[Tensorflow] Object Detection API - build your training environment
一、前期准备
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Prepare protoc
Download Protocol Buffers
Create folder: protoc and unzip it.
unsw@unsw-UX303UB$ ls
models Others protoc train_data
unsw@unsw-UX303UB$ ls protoc/
bin include readme.txt
unsw@unsw-UX303UB$ ls protoc/bin/
protoc
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Prepare model
Download model folder from tensorflow github.
unsw@unsw-UX303UB$ git clone https://github.com/tensorflow/models.git
Cloning into 'models'...
remote: Counting objects: 7518, done.
remote: Compressing objects: 100% (5/5), done.
remote: Total 7518 (delta 0), reused 1 (delta 0), pack-reused 7513
Receiving objects: 100% (7518/7518), 157.87 MiB | 1.17 MiB/s, done.
Resolving deltas: 100% (4053/4053), done.
Checking connectivity... done.
unsw@unsw-UX303UB$ ls
annotations images models Others raccoon_labels.csv xml_to_csv.py
unsw@unsw-UX303UB$ ls models/
AUTHORS CONTRIBUTING.md LICENSE README.md tutorials
CODEOWNERS ISSUE_TEMPLATE.md official research WORKSPACE
Enter: models/research/
# Set python env.
$ export PYTHONPATH=/home/unsw/Dropbox/Programmer/1-python/Tensorflow/ssd_proj/models/research/slim::pwd:pwd/slim:$PYTHONPATH
$ python object_detection/builders/model_builder_test.py
.......
----------------------------------------------------------------------
Ran 7 tests in 0.022s
OK
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Prepare train.record
Download: https://github.com/datitran/raccoon_dataset/blob/master/generate_tfrecord.py
""" Usage: # From tensorflow/models/ # Create train data: python generate_tfrecord.py --csv_input=data/train_labels.csv --output_path=train.record # Create test data: python generate_tfrecord.py --csv_input=data/test_labels.csv --output_path=test.record """ from __future__ import division from __future__ import print_function from __future__ import absolute_import import os import io import pandas as pd import tensorflow as tf from PIL import Image from object_detection.utils import dataset_util from collections import namedtuple, OrderedDict flags = tf.app.flags flags.DEFINE_string('csv_input', '', 'Path to the CSV input') flags.DEFINE_string('output_path', '', 'Path to output TFRecord') FLAGS = flags.FLAGS # TO-DO replace this with label map def class_text_to_int(row_label): if row_label == 'raccoon': return 1 else: None def split(df, group): data = namedtuple('data', ['filename', 'object']) gb = df.groupby(group) return [data(filename, gb.get_group(x)) for filename, x in zip(gb.groups.keys(), gb.groups)] def create_tf_example(group, path): with tf.gfile.GFile(os.path.join(path, '{}'.format(group.filename)), 'rb') as fid: encoded_jpg = fid.read() encoded_jpg_io = io.BytesIO(encoded_jpg) image = Image.open(encoded_jpg_io) width, height = image.size filename = group.filename.encode('utf8') image_format = b'jpg' xmins = [] xmaxs = [] ymins = [] ymaxs = [] classes_text = [] classes = [] for index, row in group.object.iterrows(): xmins.append(row['xmin'] / width) xmaxs.append(row['xmax'] / width) ymins.append(row['ymin'] / height) ymaxs.append(row['ymax'] / height) classes_text.append(row['class'].encode('utf8')) classes.append(class_text_to_int(row['class'])) tf_example = tf.train.Example(features=tf.train.Features(feature={ 'image/height': dataset_util.int64_feature(height), 'image/width': dataset_util.int64_feature(width), 'image/filename': dataset_util.bytes_feature(filename), 'image/source_id': dataset_util.bytes_feature(filename), 'image/encoded': dataset_util.bytes_feature(encoded_jpg), 'image/format': dataset_util.bytes_feature(image_format), 'image/object/bbox/xmin': dataset_util.float_list_feature(xmins), 'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs), 'image/object/bbox/ymin': dataset_util.float_list_feature(ymins), 'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs), 'image/object/class/text': dataset_util.bytes_list_feature(classes_text), 'image/object/class/label': dataset_util.int64_list_feature(classes), })) return tf_example def main(_): writer = tf.python_io.TFRecordWriter(FLAGS.output_path) path = os.path.join(os.getcwd(), 'images') examples = pd.read_csv(FLAGS.csv_input) grouped = split(examples, 'filename') for group in grouped: tf_example = create_tf_example(group, path) writer.write(tf_example.SerializeToString()) writer.close() output_path = os.path.join(os.getcwd(), FLAGS.output_path) print('Successfully created the TFRecords: {}'.format(output_path)) if __name__ == '__main__': tf.app.run()
NB: we will do everything in models/research/ where the env has been set well.
So, move data/images here for generate_tfrecord.py
unsw@unsw-UX303UB$ pwd
/home/unsw/Dropbox/Programmer/1-python/Tensorflow/ssd_proj/models/research
unsw@unsw-UX303UB$ python ../../generate_tfrecord.py --csv_input=../../data/raccoon_labels.csv --output_path=../../data/train.record
Successfully created the TFRecords: /home/unsw/Programmer/1-python/Tensorflow/ssd_proj/models/research/../../data/train.record
Now, we have got train_labels.csv (name changed from raccoon_labels.csv) train.record.
tfrecord数据文件是一种将图像数据和标签统一存储的二进制文件，能更好的利用内存，在tensorflow中快速的复制，移动，读取，存储等。
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Prepare pre-train model
Download pre-trained model: https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md
Download configure file for pre-trained model: https://github.com/tensorflow/models/tree/master/research/object_detection/samples/configs
This configure is already in our model folder:
unsw@unsw-UX303UB$ pwd
/home/unsw/Programmer/1-python/Tensorflow/ssd_proj/models/research/object_detection/samples/configs
unsw@unsw-UX303UB$ ls
faster_rcnn_inception_resnet_v2_atrous_coco.config faster_rcnn_resnet101_voc07.config faster_rcnn_resnet50_pets.config ssd_inception_v2_pets.config
faster_rcnn_inception_resnet_v2_atrous_pets.config faster_rcnn_resnet152_coco.config rfcn_resnet101_coco.config ssd_mobilenet_v1_coco.config
faster_rcnn_resnet101_coco.config faster_rcnn_resnet152_pets.config rfcn_resnet101_pets.config ssd_mobilenet_v1_pets.config
faster_rcnn_resnet101_pets.config faster_rcnn_resnet50_coco.config ssd_inception_v2_coco.config
Configure based on your own data.
1 # SSD with Mobilenet v1, configured for Oxford-IIIT Pets Dataset. 2 # Users should configure the fine_tune_checkpoint field in the train config as 3 # well as the label_map_path and input_path fields in the train_input_reader and 4 # eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that 5 # should be configured. 6 7 model { 8 ssd { 9 num_classes: 1
158 fine_tune_checkpoint: "ssd_mobilenet_v1_coco_11_06_2017/model.ckpt" 159 from_detection_checkpoint: true 160 # Note: The below line limits the training process to 200K steps, which we 161 # empirically found to be sufficient enough to train the pets dataset. This 162 # effectively bypasses the learning rate schedule (the learning rate will 163 # never decay). Remove the below line to train indefinitely. 164 num_steps: 200000 165 data_augmentation_options { 166 random_horizontal_flip { 167 } 168 } 169 data_augmentation_options { 170 ssd_random_crop { 171 } 172 } 173 } 174 175 train_input_reader: { 176 tf_record_input_reader { 177 input_path: "data/train.record" 178 } 179 label_map_path: "data/object-detection.pbtxt" 180 } 181 182 eval_config: { 183 num_examples: 2000 184 # Note: The below line limits the evaluation process to 10 evaluations. 185 # Remove the below line to evaluate indefinitely. 186 max_evals: 10 187 } 188 189 eval_input_reader: { 190 tf_record_input_reader { 191 input_path: "data/test.record" 192 } 193 label_map_path: "data/object-detection.pbtxt" 194 shuffle: false 195 num_readers: 1 196 }
As above, we need to create object-detection.pbtxt as following:
item { id: 1 name: 'raccoon' }
二、开始训练
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Prepare training
Move all configure files based on ssd_mobilenet_v1_pets.config as following:
training folder: object-detection.pbtxt and ssd_mobilenet_v1_pets.config.
data folder: train.record and train_labels.csv.
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Training on the way
Start training.
python object_detection/train.py --logtostderr --train_dir=training/ --pipeline_config_path=training/ssd_mobilenet_v1_pets.config
INFO:tensorflow:Starting Session.
INFO:tensorflow:Saving checkpoint to path training/model.ckpt
INFO:tensorflow:Starting Queues.
INFO:tensorflow:global_step/sec: 0
INFO:tensorflow:Recording summary at step 0.
INFO:tensorflow:global step 1: loss = 14.5804 (33.780 sec/step)
INFO:tensorflow:global step 2: loss = 12.6232 (19.210 sec/step)
INFO:tensorflow:global step 3: loss = 12.0996 (17.102 sec/step)
Obviously, without GPU, life will be hard. GPU as following:
INFO:tensorflow:global step 1: loss = 15.2152 (9.041 sec/step)
INFO:tensorflow:global step 2: loss = 12.7308 (0.483 sec/step)
INFO:tensorflow:global step 3: loss = 11.9776 (0.450 sec/step)
INFO:tensorflow:global step 4: loss = 11.4102 (0.402 sec/step)
INFO:tensorflow:global step 5: loss = 10.8128 (0.427 sec/step)
INFO:tensorflow:global step 6: loss = 10.1892 (0.405 sec/step)
INFO:tensorflow:global step 7: loss = 9.2219 (0.396 sec/step)
INFO:tensorflow:global step 8: loss = 9.1491 (0.421 sec/step)
INFO:tensorflow:global step 9: loss = 8.5584 (0.400 sec/step)
