Merge branch 'tensorflow:master' into panoptic-deeplab

official/projects/assemblenet/train_test.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# Lint as: python3
 import json
 import os
 import random

official/projects/backbone_reuse/README.md

+# Proper Reuse of Image Classification Features Improves Object Detection
+
+Coming soon
+1. CVPR 2022 paper
+2. Table of results
\ No newline at end of file

official/projects/basnet/configs/basnet.py

@@ -20,7 +20,7 @@ from official.core import config_definitions as cfg
 from official.core import exp_factory
 from official.modeling import hyperparams
 from official.modeling import optimization
-from official.vision.beta.configs import common
+from official.vision.configs import common
 
 
 @dataclasses.dataclass

official/projects/basnet/modeling/basnet_model.py

@@ -20,7 +20,7 @@ import tensorflow as tf
 
 from official.modeling import tf_utils
 from official.projects.basnet.modeling import nn_blocks
-from official.vision.beta.modeling.backbones import factory
+from official.vision.modeling.backbones import factory
 
 # Specifications for BASNet encoder.
 # Each element in the block configuration is in the following format:

official/projects/basnet/serving/basnet.py

@@ -17,7 +17,7 @@
 import tensorflow as tf
 
 from official.projects.basnet.tasks import basnet
-from official.vision.beta.serving import semantic_segmentation
+from official.vision.serving import semantic_segmentation
 
 
 MEAN_RGB = (0.485 * 255, 0.456 * 255, 0.406 * 255)

official/projects/basnet/serving/export_saved_model.py

@@ -41,7 +41,7 @@ from absl import flags
 from official.core import exp_factory
 from official.modeling import hyperparams
 from official.projects.basnet.serving import basnet
-from official.vision.beta.serving import export_saved_model_lib
+from official.vision.serving import export_saved_model_lib
 
 FLAGS = flags.FLAGS
 

official/projects/basnet/tasks/basnet.py

@@ -27,7 +27,7 @@ from official.projects.basnet.evaluation import metrics as basnet_metrics
 from official.projects.basnet.losses import basnet_losses
 from official.projects.basnet.modeling import basnet_model
 from official.projects.basnet.modeling import refunet
-from official.vision.beta.dataloaders import segmentation_input
+from official.vision.dataloaders import segmentation_input
 
 
 def build_basnet_model(

official/projects/basnet/train.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# Lint as: python3
 """TensorFlow Model Garden Vision training driver."""
 
 from absl import app
@@ -23,7 +22,7 @@ from official.projects.basnet.configs import basnet as basnet_cfg
 from official.projects.basnet.modeling import basnet_model
 from official.projects.basnet.modeling import refunet
 from official.projects.basnet.tasks import basnet as basenet_task
-from official.vision.beta import train
+from official.vision import train
 
 
 if __name__ == '__main__':

official/projects/const_cl/README.md

+# Contextualized Spatial-Temporal Contrastive Learning with Self-Supervision
+
+(WIP) This repository contains the official implementation of
+[Contextualized Spatio-Temporal Contrastive Learning with Self-Supervision](https://arxiv.org/abs/2112.05181)
+in TF2.

official/projects/deepmac_maskrcnn/modeling/heads/instance_heads_test.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# Lint as: python3
 """Tests for instance_heads.py."""
 
 # Import libraries

official/projects/deepmac_maskrcnn/modeling/maskrcnn_model_test.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# Lint as: python3
 """Tests for maskrcnn_model.py."""
 
 # Import libraries

official/projects/deepmac_maskrcnn/serving/detection.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# Lint as: python3
 """Detection input and model functions for serving/inference."""
 
 from typing import Dict, Mapping, Text

official/projects/deepmac_maskrcnn/serving/detection_test.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# Lint as: python3
 """Test for image detection export lib."""
 
 import io

official/projects/deepmac_maskrcnn/train.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# Lint as: python3
 """TensorFlow Model Garden Vision training driver."""
 
 from absl import app

official/projects/edgetpu/vision/configs/semantic_segmentation_config.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# Lint as: python3
 """Semantic segmentation configuration definition.
 
 The segmentation model is built using the mobilenet edgetpu v2 backbone and

official/projects/edgetpu/vision/modeling/backbones/mobilenet_edgetpu_test.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# Lint as: python3
 """Tests for MobileNet."""
 
 # Import libraries

official/projects/edgetpu/vision/tasks/image_classification_test.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# Lint as: python3
 """Tests for image classification task."""
 
 # pylint: disable=unused-import

official/projects/edgetpu/vision/tasks/semantic_segmentation_test.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# Lint as: python3
 """Tests for semantic segmentation task."""
 
 # pylint: disable=unused-import
@@ -20,12 +19,12 @@ from absl.testing import parameterized
 import orbit
 import tensorflow as tf
 
+from official import vision
 from official.core import exp_factory
 from official.modeling import optimization
 from official.projects.edgetpu.vision.configs import semantic_segmentation_config as seg_cfg
 from official.projects.edgetpu.vision.configs import semantic_segmentation_searched_config as autoseg_cfg
 from official.projects.edgetpu.vision.tasks import semantic_segmentation as img_seg_task
-from official.vision import beta
 
 
 # Dummy ADE20K TF dataset.

official/projects/edgetpu/vision/train.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# Lint as: python3
 """TensorFlow Model Garden Vision training for MobileNet-EdgeTPU."""
 
 from absl import app

official/projects/longformer/README.md

+# Longformer: The Long-Document Transformer
+
+## Modifications from Huggingface's Implementation
+
+All models require a `global_attention_size` specified in the config, setting a
+global attention for all first `global_attention_size` tokens in any sentence.
+Individual different global attention sizes for sentences are not supported.
+This setting allows running on TPUs where tensor sizes have to be determined.
+
+`_get_global_attn_indices` in `longformer_attention.py` contains how the new
+global attention indices are specified. Changed all `tf.cond` to if
+confiditions, since global attention is specified in the start now.
+
+To load weights from a pre-trained huggingface longformer, run
+`utils/convert_pretrained_pytorch_checkpoint_to_tf.py` to create a checkpoint. \
+There is also a `utils/longformer_tokenizer_to_tfrecord.py` that transformers
+pytorch longformer tokenized data to tf_records.
+
+## Steps to Fine-tune on MNLI
+#### Prepare the pre-trained checkpoint
+Option 1. Use our saved checkpoint of `allenai/longformer-base-4096` stored in cloud storage
+
+```bash
+gsutil cp -r gs://model-garden-ucsd-zihan/longformer-4096 .
+```
+Option 2. Create it directly
+
+```bash
+python3 utils/convert_pretrained_pytorch_checkpoint_to_tf.py
+```
+#### [Optional] Prepare the input file
+```bash
+python3 longformer_tokenizer_to_tfrecord.py
+```
+#### Training
+Here, we use the training data of MNLI that were uploaded to the cloud storage, you can replace it with the input files you generated.
+
+```bash
+TRAIN_DATA=task.train_data.input_path=gs://model-garden-ucsd-zihan/longformer_allenai_mnli_train.tf_record,task.validation_data.input_path=gs://model-garden-ucsd-zihan/longformer_allenai_mnli_eval.tf_record
+INIT_CHECKPOINT=longformer-4096/longformer
+PYTHONPATH=/path/to/model/garden \
+    python3 train.py \
+    --experiment=longformer/glue \
+    --config_file=experiments/glue_mnli_allenai.yaml \
+    --params_override="${TRAIN_DATA},runtime.distribution_strategy=tpu,task.init_checkpoint=${INIT_CHECKPOINT}" \
+    --tpu=local \
+    --model_dir=/path/to/outputdir \
+    --mode=train_and_eval
+```
+This should take ~ 3 hours to run, and give a performance of ~86.