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Description updated for pooling attributes #5091

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Mar 18, 2020
Merged

Description updated for pooling attributes #5091

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28 changes: 14 additions & 14 deletions include/tvm/relay/attrs/nn.h
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Original file line number Diff line number Diff line change
Expand Up @@ -494,9 +494,9 @@ struct MaxPool2DAttrs : public tvm::AttrsNode<MaxPool2DAttrs> {
"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(layout).set_default("NCHW")
.describe("Dimension ordering of data and weight. Can be 'NCHW', 'NHWC', etc."
.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"
"dimensions respectively. Pooling is applied on the '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.");
Expand Down Expand Up @@ -524,9 +524,9 @@ struct AvgPool2DAttrs : public tvm::AttrsNode<AvgPool2DAttrs> {
"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(layout).set_default("NCHW")
.describe("Dimension ordering of data and weight. Can be 'NCHW', 'NHWC', etc."
.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"
"dimensions respectively. Pooling is applied on the '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.");
Expand All @@ -541,9 +541,9 @@ struct GlobalPool2DAttrs : public tvm::AttrsNode<GlobalPool2DAttrs> {

TVM_DECLARE_ATTRS(GlobalPool2DAttrs, "relay.attrs.GlobalPool2DAttrs") {
TVM_ATTR_FIELD(layout).set_default("NCHW")
.describe("Dimension ordering of data and weight. Can be 'NCHW', 'NHWC', etc."
.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"
"dimensions respectively. Pooling is applied on the 'H' and"
"'W' dimensions.");
}
};
Expand All @@ -557,9 +557,9 @@ struct AdaptivePool2DAttrs : public tvm::AttrsNode<AdaptivePool2DAttrs> {
TVM_ATTR_FIELD(output_size).set_default(Array<IndexExpr>({}))
.describe("Output height and width.");
TVM_ATTR_FIELD(layout).set_default("NCHW")
.describe("Dimension ordering of data and weight. Can be 'NCHW', 'NHWC', etc."
.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"
"dimensions respectively. Pooling is applied on the 'H' and"
"'W' dimensions.");
}
};
Expand All @@ -572,9 +572,9 @@ struct AdaptivePool3DAttrs : public tvm::AttrsNode<AdaptivePool3DAttrs> {
TVM_ATTR_FIELD(output_size).set_default(Array<IndexExpr>({}))
.describe("Output depth, height and width.");
TVM_ATTR_FIELD(layout).set_default("NCDHW")
.describe("Dimension ordering of data and weight. Can be 'NCDHW', 'NDHWC', etc."
.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. Convolution is applied on 'D', 'H' and"
"dimensions respectively. Pooling is applied on 'D', 'H' and"
"'W' dimensions.");
}
};
Expand All @@ -600,7 +600,7 @@ struct MaxPool1DAttrs : public tvm::AttrsNode<MaxPool1DAttrs> {
"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("NCW")
.describe("Dimension ordering of data and weight. Can be 'NCW', 'NWC', etc."
.describe("Dimension ordering of input data. Can be 'NCW', 'NWC', etc."
"'N', 'C', 'W' stands for batch, channel, and width"
"dimensions respectively. Pooling is applied on the 'W' dimensions.");
TVM_ATTR_FIELD(ceil_mode).set_default(false)
Expand Down Expand Up @@ -629,7 +629,7 @@ struct AvgPool1DAttrs : public tvm::AttrsNode<AvgPool1DAttrs> {
"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("NCW")
.describe("Dimension ordering of data and weight. Can be 'NCW', 'NHC', etc."
.describe("Dimension ordering of input data. Can be 'NCW', 'NHC', etc."
"'N', 'C', 'W' stands for batch, channel, and width"
"dimensions respectively. Pooling is applied on the 'W' dimension.");
TVM_ATTR_FIELD(ceil_mode).set_default(false)
Expand Down Expand Up @@ -660,7 +660,7 @@ struct MaxPool3DAttrs : public tvm::AttrsNode<MaxPool3DAttrs> {
"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."
.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. Pooling is applied on the 'D', 'H' and"
"'W' dimensions.");
Expand Down Expand Up @@ -690,7 +690,7 @@ struct AvgPool3DAttrs : public tvm::AttrsNode<AvgPool3DAttrs> {
"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."
.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. Pooling is applied on the 'D', 'H' and"
"'W' dimensions.");
Expand Down