[TOPI] add 3D upsampling Op.

docs/langref/relay_op.rst

@@ -77,6 +77,7 @@ This level enables typical convnet models.
    tvm.relay.nn.global_max_pool2d
    tvm.relay.nn.global_avg_pool2d
    tvm.relay.nn.upsampling
+   tvm.relay.nn.upsampling3d
    tvm.relay.nn.batch_flatten
    tvm.relay.nn.pad
    tvm.relay.nn.lrn
@@ -254,6 +255,7 @@ Level 2 Definitions
 .. autofunction:: tvm.relay.nn.global_max_pool2d
 .. autofunction:: tvm.relay.nn.global_avg_pool2d
 .. autofunction:: tvm.relay.nn.upsampling
+.. autofunction:: tvm.relay.nn.upsampling3d
 .. autofunction:: tvm.relay.nn.batch_flatten
 .. autofunction:: tvm.relay.nn.pad
 .. autofunction:: tvm.relay.nn.lrn

include/tvm/relay/attrs/nn.h

@@ -589,6 +589,39 @@ struct UpSamplingAttrs : public tvm::AttrsNode<UpSamplingAttrs> {
   }
 };
 
+/*! \brief Attributes for upsampling3d operator */
+struct UpSampling3DAttrs : public tvm::AttrsNode<UpSampling3DAttrs> {
+  double scale_d;
+  double scale_h;
+  double scale_w;
+  std::string layout;
+  std::string method;
+  std::string coordinate_transformation_mode;
+
+  TVM_DECLARE_ATTRS(UpSampling3DAttrs, "relay.attrs.UpSampling3DAttrs") {
+    TVM_ATTR_FIELD(scale_d)
+        .describe("The upsampling factor for depth");
+    TVM_ATTR_FIELD(scale_h)
+        .describe("The upsampling factor for height");
+    TVM_ATTR_FIELD(scale_w)
+        .describe("The upsampling factor for width");
+    TVM_ATTR_FIELD(layout).set_default("NCDHW")
+        .describe("Dimension ordering of input data. Can be 'NCDHW', 'NDHWC', etc."
+                  "'N', 'C', 'D', 'H', 'W' stands for batch, channel, depth, height, and width"
+                  "dimensions respectively. Upsampling is applied on the 'D', 'H' and"
+                  "'W' dimensions.");
+    TVM_ATTR_FIELD(method).set_default("nearest_neighbor")
+        .describe("Specify the mode to use for scaling."
+                  "nearest_neighbor -  Nearest Neighbor"
+                  "trilinear - Trilinear Interpolation");
+    TVM_ATTR_FIELD(coordinate_transformation_mode).set_default("half_pixel")
+        .describe("Describes how to transform the coordinate in the resized tensor"
+                  "to the coordinate in the original tensor."
+                  "Refer to the ONNX Resize operator specification for details"
+                  "Available options are half_pixel, align_corners and asymmetric");
+  }
+};
+
 /*! \brief Attributes used for the padding operator */
 struct PadAttrs : public tvm::AttrsNode<PadAttrs> {
   double pad_value;

python/tvm/relay/op/nn/_nn.py

@@ -541,6 +541,25 @@ def compute_upsampling(attrs, inputs, out_dtype, target):
     align_corners = attrs.align_corners
     return [topi.nn.upsampling(inputs[0], scale_h, scale_w, layout, method, align_corners)]
 
+# upsampling3d
+reg.register_schedule("nn.upsampling3d", reg.schedule_injective)
+
+def schedule_upsampling3d(_, outs, target):
+    """Schedule definition of upsampling3d"""
+    with target:
+        return topi.generic.schedule_injective(outs)
+
+@reg.register_compute("nn.upsampling3d")
+def compute_upsampling3d(attrs, inputs, out_dtype, target):
+    scale_d = attrs.scale_d
+    scale_h = attrs.scale_h
+    scale_w = attrs.scale_w
+    layout = attrs.layout
+    method = attrs.method
+    coordinate_transformation_mode = attrs.coordinate_transformation_mode
+    return [topi.nn.upsampling3d(inputs[0], scale_d, scale_h, scale_w, layout, method,\
+        coordinate_transformation_mode)]
+
 # pad
 reg.register_schedule("nn.pad", schedule_broadcast)
 

python/tvm/relay/op/nn/nn.py

@@ -771,6 +771,58 @@ def upsampling(data,
     return _make.upsampling(data, scale_h, scale_w, layout, method, align_corners)
 
 
+def upsampling3d(data,
+                 scale_d=1,
+                 scale_h=1,
+                 scale_w=1,
+                 layout="NCDHW",
+                 method="nearest_neighbor",
+                 coordinate_transformation_mode="half_pixel"):
+    """3D Upsampling.
+
+    This operator takes data as input and does 3D scaling to the given scale factor.
+    In the default case, where the data_layout is `NCDHW`
+    with data of shape (n, c, d, h, w)
+    out will have a shape (n, c, d*scale_d, h*scale_h, w*scale_w)
+
+    method indicates the algorithm to be used while calculating the out value
+    and method can be one of ("trilinear", "nearest_neighbor")
+
+    Parameters
+    ----------
+    data : tvm.relay.Expr
+        The input data to the operator.
+
+    scale_d : tvm.relay.Expr
+        The scale factor for depth upsampling.
+
+    scale_h : tvm.relay.Expr
+        The scale factor for height upsampling.
+
+    scale_w : tvm.relay.Expr
+        The scale factor for width upsampling.
+
+    layout : str, optional
+        Layout of the input.
+
+    method : str, optional
+        Scale method to used [nearest_neighbor, bilinear, bicubic].
+
+    coordinate_transformation_mode: string, optional
+        Describes how to transform the coordinate in the resized tensor
+        to the coordinate in the original tensor.
+        Refer to the ONNX Resize operator specification for details.
+        Available options are "half_pixel", "align_corners" and "asymmetric".
+
+    Returns
+    -------
+    result : tvm.relay.Expr
+        The computed result.
+    """
+    return _make.upsampling3d(data, scale_d, scale_h, scale_w, layout, method,
+                              coordinate_transformation_mode)
+
+
 def batch_flatten(data):
     """BatchFlatten.
 

python/tvm/relay/op/op_attrs.py

@@ -63,6 +63,10 @@ class FIFOBufferAttrs(Attrs):
 class UpSamplingAttrs(Attrs):
     """Attributes for nn.upsampling"""
 
+@register_relay_attr_node
+class UpSampling3DAttrs(Attrs):
+    """Attributes for nn.upsampling3d"""
+
 @register_relay_attr_node
 class PadAttrs(Attrs):
     """Attributes for nn.pad"""

src/relay/op/nn/upsampling.cc

@@ -33,6 +33,7 @@ namespace tvm {
 namespace relay {
 
 TVM_REGISTER_NODE_TYPE(UpSamplingAttrs);
+TVM_REGISTER_NODE_TYPE(UpSampling3DAttrs);
 
 template <typename T>
 Array<Array<Layout> > UpsamplingInferCorrectLayout(
@@ -50,8 +51,11 @@ Array<Array<Layout> > UpsamplingInferCorrectLayout(
     Layout input = new_in_layouts[0];
     if (input.IndexOf(LayoutAxis::Get('W')) == raw_layout.IndexOf(LayoutAxis::Get('W')) &&
       input.IndexOf(LayoutAxis::Get('H')) == raw_layout.IndexOf(LayoutAxis::Get('H')) &&
-        !input.Contains(LayoutAxis::Get('w')) && !input.Contains(LayoutAxis::Get('h'))) {
-      params->layout = input.name();  // modify self to follow the input layout
+        !input.Contains(LayoutAxis::Get('w')) && !input.Contains(LayoutAxis::Get('h'))&&
+        (input.IndexOf(LayoutAxis::Get('D')) == -1 ||
+        (input.IndexOf(LayoutAxis::Get('D')) == raw_layout.IndexOf(LayoutAxis::Get('D')) &&
+        !input.Contains(LayoutAxis::Get('d'))))) {
+        params->layout = input.name();  // modify self to follow the input layout
     }
   }
 
@@ -108,7 +112,6 @@ Expr MakeUpSampling(Expr data,
   return CallNode::make(op, {data}, Attrs(attrs), {});
 }
 
-
 TVM_REGISTER_API("relay.op.nn._make.upsampling")
 .set_body_typed(MakeUpSampling);
 
@@ -138,5 +141,86 @@ RELAY_REGISTER_OP("nn.upsampling")
 .set_attr<TOpPattern>("TOpPattern", kInjective);
 
 
+// UpSampling3D
+bool UpSampling3DRel(const Array<Type>& types,
+                     int num_inputs,
+                     const Attrs& attrs,
+                     const TypeReporter& reporter) {
+  CHECK_EQ(types.size(), 2);
+  const auto* data = types[0].as<TensorTypeNode>();
+  if (data == nullptr) return false;
+
+  static const Layout kNCDHW("NCDHW");
+
+  const UpSampling3DAttrs* param = attrs.as<UpSampling3DAttrs>();
+  CHECK(param != nullptr);
+  const Layout in_layout(param->layout);
+
+  auto layout_converter = BijectiveLayoutNode::make(in_layout, kNCDHW);
+  CHECK(layout_converter.defined())
+    << "UpSampling3D only support input layouts that are convertible from NCDHW."
+    << " But got " << in_layout;
+
+  auto oshape = layout_converter.ForwardShape(data->shape);
+  oshape.Set(2, ir::Cast::make(oshape[2].dtype(), tvm::round(oshape[2] * param->scale_d)));
+  oshape.Set(3, ir::Cast::make(oshape[3].dtype(), tvm::round(oshape[3] * param->scale_h)));
+  oshape.Set(4, ir::Cast::make(oshape[4].dtype(), tvm::round(oshape[4] * param->scale_w)));
+
+  // assign output type
+  reporter->Assign(types[1],
+                   TensorTypeNode::make(layout_converter.BackwardShape(oshape),
+                                        data->dtype));
+  return true;
+}
+
+// Positional relay function to create upsampling3d operator
+// used by frontend FFI.
+Expr MakeUpSampling3D(Expr data,
+                      double scale_d,
+                      double scale_h,
+                      double scale_w,
+                      std::string layout,
+                      std::string method,
+                      std::string coordinate_transformation_mode) {
+  auto attrs = make_node<UpSampling3DAttrs>();
+  attrs->layout = std::move(layout);
+  attrs->method = std::move(method);
+  attrs->scale_d = scale_d;
+  attrs->scale_h = scale_h;
+  attrs->scale_w = scale_w;
+  attrs->coordinate_transformation_mode = coordinate_transformation_mode;
+  static const Op& op = Op::Get("nn.upsampling3d");
+  return CallNode::make(op, {data}, Attrs(attrs), {});
+}
+
+TVM_REGISTER_API("relay.op.nn._make.upsampling3d")
+.set_body_typed(MakeUpSampling3D);
+
+
+RELAY_REGISTER_OP("nn.upsampling3d")
+.describe(R"code(Perform upsampling on input array with nearest neighbour or
+bilinear interpolation.
+
+- **data**: data is 5D array of shape
+            (batch_size, channels, in_depth, in_height, in_width) for NCDHW
+            (batch_size, in_depth, in_height, in_width, channels) for NDHWC
+
+- **out**: Output is 5D array of shape
+           for layout NCDHW
+           (batch_size, channels, in_depth*scale, in_height*scale, in_width*scale)
+
+           for layout NDHWC
+           (batch_size, in_depth*scale, in_height*scale, in_width*scale, channels)
+
+)code" TVM_ADD_FILELINE)
+.set_attrs_type<UpSampling3DAttrs>()
+.set_num_inputs(1)
+.add_argument("data", "Tensor", "The input tensor.")
+.set_support_level(2)
+.add_type_rel("UpSampling3D", UpSampling3DRel)
+.set_attr<FInferCorrectLayout>("FInferCorrectLayout",
+  UpsamplingInferCorrectLayout<UpSampling3DAttrs>)
+.set_attr<TOpPattern>("TOpPattern", kInjective);
+
 }  // namespace relay
 }  // namespace tvm

tests/python/relay/test_op_level2.py

@@ -456,6 +456,22 @@ def test_upsampling_infer_type():
     yy = run_infer_type(y)
     assert yy.checked_type == relay.TensorType((n, c, 200, 400), "float32")
 
+def test_upsampling3d_infer_type():
+    n, c, d, h, w = tvm.var("n"), tvm.var("c"), tvm.var("d"), tvm.var("h"), tvm.var("w")
+    scale = tvm.const(2.0, "float64")
+    x = relay.var("x", relay.TensorType((n, c, d, h, w), "float32"))
+    y = relay.nn.upsampling3d(x, scale_d=2, scale_h=2, scale_w=2, layout="NCDHW", method="trilinear")
+
+    yy = run_infer_type(y)
+    assert yy.checked_type == relay.TensorType((n, c, tvm.expr.Cast("int32", tvm.round(d*scale)),
+                                                tvm.expr.Cast("int32", tvm.round(h*scale)),
+                                                tvm.expr.Cast("int32", tvm.round(w*scale))),
+                                                "float32")
+    n, c = tvm.var("n"), tvm.var("c")
+    x = relay.var("x", relay.TensorType((n, c, 100, 100, 200), "float32"))
+    y = relay.nn.upsampling3d(x, scale_d=2, scale_h=2, scale_w=2, layout="NCDHW", method="trilinear")
+    yy = run_infer_type(y)
+    assert yy.checked_type == relay.TensorType((n, c, 200, 200, 400), "float32")
 
 def _test_pool2d(opfunc, reffunc):
     n, c, h, w = tvm.var("n"), 10, 224, 224
@@ -782,6 +798,50 @@ def test_upsampling():
     _test_upsampling("NHWC", "nearest_neighbor")
     _test_upsampling("NHWC", "bilinear", True)
 
+def _test_upsampling3d(layout, method, coordinate_transformation_mode="half_pixel"):
+    n, c, d, h, w = tvm.var("n"), 8, 16, 16, 16
+    scale_d = 2.0
+    scale_h = 2.0
+    scale_w = 2.0
+    dtype = "float32"
+    def get_shape():
+        if layout == "NCDHW":
+            return (c, d, h, w), (c, int(round(d*scale_d)), int(round(h*scale_h)),\
+                                  int(round(w*scale_w)))
+        else:
+            return (d, h, w, c), (int(round(d*scale_d)), int(round(h*scale_h)),\
+                                  int(round(w*scale_w)), c)
+    ishape, oshape = get_shape()
+    x = relay.var("x", relay.TensorType((n,) + ishape, dtype))
+    y = relay.nn.upsampling3d(x, scale_d=scale_d, scale_h=scale_h, scale_w=scale_w,\
+                              layout=layout, method=method,\
+                              coordinate_transformation_mode=coordinate_transformation_mode)
+
+    yy = run_infer_type(y)
+    assert yy.checked_type == relay.TensorType((n,) + oshape, dtype)
+    dshape = (1,) + ishape
+    x = relay.var("x", shape=dshape)
+    y = relay.nn.upsampling3d(x, scale_d=scale_d, scale_h=scale_h, scale_w=scale_w,\
+                            layout=layout, method=method,\
+                            coordinate_transformation_mode=coordinate_transformation_mode)
+    func = relay.Function([x], y)
+    data = np.random.uniform(size=dshape).astype(dtype)
+    if method == "nearest_neighbor":
+        ref = topi.testing.upsampling3d_python(data, (scale_d, scale_h, scale_w), layout)
+    else:
+        ref = topi.testing.trilinear_resize3d_python(data, (int(round(d*scale_d)),\
+                                                     int(round(h*scale_h)),\
+                                                     int(round(w*scale_w))), layout)
+    for target, ctx in ctx_list():
+        executor = relay.create_executor("graph", ctx=ctx, target=target)
+        out = executor.evaluate(func)(data)
+        tvm.testing.assert_allclose(out.asnumpy(), ref, rtol=1e-5, atol=1e-5)
+
+def test_upsampling3d():
+    _test_upsampling3d("NCDHW", "nearest_neighbor")
+    _test_upsampling3d("NCDHW", "trilinear", "align_corners")
+    _test_upsampling3d("NDHWC", "nearest_neighbor")
+    _test_upsampling3d("NDHWC", "trilinear", "align_corners")
 
 def test_conv2d_int8_intrinsics():
     def _compile(ic, oc, target, data_layout, kernel_layout, dtypes):
@@ -935,6 +995,7 @@ def test_bitpack_infer_type():
     test_conv2d_infer_type()
     test_bitpack_infer_type()
     test_upsampling_infer_type()
+    test_upsampling3d_infer_type()
     test_flatten_infer_type()
     test_pad_infer_type()
     test_pad_run()
@@ -948,4 +1009,5 @@ def test_bitpack_infer_type():
     test_bitserial_conv2d_infer_type()
     test_batch_flatten()
     test_upsampling()
+    test_upsampling3d()
     test_conv2d_int8_intrinsics()