rename ops validation onnx better

update related operations readme

validation/operations/README.md

@@ -43,7 +43,7 @@ and writes the same rows to `validation_results.csv`.
 
 ## Complete inventory
 
-The suite contains 168 models. Tensor shapes, attributes, and constants are
+The suite contains 177 models. Tensor shapes, attributes, and constants are
 defined in `gen_tests.py` and in the checked-in ONNX models.
 
 ### Add (5)
@@ -64,12 +64,13 @@ defined in `gen_tests.py` and in the checked-in ONNX models.
 | `negative_axis` | Concatenates tensors using a negative axis. |
 | `three_inputs_channel_axis` | Concatenates three runtime NCHW tensors along the channel axis. |
 
-### Conv (34)
+### Conv (42)
 
 | Case | Description |
 |---|---|
 | `batch_2` | Batched Conv with SAME_UPPER padding and bias. |
 | `batch_4_pointwise` | Pointwise Conv with batch size four. |
+| `input_224_7x7_stride2` | 224x224 RGB Conv with 64 output channels, a 7x7 kernel, stride two, and bias. |
 | `depthwise_1024_channels` | Depthwise pointwise Conv with 1024 groups. |
 | `depthwise_grouped` | Depthwise-style grouped Conv with one input channel per group. |
 | `dilated_3x3` | Conv with a dilated 3x3 kernel. |
@@ -88,14 +89,21 @@ defined in `gen_tests.py` and in the checked-in ONNX models.
 | `non_square_kernel_1x3` | Conv with a non-square 1x3 kernel. |
 | `non_square_kernel_3x1` | Conv with a non-square 3x1 kernel. |
 | `non_uniform_stride` | Conv with different height and width strides. |
+| `output_channel_grouping_minimal` | Minimal 64-to-256 pointwise Conv for output-channel grouping. |
 | `pointwise_1x1` | Basic pointwise channel-mixing Conv. |
 | `pointwise_tiled_chain` | Relu and chained pointwise Convs with a tiled intermediate. |
 | `real_asymmetric_padding` | Conv with asymmetric explicit padding. |
 | `relu_conv_store` | Conv, Relu, and a second Conv to validate an intermediate stored result. |
 | `same_lower_3x3` | 3x3 Conv with SAME_LOWER padding. |
 | `same_padding_3x3` | 3x3 Conv with SAME_UPPER padding. |
-| `simple` | Hand-authored basic 2x2 Conv. |
+| `kernel_2x2` | Hand-authored Conv with a 2x2 kernel. |
 | `stride_2` | 3x3 Conv with stride two. |
+| `strategy_depthwise_16` | 16-channel depthwise 3x3 Conv for depthwise lowering. |
+| `strategy_input_k_tiled` | 32-channel 3x3 Conv sized to exercise input-K tiling. |
+| `strategy_output_channel_tiled` | 8-to-192 3x3 Conv sized to exercise output-channel tiling. |
+| `strategy_streamed_packed` | 3-to-16 3x3 Conv on 128x128 input for streamed packed lowering. |
+| `strategy_streamed_patch` | 3-to-16 3x3 Conv on 64x64 input for streamed patch lowering. |
+| `strategy_tiled_2d` | 32-to-192 3x3 Conv sized to exercise 2D tiling. |
 | `with_bias_3x3` | Multi-channel 3x3 Conv with bias. |
 | `with_constant` | Hand-authored SAME_UPPER Conv with constant weight and bias. |
 | `without_kernel_shape_attr` | Conv whose kernel shape is inferred from its weight tensor. |
@@ -135,30 +143,30 @@ defined in `gen_tests.py` and in the checked-in ONNX models.
 | `dynamic_beta` | Uses runtime operands and bias with non-default beta scaling. |
 | `dynamic_bias` | Uses runtime matrix operands and runtime bias. |
 | `dynamic_bias_alpha_beta` | Combines runtime operands and bias with alpha and beta scaling. |
-| `dynamic_transB` | Transposes a runtime right-hand matrix. |
+| `dynamic_transpose_b` | Transposes a runtime right-hand matrix. |
 | `huge_1024` | Uses 1024-wide inner and output dimensions. |
 | `large` | Exercises larger rectangular matrices. |
 | `large_k_small_n` | Uses a large reduction dimension and narrow output. |
 | `non_square` | Uses different reduction and output widths. |
 | `scalar_bias` | Broadcasts a scalar bias to the full output. |
-| `simple` | Basic Gemm with square weights. |
+| `square_weights` | Basic Gemm with square weights. |
 | `small` | Tiny Gemm for fast focused validation. |
 | `small_k_large_n` | Uses a modest reduction dimension and wide output. |
-| `transA` | Transposes the left-hand matrix. |
-| `transA_transB` | Transposes both matrix operands. |
-| `transB` | Transposes the right-hand weight matrix. |
-| `transB_with_bias` | Combines a transposed weight matrix with bias. |
+| `transpose_a` | Transposes the left-hand matrix. |
+| `transpose_a_and_b` | Transposes both matrix operands. |
+| `transpose_b` | Transposes the right-hand weight matrix. |
+| `transpose_b_with_bias` | Combines a transposed weight matrix with bias. |
 | `with_bias` | Basic matrix product with vector bias. |
 
 ### Gemv (5)
 
 | Case | Description |
 |---|---|
-| `constant` | Vector-matrix product with all inputs constant. |
-| `simple` | Basic single-row vector-matrix product. |
-| `with_heterogeneous_constant` | Adds a non-uniform constant bias pattern. |
-| `with_homogeneous_constant` | Adds a constant bias matching the output shape. |
-| `with_scalar_constant` | Adds a scalar broadcast bias. |
+| `all_constant` | Vector-matrix product with all inputs constant. |
+| `constant_weight` | Basic single-row vector-matrix product with constant weights. |
+| `non_uniform_bias` | Adds a non-uniform constant bias pattern. |
+| `uniform_bias` | Adds a uniform constant bias pattern. |
+| `scalar_bias` | Adds a scalar broadcast bias. |
 
 ### MatMul (12)
 
@@ -177,11 +185,12 @@ defined in `gen_tests.py` and in the checked-in ONNX models.
 | `vector_matrix` | Vector-matrix multiplication producing a 1D output. |
 | `yolo_attention` | YOLO11n rank-4 dynamic MatMul-scale-transpose-MatMul attention chain. |
 
-### Mul (5)
+### Mul (6)
 
 | Case | Description |
 |---|---|
 | `after_conv` | Conv followed by per-channel scaling. |
+| `after_conv_scalar_constant` | Conv followed by Mul with a scalar constant. |
 | `basic` | Elementwise Mul on two inputs with identical shapes. |
 | `channel_broadcast_1024` | Mul with NCHW per-channel broadcasting over 1024 channels. |
 | `leading_dimension_broadcast` | Mul with trailing-dimension broadcasting. |
@@ -254,7 +263,7 @@ defined in `gen_tests.py` and in the checked-in ONNX models.
 | `height_only` | Nearest-neighbor resize of only the height dimension. |
 | `nearest_2x` | Nearest-neighbor upsampling by a factor of two. |
 | `nearest_downsample` | Nearest-neighbor downsampling. |
-| `non_uniform` | Nearest-neighbor resize with different spatial scales. |
+| `non_uniform_scales` | Nearest-neighbor resize with different spatial scales. |
 | `width_only` | Nearest-neighbor resize of only the width dimension. |
 | `with_sizes` | Resize using explicit output sizes instead of scales. |