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Raptor/validation/operations/INVENTORY.md
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rework documentation 
add plot generation scripts
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# Operation test inventory
All 177 checked-in ONNX models, grouped by validator category. Each row describes
the regression or operation feature exercised; follow its link for the model.
[Generation and validation commands](README.md) remain in the short README.
[gen_tests.py](gen_tests.py) defines generated shapes, attributes, constants, and
seeds. It regenerates 160 cases; 17 fixtures are checked-in only: the five Gemv
cases, `mul/after_conv_scalar_constant`, and eleven Conv cases (the six
`strategy_*` cases plus `input_224_7x7_stride2`, `kernel_2x2`,
`output_channel_grouping_minimal`, `relu_conv_store`, `with_constant`).
Do not infer the complete inventory from generator output alone. Strategy
fixtures target particular lowering paths; actual selection depends on the
hardware profile and compiler flags.
## Add (5) — `add/`
| Case | Description |
|---|---|
| [`after_gemm`](add/after_gemm/add_after_gemm.onnx) | Gemm followed by Add with a broadcast bias vector. |
| [`basic`](add/basic/add_basic.onnx) | Elementwise Add on two inputs with identical shapes. |
| [`broadcast_row`](add/broadcast_row/add_broadcast_row.onnx) | Elementwise Add with row-vector broadcasting. |
| [`channel_broadcast_1024`](add/channel_broadcast_1024/add_channel_broadcast_1024.onnx) | NCHW per-channel broadcasting over 1024 channels. |
| [`leading_dimension_broadcast`](add/leading_dimension_broadcast/add_leading_dimension_broadcast.onnx) | Trailing-dimension broadcasting across leading dimensions. |
## Concat (3) — `concat/`
| Case | Description |
|---|---|
| [`channel_axis`](concat/channel_axis/concat_channel_axis.onnx) | Concatenates two runtime NCHW tensors along the channel axis. |
| [`negative_axis`](concat/negative_axis/concat_negative_axis.onnx) | Concatenates tensors using a negative axis. |
| [`three_inputs_channel_axis`](concat/three_inputs_channel_axis/concat_three_inputs_channel_axis.onnx) | Concatenates three runtime NCHW tensors along the channel axis. |
## Conv (42) — `conv/`
| Case | Description |
|---|---|
| [`batch_2`](conv/batch_2/conv_batch_2.onnx) | Batched Conv with SAME_UPPER padding and bias. |
| [`batch_4_pointwise`](conv/batch_4_pointwise/conv_batch_4_pointwise.onnx) | Pointwise Conv with batch size four. |
| [`input_224_7x7_stride2`](conv/input_224_7x7_stride2/conv_input_224_7x7_stride2.onnx) | 224x224 RGB Conv with 64 output channels, a 7x7 kernel, stride two, and bias. |
| [`depthwise_1024_channels`](conv/depthwise_1024_channels/conv_depthwise_1024_channels.onnx) | Depthwise pointwise Conv with 1024 groups. |
| [`depthwise_grouped`](conv/depthwise_grouped/conv_depthwise_grouped.onnx) | Depthwise-style grouped Conv with one input channel per group. |
| [`dilated_3x3`](conv/dilated_3x3/conv_dilated_3x3.onnx) | Conv with a dilated 3x3 kernel. |
| [`dynamic`](conv/dynamic/conv_dynamic.onnx) | Conv with runtime input and weight tensors. |
| [`explicit_padding`](conv/explicit_padding/conv_explicit_padding.onnx) | 3x3 Conv with symmetric explicit padding. |
| [`grouped_many_groups`](conv/grouped_many_groups/conv_grouped_many_groups.onnx) | Pointwise Conv with many groups and high channel counts. |
| [`grouped_two_groups`](conv/grouped_two_groups/conv_grouped_two_groups.onnx) | Two-group pointwise Conv with bias. |
| [`huge_pointwise_1024`](conv/huge_pointwise_1024/conv_huge_pointwise_1024.onnx) | Pointwise Conv with 1024 input and output channels. |
| [`huge_pointwise_1024_dynamic`](conv/huge_pointwise_1024_dynamic/conv_huge_pointwise_1024_dynamic.onnx) | The 1024-channel pointwise Conv with runtime weights. |
| [`kernel_3x3`](conv/kernel_3x3/conv_kernel_3x3.onnx) | Basic 3x3 Conv without padding. |
| [`kernel_equals_input_spatial`](conv/kernel_equals_input_spatial/conv_kernel_equals_input_spatial.onnx) | Conv whose kernel covers the full spatial input. |
| [`large_input_channels_1x1`](conv/large_input_channels_1x1/conv_large_input_channels_1x1.onnx) | Pointwise Conv with 1024 input channels and modest output width. |
| [`large_output_channels_1x1`](conv/large_output_channels_1x1/conv_large_output_channels_1x1.onnx) | Pointwise Conv with modest input width and 1024 output channels. |
| [`large_spatial`](conv/large_spatial/conv_large_spatial.onnx) | 3x3 Conv on a larger spatial input. |
| [`multi_channel`](conv/multi_channel/conv_multi_channel.onnx) | 3x3 Conv with multiple input and output channels. |
| [`non_square_kernel_1x3`](conv/non_square_kernel_1x3/conv_non_square_kernel_1x3.onnx) | Conv with a non-square 1x3 kernel. |
| [`non_square_kernel_3x1`](conv/non_square_kernel_3x1/conv_non_square_kernel_3x1.onnx) | Conv with a non-square 3x1 kernel. |
| [`non_uniform_stride`](conv/non_uniform_stride/conv_non_uniform_stride.onnx) | Conv with different height and width strides. |
| [`output_channel_grouping_minimal`](conv/output_channel_grouping_minimal/conv_output_channel_grouping_minimal.onnx) | Minimal 64-to-256 pointwise Conv for output-channel grouping. |
| [`pointwise_1x1`](conv/pointwise_1x1/conv_pointwise_1x1.onnx) | Basic pointwise channel-mixing Conv. |
| [`pointwise_tiled_chain`](conv/pointwise_tiled_chain/conv_pointwise_tiled_chain.onnx) | Relu and chained pointwise Convs with a tiled intermediate. |
| [`real_asymmetric_padding`](conv/real_asymmetric_padding/conv_real_asymmetric_padding.onnx) | Conv with asymmetric explicit padding. |
| [`relu_conv_store`](conv/relu_conv_store/conv_relu_conv_store.onnx) | Conv, Relu, and a second Conv to validate an intermediate stored result. |
| [`same_lower_3x3`](conv/same_lower_3x3/conv_same_lower_3x3.onnx) | 3x3 Conv with SAME_LOWER padding. |
| [`same_padding_3x3`](conv/same_padding_3x3/conv_same_padding_3x3.onnx) | 3x3 Conv with SAME_UPPER padding. |
| [`kernel_2x2`](conv/kernel_2x2/conv_kernel_2x2.onnx) | Hand-authored Conv with a 2x2 kernel. |
| [`stride_2`](conv/stride_2/conv_stride_2.onnx) | 3x3 Conv with stride two. |
| [`strategy_depthwise_16`](conv/strategy_depthwise_16/conv_strategy_depthwise_16.onnx) | 16-channel depthwise 3x3 Conv for depthwise lowering. |
| [`strategy_input_k_tiled`](conv/strategy_input_k_tiled/conv_strategy_input_k_tiled.onnx) | 32-channel 3x3 Conv sized to exercise input-K tiling. |
| [`strategy_output_channel_tiled`](conv/strategy_output_channel_tiled/conv_strategy_output_channel_tiled.onnx) | 8-to-192 3x3 Conv sized to exercise output-channel tiling. |
| [`strategy_streamed_packed`](conv/strategy_streamed_packed/conv_strategy_streamed_packed.onnx) | 3-to-16 3x3 Conv on 128x128 input for streamed packed lowering. |
| [`strategy_streamed_patch`](conv/strategy_streamed_patch/conv_strategy_streamed_patch.onnx) | 3-to-16 3x3 Conv on 64x64 input for streamed patch lowering. |
| [`strategy_tiled_2d`](conv/strategy_tiled_2d/conv_strategy_tiled_2d.onnx) | 32-to-192 3x3 Conv sized to exercise 2D tiling. |
| [`with_bias_3x3`](conv/with_bias_3x3/conv_with_bias_3x3.onnx) | Multi-channel 3x3 Conv with bias. |
| [`with_constant`](conv/with_constant/conv_with_constant.onnx) | Hand-authored SAME_UPPER Conv with constant weight and bias. |
| [`without_kernel_shape_attr`](conv/without_kernel_shape_attr/conv_without_kernel_shape_attr.onnx) | Conv whose kernel shape is inferred from its weight tensor. |
| [`yolo11n_depthwise_head`](conv/yolo11n_depthwise_head/conv_yolo11n_depthwise_head.onnx) | YOLO11n pointwise-to-depthwise head boundary at `80x80`, preserving row fragments. |
| [`yolo11n_heavy`](conv/yolo11n_heavy/conv_yolo11n_heavy.onnx) | Two largest standard YOLO11n Conv-SiLU blocks by MAC count at `64x80x80`. |
| [`yolo11n_stem`](conv/yolo11n_stem/conv_yolo11n_stem.onnx) | First two YOLO11n `Conv-SiLU` blocks at `640x640`, including the distributed activation boundary. |
## Div (6) — `div/`
| Case | Description |
|---|---|
| [`after_gemm`](div/after_gemm/div_after_gemm.onnx) | Gemm followed by Div with a broadcast divisor vector. |
| [`basic`](div/basic/div_basic.onnx) | Elementwise Div by a same-shape constant tensor. |
| [`channel_broadcast_1024`](div/channel_broadcast_1024/div_channel_broadcast_1024.onnx) | Div with NCHW per-channel broadcasting over 1024 channels. |
| [`leading_dimension_broadcast`](div/leading_dimension_broadcast/div_leading_dimension_broadcast.onnx) | Div with trailing-dimension broadcasting. |
| [`runtime_scalar_rhs`](div/runtime_scalar_rhs/div_runtime_scalar_rhs.onnx) | Div of a runtime tensor by a scalar initializer. |
| [`scalar_constant`](div/scalar_constant/div_scalar_constant.onnx) | Div with scalar broadcasting on a 2D tensor. |
## Gather (5) — `gather/`
| Case | Description |
|---|---|
| [`3d_input_axis1`](gather/3d_input_axis1/gather_3d_input_axis1.onnx) | Gathers along axis one of a 3D input. |
| [`axis0_matrix_indices`](gather/axis0_matrix_indices/gather_axis0_matrix_indices.onnx) | Gathers rows using a 2D indices tensor. |
| [`axis1`](gather/axis1/gather_axis1.onnx) | Gathers selected columns from a 2D tensor. |
| [`negative_axis`](gather/negative_axis/gather_negative_axis.onnx) | Gathers using a negative axis. |
| [`negative_indices`](gather/negative_indices/gather_negative_indices.onnx) | Gathers with negative indices along axis zero. |
## Gemm (21) — `gemm/`
| Case | Description |
|---|---|
| [`alpha_beta`](gemm/alpha_beta/gemm_alpha_beta.onnx) | Applies non-default alpha and beta scaling with bias. |
| [`bias_rank2_broadcast`](gemm/bias_rank2_broadcast/gemm_bias_rank2_broadcast.onnx) | Broadcasts a rank-2 bias across output rows. |
| [`dynamic`](gemm/dynamic/gemm_dynamic.onnx) | Uses both matrix operands at runtime. |
| [`dynamic_alpha`](gemm/dynamic_alpha/gemm_dynamic_alpha.onnx) | Uses runtime operands with non-default alpha scaling. |
| [`dynamic_beta`](gemm/dynamic_beta/gemm_dynamic_beta.onnx) | Uses runtime operands and bias with non-default beta scaling. |
| [`dynamic_bias`](gemm/dynamic_bias/gemm_dynamic_bias.onnx) | Uses runtime matrix operands and runtime bias. |
| [`dynamic_bias_alpha_beta`](gemm/dynamic_bias_alpha_beta/gemm_dynamic_bias_alpha_beta.onnx) | Combines runtime operands and bias with alpha and beta scaling. |
| [`dynamic_transpose_b`](gemm/dynamic_transpose_b/gemm_dynamic_transpose_b.onnx) | Transposes a runtime right-hand matrix. |
| [`huge_1024`](gemm/huge_1024/gemm_huge_1024.onnx) | Uses 1024-wide inner and output dimensions. |
| [`large`](gemm/large/gemm_large.onnx) | Exercises larger rectangular matrices. |
| [`large_k_small_n`](gemm/large_k_small_n/gemm_large_k_small_n.onnx) | Uses a large reduction dimension and narrow output. |
| [`non_square`](gemm/non_square/gemm_non_square.onnx) | Uses different reduction and output widths. |
| [`scalar_bias`](gemm/scalar_bias/gemm_scalar_bias.onnx) | Broadcasts a scalar bias to the full output. |
| [`square_weights`](gemm/square_weights/gemm_square_weights.onnx) | Basic Gemm with square weights. |
| [`small`](gemm/small/gemm_small.onnx) | Tiny Gemm for fast focused validation. |
| [`small_k_large_n`](gemm/small_k_large_n/gemm_small_k_large_n.onnx) | Uses a modest reduction dimension and wide output. |
| [`transpose_a`](gemm/transpose_a/gemm_transpose_a.onnx) | Transposes the left-hand matrix. |
| [`transpose_a_and_b`](gemm/transpose_a_and_b/gemm_transpose_a_and_b.onnx) | Transposes both matrix operands. |
| [`transpose_b`](gemm/transpose_b/gemm_transpose_b.onnx) | Transposes the right-hand weight matrix. |
| [`transpose_b_with_bias`](gemm/transpose_b_with_bias/gemm_transpose_b_with_bias.onnx) | Combines a transposed weight matrix with bias. |
| [`with_bias`](gemm/with_bias/gemm_with_bias.onnx) | Basic matrix product with vector bias. |
## Gemv (5) — `gemv/`
| Case | Description |
|---|---|
| [`all_constant`](gemv/all_constant/gemv_all_constant.onnx) | Vector-matrix product with all inputs constant. |
| [`constant_weight`](gemv/constant_weight/gemv_constant_weight.onnx) | Basic single-row vector-matrix product with constant weights. |
| [`non_uniform_bias`](gemv/non_uniform_bias/gemv_non_uniform_bias.onnx) | Adds a non-uniform constant bias pattern. |
| [`uniform_bias`](gemv/uniform_bias/gemv_uniform_bias.onnx) | Adds a uniform constant bias pattern. |
| [`scalar_bias`](gemv/scalar_bias/gemv_scalar_bias.onnx) | Adds a scalar broadcast bias. |
## MatMul (12) — `matmul/`
| Case | Description |
|---|---|
| [`basic`](matmul/basic/matmul_basic.onnx) | Direct 2D MatMul with constant right-hand matrix. |
| [`batched_3d`](matmul/batched_3d/matmul_batched_3d.onnx) | Batched 3D MatMul with matching batch dimensions. |
| [`batched_3d_dynamic`](matmul/batched_3d_dynamic/matmul_batched_3d_dynamic.onnx) | Batched 3D MatMul with both operands at runtime. |
| [`batched_left_constant`](matmul/batched_left_constant/matmul_batched_left_constant.onnx) | Batched 3D MatMul with constant left-hand matrix. |
| [`batched_lhs_broadcast`](matmul/batched_lhs_broadcast/matmul_batched_lhs_broadcast.onnx) | Broadcasts a 2D left-hand matrix across a batched right-hand tensor. |
| [`batched_rhs_broadcast`](matmul/batched_rhs_broadcast/matmul_batched_rhs_broadcast.onnx) | Broadcasts a 2D right-hand matrix across a batched left-hand tensor. |
| [`dynamic`](matmul/dynamic/matmul_dynamic.onnx) | Direct 2D MatMul with both operands at runtime. |
| [`huge_1024`](matmul/huge_1024/matmul_huge_1024.onnx) | Uses 1024-wide inner and output dimensions. |
| [`left_constant`](matmul/left_constant/matmul_left_constant.onnx) | Direct 2D MatMul with constant left-hand matrix. |
| [`matrix_vector`](matmul/matrix_vector/matmul_matrix_vector.onnx) | Matrix-vector multiplication producing a 1D output. |
| [`vector_matrix`](matmul/vector_matrix/matmul_vector_matrix.onnx) | Vector-matrix multiplication producing a 1D output. |
| [`yolo_attention`](matmul/yolo_attention/matmul_yolo_attention.onnx) | YOLO11n rank-4 dynamic MatMul-scale-transpose-MatMul attention chain. |
## Mul (6) — `mul/`
| Case | Description |
|---|---|
| [`after_conv`](mul/after_conv/mul_after_conv.onnx) | Conv followed by per-channel scaling. |
| [`after_conv_scalar_constant`](mul/after_conv_scalar_constant/mul_after_conv_scalar_constant.onnx) | Conv followed by Mul with a scalar constant. |
| [`basic`](mul/basic/mul_basic.onnx) | Elementwise Mul on two inputs with identical shapes. |
| [`channel_broadcast_1024`](mul/channel_broadcast_1024/mul_channel_broadcast_1024.onnx) | Mul with NCHW per-channel broadcasting over 1024 channels. |
| [`leading_dimension_broadcast`](mul/leading_dimension_broadcast/mul_leading_dimension_broadcast.onnx) | Mul with trailing-dimension broadcasting. |
| [`scalar_constant`](mul/scalar_constant/mul_scalar_constant.onnx) | Mul with scalar broadcasting. |
## Pool (15) — `pool/`
| Case | Description |
|---|---|
| [`avg_basic`](pool/avg_basic/pool_avg_basic.onnx) | AveragePool with a 2x2 kernel and unit stride. |
| [`avg_ceil_mode`](pool/avg_ceil_mode/pool_avg_ceil_mode.onnx) | AveragePool with ceil mode enabled. |
| [`avg_explicit_padding`](pool/avg_explicit_padding/pool_avg_explicit_padding.onnx) | Explicitly padded AveragePool excluding pad from the divisor. |
| [`avg_include_pad`](pool/avg_include_pad/pool_avg_include_pad.onnx) | Explicitly padded AveragePool including pad in the divisor. |
| [`avg_large_channels`](pool/avg_large_channels/pool_avg_large_channels.onnx) | AveragePool with a large channel count and small spatial extent. |
| [`avg_non_uniform_stride`](pool/avg_non_uniform_stride/pool_avg_non_uniform_stride.onnx) | AveragePool with different height and width strides. |
| [`avg_real_asymmetric_padding`](pool/avg_real_asymmetric_padding/pool_avg_real_asymmetric_padding.onnx) | AveragePool with asymmetric explicit padding. |
| [`max_after_conv`](pool/max_after_conv/pool_max_after_conv.onnx) | Conv followed by MaxPool. |
| [`max_basic`](pool/max_basic/pool_max_basic.onnx) | MaxPool with a 2x2 kernel and unit stride. |
| [`max_ceil_mode`](pool/max_ceil_mode/pool_max_ceil_mode.onnx) | MaxPool with ceil mode enabled. |
| [`max_global_style_kernel_equals_input`](pool/max_global_style_kernel_equals_input/pool_max_global_style_kernel_equals_input.onnx) | MaxPool whose kernel covers the full spatial input. |
| [`max_non_square_kernel`](pool/max_non_square_kernel/pool_max_non_square_kernel.onnx) | MaxPool with a non-square kernel. |
| [`max_real_asymmetric_padding`](pool/max_real_asymmetric_padding/pool_max_real_asymmetric_padding.onnx) | MaxPool with asymmetric explicit padding. |
| [`max_same_upper`](pool/max_same_upper/pool_max_same_upper.onnx) | MaxPool with SAME_UPPER padding. |
| [`max_stride2_multichannel`](pool/max_stride2_multichannel/pool_max_stride2_multichannel.onnx) | Multi-channel MaxPool with stride two. |
## ReduceMean (17) — `reduce_mean/`
| Case | Description |
|---|---|
| [`4d_spatial`](reduce_mean/4d_spatial/reduce_mean_4d_spatial.onnx) | Reduces height and width of an NCHW tensor while preserving rank. |
| [`4d_spatial_keepdims_0`](reduce_mean/4d_spatial_keepdims_0/reduce_mean_4d_spatial_keepdims_0.onnx) | Reduces NCHW height and width while dropping those axes. |
| [`after_conv`](reduce_mean/after_conv/reduce_mean_after_conv.onnx) | Conv followed by a spatial ReduceMean. |
| [`all_axes_keepdims_0`](reduce_mean/all_axes_keepdims_0/reduce_mean_all_axes_keepdims_0.onnx) | Reduces all axes to a scalar. |
| [`all_axes_keepdims_1`](reduce_mean/all_axes_keepdims_1/reduce_mean_all_axes_keepdims_1.onnx) | Reduces all axes while preserving rank. |
| [`basic`](reduce_mean/basic/reduce_mean_basic.onnx) | Reduces a feature dimension while preserving rank. |
| [`channel_axis_nchw`](reduce_mean/channel_axis_nchw/reduce_mean_channel_axis_nchw.onnx) | Reduces the channel axis of an NCHW tensor. |
| [`keepdims_0`](reduce_mean/keepdims_0/reduce_mean_keepdims_0.onnx) | Reduces a feature dimension and drops that axis. |
| [`large_dimension_1024`](reduce_mean/large_dimension_1024/reduce_mean_large_dimension_1024.onnx) | Reduces a dimension of length 1024. |
| [`legacy_axes_1_2_keepdims_1`](reduce_mean/legacy_axes_1_2_keepdims_1/reduce_mean_legacy_axes_1_2_keepdims_1.onnx) | Opset-18 reduction over multiple positive axes. |
| [`legacy_axis1_keepdims_0`](reduce_mean/legacy_axis1_keepdims_0/reduce_mean_legacy_axis1_keepdims_0.onnx) | Opset-18 reduction over one axis while dropping it. |
| [`legacy_axis1_keepdims_1`](reduce_mean/legacy_axis1_keepdims_1/reduce_mean_legacy_axis1_keepdims_1.onnx) | Opset-18 reduction over one axis while preserving rank. |
| [`legacy_empty_axes_noop`](reduce_mean/legacy_empty_axes_noop/reduce_mean_legacy_empty_axes_noop.onnx) | Opset-18 empty-axes no-op followed by Relu. |
| [`legacy_nchw_spatial`](reduce_mean/legacy_nchw_spatial/reduce_mean_legacy_nchw_spatial.onnx) | Opset-18 spatial reduction on NCHW input. |
| [`legacy_negative_axis`](reduce_mean/legacy_negative_axis/reduce_mean_legacy_negative_axis.onnx) | Opset-18 reduction using a negative axis. |
| [`legacy_reduce_all_keepdims_1`](reduce_mean/legacy_reduce_all_keepdims_1/reduce_mean_legacy_reduce_all_keepdims_1.onnx) | Opset-18 all-axis reduction with the axes input omitted. |
| [`negative_axis`](reduce_mean/negative_axis/reduce_mean_negative_axis.onnx) | ReduceMean using a negative axis. |
## Relu (4) — `relu/`
| Case | Description |
|---|---|
| [`4d`](relu/4d/relu_4d.onnx) | Standalone Relu on an NCHW tensor. |
| [`after_conv`](relu/after_conv/relu_after_conv.onnx) | Conv followed by Relu. |
| [`after_gemm`](relu/after_gemm/relu_after_gemm.onnx) | Gemm followed by Relu. |
| [`basic`](relu/basic/relu_basic.onnx) | Standalone Relu on a 2D tensor. |
## Reshape (4) — `reshape/`
| Case | Description |
|---|---|
| [`4d_to_2d_flatten`](reshape/4d_to_2d_flatten/reshape_4d_to_2d_flatten.onnx) | Flattens a 4D tensor into a 2D view. |
| [`infer_dim_minus_one`](reshape/infer_dim_minus_one/reshape_infer_dim_minus_one.onnx) | Uses `-1` to infer one output dimension. |
| [`same_rank`](reshape/same_rank/reshape_same_rank.onnx) | Changes shape without changing rank. |
| [`zero_copies_input_dim`](reshape/zero_copies_input_dim/reshape_zero_copies_input_dim.onnx) | Uses `0` to copy an input dimension. |
## Resize (6) — `resize/`
| Case | Description |
|---|---|
| [`height_only`](resize/height_only/resize_height_only.onnx) | Nearest-neighbor resize of only the height dimension. |
| [`nearest_2x`](resize/nearest_2x/resize_nearest_2x.onnx) | Nearest-neighbor upsampling by a factor of two. |
| [`nearest_downsample`](resize/nearest_downsample/resize_nearest_downsample.onnx) | Nearest-neighbor downsampling. |
| [`non_uniform_scales`](resize/non_uniform_scales/resize_non_uniform_scales.onnx) | Nearest-neighbor resize with different spatial scales. |
| [`width_only`](resize/width_only/resize_width_only.onnx) | Nearest-neighbor resize of only the width dimension. |
| [`with_sizes`](resize/with_sizes/resize_with_sizes.onnx) | Resize using explicit output sizes instead of scales. |
## Sigmoid (3) — `sigmoid/`
| Case | Description |
|---|---|
| [`4d`](sigmoid/4d/sigmoid_4d.onnx) | Standalone Sigmoid on an NCHW tensor. |
| [`after_gemm`](sigmoid/after_gemm/sigmoid_after_gemm.onnx) | Gemm followed by Sigmoid. |
| [`basic`](sigmoid/basic/sigmoid_basic.onnx) | Standalone Sigmoid on a 2D tensor. |
## Slice (8) — `slice/`
| Case | Description |
|---|---|
| [`2d_basic`](slice/2d_basic/slice_2d_basic.onnx) | Slices a 2D tensor with explicit axes and unit steps. |
| [`after_conv`](slice/after_conv/slice_after_conv.onnx) | Conv followed by a spatial crop. |
| [`default_axes`](slice/default_axes/slice_default_axes.onnx) | Omits axes and steps to use positional defaults. |
| [`large_channel_1024`](slice/large_channel_1024/slice_large_channel_1024.onnx) | Slices a channel range from a 1024-channel tensor. |
| [`nchw_spatial_crop`](slice/nchw_spatial_crop/slice_nchw_spatial_crop.onnx) | Crops the spatial axes of an NCHW tensor. |
| [`negative_axis`](slice/negative_axis/slice_negative_axis.onnx) | Slices using a negative axis. |
| [`negative_indices`](slice/negative_indices/slice_negative_indices.onnx) | Slices using negative indices. |
| [`step2`](slice/step2/slice_step2.onnx) | Slices using a positive step greater than one. |
## Softmax (5) — `softmax/`
| Case | Description |
|---|---|
| [`3d_last_axis`](softmax/3d_last_axis/softmax_3d_last_axis.onnx) | Softmax over the last axis of a 3D tensor. |
| [`basic`](softmax/basic/softmax_basic.onnx) | Softmax over the last dimension of a 2D tensor. |
| [`channel_axis`](softmax/channel_axis/softmax_channel_axis.onnx) | Softmax over the channel axis of an NCHW tensor. |
| [`large_dimension_1024`](softmax/large_dimension_1024/softmax_large_dimension_1024.onnx) | Softmax over a last dimension of length 1024. |
| [`negative_axis`](softmax/negative_axis/softmax_negative_axis.onnx) | Softmax using a negative axis. |
## Split (4) — `split/`
| Case | Description |
|---|---|
| [`basic`](split/basic/split_basic.onnx) | Splits a 2D tensor into two explicit output sizes. |
| [`equal_three_way`](split/equal_three_way/split_equal_three_way.onnx) | Splits a 2D tensor evenly into three outputs. |
| [`negative_axis`](split/negative_axis/split_negative_axis.onnx) | Splits using a negative axis. |
| [`uneven_channel_axis_4d`](split/uneven_channel_axis_4d/split_uneven_channel_axis_4d.onnx) | Splits an NCHW channel axis into uneven outputs. |
## Sub (6) — `sub/`
| Case | Description |
|---|---|
| [`after_gemm`](sub/after_gemm/sub_after_gemm.onnx) | Gemm followed by Sub with a broadcast constant vector. |
| [`basic`](sub/basic/sub_basic.onnx) | Elementwise Sub on two runtime inputs with identical shapes. |
| [`broadcast_row`](sub/broadcast_row/sub_broadcast_row.onnx) | Sub with a broadcast row-vector right-hand constant. |
| [`channel_broadcast_1024`](sub/channel_broadcast_1024/sub_channel_broadcast_1024.onnx) | Sub with NCHW per-channel broadcasting over 1024 channels. |
| [`constant_lhs_broadcast`](sub/constant_lhs_broadcast/sub_constant_lhs_broadcast.onnx) | Sub with a broadcast constant left-hand operand. |
| [`leading_dimension_broadcast`](sub/leading_dimension_broadcast/sub_leading_dimension_broadcast.onnx) | Sub with trailing-dimension broadcasting. |
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