generate gemm sampel for 3.4 branch

testdata/dnn/onnx/generate_onnx_models.py

@@ -11,7 +11,7 @@
 import onnxsim
 import google.protobuf.text_format
 import io
-
+from typing import Optional
 
 def assertExpected(s):
     if not (isinstance(s, str) or (sys.version_info[0] == 2 and isinstance(s, unicode))):
@@ -73,6 +73,24 @@ def save_onnx_data_and_model(input, output, name, operation, *args, **kwargs):
     model = onnx.helper.make_model(graph, producer_name=name)
     onnx.save(model, models_files)
 
+def save_data_and_onnx_model(name, input_np, output_np, onnx_model):
+    print(name + " input has sizes",  input_np.shape)
+    input_files = os.path.join("data", "input_" + name)
+    np.save(input_files, input_np.data)
+
+    print(name + " output has sizes", output_np.shape)
+    print()
+    output_files =  os.path.join("data", "output_" + name)
+    np.save(output_files, np.ascontiguousarray(output_np.data))
+
+    models_files = os.path.join("models", name + ".onnx")
+
+    onnx_model_pb = onnx._serialize(onnx_model)
+    model_def = assertONNXExpected(onnx_model_pb)
+    with open(models_files, 'wb') as file:
+        file.write(model_def.SerializeToString())
+
+
 def simplify(name, rename=False, **kwargs):
     model, check = onnxsim.simplify(name, **kwargs)
     assert check, "couldn't valide"
@@ -1665,4 +1683,46 @@ def forward(self, a, b):
 save_data_and_model_multy_inputs('output_registration', model, a, b)
 model = onnx.load('models/output_registration.onnx')
 model.graph.node[0].name = model.graph.output[0].name
-onnx.save(model, 'models/output_registration.onnx')
+onnx.save(model, 'models/output_registration.onnx')
+
+# ########################## GEMM ##########################
+# The original code is : https://github.com/onnx/onnx/blob/main/onnx/backend/test/case/node/gemm.py
+def gemm_reference_implementation(A: np.ndarray, B: np.ndarray, C: Optional[np.ndarray] = None, alpha: float = 1., beta: float = 1., transA: int = 0,
+                                  transB: int = 0) -> np.ndarray:
+    A = A if transA == 0 else A.T
+    B = B if transB == 0 else B.T
+    C = C if C is not None else np.array(0)
+
+    Y = alpha * np.dot(A, B) + beta * C
+
+    return Y
+
+## gemm without transB
+input_np = np.random.rand(2, 10).astype("float32")
+inputs = [onnx.helper.make_tensor_value_info("input1", onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[input_np.dtype], shape=input_np.shape)]
+
+weight_np = np.random.rand(10, 3).astype("float32")
+weight_tensor = onnx.helper.make_tensor('weight_tensor', data_type=onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[weight_np.dtype], dims=weight_np.shape, vals=weight_np)
+
+outputs = [onnx.helper.make_tensor_value_info("output", onnx.TensorProto.FLOAT, shape=(2, 3))]
+
+nodes = [onnx.helper.make_node("Gemm", ["input1", "weight_tensor"], ["output"])]
+
+graph = onnx.helper.make_graph(nodes,
+                            "gemm_test",
+                            inputs,
+                            outputs, initializer=[weight_tensor])
+gemm_model = onnx.helper.make_model(graph)
+output_np = gemm_reference_implementation(input_np, weight_np)
+save_data_and_onnx_model("gemm_no_transB", input_np, output_np, gemm_model)
+
+## gemm with transB = 0
+
+nodes2 = [onnx.helper.make_node("Gemm", ["input1", "weight_tensor"], ["output"], transB=0)]
+graph2 = onnx.helper.make_graph(nodes2,
+                            "gemm_test",
+                            inputs,
+                            outputs, initializer=[weight_tensor])
+gemm_model2 = onnx.helper.make_model(graph2)
+output_np = gemm_reference_implementation(input_np, weight_np)
+save_data_and_onnx_model("gemm_transB_0", input_np, output_np, gemm_model2)

testdata/dnn/onnx/models/gemm_no_transB.onnx

@@ -0,0 +1,16 @@
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