residential_are'] = 0
    dicts['city_center']=1
    dicts['beach']=2
    dicts['park']=3
    dicts['home'] = 4
    dicts['forest_path'] = 5
    dicts['bus'] = 6
    dicts['grocery_store'] = 7
    dicts['cafe/restaurant'] = 8
    dicts['car'] = 9
    dicts['train'] = 10
    dicts['metro_station'] = 11
    dicts['office'] = 12
    dicts['tram'] = 13
    dicts['library'] = 14
    # print dicts
    return dicts

def load_fold_data(data='Train',fold=1,cfg_path='../TUT-acoustic-scenes-2016-development/evaluation_setup/'
                        ,data_path='./result64/',one_hot=True,Normalization=True):
    if not os.path.exists(data_path):
        print 'please runing feature_extract_demo.py Firstly'
        exit()
    if data=='Test':
        load_file = cfg_path+'fold'+str(fold)+'_evaluate.txt'
    else:
        load_file = cfg_path+'fold'+str(fold)+'_train.txt'
    dicts = get_label_dict()
    wav_paths = []
    scene_y = []
    with open(load_file,'rb') as f:
        for line in f:
            ls = line.strip('\r\n').split('	')
            # print ls[0],ls[1],dicts.get(ls[1])
            scene_y.append(dicts.get(ls[1]))
            wav_paths.append(data_path + ls[0].split('audio/')[1] + '.jpg')
    scene_x = np.empty((len(scene_y),1,64,2584),dtype='float32')
    scene_y = np.array(scene_y,dtype='uint8')

    for i in range(scene_x.shape[0]):
        scene_x[i,0,:, :] = np.asarray(Image.open(wav_paths[i], 'r').convert('L'), dtype='float32')

    strip = 16
    win_size = 64
    step = int((2584-win_size) / strip) - 1

    frame_x = np.empty((scene_x.shape[0] * step, 1, 64, win_size), dtype='float32')
    frame_y = []
    for  i in range(scene_x.shape[0]):
        for j in range(step):
            frame_x[i*step+j,:,:,:]=scene_x[i,0,:,j*strip:j*strip+win_size]
            frame_y.append(scene_y[i])
            # plt.imshow(frame_x[i*step+j,0,:,:],cmap='gray')
            # plt.figure()
            # plt.imshow(scene_x[i,0,:,:],cmap='gray')
            # plt.show()
    frame_y = np.array(frame_y, dtype='uint8')
    if one_hot is True:
        from keras.utils import np_utils
        frame_y = np_utils.to_categorical(frame_y, 15)  # label one-hot
    if Normalization is True:
        frame_x = frame_x.astype('float32') / 255
        scene_x = scene_x.astype('float32') /255
    print 'load_'+data+'_data ok!'
    print(frame_x.shape[0], ' samples')
    return frame_x,scene_x,frame_y,scene_y

def _4_fold_train(_model,fold=1,nb_epoch=20,is_EarlyStopping = True):
    frame_x, scene_x, frame_y, scene_y = load_fold_data(data='Train',fold=fold,Normalization=True)

    frame_x_test, scene_x_test, frame_y_test, scene_y_test = load_fold_data(data='Test', fold=fold,Normalization=True)

    # print frame_x.shape,scene_x.shape
    #洗牌
    index = [i for i in range(len(frame_x))]
    random.shuffle(index)
    frame_x = frame_x[index]
    frame_y = frame_y[index]
    #采取EarlyStopping的策略防止过拟合
    if is_EarlyStopping is True:
        # early_stopping = EarlyStopping(monitor='val_acc', patience=1)
        filepath = "best_12_010_02.nn"+str(fold)
        early_stopping = ModelCheckpoint(filepath=filepath,monitor='val_acc',verbose=0,save_best_only=True)
        _model.fit(frame_x,frame_y,batch_size=64,nb_epoch=nb_epoch,verbose=2,callbacks=[early_stopping],validation_data=(frame_x_test,frame_y_test),shuffle=True)
    else:
        _model.fit(frame_x, frame_y, batch_size=24, nb_epoch=nb_epoch,validation_data=(frame_x_test,frame_y_test))

    frame_acc = _model.evaluate(frame_x_test,frame_y_test,verbose=2)
    print 'fold_'+str(fold)+'_frame_acc:_'+str(frame_acc)
    strip = 16
    win_size = 64
    step = int((2584 - win_size) / strip) - 1
    lists = []#做混淆矩阵（错分）
    f = open('./error+' + str(fold) + '.txt', 'w')  # 写入错分日志
    for i in range(scene_x_test.shape[0]):
        frame_predicts = np.empty((39,),dtype='uint8')
        # for j in range(39):#step of frames in a scene sample
        # frame_predicts = np.argmax(_model.predict(frame_x_test[i*39:i*39+39,:,:,:]),axis=-1)
        frame_predicts = _model.predict_classes(frame_x_test[i*step:i*step+step,:,:,:],verbose=0)
        scene_predict = stats.mode(frame_predicts).mode
        if scene_predict[0] == scene_y_test[i]:
            pass
        else:
            f.write(str(scene_y_test[i])+'错分为->'+str(scene_predict[0])+'\n')
            lists.append([scene_y_test[i],scene_predict[0]])
    f.close()
    scene_acc = float(scene_y_test.shape[0]-len(lists))/float(1.0*scene_y_test.shape[0])
    print 'fold_'+str(fold)+'_scene_acc_:'+str(scene_acc)
    return frame_acc,scene_acc
if __name__ == '__main__':


    # import sys
    # reload(sys)
    # print sys.getdefaultencoding()
    # sys.setdefaultencoding('gbk')#????????why?
    # name = ['./'+i for i in os.listdir('./') if i.endswith('h5')]

    #下面是载入数据的预处理
    # frame_x, scene_x, frame_y, scene_y=load_fold_data(fold=1)
    # frame_x = frame_x.astype('float32') / 255
    # scene_x = scene_x.astype('float32') /255
    # from keras.utils import np_utils
    # frame_y = np_utils.to_categorical(frame_y, 15)  # label one-hot
    # print(frame_x.shape[0], ' samples')

    #test sth
    # frame_x_test, scene_x_test, frame_y_test, scene_y_test, wav_paths = load_fold_data(data='Test', fold=1)
    # print frame_x_test.shape,wav_paths[1]
    # plt.imshow(frame_x_test[39,0,:,:],cmap='gray')
    # plt.figure()
    # plt.imshow(scene_x_test[1,0,:,:],cmap='gray')
    # plt.show()
    # print wav_paths[1]
    # exit()

    acc = []
    model = get_model()
    acc.append(_4_fold_train(model, fold=1,is_EarlyStopping=True,nb_epoch=100))
    # model.save_weights('./4_fold_1_model_12.3.h5')
    model = get_model()
    acc.append(_4_fold_train(model, fold=2,is_EarlyStopping=True,nb_epoch=100))
    # model.save_weights('./4_fold_2_model_12.3.h5')
    model = get_model()
    acc.append(_4_fold_train(model, fold=3,is_EarlyStopping=True,nb_epoch=100))
    # model.save_weights('./4_fold_3_model_12.3.h5')
    model = get_model()
    acc.append(_4_fold_train(model, fold=4,is_EarlyStopping=True,nb_epoch=100))
    # model.save_weights('./4_fold_4_model_12.3.h5')
    print acc #4_fold_acc batch=16  n_epoch=20 or 10?
    #[[1.8550737950485663, 0.68125552608311224], [0.72153158176286947, 0.84217506631299732],
    #[0.3474898756968216, 0.93477886766477369], [0.22463585421383897, 0.94766420793818051]]
    #mean = 0.8514 这个是fram-based

    #18_day15_hour2_mindcnn.h5报错 这个是由于保存的时候是gbk保存
    #载入之前的non-full的训练权重
    # model = load_model('./model.h5')
    # acc = model.evaluate(frame_x,frame_y)
    # print model.metrics_names
    # print acc
    #predicts显示的是frame-based的问题