1 files changed, 0 insertions, 116 deletions
diff --git a/compiler/bcq-tools/preserve_bcq_info b/compiler/bcq-tools/preserve_bcq_info
deleted file mode 100644
index 2ede8d4d0..000000000
--- a/compiler/bcq-tools/preserve_bcq_info
+++ /dev/null
@@ -1,116 +0,0 @@
-#!/usr/bin/env python3
-
-import tensorflow as tf
-import numpy as np
-
-import argparse
-import sys
-
-
-def _get_parser():
-    """
-    Returns an ArgumentParser for preserving BCQ information.
-    """
-    parser = argparse.ArgumentParser(
-        description=("Command line tool to preserve BCQ information"))
-
-    # Input and output path.
-    parser.add_argument(
-        "-i",
-        "--input_path",
-        type=str,
-        help="Full filepath of the input file.",
-        required=True)
-    parser.add_argument(
-        "-o",
-        "--output_path",
-        type=str,
-        help="Full filepath of the output file.",
-        required=True)
-
-    return parser
-
-
-def load_graph(frozen_graph_filename):
-    """
-    Load graph from frozen pb file
-    """
-    with tf.compat.v1.gfile.GFile(frozen_graph_filename, "rb") as f:
-        graph_def = tf.compat.v1.GraphDef()
-        graph_def.ParseFromString(f.read())
-    with tf.Graph().as_default() as graph:
-        tf.import_graph_def(graph_def, name='')
-    return graph
-
-
-def preserve_bcq_info(flags):
-    """
-    Generate unique dummy value from -1 to -N.
-
-    We use negative values to preserve BCQ information because
-    positive values may cause some confusion with real BCQ information values.
-    """
-
-    class UniqueValueGen:
-        def __init__(self):
-            self.unique_value = -1
-
-        def gen(self):
-            val = self.unique_value
-            self.unique_value = val - 1
-            return val
-
-    unique_value = UniqueValueGen()
-
-    original_graph_model = load_graph(flags.input_path)
-    original_graph_model_def = original_graph_model.as_graph_def()
-
-    new_graph = tf.compat.v1.GraphDef()
-    substitution_dict = {}
-
-    DT_INT32 = None  # Just for copying DT_INT32 attribute value
-
-    for node in original_graph_model_def.node:
-        if node.op == "Const":
-            # Because bcqinfo_do_w_x is BOOL type, we cannot add dummy value at the end.
-            # Therefore we should convert the type to INT32 type.
-            if "/bcqinfo_do_w_x" in node.name:
-                original_tensor = tf.make_ndarray(node.attr["value"].tensor)
-                substitution_dict[node.name] = tf.make_tensor_proto(
-                    [int(original_tensor[0]), unique_value.gen()], tf.int32)
-
-            preserved_bcqinfo_list = ["/bcqinfo_number_of_clusters", "/bcqinfo_size_of_clusters", 
-                "/bcqinfo_qbits_of_clusters"]
-
-            if any(name in node.name for name in preserved_bcqinfo_list):
-                original_tensor = tf.make_ndarray(
-                    node.attr["value"].tensor)  # variable name change
-                substitution_dict[node.name] = tf.make_tensor_proto(
-                    np.append(original_tensor, unique_value.gen()), tf.int32)
-                DT_INT32 = node.attr["dtype"]
-
-    for node in original_graph_model_def.node:
-        if node.name in substitution_dict:
-            new_node = new_graph.node.add()
-            new_node.op = "Const"
-            new_node.name = node.name
-            new_node.attr["dtype"].CopyFrom(DT_INT32)
-            new_node.attr["value"].tensor.CopyFrom(substitution_dict[node.name])
-        else:
-            new_node = new_graph.node.add()
-            new_node.CopyFrom(node)
-
-    tf.io.write_graph(new_graph, '.', flags.output_path, False)
-
-
-def main():
-    # Parse argument.
-    parser = _get_parser()
-    flags = parser.parse_known_args(args=sys.argv[1:])
-
-    # Generate a new pb file, which BCQ information is preserved.
-    preserve_bcq_info(flags[0])
-
-
-if __name__ == "__main__":
-    main()