Merge branch 'tensorflow:master' into panoptic-deeplab

research/object_detection/exporter.py

@@ -101,15 +101,21 @@ def rewrite_nn_resize_op(is_quantized=False):
         'FakeQuantWithMinMaxVars' if is_quantized else '*')
     stack_1_pattern = graph_matcher.OpTypePattern(
         'Pack', inputs=[input_pattern, input_pattern], ordered_inputs=False)
+    reshape_1_pattern = graph_matcher.OpTypePattern(
+        'Reshape', inputs=[stack_1_pattern, 'Const'], ordered_inputs=False)
     stack_2_pattern = graph_matcher.OpTypePattern(
-        'Pack', inputs=[stack_1_pattern, stack_1_pattern], ordered_inputs=False)
-    reshape_pattern = graph_matcher.OpTypePattern(
+        'Pack',
+        inputs=[reshape_1_pattern, reshape_1_pattern],
+        ordered_inputs=False)
+    reshape_2_pattern = graph_matcher.OpTypePattern(
         'Reshape', inputs=[stack_2_pattern, 'Const'], ordered_inputs=False)
     consumer_pattern1 = graph_matcher.OpTypePattern(
-        'Add|AddV2|Max|Mul', inputs=[reshape_pattern, '*'],
+        'Add|AddV2|Max|Mul',
+        inputs=[reshape_2_pattern, '*'],
         ordered_inputs=False)
     consumer_pattern2 = graph_matcher.OpTypePattern(
-        'StridedSlice', inputs=[reshape_pattern, '*', '*', '*'],
+        'StridedSlice',
+        inputs=[reshape_2_pattern, '*', '*', '*'],
         ordered_inputs=False)
 
     def replace_matches(consumer_pattern):
@@ -119,16 +125,17 @@ def rewrite_nn_resize_op(is_quantized=False):
       for match in matcher.match_graph(tf.get_default_graph()):
         match_counter += 1
         projection_op = match.get_op(input_pattern)
-        reshape_op = match.get_op(reshape_pattern)
+        reshape_2_op = match.get_op(reshape_2_pattern)
         consumer_op = match.get_op(consumer_pattern)
         nn_resize = tf.image.resize_nearest_neighbor(
             projection_op.outputs[0],
-            reshape_op.outputs[0].shape.dims[1:3],
+            reshape_2_op.outputs[0].shape.dims[1:3],
             align_corners=False,
-            name=os.path.split(reshape_op.name)[0] + '/resize_nearest_neighbor')
+            name=os.path.split(reshape_2_op.name)[0] +
+            '/resize_nearest_neighbor')
 
         for index, op_input in enumerate(consumer_op.inputs):
-          if op_input == reshape_op.outputs[0]:
+          if op_input == reshape_2_op.outputs[0]:
             consumer_op._update_input(index, nn_resize)  # pylint: disable=protected-access
             break
 

research/object_detection/exporter_tf1_test.py

@@ -1168,16 +1168,20 @@ class ExportInferenceGraphTest(tf.test.TestCase):
     g = tf.Graph()
     with g.as_default():
       with tf.name_scope('nearest_upsampling'):
-        x = array_ops.placeholder(dtypes.float32, shape=(8, 10, 10, 8))
-        x_stack = tf.stack([tf.stack([x] * 2, axis=3)] * 2, axis=2)
-        x_reshape = tf.reshape(x_stack, [8, 20, 20, 8])
+        x_1 = array_ops.placeholder(dtypes.float32, shape=(8, 10, 10, 8))
+        x_1_stack_1 = tf.stack([x_1] * 2, axis=3)
+        x_1_reshape_1 = tf.reshape(x_1_stack_1, [8, 10, 20, 8])
+        x_1_stack_2 = tf.stack([x_1_reshape_1] * 2, axis=2)
+        x_1_reshape_2 = tf.reshape(x_1_stack_2, [8, 20, 20, 8])
 
       with tf.name_scope('nearest_upsampling'):
         x_2 = array_ops.placeholder(dtypes.float32, shape=(8, 10, 10, 8))
-        x_stack_2 = tf.stack([tf.stack([x_2] * 2, axis=3)] * 2, axis=2)
-        x_reshape_2 = tf.reshape(x_stack_2, [8, 20, 20, 8])
+        x_2_stack_1 = tf.stack([x_2] * 2, axis=3)
+        x_2_reshape_1 = tf.reshape(x_2_stack_1, [8, 10, 20, 8])
+        x_2_stack_2 = tf.stack([x_2_reshape_1] * 2, axis=2)
+        x_2_reshape_2 = tf.reshape(x_2_stack_2, [8, 20, 20, 8])
 
-      t = x_reshape + x_reshape_2
+      t = x_1_reshape_2 + x_2_reshape_2
 
       exporter.rewrite_nn_resize_op()
 

research/object_detection/protos/center_net.proto

@@ -403,7 +403,7 @@ message CenterNet {
 
 
   // Mask prediction support using DeepMAC. See https://arxiv.org/abs/2104.00613
-  // Next ID 25
+  // Next ID 33
   message DeepMACMaskEstimation {
     // The loss used for penalizing mask predictions.
     optional ClassificationLoss classification_loss = 1;
@@ -505,6 +505,21 @@ message CenterNet {
 
     optional bool use_only_last_stage = 24 [default = false];
 
+    optional float augmented_self_supervision_max_translation = 25 [default=0.0];
+
+    optional float augmented_self_supervision_flip_probability = 26 [default=0.0];
+
+    optional float augmented_self_supervision_loss_weight = 27 [default=0.0];
+
+    optional int32 augmented_self_supervision_warmup_start = 28 [default=0];
+
+    optional int32 augmented_self_supervision_warmup_steps = 29 [default=0];
+
+    optional  AugmentedSelfSupervisionLoss augmented_self_supervision_loss = 30 [default=LOSS_DICE];
+
+    optional float augmented_self_supervision_scale_min = 31 [default=1.0];
+
+    optional float augmented_self_supervision_scale_max = 32 [default=1.0];
   }
 
   optional DeepMACMaskEstimation deepmac_mask_estimation = 14;
@@ -527,6 +542,13 @@ enum LossNormalize {
   NORMALIZE_BALANCED = 3;
 }
 
+enum AugmentedSelfSupervisionLoss {
+  LOSS_UNSET = 0;
+  LOSS_DICE = 1;
+  LOSS_MSE = 2;
+  LOSS_KL_DIV = 3;
+}
+
 message CenterNetFeatureExtractor {
   optional string type = 1;
 

research/object_detection/protos/eval.proto

@@ -3,7 +3,7 @@ syntax = "proto2";
 package object_detection.protos;
 
 // Message for configuring DetectionModel evaluation jobs (eval.py).
-// Next id - 36
+// Next id - 37
 message EvalConfig {
   optional uint32 batch_size = 25 [default = 1];
   // Number of visualization images to generate.
@@ -118,6 +118,11 @@ message EvalConfig {
   // will be ignored. This is useful for evaluating on test data that are not
   // exhaustively labeled.
   optional bool skip_predictions_for_unlabeled_class = 33 [default = false];
+
+  // If image_classes_field for a given image is empty and this field set to
+  // true, it is interpreted as if the annotations on this image were
+  // exhaustive.
+  optional bool image_classes_field_map_empty_to_ones = 36 [default = true];
 }
 
 // A message to configure parameterized evaluation metric.