Update README.md

README.md

@@ -23,7 +23,40 @@ pip install tensorflow==2.15.1 tf2onnx==1.16.1
 # use most recent opset officially supported
 python -m tf2onnx.convert \
   --saved-model <path/to/dir> \
-  --output ds_cnn.onnx --opset 18
+  --output converted_ds_cnn.onnx --opset 18
+```
+
+This version input format is NHWC.
+The following Python code fuses MatMul+Add to Gemm and folds the first Reshape operator.
+
+```python
+import onnx
+import onnxruntime
+
+import aidge_core as ai
+import aidge_onnx
+
+model_onnx = onnx.load_model("converted_ds_cnn.onnx")
+model_onnx_clean_nhwc = aidge_onnx.onnx_cleaner.clean_onnx(
+    model_onnx, {"input_1": [[1, 49, 10, 1]]}, "test_clean", opset_version=18
+)
+model = aidge_onnx.convert_onnx_to_aidge(model_onnx_clean_nhwc)
+
+# model = aidge_onnx.load_onnx("clean_ds_cnn_inferred.onnx")
+
+to_replace: set[ai.Node] = set(
+    [
+        model.get_node("StatefulPartitionedCall_functional_1_conv2d_BiasAdd__6"),
+        model.get_node("new_shape__103_out0"),
+    ]
+)
+
+model.replace(to_replace, set())
+model.set_mandatory_inputs_first()
+
+model.forward_dims(dims=[[1, 1, 49, 10]], allow_data_dependency=True)
+model_onnx_clean_nchw = aidge_onnx.convert_aidge_to_onnx(model, "ds_cnn", opset=18)
+onnx.save_model(model_onnx_clean_nchw, "ds_cnn.onnx")
 ```
 
 ## Aidge support
@@ -33,7 +66,7 @@ python -m tf2onnx.convert \
 |   Feature   | Tested in CI |
 | :---------: | :----------: |
 | ONNX import |      ✔️      |
-| Backend CPU |      ❌      |
+| Backend CPU |      ✔️      |
 | Export CPP  |      ❌      |