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Diffstat (limited to 'chapters/tensor_ops.adoc')
| -rw-r--r-- | chapters/tensor_ops.adoc | 17 |
1 files changed, 17 insertions, 0 deletions
diff --git a/chapters/tensor_ops.adoc b/chapters/tensor_ops.adoc index 6bf9bf2..9c10450 100644 --- a/chapters/tensor_ops.adoc +++ b/chapters/tensor_ops.adoc @@ -99,6 +99,9 @@ This performs an average pooling over the given input tensor. A sliding window o ---- ERROR_IF(in_t != int8_t && input_zp != 0); // Zero point only for int8_t ERROR_IF(in_t != int8_t && output_zp != 0); // Zero point only for int8_t +ERROR_IF(kernel_y < 1 || kernel_x < 1); // kernel size must be >= 1 +ERROR_IF(stride_y < 1 || stride_x < 1); +ERROR_IF(pad_top < 0 || pad_buttom < 0 || pad_left < 0 || pad_right < 0); pad = flatten([0,0], pad, [0,0]); for_each(0 <= n < N, 0 <= oy < H, 0 <= ox < W, 0 <= c < C ) { in_t output_val; @@ -170,6 +173,9 @@ Performs a 2D convolution over the given tensor input, using the weight tensor. ---- ERROR_IF(in_t != int8_t && input_zp != 0); // Zero point only for int8_t ERROR_IF(weight_t != int8_t && weight_zp != 0); +ERROR_IF(pad_top < 0 || pad_bottom < 0 || pad_left < 0 || pad_right < 0); +ERROR_IF(stride_y < 1 || stride_x < 1); +ERROR_IF(dilation_y < 1 || dilation_x < 1); pad = flatten([0,0], pad, [0,0]); for_each(0 <= n < N, 0 <= oy < H, 0 <= ox < W; 0 <= oc < OC) { acc_t acc = 0; @@ -231,6 +237,9 @@ Performs a 3D convolution over the given input tensor. ---- ERROR_IF(in_t != int8_t && input_zp != 0); // Zero point only for int8_t ERROR_IF(weight_t != int8_t && weight_zp != 0); +ERROR_IF(pad_d0 < 0 || pad_d1 < 0 || pad_top < 0 || pad_bottom < 0 || pad_left < 0 || pad_right < 0); +ERROR_IF(stride_d < 1 || stride_y < 1 || stride_x < 1); +ERROR_IF(dilation_d < 1 || dilation_y < 1 || dilation_x < 1); pad = flatten([0,0], pad, [0,0]); for_each(0 <= n < N, 0 <= od < D, 0 <= oy < H, 0 <= ox < W; 0 <= oc < OC) { acc_t acc = 0; @@ -295,6 +304,9 @@ Performs 2D convolutions separately over each channel of the given tensor input, ---- ERROR_IF(in_t != int8_t && input_zp != 0); // Zero point only for int8_t ERROR_IF(weight_t != int8_t && weight_zp != 0); +ERROR_IF(pad_top < 0 || pad_bottom < 0 || pad_left < 0 || pad_right < 0); +ERROR_IF(stride_y < 1 || stride_x < 1); +ERROR_IF(dilation_y < 1 || dilation_x < 1); pad = flatten([0,0], pad, [0,0]); for_each(0 <= n<N, 0 <= oy < H, 0 <= ox < W; 0 <= c < (C * M), 0 <= m < M) { acc_t acc = 0; @@ -447,6 +459,9 @@ None [source,c++] ---- +ERROR_IF(kernel_y < 1 || kernel_x < 1); // kernel size must be >= 1 +ERROR_IF(stride_y < 1 || stride_x < 1); +ERROR_IF(pad_top < 0 || pad_buttom < 0 || pad_left < 0 || pad_right < 0); 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; @@ -507,6 +522,8 @@ Performs a 2D transposed convolution over the given tensor input, using the weig ---- ERROR_IF(in_t != int8_t && input_zp != 0); // Zero point only allowed for int8_t ERROR_IF(weight_t != int8_t && weight_zp != 0); +ERROR_IF(out_pad_top < 0 || out_pad_left < 0); +ERROR_IF(stride_y < 1 || stride_x < 1); for_each(index in out_shape) { tensor_write<acc_t>(output, [N,OH,OW,OC], index, bias[index[3]]) } |