35fda9730bb058d37e1102208322cd68c2160f10,research/object_detection/meta_architectures/context_rcnn_lib_test.py,ContextRcnnLibTest,test_attention_block,#ContextRcnnLibTest#Any#Any#Any#,97

Before Change

    context_features = tf.ones([2, 2, 3], tf.float32)
    valid_mask = tf.constant([[True, True], [False, False]], tf.bool)
    is_training = False
    projection_layers = {context_rcnn_lib.KEY_NAME: context_rcnn_lib.ContextProjection(bottleneck_dimension, False), context_rcnn_lib.VALUE_NAME: context_rcnn_lib.ContextProjection(bottleneck_dimension, False),
                         context_rcnn_lib.QUERY_NAME: context_rcnn_lib.ContextProjection(bottleneck_dimension, False)}
    
    //Add in the feature layer because this is further down the pipeline and it isn"t automatically injected.
    projection_layers["feature"] = context_rcnn_lib.ContextProjection(output_dimension, False)

After Change

    //Add in the feature layer because this is further down the pipeline and it isn"t automatically injected.
    //projection_layers["feature"] = context_rcnn_lib.ContextProjection(output_dimension, False)

    attention_block = context_rcnn_lib.AttentionBlock(bottleneck_dimension, attention_temperature, False)
    attention_block.set_output_dimension(output_dimension)
    output_features = attention_block([input_features, context_features], is_training, valid_mask)

    // Makes sure the shape is correct.

Instances