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author | Eric Kunze <eric.kunze@arm.com> | 2022-07-08 15:52:21 -0700 |
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committer | Eric Kunze <eric.kunze@arm.com> | 2022-07-08 15:53:01 -0700 |
commit | 4b867860565221f9a1ec71ea5ceb0434a1460cb0 (patch) | |
tree | b2fad31659569654fa897366033f31dd8c5a7f56 /chapters/image.adoc | |
parent | b8522ba72949b3eca03410f931f2655ee8485fa5 (diff) | |
download | specification-4b867860565221f9a1ec71ea5ceb0434a1460cb0.tar.gz |
RESIZE: define scale as a ratio of integers
Define scaling factor as a ratio of integers so
that output dimensions can be derived from input
dimensions without rounding.
Change-Id: Iddfd9ff549edf2963bf22047e8641a348cadb35f
Signed-off-by: Eric Kunze <eric.kunze@arm.com>
Diffstat (limited to 'chapters/image.adoc')
-rw-r--r-- | chapters/image.adoc | 141 |
1 files changed, 77 insertions, 64 deletions
diff --git a/chapters/image.adoc b/chapters/image.adoc index 6f1d3cc..d6177e4 100644 --- a/chapters/image.adoc +++ b/chapters/image.adoc @@ -13,34 +13,41 @@ Resizes a tensor. Resize is only allowed in the H and W dimensions. + +The height dimension is scaled by factor (scale_y_n/scale_y_d). +The width dimension is scaled by factor (scale_x_n/scale_x_d). + The NEAREST_NEIGHBOR mode returns the value of the input tensor closest to the calculated sample position for both floating-point and integer data formats. Floating-point BILINEAR mode returns a bilinearly interpolated output value based on the four closest input sample positions. -For integer BILINEAR interpolation mode, the output value is calculated by using -the shift value along with the other parameters to create a fixed point scaling -factor for each input. These values are then summed to create the value for -output, which has 2 * shift fractional bits. To convert back to the original -integer size, the output value must be rescaled. +For integer BILINEAR interpolation mode, the output value must +be scaled by 1/(scale_y_n * scale_x_n) in a following operation to +complete the interpolation (for example with a RESCALE operator). The following examples show practical uses of the parameters: -* For approximate uniform input sampling between (0, 0) and (IH-1, IW-1) set -stride_y = ( (IH-1) * (1<<shift) ) / (OH-1), -stride_x = ( (IW-1) * (1<<shift) ) / (OW-1), -offset_x=0, offset_y=0, border_x=0, border_y=0. - -* For power of two upscale by factor (1<<k) the following parameters can -be used for fixed point upscales: -** For upscale [OH-1,OW-1] = (1<<k) * [IH-1, IW-1] set -shift=k, stride_y=1, stride_x=1, offset_x=0, offset_y=0, -border_x=0, border_y=0. -** For upscale [OH,OW] = (1<<k) * [IH,IW] set -shift=(k+1), stride_y=2, stride_x=2, offset_x=-(1<<k)+1, offset_y=-(1<<k)+1, -border_x=1<<(k-1), border_y=1<<(k-1). This samples approximately -the input area (-0.5, -0.5) to (IH-0.5, IW-0.5). +* For approximate uniform input sampling between (0, 0) and (IH - 1, IW - 1) set +** scale_y_n/scale_y_d = (OH - 1)/(IH - 1) as integer ratios +** scale_x_n/scale_x_d = (OW - 1)/(IW - 1) as integer ratios +** offset_x = 0, offset_y = 0, border_x = 0, border_y = 0 + +* For power of two upscale [OH - 1,OW - 1] = (1 << k) * [IH - 1, IW - 1], +sampling between (0,0) and (IH - 1,IW - 1), set: +** scale_y_n = (1 << k), scale_y_d = 1, offset_y = 0, border_y = 0 +** scale_x_n = (1 << k), scale_x_d = 1, offset_x = 0, border_x = 0 + +* For power of two upscale [OH,OW] = (1 << k) * [IH,IW], +sampling range approximately (-0.5, -0.5) to (IH - 0.5, IW - 0.5), set: +** scale_y_n = 2 << k, scale_y_d = 2, offset_y = -(1 << k) + 1, border_y = (1 << k) - 1 +** scale_x_n = 2 << k, scale_x_d = 2, offset_x = -(1 << k) + 1, border_x = (1 << k) - 1 + +The output dimensions can be derived from the input dimensions by inverting +the scale as described in the pseudocode. The [border_y, border_x] values +adjust the output size to allow fractional sampling beyond integer +input position (IH - 1,IW - 1). *Arguments:* @@ -48,11 +55,9 @@ the input area (-0.5, -0.5) to (IH-0.5, IW-0.5). |Argument|Type|Name|Shape|Description |Input|in_t*|input|[N,IH,IW,C]|Input tensor -|Attribute|int32_t* |output_size|[2]|[OH,OW] -|Attribute|resize_t*|stride|[2]|[stride_y, stride_x] -|Attribute|resize_t*|offset|[2]|[offset_y, offset_x] +|Attribute|int16_t *|scale|[4]|[scale_y_n, scale_y_d, scale_x_n, scale_x_d] +|Attribute|int16_t *|offset|[2]|[offset_y, offset_x] |Attribute|int32_t* |border|[2]|[border_y, border_x] -|Attribute|int32_t |shift|-|Shift value (must be zero if resize_t is float) |Attribute|mode_t|mode|-|BILINEAR or NEAREST |Output|out_t*|output|[N,OH,OW,C]|Output tensor |=== @@ -61,57 +66,65 @@ the input area (-0.5, -0.5) to (IH-0.5, IW-0.5). [source,c++] ---- -// Derive the output dimensions from the input dimensions -OH = idiv((IH-1)*(1<<shift) - offset_y, stride_y) + 1 + border_y; -OW = idiv((IW-1)*(1<<shift) - offset_x, stride_x) + 1 + border_x; // Ensure the image size is supported by GPU APIs and that for integer // implementations, position * stride does not overflow int32_t. ERROR_IF(max(OH,OW,IH,IW) >= 16384); -ERROR_IF(stride_x <= 0 || stride_y <= 0); -if (is_floating_point(resize_t)) { - // The shift attribute is not used for floating point - ERROR_IF(shift != 0); - ERROR_IF(stride_x > IW || stride_y > IH); -} else { - // if in_t=int8_t ensure that an int32_t accumulator can be used - ERROR_IF(shift < 1 || shift > 11); - // set a consistent lower limit of 1/16 downscale - // independent of the shift value to simplify implementations - ERROR_IF(stride_x >= (16 << shift)); - ERROR_IF(stride_y >= (16 << shift)); - // offset range is similarly limited to maximum 16 pixels irrespective - // of shift. Both stride and offset fit in int16_t when shift=11. - ERROR_IF(offset_x <= (-16 << shift) || offset_x >= (16 << shift)); - ERROR_IF(offset_y <= (-16 << shift) || offset_y >= (16 << shift)); -} +ERROR_IF(scale_y_n <= 0 || scale_y_d <= 0 || scale_x_n <= 0 || scale_x_d <= 0); +// if in_t=int8_t ensure that an int32_t accumulator can be used +ERROR_IF(scale_y_n > (1 << 11) || scale_x_n > (1 << 11)); +// set a consistent lower limit of 1/16 downscale to simplify implementations +ERROR_IF(scale_y_d >= 16 * scale_y_n || scale_x_d >= 16 * scale_x_n); +ERROR_IF(offset_y < -scale_y_n || offset_y >= 16 * scale_y_n); +ERROR_IF(offset_x < -scale_x_n || offset_x >= 16 * scale_x_n); +ERROR_IF(border_y < -16 * scale_y_n || border_y >= scale_y_n); +ERROR_IF(border_x < -16 * scale_x_n || border_x >= scale_x_n); +ERROR_IF(OH != idiv_check((IH - 1) * scale_y_n - offset_y + border_y, scale_y_d) + 1); +ERROR_IF(OW != idiv_check((IW - 1) * scale_x_n - offset_x + border_x, scale_x_d) + 1); for_each(0 <= n < N, 0 <= oy < OH, 0 <= ox < OW; 0 <= c < C) { - unit = (is_floating_point(resize_t)) ? 1.0 : (1 << shift); - y = oy * stride_y + offset_y; - x = ox * stride_x + offset_x; + out_t acc; + resize_t dx, dy; + resize_t unit_x, unit_y; + + unit_x = (is_floating_point(resize_t)) ? 1.0 : scale_x_n; + unit_y = (is_floating_point(resize_t)) ? 1.0 : scale_y_n; + + int32_t y = oy * scale_y_d + offset_y; + int32_t x = ox * scale_x_d + offset_x; + int16_t iy = floor(y / scale_y_n); + int16_t ix = floor(x / scale_x_n); + if (is_floating_point(resize_t)) { - iy = (int32_t)apply_floor(y); dy = y - (resize_t)iy; - ix = (int32_t)apply_floor(x); dx = x - (resize_t)ix; + dy = ((resize_t)y / (resize_t)scale_y_n) - iy; + dx = ((resize_t)x / (resize_t)scale_x_n) - ix; } else { - iy = y >> shift; dy = y - (iy<<shift); - ix = x >> shift; dx = x - (ix<<shift); + dy = y - iy * scale_y_n; + dx = y - ix * scale_x_n; } - iy0 = apply_max(iy, 0); - iy1 = apply_min(iy+1, IH-1); - ix0 = apply_max(ix, 0); - ix1 = apply_min(ix+1, IW-1); - REQUIRE(ix0 <= ix1 && iy0 <= iy1); + // Note that -1 <= iy < IH and -1 <= ix < IW + int16_t iy0 = apply_max(iy, 0); + int16_t iy1 = apply_min(iy + 1, IH - 1); + int16_t ix0 = apply_max(ix, 0); + int16_t ix1 = apply_min(ix + 1, IW - 1); if (mode==BILINEAR) { - v00 = tensor_read<in_t>(input, [N,IH,IW,C], [n,iy0,ix0,c]); - v01 = tensor_read<in_t>(input, [N,IH,IW,C], [n,iy0,ix1,c]); - v10 = tensor_read<in_t>(input, [N,IH,IW,C], [n,iy1,ix0,c]); - v11 = tensor_read<in_t>(input, [N,IH,IW,C], [n,iy1,ix1,c]); - out_t acc = v00 * (unit - dy) * (unit - dx) + v01 * (unit - dy) * dx; - acc = acc + v10 * dy * (unit-dx) + v11 * dy * dx; + in_t v00 = tensor_read<in_t>(input, [N,IH,IW,C], [n,iy0,ix0,c]); + in_t v01 = tensor_read<in_t>(input, [N,IH,IW,C], [n,iy0,ix1,c]); + in_t v10 = tensor_read<in_t>(input, [N,IH,IW,C], [n,iy1,ix0,c]); + in_t v11 = tensor_read<in_t>(input, [N,IH,IW,C], [n,iy1,ix1,c]); + acc = v00 * (unit_y - dy) * (unit_x - dx); + acc += v01 * (unit_y - dy) * dx; + acc += v10 * dy * (unit_x - dx); + acc += v11 * dy * dx; tensor_write<out_t>(output, [N,OH,OW,C], [n,oy,ox,c], acc); } else if (mode==NEAREST) { - iy = (dy >= unit/2) ? iy1 : iy0; - ix = (dx >= unit/2) ? ix1 : ix0; - v = tensor_read<in_t>(input, [N,IH,IW,C], [n,iy,ix,c]); + int32_t iy, ix; + if (is_floating_point(resize_t)) { + iy = (dy >= 0.5) ? iy1 : iy0; + ix = (dx >= 0.5) ? ix1 : ix0; + } else { + iy = (2 * dy >= scale_y_n) ? iy1 : iy0; + ix = (2 * dx >= scale_x_n) ? ix1 : ix0; + } + in_t v = tensor_read<in_t>(input, [N,IH,IW,C], [n,iy,ix,c]); tensor_write<out_t>(output, [N,OH,OW,C], [n,oy,ox,c], v); } } |