Move network_config in dedicated file

compile_config/network_config.yaml

@@ -0,0 +1,12 @@
+last_nodes:
+  - /Reshape_output_0
+  - /Flatten_output_0
+  - /Reshape_1_output_0
+global_config:
+  weight_symmetric: true
+optimization_options:
+  - -segmentation_json ./CenterNet-R18-ONNX-fp32/compile_config/l2seg_csm_16mb.json
+  - -enable_shallow_conv_opt
+  - -enable_dma_fusion
+  - -stu_weight
+  - -distributed_coef_stu

int8/benchmarks/x5h_mwmx_npu_apm80_12core.yaml

@@ -41,31 +41,15 @@ memory:
 power:
   avg_w: null
 
-network_configuration:
-  key: centernet_r18_8xb16_crop512_140e_coco
-  entry_yaml: |
-    last_nodes:
-      - /Reshape_output_0
-      - /Flatten_output_0
-      - /Reshape_1_output_0
-    global_config:
-      weight_symmetric: true
-    optimization_options:
-      - -segmentation_json ./CenterNet-R18-ONNX-fp32/compile_config/l2seg_csm_16mb.json
-      - -enable_shallow_conv_opt
-      - -enable_dma_fusion
-      - -stu_weight
-      - -distributed_coef_stu
-
 # Exact commands verified against the NNAC "Getting Started" chapter for this
-# model (it is that chapter's own worked example), plus the network_configuration
-# block verified against the internal model_zoo compile config for this model's
+# model (it is that chapter's own worked example), plus the network config
+# verified against the internal model_zoo compile config for this model's
 # 12-core slice (see compile_config/l2seg_csm_16mb.json). Rendered by the
 # AI-Dashboard in place of the generic placeholder flow — see
 # downloadRunHTML() / parse_reproduce(). NOTE: the 1-core vs. 12-core split
-# (see `configuration` above) is set at compile time via
-# ${WORKDIR}/nnac_config/config.yaml, not a host_app flag — the on-board run
-# command below is identical to the 1-core run.
+# (see `configuration` above) is set at compile time via the
+# --network-config compile_config/network_config.yaml flag on legalize.py —
+# the on-board run command below is identical to the 1-core run.
 reproduce:
   steps:
     - title: Activate the Python environment
@@ -76,25 +60,9 @@ reproduce:
       kind: note
     - title: Download the ONNX model and compile config
       command: hf download Renesas/CenterNet-R18-ONNX --repo-type model --include "fp32/*" "compile_config/*" --local-dir ./CenterNet-R18-ONNX-fp32
-    - title: "Insert this model's entry into network_configuration: in nnac_config/config.yaml"
-      command: |
-        centernet_r18_8xb16_crop512_140e_coco:
-          last_nodes:
-            - /Reshape_output_0
-            - /Flatten_output_0
-            - /Reshape_1_output_0
-          global_config:
-            weight_symmetric: true
-          optimization_options:
-            - -segmentation_json ./CenterNet-R18-ONNX-fp32/compile_config/l2seg_csm_16mb.json
-            - -enable_shallow_conv_opt
-            - -enable_dma_fusion
-            - -stu_weight
-            - -distributed_coef_stu
-      kind: snippet
     - title: Compile with the NNAC toolchain (INT8 auto-cast from the FP32 graph)
       command: |
-        python3 nnac_frontend/legalize.py -d binary/nnx ./CenterNet-R18-ONNX-fp32/fp32/centernet_r18_8xb16_crop512_140e_coco.onnx --num-core 12
+        python3 nnac_frontend/legalize.py -d binary/nnx ./CenterNet-R18-ONNX-fp32/fp32/centernet_r18_8xb16_crop512_140e_coco.onnx --num-core 12 --network-config ./CenterNet-R18-ONNX-fp32/compile_config/network_config.yaml
     - title: Set up the R-Car X5H board
       command: Configure the board per the AI Compiler (NNAC) "Getting Started" guide, section 3.4 (host TFTP/NFS setup, bootloader flashing, U-Boot, Linux boot, login) -- exact steps depend on your board/network setup.
       kind: note

int8/benchmarks/x5h_mwmx_npu_apm80_3core.yaml

@@ -41,22 +41,6 @@ memory:
 power:
   avg_w: null
 
-network_configuration:
-  key: centernet_r18_8xb16_crop512_140e_coco
-  entry_yaml: |
-    last_nodes:
-      - /Reshape_output_0
-      - /Flatten_output_0
-      - /Reshape_1_output_0
-    global_config:
-      weight_symmetric: true
-    optimization_options:
-      - -segmentation_json ./CenterNet-R18-ONNX-fp32/compile_config/l2seg_csm_16mb.json
-      - -enable_shallow_conv_opt
-      - -enable_dma_fusion
-      - -stu_weight
-      - -distributed_coef_stu
-
 reproduce:
   steps:
     - title: Activate the Python environment
@@ -65,27 +49,11 @@ reproduce:
         (huggingface_hub) and the NNAC toolchain installed, e.g.
         `source nnac_venv/bin/activate` -- path depends on your toolchain install.
       kind: note
-    - title: Download the ONNX model
-      command: hf download Renesas/CenterNet-R18-ONNX --repo-type=model --include "fp32/*" --local-dir ./CenterNet-R18-ONNX-fp32
-    - title: "Insert this model's entry into network_configuration: in nnac_config/config.yaml"
-      command: |
-        centernet_r18_8xb16_crop512_140e_coco:
-          last_nodes:
-            - /Reshape_output_0
-            - /Flatten_output_0
-            - /Reshape_1_output_0
-          global_config:
-            weight_symmetric: true
-          optimization_options:
-            - -segmentation_json ./CenterNet-R18-ONNX-fp32/compile_config/l2seg_csm_16mb.json
-            - -enable_shallow_conv_opt
-            - -enable_dma_fusion
-            - -stu_weight
-            - -distributed_coef_stu
-      kind: snippet
+    - title: Download the ONNX model and compile config
+      command: hf download Renesas/CenterNet-R18-ONNX --repo-type model --include "fp32/*" "compile_config/*" --local-dir ./CenterNet-R18-ONNX-fp32
     - title: Compile with the NNAC toolchain (INT8 auto-cast from the FP32 graph)
       command: |
-        python3 nnac_frontend/legalize.py -d binary/nnx ./CenterNet-R18-ONNX-fp32/fp32/centernet_r18_8xb16_crop512_140e_coco.onnx --num-core 3
+        python3 nnac_frontend/legalize.py -d binary/nnx ./CenterNet-R18-ONNX-fp32/fp32/centernet_r18_8xb16_crop512_140e_coco.onnx --num-core 3 --network-config ./CenterNet-R18-ONNX-fp32/compile_config/network_config.yaml
     - title: Set up the R-Car X5H board
       command: Configure the board per the AI Compiler (NNAC) "Getting Started" guide, section 3.4 (host TFTP/NFS setup, bootloader flashing, U-Boot, Linux boot, login) -- exact steps depend on your board/network setup.
       kind: note

int8/benchmarks/x5h_mwmx_npu_apm80_4core.yaml

@@ -41,22 +41,6 @@ memory:
 power:
   avg_w: null
 
-network_configuration:
-  key: centernet_r18_8xb16_crop512_140e_coco
-  entry_yaml: |
-    last_nodes:
-      - /Reshape_output_0
-      - /Flatten_output_0
-      - /Reshape_1_output_0
-    global_config:
-      weight_symmetric: true
-    optimization_options:
-      - -segmentation_json ./CenterNet-R18-ONNX-fp32/compile_config/l2seg_csm_16mb.json
-      - -enable_shallow_conv_opt
-      - -enable_dma_fusion
-      - -stu_weight
-      - -distributed_coef_stu
-
 reproduce:
   steps:
     - title: Activate the Python environment
@@ -65,27 +49,11 @@ reproduce:
         (huggingface_hub) and the NNAC toolchain installed, e.g.
         `source nnac_venv/bin/activate` -- path depends on your toolchain install.
       kind: note
-    - title: Download the ONNX model
-      command: hf download Renesas/CenterNet-R18-ONNX --repo-type=model --include "fp32/*" --local-dir ./CenterNet-R18-ONNX-fp32
-    - title: "Insert this model's entry into network_configuration: in nnac_config/config.yaml"
-      command: |
-        centernet_r18_8xb16_crop512_140e_coco:
-          last_nodes:
-            - /Reshape_output_0
-            - /Flatten_output_0
-            - /Reshape_1_output_0
-          global_config:
-            weight_symmetric: true
-          optimization_options:
-            - -segmentation_json ./CenterNet-R18-ONNX-fp32/compile_config/l2seg_csm_16mb.json
-            - -enable_shallow_conv_opt
-            - -enable_dma_fusion
-            - -stu_weight
-            - -distributed_coef_stu
-      kind: snippet
+    - title: Download the ONNX model and compile config
+      command: hf download Renesas/CenterNet-R18-ONNX --repo-type model --include "fp32/*" "compile_config/*" --local-dir ./CenterNet-R18-ONNX-fp32
     - title: Compile with the NNAC toolchain (INT8 auto-cast from the FP32 graph)
       command: |
-        python3 nnac_frontend/legalize.py -d binary/nnx ./CenterNet-R18-ONNX-fp32/fp32/centernet_r18_8xb16_crop512_140e_coco.onnx --num-core 4
+        python3 nnac_frontend/legalize.py -d binary/nnx ./CenterNet-R18-ONNX-fp32/fp32/centernet_r18_8xb16_crop512_140e_coco.onnx --num-core 4 --network-config ./CenterNet-R18-ONNX-fp32/compile_config/network_config.yaml
     - title: Set up the R-Car X5H board
       command: Configure the board per the AI Compiler (NNAC) "Getting Started" guide, section 3.4 (host TFTP/NFS setup, bootloader flashing, U-Boot, Linux boot, login) -- exact steps depend on your board/network setup.
       kind: note

int8/benchmarks/x5h_mwmx_npu_apm80_6core.yaml

@@ -41,22 +41,6 @@ memory:
 power:
   avg_w: null
 
-network_configuration:
-  key: centernet_r18_8xb16_crop512_140e_coco
-  entry_yaml: |
-    last_nodes:
-      - /Reshape_output_0
-      - /Flatten_output_0
-      - /Reshape_1_output_0
-    global_config:
-      weight_symmetric: true
-    optimization_options:
-      - -segmentation_json ./CenterNet-R18-ONNX-fp32/compile_config/l2seg_csm_16mb.json
-      - -enable_shallow_conv_opt
-      - -enable_dma_fusion
-      - -stu_weight
-      - -distributed_coef_stu
-
 reproduce:
   steps:
     - title: Activate the Python environment
@@ -65,27 +49,11 @@ reproduce:
         (huggingface_hub) and the NNAC toolchain installed, e.g.
         `source nnac_venv/bin/activate` -- path depends on your toolchain install.
       kind: note
-    - title: Download the ONNX model
-      command: hf download Renesas/CenterNet-R18-ONNX --repo-type=model --include "fp32/*" --local-dir ./CenterNet-R18-ONNX-fp32
-    - title: "Insert this model's entry into network_configuration: in nnac_config/config.yaml"
-      command: |
-        centernet_r18_8xb16_crop512_140e_coco:
-          last_nodes:
-            - /Reshape_output_0
-            - /Flatten_output_0
-            - /Reshape_1_output_0
-          global_config:
-            weight_symmetric: true
-          optimization_options:
-            - -segmentation_json ./CenterNet-R18-ONNX-fp32/compile_config/l2seg_csm_16mb.json
-            - -enable_shallow_conv_opt
-            - -enable_dma_fusion
-            - -stu_weight
-            - -distributed_coef_stu
-      kind: snippet
+    - title: Download the ONNX model and compile config
+      command: hf download Renesas/CenterNet-R18-ONNX --repo-type model --include "fp32/*" "compile_config/*" --local-dir ./CenterNet-R18-ONNX-fp32
     - title: Compile with the NNAC toolchain (INT8 auto-cast from the FP32 graph)
       command: |
-        python3 nnac_frontend/legalize.py -d binary/nnx ./CenterNet-R18-ONNX-fp32/fp32/centernet_r18_8xb16_crop512_140e_coco.onnx --num-core 6
+        python3 nnac_frontend/legalize.py -d binary/nnx ./CenterNet-R18-ONNX-fp32/fp32/centernet_r18_8xb16_crop512_140e_coco.onnx --num-core 6 --network-config ./CenterNet-R18-ONNX-fp32/compile_config/network_config.yaml
     - title: Set up the R-Car X5H board
       command: Configure the board per the AI Compiler (NNAC) "Getting Started" guide, section 3.4 (host TFTP/NFS setup, bootloader flashing, U-Boot, Linux boot, login) -- exact steps depend on your board/network setup.
       kind: note