Internal change

PiperOrigin-RevId: 454634340

official/vision/configs/backbones.py

@@ -45,6 +45,8 @@ class DilatedResNet(hyperparams.Config):
   last_stage_repeats: int = 1
   se_ratio: float = 0.0
   stochastic_depth_drop_rate: float = 0.0
+  resnetd_shortcut: bool = False
+  replace_stem_max_pool: bool = False
 
 
 @dataclasses.dataclass

official/vision/modeling/backbones/resnet_deeplab.py

@@ -75,6 +75,8 @@ class DilatedResNet(tf.keras.Model):
       input_specs: tf.keras.layers.InputSpec = layers.InputSpec(
           shape=[None, None, None, 3]),
       stem_type: str = 'v0',
+      resnetd_shortcut: bool = False,
+      replace_stem_max_pool: bool = False,
       se_ratio: Optional[float] = None,
       init_stochastic_depth_rate: float = 0.0,
       multigrid: Optional[Tuple[int]] = None,
@@ -96,6 +98,10 @@ class DilatedResNet(tf.keras.Model):
       input_specs: A `tf.keras.layers.InputSpec` of the input tensor.
       stem_type: A `str` of stem type. Can be `v0` or `v1`. `v1` replaces 7x7
         conv by 3 3x3 convs.
+      resnetd_shortcut: A `bool` of whether to use ResNet-D shortcut in
+        downsampling blocks.
+      replace_stem_max_pool: A `bool` of whether to replace the max pool in stem
+        with a stride-2 conv,
       se_ratio: A `float` or None. Ratio of the Squeeze-and-Excitation layer.
       init_stochastic_depth_rate: A `float` of initial stochastic depth rate.
       multigrid: A tuple of the same length as the number of blocks in the last
@@ -128,6 +134,8 @@ class DilatedResNet(tf.keras.Model):
     self._kernel_regularizer = kernel_regularizer
     self._bias_regularizer = bias_regularizer
     self._stem_type = stem_type
+    self._resnetd_shortcut = resnetd_shortcut
+    self._replace_stem_max_pool = replace_stem_max_pool
     self._se_ratio = se_ratio
     self._init_stochastic_depth_rate = init_stochastic_depth_rate
 
@@ -200,6 +208,22 @@ class DilatedResNet(tf.keras.Model):
     else:
       raise ValueError('Stem type {} not supported.'.format(stem_type))
 
+    if replace_stem_max_pool:
+      x = layers.Conv2D(
+          filters=64,
+          kernel_size=3,
+          strides=2,
+          use_bias=False,
+          padding='same',
+          kernel_initializer=self._kernel_initializer,
+          kernel_regularizer=self._kernel_regularizer,
+          bias_regularizer=self._bias_regularizer)(
+              x)
+      x = self._norm(
+          axis=bn_axis, momentum=norm_momentum, epsilon=norm_epsilon)(
+              x)
+      x = tf_utils.get_activation(activation, use_keras_layer=True)(x)
+    else:
       x = layers.MaxPool2D(pool_size=3, strides=2, padding='same')(x)
 
     normal_resnet_stage = int(np.math.log2(self._output_stride)) - 2
@@ -296,6 +320,7 @@ class DilatedResNet(tf.keras.Model):
         use_projection=True,
         stochastic_depth_drop_rate=stochastic_depth_drop_rate,
         se_ratio=self._se_ratio,
+        resnetd_shortcut=self._resnetd_shortcut,
         kernel_initializer=self._kernel_initializer,
         kernel_regularizer=self._kernel_regularizer,
         bias_regularizer=self._bias_regularizer,
@@ -311,6 +336,7 @@ class DilatedResNet(tf.keras.Model):
           dilation_rate=dilation_rate * multigrid[i],
           use_projection=False,
           stochastic_depth_drop_rate=stochastic_depth_drop_rate,
+          resnetd_shortcut=self._resnetd_shortcut,
           se_ratio=self._se_ratio,
           kernel_initializer=self._kernel_initializer,
           kernel_regularizer=self._kernel_regularizer,
@@ -328,6 +354,8 @@ class DilatedResNet(tf.keras.Model):
         'model_id': self._model_id,
         'output_stride': self._output_stride,
         'stem_type': self._stem_type,
+        'resnetd_shortcut': self._resnetd_shortcut,
+        'replace_stem_max_pool': self._replace_stem_max_pool,
         'se_ratio': self._se_ratio,
         'init_stochastic_depth_rate': self._init_stochastic_depth_rate,
         'activation': self._activation,
@@ -367,6 +395,8 @@ def build_dilated_resnet(
       output_stride=backbone_cfg.output_stride,
       input_specs=input_specs,
       stem_type=backbone_cfg.stem_type,
+      resnetd_shortcut=backbone_cfg.resnetd_shortcut,
+      replace_stem_max_pool=backbone_cfg.replace_stem_max_pool,
       se_ratio=backbone_cfg.se_ratio,
       init_stochastic_depth_rate=backbone_cfg.stochastic_depth_drop_rate,
       multigrid=backbone_cfg.multigrid,

official/vision/modeling/backbones/resnet_deeplab_test.py

@@ -52,13 +52,17 @@ class ResNetTest(parameterized.TestCase, tf.test.TestCase):
     ], endpoints[str(int(np.math.log2(output_stride)))].shape.as_list())
 
   @parameterized.parameters(
-      ('v0', None, 0.0),
-      ('v1', None, 0.0),
-      ('v1', 0.25, 0.0),
-      ('v1', 0.25, 0.2),
+      ('v0', None, 0.0, False, False),
+      ('v1', None, 0.0, False, False),
+      ('v1', 0.25, 0.0, False, False),
+      ('v1', 0.25, 0.2, False, False),
+      ('v1', 0.25, 0.0, True, False),
+      ('v1', 0.25, 0.2, False, True),
+      ('v1', None, 0.2, True, True),
   )
   def test_network_features(self, stem_type, se_ratio,
-                            init_stochastic_depth_rate):
+                            init_stochastic_depth_rate, resnetd_shortcut,
+                            replace_stem_max_pool):
     """Test additional features of ResNet models."""
     input_size = 128
     model_id = 50
@@ -71,6 +75,8 @@ class ResNetTest(parameterized.TestCase, tf.test.TestCase):
         model_id=model_id,
         output_stride=output_stride,
         stem_type=stem_type,
+        resnetd_shortcut=resnetd_shortcut,
+        replace_stem_max_pool=replace_stem_max_pool,
         se_ratio=se_ratio,
         init_stochastic_depth_rate=init_stochastic_depth_rate)
     inputs = tf.keras.Input(shape=(input_size, input_size, 3), batch_size=1)
@@ -120,6 +126,8 @@ class ResNetTest(parameterized.TestCase, tf.test.TestCase):
         stem_type='v0',
         se_ratio=0.25,
         init_stochastic_depth_rate=0.2,
+        resnetd_shortcut=False,
+        replace_stem_max_pool=False,
         use_sync_bn=False,
         activation='relu',
         norm_momentum=0.99,