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

official/vision/beta/projects/panoptic_maskrcnn/tasks/panoptic_maskrcnn.py

@@ -13,20 +13,21 @@
 # limitations under the License.
 
 """Panoptic MaskRCNN task definition."""
-from typing import Any, List, Mapping, Optional, Tuple, Dict
+from typing import Any, Dict, List, Mapping, Optional, Tuple
+
 from absl import logging
 import tensorflow as tf
 
 from official.common import dataset_fn
 from official.core import task_factory
-from official.vision.beta.dataloaders import input_reader_factory
-from official.vision.beta.evaluation import panoptic_quality_evaluator
-from official.vision.beta.evaluation import segmentation_metrics
-from official.vision.beta.losses import segmentation_losses
 from official.vision.beta.projects.panoptic_maskrcnn.configs import panoptic_maskrcnn as exp_cfg
 from official.vision.beta.projects.panoptic_maskrcnn.dataloaders import panoptic_maskrcnn_input
 from official.vision.beta.projects.panoptic_maskrcnn.modeling import factory
-from official.vision.beta.tasks import maskrcnn
+from official.vision.dataloaders import input_reader_factory
+from official.vision.evaluation import panoptic_quality_evaluator
+from official.vision.evaluation import segmentation_metrics
+from official.vision.losses import segmentation_losses
+from official.vision.tasks import maskrcnn
 
 
 @task_factory.register_task_cls(exp_cfg.PanopticMaskRCNNTask)

official/vision/beta/projects/panoptic_maskrcnn/tasks/panoptic_maskrcnn_test.py

@@ -18,10 +18,10 @@ import os
 from absl.testing import parameterized
 import tensorflow as tf
 
-from official.vision.beta.configs import decoders as decoder_cfg
-from official.vision.beta.configs import semantic_segmentation as segmentation_cfg
 from official.vision.beta.projects.panoptic_maskrcnn.configs import panoptic_maskrcnn as cfg
 from official.vision.beta.projects.panoptic_maskrcnn.tasks import panoptic_maskrcnn
+from official.vision.configs import decoders as decoder_cfg
+from official.vision.configs import semantic_segmentation as segmentation_cfg
 
 
 class PanopticMaskRCNNTaskTest(tf.test.TestCase, parameterized.TestCase):

official/vision/beta/projects/panoptic_maskrcnn/train.py

@@ -17,7 +17,7 @@
 from absl import app
 
 from official.common import flags as tfm_flags
-from official.vision.beta import train
+from official.vision import train
 from official.vision.beta.projects.panoptic_maskrcnn.configs import panoptic_maskrcnn as cfg  # pylint: disable=unused-import
 from official.vision.beta.projects.panoptic_maskrcnn.tasks import panoptic_maskrcnn as task  # pylint: disable=unused-import
 

official/vision/beta/projects/simclr/common/registry_imports.py

@@ -15,7 +15,7 @@
 """All necessary imports for registration."""
 
 # pylint: disable=unused-import
-from official.common import registry_imports
+from official.vision import registry_imports
 from official.vision.beta.projects.simclr.configs import simclr
 from official.vision.beta.projects.simclr.losses import contrastive_losses
 from official.vision.beta.projects.simclr.modeling import simclr_model

official/vision/beta/projects/simclr/configs/multitask_config.py

@@ -20,10 +20,10 @@ from typing import List, Tuple
 from official.core import exp_factory
 from official.modeling import hyperparams
 from official.modeling.multitask import configs as multitask_configs
-from official.vision.beta.configs import backbones
-from official.vision.beta.configs import common
 from official.vision.beta.projects.simclr.configs import simclr as simclr_configs
 from official.vision.beta.projects.simclr.modeling import simclr_model
+from official.vision.configs import backbones
+from official.vision.configs import common
 
 
 @dataclasses.dataclass

official/vision/beta/projects/simclr/configs/simclr.py

@@ -21,9 +21,9 @@ 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 backbones
-from official.vision.beta.configs import common
 from official.vision.beta.projects.simclr.modeling import simclr_model
+from official.vision.configs import backbones
+from official.vision.configs import common
 
 
 @dataclasses.dataclass

official/vision/beta/projects/simclr/dataloaders/simclr_input.py

@@ -40,11 +40,11 @@ from typing import List
 
 import tensorflow as tf
 
-from official.vision.beta.dataloaders import decoder
-from official.vision.beta.dataloaders import parser
-from official.vision.beta.ops import preprocess_ops
 from official.vision.beta.projects.simclr.dataloaders import preprocess_ops as simclr_preprocess_ops
 from official.vision.beta.projects.simclr.modeling import simclr_model
+from official.vision.dataloaders import decoder
+from official.vision.dataloaders import parser
+from official.vision.ops import preprocess_ops
 
 
 class Decoder(decoder.Decoder):

official/vision/beta/projects/simclr/modeling/multitask_model.py

@@ -14,15 +14,15 @@
 
 """Multi-task image multi-taskSimCLR model definition."""
 from typing import Dict, Text
-from absl import logging
 
+from absl import logging
 import tensorflow as tf
 
 from official.modeling.multitask import base_model
-from official.vision.beta.modeling import backbones
 from official.vision.beta.projects.simclr.configs import multitask_config as simclr_multitask_config
 from official.vision.beta.projects.simclr.heads import simclr_head
 from official.vision.beta.projects.simclr.modeling import simclr_model
+from official.vision.modeling import backbones
 
 PROJECTION_OUTPUT_KEY = 'projection_outputs'
 SUPERVISED_OUTPUT_KEY = 'supervised_outputs'
@@ -110,7 +110,8 @@ class SimCLRMTModel(base_model.MultiTaskBaseModel):
       pretrained_items = dict(
           backbone=self._backbone, projection_head=self._projection_head)
     else:
-      assert ("Only 'backbone_projection' or 'backbone' can be used to "
+      raise ValueError(
+          "Only 'backbone_projection' or 'backbone' can be used to "
           'initialize the model.')
 
     ckpt = tf.train.Checkpoint(**pretrained_items)

official/vision/beta/projects/simclr/modeling/simclr_model_test.py

@@ -14,13 +14,12 @@
 
 """Test for SimCLR model."""
 from absl.testing import parameterized
-
 import numpy as np
 import tensorflow as tf
 
-from official.vision.beta.modeling import backbones
 from official.vision.beta.projects.simclr.heads import simclr_head
 from official.vision.beta.projects.simclr.modeling import simclr_model
+from official.vision.modeling import backbones
 
 
 class SimCLRModelTest(parameterized.TestCase, tf.test.TestCase):

official/vision/beta/projects/simclr/tasks/simclr.py

@@ -36,12 +36,12 @@ from official.core import task_factory
 from official.modeling import optimization
 from official.modeling import performance
 from official.modeling import tf_utils
-from official.vision.beta.modeling import backbones
 from official.vision.beta.projects.simclr.configs import simclr as exp_cfg
 from official.vision.beta.projects.simclr.dataloaders import simclr_input
 from official.vision.beta.projects.simclr.heads import simclr_head
 from official.vision.beta.projects.simclr.losses import contrastive_losses
 from official.vision.beta.projects.simclr.modeling import simclr_model
+from official.vision.modeling import backbones
 
 OptimizationConfig = optimization.OptimizationConfig
 RuntimeConfig = config_definitions.RuntimeConfig
@@ -157,7 +157,8 @@ class SimCLRPretrainTask(base_task.Task):
       status = ckpt.read(ckpt_dir_or_file)
       status.expect_partial().assert_existing_objects_matched()
     else:
-      assert "Only 'all' or 'backbone' can be used to initialize the model."
+      raise ValueError(
+          "Only 'all' or 'backbone' can be used to initialize the model.")
 
     logging.info('Finished loading pretrained checkpoint from %s',
                  ckpt_dir_or_file)
@@ -335,7 +336,8 @@ class SimCLRPretrainTask(base_task.Task):
 
   def validation_step(self, inputs, model, metrics=None):
     if self.task_config.model.supervised_head is None:
-      assert 'Skipping eval during pretraining without supervised head.'
+      raise ValueError(
+          'Skipping eval during pretraining without supervised head.')
 
     features, labels = inputs
     if self.task_config.evaluation.one_hot:
@@ -467,7 +469,8 @@ class SimCLRFinetuneTask(base_task.Task):
       status = ckpt.read(ckpt_dir_or_file)
       status.expect_partial().assert_existing_objects_matched()
     else:
-      assert "Only 'all' or 'backbone' can be used to initialize the model."
+      raise ValueError(
+          "Only 'all' or 'backbone' can be used to initialize the model.")
 
     # If the checkpoint is from pretraining, reset the following parameters
     model.backbone_trainable = self.task_config.model.backbone_trainable

official/vision/beta/projects/yolo/common/__init__.py

+# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+

official/vision/modeling/layers/nn_blocks.py

@@ -714,7 +714,8 @@ class InvertedBottleneckBlock(tf.keras.layers.Layer):
         'use_depthwise': self._use_depthwise,
         'use_residual': self._use_residual,
         'norm_momentum': self._norm_momentum,
-        'norm_epsilon': self._norm_epsilon
+        'norm_epsilon': self._norm_epsilon,
+        'output_intermediate_endpoints': self._output_intermediate_endpoints
     }
     base_config = super(InvertedBottleneckBlock, self).get_config()
     return dict(list(base_config.items()) + list(config.items()))