No public description

PiperOrigin-RevId: 569290058

Author: tensorflower-gardener

official/vision/configs/maskrcnn.py

@@ -46,6 +46,13 @@ class Parser(hyperparams.Config):
   rpn_batch_size_per_im: int = 256
   rpn_fg_fraction: float = 0.5
   mask_crop_size: int = 112
+  pad: bool = True  # Only support `pad = True`.
+
+  def __post_init__(self, *args, **kwargs):
+    """Validates the configuration."""
+    if not self.pad:
+      raise ValueError('`maskrcnn.Parser` only supports `pad = True`.')
+    super().__post_init__(*args, **kwargs)
 
 
 @dataclasses.dataclass

official/vision/configs/retinanet.py

@@ -59,6 +59,7 @@ class Parser(hyperparams.Config):
   max_num_instances: int = 100
   # Can choose AutoAugment and RandAugment.
   aug_type: Optional[common.Augmentation] = None
+  pad: bool = True
 
   # Keep for backward compatibility. Not used.
   aug_policy: Optional[str] = None

official/vision/serving/detection.py

@@ -14,11 +14,13 @@
 
 """Detection input and model functions for serving/inference."""
 
+import math
 from typing import Mapping, Tuple
 
 from absl import logging
 import tensorflow as tf
 
+from official.core import config_definitions as cfg
 from official.vision import configs
 from official.vision.modeling import factory
 from official.vision.ops import anchor
@@ -30,6 +32,34 @@
 class DetectionModule(export_base.ExportModule):
   """Detection Module."""
 
+  def __init__(
+      self,
+      params: cfg.ExperimentConfig,
+      *,
+      input_image_size: list[int],
+      **kwargs,
+  ):
+    """Initializes a detection module for export.
+
+    Args:
+      params: Experiment params.
+      input_image_size: List or Tuple of size of the input image. For 2D image,
+        it is [height, width].
+      **kwargs: All other kwargs are passed to `export_base.ExportModule`; see
+        the documentation on `export_base.ExportModule` for valid arguments.
+    """
+    if params.task.train_data.parser.pad:
+      self._padded_size = preprocess_ops.compute_padded_size(
+          input_image_size, 2**params.task.model.max_level
+      )
+    else:
+      self._padded_size = input_image_size
+    super().__init__(
+        params=params,
+        input_image_size=input_image_size,
+        **kwargs,
+    )
+
   def _build_model(self):
 
     nms_versions_supporting_dynamic_batch_size = {'batched', 'v2', 'v3'}
@@ -40,8 +70,8 @@ def _build_model(self):
                    'does not support with dynamic batch size.', nms_version)
       self.params.task.model.detection_generator.nms_version = 'batched'
 
-    input_specs = tf.keras.layers.InputSpec(shape=[self._batch_size] +
-                                            self._input_image_size + [3])
+    input_specs = tf.keras.layers.InputSpec(shape=[
+        self._batch_size, *self._padded_size, 3])
 
     if isinstance(self.params.task.model, configs.maskrcnn.MaskRCNN):
       model = factory.build_maskrcnn(
@@ -64,23 +94,21 @@ def _build_anchor_boxes(self):
         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]))
+    return input_anchor(image_size=self._padded_size)
 
   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=preprocess_ops.MEAN_RGB, scale=preprocess_ops.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),
+        padded_size=self._padded_size,
         aug_scale_min=1.0,
-        aug_scale_max=1.0)
+        aug_scale_max=1.0,
+    )
     anchor_boxes = self._build_anchor_boxes()
 
     return image, anchor_boxes, image_info
@@ -128,7 +156,7 @@ def preprocess(
       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],
+      images_spec = tf.TensorSpec(shape=self._padded_size + [3],
                                   dtype=tf.float32)
 
       num_anchors = model_params.anchor.num_scales * len(
@@ -137,8 +165,9 @@ def preprocess(
       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
+                math.ceil(self._padded_size[0] / 2**level),
+                math.ceil(self._padded_size[1] / 2**level),
+                num_anchors,
             ],
             dtype=tf.float32)
         anchor_shapes.append((str(level), anchor_level_spec))