Merge branch 'panoptic-segmentation' into panoptic-deeplab-modeling

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 24
+  // Next ID 25
   message DeepMACMaskEstimation {
     // The loss used for penalizing mask predictions.
     optional ClassificationLoss classification_loss = 1;
@@ -485,6 +485,14 @@ message CenterNet {
 
     optional int32 color_consistency_warmup_start = 23 [default=0];
 
+    // DeepMAC has been refactored to process the entire batch at once,
+    // instead of the previous (simple) approach of processing one sample at
+    // a time. Because of this, the memory consumption has increased and
+    // it's crucial to only feed the mask head the last stage outputs
+    // from the hourglass. Doing so halves the memory requirement of the
+    // mask head and does not cause a drop in evaluation metrics.
+    optional bool use_only_last_stage = 24 [default=false];
+
   }
 
   optional DeepMACMaskEstimation deepmac_mask_estimation = 14;

research/seq_flow_lite/layers/BUILD

@@ -76,3 +76,29 @@ py_strict_library(
         "//tf_ops:sequence_string_projection_op_v2_py",  # sequence projection
     ],
 )
+
+py_strict_library(
+    name = "misc_layers",
+    srcs = ["misc_layers.py"],
+    srcs_version = "PY3",
+    deps = [
+        # package tensorflow
+        "//layers:base_layers",  # sequence projection
+        "//layers:dense_layers",  # sequence projection
+        "//layers:quantization_layers",  # sequence projection
+    ],
+)
+
+py_strict_library(
+    name = "qrnn_layers",
+    srcs = ["qrnn_layers.py"],
+    srcs_version = "PY3",
+    deps = [
+        ":base_layers",
+        ":conv_layers",
+        ":dense_layers",
+        ":quantization_layers",
+        # package tensorflow
+        "//tf_ops:tf_custom_ops_py",  # sequence projection
+    ],
+)

research/seq_flow_lite/models/sgnn/BUILD

@@ -90,6 +90,7 @@ py_binary(
     main = "run_tflite.py",
     python_version = "PY3",
     deps = [
+        ":sgnn_projection_op_resolver",
         # Expect numpy installed
         # package TFLite flex delegate
         # package TFLite interpreter

research/seq_flow_lite/models/sgnn/sgnn.py

@@ -43,8 +43,6 @@ Hparams = collections.namedtuple(
 
 def preprocess(text):
   """Normalize the text, and return tokens."""
-  assert len(text.get_shape().as_list()) == 2
-  assert text.get_shape().as_list()[-1] == 1
   text = tf.reshape(text, [-1])
   text = tf_text.case_fold_utf8(text)
   tokenizer = tflite_text_api.WhitespaceTokenizer()