Adds a "drop_remainder" option for input readers to handle "exact" evals when batch_size>1.

PiperOrigin-RevId: 339071313

research/object_detection/builders/dataset_builder.py

@@ -242,7 +242,8 @@ def build(input_reader_config, batch_size=None, transform_input_data_fn=None,
       dataset = dataset_map_fn(dataset, transform_input_data_fn,
                                batch_size, input_reader_config)
     if batch_size:
-      dataset = dataset.batch(batch_size, drop_remainder=True)
+      dataset = dataset.batch(batch_size,
+                              drop_remainder=input_reader_config.drop_remainder)
     dataset = dataset.prefetch(input_reader_config.num_prefetch_batches)
     return dataset
 

research/object_detection/protos/input_reader.proto

@@ -30,7 +30,7 @@ enum InputType {
   TF_SEQUENCE_EXAMPLE = 2;  // TfSequenceExample Input
 }
 
-// Next id: 35
+// Next id: 36
 message InputReader {
   // Name of input reader. Typically used to describe the dataset that is read
   // by this input reader.
@@ -93,6 +93,9 @@ message InputReader {
   // Number of parallel decode ops to apply.
   optional uint32 num_parallel_map_calls = 14 [default = 64, deprecated = true];
 
+  // Drop remainder when batch size does not divide dataset size.
+  optional bool drop_remainder = 35 [default = true];
+
   // If positive, TfExampleDecoder will try to decode rasters of additional
   // channels from tf.Examples.
   optional int32 num_additional_channels = 18 [default = 0];