[Relay,Topi] Correlation

include/tvm/relay/attrs/nn.h

@@ -1203,6 +1203,36 @@ struct SubPixelAttrs : public tvm::AttrsNode<SubPixelAttrs> {
   }
 };  // struct SubPixelAttrs
 
+/*! \brief Attributes used in correlation operators */
+struct CorrelationAttrs : public tvm::AttrsNode<CorrelationAttrs> {
+  int kernel_size;
+  int max_displacement;
+  int stride1;
+  int stride2;
+  Array<IndexExpr> padding;
+  bool is_multiply;
+  String layout;
+
+  TVM_DECLARE_ATTRS(CorrelationAttrs, "relay.attrs.CorrelationAttrs") {
+    TVM_ATTR_FIELD(kernel_size)
+        .describe("Kernel size for correlation, must be an odd number.")
+        .set_default(1);
+    TVM_ATTR_FIELD(max_displacement).describe("Max displacement of Correlation.").set_default(1);
+    TVM_ATTR_FIELD(stride1).describe("Stride for data1.").set_default(1);
+    TVM_ATTR_FIELD(stride2).describe("Stride for data2.").set_default(1);
+    TVM_ATTR_FIELD(padding)
+        .describe("Padding for data1 and data2.")
+        .set_default(Array<IndexExpr>{0, 0});
+    TVM_ATTR_FIELD(is_multiply)
+        .describe("Operation type is either multiplication or substraction.")
+        .set_default(true);
+    TVM_ATTR_FIELD(layout).set_default("NCHW").describe(
+        "Dimension ordering of input data. Can be 'NCHW', 'NHWC', etc."
+        "'N', 'C', 'H', 'W' stands for batch, channel, height, and width"
+        "dimensions respectively.");
+  }
+};  // struct CorrelationAttrs
+
 }  // namespace relay
 }  // namespace tvm
 #endif  // TVM_RELAY_ATTRS_NN_H_

python/tvm/relay/frontend/mxnet.py

@@ -1104,6 +1104,19 @@ def _mx_space_to_depth(inputs, attrs):
     return _op.nn.space_to_depth(*inputs, **new_attrs)
 
 
+def _mx_correlation(inputs, attrs):
+    assert len(inputs) == 2
+    new_attrs = {}
+    new_attrs["kernel_size"] = attrs.get_int("kernel_size", 1)
+    new_attrs["max_displacement"] = attrs.get_int("max_displacement", 1)
+    new_attrs["stride1"] = attrs.get_int("stride1", 1)
+    new_attrs["stride2"] = attrs.get_int("stride2", 1)
+    new_attrs["padding"] = attrs.get_int("pad_size", 0)
+    new_attrs["is_multiply"] = attrs.get_bool("is_multiply", True)
+    new_attrs["layout"] = "NCHW"
+    return _op.nn.correlation(*inputs, **new_attrs)
+
+
 def _mx_contrib_fifo_buffer(inputs, attrs):
     new_attrs = {}
     new_attrs['axis'] = attrs.get_int('axis')
@@ -1942,6 +1955,7 @@ def impl(inputs, input_types):
     "one_hot"           : _mx_one_hot,
     "depth_to_space"    : _mx_depth_to_space,
     "space_to_depth"    : _mx_space_to_depth,
+    "Correlation"       : _mx_correlation,
     # vision
     "_contrib_BilinearResize2D" : _mx_resize,
     "_contrib_MultiBoxPrior" : _mx_multibox_prior,

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

@@ -563,6 +563,11 @@ def compute_space_to_depth(attrs, inputs, out_dtype):
 reg.register_pattern("nn.space_to_depth", OpPattern.INJECTIVE)
 
 
+# correlation
+reg.register_strategy("nn.correlation", strategy.correlation_strategy)
+reg.register_pattern("nn.correlation", OpPattern.OUT_ELEMWISE_FUSABLE)
+
+
 #####################
 #  Shape functions  #
 #####################

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

@@ -2761,3 +2761,84 @@ def global_avg_pool3d(data,
     """
     output_size = [1, 1, 1]
     return _make.adaptive_avg_pool3d(data, output_size, layout)
+
+
+def correlation(data1, data2, kernel_size, max_displacement, stride1, stride2, padding,
+                is_multiply, layout):
+    r"""Applies correlation to inputs.
+
+    The correlation layer performs multiplicative patch comparisons between two feature maps.
+    Given two multi-channel feature maps :math:`f_{1}, f_{2}`, with :math:`w`, :math:`h`, and
+    :math:`c` being their width, height, and number of channels, the correlation layer lets the
+    network compare each patch from :math:`f_{1}` with each patch from :math:`f_{2}`.
+
+    For now we consider only a single comparison of two patches. The 'correlation' of two patches
+    centered at :math:`x_{1}` in the first map and :math:`x_{2}` in the second map is then defined
+    as:
+
+    .. math::
+    c(x_{1}, x_{2}) = \sum_{o \in [-k,k] \times [-k,k]} <f_{1}(x_{1} + o), f_{2}(x_{2} + o)>
+
+    for a square patch of size :math:`K:=2k+1`.
+
+    Note that the equation above is identical to one step of a convolution in neural networks, but
+    instead of convolving data with a filter, it convolves data with other    data. For this
+    reason, it has no training weights.
+
+    Computing :math:`c(x_{1}, x_{2})` involves :math:`c * K^{2}` multiplications. Comparing all
+    patch combinations involves :math:`w^{2}*h^{2}` such computations.
+
+    Given a maximum displacement :math:`d`, for each location :math:`x_{1}` it computes
+    correlations :math:`c(x_{1}, x_{2})` only in a neighborhood of size :math:`D:=2d+1`,
+    by limiting the range of :math:`x_{2}`. We use strides :math:`s_{1}, s_{2}`, to quantize
+    :math:`x_{1}` globally and to quantize :math:`x_{2}` within the neighborhood
+    centered around :math:`x_{1}`.
+
+    The final output is defined by the following expression:
+
+    .. math::
+    out[n, q, i, j] = c(x_{i, j}, x_{q})
+
+    where :math:`i` and :math:`j` enumerate spatial locations in :math:`f_{1}`, and :math:`q`
+    denotes the :math:`q^{th}` neighborhood of :math:`x_{i,j}`.
+
+    Parameters
+    ----------
+    data1 : tvm.te.Tensor
+        4-D with shape [batch, channel, height, width]
+
+    data2 : tvm.te.Tensor
+        4-D with shape [batch, channel, height, width]
+
+    kernel_size: int
+        Kernel size for correlation, must be an odd number
+
+    max_displacement: int
+        Max displacement of Correlation
+
+    stride1: int
+        Stride for data1
+
+    stride2: int
+        Stride for data2 within the neightborhood centered around data1
+
+    padding : int or a list/tuple of 2 or 4 ints
+        Padding size, or
+        [pad_height, pad_width] for 2 ints, or
+        [pad_top, pad_left, pad_bottom, pad_right] for 4 ints
+
+    is_multiply: bool
+        operation type is either multiplication or substraction
+
+    layout: str
+        layout of data1, data2 and the output
+
+    Returns
+    -------
+    Output : tvm.te.Tensor
+        4-D with shape [batch, out_channel, out_height, out_width]
+    """
+    if isinstance(padding, int):
+        padding = (padding, padding)
+    return _make.correlation(data1, data2, kernel_size, max_displacement, stride1, stride2,
+                             padding, is_multiply, layout)

python/tvm/relay/op/op_attrs.py

@@ -357,3 +357,8 @@ class DilateAttrs(Attrs):
 @tvm._ffi.register_object("relay.attrs.SubPixelAttrs")
 class SubPixelAttrs(Attrs):
     """Attributes used in depth to space and space to depth operators"""
+
+
+@tvm._ffi.register_object("relay.attrs.CorrelationAttrs")
+class CorrelationAttrs(Attrs):
+    """Attributes used in correlation operators"""

python/tvm/relay/op/strategy/cuda.py

@@ -590,3 +590,15 @@ def winograd_judge(N, H, W, KH, KW, CI, CO, padding, stride_h,
                               stride_h == 1 and stride_w == 1 and \
                               dilation_h == 1 and dilation_w == 1
     return judge_winograd_tensorcore, judge_winograd_shape
+
+@correlation_strategy.register(["cuda", "gpu"])
+def correlation_strategy_cuda(attrs, inputs, out_type, target):
+    """correlation cuda strategy"""
+    layout = attrs.layout
+    assert layout == "NCHW", "Only support NCHW layout"
+    strategy = _op.OpStrategy()
+    strategy.add_implementation(
+        wrap_compute_correlation(topi.cuda.correlation_nchw),
+        wrap_topi_schedule(topi.cuda.schedule_correlation_nchw),
+        name="correlation.cuda")
+    return strategy

python/tvm/relay/op/strategy/generic.py

@@ -829,3 +829,30 @@ def bitserial_dense_strategy(attrs, inputs, out_type, target):
         wrap_topi_schedule(topi.generic.schedule_bitserial_dense),
         name="bitserial_dense.generic")
     return strategy
+
+# correlation
+def wrap_compute_correlation(topi_compute):
+    """wrap correlation topi compute"""
+    def _compute_correlation(attrs, inputs, out_type):
+        kernel_size = attrs.kernel_size
+        max_displacement = attrs.max_displacement
+        stride1 = attrs.stride1
+        stride2 = attrs.stride2
+        padding = get_const_tuple(attrs.padding)
+        is_multiply = attrs.is_multiply
+        return [topi_compute(inputs[0], inputs[1], kernel_size, max_displacement, stride1, stride2,
+                             padding, is_multiply)]
+    return _compute_correlation
+
+@override_native_generic_func("correlation_strategy")
+def correlation_strategy(attrs, inputs, out_type, target):
+    """correlation generic strategy"""
+    logger.warning("correlation is not optimized for this platform.")
+    layout = attrs.layout
+    assert layout == "NCHW", "Only support NCHW layout"
+    strategy = _op.OpStrategy()
+    strategy.add_implementation(
+        wrap_compute_correlation(topi.nn.correlation_nchw),
+        wrap_topi_schedule(topi.generic.schedule_correlation_nchw),
+        name="correlation.generic")
+    return strategy

src/relay/op/nn/correlation.cc

@@ -0,0 +1,136 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file correlation.cc
+ * \brief Correlation operators
+ */
+#include <topi/nn.h>
+#include <tvm/relay/attrs/nn.h>
+#include <tvm/relay/op.h>
+#include <tvm/tir/data_layout.h>
+#include <tvm/tir/op.h>
+
+#include <vector>
+
+#include "../op_common.h"
+
+namespace tvm {
+namespace relay {
+
+// relay.nn.correlation
+TVM_REGISTER_NODE_TYPE(CorrelationAttrs);
+
+Array<Array<Layout>> CorrelationInferCorrectLayout(const Attrs& attrs,
+                                                   const Array<Layout>& new_in_layouts,
+                                                   const Array<Layout>& old_in_layouts,
+                                                   const Array<tvm::relay::Type>& old_in_types) {
+  const auto* params = attrs.as<CorrelationAttrs>();
+  Layout layout{params->layout};
+  return Array<Array<Layout>>{{layout, layout}, {layout}};
+}
+
+// Positional relay function to create correlation operator
+// used by frontend FFI.
+Expr MakeCorrelation(Expr data1, Expr data2, int kernel_size, int max_displacement, int stride1,
+                     int stride2, Array<IndexExpr> padding, bool is_multiply, String layout) {
+  auto attrs = make_object<CorrelationAttrs>();
+  attrs->kernel_size = kernel_size;
+  attrs->max_displacement = max_displacement;
+  attrs->stride1 = stride1;
+  attrs->stride2 = stride2;
+  attrs->padding = padding;
+  attrs->is_multiply = is_multiply;
+  attrs->layout = layout;
+  static const Op& op = Op::Get("nn.correlation");
+  return Call(op, {data1, data2}, Attrs(attrs), {});
+}
+
+bool CorrelationRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
+                    const TypeReporter& reporter) {
+  CHECK_EQ(types.size(), 3);
+  const auto* data1 = types[0].as<TensorTypeNode>();
+  const auto* data2 = types[1].as<TensorTypeNode>();
+  if (data1 == nullptr || data2 == nullptr) return false;
+
+  const CorrelationAttrs* param = attrs.as<CorrelationAttrs>();
+  CHECK(param != nullptr);
+  CHECK_EQ(param->layout, "NCHW") << "layout not supported.";
+  IndexExpr pad_h, pad_w;
+  GetPaddingHeightWidth(param->padding, &pad_h, &pad_w);
+  IndexExpr padded_height = data1->shape[2] + pad_h;
+  IndexExpr padded_width = data2->shape[3] + pad_w;
+  int kernel_radius = (param->kernel_size - 1) / 2;
+  int border_size = param->max_displacement + kernel_radius;
+  int displacement_radius = param->max_displacement / param->stride2;
+  int displacement_size = 2 * displacement_radius + 1;
+  int out_channel = displacement_size * displacement_size;
+  IndexExpr out_height =
+      indexdiv((padded_height - 2 * border_size + param->stride1 - 1), param->stride1);
+  IndexExpr out_width =
+      indexdiv((padded_width - 2 * border_size + param->stride1 - 1), param->stride1);
+  Array<tvm::PrimExpr> oshape{data1->shape[0], out_channel, out_height, out_width};
+  // assign output type
+  reporter->Assign(types[2], TensorType(oshape, data1->dtype));
+  return true;
+}
+
+TVM_REGISTER_GLOBAL("relay.op.nn._make.correlation").set_body_typed(MakeCorrelation);
+
+RELAY_REGISTER_OP("nn.correlation")
+    .describe(R"code(Applies correlation to inputs.
+
+The correlation layer performs multiplicative patch comparisons between two feature maps.
+Given two multi-channel feature maps :math:`f_{1}, f_{2}`, with :math:`w`, :math:`h`, and :math:`c` being their width, height, and number of channels,
+the correlation layer lets the network compare each patch from :math:`f_{1}` with each patch from :math:`f_{2}`.
+
+For now we consider only a single comparison of two patches. The 'correlation' of two patches centered at :math:`x_{1}` in the first map and
+:math:`x_{2}` in the second map is then defined as:
+
+.. math::
+   c(x_{1}, x_{2}) = \sum_{o \in [-k,k] \times [-k,k]} <f_{1}(x_{1} + o), f_{2}(x_{2} + o)>
+
+for a square patch of size :math:`K:=2k+1`.
+
+Note that the equation above is identical to one step of a convolution in neural networks, but instead of convolving data with a filter, it convolves data with other
+data. For this reason, it has no training weights.
+
+Computing :math:`c(x_{1}, x_{2})` involves :math:`c * K^{2}` multiplications. Comparing all patch combinations involves :math:`w^{2}*h^{2}` such computations.
+
+Given a maximum displacement :math:`d`, for each location :math:`x_{1}` it computes correlations :math:`c(x_{1}, x_{2})` only in a neighborhood of size :math:`D:=2d+1`,
+by limiting the range of :math:`x_{2}`. We use strides :math:`s_{1}, s_{2}`, to quantize :math:`x_{1}` globally and to quantize :math:`x_{2}` within the neighborhood
+centered around :math:`x_{1}`.
+
+The final output is defined by the following expression:
+
+.. math::
+  out[n, q, i, j] = c(x_{i, j}, x_{q})
+
+where :math:`i` and :math:`j` enumerate spatial locations in :math:`f_{1}`, and :math:`q` denotes the :math:`q^{th}` neighborhood of :math:`x_{i,j}`.
+)code" TVM_ADD_FILELINE)
+    .set_attrs_type<CorrelationAttrs>()
+    .set_num_inputs(2)
+    .add_argument("data1", "Tensor", "Input data1 to the correlation.")
+    .add_argument("data2", "Tensor", "Input data2 to the correlation.")
+    .set_support_level(2)
+    .set_attr<FInferCorrectLayout>("FInferCorrectLayout", CorrelationInferCorrectLayout)
+    .add_type_rel("Correlation", CorrelationRel);
+
+}  // namespace relay
+}  // namespace tvm