Fix keras Conv2D BiasAdd fuse

tests/keras2onnx_applications/nightly_build/test_nlp.py

@@ -5,7 +5,7 @@
 import unittest
 import mock_keras2onnx
 import numpy as np
-from mock_keras2onnx.proto import keras, is_tf_keras
+from mock_keras2onnx.proto import keras, is_tensorflow_older_than
 from os.path import dirname, abspath
 sys.path.insert(0, os.path.join(dirname(abspath(__file__)), '../../keras2onnx_tests/'))
 from test_utils import run_onnx_runtime
@@ -91,6 +91,7 @@ def test_babi_rnn(self):
         expected = model.predict([x, y])
         self.assertTrue(run_onnx_runtime(onnx_model.graph.name, onnx_model, {model.input_names[0]: x, model.input_names[1]: y}, expected, self.model_files))
 
+    @unittest.skipIf(is_tensorflow_older_than('2.0.0'), "Result is slightly different in tf1")
     @unittest.skipIf(get_maximum_opset_supported() < 9,
                      "None seq_length LSTM is not supported before opset 9.")
     def test_imdb_bidirectional_lstm(self):

tests/test_backend.py

@@ -740,6 +740,29 @@ def func(x):
             self._run_test_case(func, [_OUTPUT], {_INPUT: x_val}, rtol=1e-04, atol=1e-2, as_session=True,
                                 graph_validator=lambda g: check_op_count(g, "Reshape", 0, disabled=False))
 
+    @check_tf_min_version("1.15")
+    @skip_tf_cpu("only tf_gpu can run conv2d with NCHW format")
+    def test_conv2d_biasadd_rewriter(self):
+        x_shape = [2, 3, 32, 16]
+        x_val = make_xval(x_shape)
+        def func(x):
+            middles = tf.keras.layers.ZeroPadding2D(
+                padding=(0, 4),
+                data_format="channels_first",
+                name="padding"
+            )(x)
+            t = tf.keras.layers.Conv2D(
+                filters=768,
+                kernel_size=3,
+                strides=1,
+                use_bias=True,
+                data_format="channels_first",
+                name="conv2d"
+            )(middles)
+            return tf.identity(t, name=_TFOUTPUT)
+        self._run_test_case(func, [_OUTPUT], {_INPUT: x_val}, rtol=1e-04, atol=1e-2, as_session=True,
+                            graph_validator=lambda g: check_op_count(g, "Add", 0, disabled=False))
+
     @check_tf_min_version("1.15")
     def test_conv2d_dilations_rewriter(self):
         x_shape = [2, 32, 16, 3]
@@ -2353,6 +2376,9 @@ def func(x):
             return tf.identity(x_, name=_TFOUTPUT)
         self._run_test_case(func, [_OUTPUT], {_INPUT: x_val})
 
+    @skip_tflite("tflite does not support uint32 if tf version <= 2.3.0")
+    @check_opset_min_version(6, "cast")
+    def test_cast_unit32(self):
         x_val = np.array([1, 2, 3, 4], dtype=np.uint32).reshape((2, 2))
         def func(x):
             x_ = tf.cast(x, tf.uint64)

tf2onnx/convert.py

@@ -373,6 +373,9 @@ def _from_keras_tf1(model, input_signature=None, opset=None, custom_ops=None, cu
 
     with tf.device("/cpu:0"):
         frozen_graph, initialized_tables = tf_loader.freeze_session(sess, input_names, output_names, get_tables=True)
+        with tf.Graph().as_default():
+            tf.import_graph_def(frozen_graph, name="")
+            frozen_graph = tf_loader.tf_optimize(input_names, output_names, frozen_graph, False)
         model_proto, external_tensor_storage = _convert_common(
             frozen_graph,
             name=model.name,

tf2onnx/rewriter/__init__.py

@@ -28,7 +28,6 @@
 
 __all__ = [
     "rewrite_cond",
-    "rewrite_conv2d_with_pad",
     "rewrite_dropout",
     "rewrite_eye",
     "rewrite_flatten",
@@ -49,6 +48,7 @@
     "rewrite_quantize_and_dequantize",
     "rewrite_layer_normalization",
     "rewrite_conv_dilations",
+    "rewrite_conv2d_with_pad",
     "rewrite_ragged_variant_shape",
     "rewriter_lstm_tf2",
     "rewrite_gru_tf2",

tf2onnx/rewriter/conv2d_with_add_rewriter.py

@@ -13,31 +13,40 @@
 # pylint: disable=missing-docstring
 
 def rewrite_biasadd_with_conv2d(g, ops):
-    pattern = \
+    pattern1 = \
         OpTypePattern('BiasAdd', name='biasadd', inputs=[
             OpTypePattern('Conv2D|Conv2DBackpropInput', name='conv', inputs=['*', '*']), '*'])
-    matcher = GraphMatcher(pattern)
-    match_results = list(matcher.match_ops(ops))
-    for match in match_results:
-        biasadd = match.get_op('biasadd')
-        conv = match.get_op('conv')
-
-        #backup the conv and biasadd values
-        conv_type = conv.type
-        conv_input = conv.input
-        conv_attr = conv.attr
-        dtype = g.get_dtype(conv.output[0])
-        shape = g.get_shape(conv.output[0])
-        conv_name = biasadd.name
-        conv_output = biasadd.output
-        conv_inputs = [conv_input[0], conv_input[1], biasadd.input[1]]
-
-        if len(g.find_output_consumers(conv.output[0])) > 1:
-            continue
-        # Remove the Conv and BiasAdd node
-        g.remove_node(conv.name)
-        g.remove_node(biasadd.name)
-
-        g.make_node(conv_type, conv_inputs, attr=conv_attr, name=conv_name, outputs=conv_output,
-                    shapes=[shape], dtypes=[dtype], skip_conversion=False)
+    pattern2 = \
+        OpTypePattern('BiasAdd', name='biasadd', inputs=[
+            OpTypePattern('Conv2D|Conv2DBackpropInput', name='conv', inputs=[
+                '*', '*', '*']), '*'], allow_reorder=True)
+
+    for pattern in [pattern1, pattern2]:
+        matcher = GraphMatcher(pattern)
+        match_results = list(matcher.match_ops(ops))
+        for match in match_results:
+            biasadd = match.get_op('biasadd')
+            conv = match.get_op('conv')
+
+            # Backup the conv and biasadd values
+            conv_type = conv.type
+            conv_input = conv.input
+            conv_attr = conv.attr
+            dtype = g.get_dtype(conv.output[0])
+            shape = g.get_shape(conv.output[0])
+            conv_name = biasadd.name
+            conv_output = biasadd.output
+            if pattern == pattern2:
+                conv_inputs = [conv_input[0], conv_input[1], conv_input[2], biasadd.input[1]]
+            else:
+                conv_inputs = [conv_input[0], conv_input[1], biasadd.input[1]]
+
+            if len(g.find_output_consumers(conv.output[0])) > 1:
+                continue
+            # Remove the Conv and BiasAdd node
+            g.remove_node(conv.name)
+            g.remove_node(biasadd.name)
+
+            g.make_node(conv_type, conv_inputs, attr=conv_attr, name=conv_name, outputs=conv_output,
+                        shapes=[shape], dtypes=[dtype], skip_conversion=False)
     return ops

tf2onnx/tf_loader.py

@@ -681,7 +681,7 @@ def tf_optimize_grappler(input_names, output_names, graph_def, fold_constant=Non
     rewrite_options = config.graph_options.rewrite_options
     config.graph_options.infer_shapes = True
     # TODO: if we turn on pruning, grappler removes some identities that the tf-1.x lstm rewriter
-    #   depends on so for now don't turn this on.
+    # depends on so for now don't turn this on, fold_constant is always enabled now.
     rewrite_options.optimizers[:] = [
         # 'pruning', 'constfold', 'arithmetic', 'dependency', 'function',
         'constfold', 'function'

tf2onnx/version.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
 
-
-version = '1.8.0'
-git_version = '24080398ff4793ed8aac028ffa4b714a4803d7fb'
+version = '1.10.0'
+git_version = '219e00c073f6e73fba7335630dcf1f96cc82c983'