Added experiment config and dataset preparation script (#11)

* added shell scipt to prepare dataset

* fixed path errors

* added 1x and 3x experiment configs

* fixed formatting

official/vision/beta/data/process_coco_panoptic.sh

+#!/bin/bash
+
+sudo apt update
+sudo apt install unzip aria2 -y
+
+DATA_DIR=$1
+aria2c -j 8 -Z \
+  http://images.cocodataset.org/annotations/annotations_trainval2017.zip \
+  http://images.cocodataset.org/annotations/panoptic_annotations_trainval2017.zip \
+  http://images.cocodataset.org/zips/train2017.zip \
+  http://images.cocodataset.org/zips/val2017.zip \
+  --dir=$DATA_DIR;
+
+unzip $DATA_DIR/"*".zip -d $DATA_DIR;
+mkdir $DATA_DIR/zips && mv $DATA_DIR/*.zip $DATA_DIR/zips;
+unzip $DATA_DIR/annotations/panoptic_train2017.zip -d $DATA_DIR
+unzip $DATA_DIR/annotations/panoptic_val2017.zip -d $DATA_DIR
+
+python3 official/vision/beta/data/create_coco_tf_record.py \
+  --logtostderr  \
+  --image_dir="$DATA_DIR/val2017" \
+  --object_annotations_file="$DATA_DIR/annotations/instances_val2017.json"  \
+  --output_file_prefix="$DATA_DIR/tfrecords/val"  \
+  --panoptic_annotations_file="$DATA_DIR/annotations/panoptic_val2017.json" \
+  --panoptic_masks_dir="$DATA_DIR/panoptic_val2017" \
+  --num_shards=8 \
+  --include_masks \
+  --include_panoptic_masks
+
+
+python3 official/vision/beta/data/create_coco_tf_record.py \
+  --logtostderr  \
+  --image_dir="$DATA_DIR/train2017" \
+  --object_annotations_file="$DATA_DIR/annotations/instances_train2017.json"  \
+  --output_file_prefix="$DATA_DIR/tfrecords/train"  \
+  --panoptic_annotations_file="$DATA_DIR/annotations/panoptic_train2017.json" \
+  --panoptic_masks_dir="$DATA_DIR/panoptic_train2017" \
+  --num_shards=32 \
+  --include_masks \
+  --include_panoptic_masks

official/vision/beta/projects/panoptic_maskrcnn/README.md

@@ -20,58 +20,12 @@ prerequisites.
 $ git clone https://github.com/tensorflow/models.git
 $ cd models
 $ pip3 install -r official/requirements.txt
+$ export PYTHONPATH=$(pwd)
 ```
 
 ## Preparing Dataset
-### Download and extract COCO dataset
 ```bash
-$ sudo apt update
-$ sudo apt install unzip aria2 -y
-
-$ export DATA_DIR=<path-to-store-tfrecords>
-$ aria2c -j 8 -Z \
-  http://images.cocodataset.org/annotations/annotations_trainval2017.zip \
-  http://images.cocodataset.org/annotations/panoptic_annotations_trainval2017.zip \
-  http://images.cocodataset.org/zips/train2017.zip \
-  http://images.cocodataset.org/zips/val2017.zip \
-  --dir=$DATA_DIR;
-
-$ unzip $DATA_DIR/"*".zip -d $DATA_DIR;
-$ mkdir $DATA_DIR/zips && mv $DATA_DIR/*.zip $DATA_DIR/zips;
-$ unzip $DATA_DIR/annotations/panoptic_train2017.zip -d $DATA_DIR
-$ unzip $DATA_DIR/annotations/panoptic_val2017.zip -d $DATA_DIR
-```
-
-### Create TFrecords
-```bash
-$ cd official/vision/beta/data
-
-$ python3 create_coco_tf_record.py \
-  --logtostderr  \
-  --image_dir="$DATA_DIR/val2017" \
-  --object_annotations_file="$DATA_DIR/annotations/instances_val2017.json"  \
-  --output_file_prefix="$DATA_DIR/tfrecords/val"  \
-  --panoptic_annotations_file="$DATA_DIR/annotations/panoptic_val2017.json" \
-  --panoptic_masks_dir="$DATA_DIR/panoptic_val2017" \
-  --num_shards=8 \
-  --include_masks \
-  --include_panoptic_masks
-
-
-$ python3 create_coco_tf_record.py \
-  --logtostderr  \
-  --image_dir="$DATA_DIR/train2017" \
-  --object_annotations_file="$DATA_DIR/annotations/instances_train2017.json"  \
-  --output_file_prefix="$DATA_DIR/tfrecords/train"  \
-  --panoptic_annotations_file="$DATA_DIR/annotations/panoptic_train2017.json" \
-  --panoptic_masks_dir="$DATA_DIR/panoptic_train2017" \
-  --num_shards=32 \
-  --include_masks \
-  --include_panoptic_masks
-```
-### Upload tfrecords to a Google Cloud Storage Bucket
-```bash
-$ gsutil -m cp -r "$DATA_DIR/tfrecords" gs://<bucket-details>
+$ ./official/vision/beta/data/process_coco_panoptic.sh <path-to-data-directory>
 ```
 
 ## Launch Training
@@ -82,9 +36,9 @@ $ export ANNOTATION_FILE="gs://<path-to-coco-annotation-json>"
 $ export TRAIN_DATA="gs://<path-to-train-data>"
 $ export EVAL_DATA="gs://<path-to-eval-data>"
 $ export OVERRIDES="task.validation_data.input_path=${EVAL_DATA},\
-  task.train_data.input_path=${TRAIN_DATA},\
-  task.annotation_file=${ANNOTATION_FILE},\
-  runtime.distribution_strategy=tpu"
+task.train_data.input_path=${TRAIN_DATA},\
+task.annotation_file=${ANNOTATION_FILE},\
+runtime.distribution_strategy=tpu"
 
 
 $ python3 train.py \
@@ -104,11 +58,11 @@ $ export ANNOTATION_FILE="gs://<path-to-coco-annotation-json>"
 $ export TRAIN_DATA="gs://<path-to-train-data>"
 $ export EVAL_DATA="gs://<path-to-eval-data>"
 $ export OVERRIDES="task.validation_data.input_path=${EVAL_DATA}, \
-  task.train_data.input_path=${TRAIN_DATA}, \
-  task.annotation_file=${ANNOTATION_FILE}, \
-  runtime.distribution_strategy=mirrored, \
-  runtime.mixed_precision_dtype=$PRECISION, \
-  runtime.num_gpus=$NUM_GPUS"
+task.train_data.input_path=${TRAIN_DATA}, \
+task.annotation_file=${ANNOTATION_FILE}, \
+runtime.distribution_strategy=mirrored, \
+runtime.mixed_precision_dtype=$PRECISION, \
+runtime.num_gpus=$NUM_GPUS"
 
 
 $ python3 train.py \
@@ -117,7 +71,7 @@ $ python3 train.py \
   --model_dir $MODEL_DIR \
   --params_override=$OVERRIDES
 ```
-**Note**: The [PanopticSegmentationGenerator](https://github.com/tensorflow/models/blob/ac7f9e7f2d0508913947242bad3e23ef7cae5a43/official/vision/beta/projects/panoptic_maskrcnn/modeling/layers/panoptic_segmentation_generator.py#L22) layer uses dynamic shapes and hence generating panoptic masks is not supported on Cloud TPUs. Running evaluation on Cloud TPUs is not supported for the same reson. 
+**Note**: The [PanopticSegmentationGenerator](https://github.com/tensorflow/models/blob/ac7f9e7f2d0508913947242bad3e23ef7cae5a43/official/vision/beta/projects/panoptic_maskrcnn/modeling/layers/panoptic_segmentation_generator.py#L22) layer uses dynamic shapes and hence generating panoptic masks is not supported on Cloud TPUs. Running evaluation on Cloud TPUs is not supported for the same reason. However, training is both supported on Cloud TPUs and GPUs.
 ## Pretrained Models
 Backbone     | Schedule     | Experiment name             | Box mAP |  Mask mAP  | Overall PQ | Things PQ | Stuff PQ | Checkpoints
 :------------| :----------- | :---------------------------| ------- | ---------- | ---------- | --------- | -------- | ------------:
@@ -128,7 +82,6 @@ ResNet-50    | 3x           | `panoptic_fpn_coco`         | 40.64   |   36.29
 
 ___
 ## Citation
-
 ```
 @misc{kirillov2019panoptic,
       title={Panoptic Feature Pyramid Networks}, 

official/vision/beta/projects/panoptic_maskrcnn/configs/experiments/r50fpn_1x_coco.yaml

+runtime:
+  distribution_strategy: 'tpu'
+  mixed_precision_dtype: 'bfloat16'
+
+task:
+  init_checkpoint: 'gs://cloud-tpu-checkpoints/vision-2.0/resnet50_imagenet/ckpt-28080'
+  annotation_file: 'coco/instances_val2017.json'
+  train_data:
+    global_batch_size: 64
+  validation_data:
+    global_batch_size: 8
+
+trainer:
+  train_steps: 22500
+  optimizer_config:
+    learning_rate:
+      type: 'stepwise'
+      stepwise:
+        boundaries: [15000, 20000]
+        values: [0.12, 0.012, 0.0012]
+    warmup:
+      type: 'linear'
+      linear:
+        warmup_steps: 500
+        warmup_learning_rate: 0.0067

official/vision/beta/projects/panoptic_maskrcnn/configs/experiments/r50fpn_3x_coco.yaml

+runtime:
+  distribution_strategy: 'tpu'
+  mixed_precision_dtype: 'bfloat16'
+
+task:
+  init_checkpoint: 'gs://cloud-tpu-checkpoints/vision-2.0/resnet50_imagenet/ckpt-28080'
+  annotation_file: 'coco/instances_val2017.json'
+  train_data:
+    global_batch_size: 64
+  validation_data:
+    global_batch_size: 8
+
+trainer:
+  train_steps: 67500
+  optimizer_config:
+    learning_rate:
+      type: 'stepwise'
+      stepwise:
+        boundaries: [45000, 60000]
+        values: [0.12, 0.012, 0.0012]
+    warmup:
+      type: 'linear'
+      linear:
+        warmup_steps: 500
+        warmup_learning_rate: 0.0067