# --coding:utf---

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

  cnt =

  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')

#print(train_num)

#input('wait...')

# 数据准备

IM_WIDTH, IM_HEIGHT = ,  #InceptionV3指定的图片尺寸

FC_SIZE =                 # 全连接层的节点个数

##NB_IV3_LAYERS_TO_FREEZE =   # 冻结层的数量

NB_IV3_LAYERS_TO_FREEZE =   # 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=

nb_epoch =

batch_size = 

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=,

  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=,

  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  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)

# --coding:utf---

# 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 = (, ) #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=)

  x = preprocess_input(x)

  preds = model.predict(x)

  return preds[]

# 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([, ], preds, alpha=0.5)

  plt.yticks([, ], labels)

  plt.xlabel('Probability')

  plt.xlim(,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

# -*- coding: utf- -*-

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[:-] + '.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 + ))

        tf.identity(h5_model.output[i],out_prefix + str(i + ))

    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')

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