#include "megdnn/oprs.h"
#include "src/common/utils.h"
namespace megdnn {
void PoolingBase::deduce_layout_fwd(const TensorLayout& src, TensorLayout& dst) {
auto errmsg =
megdnn_layout_msg(src) + ", " + megdnn_layout_msg(dst) + ", " +
"pad_h=" + std::to_string(param().pad_h) + ", " +
"pad_w=" + std::to_string(param().pad_w) + ", " +
"stride_h=" + std::to_string(param().stride_h) + ", " +
"stride_w=" + std::to_string(param().stride_w) + ", " +
"window_h=" + std::to_string(param().window_h) + ", " +
"window_w=" + std::to_string(param().window_w) + ", " +
"is_max=" + std::to_string(param().mode == Mode::MAX) + ", " +
"is_nhwc=" + std::to_string(param().format == Param::Format::NHWC) + ", " +
"is_nhwcd4=" + std::to_string(param().format == Param::Format::NHWCD4);
auto errmsg_c = errmsg.c_str();
MEGDNN_MARK_USED_VAR(errmsg_c);
megdnn_assert_contiguous(src);
size_t spatial_pos, c_pos, batch_pos = 0;
if (param().format == Param::Format::NCHW) {
megdnn_assert(src.ndim == 4_z, "%s", errmsg_c);
spatial_pos = 2;
c_pos = 1;
} else if (param().format == Param::Format::NHWC) {
megdnn_assert(src.ndim == 4_z, "%s", errmsg_c);
spatial_pos = 1;
c_pos = 3;
} else if (
param().format == Param::Format::NCHW4 ||
param().format == Param::Format::NCHW44 ||
param().format == Param::Format::NCHW88 ||
param().format == Param::Format::NCHW32 ||
param().format == Param::Format::NCHW64) {
megdnn_assert(src.ndim == 5_z, "%s", errmsg_c);
spatial_pos = 2;
c_pos = 1;
} else if (param().format == Param::Format::CHWN4) {
spatial_pos = 1;
c_pos = 0;
batch_pos = 3;
} else {
megdnn_assert(
param().format == Param::Format::NHWCD4 && src.ndim == 5_z, "%s",
errmsg_c);
spatial_pos = 1;
c_pos = 2;
}
size_t n = src[batch_pos];
size_t c = src[c_pos];
size_t ih = src[spatial_pos];
size_t iw = src[spatial_pos + 1];
if (param().format == Param::Format::NHWCD4) {
c *= 4;
iw = src[spatial_pos + 2];
}
if (param().format == Param::Format::NCHW4 ||
param().format == Param::Format::NCHW44 ||
param().format == Param::Format::CHWN4) {
c *= 4;
}
if (param().format == Param::Format::NCHW88) {
c *= 8;
}
if (param().format == Param::Format::NCHW32) {
c *= 32;
}
if (param().format == Param::Format::NCHW64) {
c *= 64;
}
size_t oh, ow;
size_t fh = this->param().window_h;
size_t fw = this->param().window_w;
size_t sh = this->param().stride_h;
size_t sw = this->param().stride_w;
size_t ph = this->param().pad_h;
size_t pw = this->param().pad_w;
if (ph >= fh || pw >= fw) {
megdnn_log_warn(
"pooling padding size (%zu %zu) should not be bigger than "
"window size (%zu %zu), it only can be used in CaffePooling",
pw, ph, fw, fh);
}
infer_conv_shape2d(ih, iw, fh, fw, sh, sw, ph, pw, oh, ow);
if (param().format == Param::Format::NCHW) {
dst = TensorLayout(TensorShape({n, c, oh, ow}), src.dtype);
} else if (param().format == Param::Format::NHWC) {
megdnn_assert(param().format == Param::Format::NHWC, "invalid pooling format");
dst = TensorLayout({n, oh, ow, c}, src.dtype, src.format);
} else if (
param().format == Param::Format::NCHW4 ||
param().format == Param::Format::NCHW44) {
dst = TensorLayout{{n, c / 4, oh, ow, 4}, src.dtype, src.format};
} else if (param().format == Param::Format::NCHW88) {
dst = TensorLayout{{n, c / 8, oh, ow, 8}, src.dtype, src.format};
} else if (param().format == Param::Format::NCHW32) {
dst = TensorLayout{{n, c / 32, oh, ow, 32}, src.dtype, src.format};
} else if (param().format == Param::Format::NCHW64) {
dst = TensorLayout{{n, c / 64, oh, ow, 64}, src.dtype, src.format};
} else if (param().format == Param::Format::CHWN4) {
dst = TensorLayout{{c / 4, oh, ow, n, 4}, src.dtype, src.format};
} else {
megdnn_assert(
param().format == Param::Format::NHWCD4, "invalid pooling format");
dst = TensorLayout{{n, oh, c / 4, ow, 4}, src.dtype, src.format};
}
}
void PoolingBase::check_layout_fwd(const TensorLayout& src, const TensorLayout& dst) {
TensorLayout dst_expected;
megdnn_assert_eq_dtype(src, dst);
deduce_layout_fwd(src, dst_expected);
megdnn_assert_eq_layout(dst_expected, dst);
megdnn_assert(src.dtype == dst.dtype);
megdnn_assert(
src.dtype.category() == DTypeCategory::FLOAT ||
src.dtype == dtype::Int8() ||
src.dtype.category() == DTypeCategory::QUANTIZED);
}
void PoolingForward::deduce_layout(const TensorLayout& src, TensorLayout& dst) {
deduce_layout_fwd(src, dst);
}
void PoolingForward::check_exec(
const TensorLayout& src, const TensorLayout& dst, size_t workspace_in_bytes) {
check_layout_fwd(src, dst);
auto required_workspace_in_bytes = get_workspace_in_bytes(src, dst);
megdnn_assert(workspace_in_bytes >= required_workspace_in_bytes);
}
void PoolingBackward::check_exec(
const TensorLayout& src, const TensorLayout& dst, const TensorLayout& diff,
const TensorLayout& grad, size_t workspace_in_bytes) {
check_layout_fwd(src, dst);
megdnn_assert_eq_layout(src, grad);
megdnn_assert_eq_layout(dst, diff);
auto required_workspace_in_bytes = get_workspace_in_bytes(src, dst, diff, grad);
megdnn_assert(workspace_in_bytes >= required_workspace_in_bytes);
}
}