Object detection changes: (#7208)

257914648  by lzc:

    Internal changes

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257525973  by Zhichao Lu:

    Fixes bug that silently prevents checkpoints from loading when training w/ eager + functions. Also sets up scripts to run training.

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257296614  by Zhichao Lu:

    Adding detection_features to model outputs

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257234565  by Zhichao Lu:

    Fix wrong order of `classes_with_max_scores` in class-agnostic NMS caused by
    sorting in partitioned-NMS.

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257232002  by ronnyvotel:

    Supporting `filter_nonoverlapping` option in np_box_list_ops.clip_to_window().

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257198282  by Zhichao Lu:

    Adding the focal loss and l1 loss from the Objects as Points paper.

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257089535  by Zhichao Lu:

    Create Keras based ssd + resnetv1 + fpn.

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257087407  by Zhichao Lu:

    Make object_detection/data_decoders Python3-compatible.

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257004582  by Zhichao Lu:

    Updates _decode_raw_data_into_masks_and_boxes to the latest binary masks-to-string encoding fo...

research/object_detection/models/faster_rcnn_pnas_feature_extractor.py

@@ -21,6 +21,7 @@ Based on PNASNet model: https://arxiv.org/abs/1712.00559
 import tensorflow as tf
 
 from object_detection.meta_architectures import faster_rcnn_meta_arch
+from object_detection.utils import variables_helper
 from nets.nasnet import nasnet_utils
 from nets.nasnet import pnasnet
 
@@ -302,7 +303,7 @@ class FasterRCNNPNASFeatureExtractor(
       the model graph.
     """
     variables_to_restore = {}
-    for variable in tf.global_variables():
+    for variable in variables_helper.get_global_variables_safely():
       if variable.op.name.startswith(
           first_stage_feature_extractor_scope):
         var_name = variable.op.name.replace(

research/object_detection/models/faster_rcnn_resnet_v1_feature_extractor.py

@@ -44,7 +44,8 @@ class FasterRCNNResnetV1FeatureExtractor(
                first_stage_features_stride,
                batch_norm_trainable=False,
                reuse_weights=None,
-               weight_decay=0.0):
+               weight_decay=0.0,
+               activation_fn=tf.nn.relu):
     """Constructor.
 
     Args:
@@ -55,6 +56,7 @@ class FasterRCNNResnetV1FeatureExtractor(
       batch_norm_trainable: See base class.
       reuse_weights: See base class.
       weight_decay: See base class.
+      activation_fn: Activaton functon to use in Resnet V1 model.
 
     Raises:
       ValueError: If `first_stage_features_stride` is not 8 or 16.
@@ -63,9 +65,10 @@ class FasterRCNNResnetV1FeatureExtractor(
       raise ValueError('`first_stage_features_stride` must be 8 or 16.')
     self._architecture = architecture
     self._resnet_model = resnet_model
-    super(FasterRCNNResnetV1FeatureExtractor, self).__init__(
-        is_training, first_stage_features_stride, batch_norm_trainable,
-        reuse_weights, weight_decay)
+    self._activation_fn = activation_fn
+    super(FasterRCNNResnetV1FeatureExtractor,
+          self).__init__(is_training, first_stage_features_stride,
+                         batch_norm_trainable, reuse_weights, weight_decay)
 
   def preprocess(self, resized_inputs):
     """Faster R-CNN Resnet V1 preprocessing.
@@ -125,6 +128,7 @@ class FasterRCNNResnetV1FeatureExtractor(
           resnet_utils.resnet_arg_scope(
               batch_norm_epsilon=1e-5,
               batch_norm_scale=True,
+              activation_fn=self._activation_fn,
               weight_decay=self._weight_decay)):
         with tf.variable_scope(
             self._architecture, reuse=self._reuse_weights) as var_scope:
@@ -159,6 +163,7 @@ class FasterRCNNResnetV1FeatureExtractor(
           resnet_utils.resnet_arg_scope(
               batch_norm_epsilon=1e-5,
               batch_norm_scale=True,
+              activation_fn=self._activation_fn,
               weight_decay=self._weight_decay)):
         with slim.arg_scope([slim.batch_norm],
                             is_training=self._train_batch_norm):
@@ -182,7 +187,8 @@ class FasterRCNNResnet50FeatureExtractor(FasterRCNNResnetV1FeatureExtractor):
                first_stage_features_stride,
                batch_norm_trainable=False,
                reuse_weights=None,
-               weight_decay=0.0):
+               weight_decay=0.0,
+               activation_fn=tf.nn.relu):
     """Constructor.
 
     Args:
@@ -191,15 +197,16 @@ class FasterRCNNResnet50FeatureExtractor(FasterRCNNResnetV1FeatureExtractor):
       batch_norm_trainable: See base class.
       reuse_weights: See base class.
       weight_decay: See base class.
+      activation_fn: See base class.
 
     Raises:
       ValueError: If `first_stage_features_stride` is not 8 or 16,
         or if `architecture` is not supported.
     """
-    super(FasterRCNNResnet50FeatureExtractor, self).__init__(
-        'resnet_v1_50', resnet_v1.resnet_v1_50, is_training,
+    super(FasterRCNNResnet50FeatureExtractor,
+          self).__init__('resnet_v1_50', resnet_v1.resnet_v1_50, is_training,
                          first_stage_features_stride, batch_norm_trainable,
-        reuse_weights, weight_decay)
+                         reuse_weights, weight_decay, activation_fn)
 
 
 class FasterRCNNResnet101FeatureExtractor(FasterRCNNResnetV1FeatureExtractor):
@@ -210,7 +217,8 @@ class FasterRCNNResnet101FeatureExtractor(FasterRCNNResnetV1FeatureExtractor):
                first_stage_features_stride,
                batch_norm_trainable=False,
                reuse_weights=None,
-               weight_decay=0.0):
+               weight_decay=0.0,
+               activation_fn=tf.nn.relu):
     """Constructor.
 
     Args:
@@ -219,15 +227,16 @@ class FasterRCNNResnet101FeatureExtractor(FasterRCNNResnetV1FeatureExtractor):
       batch_norm_trainable: See base class.
       reuse_weights: See base class.
       weight_decay: See base class.
+      activation_fn: See base class.
 
     Raises:
       ValueError: If `first_stage_features_stride` is not 8 or 16,
         or if `architecture` is not supported.
     """
-    super(FasterRCNNResnet101FeatureExtractor, self).__init__(
-        'resnet_v1_101', resnet_v1.resnet_v1_101, is_training,
+    super(FasterRCNNResnet101FeatureExtractor,
+          self).__init__('resnet_v1_101', resnet_v1.resnet_v1_101, is_training,
                          first_stage_features_stride, batch_norm_trainable,
-        reuse_weights, weight_decay)
+                         reuse_weights, weight_decay, activation_fn)
 
 
 class FasterRCNNResnet152FeatureExtractor(FasterRCNNResnetV1FeatureExtractor):
@@ -238,7 +247,8 @@ class FasterRCNNResnet152FeatureExtractor(FasterRCNNResnetV1FeatureExtractor):
                first_stage_features_stride,
                batch_norm_trainable=False,
                reuse_weights=None,
-               weight_decay=0.0):
+               weight_decay=0.0,
+               activation_fn=tf.nn.relu):
     """Constructor.
 
     Args:
@@ -247,12 +257,13 @@ class FasterRCNNResnet152FeatureExtractor(FasterRCNNResnetV1FeatureExtractor):
       batch_norm_trainable: See base class.
       reuse_weights: See base class.
       weight_decay: See base class.
+      activation_fn: See base class.
 
     Raises:
       ValueError: If `first_stage_features_stride` is not 8 or 16,
         or if `architecture` is not supported.
     """
-    super(FasterRCNNResnet152FeatureExtractor, self).__init__(
-        'resnet_v1_152', resnet_v1.resnet_v1_152, is_training,
+    super(FasterRCNNResnet152FeatureExtractor,
+          self).__init__('resnet_v1_152', resnet_v1.resnet_v1_152, is_training,
                          first_stage_features_stride, batch_norm_trainable,
-        reuse_weights, weight_decay)
+                         reuse_weights, weight_decay, activation_fn)

research/object_detection/models/feature_map_generators.py

@@ -79,14 +79,19 @@ def create_conv_block(
   """
   layers = []
   if use_depthwise:
-    layers.append(tf.keras.layers.SeparableConv2D(
-        depth,
-        [kernel_size, kernel_size],
+    kwargs = conv_hyperparams.params()
+    # Both the regularizer and initializer apply to the depthwise layer,
+    # so we remap the kernel_* to depthwise_* here.
+    kwargs['depthwise_regularizer'] = kwargs['kernel_regularizer']
+    kwargs['depthwise_initializer'] = kwargs['kernel_initializer']
+    layers.append(
+        tf.keras.layers.SeparableConv2D(
+            depth, [kernel_size, kernel_size],
             depth_multiplier=1,
             padding=padding,
             strides=stride,
             name=layer_name + '_depthwise_conv',
-        **conv_hyperparams.params()))
+            **kwargs))
   else:
     layers.append(tf.keras.layers.Conv2D(
         depth,

research/object_detection/models/keras_models/mobilenet_v2.py

@@ -160,7 +160,12 @@ class _LayersOverride(object):
     """
     if self._conv_hyperparams:
       kwargs = self._conv_hyperparams.params(**kwargs)
+      # Both the regularizer and initializer apply to the depthwise layer in
+      # MobilenetV1, so we remap the kernel_* to depthwise_* here.
+      kwargs['depthwise_regularizer'] = kwargs['kernel_regularizer']
+      kwargs['depthwise_initializer'] = kwargs['kernel_initializer']
     else:
+      kwargs['depthwise_regularizer'] = self.regularizer
       kwargs['depthwise_initializer'] = self.initializer
 
     kwargs['padding'] = 'same'