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op {
graph_op_name: "MaxPoolWithArgmax"
in_arg {
name: "input"
description: <<END
4-D with shape `[batch, height, width, channels]`. Input to pool over.
END
}
out_arg {
name: "output"
description: <<END
The max pooled output tensor.
END
}
out_arg {
name: "argmax"
description: <<END
4-D. The flattened indices of the max values chosen for each output.
END
}
attr {
name: "ksize"
description: <<END
The size of the window for each dimension of the input tensor.
END
}
attr {
name: "strides"
description: <<END
The stride of the sliding window for each dimension of the
input tensor.
END
}
attr {
name: "padding"
description: <<END
The type of padding algorithm to use.
END
}
summary: "Performs max pooling on the input and outputs both max values and indices."
description: <<END
The indices in `argmax` are flattened, so that a maximum value at position
`[b, y, x, c]` becomes flattened index
`((b * height + y) * width + x) * channels + c`.
The indices returned are always in `[0, height) x [0, width)` before flattening,
even if padding is involved and the mathematically correct answer is outside
(either negative or too large). This is a bug, but fixing it is difficult to do
in a safe backwards compatible way, especially due to flattening.
END
}
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