Remove build model from quant model zoo

Signed-off-by: gcunhase <[email protected]>

Author: gcunhase

tests/_test_utils/onnx_quantization/lib_test_models.py

@@ -554,62 +554,3 @@ def build_convtranspose_conv_residual_model():
     onnx.checker.check_model(model_inferred)
 
     return model_inferred
-
-
-def build_matmul_relu_model(ir_version=12):
-    # Define your model inputs and outputs
-    input_names = ["input_0"]
-    output_names = ["output_0"]
-    input_shapes = [(1, 1024, 1024)]
-    output_shapes = [(1, 1024, 16)]
-
-    inputs = [
-        helper.make_tensor_value_info(input_name, onnx.TensorProto.FLOAT, input_shape)
-        for input_name, input_shape in zip(input_names, input_shapes)
-    ]
-    outputs = [
-        helper.make_tensor_value_info(output_name, onnx.TensorProto.FLOAT, output_shape)
-        for output_name, output_shape in zip(output_names, output_shapes)
-    ]
-
-    # Create the ONNX graph with the nodes
-    nodes = [
-        helper.make_node(
-            op_type="MatMul",
-            inputs=["input_0", "weights_1"],
-            outputs=["matmul1_matmul/MatMul:0"],
-            name="matmul1_matmul/MatMul",
-        ),
-        helper.make_node(
-            op_type="Relu",
-            inputs=["matmul1_matmul/MatMul:0"],
-            outputs=["output_0"],
-            name="relu1_relu/Relu",
-        ),
-    ]
-
-    # Create the ONNX initializers
-    initializers = [
-        helper.make_tensor(
-            name="weights_1",
-            data_type=onnx.TensorProto.FLOAT,
-            dims=(1024, 16),
-            vals=np.random.uniform(low=0.5, high=1.0, size=1024 * 16),
-        ),
-    ]
-
-    # Create the ONNX graph with the nodes and initializers
-    graph = helper.make_graph(
-        nodes, f"matmul_relu_ir_{ir_version}", inputs, outputs, initializer=initializers
-    )
-
-    # Create the ONNX model
-    model = helper.make_model(graph)
-    model.opset_import[0].version = 13
-    model.ir_version = ir_version
-
-    # Check the ONNX model
-    model_inferred = onnx.shape_inference.infer_shapes(model)
-    onnx.checker.check_model(model_inferred)
-
-    return model_inferred