add the tf1 keras missing optimization

Signed-off-by: hwangdeyu <[email protected]>

Author: hwangdeyu

tests/keras2onnx_unit_tests/test_layers.py

@@ -1198,28 +1198,6 @@ def test_conv2d_transpose_2(runner, padding):
     assert runner(onnx_model.graph.name, onnx_model, data, expected)
 
 
-def test_conv2d_bias_add(runner):
-    size = 128
-    input_img = Input((size, size, 64))
-    x = tf.keras.layers.ZeroPadding2D(
-        padding=(0, 4),
-        data_format="channels_first",
-        name="padding")(input_img)
-    y = tf.keras.layers.Conv2D(
-        filters=1,
-        kernel_size=(9, 9),
-        strides=(1, 1),
-        use_bias=True,
-        data_format="channels_first",
-        name="conv2d",
-    )(x)
-    model = tf.keras.Model(inputs=input_img, outputs=y)
-    data = np.random.rand(1, size, size, 64).astype(np.float32)
-    onnx_model = convert_keras(model, model.name)
-    expected = model.predict(data)
-    assert runner(onnx_model.graph.name, onnx_model, data, expected)
-
-
 def test_conv2d_padding_same(conv2_runner):
     conv2_runner(3, 5, (2, 2), (1, 1), (5, 5), padding='same')
     conv2_runner(8, 16, (1, 1), (2, 2), (60, 60), padding='same')

tests/test_backend.py

@@ -740,6 +740,26 @@ 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")
+    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, constant_fold=False,
+                            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]

tf2onnx/convert.py

@@ -373,6 +373,10 @@ 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)
+        tf_loader.tf_reset_default_graph()
+        tf.import_graph_def(frozen_graph, name='')
+        sess = tf.keras.backend.get_session(model.outputs)
+        frozen_graph = tf_loader.tf_optimize(input_names, output_names, frozen_graph)
         model_proto, external_tensor_storage = _convert_common(
             frozen_graph,
             name=model.name,

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'