diff options
| -rw-r--r-- | chapters/ewise_binary.adoc | 9 | ||||
| -rw-r--r-- | chapters/operators.adoc | 2 | ||||
| -rw-r--r-- | chapters/tensor_ops.adoc | 20 |
3 files changed, 19 insertions, 12 deletions
diff --git a/chapters/ewise_binary.adoc b/chapters/ewise_binary.adoc index 2b1eadd..1a54a99 100644 --- a/chapters/ewise_binary.adoc +++ b/chapters/ewise_binary.adoc @@ -58,7 +58,7 @@ Axis of size 1 will be broadcast, as necessary. Rank of input tensors must match |Input|in_t*|input1|shape1|Input tensor |Input|in_t*|input2|shape2|Input tensor with the same rank as input1 -|Input|bool_t |round |- | If true then the shift is rounded +|Input|bool_t|round|-|If true then the shift is rounded |Output|in_t*|output|shape|Output tensor with broadcast shape if necessary |=== @@ -71,7 +71,12 @@ for_each(index in shape) { index2 = apply_broadcast(shape, shape2, index); in_t value1 = tensor_read<in_t>(input1, shape1, index1); in_t value2 = tensor_read<in_t>(input2, shape2, index2); - REQUIRE(0 <= value2 && value2 <= 31); + + // Ensure that shift amount is appropriate for the data type + REQUIRE((in_t == int32_t && 0 <= value2 && value2 <= 31) || + (in_t == int16_t && 0 <= value2 && value2 <= 15) || + (in_t == int8_t && 0 <= value2 && value2 <= 7)); + in_t acc = value1 >> value2; if (round == true && value2 > 0 && (value1 >> (value2 - 1)) & 1 != 0) { acc = acc + 1; diff --git a/chapters/operators.adoc b/chapters/operators.adoc index 264063a..bec93c1 100644 --- a/chapters/operators.adoc +++ b/chapters/operators.adoc @@ -58,8 +58,6 @@ tosa_execute_graph(tosa_graph_t graph, tosa_list_t input_list, tosa_list_t outpu } ---- -Note that within the graph, each input operand is instantiated as a PLACEHOLDER operator. - include::tensor_ops.adoc[] include::activation_funcs.adoc[] diff --git a/chapters/tensor_ops.adoc b/chapters/tensor_ops.adoc index 9a1c035..7334f67 100644 --- a/chapters/tensor_ops.adoc +++ b/chapters/tensor_ops.adoc @@ -77,7 +77,7 @@ This performs an average pooling over the given input tensor. A sliding window o |=== |Argument|Type|Name|Shape|Description -|Input|in_t*|input|[N,H,W,C]|Input tensor 4D +|Input|in_t*|input|[N,IH,IW,C]|Input tensor 4D |Attribute|int*|kernel|[2]|[kernel_y, kernel_x] |Attribute|int*|stride|[2]|[stride_y, stride_x] |Attribute|int*|pad|[4]|[pad_top, pad_bottom, pad_left, pad_right] @@ -109,9 +109,13 @@ for_each(0 <= n < N, 0 <= oy < H, 0 <= ox < W, 0 <= c < C ) { for_each(0 <= ky < kernel_y, 0 <= kx < kernel_x) { y = iy + ky; x = ix + kx; - acc_t value = tensor_read<in_t>(input, [N,IH,IW,IC], [n,y,x,c], input_zp, pad); + acc_t value = tensor_read<in_t>(input, [N,IH,IW,C], [n,y,x,c], input_zp, pad); acc = apply_add<acc_t>(acc, value); - if (0 <= y < IH and 0 <= x < IW) count++ + // Only values from the input tensor are used to calculate the + // average, padding does not count + if (0 <= y < IH and 0 <= x < IW) { + count++; + } } if (is_float(out_t)) { output_val = acc / (float)count; @@ -120,7 +124,7 @@ for_each(0 <= n < N, 0 <= oy < H, 0 <= ox < W, 0 <= c < C ) { acc = apply_scale_32(acc, scale.multiplier, scale.shift, false); output_val = (in_t)apply_clip<acc_t>(acc + output_zp, minimum<in_t>, maximum<in_t>) } - tensor_write<in_t>(output, [N,H,W,OC], [n,oy,ox,oc], output_val); + tensor_write<in_t>(output, [N,H,W,C], [n,oy,ox,c], output_val); } ---- @@ -428,7 +432,7 @@ This performs a max pooling over the given input tensor. A sliding window of siz |=== |Argument|Type|Name|Shape|Description -|Input|in_t*|input|[N,H,W,C]|Input tensor 4D +|Input|in_t*|input|[N,IH,IW,C]|Input tensor 4D |Attribute|int*|kernel|[2]|[kernel_y, kernel_x] |Attribute|int*|stride|[2]|[stride_y, stride_x] |Attribute|int*|pad|[4]|[pad_top, pad_bottom, pad_left, pad_right] @@ -448,13 +452,13 @@ for_each(0 <= n < N, 0 <= oy < H, 0 <= ox < W, 0 <= c < C ) { in_t acc = minimum_value<in_t>; iy = oy * stride_y - pad_top; ix = ox * stride_x - pad_left; - for_each( 0<=ky<kernel_y, 0<=kx<kernel_x ) { + for_each( 0 <= ky < kernel_y, 0 <= kx < kernel_x ) { y = iy + ky; x = ix + kx; - in_t value = tensor_read<in_t>(input, [N,IH,IW,IC], [n,y,x,c], pad); + in_t value = tensor_read<in_t>(input, [N,IH,IW,C], [n,y,x,c], pad); acc = apply_max(acc, value); } - tensor_write<in_t>(output, [N,H,W,OC], [n,oy,ox,oc], acc); + tensor_write<in_t>(output, [N,H,W,C], [n,oy,ox,c], acc); } ---- |