diff options
Diffstat (limited to 'chapters/tensor_ops.adoc')
| -rw-r--r-- | chapters/tensor_ops.adoc | 30 |
1 files changed, 15 insertions, 15 deletions
diff --git a/chapters/tensor_ops.adoc b/chapters/tensor_ops.adoc index b2c220e..6780b1c 100644 --- a/chapters/tensor_ops.adoc +++ b/chapters/tensor_ops.adoc @@ -31,7 +31,7 @@ None [source,c++] ---- -assert(axis >= 0 && axis < rank(shape1) && rank(shape1) <= 4); +REQUIRE(axis >= 0 && axis < rank(shape1) && rank(shape1) <= 4); if (axis == 0) { left_shape = []; } else { @@ -42,7 +42,7 @@ if (axis == rank(shape1)-1) { } else { right_shape = shape1[axis+1:rank(shape1) - 1]; } -assert(flatten(left_shape, right_shape) == shape); +REQUIRE(flatten(left_shape, right_shape) == shape); for_each(left_index in left_shape) { for_each(right_index in right_shape) { in_t max_value = minimum_value<in_t>; @@ -97,8 +97,8 @@ This performs an average pooling over the given input tensor. A sliding window o [source,c++] ---- -assert(in_t == int8_t || input_zp == 0); // Zero point only for int8_t -assert(in_t == int8_t || output_zp == 0); // Zero point only for int8_t +REQUIRE(in_t == int8_t || input_zp == 0); // Zero point only for int8_t +REQUIRE(in_t == int8_t || output_zp == 0); // Zero point only for int8_t 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; @@ -164,8 +164,8 @@ Performs a 2D convolution over the given tensor input, using the weight tensor. [source,c++] ---- -assert(in_t == int8_t || input_zp == 0); // Zero point only for int8_t -assert(weight_t == int8_t || weight_zp == 0); +REQUIRE(in_t == int8_t || input_zp == 0); // Zero point only for int8_t +REQUIRE(weight_t == int8_t || weight_zp == 0); 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; @@ -225,8 +225,8 @@ Performs a 3D convolution over the given input tensor. [source,c++] ---- -assert(in_t == int8_t || input_zp == 0); // Zero point only for int8_t -assert(weight_t == int8_t || weight_zp == 0); +REQUIRE(in_t == int8_t || input_zp == 0); // Zero point only for int8_t +REQUIRE(weight_t == int8_t || weight_zp == 0); 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; @@ -289,8 +289,8 @@ Performs 2D convolutions separately over each channel of the given tensor input, [source,c++] ---- -assert(in_t == int8_t || input_zp == 0); // Zero point only for int8_t -assert(weight_t == int8_t || weight_zp == 0); +REQUIRE(in_t == int8_t || input_zp == 0); // Zero point only for int8_t +REQUIRE(weight_t == int8_t || weight_zp == 0); 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; @@ -347,8 +347,8 @@ Performs a fully connected network. [source,c++] ---- -assert(in_t == int8_t || input_zp == 0); // Zero point only for int8_t -assert(weight_t == int8_t || weight_zp == 0); +REQUIRE(in_t == int8_t || input_zp == 0); // Zero point only for int8_t +REQUIRE(weight_t == int8_t || weight_zp == 0); for_each(0 <= n < N, 0 <= oc < OC) { acc_t acc = 0; for_each(0 <= ic < IC) { @@ -398,7 +398,7 @@ Performs two dimensional matrix multiplications. This allows both inputs to be a [source,c++] ---- -assert(in_t == int8_t || (A_zp == 0 && B_zp == 0)); // Zero point only for int8_t +REQUIRE(in_t == int8_t || (A_zp == 0 && B_zp == 0)); // Zero point only for int8_t for_each(0 <= n < N, 0 <= h < H, 0 <= w < W) { acc_t acc = 0; for_each(0 <= c < C) { @@ -499,8 +499,8 @@ Performs a 2D transposed convolution over the given tensor input, using the weig [source,c++] ---- -assert(in_t == int8_t || input_zp == 0); // Zero point only allowed for int8_t -assert(weight_t == int8_t || weight_zp == 0); +REQUIRE(in_t == int8_t || input_zp == 0); // Zero point only allowed for int8_t +REQUIRE(weight_t == int8_t || weight_zp == 0); for_each(index in out_shape) { tensor_write<acc_t>(output, [N,OH,OW,OC], index, bias[index[3]]) } |