Respect --load when exporting

DeepSpeech.py

@@ -21,7 +21,6 @@ from datetime import datetime
 from ds_ctcdecoder import ctc_beam_search_decoder, Scorer
 from evaluate import evaluate
 from six.moves import zip, range
-from tensorflow.python.tools import freeze_graph, strip_unused_lib
 from util.config import Config, initialize_globals
 from util.checkpoints import load_or_init_graph
 from util.feeding import create_dataset, samples_to_mfccs, audiofile_to_features
@@ -733,49 +732,40 @@ def export():
     if FLAGS.export_language:
         outputs['metadata_language'] = tf.constant([FLAGS.export_language.encode('utf-8')], name='metadata_language')
 
+    # Prevent further graph changes
+    tfv1.get_default_graph().finalize()
+
     output_names_tensors = [tensor.op.name for tensor in outputs.values() if isinstance(tensor, Tensor)]
     output_names_ops = [op.name for op in outputs.values() if isinstance(op, Operation)]
-    output_names = ",".join(output_names_tensors + output_names_ops)
+    output_names = output_names_tensors + output_names_ops
 
-    # Create a saver using variables from the above newly created graph
-    saver = tfv1.train.Saver()
+    with tf.Session() as session:
+        # Restore variables from checkpoint
+        if FLAGS.load == 'auto':
+            method_order = ['best', 'last']
+        else:
+            method_order = [FLAGS.load]
+        load_or_init_graph(session, method_order)
 
-    # Restore variables from training checkpoint
-    checkpoint = tf.train.get_checkpoint_state(FLAGS.save_checkpoint_dir)
-    checkpoint_path = checkpoint.model_checkpoint_path
+        output_filename = FLAGS.export_name + '.pb'
+        if FLAGS.remove_export:
+            if os.path.isdir(FLAGS.export_dir):
+                log_info('Removing old export')
+                shutil.rmtree(FLAGS.export_dir)
 
-    output_filename = FLAGS.export_name + '.pb'
-    if FLAGS.remove_export:
-        if os.path.isdir(FLAGS.export_dir):
-            log_info('Removing old export')
-            shutil.rmtree(FLAGS.export_dir)
-    try:
         output_graph_path = os.path.join(FLAGS.export_dir, output_filename)
 
         if not os.path.isdir(FLAGS.export_dir):
             os.makedirs(FLAGS.export_dir)
 
-        def do_graph_freeze(output_file=None, output_node_names=None, variables_blacklist=''):
-            frozen = freeze_graph.freeze_graph_with_def_protos(
-                input_graph_def=tfv1.get_default_graph().as_graph_def(),
-                input_saver_def=saver.as_saver_def(),
-                input_checkpoint=checkpoint_path,
-                output_node_names=output_node_names,
-                restore_op_name=None,
-                filename_tensor_name=None,
-                output_graph=output_file,
-                clear_devices=False,
-                variable_names_blacklist=variables_blacklist,
-                initializer_nodes='')
+        frozen_graph = tfv1.graph_util.convert_variables_to_constants(
+            sess=session,
+            input_graph_def=tfv1.get_default_graph().as_graph_def(),
+            output_node_names=output_names)
 
-            input_node_names = []
-            return strip_unused_lib.strip_unused(
-                input_graph_def=frozen,
-                input_node_names=input_node_names,
-                output_node_names=output_node_names.split(','),
-                placeholder_type_enum=tf.float32.as_datatype_enum)
-
-        frozen_graph = do_graph_freeze(output_node_names=output_names)
+        frozen_graph = tfv1.graph_util.extract_sub_graph(
+            graph_def=frozen_graph,
+            dest_nodes=output_names)
 
         if not FLAGS.export_tflite:
             with open(output_graph_path, 'wb') as fout:
@@ -784,7 +774,7 @@ def export():
             output_tflite_path = os.path.join(FLAGS.export_dir, output_filename.replace('.pb', '.tflite'))
 
             converter = tf.lite.TFLiteConverter(frozen_graph, input_tensors=inputs.values(), output_tensors=outputs.values())
-            converter.optimizations = [ tf.lite.Optimize.DEFAULT ]
+            converter.optimizations = [tf.lite.Optimize.DEFAULT]
             # AudioSpectrogram and Mfcc ops are custom but have built-in kernels in TFLite
             converter.allow_custom_ops = True
             tflite_model = converter.convert()
@@ -792,11 +782,7 @@ def export():
             with open(output_tflite_path, 'wb') as fout:
                 fout.write(tflite_model)
 
-            log_info('Exported model for TF Lite engine as {}'.format(os.path.basename(output_tflite_path)))
-
         log_info('Models exported at %s' % (FLAGS.export_dir))
-    except RuntimeError as e:
-        log_error(str(e))
 
 def package_zip():
     # --export_dir path/to/export/LANG_CODE/ => path/to/export/LANG_CODE.zip