Fix ResizeBilinear and ResizeNearestNeighbor and update related tests (onnx#2130)

1) Fix tf ResizeBilinear and ResizeNearestNeighbor op with align_corners=True or half_pixel_centers=True
2) Update related tests to make input data include x.5 and add tests for align_corners=True

Signed-off-by: cosine <[email protected]>
Co-authored-by: Jay Zhang <[email protected]>

Author: cosineFish

tests/test_backend.py

@@ -3192,7 +3192,7 @@ def func(x):
     def test_resize_nearest_neighbor(self):
         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_val = np.arange(1, 1 + np.prod(x_shape)/10, 0.1).astype("float32").reshape(x_shape)
         def func(x):
             x_new_size_ = tf.constant(x_new_size)
             x_ = resize_nearest_neighbor(x, x_new_size_)
@@ -3202,7 +3202,7 @@ def func(x):
     @check_opset_min_version(9, "resize_nearest_neighbor")
     def test_resize_nearest_neighbor_with_non_const(self):
         x_shape = [3, 10, 8, 5]
-        x_val = np.arange(1, 1 + np.prod(x_shape), dtype=np.float32).reshape(x_shape)
+        x_val = np.arange(1, 1 + np.prod(x_shape)/10, 0.1, dtype=np.float32).reshape(x_shape)
         x_new_size = np.array([20, 16]).astype(np.int32)
         def func(x, x_new_size_):
             x_ = resize_nearest_neighbor(x, x_new_size_)
@@ -3221,13 +3221,26 @@ def func(x):
             return tf.identity(x_, name=_TFOUTPUT)
         _ = self._run_test_case(func, [_OUTPUT], {_INPUT: x_val})
 
+    @skip_caffe2_backend()
+    @check_tf_min_version("1.14")
+    @check_opset_min_version(11, "coordinate_transformation_mode attr of resize_bilinear")
+    def test_resize_bilinear_align_coreners(self):
+        x_shape = [1, 15, 20, 2]
+        x_new_size = [30, 40]
+        x_val = np.arange(1, 1 + np.prod(x_shape)/10, 0.1).astype("float32").reshape(x_shape)
+        def func(x):
+            x_new_size_ = tf.constant(x_new_size)
+            x_ = resize_bilinear(x, x_new_size_, align_corners=True)
+            return tf.identity(x_, name=_TFOUTPUT)
+        _ = self._run_test_case(func, [_OUTPUT], {_INPUT: x_val})
+
     @skip_caffe2_backend()
     @check_tf_min_version("1.14")
     @check_opset_min_version(11, "coordinate_transformation_mode attr")
     def test_resize_bilinear_half_pixel_centers(self):
         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_val = np.arange(1, 1 + np.prod(x_shape)/10, 0.1).astype("float32").reshape(x_shape)
         def func(x):
             x_new_size_ = tf.constant(x_new_size)
             x_ = resize_bilinear(x, x_new_size_, half_pixel_centers=True)
@@ -3237,7 +3250,7 @@ def func(x):
     @check_opset_min_version(9, "resize_bilinear")
     def test_resize_bilinear_with_non_const(self):
         x_shape = [3, 10, 8, 5]
-        x_val = np.arange(1, 1 + np.prod(x_shape), dtype=np.float32).reshape(x_shape)
+        x_val = np.arange(1, 1 + np.prod(x_shape)/10, 0.1, dtype=np.float32).reshape(x_shape)
         x_new_size = np.array([20, 16]).astype(np.int32)
         def func(x, x_new_size_):
             x_ = resize_bilinear(x, x_new_size_)
@@ -3248,7 +3261,7 @@ def func(x, x_new_size_):
     def test_resize_bilinear_with_non_const2(self):
         # scales has an element larger than 1 and also has an element less that 1
         x_shape = [3, 100, 8, 5]
-        x_val = np.arange(1, 1 + np.prod(x_shape), dtype=np.float32).reshape(x_shape)
+        x_val = np.arange(1, 1 + np.prod(x_shape)/10, 0.1, dtype=np.float32).reshape(x_shape)
         x_new_size = np.array([20, 16]).astype(np.int32)
         def func(x, x_new_size_):
             x_ = resize_bilinear(x, x_new_size_)
@@ -3259,7 +3272,7 @@ def func(x, x_new_size_):
     @check_opset_min_version(11, "resize_bilinear_v2")
     def test_resize_bilinear_v2_with_non_const(self):
         x_shape = [3, 10, 8, 5]
-        x_val = np.arange(1, 1 + np.prod(x_shape), dtype=np.float32).reshape(x_shape)
+        x_val = np.arange(1, 1 + np.prod(x_shape)/10, 0.1, dtype=np.float32).reshape(x_shape)
         x_new_size = np.array([20, 16]).astype(np.int32)
         def func(x, x_new_size_):
             x_ = resize_bilinear_v2(x, x_new_size_)
@@ -3304,7 +3317,7 @@ def func(x, y):
     def test_resize_bicubic(self):
         x_shape = [1, 15, 20, 2]
         new_size_val = np.array([30, 40], dtype=np.int32)
-        x_val = np.arange(1, 1 + np.prod(x_shape)).astype("float32").reshape(x_shape)
+        x_val = np.arange(1, 1 + np.prod(x_shape)/10, 0.1).astype("float32").reshape(x_shape)
         def func(x, new_size):
             y = tf.image.resize(x, new_size, method=tf.image.ResizeMethod.BICUBIC)
             return tf.identity(y, name=_TFOUTPUT)
@@ -3314,7 +3327,7 @@ def func(x, new_size):
     def test_resize_nearest_neighbor2(self):
         x_shape = [1, 300, 20, 2]
         x_new_size = [30, 40]
-        x_val = np.arange(1, 1 + np.prod(x_shape)).astype("float32").reshape(x_shape)
+        x_val = np.arange(1, 1 + np.prod(x_shape)/10, 0.1).astype("float32").reshape(x_shape)
         def func(x):
             x_new_size_ = tf.constant(x_new_size)
             x_ = resize_nearest_neighbor(x, x_new_size_)
@@ -3326,13 +3339,25 @@ def func(x):
     def test_resize_nearest_neighbor_half_pixel_centers(self):
         x_shape = [1, 10, 20, 2]
         x_new_size = [20, 40]
-        x_val = np.arange(1, 1 + np.prod(x_shape)).astype("float32").reshape(x_shape)
+        x_val = np.arange(1, 1 + np.prod(x_shape)/10, 0.1).astype("float32").reshape(x_shape)
         def func(x):
             x_new_size_ = tf.constant(x_new_size)
             x_ = resize_nearest_neighbor(x, x_new_size_, half_pixel_centers=True)
             return tf.identity(x_, name=_TFOUTPUT)
         _ = self._run_test_case(func, [_OUTPUT], {_INPUT: x_val})
 
+    @check_tf_min_version("1.14")
+    @check_opset_min_version(11, "coordinate_transformation_mode and nearest_mode attr")
+    def test_resize_nearest_neighbor_align_corners(self):
+        x_shape = [1, 10, 20, 2]
+        x_new_size = [20, 40]
+        x_val = np.arange(1, 1 + np.prod(x_shape)/10, 0.1).astype("float32").reshape(x_shape)
+        def func(x):
+            x_new_size_ = tf.constant(x_new_size)
+            x_ = resize_nearest_neighbor(x, x_new_size_, align_corners=True)
+            return tf.identity(x_, name=_TFOUTPUT)
+        _ = self._run_test_case(func, [_OUTPUT], {_INPUT: x_val})
+
     @check_opset_min_version(9, "fill")
     def test_fill_float32(self):
         x_shape = [1, 15, 20, 2]

tf2onnx/onnx_opset/nn.py

@@ -1409,11 +1409,11 @@ def version_11(cls, ctx, node, **kwargs):
         nearest_mode = "floor"
         if "align_corners" in node.attr and node.attr["align_corners"].i:
             transformation_mode = "align_corners"
+            nearest_mode = "round_prefer_ceil"
         if "half_pixel_centers" in node.attr and node.attr["half_pixel_centers"].i:
             if node.type == "ResizeNearestNeighbor" and not ctx.is_target(constants.TARGET_TENSORRT):
                 # TensorRT only supports nearest_mode = "floor" for mode = "nearest"
-                transformation_mode = "half_pixel"
-                nearest_mode = "round_prefer_ceil"
+                transformation_mode = "tf_half_pixel_for_nn"
             else:
                 transformation_mode = "half_pixel"
         attr = {"mode": mode, "nearest_mode": nearest_mode, "coordinate_transformation_mode": transformation_mode,
@@ -1435,6 +1435,12 @@ def _convert_since_9(cls, ctx, node, op_type, use_target_size=False):
         # wants the input to be NHWC - adjust target_shape to this.
         utils.make_sure(node.type != "ResizeBicubic", "Opset 11 is required for bicubic interpolation for node %s",
                         node.name)
+        if "align_corners" in node.attr:
+            utils.make_sure(not node.attr["align_corners"].i,
+                            "Opset 11 is required for align_corners=True for node %s", node.name)
+        if "half_pixel_centers" in node.attr:
+            utils.make_sure(not node.attr["half_pixel_centers"].i,
+                            "Opset 11 is required for half_pixel_centers=True for node %s", node.name)
         mode = "linear" if node.type == "ResizeBilinear" else "nearest"
 
         # because onnxruntime only supports to scale the last two dims so transpose is inserted