Internal change

PiperOrigin-RevId: 321666729

research/object_detection/models/keras_models/resnet_v1.py

@@ -21,6 +21,7 @@ from __future__ import print_function
 
 import tensorflow.compat.v1 as tf
 
+from tensorflow.python.keras.applications import resnet
 from object_detection.core import freezable_batch_norm
 from object_detection.models.keras_models import model_utils
 
@@ -95,11 +96,11 @@ class _LayersOverride(object):
     self.regularizer = tf.keras.regularizers.l2(weight_decay)
     self.initializer = tf.variance_scaling_initializer()
 
-  def _FixedPaddingLayer(self, kernel_size, rate=1):
+  def _FixedPaddingLayer(self, kernel_size, rate=1):  # pylint: disable=invalid-name
     return tf.keras.layers.Lambda(
         lambda x: _fixed_padding(x, kernel_size, rate))
 
-  def Conv2D(self, filters, kernel_size, **kwargs):
+  def Conv2D(self, filters, kernel_size, **kwargs):  # pylint: disable=invalid-name
     """Builds a Conv2D layer according to the current Object Detection config.
 
     Overrides the Keras Resnet application's convolutions with ones that
@@ -141,7 +142,7 @@ class _LayersOverride(object):
     else:
       return tf.keras.layers.Conv2D(filters, kernel_size, **kwargs)
 
-  def Activation(self, *args, **kwargs):  # pylint: disable=unused-argument
+  def Activation(self, *args, **kwargs):  # pylint: disable=unused-argument,invalid-name
     """Builds an activation layer.
 
     Overrides the Keras application Activation layer specified by the
@@ -163,7 +164,7 @@ class _LayersOverride(object):
     else:
       return tf.keras.layers.Lambda(tf.nn.relu, name=name)
 
-  def BatchNormalization(self, **kwargs):
+  def BatchNormalization(self, **kwargs):  # pylint: disable=invalid-name
     """Builds a normalization layer.
 
     Overrides the Keras application batch norm with the norm specified by the
@@ -191,7 +192,7 @@ class _LayersOverride(object):
           momentum=self._default_batchnorm_momentum,
           **kwargs)
 
-  def Input(self, shape):
+  def Input(self, shape):  # pylint: disable=invalid-name
     """Builds an Input layer.
 
     Overrides the Keras application Input layer with one that uses a
@@ -219,7 +220,7 @@ class _LayersOverride(object):
         input=input_tensor, shape=[None] + shape)
     return model_utils.input_layer(shape, placeholder_with_default)
 
-  def MaxPooling2D(self, pool_size, **kwargs):
+  def MaxPooling2D(self, pool_size, **kwargs):  # pylint: disable=invalid-name
     """Builds a MaxPooling2D layer with default padding as 'SAME'.
 
     This is specified by the default resnet arg_scope in slim.
@@ -237,7 +238,7 @@ class _LayersOverride(object):
   # Add alias as Keras also has it.
   MaxPool2D = MaxPooling2D  # pylint: disable=invalid-name
 
-  def ZeroPadding2D(self, padding, **kwargs):  # pylint: disable=unused-argument
+  def ZeroPadding2D(self, padding, **kwargs):  # pylint: disable=unused-argument,invalid-name
     """Replaces explicit padding in the Keras application with a no-op.
 
     Args:
@@ -395,3 +396,146 @@ def resnet_v1_152(batchnorm_training,
   return tf.keras.applications.resnet.ResNet152(
       layers=layers_override, **kwargs)
 # pylint: enable=invalid-name
+
+
+# The following codes are based on the existing keras ResNet model pattern:
+# google3/third_party/tensorflow/python/keras/applications/resnet.py
+def block_basic(x,
+                filters,
+                kernel_size=3,
+                stride=1,
+                conv_shortcut=False,
+                name=None):
+  """A residual block for ResNet18/34.
+
+  Arguments:
+      x: input tensor.
+      filters: integer, filters of the bottleneck layer.
+      kernel_size: default 3, kernel size of the bottleneck layer.
+      stride: default 1, stride of the first layer.
+      conv_shortcut: default False, use convolution shortcut if True, otherwise
+        identity shortcut.
+      name: string, block label.
+
+  Returns:
+    Output tensor for the residual block.
+  """
+  layers = tf.keras.layers
+  bn_axis = 3 if tf.keras.backend.image_data_format() == 'channels_last' else 1
+
+  preact = layers.BatchNormalization(
+      axis=bn_axis, epsilon=1.001e-5, name=name + '_preact_bn')(
+          x)
+  preact = layers.Activation('relu', name=name + '_preact_relu')(preact)
+
+  if conv_shortcut:
+    shortcut = layers.Conv2D(
+        filters, 1, strides=1, name=name + '_0_conv')(
+            preact)
+  else:
+    shortcut = layers.MaxPooling2D(1, strides=stride)(x) if stride > 1 else x
+
+  x = layers.ZeroPadding2D(
+      padding=((1, 1), (1, 1)), name=name + '_1_pad')(
+          preact)
+  x = layers.Conv2D(
+      filters, kernel_size, strides=1, use_bias=False, name=name + '_1_conv')(
+          x)
+  x = layers.BatchNormalization(
+      axis=bn_axis, epsilon=1.001e-5, name=name + '_1_bn')(
+          x)
+  x = layers.Activation('relu', name=name + '_1_relu')(x)
+
+  x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)), name=name + '_2_pad')(x)
+  x = layers.Conv2D(
+      filters,
+      kernel_size,
+      strides=stride,
+      use_bias=False,
+      name=name + '_2_conv')(
+          x)
+  x = layers.BatchNormalization(
+      axis=bn_axis, epsilon=1.001e-5, name=name + '_2_bn')(
+          x)
+  x = layers.Activation('relu', name=name + '_2_relu')(x)
+  x = layers.Add(name=name + '_out')([shortcut, x])
+  return x
+
+
+def stack_basic(x, filters, blocks, stride1=2, name=None):
+  """A set of stacked residual blocks for ResNet18/34.
+
+  Arguments:
+      x: input tensor.
+      filters: integer, filters of the bottleneck layer in a block.
+      blocks: integer, blocks in the stacked blocks.
+      stride1: default 2, stride of the first layer in the first block.
+      name: string, stack label.
+
+  Returns:
+      Output tensor for the stacked blocks.
+  """
+  x = block_basic(x, filters, conv_shortcut=True, name=name + '_block1')
+  for i in range(2, blocks):
+    x = block_basic(x, filters, name=name + '_block' + str(i))
+  x = block_basic(
+      x, filters, stride=stride1, name=name + '_block' + str(blocks))
+  return x
+
+
+def resnet_v1_18(include_top=True,
+                 weights='imagenet',
+                 input_tensor=None,
+                 input_shape=None,
+                 pooling=None,
+                 classes=1000,
+                 classifier_activation='softmax'):
+  """Instantiates the ResNet18 architecture."""
+
+  def stack_fn(x):
+    x = stack_basic(x, 64, 2, stride1=1, name='conv2')
+    x = stack_basic(x, 128, 2, name='conv3')
+    x = stack_basic(x, 256, 2, name='conv4')
+    return stack_basic(x, 512, 2, name='conv5')
+
+  return resnet.ResNet(
+      stack_fn,
+      True,
+      True,
+      'resnet18',
+      include_top,
+      weights,
+      input_tensor,
+      input_shape,
+      pooling,
+      classes,
+      classifier_activation=classifier_activation)
+
+
+def resnet_v1_34(include_top=True,
+                 weights='imagenet',
+                 input_tensor=None,
+                 input_shape=None,
+                 pooling=None,
+                 classes=1000,
+                 classifier_activation='softmax'):
+  """Instantiates the ResNet34 architecture."""
+
+  def stack_fn(x):
+    x = stack_basic(x, 64, 3, stride1=1, name='conv2')
+    x = stack_basic(x, 128, 4, name='conv3')
+    x = stack_basic(x, 256, 6, name='conv4')
+    return stack_basic(x, 512, 3, name='conv5')
+
+  return resnet.ResNet(
+      stack_fn,
+      True,
+      True,
+      'resnet34',
+      include_top,
+      weights,
+      input_tensor,
+      input_shape,
+      pooling,
+      classes,
+      classifier_activation=classifier_activation)

research/object_detection/models/keras_models/resnet_v1_tf2_test.py

@@ -20,12 +20,13 @@ object detection. To verify the consistency of the two models, we compare:
   2. Number of global variables.
 """
 import unittest
+
+from absl.testing import parameterized
 import numpy as np
 from six.moves import zip
 import tensorflow.compat.v1 as tf
 
 from google.protobuf import text_format
-
 from object_detection.builders import hyperparams_builder
 from object_detection.models.keras_models import resnet_v1
 from object_detection.protos import hyperparams_pb2
@@ -180,5 +181,46 @@ class ResnetV1Test(test_case.TestCase):
       self.assertEqual(len(variables), var_num)
 
 
+class ResnetShapeTest(test_case.TestCase, parameterized.TestCase):
+
+  @unittest.skipIf(tf_version.is_tf1(), 'Skipping TF2.X only test.')
+  @parameterized.parameters(
+      {
+          'resnet_type':
+              'resnet_v1_34',
+          'output_layer_names': [
+              'conv2_block3_out', 'conv3_block4_out', 'conv4_block6_out',
+              'conv5_block3_out'
+          ]
+      }, {
+          'resnet_type':
+              'resnet_v1_18',
+          'output_layer_names': [
+              'conv2_block2_out', 'conv3_block2_out', 'conv4_block2_out',
+              'conv5_block2_out'
+          ]
+      })
+  def test_output_shapes(self, resnet_type, output_layer_names):
+    if resnet_type == 'resnet_v1_34':
+      model = resnet_v1.resnet_v1_34(weights=None)
+    else:
+      model = resnet_v1.resnet_v1_18(weights=None)
+    outputs = [
+        model.get_layer(output_layer_name).output
+        for output_layer_name in output_layer_names
+    ]
+    resnet_model = tf.keras.models.Model(inputs=model.input, outputs=outputs)
+    outputs = resnet_model(np.zeros((2, 64, 64, 3), dtype=np.float32))
+
+    # Check the shape of 'conv2_block3_out':
+    self.assertEqual(outputs[0].shape, [2, 16, 16, 64])
+    # Check the shape of 'conv3_block4_out':
+    self.assertEqual(outputs[1].shape, [2, 8, 8, 128])
+    # Check the shape of 'conv4_block6_out':
+    self.assertEqual(outputs[2].shape, [2, 4, 4, 256])
+    # Check the shape of 'conv5_block3_out':
+    self.assertEqual(outputs[3].shape, [2, 2, 2, 512])
+
+
 if __name__ == '__main__':
   tf.test.main()