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Fine-tuning a model from an existing checkpoint

This section wil describe how to fine-tune your own model from the existing checkpoint of Inception V3. This tutorial is referenced from https://github.com/tensorflow/models/tree/master/research/slim#fine-tuning-a-model-from-an-existing-checkpoint with some changes to train your own model. The example training dataset is hospital dataset, which is used to recognize doctor and patient.

I/ Prepare dataset to train

Create training directory ./data/hospital/hospital_photos. Then download hospital dataset photos to train. Each category is stored in a separated folder whose name is "class" to describe. (e.g. doctor, patient). Save these folders in ./data/hospital/hospital_photos/ Dataset storage will be in that structure:

data
└── hospital
    └── hospital_photos
        ├── doctor
        └── patient

II/ Modify set-up file to convert dataset to TRRecord format

  1. Copy datasets/convert_poses.py to datasets/convert_hospital.py
cp datasets/convert_poses.py datasets/convert_hospital.py
  1. Open file datasets/convert_hospital.py, edit _NUM_VALIDATION variable to desired number of validation photos. This number depends on the size of your dataset. Then replace all words "poses" by "hospital" in this file.
  2. Open file download_and_convert_data.py
  • Add from datasets import convert_hospital
  • Add hospital to tf.app.flags.DEFINE_string
  • Add command convert_hospital.run(FLAGS.dataset_dir) to "if" in main()

III/ Convert to TFRecord Format

For each dataset, we'll need to download the raw data and convert it to TensorFlow's native TFRecord format. Each TFRecord contains a TF-Example protocol buffer.

python3 download_and_convert_data.py --dataset_name=hospital --dataset_dir=./data/hospital

IV/ Modify set-up file to train model

  1. Copy datasets/poses.py to datasets/hospital.py.
cp ./datasets/poses.py ./datasets/hospital.py
  1. Open file datasets/hospital.py,
  • Edit SPLITS_TO_SIZES to the number of photos used for training and validation.
  • Edit _NUM_CLASSES to 2 (because there are 2 classes: doctor and patient).
  • Edit _FILE_PATTERN to hospital%s_*.tfrecord_
  • Replace all words "poses" by "hospital"
  1. Open file datasets/dataset_factory.py
  • Add from datasets import hospital
  • Add 'hospital': hospital, to datasets_map

V/ Download Checkpoint of pre-trained model of Inception V3 and fine-tune your own model

  1. Download Checkpoint of InceptionV3 pre-trained model
mkdir ./my_checkpoints
wget http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz
tar -xvf inception_v3_2016_08_28.tar.gz
mv inception_v3.ckpt ./my_checkpoints/
rm inception_v3_2016_08_28.tar.gz
  1. Fine-tune your own model To indicate a checkpoint from which to fine-tune, we'll call training with the --checkpoint_path flag and assign it an absolute path to a checkpoint file.

When fine-tuning a model, we need to be careful about restoring checkpoint weights. In particular, when we fine-tune a model on a new task with a different number of output labels, we won't be able restore the final logits (classifier) layer. For this, we'll use the --checkpoint_exclude_scopes flag. This flag hinders certain variables from being loaded. When fine-tuning on a classification task using a different number of classes than the trained model, the new model will have a final 'logits' layer whose dimensions differ from the pre-trained model. For example, if fine-tuning an ImageNet-trained model on Hospital, the pre-trained logits layer will have dimensions [2048 x 1001] but our new logits layer will have dimensions [2048 x 2]. Consequently, this flag indicates to TF-Slim to avoid loading these weights from the checkpoint.

Keep in mind that warm-starting from a checkpoint affects the model's weights only during the initialization of the model. Once a model has started training, a new checkpoint will be created in --train_dir. If the fine-tuning training is stopped and restarted, this new checkpoint will be the one from which weights are restored and not the --checkpoint_path. Consequently, the flags --checkpoint_path and --checkpoint_exclude_scopes are only used during the 0-th global step (model initialization). Typically for fine-tuning one only want train a sub-set of layers, so the flag --trainable_scopes allows to specify which subsets of layers should trained, the rest would remain frozen.

Below we give an example of fine-tuning inception-v3 on Hos, inception_v3 was trained on ImageNet with 1000 class labels, but the Hospital dataset only have 2 classes. Since the dataset is quite small we will only train the new layers.

python3 train_image_classifier.py \
    --train_dir=./hospital_models/inception_v3 \
    --dataset_dir=./data/hospital \
    --dataset_name=hospital \
    --dataset_split_name=train \
    --model_name=inception_v3 \
    --checkpoint_path=my_checkpoints/inception_v3.ckpt \
    --checkpoint_exclude_scopes=InceptionV3/Logits,InceptionV3/AuxLogits \
    --trainable_scopes=InceptionV3/Logits,InceptionV3/AuxLogits \
    --max_number_of_steps=5000 \
    --batch_size=32 \
    --learning_rate=0.01 \
    --learning_rate_decay_type=fixed \
    --save_interval_secs=60 \
    --save_summaries_secs=60 \
    --log_every_n_steps=100 \
    --optimizer=rmsprop \
    --weight_decay=0.00004

For more information about Gradient Descent optimizer algorithm, you can refer at: http://ruder.io/optimizing-gradient-descent/index.html

VI/ Evaluating performance of a model

To evaluate the performance of a model, you can use the eval_image_classifier.py script, as shown below.

 python3 eval_image_classifier.py \
    --alsologtostderr \
    --checkpoint_path=./hospital_models/inception_v3/model.ckpt-5000 \
    --dataset_dir=./data/hospital \
    --dataset_name=hospital \
    --dataset_split_name=validation \
    --model_name=inception_v3

VII/ Exporting the Inference Graph

Saves out a GraphDef containing the architecture of the model. To use it with a model name defined by slim, run:

python3 export_inference_graph.py \
  --alsologtostderr \
  --model_name=inception_v3 \
  --output_file=./hospital_icpv3_inf_graph.pb \
  --dataset_name=hospital

VIII/ Freezing the exported Graph

If you then want to use the resulting model with your own or pretrained checkpoints as part of a mobile model, you can run freeze_graph to get a graph def with the variables inlined as constants using:

python3 freeze_graph.py \
--input_graph=./hospital_icpv3_inf_graph.pb \
--input_checkpoint=./hospital_models/inception_v3/model.ckpt-5000 \
--input_binary=true \
--output_graph=./hospital_icpv3_frz_graph.pb \
--output_node_names=InceptionV3/Predictions/Reshape_1

IX/ Test your model

Run the code below:

python3 label_image.py \
--graph=./hospital_icpv3_frz_graph.pb \
--labels=./data/hospital/labels.txt \
--image=<PATH_TO_IMAGE>