Merge branch 'panoptic-segmentation' into panoptic-deeplab-modeling

official/vision/detection/ops/nms.py → official/legacy/detection/ops/nms.py

@@ -20,7 +20,7 @@ from __future__ import print_function
 
 import tensorflow as tf
 
-from official.vision.detection.utils import box_utils
+from official.legacy.detection.utils import box_utils
 
 NMS_TILE_SIZE = 512
 

official/vision/detection/ops/postprocess_ops.py → official/legacy/detection/ops/postprocess_ops.py

@@ -22,8 +22,8 @@ import functools
 
 import tensorflow as tf
 
-from official.vision.detection.ops import nms
-from official.vision.detection.utils import box_utils
+from official.legacy.detection.ops import nms
+from official.legacy.detection.utils import box_utils
 
 
 def generate_detections_factory(params):

official/vision/detection/ops/roi_ops.py → official/legacy/detection/ops/roi_ops.py

@@ -20,8 +20,8 @@ from __future__ import print_function
 
 import tensorflow as tf
 
-from official.vision.detection.ops import nms
-from official.vision.detection.utils import box_utils
+from official.legacy.detection.ops import nms
+from official.legacy.detection.utils import box_utils
 
 
 def multilevel_propose_rois(rpn_boxes,

official/vision/detection/ops/target_ops.py → official/legacy/detection/ops/target_ops.py

@@ -20,8 +20,8 @@ from __future__ import print_function
 
 import tensorflow as tf
 
-from official.vision.detection.ops import spatial_transform_ops
-from official.vision.detection.utils import box_utils
+from official.legacy.detection.ops import spatial_transform_ops
+from official.legacy.detection.utils import box_utils
 from official.vision.utils.object_detection import balanced_positive_negative_sampler
 
 

official/vision/detection/utils/dataloader_utils.py → official/legacy/detection/utils/dataloader_utils.py

@@ -16,7 +16,7 @@
 
 import tensorflow as tf
 
-from official.vision.detection.utils import input_utils
+from official.legacy.detection.utils import input_utils
 
 
 def process_source_id(source_id):

official/vision/detection/utils/input_utils.py → official/legacy/detection/utils/input_utils.py

@@ -18,7 +18,7 @@ import math
 
 import tensorflow as tf
 
-from official.vision.detection.utils import box_utils
+from official.legacy.detection.utils import box_utils
 from official.vision.utils.object_detection import preprocessor
 
 

official/legacy/image_classification/README.md

+# Image Classification
+
+**Warning:** the features in the `image_classification/` folder have been fully
+integrated into vision/beta. Please use the [new code base](../../vision/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.
+

official/vision/image_classification/augment_test.py → official/legacy/image_classification/augment_test.py

@@ -22,7 +22,7 @@ from absl.testing import parameterized
 
 import tensorflow as tf
 
-from official.vision.image_classification import augment
+from official.legacy.image_classification import augment
 
 
 def get_dtype_test_cases():

official/vision/image_classification/classifier_trainer.py → official/legacy/image_classification/classifier_trainer.py

@@ -17,25 +17,25 @@
 
 import os
 import pprint
-from typing import Any, Tuple, Text, Optional, Mapping
+from typing import Any, Mapping, Optional, Text, Tuple
 
 from absl import app
 from absl import flags
 from absl import logging
 import tensorflow as tf
 from official.common import distribute_utils
+from official.legacy.image_classification import callbacks as custom_callbacks
+from official.legacy.image_classification import dataset_factory
+from official.legacy.image_classification import optimizer_factory
+from official.legacy.image_classification.configs import base_configs
+from official.legacy.image_classification.configs import configs
+from official.legacy.image_classification.efficientnet import efficientnet_model
+from official.legacy.image_classification.resnet import common
+from official.legacy.image_classification.resnet import resnet_model
 from official.modeling import hyperparams
 from official.modeling import performance
 from official.utils import hyperparams_flags
 from official.utils.misc import keras_utils
-from official.vision.image_classification import callbacks as custom_callbacks
-from official.vision.image_classification import dataset_factory
-from official.vision.image_classification import optimizer_factory
-from official.vision.image_classification.configs import base_configs
-from official.vision.image_classification.configs import configs
-from official.vision.image_classification.efficientnet import efficientnet_model
-from official.vision.image_classification.resnet import common
-from official.vision.image_classification.resnet import resnet_model
 
 
 def get_models() -> Mapping[str, tf.keras.Model]:

official/vision/image_classification/classifier_trainer_test.py → official/legacy/image_classification/classifier_trainer_test.py

@@ -34,8 +34,8 @@ import tensorflow as tf
 
 from tensorflow.python.distribute import combinations
 from tensorflow.python.distribute import strategy_combinations
+from official.legacy.image_classification import classifier_trainer
 from official.utils.flags import core as flags_core
-from official.vision.image_classification import classifier_trainer
 
 
 classifier_trainer.define_classifier_flags()

official/vision/image_classification/classifier_trainer_util_test.py → official/legacy/image_classification/classifier_trainer_util_test.py

@@ -25,10 +25,10 @@ import os
 from absl.testing import parameterized
 import tensorflow as tf
 
-from official.vision.image_classification import classifier_trainer
-from official.vision.image_classification import dataset_factory
-from official.vision.image_classification import test_utils
-from official.vision.image_classification.configs import base_configs
+from official.legacy.image_classification import classifier_trainer
+from official.legacy.image_classification import dataset_factory
+from official.legacy.image_classification import test_utils
+from official.legacy.image_classification.configs import base_configs
 
 
 def get_trivial_model(num_classes: int) -> tf.keras.Model: