Merge branch 'master' of https://github.com/tensorflow/models

research/object_detection/models/center_net_mobilenet_v2_fpn_feature_extractor_tf2_test.py

@@ -103,6 +103,30 @@ class CenterNetMobileNetV2FPNFeatureExtractorTest(test_case.TestCase):
     # a depth multiplier of 2.
     self.assertEqual(64, first_conv.filters)
 
+  def test_center_net_mobilenet_v2_fpn_feature_extractor_interpolation(self):
+
+    channel_means = (0., 0., 0.)
+    channel_stds = (1., 1., 1.)
+    bgr_ordering = False
+    model = (
+        center_net_mobilenet_v2_fpn_feature_extractor.mobilenet_v2_fpn(
+            channel_means, channel_stds, bgr_ordering, use_separable_conv=True,
+            upsampling_interpolation='bilinear'))
+
+    def graph_fn():
+      img = np.zeros((8, 224, 224, 3), dtype=np.float32)
+      processed_img = model.preprocess(img)
+      return model(processed_img)
+
+    outputs = self.execute(graph_fn, [])
+    self.assertEqual(outputs.shape, (8, 56, 56, 24))
+
+    # Verify the upsampling layers in the FPN use 'bilinear' interpolation.
+    fpn = model.get_layer('model_1')
+    for layer in fpn.layers:
+      if 'up_sampling2d' in layer.name:
+        self.assertEqual('bilinear', layer.interpolation)
+
 
 if __name__ == '__main__':
   tf.test.main()

research/object_detection/models/center_net_resnet_feature_extractor.py

@@ -126,14 +126,8 @@ class CenterNetResnetFeatureExtractor(CenterNetFeatureExtractor):
     return 4
 
   @property
-  def supported_sub_model_types(self):
-    return ['classification']
-
-  def get_sub_model(self, sub_model_type):
-    if sub_model_type == 'classification':
-      return self._base_model
-    else:
-      ValueError('Sub model type "{}" not supported.'.format(sub_model_type))
+  def classification_backbone(self):
+    return self._base_model
 
 
 def resnet_v2_101(channel_means, channel_stds, bgr_ordering, **kwargs):

research/object_detection/models/center_net_resnet_v1_fpn_feature_extractor.py

@@ -162,14 +162,8 @@ class CenterNetResnetV1FpnFeatureExtractor(CenterNetFeatureExtractor):
     return 4
 
   @property
-  def supported_sub_model_types(self):
-    return ['classification']
-
-  def get_sub_model(self, sub_model_type):
-    if sub_model_type == 'classification':
-      return self._base_model
-    else:
-      ValueError('Sub model type "{}" not supported.'.format(sub_model_type))
+  def classification_backbone(self):
+    return self._base_model
 
 
 def resnet_v1_101_fpn(channel_means, channel_stds, bgr_ordering, **kwargs):

research/object_detection/protos/center_net.proto

@@ -440,11 +440,17 @@ message CenterNetFeatureExtractor {
   optional bool use_depthwise = 5 [default = false];
 
 
-  // Depth multiplier. Only valid for specific models (e.g. MobileNet). See subclasses of `CenterNetFeatureExtractor`.
+  // Depth multiplier. Only valid for specific models (e.g. MobileNet). See
+  // subclasses of `CenterNetFeatureExtractor`.
   optional float depth_multiplier = 9 [default = 1.0];
 
   // Whether to use separable convolutions. Only valid for specific
   // models. See subclasses of `CenterNetFeatureExtractor`.
   optional bool use_separable_conv = 10 [default = false];
+
+  // Which interpolation method to use for the upsampling ops in the FPN.
+  // Currently only valid for CenterNetMobileNetV2FPNFeatureExtractor. The value
+  // can be on of 'nearest' or 'bilinear'.
+  optional string upsampling_interpolation = 11 [default = 'nearest'];
 }
 

research/object_detection/protos/train.proto

@@ -40,12 +40,26 @@ message TrainConfig {
   // extractor variables trained outside of object detection.
   optional string fine_tune_checkpoint = 7 [default=""];
 
-  // Type of checkpoint to restore variables from, e.g. 'classification'
-  // 'detection', `fine_tune`, `full`. Controls which variables are restored
-  // from the pre-trained checkpoint. For meta architecture specific valid
-  // values of this parameter, see the restore_map (TF1) or
+  // This option controls how variables are restored from the (pre-trained)
+  // fine_tune_checkpoint. For TF2 models, 3 different types are supported:
+  //   1. "classification": Restores only the classification backbone part of
+  //        the feature extractor. This option is typically used when you want
+  //        to train a detection model starting from a pre-trained image
+  //        classification model, e.g. a ResNet model pre-trained on ImageNet.
+  //   2. "detection": Restores the entire feature extractor. The only parts
+  //        of the full detection model that are not restored are the box and
+  //        class prediction heads. This option is typically used when you want
+  //        to use a pre-trained detection model and train on a new dataset or
+  //        task which requires different box and class prediction heads.
+  //   3. "full": Restores the entire detection model, including the
+  //        feature extractor, its classification backbone, and the prediction
+  //        heads. This option should only be used when the pre-training and
+  //        fine-tuning tasks are the same. Otherwise, the model's parameters
+  //        may have incompatible shapes, which will cause errors when
+  //        attempting to restore the checkpoint.
+  // For more details about this parameter, see the restore_map (TF1) or
   // restore_from_object (TF2) function documentation in the
-  // /meta_architectures/*meta_arch.py files
+  // /meta_architectures/*meta_arch.py files.
   optional string fine_tune_checkpoint_type = 22 [default=""];
 
   // Either "v1" or "v2". If v1, restores the checkpoint using the tensorflow

research/object_detection/utils/variables_helper.py

@@ -21,6 +21,7 @@ from __future__ import division
 from __future__ import print_function
 
 import logging
+import os
 import re
 
 import tensorflow.compat.v1 as tf
@@ -29,6 +30,19 @@ import tf_slim as slim
 from tensorflow.python.ops import variables as tf_variables
 
 
+# Maps checkpoint types to variable name prefixes that are no longer
+# supported
+DETECTION_FEATURE_EXTRACTOR_MSG = """\
+The checkpoint type 'detection' is not supported when it contains variable
+names with 'feature_extractor'. Please download the new checkpoint file
+from model zoo.
+"""
+
+DEPRECATED_CHECKPOINT_MAP = {
+    'detection': ('feature_extractor', DETECTION_FEATURE_EXTRACTOR_MSG)
+}
+
+
 # TODO(derekjchow): Consider replacing with tf.contrib.filter_variables in
 # tensorflow/contrib/framework/python/ops/variables.py
 def filter_variables(variables, filter_regex_list, invert=False):
@@ -176,3 +190,41 @@ def get_global_variables_safely():
                        "executing eagerly. Use a Keras model's `.variables` "
                        "attribute instead.")
   return tf.global_variables()
+
+
+def ensure_checkpoint_supported(checkpoint_path, checkpoint_type, model_dir):
+  """Ensures that the given checkpoint can be properly loaded.
+
+  Performs the following checks
+  1. Raises an error if checkpoint_path and model_dir are same.
+  2. Checks that checkpoint_path does not contain a deprecated checkpoint file
+     by inspecting its variables.
+
+  Args:
+    checkpoint_path: str, path to checkpoint.
+    checkpoint_type: str, denotes the type of checkpoint.
+    model_dir: The model directory to store intermediate training checkpoints.
+
+  Raises:
+    RuntimeError: If
+      1. We detect an deprecated checkpoint file.
+      2. model_dir and checkpoint_path are in the same directory.
+  """
+  variables = tf.train.list_variables(checkpoint_path)
+
+  if checkpoint_type in DEPRECATED_CHECKPOINT_MAP:
+    blocked_prefix, msg = DEPRECATED_CHECKPOINT_MAP[checkpoint_type]
+    for var_name, _ in variables:
+      if var_name.startswith(blocked_prefix):
+        tf.logging.error('Found variable name - %s with prefix %s', var_name,
+                         blocked_prefix)
+        raise RuntimeError(msg)
+
+  checkpoint_path_dir = os.path.abspath(os.path.dirname(checkpoint_path))
+  model_dir = os.path.abspath(model_dir)
+
+  if model_dir == checkpoint_path_dir:
+    raise RuntimeError(
+        ('Checkpoint dir ({}) and model_dir ({}) cannot be same.'.format(
+            checkpoint_path_dir, model_dir) +
+         (' Please set model_dir to a different path.')))