1. 训练
# --coding:utf-8-- import os import sys import glob import argparse import matplotlib.pyplot as plt from keras import __version__ from keras.applications.inception_v3 import InceptionV3, preprocess_input #from keras.applications.inception_v3_matt import InceptionV3, preprocess_input from keras.models import Model from keras.layers import Dense, GlobalAveragePooling2D from keras.preprocessing.image import ImageDataGenerator from keras.optimizers import SGD def get_nb_files(directory): """Get number of files by searching directory recursively""" if not os.path.exists(directory): return 0 cnt = 0 for r, dirs, files in os.walk(directory): for dr in dirs: cnt += len(glob.glob(os.path.join(r, dr + "/*"))) return cnt #train_num = get_nb_files('/home/pandafish/AnacondaProjects/Inceptionv3/dataset_my/train') 2500 #print(train_num) #input('wait...') # 数据准备 IM_WIDTH, IM_HEIGHT = 299, 299 #InceptionV3指定的图片尺寸 FC_SIZE = 1024 # 全连接层的节点个数 ##NB_IV3_LAYERS_TO_FREEZE = 172 # 冻结层的数量 NB_IV3_LAYERS_TO_FREEZE = 0 # 冻结层的数量 train_dir = '/home/dl/local_repo/data/mosaic1/mosaic_train' # 训练集数据 val_dir = '/home/dl/local_repo/data/mosaic1/mosaic_valid' # 验证集数据 output_model_file = '/home/dl/local_repo/data/mosaic1/mosaic.hdf5' wights_path = '/home/dl/local_repo/data/mosaic1/mosaic.h5' nb_classes= 2 nb_epoch = 10 batch_size = 32 nb_train_samples = get_nb_files(train_dir) # 训练样本个数 nb_classes = len(glob.glob(train_dir + "/*")) # 分类数 nb_val_samples = get_nb_files(val_dir) #验证集样本个数 nb_epoch = int(nb_epoch) # epoch数量 batch_size = int(batch_size) # 图片生成器 train_datagen = ImageDataGenerator( preprocessing_function=preprocess_input, ##rotation_range=30, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, zoom_range=0.2, horizontal_flip=True ) test_datagen = ImageDataGenerator( preprocessing_function=preprocess_input, ##rotation_range=30, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, zoom_range=0.2, horizontal_flip=True ) # 训练数据与测试数据 train_generator = train_datagen.flow_from_directory( train_dir, target_size=(IM_WIDTH, IM_HEIGHT), batch_size=batch_size,class_mode='categorical') validation_generator = test_datagen.flow_from_directory( val_dir, target_size=(IM_WIDTH, IM_HEIGHT), batch_size=batch_size,class_mode='categorical') # 添加新层 def add_new_last_layer(base_model, nb_classes): """ 添加最后的层 输入 base_model和分类数量 输出 新的keras的model """ x = base_model.output x = GlobalAveragePooling2D()(x) x = Dense(FC_SIZE, activation='relu')(x) #new FC layer, random init predictions = Dense(nb_classes, activation='softmax')(x) #new softmax layer model = Model(input=base_model.input, output=predictions) return model # 冻上NB_IV3_LAYERS之前的层 def setup_to_finetune(model): """Freeze the bottom NB_IV3_LAYERS and retrain the remaining top layers. note: NB_IV3_LAYERS corresponds to the top 2 inception blocks in the inceptionv3 arch Args: model: keras model """ for layer in model.layers[:NB_IV3_LAYERS_TO_FREEZE]: layer.trainable = False for layer in model.layers[NB_IV3_LAYERS_TO_FREEZE:]: layer.trainable = True model.compile(optimizer=SGD(lr=0.0001, momentum=0.9), loss='categorical_crossentropy', metrics=['accuracy']) # 设置网络结构 model = InceptionV3(weights='imagenet', include_top=False) model = add_new_last_layer(model, nb_classes) setup_to_finetune(model) # 模式二训练 history_ft = model.fit_generator( train_generator, samples_per_epoch=nb_train_samples, nb_epoch=nb_epoch, validation_data=validation_generator, nb_val_samples=nb_val_samples, class_weight='auto1') # 模型保存 model.save(output_model_file) model.save_weights(wights_path) # 画图 def plot_training(history): acc = history.history['acc'] val_acc = history.history['val_acc'] loss = history.history['loss'] val_loss = history.history['val_loss'] epochs = range(len(acc)) plt.plot(epochs, acc, 'r.') plt.plot(epochs, val_acc, 'r') plt.title('Training and validation accuracy') plt.figure() plt.plot(epochs, loss, 'r.') plt.plot(epochs, val_loss, 'r-') plt.title('Training and validation loss') plt.show() # 训练的acc_loss图 plot_training(history_ft)
2 . 服务器上测试图片
# --coding:utf-8-- # 定义层 import sys import argparse import numpy as np from PIL import Image from io import BytesIO import matplotlib.pyplot as plt from keras.preprocessing import image from keras.models import load_model from keras.applications.inception_v3 import preprocess_input # 狂阶图片指定尺寸 target_size = (299, 299) #fixed size for InceptionV3 architecture # 预测函数 # 输入:model,图片,目标尺寸 # 输出:预测predict def predict(model, img, target_size): """Run model prediction on image Args: model: keras model img: PIL format image target_size: (w,h) tuple Returns: list of predicted labels and their probabilities """ if img.size != target_size: img = img.resize(target_size) x = image.img_to_array(img) x = np.expand_dims(x, axis=0) x = preprocess_input(x) preds = model.predict(x) return preds[0] # 画图函数 # 预测之后画图,这里默认是猫狗,当然可以修改label labels = ("mosaic", "normal") def plot_preds(image, preds,labels): """Displays image and the top-n predicted probabilities in a bar graph Args: image: PIL image preds: list of predicted labels and their probabilities """ plt.imshow(image) plt.axis('off') plt.figure() plt.barh([0, 1], preds, alpha=0.5) plt.yticks([0, 1], labels) plt.xlabel('Probability') plt.xlim(0,1.01) plt.tight_layout() plt.show() # 载入模型 model = load_model('/home/dl/local_repo/data/mosaic1/mosaic.model') # 本地图片 img = Image.open('test.jpg') preds = predict(model, img, target_size) print preds ##plot_preds(img, preds,labels)##因为没有显示器 所以不画图了
3. hdf5 转为 pb
# -*- coding: utf-8 -*- from keras.models import load_model import tensorflow as tf import os import os.path as osp from keras import backend as K #路径参数 # input_path = 'input path' # weight_file = 'weight.h5' # weight_file_path = osp.join(input_path,weight_file) weight_file = 'mosaic.hdf5' weight_file_path = '/home/dl/local_repo/data/mosaic1/mosaic.hdf5' output_graph_name = weight_file[:-4] + '.pb' #转换函数 def h5_to_pb(h5_model,output_dir,model_name,out_prefix = "output_",log_tensorboard = True): if osp.exists(output_dir) == False: os.mkdir(output_dir) out_nodes = [] for i in range(len(h5_model.outputs)): out_nodes.append(out_prefix + str(i + 1)) tf.identity(h5_model.output[i],out_prefix + str(i + 1)) sess = K.get_session() from tensorflow.python.framework import graph_util,graph_io init_graph = sess.graph.as_graph_def() main_graph = graph_util.convert_variables_to_constants(sess,init_graph,out_nodes) graph_io.write_graph(main_graph,output_dir,name = model_name,as_text = False) if log_tensorboard: from tensorflow.python.tools import import_pb_to_tensorboard import_pb_to_tensorboard.import_to_tensorboard(osp.join(output_dir,model_name),output_dir) #输出路径 output_dir = osp.join(os.getcwd(),"trans_model") #加载模型 h5_model = load_model(weight_file_path) h5_to_pb(h5_model,output_dir = output_dir,model_name = output_graph_name) print('model saved')
4. Opencv 测试 参考 : opencv-4.0.1modulesdnn est est_tf_importer.cpp
TEST(Test_TensorFlow, read_inception) { Net net; { const string model = findDataFile("dnn/tensorflow_inception_graph.pb", false); net = readNetFromTensorflow(model); ASSERT_FALSE(net.empty()); } net.setPreferableBackend(DNN_BACKEND_OPENCV); Mat sample = imread(_tf("grace_hopper_227.png")); ASSERT_TRUE(!sample.empty()); Mat input; resize(sample, input, Size(299, 299)); input -= 128; // mean sub Mat inputBlob = blobFromImage(input); net.setInput(inputBlob, "input"); Mat out = net.forward("softmax2"); std::cout << out.dims << std::endl; }
错误及解决办法
1. readNetFromTensorflow crash .
![](https://img2018.cnblogs.com/blog/1135673/201903/1135673-20190327102912590-1395429923.png)
这里是由于FusedBatchNorm 越界了 blobs的size是3 取不到blobs[3]
2. 提示libnvinfer.so.5 找不到
下载对应版本的 TensorRT https://developer.nvidia.com/nvidia-tensorrt-5x-download
tar ***.tar.gz
cd TensorRT*/lib
cp libnvinfer.so.5.0.2 /usr/lib/x86_64-linux-gnu/
ln -s /usr/lib/x86_64-linux-gnu/libnvinfer.so.5.0.2 /usr/lib/x86_64-linux-gnu/libnvinfer.so.5