Internal change

PiperOrigin-RevId: 407843815

official/vision/__init__.py

-# Copyright 2021 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/beta/evaluation/segmentation_metrics.py

@@ -128,7 +128,7 @@ class MeanIoU(tf.keras.metrics.MeanIoU):
 class PerClassIoU(iou.PerClassIoU):
   """Per Class IoU metric for semantic segmentation.
 
-  This class utilizes keras_cv.metrics.PerClassIoU to perform batched per class
+  This class utilizes iou.PerClassIoU to perform batched per class
   iou when both input images and groundtruth masks are resized to the same size
   (rescale_predictions=False). It also computes per class iou on groundtruth
   original sizes, in which case, each prediction is rescaled back to the

official/vision/beta/modeling/layers/deeplab.py

@@ -17,7 +17,6 @@
 import tensorflow as tf
 
 
-@tf.keras.utils.register_keras_serializable(package='keras_cv')
 class SpatialPyramidPooling(tf.keras.layers.Layer):
   """Implements the Atrous Spatial Pyramid Pooling.
 

official/vision/beta/modeling/layers/roi_sampler.py

@@ -14,10 +14,13 @@
 
 """Contains definitions of ROI sampler."""
 # Import libraries
+
 import tensorflow as tf
 
-from official.vision import keras_cv
 from official.vision.beta.modeling.layers import box_sampler
+from official.vision.beta.ops import box_matcher
+from official.vision.beta.ops import iou_similarity
+from official.vision.beta.ops import target_gather
 
 
 @tf.keras.utils.register_keras_serializable(package='Vision')
@@ -64,14 +67,14 @@ class ROISampler(tf.keras.layers.Layer):
         'skip_subsampling': skip_subsampling,
     }
 
-    self._sim_calc = keras_cv.ops.IouSimilarity()
-    self._box_matcher = keras_cv.ops.BoxMatcher(
+    self._sim_calc = iou_similarity.IouSimilarity()
+    self._box_matcher = box_matcher.BoxMatcher(
         thresholds=[
             background_iou_low_threshold, background_iou_high_threshold,
             foreground_iou_threshold
         ],
         indicators=[-3, -1, -2, 1])
-    self._target_gather = keras_cv.ops.TargetGather()
+    self._target_gather = target_gather.TargetGather()
 
     self._sampler = box_sampler.BoxSampler(
         num_sampled_rois, foreground_fraction)

official/vision/beta/ops/anchor.py

@@ -15,9 +15,15 @@
 """Anchor box and labeler definition."""
 
 import collections
+
 # Import libraries
+
 import tensorflow as tf
-from official.vision import keras_cv
+
+from official.vision.beta.ops import anchor_generator
+from official.vision.beta.ops import box_matcher
+from official.vision.beta.ops import iou_similarity
+from official.vision.beta.ops import target_gather
 from official.vision.detection.utils.object_detection import balanced_positive_negative_sampler
 from official.vision.detection.utils.object_detection import box_list
 from official.vision.detection.utils.object_detection import faster_rcnn_box_coder
@@ -132,9 +138,9 @@ class AnchorLabeler(object):
         upper-bound threshold to assign negative labels for anchors. An anchor
         with a score below the threshold is labeled negative.
     """
-    self.similarity_calc = keras_cv.ops.IouSimilarity()
-    self.target_gather = keras_cv.ops.TargetGather()
-    self.matcher = keras_cv.ops.BoxMatcher(
+    self.similarity_calc = iou_similarity.IouSimilarity()
+    self.target_gather = target_gather.TargetGather()
+    self.matcher = box_matcher.BoxMatcher(
         thresholds=[unmatched_threshold, match_threshold],
         indicators=[-1, -2, 1],
         force_match_for_each_col=True)
@@ -343,7 +349,7 @@ def build_anchor_generator(min_level, max_level, num_scales, aspect_ratios,
     stride = 2**level
     strides[str(level)] = stride
     anchor_sizes[str(level)] = anchor_size * stride
-  anchor_gen = keras_cv.ops.AnchorGenerator(
+  anchor_gen = anchor_generator.AnchorGenerator(
       anchor_sizes=anchor_sizes,
       scales=scales,
       aspect_ratios=aspect_ratios,

official/vision/keras_cv/ops/anchor_generator_test.py → official/vision/beta/ops/anchor_generator_test.py

@@ -16,7 +16,7 @@
 
 from absl.testing import parameterized
 import tensorflow as tf
-from official.vision.keras_cv.ops import anchor_generator
+from official.vision.beta.ops import anchor_generator
 
 
 class AnchorGeneratorTest(parameterized.TestCase, tf.test.TestCase):

official/vision/keras_cv/ops/box_matcher_test.py → official/vision/beta/ops/box_matcher_test.py

@@ -16,7 +16,7 @@
 
 import tensorflow as tf
 
-from official.vision.keras_cv.ops import box_matcher
+from official.vision.beta.ops import box_matcher
 
 
 class BoxMatcherTest(tf.test.TestCase):

official/vision/keras_cv/ops/iou_similarity_test.py → official/vision/beta/ops/iou_similarity_test.py

@@ -16,7 +16,7 @@
 
 import tensorflow as tf
 
-from official.vision.keras_cv.ops import iou_similarity
+from official.vision.beta.ops import iou_similarity
 
 
 class BoxMatcherTest(tf.test.TestCase):

official/vision/keras_cv/ops/target_gather_test.py → official/vision/beta/ops/target_gather_test.py

@@ -16,7 +16,7 @@
 
 import tensorflow as tf
 
-from official.vision.keras_cv.ops import target_gather
+from official.vision.beta.ops import target_gather
 
 
 class TargetGatherTest(tf.test.TestCase):

official/vision/beta/tasks/retinanet.py

@@ -13,14 +13,14 @@
 # limitations under the License.
 
 """RetinaNet task definition."""
-from typing import Any, Optional, List, Tuple, Mapping
+from typing import Any, List, Mapping, Optional, Tuple
 
 from absl import logging
 import tensorflow as tf
+
 from official.common import dataset_fn
 from official.core import base_task
 from official.core import task_factory
-from official.vision import keras_cv
 from official.vision.beta.configs import retinanet as exp_cfg
 from official.vision.beta.dataloaders import input_reader_factory
 from official.vision.beta.dataloaders import retinanet_input
@@ -28,6 +28,8 @@ from official.vision.beta.dataloaders import tf_example_decoder
 from official.vision.beta.dataloaders import tfds_factory
 from official.vision.beta.dataloaders import tf_example_label_map_decoder
 from official.vision.beta.evaluation import coco_evaluator
+from official.vision.beta.losses import focal_loss
+from official.vision.beta.losses import loss_utils
 from official.vision.beta.modeling import factory
 
 
@@ -155,9 +157,9 @@ class RetinaNetTask(base_task.Task):
       if head.name not in outputs['attribute_outputs']:
         raise ValueError(f'Attribute {head.name} not found in model outputs.')
 
-      y_true_att = keras_cv.losses.multi_level_flatten(
+      y_true_att = loss_utils.multi_level_flatten(
           labels['attribute_targets'][head.name], last_dim=head.size)
-      y_pred_att = keras_cv.losses.multi_level_flatten(
+      y_pred_att = loss_utils.multi_level_flatten(
           outputs['attribute_outputs'][head.name], last_dim=head.size)
       if head.type == 'regression':
         att_loss_fn = tf.keras.losses.Huber(
@@ -180,7 +182,7 @@ class RetinaNetTask(base_task.Task):
     params = self.task_config
     attribute_heads = self.task_config.model.head.attribute_heads
 
-    cls_loss_fn = keras_cv.losses.FocalLoss(
+    cls_loss_fn = focal_loss.FocalLoss(
         alpha=params.losses.focal_loss_alpha,
         gamma=params.losses.focal_loss_gamma,
         reduction=tf.keras.losses.Reduction.SUM)
@@ -194,14 +196,14 @@ class RetinaNetTask(base_task.Task):
     num_positives = tf.reduce_sum(box_sample_weight) + 1.0
     cls_sample_weight = cls_sample_weight / num_positives
     box_sample_weight = box_sample_weight / num_positives
-    y_true_cls = keras_cv.losses.multi_level_flatten(
+    y_true_cls = loss_utils.multi_level_flatten(
         labels['cls_targets'], last_dim=None)
     y_true_cls = tf.one_hot(y_true_cls, params.model.num_classes)
-    y_pred_cls = keras_cv.losses.multi_level_flatten(
+    y_pred_cls = loss_utils.multi_level_flatten(
         outputs['cls_outputs'], last_dim=params.model.num_classes)
-    y_true_box = keras_cv.losses.multi_level_flatten(
+    y_true_box = loss_utils.multi_level_flatten(
         labels['box_targets'], last_dim=4)
-    y_pred_box = keras_cv.losses.multi_level_flatten(
+    y_pred_box = loss_utils.multi_level_flatten(
         outputs['box_outputs'], last_dim=4)
 
     cls_loss = cls_loss_fn(

official/vision/detection/dataloader/anchor.py

@@ -21,7 +21,7 @@ from __future__ import print_function
 import collections
 
 import tensorflow as tf
-from official.vision import keras_cv
+from official.vision.beta.ops import iou_similarity
 from official.vision.detection.utils import box_utils
 from official.vision.detection.utils.object_detection import argmax_matcher
 from official.vision.detection.utils.object_detection import balanced_positive_negative_sampler
@@ -135,7 +135,7 @@ class AnchorLabeler(object):
         upper-bound threshold to assign negative labels for anchors. An anchor
         with a score below the threshold is labeled negative.
     """
-    similarity_calc = keras_cv.ops.IouSimilarity()
+    similarity_calc = iou_similarity.IouSimilarity()
     matcher = argmax_matcher.ArgMaxMatcher(
         match_threshold,
         unmatched_threshold=unmatched_threshold,
@@ -341,7 +341,7 @@ class OlnAnchorLabeler(RpnAnchorLabeler):
         unmatched_threshold=unmatched_threshold,
         rpn_batch_size_per_im=rpn_batch_size_per_im,
         rpn_fg_fraction=rpn_fg_fraction)
-    similarity_calc = keras_cv.ops.IouSimilarity()
+    similarity_calc = iou_similarity.IouSimilarity()
     matcher = argmax_matcher.ArgMaxMatcher(
         match_threshold,
         unmatched_threshold=unmatched_threshold,

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official/vision/keras_cv/README.md

-# keras-cv
-
-## Losses
-
-*   [FocalLoss](losses/focal_loss.py) implements Focal loss as described in
-    ["Focal Loss for Dense Object Detection"](https://arxiv.org/abs/1708.02002).
-
-
-## Ops
-
-Ops are used in data pipeline for pre-compute labels, weights.
-
-*   [IOUSimilarity](ops/iou_similarity.py) implements Intersection-Over-Union.

official/vision/keras_cv/__init__.py

-# Copyright 2021 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.
-
-"""Keras-CV package definition."""
-# pylint: disable=wildcard-import
-from official.vision.keras_cv import losses
-from official.vision.keras_cv import ops