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Image Classification

This folder contains TF 2.0 model examples for image classification:

  • MNIST
  • Classifier Trainer, a framework that uses the Keras compile/fit methods for image classification models, including:
    • ResNet
    • EfficientNet1

Before you begin

Please make sure that you have the latest version of TensorFlow installed and add the models folder to your Python path.

ImageNet preparation

Using TFDS

classifier_trainer.py supports ImageNet with TensorFlow Datasets (TFDS).

Please see the following example snippet for more information on how to use TFDS to download and prepare datasets, and specifically the TFDS ImageNet readme for manual download instructions.

Legacy TFRecords

Download the ImageNet dataset and convert it to TFRecord format. The following script and README provide a few options.

Note that the legacy ResNet runners, e.g. resnet/resnet_ctl_imagenet_main.py require TFRecords whereas classifier_trainer.py can use both by setting the builder to 'records' or 'tfds' in the configurations.

Running on Cloud TPUs

Note: These models will not work with TPUs on Colab.

You can train image classification models on Cloud TPUs using tf.distribute.experimental.TPUStrategy. If you are not familiar with Cloud TPUs, it is strongly recommended that you go through the quickstart to learn how to create a TPU and GCE VM.

Running on multiple GPU hosts

You can also train these models on multiple hosts, each with GPUs, using tf.distribute.experimental.MultiWorkerMirroredStrategy.

The easiest way to run multi-host benchmarks is to set the TF_CONFIG appropriately at each host. e.g., to run using MultiWorkerMirroredStrategy on 2 hosts, the cluster in TF_CONFIG should have 2 host:port entries, and host i should have the task in TF_CONFIG set to {"type": "worker", "index": i}. MultiWorkerMirroredStrategy will automatically use all the available GPUs at each host.

MNIST

To download the data and run the MNIST sample model locally for the first time, run one of the following command:

python3 mnist_main.py \
  --model_dir=$MODEL_DIR \
  --data_dir=$DATA_DIR \
  --train_epochs=10 \
  --distribution_strategy=one_device \
  --num_gpus=$NUM_GPUS \
  --download

To train the model on a Cloud TPU, run the following command:

python3 mnist_main.py \
  --tpu=$TPU_NAME \
  --model_dir=$MODEL_DIR \
  --data_dir=$DATA_DIR \
  --train_epochs=10 \
  --distribution_strategy=tpu \
  --download

Note: the --download flag is only required the first time you run the model.

Classifier Trainer

The classifier trainer is a unified framework for running image classification models using Keras's compile/fit methods. Experiments should be provided in the form of YAML files, some examples are included within the configs/examples folder. Please see configs/examples for more example configurations.

The provided configuration files use a per replica batch size and is scaled by the number of devices. For instance, if batch size = 64, then for 1 GPU the global batch size would be 64 * 1 = 64. For 8 GPUs, the global batch size would be 64 * 8 = 512. Similarly, for a v3-8 TPU, the global batch size would be 64 * 8 = 512, and for a v3-32, the global batch size is 64 * 32 = 2048.

ResNet50

On GPU:

python3 classifier_trainer.py \
  --mode=train_and_eval \
  --model_type=resnet \
  --dataset=imagenet \
  --model_dir=$MODEL_DIR \
  --data_dir=$DATA_DIR \
  --config_file=configs/examples/resnet/imagenet/gpu.yaml \
  --params_override='runtime.num_gpus=$NUM_GPUS'

To train on multiple hosts, each with GPUs attached using MultiWorkerMirroredStrategy please update runtime section in gpu.yaml (or override using --params_override) with:

# gpu.yaml
runtime:
  distribution_strategy: 'multi_worker_mirrored'
  worker_hosts: '$HOST1:port,$HOST2:port'
  num_gpus: $NUM_GPUS
  task_index: 0

By having task_index: 0 on the first host and task_index: 1 on the second and so on. $HOST1 and $HOST2 are the IP addresses of the hosts, and port can be chosen any free port on the hosts. Only the first host will write TensorBoard Summaries and save checkpoints.

On TPU:

python3 classifier_trainer.py \
  --mode=train_and_eval \
  --model_type=resnet \
  --dataset=imagenet \
  --tpu=$TPU_NAME \
  --model_dir=$MODEL_DIR \
  --data_dir=$DATA_DIR \
  --config_file=configs/examples/resnet/imagenet/tpu.yaml

EfficientNet

Note: EfficientNet development is a work in progress.

On GPU:

python3 classifier_trainer.py \
  --mode=train_and_eval \
  --model_type=efficientnet \
  --dataset=imagenet \
  --model_dir=$MODEL_DIR \
  --data_dir=$DATA_DIR \
  --config_file=configs/examples/efficientnet/imagenet/efficientnet-b0-gpu.yaml \
  --params_override='runtime.num_gpus=$NUM_GPUS'

On TPU:

python3 classifier_trainer.py \
  --mode=train_and_eval \
  --model_type=efficientnet \
  --dataset=imagenet \
  --tpu=$TPU_NAME \
  --model_dir=$MODEL_DIR \
  --data_dir=$DATA_DIR \
  --config_file=configs/examples/efficientnet/imagenet/efficientnet-b0-tpu.yaml

Note that the number of GPU devices can be overridden in the command line using --params_overrides. The TPU does not need this override as the device is fixed by providing the TPU address or name with the --tpu flag.

Footnotes

  1. Currently a work in progress. We cannot match "AutoAugment (AA)" in the original version. For more information about other types of models, please refer to this README file. ↩