add support for tf.image.resize_bilinear, fix issue in tf.image.resize_nearest_neighbor, numerical correct against onnxmsrtnext

Author: guschmue

setup.py

@@ -74,11 +74,11 @@ def run(self):
     version=VersionInfo.version,
     description='Tensorflow to ONNX converter',
     setup_requires=['pytest-runner'],
-    tests_require=['numpy', 'pytest', 'pytest-cov', 'psutil', 'graphviz'],
+    tests_require=['pytest', 'pytest-cov', 'psutil', 'graphviz', 'pyyaml'],
     cmdclass=cmdclass,
     packages=find_packages(),
     author='[email protected]',
     author_email='[email protected]',
     url='https://github.com/onnx/tensorflow-onnx',
-    install_requires=['pyyaml', 'onnx>=1.2']
+    install_requires=['numpy>=1.14.1', 'onnx>=1.2']
 )

tests/test_backend.py

@@ -823,10 +823,10 @@ def test_strided_slice2(self):
         actual, expected = self._run(output, {x: x_val}, {_INPUT: x_val})
         self.assertAllClose(expected, actual)
 
-    @unittest.skip
+    @unittest.skipIf(BACKEND in ["caffe2", "onnxmsrt"], "not correctly supported")
     def test_resize_nearest_neighbor(self):
         # this should work but no runtime I tried supports it.
-        x_shape = [1, 15, 20, 3]
+        x_shape = [1, 15, 20, 2]
         x_new_size = [30, 40]
         x_val = np.arange(1, 1 + np.prod(x_shape)).astype("float32").reshape(x_shape)
         x = tf.placeholder(tf.float32, x_shape, name=_TFINPUT)
@@ -836,6 +836,19 @@ def test_resize_nearest_neighbor(self):
         actual, expected = self._run(output, {x: x_val}, {_INPUT: x_val})
         self.assertAllClose(expected, actual)
 
+    @unittest.skipIf(BACKEND in ["caffe2", "onnxmsrt"], "not correctly supported")
+    def test_resize_bilinear(self):
+        # this should work but no runtime I tried supports it.
+        x_shape = [1, 15, 20, 2]
+        x_new_size = [30, 40]
+        x_val = np.arange(1, 1 + np.prod(x_shape)).astype("float32").reshape(x_shape)
+        x = tf.placeholder(tf.float32, x_shape, name=_TFINPUT)
+        x_new_size_ = tf.constant(x_new_size)
+        x_ = tf.image.resize_bilinear(x, x_new_size_)
+        output = tf.identity(x_, name=_TFOUTPUT)
+        actual, expected = self._run(output, {x: x_val}, {_INPUT: x_val})
+        self.assertAllClose(expected, actual)
+
     @unittest.skip
     def test_fill(self):
         # no official fill op in onnx

tf2onnx/tfonnx.py

@@ -749,18 +749,22 @@ def lrn_op(ctx, node, name, args):
     return node
 
 
-def upsample_op(ctx, node, name, args):
-    node.type = "Upsample"
+def upsample_op7(ctx, node, name, args):
+    mode = args[0]
     shape = ctx.get_shape(node.input[0])
     target_shape = node.inputs[1].get_tensor_value()
     # https://www.tensorflow.org/api_docs/python/tf/image/resize_nearest_neighbor
     # wants the input to be NHWC - adjust target_shape to this.
     n, h, w, c = shape
     nh, nw = target_shape
-    scaler = [float(n), float(nh) / h, float(nw) / w, float(c)]
+    # scaler = [float(n), float(nh) / h, float(nw) / w, float(c)]
+    scaler = [float(nh) / h, float(nw) / w]
     node.set_attr("scales", scaler)
+    node.set_attr("mode", mode)
     ctx.remove_input(node, node.input[1])
-    return node
+    node.data_format = "NHWC"
+    nodes = conv_convert_inputs(ctx, node, with_kernel=False)
+    return nodes
 
 
 def multinomial_op(ctx, node, name, args):
@@ -897,7 +901,8 @@ def spacetodepth_op(ctx, node, name, args):
 
 _OPSET_7 = {
     "Tile": (tile_op7, []),
-    "ResizeNearestNeighbor": (upsample_op, []),
+    "ResizeNearestNeighbor": (upsample_op7, ["Upsample", "nearest"]),
+    "ResizeBilinear": (upsample_op7, ["Upsample", "linear"]),
     "BiasAdd": (biasadd_op7, []),
     "BiasAddV1": (biasadd_op7, []),
     "Add": (broadcast_op7, []),