Merge branch 'tensorflow:master' into panoptic-deeplab

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

@@ -22,10 +22,10 @@ import numpy as np
 from official.core import config_definitions as cfg
 from official.core import exp_factory
 from official.modeling import hyperparams
-from official.vision.beta.configs import common
 from official.vision.beta.projects.yolo import optimization
 from official.vision.beta.projects.yolo.configs import backbones
 from official.vision.beta.projects.yolo.configs import decoders
+from official.vision.configs import common
 
 
 # pytype: disable=annotation-type-mismatch

official/vision/beta/projects/yolo/dataloaders/classification_input.py

@@ -14,8 +14,8 @@
 
 """Classification decoder and parser."""
 import tensorflow as tf
-from official.vision.beta.dataloaders import classification_input
-from official.vision.beta.ops import preprocess_ops
+from official.vision.dataloaders import classification_input
+from official.vision.ops import preprocess_ops
 
 
 class Parser(classification_input.Parser):

official/vision/beta/projects/yolo/dataloaders/tf_example_decoder.py

@@ -19,7 +19,7 @@ protos for object detection.
 """
 import tensorflow as tf
 
-from official.vision.beta.dataloaders import tf_example_decoder
+from official.vision.dataloaders import tf_example_decoder
 
 
 def _coco91_to_80(classif, box, areas, iscrowds):

official/vision/beta/projects/yolo/dataloaders/yolo_input.py

@@ -15,12 +15,12 @@
 """Detection Data parser and processing for YOLO."""
 import tensorflow as tf
 
-from official.vision.beta.dataloaders import parser
-from official.vision.beta.dataloaders import utils
-from official.vision.beta.ops import box_ops as bbox_ops
-from official.vision.beta.ops import preprocess_ops
 from official.vision.beta.projects.yolo.ops import anchor
 from official.vision.beta.projects.yolo.ops import preprocessing_ops
+from official.vision.dataloaders import parser
+from official.vision.dataloaders import utils
+from official.vision.ops import box_ops as bbox_ops
+from official.vision.ops import preprocess_ops
 
 
 class Parser(parser.Parser):

official/vision/beta/projects/yolo/modeling/backbones/darknet.py

@@ -36,11 +36,12 @@ Darknets are used mainly for object detection in:
 """
 
 import collections
+
 import tensorflow as tf
 
 from official.modeling import hyperparams
-from official.vision.beta.modeling.backbones import factory
 from official.vision.beta.projects.yolo.modeling.layers import nn_blocks
+from official.vision.modeling.backbones import factory
 
 
 class BlockConfig:

official/vision/beta/projects/yolo/modeling/backbones/darknet_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 yolo."""
 
 from absl.testing import parameterized

official/vision/beta/projects/yolo/modeling/decoders/yolo_decoder.py

@@ -13,13 +13,13 @@
 # limitations under the License.
 
 """Feature Pyramid Network and Path Aggregation variants used in YOLO."""
-from typing import Mapping, Union, Optional
+from typing import Mapping, Optional, Union
 
 import tensorflow as tf
 
 from official.modeling import hyperparams
-from official.vision.beta.modeling.decoders import factory
 from official.vision.beta.projects.yolo.modeling.layers import nn_blocks
+from official.vision.modeling.decoders import factory
 
 # model configurations
 # the structure is as follows. model version, {v3, v4, v#, ... etc}

official/vision/beta/projects/yolo/modeling/decoders/yolo_decoder_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 YOLO."""
 
 # Import libraries

official/vision/beta/projects/yolo/modeling/factory.py

@@ -15,13 +15,13 @@
 """Contains common factory functions yolo neural networks."""
 
 from absl import logging
-from official.vision.beta.modeling.backbones import factory as backbone_factory
-from official.vision.beta.modeling.decoders import factory as decoder_factory
 
 from official.vision.beta.projects.yolo.configs import yolo
 from official.vision.beta.projects.yolo.modeling import yolo_model
 from official.vision.beta.projects.yolo.modeling.heads import yolo_head
 from official.vision.beta.projects.yolo.modeling.layers import detection_generator
+from official.vision.modeling.backbones import factory as backbone_factory
+from official.vision.modeling.decoders import factory as decoder_factory
 
 
 def build_yolo_detection_generator(model_config: yolo.Yolo, anchor_boxes):

official/vision/beta/projects/yolo/modeling/heads/yolo_head_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 yolo heads."""
 
 # Import libraries

official/vision/beta/projects/yolo/modeling/layers/detection_generator.py

@@ -15,10 +15,10 @@
 """Contains common building blocks for yolo layer (detection layer)."""
 import tensorflow as tf
 
-from official.vision.beta.modeling.layers import detection_generator
 from official.vision.beta.projects.yolo.losses import yolo_loss
 from official.vision.beta.projects.yolo.ops import box_ops
 from official.vision.beta.projects.yolo.ops import loss_utils
+from official.vision.modeling.layers import detection_generator
 
 
 class YoloLayer(tf.keras.Model):

official/vision/beta/projects/yolo/modeling/layers/nn_blocks.py

@@ -18,7 +18,7 @@ from typing import Callable, List, Tuple
 import tensorflow as tf
 
 from official.modeling import tf_utils
-from official.vision.beta.ops import spatial_transform_ops
+from official.vision.ops import spatial_transform_ops
 
 
 class Identity(tf.keras.layers.Layer):

official/vision/beta/projects/yolo/modeling/layers/nn_blocks_test.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# Lint as: python3
 from absl.testing import parameterized
 import numpy as np
 import tensorflow as tf

official/vision/beta/projects/yolo/ops/mosaic.py

@@ -14,12 +14,13 @@
 
 """Mosaic op."""
 import random
+
 import tensorflow as tf
 import tensorflow_addons as tfa
 
-from official.vision.beta.ops import box_ops
-from official.vision.beta.ops import preprocess_ops
 from official.vision.beta.projects.yolo.ops import preprocessing_ops
+from official.vision.ops import box_ops
+from official.vision.ops import preprocess_ops
 
 
 class Mosaic:

official/vision/beta/projects/yolo/ops/preprocessing_ops.py

@@ -19,7 +19,7 @@ import numpy as np
 import tensorflow as tf
 import tensorflow_addons as tfa
 
-from official.vision.beta.ops import box_ops as bbox_ops
+from official.vision.ops import box_ops as bbox_ops
 
 PAD_VALUE = 114
 GLOBAL_SEED_SET = False

official/vision/beta/projects/yolo/ops/preprocessing_ops_test.py

@@ -17,8 +17,8 @@ from absl.testing import parameterized
 import numpy as np
 import tensorflow as tf
 
-from official.vision.beta.ops import box_ops as bbox_ops
 from official.vision.beta.projects.yolo.ops import preprocessing_ops
+from official.vision.ops import box_ops as bbox_ops
 
 
 class InputUtilsTest(parameterized.TestCase, tf.test.TestCase):

official/vision/beta/projects/yolo/tasks/image_classification.py

@@ -15,12 +15,12 @@
 """Image classification task definition."""
 from official.common import dataset_fn
 from official.core import task_factory
-from official.vision.beta.dataloaders import classification_input as classification_input_base
-from official.vision.beta.dataloaders import input_reader_factory
-from official.vision.beta.dataloaders import tfds_factory
 from official.vision.beta.projects.yolo.configs import darknet_classification as exp_cfg
 from official.vision.beta.projects.yolo.dataloaders import classification_input
-from official.vision.beta.tasks import image_classification
+from official.vision.dataloaders import classification_input as classification_input_base
+from official.vision.dataloaders import input_reader_factory
+from official.vision.dataloaders import tfds_factory
+from official.vision.tasks import image_classification
 
 
 @task_factory.register_task_cls(exp_cfg.ImageClassificationTask)

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

@@ -26,10 +26,6 @@ from official.core import config_definitions
 from official.core import input_reader
 from official.core import task_factory
 from official.modeling import performance
-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.ops import box_ops
 from official.vision.beta.projects.yolo import optimization
 from official.vision.beta.projects.yolo.configs import yolo as exp_cfg
 from official.vision.beta.projects.yolo.dataloaders import tf_example_decoder
@@ -39,6 +35,10 @@ from official.vision.beta.projects.yolo.ops import kmeans_anchors
 from official.vision.beta.projects.yolo.ops import mosaic
 from official.vision.beta.projects.yolo.ops import preprocessing_ops
 from official.vision.beta.projects.yolo.tasks import task_utils
+from official.vision.dataloaders import tfds_factory
+from official.vision.dataloaders.google import tf_example_label_map_decoder
+from official.vision.evaluation import coco_evaluator
+from official.vision.ops import box_ops
 
 OptimizationConfig = optimization.OptimizationConfig
 RuntimeConfig = config_definitions.RuntimeConfig

official/vision/beta/serving/__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/beta/serving/detection.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.
-
-# Lint as: python3
-"""Detection input and model functions for serving/inference."""
-
-from typing import Mapping, Text
-import tensorflow as tf
-
-from official.vision.beta import configs
-from official.vision.beta.modeling import factory
-from official.vision.beta.ops import anchor
-from official.vision.beta.ops import box_ops
-from official.vision.beta.ops import preprocess_ops
-from official.vision.beta.serving import export_base
-
-
-MEAN_RGB = (0.485 * 255, 0.456 * 255, 0.406 * 255)
-STDDEV_RGB = (0.229 * 255, 0.224 * 255, 0.225 * 255)
-
-
-class DetectionModule(export_base.ExportModule):
-  """Detection Module."""
-
-  def _build_model(self):
-
-    if self._batch_size is None:
-      raise ValueError('batch_size cannot be None for detection models.')
-    input_specs = tf.keras.layers.InputSpec(shape=[self._batch_size] +
-                                            self._input_image_size + [3])
-
-    if isinstance(self.params.task.model, configs.maskrcnn.MaskRCNN):
-      model = factory.build_maskrcnn(
-          input_specs=input_specs, model_config=self.params.task.model)
-    elif isinstance(self.params.task.model, configs.retinanet.RetinaNet):
-      model = factory.build_retinanet(
-          input_specs=input_specs, model_config=self.params.task.model)
-    else:
-      raise ValueError('Detection module not implemented for {} model.'.format(
-          type(self.params.task.model)))
-
-    return model
-
-  def _build_anchor_boxes(self):
-    """Builds and returns anchor boxes."""
-    model_params = self.params.task.model
-    input_anchor = anchor.build_anchor_generator(
-        min_level=model_params.min_level,
-        max_level=model_params.max_level,
-        num_scales=model_params.anchor.num_scales,
-        aspect_ratios=model_params.anchor.aspect_ratios,
-        anchor_size=model_params.anchor.anchor_size)
-    return input_anchor(
-        image_size=(self._input_image_size[0], self._input_image_size[1]))
-
-  def _build_inputs(self, image):
-    """Builds detection model inputs for serving."""
-    model_params = self.params.task.model
-    # Normalizes image with mean and std pixel values.
-    image = preprocess_ops.normalize_image(image,
-                                           offset=MEAN_RGB,
-                                           scale=STDDEV_RGB)
-
-    image, image_info = preprocess_ops.resize_and_crop_image(
-        image,
-        self._input_image_size,
-        padded_size=preprocess_ops.compute_padded_size(
-            self._input_image_size, 2**model_params.max_level),
-        aug_scale_min=1.0,
-        aug_scale_max=1.0)
-    anchor_boxes = self._build_anchor_boxes()
-
-    return image, anchor_boxes, image_info
-
-  def preprocess(self, images: tf.Tensor) -> (
-      tf.Tensor, Mapping[Text, tf.Tensor], tf.Tensor):
-    """Preprocess inputs to be suitable for the model.
-
-    Args:
-      images: The images tensor.
-    Returns:
-      images: The images tensor cast to float.
-      anchor_boxes: Dict mapping anchor levels to anchor boxes.
-      image_info: Tensor containing the details of the image resizing.
-
-    """
-    model_params = self.params.task.model
-    with tf.device('cpu:0'):
-      images = tf.cast(images, dtype=tf.float32)
-
-      # Tensor Specs for map_fn outputs (images, anchor_boxes, and image_info).
-      images_spec = tf.TensorSpec(shape=self._input_image_size + [3],
-                                  dtype=tf.float32)
-
-      num_anchors = model_params.anchor.num_scales * len(
-          model_params.anchor.aspect_ratios) * 4
-      anchor_shapes = []
-      for level in range(model_params.min_level, model_params.max_level + 1):
-        anchor_level_spec = tf.TensorSpec(
-            shape=[
-                self._input_image_size[0] // 2**level,
-                self._input_image_size[1] // 2**level, num_anchors
-            ],
-            dtype=tf.float32)
-        anchor_shapes.append((str(level), anchor_level_spec))
-
-      image_info_spec = tf.TensorSpec(shape=[4, 2], dtype=tf.float32)
-
-      images, anchor_boxes, image_info = tf.nest.map_structure(
-          tf.identity,
-          tf.map_fn(
-              self._build_inputs,
-              elems=images,
-              fn_output_signature=(images_spec, dict(anchor_shapes),
-                                   image_info_spec),
-              parallel_iterations=32))
-
-      return images, anchor_boxes, image_info
-
-  def serve(self, images: tf.Tensor):
-    """Cast image to float and run inference.
-
-    Args:
-      images: uint8 Tensor of shape [batch_size, None, None, 3]
-    Returns:
-      Tensor holding detection output logits.
-    """
-
-    # Skip image preprocessing when input_type is tflite so it is compatible
-    # with TFLite quantization.
-    if self._input_type != 'tflite':
-      images, anchor_boxes, image_info = self.preprocess(images)
-    else:
-      with tf.device('cpu:0'):
-        anchor_boxes = self._build_anchor_boxes()
-        # image_info is a 3D tensor of shape [batch_size, 4, 2]. It is in the
-        # format of [[original_height, original_width],
-        # [desired_height, desired_width], [y_scale, x_scale],
-        # [y_offset, x_offset]]. When input_type is tflite, input image is
-        # supposed to be preprocessed already.
-        image_info = tf.convert_to_tensor([[
-            self._input_image_size, self._input_image_size, [1.0, 1.0], [0, 0]
-        ]],
-                                          dtype=tf.float32)
-    input_image_shape = image_info[:, 1, :]
-
-    # To overcome keras.Model extra limitation to save a model with layers that
-    # have multiple inputs, we use `model.call` here to trigger the forward
-    # path. Note that, this disables some keras magics happens in `__call__`.
-    detections = self.model.call(
-        images=images,
-        image_shape=input_image_shape,
-        anchor_boxes=anchor_boxes,
-        training=False)
-
-    if self.params.task.model.detection_generator.apply_nms:
-      # For RetinaNet model, apply export_config.
-      # TODO(huizhongc): Add export_config to fasterrcnn and maskrcnn as needed.
-      if isinstance(self.params.task.model, configs.retinanet.RetinaNet):
-        export_config = self.params.task.export_config
-        # Normalize detection box coordinates to [0, 1].
-        if export_config.output_normalized_coordinates:
-          detection_boxes = (
-              detections['detection_boxes'] /
-              tf.tile(image_info[:, 2:3, :], [1, 1, 2]))
-          detections['detection_boxes'] = box_ops.normalize_boxes(
-              detection_boxes, image_info[:, 0:1, :])
-
-        # Cast num_detections and detection_classes to float. This allows the
-        # model inference to work on chain (go/chain) as chain requires floating
-        # point outputs.
-        if export_config.cast_num_detections_to_float:
-          detections['num_detections'] = tf.cast(
-              detections['num_detections'], dtype=tf.float32)
-        if export_config.cast_detection_classes_to_float:
-          detections['detection_classes'] = tf.cast(
-              detections['detection_classes'], dtype=tf.float32)
-
-      final_outputs = {
-          'detection_boxes': detections['detection_boxes'],
-          'detection_scores': detections['detection_scores'],
-          'detection_classes': detections['detection_classes'],
-          'num_detections': detections['num_detections']
-      }
-    else:
-      final_outputs = {
-          'decoded_boxes': detections['decoded_boxes'],
-          'decoded_box_scores': detections['decoded_box_scores']
-      }
-
-    if 'detection_masks' in detections.keys():
-      final_outputs['detection_masks'] = detections['detection_masks']
-
-    final_outputs.update({'image_info': image_info})
-    return final_outputs