deprecate verbose argument for process_tf_graph

Author: nbcsm

tests/run_pretrained_models.py

@@ -153,7 +153,7 @@ def run_tensorflow(self, sess, inputs):
 
     def to_onnx(self, tf_graph, opset=None, extra_opset=None, shape_override=None, input_names=None):
         """Convert graph to tensorflow."""
-        return process_tf_graph(tf_graph, continue_on_error=False, verbose=True, opset=opset,
+        return process_tf_graph(tf_graph, continue_on_error=False, opset=opset,
                                 extra_opset=extra_opset, target=Test.target, shape_override=shape_override,
                                 input_names=input_names, output_names=self.output_names)
 

tf2onnx/convert.py

@@ -112,7 +112,6 @@ def main():
     with tf.Session(graph=tf_graph):
         g = process_tf_graph(tf_graph,
                              continue_on_error=args.continue_on_error,
-                             verbose=args.verbose,
                              target=args.target,
                              opset=args.opset,
                              custom_op_handlers=custom_ops,

tf2onnx/rewriter/custom_rnn_rewriter.py

@@ -7,19 +7,21 @@
 
 from __future__ import division
 from __future__ import print_function
+
 import logging
 import sys
 import traceback
+
 from onnx import onnx_pb
 import numpy as np
+
 from tf2onnx.rewriter.loop_rewriter_base import LoopRewriterBase, Context
 from tf2onnx.rewriter.rnn_utils import REWRITER_RESULT, get_rnn_scope_name, parse_rnn_loop
-from tf2onnx.tfonnx import utils
-
-
+from tf2onnx import utils
 
 logger = logging.getLogger(__name__)
 
+
 # pylint: disable=missing-docstring,invalid-name,unused-argument,using-constant-test,broad-except,protected-access
 
 
@@ -159,7 +161,6 @@ def _connect_scan_with_output(self, context, scan_node):
                     self.g.replace_all_inputs(self.g.get_nodes(), out_tensor_value_info.id, scan_node.output[index])
             index += 1
 
-
     def _adapt_scan_sequence_input_or_output(self, target_name, input_id, handle_output=False):
         nodes_to_add = []
         shape_node = self.g.make_node("Shape", [input_id])
@@ -198,7 +199,7 @@ def _adapt_scan_sequence_input_or_output(self, target_name, input_id, handle_out
             else:
                 # add a fake batch size : 1
                 fake_batch_size_node = self.g.make_const(utils.make_name(target_name + "_target_shape"),
-                                                         np.array([1,], dtype=np.int64))
+                                                         np.array([1], dtype=np.int64))
                 nodes_to_add.append(fake_batch_size_node)
                 new_shape_node = self.g.make_node("Concat",
                                                   [fake_batch_size_node.output[0], shape_node.output[0]],

tf2onnx/rewriter/loop_rewriter.py

@@ -7,16 +7,17 @@
 
 from __future__ import division
 from __future__ import print_function
+
 import logging
 import sys
 import traceback
+
 from onnx import TensorProto
 import numpy as np
+
 from tf2onnx.rewriter.loop_rewriter_base import LoopRewriterBase, Context
 from tf2onnx.rewriter.rnn_utils import REWRITER_RESULT
-from tf2onnx.tfonnx import utils
-
-
+from tf2onnx import utils
 
 logger = logging.getLogger(__name__)
 

tf2onnx/tfonnx.py

@@ -10,7 +10,6 @@
 from __future__ import unicode_literals
 
 import collections
-import logging
 import sys
 import traceback
 
@@ -23,12 +22,12 @@
 import tf2onnx
 import tf2onnx.onnx_opset  # pylint: disable=unused-import
 import tf2onnx.custom_opsets  # pylint: disable=unused-import
-from tf2onnx import constants, schemas, utils, handler
 from tf2onnx.graph import Graph
 from tf2onnx.graph_matcher import OpTypePattern, GraphMatcher
 from tf2onnx.rewriter import *  # pylint: disable=wildcard-import
 from tf2onnx.shape_inference import infer_shape_for_graph
 from tf2onnx.utils import port_name
+from . import constants, logging, schemas, utils, handler
 
 logger = logging.getLogger(__name__)
 
@@ -669,7 +668,7 @@ def process_tf_graph(tf_graph, continue_on_error=False, verbose=False, target=No
         Args:
             tf_graph: tensorflow graph
             continue_on_error: if an op can't be processed (aka there is no mapping), continue
-            verbose: print summary stats
+            verbose: print summary stats (deprecated)
             target: list of workarounds applied to help certain platforms
             opset: the opset to be used (int, default is latest)
             custom_op_handlers: dictionary of custom ops handlers
@@ -682,6 +681,11 @@ def process_tf_graph(tf_graph, continue_on_error=False, verbose=False, target=No
         Return:
             onnx graph
     """
+    # TODO: remove verbose argument in future release
+    if verbose:
+        logger.warning("Argument verbose for process_tf_graph is deprecated. Please use --verbose option instead.")
+    del verbose
+
     opset = utils.find_opset(opset)
     print("using tensorflow={}, onnx={}, opset={}, tfonnx={}/{}".format(
         tf.__version__, utils.get_onnx_version(), opset,
@@ -795,10 +799,11 @@ def compat_handler(ctx, node, **kwargs):
 
     g.update_proto()
 
-    if verbose:
-        print("tensorflow ops: {}".format(op_cnt))
-        print("tensorflow attr: {}".format(attr_cnt))
-        print("onnx mapped: {}".format(mapped_op))
-        print("onnx unmapped: {}".format(unmapped_op))
+    logger.verbose(
+        "Summay Stats:\n"
+        "\ttensorflow ops: {}\n"
+        "\ttensorflow attr: {}\n"
+        "\tonnx mapped: {}\n"
+        "\tonnx unmapped: {}".format(op_cnt, attr_cnt, mapped_op, unmapped_op))
 
     return g