打造本身的圖像識別模型
3.1 微調的原理
在已經訓練好的模型中，對指定層進行參數的微調，使之適應新的問題。node
3.2 數據準備
- 將數據集切分紅訓練集和驗證集
- 將數據轉換爲tfrecord格式
首先須要將數據轉換成tfrecord的形式。在data_prepare文件夾下，運行：python
python data_convert.py -t pic/ \ --train-shards 2 \ --validation-shards 2 \ --num-threads 2 \ --dataset-name satellite
python data_convert.py -t pic/ --train-shards 2 --validation-shards 2 --num-threads 2 --dataset-name satellite
tfrecord.pygit
# coding:utf-8
# Copyright 2016 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Converts image data to TFRecords file format with Example protos.
The image data set is expected to reside in JPEG files located in the
following directory structure.
data_dir/label_0/image0.jpeg
data_dir/label_0/image1.jpg
...
data_dir/label_1/weird-image.jpeg
data_dir/label_1/my-image.jpeg
...
where the sub-directory is the unique label associated with these images.
This TensorFlow script converts the training and evaluation data into
a sharded data set consisting of TFRecord files
train_directory/train-00000-of-01024
train_directory/train-00001-of-01024
...
train_directory/train-00127-of-01024
and
validation_directory/validation-00000-of-00128
validation_directory/validation-00001-of-00128
...
validation_directory/validation-00127-of-00128
where we have selected 1024 and 128 shards for each data set. Each record
within the TFRecord file is a serialized Example proto. The Example proto
contains the following fields:
image/encoded: string containing JPEG encoded image in RGB colorspace
image/height: integer, image height in pixels
image/width: integer, image width in pixels
image/colorspace: string, specifying the colorspace, always 'RGB'
image/channels: integer, specifying the number of channels, always 3
image/format: string, specifying the format, always'JPEG'
image/filename: string containing the basename of the image file
e.g. 'n01440764_10026.JPEG' or 'ILSVRC2012_val_00000293.JPEG'
image/class/label: integer specifying the index in a classification layer. start from "class_label_base"
image/class/text: string specifying the human-readable version of the label
e.g. 'dog'
If you data set involves bounding boxes, please look at build_imagenet_data.py.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from datetime import datetime
import os
import random
import sys
import threading
import numpy as np
import tensorflow as tf
import logging
def _int64_feature(value):
"""Wrapper for inserting int64 features into Example proto."""
if not isinstance(value, list):
value = [value]
return tf.train.Feature(int64_list=tf.train.Int64List(value=value))
def _bytes_feature(value):
"""Wrapper for inserting bytes features into Example proto."""
value = tf.compat.as_bytes(value)
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
def _convert_to_example(filename, image_buffer, label, text, height, width):
"""Build an Example proto for an example.
Args:
filename: string, path to an image file, e.g., '/path/to/example.JPG'
image_buffer: string, JPEG encoding of RGB image
label: integer, identifier for the ground truth for the network
text: string, unique human-readable, e.g. 'dog'
height: integer, image height in pixels
width: integer, image width in pixels
Returns:
Example proto
"""
colorspace = 'RGB'
channels = 3
image_format = 'JPEG'
example = tf.train.Example(features=tf.train.Features(feature={
'image/height': _int64_feature(height),
'image/width': _int64_feature(width),
'image/colorspace': _bytes_feature(colorspace),
'image/channels': _int64_feature(channels),
'image/class/label': _int64_feature(label),
'image/class/text': _bytes_feature(text),
'image/format': _bytes_feature(image_format),
'image/filename': _bytes_feature(os.path.basename(filename)),
'image/encoded': _bytes_feature(image_buffer)}))
return example
class ImageCoder(object):
"""Helper class that provides TensorFlow image coding utilities."""
def __init__(self):
# Create a single Session to run all image coding calls.
self._sess = tf.Session()
# Initializes function that converts PNG to JPEG data.
self._png_data = tf.placeholder(dtype=tf.string)
image = tf.image.decode_png(self._png_data, channels=3)
self._png_to_jpeg = tf.image.encode_jpeg(image, format='rgb', quality=100)
# Initializes function that decodes RGB JPEG data.
self._decode_jpeg_data = tf.placeholder(dtype=tf.string)
self._decode_jpeg = tf.image.decode_jpeg(self._decode_jpeg_data, channels=3)
def png_to_jpeg(self, image_data):
return self._sess.run(self._png_to_jpeg,
feed_dict={self._png_data: image_data})
def decode_jpeg(self, image_data):
image = self._sess.run(self._decode_jpeg,
feed_dict={self._decode_jpeg_data: image_data})
assert len(image.shape) == 3
assert image.shape[2] == 3
return image
def _is_png(filename):
"""Determine if a file contains a PNG format image.
Args:
filename: string, path of the image file.
Returns:
boolean indicating if the image is a PNG.
"""
return '.png' in filename
def _process_image(filename, coder):
"""Process a single image file.
Args:
filename: string, path to an image file e.g., '/path/to/example.JPG'.
coder: instance of ImageCoder to provide TensorFlow image coding utils.
Returns:
image_buffer: string, JPEG encoding of RGB image.
height: integer, image height in pixels.
width: integer, image width in pixels.
"""
# Read the image file.
with open(filename, 'rb') as f:
image_data = f.read()
# Convert any PNG to JPEG's for consistency.
if _is_png(filename):
logging.info('Converting PNG to JPEG for %s' % filename)
image_data = coder.png_to_jpeg(image_data)
# Decode the RGB JPEG.
image = coder.decode_jpeg(image_data)
# Check that image converted to RGB
assert len(image.shape) == 3
height = image.shape[0]
width = image.shape[1]
assert image.shape[2] == 3
return image_data, height, width
def _process_image_files_batch(coder, thread_index, ranges, name, filenames,
texts, labels, num_shards, command_args):
"""Processes and saves list of images as TFRecord in 1 thread.
Args:
coder: instance of ImageCoder to provide TensorFlow image coding utils.
thread_index: integer, unique batch to run index is within [0, len(ranges)).
ranges: list of pairs of integers specifying ranges of each batches to
analyze in parallel.
name: string, unique identifier specifying the data set
filenames: list of strings; each string is a path to an image file
texts: list of strings; each string is human readable, e.g. 'dog'
labels: list of integer; each integer identifies the ground truth
num_shards: integer number of shards for this data set.
"""
# Each thread produces N shards where N = int(num_shards / num_threads).
# For instance, if num_shards = 128, and the num_threads = 2, then the first
# thread would produce shards [0, 64).
num_threads = len(ranges)
assert not num_shards % num_threads
num_shards_per_batch = int(num_shards / num_threads)
shard_ranges = np.linspace(ranges[thread_index][0],
ranges[thread_index][1],
num_shards_per_batch + 1).astype(int)
num_files_in_thread = ranges[thread_index][1] - ranges[thread_index][0]
counter = 0
for s in range(num_shards_per_batch):
# Generate a sharded version of the file name, e.g. 'train-00002-of-00010'
shard = thread_index * num_shards_per_batch + s
output_filename = '%s_%s_%.5d-of-%.5d.tfrecord' % (command_args.dataset_name, name, shard, num_shards)
output_file = os.path.join(command_args.output_directory, output_filename)
writer = tf.python_io.TFRecordWriter(output_file)
shard_counter = 0
files_in_shard = np.arange(shard_ranges[s], shard_ranges[s + 1], dtype=int)
for i in files_in_shard:
filename = filenames[i]
label = labels[i]
text = texts[i]
image_buffer, height, width = _process_image(filename, coder)
example = _convert_to_example(filename, image_buffer, label,
text, height, width)
writer.write(example.SerializeToString())
shard_counter += 1
counter += 1
if not counter % 1000:
logging.info('%s [thread %d]: Processed %d of %d images in thread batch.' %
(datetime.now(), thread_index, counter, num_files_in_thread))
sys.stdout.flush()
writer.close()
logging.info('%s [thread %d]: Wrote %d images to %s' %
(datetime.now(), thread_index, shard_counter, output_file))
sys.stdout.flush()
shard_counter = 0
logging.info('%s [thread %d]: Wrote %d images to %d shards.' %
(datetime.now(), thread_index, counter, num_files_in_thread))
sys.stdout.flush()
def _process_image_files(name, filenames, texts, labels, num_shards, command_args):
"""Process and save list of images as TFRecord of Example protos.
Args:
name: string, unique identifier specifying the data set
filenames: list of strings; each string is a path to an image file
texts: list of strings; each string is human readable, e.g. 'dog'
labels: list of integer; each integer identifies the ground truth
num_shards: integer number of shards for this data set.
"""
assert len(filenames) == len(texts)
assert len(filenames) == len(labels)
# Break all images into batches with a [ranges[i][0], ranges[i][1]].
spacing = np.linspace(0, len(filenames), command_args.num_threads + 1).astype(np.int)
ranges = []
for i in range(len(spacing) - 1):
ranges.append([spacing[i], spacing[i + 1]])
# Launch a thread for each batch.
logging.info('Launching %d threads for spacings: %s' % (command_args.num_threads, ranges))
sys.stdout.flush()
# Create a mechanism for monitoring when all threads are finished.
coord = tf.train.Coordinator()
# Create a generic TensorFlow-based utility for converting all image codings.
coder = ImageCoder()
threads = []
for thread_index in range(len(ranges)):
args = (coder, thread_index, ranges, name, filenames,
texts, labels, num_shards, command_args)
t = threading.Thread(target=_process_image_files_batch, args=args)
t.start()
threads.append(t)
# Wait for all the threads to terminate.
coord.join(threads)
logging.info('%s: Finished writing all %d images in data set.' %
(datetime.now(), len(filenames)))
sys.stdout.flush()
def _find_image_files(data_dir, labels_file, command_args):
"""Build a list of all images files and labels in the data set.
Args:
data_dir: string, path to the root directory of images.
Assumes that the image data set resides in JPEG files located in
the following directory structure.
data_dir/dog/another-image.JPEG
data_dir/dog/my-image.jpg
where 'dog' is the label associated with these images.
labels_file: string, path to the labels file.
The list of valid labels are held in this file. Assumes that the file
contains entries as such:
dog
cat
flower
where each line corresponds to a label. We map each label contained in
the file to an integer starting with the integer 0 corresponding to the
label contained in the first line.
Returns:
filenames: list of strings; each string is a path to an image file.
texts: list of strings; each string is the class, e.g. 'dog'
labels: list of integer; each integer identifies the ground truth.
"""
logging.info('Determining list of input files and labels from %s.' % data_dir)
unique_labels = [l.strip() for l in tf.gfile.FastGFile(
labels_file, 'r').readlines()]
labels = []
filenames = []
texts = []
# Leave label index 0 empty as a background class.
"""很是重要，這裏咱們調整label從0開始以符合定義"""
label_index = command_args.class_label_base
# Construct the list of JPEG files and labels.
for text in unique_labels:
jpeg_file_path = '%s/%s/*' % (data_dir, text)
matching_files = tf.gfile.Glob(jpeg_file_path)
labels.extend([label_index] * len(matching_files))
texts.extend([text] * len(matching_files))
filenames.extend(matching_files)
if not label_index % 100:
logging.info('Finished finding files in %d of %d classes.' % (
label_index, len(labels)))
label_index += 1
# Shuffle the ordering of all image files in order to guarantee
# random ordering of the images with respect to label in the
# saved TFRecord files. Make the randomization repeatable.
shuffled_index = list(range(len(filenames)))
random.seed(12345)
random.shuffle(shuffled_index)
filenames = [filenames[i] for i in shuffled_index]
texts = [texts[i] for i in shuffled_index]
labels = [labels[i] for i in shuffled_index]
logging.info('Found %d JPEG files across %d labels inside %s.' %
(len(filenames), len(unique_labels), data_dir))
# print(labels)
return filenames, texts, labels
def _process_dataset(name, directory, num_shards, labels_file, command_args):
"""Process a complete data set and save it as a TFRecord.
Args:
name: string, unique identifier specifying the data set.
directory: string, root path to the data set.
num_shards: integer number of shards for this data set.
labels_file: string, path to the labels file.
"""
filenames, texts, labels = _find_image_files(directory, labels_file, command_args)
_process_image_files(name, filenames, texts, labels, num_shards, command_args)
def check_and_set_default_args(command_args):
if not (hasattr(command_args, 'train_shards')) or command_args.train_shards is None:
command_args.train_shards = 5
if not (hasattr(command_args, 'validation_shards')) or command_args.validation_shards is None:
command_args.validation_shards = 5
if not (hasattr(command_args, 'num_threads')) or command_args.num_threads is None:
command_args.num_threads = 5
if not (hasattr(command_args, 'class_label_base')) or command_args.class_label_base is None:
command_args.class_label_base = 0
if not (hasattr(command_args, 'dataset_name')) or command_args.dataset_name is None:
command_args.dataset_name = ''
assert not command_args.train_shards % command_args.num_threads, (
'Please make the command_args.num_threads commensurate with command_args.train_shards')
assert not command_args.validation_shards % command_args.num_threads, (
'Please make the command_args.num_threads commensurate with '
'command_args.validation_shards')
assert command_args.train_directory is not None
assert command_args.validation_directory is not None
assert command_args.labels_file is not None
assert command_args.output_directory is not None
def main(command_args):
"""
command_args:須要有如下屬性：
command_args.train_directory 訓練集所在的文件夾。這個文件夾下面，每一個文件夾的名字表明label名稱，再下面就是圖片。
command_args.validation_directory 驗證集所在的文件夾。這個文件夾下面，每一個文件夾的名字表明label名稱，再下面就是圖片。
command_args.labels_file 一個文件。每一行表明一個label名稱。
command_args.output_directory 一個文件夾，表示最後輸出的位置。
command_args.train_shards 將訓練集分紅多少份。
command_args.validation_shards 將驗證集分紅多少份。
command_args.num_threads 線程數。必須是上面兩個參數的約數。
command_args.class_label_base 很重要！真正的tfrecord中，每一個class的label號從多少開始，默認爲0（在models/slim中就是從0開始的）
command_args.dataset_name 字符串，輸出的時候的前綴。
圖片不能夠有損壞。不然會致使線程提早退出。
"""
check_and_set_default_args(command_args)
logging.info('Saving results to %s' % command_args.output_directory)
# Run it!
_process_dataset('validation', command_args.validation_directory,
command_args.validation_shards, command_args.labels_file, command_args)
_process_dataset('train', command_args.train_directory,
command_args.train_shards, command_args.labels_file, command_args)
data_convert.pygithub
# coding:utf-8
from __future__ import absolute_import
import argparse
import os
import logging
from src.tfrecord import main
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('-t', '--tensorflow-data-dir', default='pic/')
parser.add_argument('--train-shards', default=2, type=int)
parser.add_argument('--validation-shards', default=2, type=int)
parser.add_argument('--num-threads', default=2, type=int)
parser.add_argument('--dataset-name', default='satellite', type=str)
return parser.parse_args()
if __name__ == '__main__':
logging.basicConfig(level=logging.INFO)
args = parse_args()
args.tensorflow_dir = args.tensorflow_data_dir
args.train_directory = os.path.join(args.tensorflow_dir, 'train')
args.validation_directory = os.path.join(args.tensorflow_dir, 'validation')
args.output_directory = args.tensorflow_dir
args.labels_file = os.path.join(args.tensorflow_dir, 'label.txt')
if os.path.exists(args.labels_file) is False:
logging.warning('Can\'t find label.txt. Now create it.')
all_entries = os.listdir(args.train_directory)
dirnames = []
for entry in all_entries:
if os.path.isdir(os.path.join(args.train_directory, entry)):
dirnames.append(entry)
with open(args.labels_file, 'w') as f:
for dirname in dirnames:
f.write(dirname + '\n')
main(args)
這樣在pic文件夾下就會生成4個tfrecord文件和1個label.txt文件。spring
3.3 使用TensorFlow Slim 微調模型
3.3.1 下載TensorFlow Slim源代碼
3.3.2 定義新的datasets 文件
參考3.3.2小節對Slim源碼作修改。express
3.3.3 準備訓練文件夾
在slim文件夾下新建一個satellite目錄。在這個目錄下作下面幾件事情：apache
- 新建一個data 目錄，並將第3.2中準備好的5個轉換好格式的訓練數據複製進去。
- 新建一個空的train_dir目錄，用來保存訓練過程當中的日誌和模型。
- 新建一個pretrained目錄，在slim的GitHub頁面找到Inception V3 模型的下載地址http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz 下載並解壓後，會獲得一個inception_v3.ckpt 文件，將該文件複製到pretrained 目錄下（這個文件在chapter_3_data/文件中也提供了）
3.3.4 開始訓練網絡
（在slim文件夾下運行）訓練Logits層：
使用GPU問題
異常日誌：app
Cannot assign a device for operation 'InceptionV3/Conv2d_1a_3x3/weights/Initializer/truncated_normal/TruncatedNormal': Could not satisfy explicit device specification '' because the node was colocated with a group of nodes t
hat required incompatible device '/device:GPU:0'
經分析，與GPU使用有關
解決方法：
1.不使用GPU
參考https://github.com/tensorflow/models/issues/1823
In the train_image_classifier.py script line 40 there is:
tf.app.flags.DEFINE_boolean('clone_on_cpu', False, 'Use CPUs to deploy clones.')
Turn this function to True if you will to use CPUs instead of GPUs which seems to be enabled by default.
python train_image_classifier.py \ --train_dir=satellite/train_dir \ --dataset_name=satellite \ --dataset_split_name=train \ --dataset_dir=satellite/data \ --model_name=inception_v3 \ --checkpoint_path=satellite/pretrained/inception_v3.ckpt \ --checkpoint_exclude_scopes=InceptionV3/Logits,InceptionV3/AuxLogits \ --trainable_scopes=InceptionV3/Logits,InceptionV3/AuxLogits \ --max_number_of_steps=100000 \ --batch_size=32 \ --learning_rate=0.001 \ --learning_rate_decay_type=fixed \ --save_interval_secs=300 \ --save_summaries_secs=2 \ --log_every_n_steps=10 \ --optimizer=rmsprop \ --weight_decay=0.00004
python train_image_classifier.py --train_dir=satellite/train_dir --dataset_name=satellite --dataset_split_name=train --dataset_dir=satellite/data --model_name=inception_v3 --checkpoint_path=satellite/pretrained/inception_v3.ckpt --checkpoint_exclude_scopes=InceptionV3/Logits,InceptionV3/AuxLogits --trainable_scopes=InceptionV3/Logits,InceptionV3/AuxLogits --max_number_of_steps=100000 --batch_size=32 --learning_rate=0.001 --learning_rate_decay_type=fixed --save_interval_secs=300 --save_summaries_secs=2 --log_every_n_steps=10 --optimizer=rmsprop --weight_decay=0.00004
訓練全部層：less
python train_image_classifier.py \ --train_dir=satellite/train_dir \ --dataset_name=satellite \ --dataset_split_name=train \ --dataset_dir=satellite/data \ --model_name=inception_v3 \ --checkpoint_path=satellite/pretrained/inception_v3.ckpt \ --checkpoint_exclude_scopes=InceptionV3/Logits,InceptionV3/AuxLogits \ --max_number_of_steps=100000 \ --batch_size=32 \ --learning_rate=0.001 \ --learning_rate_decay_type=fixed \ --save_interval_secs=300 \ --save_summaries_secs=10 \ --log_every_n_steps=1 \ --optimizer=rmsprop \ --weight_decay=0.00004
python train_image_classifier.py --train_dir=satellite/train_dir --dataset_name=satellite --dataset_split_name=train --dataset_dir=satellite/data --model_name=inception_v3 --checkpoint_path=satellite/pretrained/inception_v3.ckpt --checkpoint_exclude_scopes=InceptionV3/Logits,InceptionV3/AuxLogits --max_number_of_steps=100000 --batch_size=32 --learning_rate=0.001 --learning_rate_decay_type=fixed --save_interval_secs=300 --save_summaries_secs=10 --log_every_n_steps=1 --optimizer=rmsprop --weight_decay=0.00004
3.3.6 驗證模型準確率
在slim文件夾下運行：
python eval_image_classifier.py \ --checkpoint_path=satellite/train_dir \ --eval_dir=satellite/eval_dir \ --dataset_name=satellite \ --dataset_split_name=validation \ --dataset_dir=satellite/data \ --model_name=inception_v3
python eval_image_classifier.py --checkpoint_path=satellite/train_dir --eval_dir=satellite/eval_dir --dataset_name=satellite --dataset_split_name=validation --dataset_dir=satellite/data --model_name=inception_v3
3.3.7 TensorBoard 可視化與超參數選擇
打開TensorBoard：
tensorboard --logdir satellite/train_dir
3.3.8 導出模型並對單張圖片進行識別
在slim文件夾下運行：
python export_inference_graph.py \ --alsologtostderr \ --model_name=inception_v3 \ --output_file=satellite/inception_v3_inf_graph.pb \ --dataset_name satellite
python export_inference_graph.py --alsologtostderr --model_name=inception_v3 --output_file=satellite/inception_v3_inf_graph.pb --dataset_name satellite
在chapter_3文件夾下運行（需將5271改爲train_dir中保存的實際的模型訓練步數）：
python freeze_graph.py \ --input_graph slim/satellite/inception_v3_inf_graph.pb \ --input_checkpoint slim/satellite/train_dir/model.ckpt-5271 \ --input_binary true \ --output_node_names InceptionV3/Predictions/Reshape_1 \ --output_graph slim/satellite/frozen_graph.pb
python freeze_graph.py --input_graph slim/satellite/inception_v3_inf_graph.pb --input_checkpoint slim/satellite/train_dir/model.ckpt-100 --input_binary true --output_node_names InceptionV3/Predictions/Reshape_1 --output_graph slim/satellite/frozen_graph.pb
運行導出模型分類單張圖片：
python classify_image_inception_v3.py \ --model_path slim/satellite/frozen_graph.pb \ --label_path data_prepare/pic/label.txt \ --image_file test_image.jpg
python classify_image_inception_v3.py --model_path slim/satellite/frozen_graph.pb --label_path data_prepare/pic/label.txt --image_file test_image.jpg
拓展閱讀
- TensorFlow Slim 是TensorFlow 中用於定義、訓練和驗證複雜網絡的 高層API。官方已經使用TF-Slim 定義了一些經常使用的圖像識別模型， 如AlexNet、VGGNet、Inception模型、ResNet等。本章介紹的Inception V3 模型也是其中之一， 詳細文檔請參考： https://github.com/tensorflow/models/tree/master/research/slim。
- 在第3.2節中，將圖片數據轉換成了TFRecord文件。TFRecord 是 TensorFlow 提供的用於高速讀取數據的文件格式。讀者能夠參考博文（ http://warmspringwinds.github.io/tensorflow/tf-slim/2016/12/21/tfrecords-guide/ ）詳細瞭解如何將數據轉換爲TFRecord 文件，以及 如何從TFRecord 文件中讀取數據。
- Inception V3 是Inception 模型（即GoogLeNet）的改進版，能夠參考論文Rethinking the Inception Architecture for Computer Vision 瞭解 其結構細節。
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相關文章
- 1. 打造本身的圖像識別模型
- 2. ImageNet打造自己的圖像識別
- 3. 手寫數字識別模型識別本身的圖片
- 4. 圖像識別模型
- 5. 量身打造本身的MyEclipse（多圖）
- 6. 打造本身的
- 7. 告別原生，打造本身的topBar
- 8. lmageNet圖像識別模型簡介
- 9. swift:打造你本身的折線圖
- 10. 圖像識別、圖像模糊檢測