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new 2b66123 [TOPI, Relay refactor] Move Dilation2d from nn to image
namespace (#5110)
2b66123 is described below
commit 2b6612317bbd3768fe15dcae57aaa17c59635716
Author: masahi <[email protected]>
AuthorDate: Sat Mar 21 07:20:13 2020 +0900
[TOPI, Relay refactor] Move Dilation2d from nn to image namespace (#5110)
---
docs/api/python/topi.rst | 3 +-
docs/langref/relay_op.rst | 4 +-
include/tvm/relay/attrs/image.h | 35 +++++
include/tvm/relay/attrs/nn.h | 37 -----
python/tvm/relay/op/image/_image.py | 8 +
python/tvm/relay/op/image/image.py | 57 ++++++++
python/tvm/relay/op/nn/_nn.py | 3 -
python/tvm/relay/op/nn/nn.py | 57 --------
python/tvm/relay/op/op_attrs.py | 12 +-
python/tvm/relay/op/strategy/generic.py | 4 +-
src/relay/op/image/dilation2d.cc | 161 +++++++++++++++++++++
src/relay/op/image/resize.cc | 2 +-
src/relay/op/nn/convolution.cc | 61 --------
src/relay/op/nn/convolution.h | 71 ---------
tests/python/relay/test_op_level2.py | 109 --------------
tests/python/relay/test_op_level5.py | 109 ++++++++++++++
topi/python/topi/generic/__init__.py | 1 +
.../topi/{image/__init__.py => generic/default.py} | 20 ++-
topi/python/topi/generic/{sort.py => image.py} | 36 ++---
topi/python/topi/generic/nn.py | 45 +-----
topi/python/topi/generic/search.py | 3 +-
topi/python/topi/generic/sort.py | 5 +-
topi/python/topi/generic/vision.py | 14 +-
topi/python/topi/image/__init__.py | 1 +
topi/python/topi/{nn => image}/dilation2d.py | 8 +-
topi/python/topi/nn/__init__.py | 1 -
26 files changed, 428 insertions(+), 439 deletions(-)
diff --git a/docs/api/python/topi.rst b/docs/api/python/topi.rst
index 39b120f..49fd94d 100644
--- a/docs/api/python/topi.rst
+++ b/docs/api/python/topi.rst
@@ -57,7 +57,6 @@ List of operators
topi.nn.relu
topi.nn.leaky_relu
topi.nn.dilate
- topi.nn.dilation2d
topi.nn.pool
topi.nn.global_pool
topi.nn.adaptive_pool
@@ -106,6 +105,7 @@ List of operators
topi.layout_transform
topi.image.resize
topi.image.crop_and_resize
+ topi.image.dilation2d
topi.argsort
topi.topk
topi.sequence_mask
@@ -198,7 +198,6 @@ topi.nn
.. autofunction:: topi.nn.upsampling
.. autofunction:: topi.nn.softmax
.. autofunction:: topi.nn.dense
-.. autofunction:: topi.nn.dilation2d
.. autofunction:: topi.nn.batch_matmul
.. autofunction:: topi.nn.log_softmax
.. autofunction:: topi.nn.conv2d_nchw
diff --git a/docs/langref/relay_op.rst b/docs/langref/relay_op.rst
index 94ba19d..00c7ab9 100644
--- a/docs/langref/relay_op.rst
+++ b/docs/langref/relay_op.rst
@@ -70,7 +70,6 @@ This level enables typical convnet models.
tvm.relay.nn.conv2d
tvm.relay.nn.conv2d_transpose
tvm.relay.nn.dense
- tvm.relay.nn.dilation2d
tvm.relay.nn.max_pool2d
tvm.relay.nn.max_pool3d
tvm.relay.nn.avg_pool2d
@@ -171,6 +170,7 @@ This level enables additional math and transform operators.
tvm.relay.image.resize
tvm.relay.image.crop_and_resize
+ tvm.relay.image.dilation2d
tvm.relay.vision.multibox_prior
tvm.relay.vision.multibox_transform_loc
tvm.relay.vision.nms
@@ -250,7 +250,6 @@ Level 2 Definitions
.. autofunction:: tvm.relay.nn.conv2d
.. autofunction:: tvm.relay.nn.conv2d_transpose
.. autofunction:: tvm.relay.nn.dense
-.. autofunction:: tvm.relay.nn.dilation2d
.. autofunction:: tvm.relay.nn.max_pool2d
.. autofunction:: tvm.relay.nn.max_pool3d
.. autofunction:: tvm.relay.nn.avg_pool2d
@@ -339,6 +338,7 @@ Level 5 Definitions
-------------------
.. autofunction:: tvm.relay.image.resize
.. autofunction:: tvm.relay.image.crop_and_resize
+.. autofunction:: tvm.relay.image.dilation2d
.. autofunction:: tvm.relay.vision.multibox_prior
.. autofunction:: tvm.relay.vision.multibox_transform_loc
.. autofunction:: tvm.relay.vision.nms
diff --git a/include/tvm/relay/attrs/image.h b/include/tvm/relay/attrs/image.h
index 4bf40e3..52bb2ef 100644
--- a/include/tvm/relay/attrs/image.h
+++ b/include/tvm/relay/attrs/image.h
@@ -91,6 +91,41 @@ struct CropAndResizeAttrs : public
tvm::AttrsNode<CropAndResizeAttrs> {
}
};
+/*! \brief Attributes used in dilation operators */
+struct Dilation2DAttrs : public tvm::AttrsNode<Dilation2DAttrs> {
+ Array<IndexExpr> strides;
+ Array<IndexExpr> padding;
+ Array<IndexExpr> dilations;
+ std::string data_layout;
+ std::string kernel_layout;
+ DataType out_dtype;
+
+ TVM_DECLARE_ATTRS(Dilation2DAttrs, "relay.attrs.Dilation2DAttrs") {
+ TVM_ATTR_FIELD(strides).set_default(Array<IndexExpr>({1, 1}))
+ .describe("Specifies the strides of the sliding window.
[stride_height, stride_width].");
+ TVM_ATTR_FIELD(padding).set_default(Array<IndexExpr>({0, 0}))
+ .describe("If padding is non-zero, then the input is implicitly
zero-padded"
+ "Padding support both symmetric and asymmetric as"
+ "one int : same padding used on all sides"
+ "two int : bottom, right will use same padding as top, left"
+ "four int : padding width in the order of (top, left,
bottom, right)");
+ TVM_ATTR_FIELD(dilations).set_default(Array<IndexExpr>({1, 1}))
+ .describe("Specifies the dilation rate to use. [dilation_height,
dilation_width]");
+ TVM_ATTR_FIELD(data_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. Convolution is applied on the 'H'
and"
+ "'W' dimensions.");
+ TVM_ATTR_FIELD(kernel_layout).set_default("IHW")
+ .describe("Dimension ordering of weight. Can be 'IHW', 'HWI', etc."
+ "'I', 'H', 'W' stands for input_channel, height, and width"
+ "dimensions respectively.");
+ TVM_ATTR_FIELD(out_dtype)
+ .set_default(NullValue<DataType>())
+ .describe("Output data type, set to explicit type under mixed
precision setting");
+ }
+};
+
} // namespace relay
} // namespace tvm
#endif // TVM_RELAY_ATTRS_IMAGE_H_
diff --git a/include/tvm/relay/attrs/nn.h b/include/tvm/relay/attrs/nn.h
index dee4aa9..5794ddd 100644
--- a/include/tvm/relay/attrs/nn.h
+++ b/include/tvm/relay/attrs/nn.h
@@ -155,43 +155,6 @@ struct Conv2DAttrs : public tvm::AttrsNode<Conv2DAttrs> {
}
};
-
-/*! \brief Attributes used in dilation operators */
-struct Dilation2DAttrs : public tvm::AttrsNode<Dilation2DAttrs> {
- Array<IndexExpr> strides;
- Array<IndexExpr> padding;
- Array<IndexExpr> dilations;
- std::string data_layout;
- std::string kernel_layout;
- DataType out_dtype;
-
- TVM_DECLARE_ATTRS(Dilation2DAttrs, "relay.attrs.Dilation2DAttrs") {
- TVM_ATTR_FIELD(strides).set_default(Array<IndexExpr>({1, 1}))
- .describe("Specifies the strides of the sliding window.
[stride_height, stride_width].");
- TVM_ATTR_FIELD(padding).set_default(Array<IndexExpr>({0, 0}))
- .describe("If padding is non-zero, then the input is implicitly
zero-padded"
- "Padding support both symmetric and asymmetric as"
- "one int : same padding used on all sides"
- "two int : bottom, right will use same padding as top, left"
- "four int : padding width in the order of (top, left,
bottom, right)");
- TVM_ATTR_FIELD(dilations).set_default(Array<IndexExpr>({1, 1}))
- .describe("Specifies the dilation rate to use. [dilation_height,
dilation_width]");
- TVM_ATTR_FIELD(data_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. Convolution is applied on the 'H'
and"
- "'W' dimensions.");
- TVM_ATTR_FIELD(kernel_layout).set_default("IHW")
- .describe("Dimension ordering of weight. Can be 'IHW', 'HWI', etc."
- "'I', 'H', 'W' stands for input_channel, height, and width"
- "dimensions respectively.");
- TVM_ATTR_FIELD(out_dtype)
- .set_default(NullValue<DataType>())
- .describe("Output data type, set to explicit type under mixed
precision setting");
- }
-};
-
-
/*! \brief Attributes used in winograd weight transformation operators */
struct Conv2DWinogradWeightTransformAttrs :
public tvm::AttrsNode<Conv2DWinogradWeightTransformAttrs> {
diff --git a/python/tvm/relay/op/image/_image.py
b/python/tvm/relay/op/image/_image.py
index b98b2bc..ba9d62a 100644
--- a/python/tvm/relay/op/image/_image.py
+++ b/python/tvm/relay/op/image/_image.py
@@ -20,6 +20,9 @@ from __future__ import absolute_import
import topi
from .. import op as reg
+from .. import strategy
+from ..op import OpPattern
+
# resize
@reg.register_compute("image.resize")
@@ -47,3 +50,8 @@ def compute_crop_and_resize(attrs, inputs, out_type):
extrapolation_value, out_dtype)]
reg.register_injective_schedule("image.crop_and_resize")
+
+
+# dilation2d
+reg.register_strategy("image.dilation2d", strategy.dilation2d_strategy)
+reg.register_pattern("image.dilation2d", OpPattern.OUT_ELEMWISE_FUSABLE)
diff --git a/python/tvm/relay/op/image/image.py
b/python/tvm/relay/op/image/image.py
index 284d602..284bd4a 100644
--- a/python/tvm/relay/op/image/image.py
+++ b/python/tvm/relay/op/image/image.py
@@ -113,3 +113,60 @@ def crop_and_resize(data,
"""
return _make.crop_and_resize(data, boxes, box_indices, crop_size,
layout, method, extrapolation_value,
out_dtype)
+
+
+def dilation2d(data,
+ weight,
+ strides=(1, 1),
+ padding=(0, 0),
+ dilations=(1, 1),
+ data_layout="NCHW",
+ kernel_layout="IHW",
+ out_dtype=""):
+ r"""Morphological Dilation 2D.
+ This operator takes the weight as the dilation kernel and dilates it with
+ data to produce an output. In the default case, where the data_layout is
`NCHW`
+ and kernel_layout is `OIHW`, dilation2d takes in a data Tensor with shape
+ `(batch_size, in_channels, height, width)`, and a weight Tensor with shape
+ `(channels, kernel_height, kernel_width)` to produce an output Tensor
+ with the following rule:
+
+ .. math::
+ \mbox{out}[b, c, y, x] = \max_{dy, dx}
+ \mbox{data}[b, c, \mbox{strides}[0] * y + dy, \mbox{strides}[1] *
x + dx] +
+ \mbox{weight}[c, dy, dx]
+
+ Padding and dilation are applied to data and weight respectively before
the computation.
+ This operator accepts data layout specification. Semantically, the operator
+ will convert the layout to the canonical layout
+ (`NCHW` for data and `IHW` for weight) and perform the computation.
+
+ weight : tvm.relay.Expr
+ The weight expressions.
+
+ strides : Optional[Tuple[int]]
+ The strides of convolution.
+
+ padding : Optional[Tuple[int]]
+ The padding of convolution on both sides of inputs before convolution.
+
+ dilations : Optional[Tuple[int]]
+ Specifies the dilation rate to be used for dilated convolution.
+
+ data_layout : Optional[str]
+ Layout of the input.
+
+ kernel_layout : Optional[str]
+ Layout of the weight.
+
+ out_dtype : Optional[str]
+ Specifies the output data type.
+
+ Returns
+ -------
+ result : tvm.relay.Expr
+ The computed result.
+ """
+
+ return _make.dilation2d(data, weight, strides, padding, dilations,
data_layout,
+ kernel_layout, out_dtype)
diff --git a/python/tvm/relay/op/nn/_nn.py b/python/tvm/relay/op/nn/_nn.py
index a9bd900..aa35fa2 100644
--- a/python/tvm/relay/op/nn/_nn.py
+++ b/python/tvm/relay/op/nn/_nn.py
@@ -178,9 +178,6 @@ def legalize_conv2d_transpose(attrs, inputs, types):
reg.register_strategy("nn.conv3d", strategy.conv3d_strategy)
reg.register_pattern("nn.conv3d", OpPattern.OUT_ELEMWISE_FUSABLE)
-# dilation2d
-reg.register_strategy("nn.dilation2d", strategy.dilation2d_strategy)
-reg.register_pattern("nn.dilation2d", OpPattern.OUT_ELEMWISE_FUSABLE)
# conv1d_transpose
reg.register_strategy("nn.conv1d_transpose",
strategy.conv1d_transpose_strategy)
diff --git a/python/tvm/relay/op/nn/nn.py b/python/tvm/relay/op/nn/nn.py
index 66c4ec3..c62b1cf 100644
--- a/python/tvm/relay/op/nn/nn.py
+++ b/python/tvm/relay/op/nn/nn.py
@@ -2463,60 +2463,3 @@ def adaptive_avg_pool3d(data,
"""
output_size = [] or output_size
return _make.adaptive_avg_pool3d(data, output_size, layout)
-
-
-def dilation2d(data,
- weight,
- strides=(1, 1),
- padding=(0, 0),
- dilations=(1, 1),
- data_layout="NCHW",
- kernel_layout="IHW",
- out_dtype=""):
- r"""Dilation 2D.
- This operator takes the weight as the dilation kernel and dilates it with
- data to produce an output. In the default case, where the data_layout is
`NCHW`
- and kernel_layout is `OIHW`, dilation2d takes in a data Tensor with shape
- `(batch_size, in_channels, height, width)`, and a weight Tensor with shape
- `(channels, kernel_height, kernel_width)` to produce an output Tensor
- with the following rule:
-
- .. math::
- \mbox{out}[b, c, y, x] = \max_{dy, dx}
- \mbox{data}[b, c, \mbox{strides}[0] * y + dy, \mbox{strides}[1] *
x + dx] +
- \mbox{weight}[c, dy, dx]
-
- Padding and dilation are applied to data and weight respectively before
the computation.
- This operator accepts data layout specification. Semantically, the operator
- will convert the layout to the canonical layout
- (`NCHW` for data and `IHW` for weight) and perform the computation.
-
- weight : tvm.relay.Expr
- The weight expressions.
-
- strides : Optional[Tuple[int]]
- The strides of convolution.
-
- padding : Optional[Tuple[int]]
- The padding of convolution on both sides of inputs before convolution.
-
- dilations : Optional[Tuple[int]]
- Specifies the dilation rate to be used for dilated convolution.
-
- data_layout : Optional[str]
- Layout of the input.
-
- kernel_layout : Optional[str]
- Layout of the weight.
-
- out_dtype : Optional[str]
- Specifies the output data type.
-
- Returns
- -------
- result : tvm.relay.Expr
- The computed result.
- """
-
- return _make.dilation2d(data, weight, strides, padding, dilations,
data_layout,
- kernel_layout, out_dtype)
diff --git a/python/tvm/relay/op/op_attrs.py b/python/tvm/relay/op/op_attrs.py
index b7f2879..edc2160 100644
--- a/python/tvm/relay/op/op_attrs.py
+++ b/python/tvm/relay/op/op_attrs.py
@@ -44,11 +44,6 @@ class Conv2DWinogradNNPACKWeightTransformAttrs(Attrs):
"""Attributes for nn.contrib_conv2d_winograd_nnpack_weight_transform"""
-@tvm._ffi.register_object("relay.attrs.Dilation2DAttrs")
-class Dilation2DAttrs(Attrs):
- """Attributes for nn.dilation2d"""
-
-
@tvm._ffi.register_object("relay.attrs.GlobalPool2DAttrs")
class GlobalPool2DAttrs(Attrs):
"""Attributes for nn.global_pool"""
@@ -124,10 +119,17 @@ class DeformableConv2DAttrs(Attrs):
class ResizeAttrs(Attrs):
"""Attributes for image.resize"""
+
@tvm._ffi.register_object("relay.attrs.CropAndResizeAttrs")
class CropAndResizeAttrs(Attrs):
"""Attributes for image.crop_and_resize"""
+
+@tvm._ffi.register_object("relay.attrs.Dilation2DAttrs")
+class Dilation2DAttrs(Attrs):
+ """Attributes for image.dilation2d"""
+
+
@tvm._ffi.register_object("relay.attrs.ArgsortAttrs")
class ArgsortAttrs(Attrs):
"""Attributes for algorithm.argsort"""
diff --git a/python/tvm/relay/op/strategy/generic.py
b/python/tvm/relay/op/strategy/generic.py
index e849f8c..573df36 100644
--- a/python/tvm/relay/op/strategy/generic.py
+++ b/python/tvm/relay/op/strategy/generic.py
@@ -479,13 +479,13 @@ def dilation2d_strategy(attrs, inputs, out_type, target):
if layout == "NCHW":
assert kernel_layout == "IHW"
strategy.add_implementation(
- wrap_compute_dilation2d(topi.nn.dilation2d_nchw),
+ wrap_compute_dilation2d(topi.image.dilation2d_nchw),
wrap_topi_schedule(topi.generic.schedule_dilation2d_nchw),
name="dilation2d_nchw.generic")
elif layout == "NHWC":
assert kernel_layout == "HWI"
strategy.add_implementation(
- wrap_compute_dilation2d(topi.nn.dilation2d_nhwc),
+ wrap_compute_dilation2d(topi.image.dilation2d_nhwc),
wrap_topi_schedule(topi.generic.schedule_dilation2d_nhwc),
name="dilation2d_nhwc.generic")
else:
diff --git a/src/relay/op/image/dilation2d.cc b/src/relay/op/image/dilation2d.cc
new file mode 100644
index 0000000..55c49d7
--- /dev/null
+++ b/src/relay/op/image/dilation2d.cc
@@ -0,0 +1,161 @@
+/*
+ * 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 dilation2d.cc
+ * \brief Morphological dilation operator
+ */
+#include <tvm/tir/data_layout.h>
+#include <tvm/relay/op.h>
+#include <tvm/relay/attrs/image.h>
+#include "../op_common.h"
+
+namespace tvm {
+namespace relay {
+
+// relay.image.dilation2d
+TVM_REGISTER_NODE_TYPE(Dilation2DAttrs);
+
+template<typename T>
+Array<Array<Layout> > Dilation2DInferCorrectLayout(
+ const Attrs& attrs,
+ const Array<Layout>& new_in_layouts,
+ const Array<Layout>& old_in_layouts,
+ const Array<tvm::relay::Type> &old_in_types) {
+ const T* params = attrs.as<T>();
+
+ return Array<Array<Layout> >{{params->data_layout, params->kernel_layout},
+ {params->data_layout}};
+}
+
+// Positional relay function to create dilation2d operator
+// used by frontend FFI.
+Expr MakeDilation2D(Expr data,
+ Expr weight,
+ Array<IndexExpr> strides,
+ Array<IndexExpr> padding,
+ Array<IndexExpr> dilations,
+ std::string data_layout,
+ std::string kernel_layout,
+ DataType out_dtype) {
+ auto attrs = make_object<Dilation2DAttrs>();
+ attrs->strides = std::move(strides);
+ attrs->padding = std::move(padding);
+ attrs->dilations = std::move(dilations);
+ attrs->data_layout = std::move(data_layout);
+ attrs->kernel_layout = std::move(kernel_layout);
+ attrs->out_dtype = std::move(out_dtype);
+ static const Op& op = Op::Get("image.dilation2d");
+ return CallNode::make(op, {data, weight}, Attrs(attrs), {});
+}
+
+template <typename AttrType>
+bool Dilation2DRel(const Array<Type>& types, int num_inputs, const Attrs&
attrs,
+ const TypeReporter& reporter) {
+ CHECK_EQ(types.size(), 3);
+ const auto* data = types[0].as<TensorTypeNode>();
+ const auto* weight = types[1].as<TensorTypeNode>();
+ if (data == nullptr) return false;
+ static const Layout kNCHW("NCHW");
+ static const Layout kOIHW("IHW");
+
+ const AttrType* param = attrs.as<AttrType>();
+ CHECK(param != nullptr);
+ const Layout in_layout(param->data_layout);
+ const Layout kernel_layout(param->kernel_layout);
+
+ const auto trans_in_layout = BijectiveLayoutNode::make(in_layout, kNCHW);
+ CHECK(trans_in_layout.defined())
+ << "Dilation2D only support input layouts that are convertible from
NCHW."
+ << " But got " << in_layout;
+
+ const auto trans_kernel_layout = BijectiveLayoutNode::make(kernel_layout,
kOIHW);
+ CHECK(trans_kernel_layout.defined())
+ << "Dilation2D only support kernel layouts that are convertible from
OIHW."
+ << " But got " << kernel_layout;
+
+ Layout out_layout(param->data_layout);
+ const auto trans_out_layout = BijectiveLayoutNode::make(out_layout, kNCHW);
+ CHECK(trans_out_layout.defined())
+ << "Dilation2D only support output layouts that are convertible from
NCHW."
+ << " But got " << out_layout;
+
+ Array<IndexExpr> dshape_nchw = trans_in_layout.ForwardShape(data->shape);
+
+ IndexExpr channels, dilated_ksize_y, dilated_ksize_x;
+
+ // use weight to infer the conv shape.
+ if (weight == nullptr) return false;
+ auto wshape = trans_kernel_layout.ForwardShape(weight->shape);
+ channels = wshape[0];
+
+ dilated_ksize_y = 1 + (wshape[1] - 1) * param->dilations[0];
+ dilated_ksize_x = 1 + (wshape[2] - 1) * param->dilations[1];
+
+ // dilation
+ Array<IndexExpr> oshape({dshape_nchw[0], channels, 0, 0});
+ IndexExpr pad_h, pad_w;
+ GetPaddingHeightWidth(param->padding, &pad_h, &pad_w);
+ if (!dshape_nchw[2].as<tir::AnyNode>()) {
+ oshape.Set(2, indexdiv(dshape_nchw[2] + pad_h - dilated_ksize_y,
+ param->strides[0]) + 1);
+ } else {
+ oshape.Set(2, dshape_nchw[2]);
+ }
+
+ if (!dshape_nchw[3].as<tir::AnyNode>()) {
+ oshape.Set(3, indexdiv(dshape_nchw[3] + pad_w - dilated_ksize_x,
+ param->strides[1]) + 1);
+ } else {
+ oshape.Set(3, dshape_nchw[3]);
+ }
+
+ DataType out_dtype = param->out_dtype;
+ if (out_dtype.bits() == 0) {
+ out_dtype = data->dtype;
+ }
+ oshape = trans_out_layout.BackwardShape(oshape);
+ // assign output type
+ reporter->Assign(types[2], TensorType(oshape, out_dtype));
+ return true;
+}
+
+TVM_REGISTER_GLOBAL("relay.op.image._make.dilation2d")
+.set_body_typed(MakeDilation2D);
+
+
+RELAY_REGISTER_OP("image.dilation2d")
+.describe(R"code(Computes grayscale dilation of 4D input and 3D filter.
+- **data**: This depends on the `layout` parameter. Input is 4D array of shape
+ (batch_size, in_channels, height, width) if `layout` is `NCHW`.
+- **weight**: (in_channels, height, width)
+- **out**: This depends on the `layout` parameter. Output is 4D array of shape
+ (batch_size, channels, out_height, out_width) if `layout` is
`NCHW`.
+)code" TVM_ADD_FILELINE)
+.set_attrs_type<Dilation2DAttrs>()
+.set_num_inputs(2)
+.add_argument("data", "Tensor", "The input tensor.")
+.add_argument("weight", "Tensor", "The weight tensor.")
+.set_support_level(2)
+.add_type_rel("Dilation2D", Dilation2DRel<Dilation2DAttrs>)
+.set_attr<FInferCorrectLayout>("FInferCorrectLayout",
+ Dilation2DInferCorrectLayout<Dilation2DAttrs>);
+
+} // namespace relay
+} // namespace tvm
diff --git a/src/relay/op/image/resize.cc b/src/relay/op/image/resize.cc
index 4349e09..99d97b2 100644
--- a/src/relay/op/image/resize.cc
+++ b/src/relay/op/image/resize.cc
@@ -19,7 +19,7 @@
/*!
* \file resize.cc
- * \brief Image operators
+ * \brief Image resize operators
*/
#include <tvm/tir/data_layout.h>
#include <tvm/relay/op.h>
diff --git a/src/relay/op/nn/convolution.cc b/src/relay/op/nn/convolution.cc
index 25dca08..0d10253 100644
--- a/src/relay/op/nn/convolution.cc
+++ b/src/relay/op/nn/convolution.cc
@@ -1023,66 +1023,5 @@ Expr MakeDeformableConv2D(Expr data,
TVM_REGISTER_GLOBAL("relay.op.nn._make.deformable_conv2d")
.set_body_typed(MakeDeformableConv2D);
-// relay.nn.dilation2d
-TVM_REGISTER_NODE_TYPE(Dilation2DAttrs);
-
-template<typename T>
-Array<Array<Layout> > Dilation2DInferCorrectLayout(
- const Attrs& attrs,
- const Array<Layout>& new_in_layouts,
- const Array<Layout>& old_in_layouts,
- const Array<tvm::relay::Type> &old_in_types) {
- const T* params = attrs.as<T>();
-
- // We always make other operators to fit the layouts of convolution layers
- // So this inference ignores all inputs
- return Array<Array<Layout> >{{params->data_layout, params->kernel_layout},
- {params->data_layout}};
-}
-
-// Positional relay function to create dilation2d operator
-// used by frontend FFI.
-Expr MakeDilation2D(Expr data,
- Expr weight,
- Array<IndexExpr> strides,
- Array<IndexExpr> padding,
- Array<IndexExpr> dilations,
- std::string data_layout,
- std::string kernel_layout,
- DataType out_dtype) {
- auto attrs = make_object<Dilation2DAttrs>();
- attrs->strides = std::move(strides);
- attrs->padding = std::move(padding);
- attrs->dilations = std::move(dilations);
- attrs->data_layout = std::move(data_layout);
- attrs->kernel_layout = std::move(kernel_layout);
- attrs->out_dtype = std::move(out_dtype);
- static const Op& op = Op::Get("nn.dilation2d");
- return CallNode::make(op, {data, weight}, Attrs(attrs), {});
-}
-
-
-TVM_REGISTER_GLOBAL("relay.op.nn._make.dilation2d")
-.set_body_typed(MakeDilation2D);
-
-
-RELAY_REGISTER_OP("nn.dilation2d")
-.describe(R"code(Computes grayscale dilation of 4D input and 3D filter.
-- **data**: This depends on the `layout` parameter. Input is 4D array of shape
- (batch_size, in_channels, height, width) if `layout` is `NCHW`.
-- **weight**: (in_channels, height, width)
-- **out**: This depends on the `layout` parameter. Output is 4D array of shape
- (batch_size, channels, out_height, out_width) if `layout` is
`NCHW`.
-)code" TVM_ADD_FILELINE)
-.set_attrs_type<Dilation2DAttrs>()
-.set_num_inputs(2)
-.add_argument("data", "Tensor", "The input tensor.")
-.add_argument("weight", "Tensor", "The weight tensor.")
-.set_support_level(2)
-.add_type_rel("Dilation2D", Dilation2DRel<Dilation2DAttrs>)
-.set_attr<FInferCorrectLayout>("FInferCorrectLayout",
- Dilation2DInferCorrectLayout<Dilation2DAttrs>);
-
-
} // namespace relay
} // namespace tvm
diff --git a/src/relay/op/nn/convolution.h b/src/relay/op/nn/convolution.h
index e3fa02d..e3b4a85 100644
--- a/src/relay/op/nn/convolution.h
+++ b/src/relay/op/nn/convolution.h
@@ -360,77 +360,6 @@ bool Conv3DRel(const Array<Type>& types, int num_inputs,
const Attrs& attrs,
return true;
}
-template <typename AttrType>
-bool Dilation2DRel(const Array<Type>& types, int num_inputs, const Attrs&
attrs,
- const TypeReporter& reporter) {
- CHECK_EQ(types.size(), 3);
- const auto* data = types[0].as<TensorTypeNode>();
- const auto* weight = types[1].as<TensorTypeNode>();
- if (data == nullptr) return false;
- static const Layout kNCHW("NCHW");
- static const Layout kOIHW("IHW");
-
- const AttrType* param = attrs.as<AttrType>();
- CHECK(param != nullptr);
- const Layout in_layout(param->data_layout);
- const Layout kernel_layout(param->kernel_layout);
-
- const auto trans_in_layout = BijectiveLayoutNode::make(in_layout, kNCHW);
- CHECK(trans_in_layout.defined())
- << "Dilation2D only support input layouts that are convertible from
NCHW."
- << " But got " << in_layout;
-
- const auto trans_kernel_layout = BijectiveLayoutNode::make(kernel_layout,
kOIHW);
- CHECK(trans_kernel_layout.defined())
- << "Dilation2D only support kernel layouts that are convertible from
OIHW."
- << " But got " << kernel_layout;
-
- Layout out_layout(param->data_layout);
- const auto trans_out_layout = BijectiveLayoutNode::make(out_layout, kNCHW);
- CHECK(trans_out_layout.defined())
- << "Dilation2D only support output layouts that are convertible from
NCHW."
- << " But got " << out_layout;
-
- Array<IndexExpr> dshape_nchw = trans_in_layout.ForwardShape(data->shape);
-
- IndexExpr channels, dilated_ksize_y, dilated_ksize_x;
-
- // use weight to infer the conv shape.
- if (weight == nullptr) return false;
- auto wshape = trans_kernel_layout.ForwardShape(weight->shape);
- channels = wshape[0];
-
- dilated_ksize_y = 1 + (wshape[1] - 1) * param->dilations[0];
- dilated_ksize_x = 1 + (wshape[2] - 1) * param->dilations[1];
-
- // dilation
- Array<IndexExpr> oshape({dshape_nchw[0], channels, 0, 0});
- IndexExpr pad_h, pad_w;
- GetPaddingHeightWidth(param->padding, &pad_h, &pad_w);
- if (!dshape_nchw[2].as<tir::AnyNode>()) {
- oshape.Set(2, indexdiv(dshape_nchw[2] + pad_h - dilated_ksize_y,
- param->strides[0]) + 1);
- } else {
- oshape.Set(2, dshape_nchw[2]);
- }
-
- if (!dshape_nchw[3].as<tir::AnyNode>()) {
- oshape.Set(3, indexdiv(dshape_nchw[3] + pad_w - dilated_ksize_x,
- param->strides[1]) + 1);
- } else {
- oshape.Set(3, dshape_nchw[3]);
- }
-
- DataType out_dtype = param->out_dtype;
- if (out_dtype.bits() == 0) {
- out_dtype = data->dtype;
- }
- oshape = trans_out_layout.BackwardShape(oshape);
- // assign output type
- reporter->Assign(types[2], TensorType(oshape, out_dtype));
- return true;
-}
-
template<typename T>
Array<Array<Layout> > ConvInferCorrectLayout(
const Attrs& attrs,
diff --git a/tests/python/relay/test_op_level2.py
b/tests/python/relay/test_op_level2.py
index 53a5aa3..7a42fc3 100644
--- a/tests/python/relay/test_op_level2.py
+++ b/tests/python/relay/test_op_level2.py
@@ -1219,113 +1219,6 @@ def test_depthwise_conv2d_int8():
graph, lib, params = relay.build(func, target,
params=parameters)
-def test_dilation2d_infer_type():
- # symbolic in batch dimension
- n, h, w, c = te.var("n"), 224, 224, 10
- x = relay.var("x", relay.ty.TensorType((n, c, h, w), "float32"))
- kc, kh, kw = 10, 8, 8
- w = relay.var("w", relay.ty.TensorType((kc, kw, kh), "float32"))
- y = relay.nn.dilation2d(x, w,
- # kernel_size=(3, 3),
- strides=[1, 1, 1, 1],
- dilations=[1, 1, 1, 1],
- padding=[0, 0, 0, 0])
- yy = run_infer_type(y)
- assert yy.checked_type == relay.TensorType(
- (n, 10, 217, 217), "float32")
-
-
-def test_dilation2d_run():
- def run_test_dilation2d(indata, kernel, out,
- dtype='float32',
- strides=[1, 1],
- padding=[0, 0],
- dilations=[1, 1],
- except_targets=['cuda'],
- **attrs):
-
- dshape = indata.shape
- kshape = kernel.shape
-
- if except_targets is None:
- except_targets = []
-
- x = relay.var("x", shape=dshape, dtype=dtype)
- w = relay.var("w", shape=kshape, dtype=dtype)
- y = relay.nn.dilation2d(x, w,
- strides=strides,
- dilations=dilations,
- padding=padding,
- **attrs)
- func = relay.Function([x, w], y)
-
- for target, ctx in ctx_list():
- if target in except_targets:
- continue
- intrp = relay.create_executor("graph", ctx=ctx, target=target)
- op_res = intrp.evaluate(func)(indata, kernel)
- tvm.testing.assert_allclose(op_res.asnumpy(), out, rtol=1e-5,
atol=1e-5)
-
- def _convert_data(indata, kernel, out, layout=None):
- indata = np.asarray(indata)
- kernel = np.asarray(kernel)
- out = np.asarray(out)
- if layout == 'NCHW':
- indata = indata.transpose([0, 3, 1, 2])
- kernel = kernel.transpose([2, 0, 1])
- out = out.transpose([0, 3, 1, 2])
- return indata, kernel, out
-
- image = [[[[.1], [.2]], [[.3], [.4]]]]
- kernel = [[[.4], [.3]], [[.1], [.0]]]
- out = [[[[.5]]]]
- run_test_dilation2d(*_convert_data(image, kernel, out, layout='NCHW'))
- run_test_dilation2d(*_convert_data(image, kernel, out),
data_layout='NHWC', kernel_layout='HWI')
-
- image = [[[[.1], [.2]], [[.3], [.4]]]]
- kernel = [[[.4], [.3]], [[.1], [.0]]]
- out = [[[[.5], [.6]], [[.7], [.8]]]]
- run_test_dilation2d(*_convert_data(image, kernel, out, layout='NCHW'),
padding=[0, 0, 1, 1])
- run_test_dilation2d(*_convert_data(image, kernel, out), padding=[0, 0, 1,
1],
- data_layout='NHWC', kernel_layout='HWI')
-
- image = [[[[.1, .2, .0], [.2, .3, .1]], [[.3, .4, .2], [.4, .5, .3]]]]
- kernel = [[[.4, .5, .3], [.3, .4, .2]], [[.1, .2, .0], [.0, .1, -.1]]]
- out = [[[[.5, .7, .3], [.6, .8, .4]], [[.7, .9, .5], [.8, 1., .6]]]]
- run_test_dilation2d(*_convert_data(image, kernel, out, layout='NCHW'),
padding=[0, 0, 1, 1])
- run_test_dilation2d(*_convert_data(image, kernel, out), padding=[0, 0, 1,
1],
- data_layout='NHWC', kernel_layout='HWI')
-
- image = [[[[.1], [.2]], [[.3], [.4]]], [[[.2], [.3]], [[.4], [.5]]]]
- kernel = [[[.4], [.3]], [[.1], [.0]]]
- out = [[[[.5], [.6]], [[.7], [.8]]], [[[.6], [.7]], [[.8], [.9]]]]
- run_test_dilation2d(*_convert_data(image, kernel, out, layout='NCHW'),
padding=[0, 0, 1, 1])
- run_test_dilation2d(*_convert_data(image, kernel, out), padding=[0, 0, 1,
1],
- data_layout='NHWC', kernel_layout='HWI')
-
- image = [[[[.1], [.2]], [[.3], [.4]]]]
- kernel = [[[.4], [.3]]]
- out = [[[[.5]], [[.7]]]]
- run_test_dilation2d(*_convert_data(image, kernel, out, layout='NCHW'))
- run_test_dilation2d(*_convert_data(image, kernel, out),
- data_layout='NHWC', kernel_layout='HWI')
-
- image = [[[[.1], [.2], [.3]], [[.4], [.5], [.6]], [[.7], [.8], [.9]]]]
- kernel = [[[.4], [.3]], [[.1], [.2]]]
- out = [[[[.7], [.8], [.6]], [[1.0], [1.1], [.9]], [[.8], [.9], [.9]]]]
- run_test_dilation2d(*_convert_data(image, kernel, out, layout='NCHW'),
padding=[1, 1], dilations=[2, 2])
- run_test_dilation2d(*_convert_data(image, kernel, out), padding=[1, 1],
dilations=[2, 2],
- data_layout='NHWC', kernel_layout='HWI')
-
- image = [[[[.1], [.2], [.3], [.4]], [[.5], [.6], [.7], [.8]],
- [[.9], [1.0], [1.1], [1.2]]]]
- kernel = [[[.4], [.3]], [[.1], [.2]]]
- out = [[[[.8], [1.0]], [[1.2], [1.4]]]]
- run_test_dilation2d(*_convert_data(image, kernel, out, layout='NCHW'),
strides=[1, 2])
- run_test_dilation2d(*_convert_data(image, kernel, out), strides=[1, 2],
- data_layout='NHWC', kernel_layout='HWI')
-
-
def test_bitserial_conv2d_infer_type():
# Basic shape test with ambiguous batch.
n, c, h, w = te.size_var("n"), 32, 224, 224
@@ -1381,5 +1274,3 @@ if __name__ == "__main__":
test_upsampling3d()
test_conv2d_int8_intrinsics()
test_depthwise_conv2d_int8()
- test_dilation2d_infer_type()
- test_dilation2d_run()
diff --git a/tests/python/relay/test_op_level5.py
b/tests/python/relay/test_op_level5.py
index 8fd05da..2dc549f 100644
--- a/tests/python/relay/test_op_level5.py
+++ b/tests/python/relay/test_op_level5.py
@@ -671,6 +671,113 @@ def test_space_to_depth():
verify_space_to_depth((1, 4, 4, 4), 2, layout)
+def test_dilation2d_infer_type():
+ # symbolic in batch dimension
+ n, h, w, c = te.var("n"), 224, 224, 10
+ x = relay.var("x", relay.ty.TensorType((n, c, h, w), "float32"))
+ kc, kh, kw = 10, 8, 8
+ w = relay.var("w", relay.ty.TensorType((kc, kw, kh), "float32"))
+ y = relay.image.dilation2d(x, w,
+ # kernel_size=(3, 3),
+ strides=[1, 1, 1, 1],
+ dilations=[1, 1, 1, 1],
+ padding=[0, 0, 0, 0])
+ yy = run_infer_type(y)
+ assert yy.checked_type == relay.TensorType(
+ (n, 10, 217, 217), "float32")
+
+
+def test_dilation2d_run():
+ def run_test_dilation2d(indata, kernel, out,
+ dtype='float32',
+ strides=[1, 1],
+ padding=[0, 0],
+ dilations=[1, 1],
+ except_targets=['cuda'],
+ **attrs):
+
+ dshape = indata.shape
+ kshape = kernel.shape
+
+ if except_targets is None:
+ except_targets = []
+
+ x = relay.var("x", shape=dshape, dtype=dtype)
+ w = relay.var("w", shape=kshape, dtype=dtype)
+ y = relay.image.dilation2d(x, w,
+ strides=strides,
+ dilations=dilations,
+ padding=padding,
+ **attrs)
+ func = relay.Function([x, w], y)
+
+ for target, ctx in ctx_list():
+ if target in except_targets:
+ continue
+ intrp = relay.create_executor("graph", ctx=ctx, target=target)
+ op_res = intrp.evaluate(func)(indata, kernel)
+ tvm.testing.assert_allclose(op_res.asnumpy(), out, rtol=1e-5,
atol=1e-5)
+
+ def _convert_data(indata, kernel, out, layout=None):
+ indata = np.asarray(indata)
+ kernel = np.asarray(kernel)
+ out = np.asarray(out)
+ if layout == 'NCHW':
+ indata = indata.transpose([0, 3, 1, 2])
+ kernel = kernel.transpose([2, 0, 1])
+ out = out.transpose([0, 3, 1, 2])
+ return indata, kernel, out
+
+ image = [[[[.1], [.2]], [[.3], [.4]]]]
+ kernel = [[[.4], [.3]], [[.1], [.0]]]
+ out = [[[[.5]]]]
+ run_test_dilation2d(*_convert_data(image, kernel, out, layout='NCHW'))
+ run_test_dilation2d(*_convert_data(image, kernel, out),
data_layout='NHWC', kernel_layout='HWI')
+
+ image = [[[[.1], [.2]], [[.3], [.4]]]]
+ kernel = [[[.4], [.3]], [[.1], [.0]]]
+ out = [[[[.5], [.6]], [[.7], [.8]]]]
+ run_test_dilation2d(*_convert_data(image, kernel, out, layout='NCHW'),
padding=[0, 0, 1, 1])
+ run_test_dilation2d(*_convert_data(image, kernel, out), padding=[0, 0, 1,
1],
+ data_layout='NHWC', kernel_layout='HWI')
+
+ image = [[[[.1, .2, .0], [.2, .3, .1]], [[.3, .4, .2], [.4, .5, .3]]]]
+ kernel = [[[.4, .5, .3], [.3, .4, .2]], [[.1, .2, .0], [.0, .1, -.1]]]
+ out = [[[[.5, .7, .3], [.6, .8, .4]], [[.7, .9, .5], [.8, 1., .6]]]]
+ run_test_dilation2d(*_convert_data(image, kernel, out, layout='NCHW'),
padding=[0, 0, 1, 1])
+ run_test_dilation2d(*_convert_data(image, kernel, out), padding=[0, 0, 1,
1],
+ data_layout='NHWC', kernel_layout='HWI')
+
+ image = [[[[.1], [.2]], [[.3], [.4]]], [[[.2], [.3]], [[.4], [.5]]]]
+ kernel = [[[.4], [.3]], [[.1], [.0]]]
+ out = [[[[.5], [.6]], [[.7], [.8]]], [[[.6], [.7]], [[.8], [.9]]]]
+ run_test_dilation2d(*_convert_data(image, kernel, out, layout='NCHW'),
padding=[0, 0, 1, 1])
+ run_test_dilation2d(*_convert_data(image, kernel, out), padding=[0, 0, 1,
1],
+ data_layout='NHWC', kernel_layout='HWI')
+
+ image = [[[[.1], [.2]], [[.3], [.4]]]]
+ kernel = [[[.4], [.3]]]
+ out = [[[[.5]], [[.7]]]]
+ run_test_dilation2d(*_convert_data(image, kernel, out, layout='NCHW'))
+ run_test_dilation2d(*_convert_data(image, kernel, out),
+ data_layout='NHWC', kernel_layout='HWI')
+
+ image = [[[[.1], [.2], [.3]], [[.4], [.5], [.6]], [[.7], [.8], [.9]]]]
+ kernel = [[[.4], [.3]], [[.1], [.2]]]
+ out = [[[[.7], [.8], [.6]], [[1.0], [1.1], [.9]], [[.8], [.9], [.9]]]]
+ run_test_dilation2d(*_convert_data(image, kernel, out, layout='NCHW'),
padding=[1, 1], dilations=[2, 2])
+ run_test_dilation2d(*_convert_data(image, kernel, out), padding=[1, 1],
dilations=[2, 2],
+ data_layout='NHWC', kernel_layout='HWI')
+
+ image = [[[[.1], [.2], [.3], [.4]], [[.5], [.6], [.7], [.8]],
+ [[.9], [1.0], [1.1], [1.2]]]]
+ kernel = [[[.4], [.3]], [[.1], [.2]]]
+ out = [[[[.8], [1.0]], [[1.2], [1.4]]]]
+ run_test_dilation2d(*_convert_data(image, kernel, out, layout='NCHW'),
strides=[1, 2])
+ run_test_dilation2d(*_convert_data(image, kernel, out), strides=[1, 2],
+ data_layout='NHWC', kernel_layout='HWI')
+
+
if __name__ == "__main__":
test_resize_infer_type()
test_resize()
@@ -687,3 +794,5 @@ if __name__ == "__main__":
test_deformable_conv2d()
test_depth_to_space()
test_space_to_depth()
+ test_dilation2d_infer_type()
+ test_dilation2d_run()
diff --git a/topi/python/topi/generic/__init__.py
b/topi/python/topi/generic/__init__.py
index bf45bc3..6171317 100644
--- a/topi/python/topi/generic/__init__.py
+++ b/topi/python/topi/generic/__init__.py
@@ -38,3 +38,4 @@ from .extern import *
from .vision import *
from .sort import *
from .search import *
+from .image import *
diff --git a/topi/python/topi/image/__init__.py
b/topi/python/topi/generic/default.py
similarity index 56%
copy from topi/python/topi/image/__init__.py
copy to topi/python/topi/generic/default.py
index 90c1cf6..d4c642a 100644
--- a/topi/python/topi/image/__init__.py
+++ b/topi/python/topi/generic/default.py
@@ -14,9 +14,21 @@
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
+# pylint: disable=invalid-name,unused-argument
+"""The default schedule used by various operators"""
+import tvm
+from tvm import te
-# pylint: disable=wildcard-import
-"""IMAGE network operators"""
-from __future__ import absolute_import as _abs
-from .resize import *
+def default_schedule(outs, auto_inline):
+ """Default schedule for llvm."""
+ target = tvm.target.Target.current(allow_none=False)
+ outs = [outs] if isinstance(outs, te.tensor.Tensor) else outs
+ if target.target_name != "llvm":
+ raise RuntimeError("schedule not registered for '%s'" % target)
+ s = te.create_schedule([x.op for x in outs])
+ if auto_inline:
+ x = outs[0]
+ te.schedule.AutoInlineInjective(s)
+ s[x].fuse(s[x].op.axis)
+ return s
diff --git a/topi/python/topi/generic/sort.py
b/topi/python/topi/generic/image.py
similarity index 62%
copy from topi/python/topi/generic/sort.py
copy to topi/python/topi/generic/image.py
index 9eca588..44d134d 100644
--- a/topi/python/topi/generic/sort.py
+++ b/topi/python/topi/generic/image.py
@@ -14,39 +14,35 @@
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
-# pylint: disable=invalid-name, no-member
-"""Generic vision operators"""
-from __future__ import absolute_import as _abs
-from .vision import _default_schedule
+"""Generic image operators"""
+from .default import default_schedule as _default_schedule
-def schedule_argsort(outs):
- """Schedule for argsort operator.
+def schedule_dilation2d_nchw(outs):
+ """Schedule for dilation2d
Parameters
----------
- outs: Array of Tensor
- The indices that would sort an input array along
- the given axis.
-
+ outs : Array of Tensor
+ The computation graph description of dilation2d
+ in the format of an array of tensors.
Returns
-------
- s: Schedule
- The computation schedule for the op.
+ sch : Schedule
+ The computation schedule for the op.
"""
return _default_schedule(outs, False)
-def schedule_topk(outs):
- """Schedule for topk operator.
+def schedule_dilation2d_nhwc(outs):
+ """Schedule for dilation2d
Parameters
----------
- outs: Array of Tensor
- The indices that would sort an input array along
- the given axis.
-
+ outs : Array of Tensor
+ The computation graph description of dilation2d
+ in the format of an array of tensors.
Returns
-------
- s: Schedule
- The computation schedule for the op.
+ sch : Schedule
+ The computation schedule for the op.
"""
return _default_schedule(outs, False)
diff --git a/topi/python/topi/generic/nn.py b/topi/python/topi/generic/nn.py
index 7177d04..43b1282 100644
--- a/topi/python/topi/generic/nn.py
+++ b/topi/python/topi/generic/nn.py
@@ -16,21 +16,8 @@
# under the License.
# pylint: disable=invalid-name,unused-argument
"""Generic nn operators"""
-import tvm
from tvm import te
-
-def _default_schedule(outs, auto_inline):
- """Default schedule for llvm."""
- target = tvm.target.Target.current(allow_none=False)
- outs = [outs] if isinstance(outs, te.tensor.Tensor) else outs
- if target.target_name not in ("llvm", "c"):
- raise RuntimeError("schedule not registered for '%s'" % target)
- s = te.create_schedule([x.op for x in outs])
- if auto_inline:
- x = outs[0]
- te.schedule.AutoInlineInjective(s)
- s[x].fuse(s[x].op.axis)
- return s
+from .default import default_schedule as _default_schedule
def schedule_conv1d_ncw(outs):
@@ -648,33 +635,3 @@ def schedule_batch_matmul(outs):
The computation schedule for the op.
"""
return _default_schedule(outs, False)
-
-
-def schedule_dilation2d_nchw(outs):
- """Schedule for dilation2d
- Parameters
- ----------
- outs : Array of Tensor
- The computation graph description of dilation2d
- in the format of an array of tensors.
- Returns
- -------
- sch : Schedule
- The computation schedule for the op.
- """
- return _default_schedule(outs, False)
-
-
-def schedule_dilation2d_nhwc(outs):
- """Schedule for dilation2d
- Parameters
- ----------
- outs : Array of Tensor
- The computation graph description of dilation2d
- in the format of an array of tensors.
- Returns
- -------
- sch : Schedule
- The computation schedule for the op.
- """
- return _default_schedule(outs, False)
diff --git a/topi/python/topi/generic/search.py
b/topi/python/topi/generic/search.py
index 69f2366..91b7635 100644
--- a/topi/python/topi/generic/search.py
+++ b/topi/python/topi/generic/search.py
@@ -17,7 +17,8 @@
# pylint: disable=invalid-name, no-member
"""Generic search operators"""
from __future__ import absolute_import as _abs
-from .vision import _default_schedule
+from .default import default_schedule as _default_schedule
+
def schedule_argwhere(outs):
"""Schedule for argwhere operator.
diff --git a/topi/python/topi/generic/sort.py b/topi/python/topi/generic/sort.py
index 9eca588..1d5a30d 100644
--- a/topi/python/topi/generic/sort.py
+++ b/topi/python/topi/generic/sort.py
@@ -15,9 +15,10 @@
# specific language governing permissions and limitations
# under the License.
# pylint: disable=invalid-name, no-member
-"""Generic vision operators"""
+"""Generic sort operators"""
from __future__ import absolute_import as _abs
-from .vision import _default_schedule
+from .default import default_schedule as _default_schedule
+
def schedule_argsort(outs):
"""Schedule for argsort operator.
diff --git a/topi/python/topi/generic/vision.py
b/topi/python/topi/generic/vision.py
index 3935250..edf1a48 100644
--- a/topi/python/topi/generic/vision.py
+++ b/topi/python/topi/generic/vision.py
@@ -18,21 +18,9 @@
"""Generic vision operators"""
from __future__ import absolute_import as _abs
import tvm
-from tvm import te
from .. import cpp
+from .default import default_schedule as _default_schedule
-def _default_schedule(outs, auto_inline):
- """Default schedule for llvm."""
- target = tvm.target.Target.current(allow_none=False)
- outs = [outs] if isinstance(outs, te.tensor.Tensor) else outs
- if target.target_name != "llvm":
- raise RuntimeError("schedule not registered for '%s'" % target)
- s = te.create_schedule([x.op for x in outs])
- if auto_inline:
- x = outs[0]
- te.schedule.AutoInlineInjective(s)
- s[x].fuse(s[x].op.axis)
- return s
def schedule_reorg(outs):
"""Schedule for reorg
diff --git a/topi/python/topi/image/__init__.py
b/topi/python/topi/image/__init__.py
index 90c1cf6..86b9825 100644
--- a/topi/python/topi/image/__init__.py
+++ b/topi/python/topi/image/__init__.py
@@ -20,3 +20,4 @@
from __future__ import absolute_import as _abs
from .resize import *
+from .dilation2d import *
diff --git a/topi/python/topi/nn/dilation2d.py
b/topi/python/topi/image/dilation2d.py
similarity index 97%
rename from topi/python/topi/nn/dilation2d.py
rename to topi/python/topi/image/dilation2d.py
index 9cb4284..a71866e 100644
--- a/topi/python/topi/nn/dilation2d.py
+++ b/topi/python/topi/image/dilation2d.py
@@ -20,12 +20,12 @@
from __future__ import absolute_import as _abs
from tvm import te
from topi.util import simplify
-from .pad import pad
-from .util import get_pad_tuple
+from ..nn.pad import pad
+from ..nn.util import get_pad_tuple
def dilation2d_nchw(input, filter, stride, padding, dilations, out_dtype=None):
- """Dilation2D operator in NCHW layout.
+ """Morphological dilation operator in NCHW layout.
Parameters
----------
@@ -96,7 +96,7 @@ def dilation2d_nchw(input, filter, stride, padding,
dilations, out_dtype=None):
def dilation2d_nhwc(input, filter, stride, padding, dilations, out_dtype=None):
- """Dilation2D operator in NHWC layout.
+ """Morphological 2d dilation NHWC layout.
Parameters
----------
diff --git a/topi/python/topi/nn/__init__.py b/topi/python/topi/nn/__init__.py
index 1b1067f..bd806b9 100644
--- a/topi/python/topi/nn/__init__.py
+++ b/topi/python/topi/nn/__init__.py
@@ -24,7 +24,6 @@ from .conv2d import *
from .conv3d import *
from .deformable_conv2d import *
from .depthwise_conv2d import *
-from .dilation2d import *
from .elemwise import *
from .dilate import *
from .flatten import *
