Internal change.

PiperOrigin-RevId: 421362994

Author: fyangf

official/vision/image_classification/README.md

@@ -1,185 +1,3 @@
-# Image Classification
-
-**Warning:** the features in the `image_classification/` folder have been fully
-intergrated into vision/beta. Please use the [new code base](../beta/README.md).
-
-This folder contains TF 2.0 model examples for image classification:
-
-* [MNIST](#mnist)
-* [Classifier Trainer](#classifier-trainer), a framework that uses the Keras
-compile/fit methods for image classification models, including:
-  * ResNet
-  * EfficientNet[^1]
-
-[^1]: Currently a work in progress. We cannot match "AutoAugment (AA)" in [the original version](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet).
-For more information about other types of models, please refer to this
-[README file](../../README.md).
-
-## Before you begin
-Please make sure that you have the latest version of TensorFlow
-installed and
-[add the models folder to your Python path](/official/#running-the-models).
-
-### ImageNet preparation
-
-#### Using TFDS
-`classifier_trainer.py` supports ImageNet with
-[TensorFlow Datasets (TFDS)](https://www.tensorflow.org/datasets/overview).
-
-Please see the following [example snippet](https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/scripts/download_and_prepare.py)
-for more information on how to use TFDS to download and prepare datasets, and
-specifically the [TFDS ImageNet readme](https://github.com/tensorflow/datasets/blob/master/docs/catalog/imagenet2012.md)
-for manual download instructions.
-
-#### Legacy TFRecords
-Download the ImageNet dataset and convert it to TFRecord format.
-The following [script](https://github.com/tensorflow/tpu/blob/master/tools/datasets/imagenet_to_gcs.py)
-and [README](https://github.com/tensorflow/tpu/tree/master/tools/datasets#imagenet_to_gcspy)
-provide a few options.
-
-Note that the legacy ResNet runners, e.g. [resnet/resnet_ctl_imagenet_main.py](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.TPUStrategy](https://www.tensorflow.org/api_docs/python/tf.distribute.TPUStrategy?version=nightly).
-If you are not familiar with Cloud TPUs, it is strongly recommended that you go
-through the
-[quickstart](https://cloud.google.com/tpu/docs/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](https://www.tensorflow.org/api_docs/python/tf/distribute/experimental/MultiWorkerMirroredStrategy).
-
-The easiest way to run multi-host benchmarks is to set the
-[`TF_CONFIG`](https://www.tensorflow.org/guide/distributed_training#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:
-
-```bash
-python3 mnist_main.py \
-  --model_dir=$MODEL_DIR \
-  --data_dir=$DATA_DIR \
-  --train_epochs=10 \
-  --distribution_strategy=one_device \
-  --num_gpus=$NUM_GPUS \
-  --download
-```
-
-To train the model on a Cloud TPU, run the following command:
-
-```bash
-python3 mnist_main.py \
-  --tpu=$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](./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:
-```bash
-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=$NUM_GPUS'
-```
-
-To train on multiple hosts, each with GPUs attached using
-[MultiWorkerMirroredStrategy](https://www.tensorflow.org/api_docs/python/tf/distribute/experimental/MultiWorkerMirroredStrategy)
-please update `runtime` section in gpu.yaml
-(or override using `--params_override`) with:
-
-```YAML
-# gpu.yaml
-runtime:
-  distribution_strategy: 'multi_worker_mirrored'
-  worker_hosts: '$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 `$HOST2` are the IP addresses of the hosts, and `port`
-can be chosen any free port on the hosts. Only the first host will write
-TensorBoard Summaries and save checkpoints.
-
-#### On TPU:
-```bash
-python3 classifier_trainer.py \
-  --mode=train_and_eval \
-  --model_type=resnet \
-  --dataset=imagenet \
-  --tpu=$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:
-```bash
-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=$NUM_GPUS'
-```
-
-
-#### On TPU:
-```bash
-python3 classifier_trainer.py \
-  --mode=train_and_eval \
-  --model_type=efficientnet \
-  --dataset=imagenet \
-  --tpu=$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.
-
+This repository is deprecated and replaced by the solid
+implementations inside vision/beta/. All the content has been moved to
+[official/legacy/image_classification](https://github.com/tensorflow/models/tree/master/official/legacy/image_classification).

official/vision/image_classification/__init__.py

@@ -12,3 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+"""Deprecating the vision/detection folder."""
+raise ImportError(
+    'This module has been moved to official/legacy/image_classification')