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[TOPI] implement pool3d op #4478

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Dec 12, 2019
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[TOPI] implement pool3d op #4478

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f5b3be9
[TOPI] implement pool3d op
optima2005 Dec 7, 2019
2d6b77b
use PoolInferCorrectLayout for both 2d and 3d pooling
optima2005 Dec 8, 2019
89c69d8
unify MakeMaxPool and MakeAvgPool
optima2005 Dec 12, 2019
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62 changes: 62 additions & 0 deletions include/tvm/relay/attrs/nn.h
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Original file line number Diff line number Diff line change
Expand Up @@ -406,6 +406,68 @@ struct AdaptivePool2DAttrs : public tvm::AttrsNode<AdaptivePool2DAttrs> {
};


/*! \brief Attributes for 3D max pool operator */
struct MaxPool3DAttrs : public tvm::AttrsNode<MaxPool3DAttrs> {
Array<IndexExpr> pool_size;
Array<IndexExpr> strides;
Array<IndexExpr> padding;
std::string layout;
bool ceil_mode;

TVM_DECLARE_ATTRS(MaxPool3DAttrs, "relay.attrs.MaxPool3DAttrs") {
TVM_ATTR_FIELD(pool_size)
.describe("Size of the pooling windows.");
TVM_ATTR_FIELD(strides).set_default(Array<IndexExpr>({1, 1, 1}))
.describe("Specifies the strides of the convolution.");
TVM_ATTR_FIELD(padding).set_default(Array<IndexExpr>({0, 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"
"three int : back, bottom, right will use same padding as front, top, left"
"six int : padding width in the order of (front, top, left, back, bottom, right)");
TVM_ATTR_FIELD(layout).set_default("NCDHW")
.describe("Dimension ordering of data and weight. Can be 'NCDHW', 'NDHWC', etc."
"'N', 'C', 'D', 'H', 'W' stands for batch, channel, depth, height, and width"
"dimensions respectively. Pooling is applied on the 'D', 'H' and"
"'W' dimensions.");
TVM_ATTR_FIELD(ceil_mode).set_default(false)
.describe("When true, will use ceil instead of floor to compute the output shape.");
}
};

/*! \brief Attributes for 3D avg pool operator */
struct AvgPool3DAttrs : public tvm::AttrsNode<AvgPool3DAttrs> {
Array<IndexExpr> pool_size;
Array<IndexExpr> strides;
Array<IndexExpr> padding;
std::string layout;
bool ceil_mode;
bool count_include_pad;

TVM_DECLARE_ATTRS(AvgPool3DAttrs, "relay.attrs.AvgPool3DAttrs") {
TVM_ATTR_FIELD(pool_size)
.describe("Size of the pooling windows.");
TVM_ATTR_FIELD(strides).set_default(Array<IndexExpr>({1, 1, 1}))
.describe("Specifies the strides of the convolution.");
TVM_ATTR_FIELD(padding).set_default(Array<IndexExpr>({0, 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"
"three int : back, bottom, right will use same padding as front, top, left"
"six int : padding width in the order of (front, top, left, back, bottom, right)");
TVM_ATTR_FIELD(layout).set_default("NCDHW")
.describe("Dimension ordering of data and weight. Can be 'NCDHW', 'NDHWC', etc."
"'N', 'C', 'D', 'H', 'W' stands for batch, channel, depth, height, and width"
"dimensions respectively. Pooling is applied on the 'D', 'H' and"
"'W' dimensions.");
TVM_ATTR_FIELD(ceil_mode).set_default(false)
.describe("When true, will use ceil instead of floor to compute the output shape.");
TVM_ATTR_FIELD(count_include_pad).set_default(false)
.describe("When true, will include padding to compute the average");
}
};


/*! \brief Attributes for dense operator */
struct DenseAttrs : public tvm::AttrsNode<DenseAttrs> {
IndexExpr units;
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