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/* | ||
* 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. | ||
*/ | ||
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/*! | ||
* \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> | ||
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#include <vector> | ||
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#include "../op_common.h" | ||
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namespace tvm { | ||
namespace relay { | ||
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// relay.nn.correlation | ||
TVM_REGISTER_NODE_TYPE(CorrelationAttrs); | ||
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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}}; | ||
} | ||
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// 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), {}); | ||
} | ||
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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; | ||
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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; | ||
} | ||
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TVM_REGISTER_GLOBAL("relay.op.nn._make.correlation").set_body_typed(MakeCorrelation); | ||
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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); | ||
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} // namespace relay | ||
} // namespace tvm |
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