gen_onnx.py

import numpy as np
import onnx
from onnx import helper
from onnx import numpy_helper
from onnx import AttributeProto, TensorProto, GraphProto

def onnx_test(op_test):
    def run_test():
        op_info = op_test()
        if len(op_info) > 3:
            graph_def = helper.make_graph(
                op_info[0],
                op_test.__name__,
                op_info[1],
                op_info[2],
                initializer=op_info[3]
            )
        else:
            graph_def = helper.make_graph(
                op_info[0], 
                op_test.__name__, 
                op_info[1], 
                op_info[2]
            )
        model_def = helper.make_model(graph_def, producer_name=op_test.__name__)
        onnx.save(model_def, '{}.onnx'.format(op_test.__name__))
    return run_test

@onnx_test
def acos_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])

    node = onnx.helper.make_node(
        'Acos',
        inputs=['x'],
        outputs=['y'],
    )

    return ([node], [x], [y])

@onnx_test
def add_bcast_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 4])
    z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [2, 3, 4,5])

    node = onnx.helper.make_node(
        'Add',
        inputs=['0', '1'],
        broadcast=1,
        axis=1,
        outputs=['2']
    )

    return ([node], [x,y], [z])

@onnx_test
def add_fp16_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT16, [1])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT16, [1])
    z = helper.make_tensor_value_info('2', TensorProto.FLOAT16, [1])

    node = onnx.helper.make_node(
        'Add',
        inputs=['0', '1'],
        outputs=['2'],
    )

    return (
        [node],
        [x,y],
        [z],
        # '0' -> 1.5, '1' -> 2.5
        [onnx.helper.make_tensor('0', TensorProto.FLOAT16, [1], [15872]),
        onnx.helper.make_tensor('1', TensorProto.FLOAT16, [1], [16640])]
    )

    model_def = helper.make_model(graph_def, producer_name=('add-fp16-example'))
    onnx.save(model_def, 'add_fp16_test.onnx')

@onnx_test
def add_scalar_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [])
    z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [2, 3, 4,5])

    node = onnx.helper.make_node(
        'Add',
        inputs=['0', '1'],
        outputs=['2']
    )

    return (
        [node],
        [x,y],
        [z],
        [helper.make_tensor('1', TensorProto.FLOAT, [], [1])]
    )

@onnx_test
def argmax_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 6])

    node = onnx.helper.make_node(
        'ArgMax',
        inputs=['x'],
        outputs=['y'],
        axis=2,
        keepdims = 0
    )

    return ([node], [x], [y])

@onnx_test
def argmin_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 5])

    node = onnx.helper.make_node(
        'ArgMin',
        inputs=['x'],
        outputs=['y'],
        axis=3,
        keepdims = 0
    )

    return ([node], [x], [y])

@onnx_test
def asin_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])

    node = onnx.helper.make_node(
        'Asin',
        inputs=['x'],
        outputs=['y'],
    )

    return ([node], [x], [y])


@onnx_test
def atan_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])

    node = onnx.helper.make_node(
        'Atan',
        inputs=['x'],
        outputs=['y'],
    )
    
    return ([node], [x], [y])

@onnx_test
def cast_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT16, [10])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])

    node = onnx.helper.make_node(
        'Cast',
        inputs=['x'],
        outputs=['y'],
        to = 1
    )
    
    return ([node], [x], [y])

@onnx_test
def clip_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3])


    node = onnx.helper.make_node(
        'Clip',
        inputs=['0'],
        outputs=['1'],
        max=6.0,
        min=0.0
    )

    return ([node], [x], [y])

@onnx_test
def concat_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 4, 3])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [7, 4, 3])
    z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [9, 4, 3])

    node = onnx.helper.make_node(
        'Concat',
        inputs=['0', '1'],
        axis=0,
        outputs=['2'],
    )

    return ([node], [x,y], [z])

@onnx_test
def constant_test():
    x = np.array([0, 1, 2])
    y = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
    
    node = onnx.helper.make_node(
        'Constant',
        inputs=[],
        outputs=['0'],
        value=onnx.helper.make_tensor(
            name='const_tensor',
            data_type=TensorProto.FLOAT,
            dims=x.shape,
            vals=x.flatten().astype(float),
        ),
    )

    return ([node], [], [y])

@onnx_test
def constant_fill_test():
    value = helper.make_tensor_value_info('value', TensorProto.FLOAT, [2, 3])

    node = onnx.helper.make_node(
        'ConstantFill',
        inputs=[],
        outputs=['value'],
        dtype = 1,
        value = 1.0,
        shape = [2, 3],
        input_as_shape = 0,
    )

    return ([node], [], [value])

@onnx_test
def constant_fill_input_as_shape_test():
    np_shape = np.array([2, 3])
    shape = helper.make_tensor_value_info('shape', TensorProto.INT32, [2])
    value = helper.make_tensor_value_info('value', TensorProto.FLOAT, [2, 3])

    ts_shape = helper.make_tensor(
        name = 'shape_tensor',
        data_type = TensorProto.INT32,
        dims = np_shape.shape,
        vals = np_shape.flatten().astype(int)
    )

    const_shape_node = onnx.helper.make_node(
        'Constant',
        inputs=[],
        outputs=['shape'],
        value=ts_shape,
    )

    node = onnx.helper.make_node(
        'ConstantFill',
        inputs=['shape'],
        outputs=['value'],
        dtype = 1,
        value = 1.0,
        input_as_shape = 1,
    )

    return ([const_shape_node, node], [], [value])

@onnx_test
def constant_scalar_test():
    x = np.array([1])
    y = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1])

    node = onnx.helper.make_node(
        'Constant',
        inputs=[],
        outputs=['0'],
        value=onnx.helper.make_tensor(
            name='const_tensor',
            data_type=TensorProto.INT32,
            dims=x.shape,
            vals=x.flatten().astype(int),
        ),
    )

    return ([node], [], [y])

@onnx_test
def const_of_shape_empty_input_test():
    tensor_val = onnx.helper.make_tensor(
        'value',
        onnx.TensorProto.INT64, [1],[10]
    )
    shape_val = np.array([2, 3, 4]).astype(np.int64)
    empty_val = np.array([]).astype(np.int64)
    empty_ts = helper.make_tensor(
        name='empty_tensor',
        data_type = TensorProto.INT32,
        dims=empty_val.shape,
        vals=empty_val.flatten().astype(int)
    )
    shape_const = helper.make_node(
        'Constant',
        inputs=[],
        outputs=['shape'],
        value=empty_ts,
    )
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 3, 4])

    node = onnx.helper.make_node(
        'ConstantOfShape',
        inputs=['shape'],
        outputs=['y'],
        value = tensor_val,
    )

    return ([shape_const, node], [], [y])

@onnx_test
def const_of_shape_float_test():
    tensor_val = onnx.helper.make_tensor(
        'value',
        onnx.TensorProto.FLOAT, [1],[10])

    shape_val = np.array([2, 3, 4]).astype(np.int64)
    shape_ts = helper.make_tensor(
        name = 'shape_tensor',
        data_type = TensorProto.INT32,
        dims = shape_val.shape,
        vals = shape_val.flatten().astype(int)
    )

    shape_const = helper.make_node(
        'Constant',
        inputs=[],
        outputs=['shape'],
        value=shape_ts,
    )
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 3, 4])

    node = onnx.helper.make_node(
        'ConstantOfShape',
        inputs=['shape'],
        outputs=['y'],
        value = tensor_val
    )

    return ([shape_const, node], [], [y])

@onnx_test
def const_of_shape_int64_test():
    tensor_val = onnx.helper.make_tensor(
        'value',
        onnx.TensorProto.INT64, [1],[10]
    )
    shape_val = np.array([2, 3, 4]).astype(np.int64)
    shape_ts = helper.make_tensor(
        name = 'shape_tensor',
        data_type = TensorProto.INT32,
        dims = shape_val.shape,
        vals = shape_val.flatten().astype(int)
    )
    shape_const = helper.make_node(
            'Constant',
            inputs=[],
            outputs=['shape'],
            value=shape_ts,
    )
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 3, 4])
    
    node = onnx.helper.make_node(
        'ConstantOfShape',
        inputs=['shape'],
        outputs=['y'],
        value = tensor_val
    )

    return ([shape_const, node], [], [y])

@onnx_test
def const_of_shape_no_value_attr_test():
    shape_val = np.array([2, 3, 4]).astype(np.int64)
    shape_ts = helper.make_tensor(
        name = 'shape_tensor',
        data_type = TensorProto.INT32,
        dims = shape_val.shape,
        vals = shape_val.flatten().astype(int)
    )
    shape_const = helper.make_node(
        'Constant',
        inputs=[],
        outputs=['shape'],
        value=shape_ts,
    )
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 3, 4])
    
    node = onnx.helper.make_node(
        'ConstantOfShape',
        inputs=['shape'],
        outputs=['y'],
    )

    return ([shape_const, node], [], [y])

@onnx_test
def conv_autopad_fail_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 32, 32])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 1, 1])
    out = helper.make_tensor_value_info('2', TensorProto.FLOAT, [1, 1, 34, 34])

    node = onnx.helper.make_node(
        'Conv',
        inputs=['0', '1'],
        outputs=['2'],
        dilations = [1, 1], 
        strides = [1, 1],
        auto_pad = 'SAME',
        pads = [0,0,1,1,0,0,1,1]
    )

    return ([node], [x,y], [out])

@onnx_test
def conv_bias_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 32, 32])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 5, 5])
    z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [1])
    out = helper.make_tensor_value_info('3', TensorProto.FLOAT, [1, 2, 28, 28])

    node = onnx.helper.make_node(
        'Conv',
        inputs=['0', '1', '2'],
        outputs=['3'],
        dilations = [1, 1], 
        strides = [1, 1]
    )

    return ([node], [x,y,z], [out])

@onnx_test
def conv_bn_relu_maxpool_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 32, 32])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 5, 5])
    z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [1])
    m = helper.make_tensor_value_info('3', TensorProto.FLOAT, [1])
    n = helper.make_tensor_value_info('4', TensorProto.FLOAT, [1])
    k = helper.make_tensor_value_info('5', TensorProto.FLOAT, [1])
    l = helper.make_tensor_value_info('6', TensorProto.FLOAT, [1])
    out = helper.make_tensor_value_info('10', TensorProto.FLOAT, [1, 1, 14, 14])

    node0 = onnx.helper.make_node(
        'Conv',
        inputs=['0', '1', '2'],
        outputs=['7'],
        dilations = [1, 1], 
        strides = [1, 1],
        pads = [0, 0, 0, 0]
    )

    node1 = onnx.helper.make_node(
        'BatchNormalization',
        inputs=['7', '3', '4', '5', '6'],
        outputs=['8'],
        epsilon = 9.99999974737875e-06, 
        momentum = 0.899999976158142
    )

    node2 = onnx.helper.make_node(
        'Relu',
        inputs=['8'],
        outputs=['9']
    )
    node3 = onnx.helper.make_node(
        'MaxPool',
        inputs=['9'],
        outputs=['10'],
        pads = [0, 0, 0, 0], 
        strides = [2, 2],
        kernel_shape=[2,2]
    )

    return (
        [node0, node1, node2, node3],
        [x, y, z, m, n, k, l],
        [out]
    )

@onnx_test
def conv_relu_maxpool_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 32, 32])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 5, 5])
    z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [1])
    out = helper.make_tensor_value_info('5', TensorProto.FLOAT, [1, 1, 14, 14])

    node1 = onnx.helper.make_node(
        'Conv',
        inputs=['0', '1', '2'],
        outputs=['3'],
        dilations = [1, 1], 
        strides = [1, 1],
        pads = [0, 0, 0, 0]
    )

    node2 = onnx.helper.make_node(
        'Relu',
        inputs=['3'],
        outputs=['4']
    )

    node3 = onnx.helper.make_node(
        'MaxPool',
        inputs=['4'],
        outputs=['5'],
        pads = [0, 0, 0, 0], 
        strides = [2, 2],
        kernel_shape=[2,2]
    )

    return (
        [node1, node2, node3],
        [x, y, z],
        [out]
    )

@onnx_test
def conv_relu_maxpool_x2_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 32, 32])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [5, 3, 5, 5])
    z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [5])
    m = helper.make_tensor_value_info('3', TensorProto.FLOAT, [1, 5, 5, 5])
    n = helper.make_tensor_value_info('4', TensorProto.FLOAT, [1])
    out = helper.make_tensor_value_info('10', TensorProto.FLOAT, [1, 1, 5, 5])

    node1 = onnx.helper.make_node(
        'Conv',
        inputs=['0', '1', '2'],
        outputs=['5'],
        dilations = [1, 1], 
        strides = [1, 1],
        pads = [0, 0, 0, 0]
    )

    node2 = onnx.helper.make_node(
        'Relu',
        inputs=['5'],
        outputs=['6']
    )

    node3 = onnx.helper.make_node(
        'MaxPool',
        inputs=['6'],
        outputs=['7'],
        pads = [0, 0, 0, 0], 
        strides = [2, 2],
        kernel_shape=[2,2]
    )

    node4 = onnx.helper.make_node(
        'Conv',
        inputs=['7', '3', '4'],
        outputs=['8'],
        dilations = [1, 1], 
        strides = [1, 1],
        pads = [0, 0, 0, 0]
    )

    node5 = onnx.helper.make_node(
        'Relu',
        inputs=['8'],
        outputs=['9']
    )

    node6 = onnx.helper.make_node(
        'MaxPool',
        inputs=['9'],
        outputs=['10'],
        pads = [0, 0, 0, 0], 
        strides = [2, 2],
        kernel_shape=[2,2]
        )

    return (
        [node1, node2, node3, node4, node5, node6],
        [x, y, z, m, n],
        [out]
    )

@onnx_test
def cos_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])

    node = onnx.helper.make_node(
        'Cos',
        inputs=['x'],
        outputs=['y'],
    )

    return ([node], [x], [y])

@onnx_test
def cosh_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1])

    node = onnx.helper.make_node(
        'Cosh',
        inputs=['x'],
        outputs=['y'],
    )

    return ([node], [x], [y])

@onnx_test
def dropout_test():
        x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 2, 2])
        y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 2, 2])

        node = onnx.helper.make_node(
            'Dropout',
            inputs=['0'],
            outputs=['1'],
        )

        return ([node], [x], [y])

@onnx_test
def elu_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3])

    node = onnx.helper.make_node(
        'Elu',
        inputs=['0'],
        outputs=['1'],
        alpha=0.01
    )

    return ([node], [x], [y])

@onnx_test
def erf_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10, 15])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10, 15])

    node = onnx.helper.make_node(
        'Erf',
        inputs=['x'],
        outputs=['y'],
    )

    return ([node], [x], [y])

@onnx_test
def exp_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])

    node = onnx.helper.make_node(
        'Exp',
        inputs=['x'],
        outputs=['y'],
    )

    return ([node], [x], [y])

@onnx_test
def expand_test():
    shape_val = np.array([2, 3, 4, 5]).astype(np.int64)
    shape_ts = helper.make_tensor(
        name = 'shape_tensor',
        data_type = TensorProto.INT32,
        dims = shape_val.shape,
        vals = shape_val.flatten().astype(int)
    )
    shape_const = helper.make_node(
        'Constant',
        inputs=[],
        outputs=['shape'],
        value=shape_ts,
    )
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 1, 1])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 3, 4, 5])
    
    node = onnx.helper.make_node(
        'Expand',
        inputs=['x', 'shape'],
        outputs=['y']
    )

    return ([shape_const,node], [x], [y])

@onnx_test
def flatten_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
    y = helper.make_tensor_value_info('2', TensorProto.FLOAT, [6,  20])
    y2 = helper.make_tensor_value_info('3', TensorProto.FLOAT, [2, 60])

    node = onnx.helper.make_node(
        'Flatten',
        inputs=['0'],
        axis=2,
        outputs=['2']
    )

    node2 = onnx.helper.make_node(
        'Flatten',
        inputs=['0'],
        outputs=['3']
    )

    return ([node,node2], [x], [y,y2])

@onnx_test
def gather_test():
    x = helper.make_tensor_value_info('data', TensorProto.FLOAT, [3, 4, 5, 6])
    i = helper.make_tensor_value_info('indices', TensorProto.INT32, [2, 3, 4, 5])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 3, 4, 5])

    node = onnx.helper.make_node(
        'Gather',
        inputs=['data', 'indices'],
        outputs=['y'],
        axis=1,
    )

    return ([node], [x,i], [y])

@onnx_test
def gemm_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [5, 7])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [11, 5])
    z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [])
    a = helper.make_tensor_value_info('3', TensorProto.FLOAT, [7, 11])

    node = onnx.helper.make_node(
        'Gemm',
        inputs=['0', '1', '2'],
        outputs=['3'],
        alpha=2.0,
        beta=2.0,
        transA=1,
        transB=1
    )

    return ([node], [x,y,z], [a])

@onnx_test
def gemm_ex_test():
    m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 1, 5, 6])
    m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [1, 1, 5, 7])
    m3 = helper.make_tensor_value_info('3', TensorProto.FLOAT, [1, 1, 6, 7])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 1, 6, 7])

    node = onnx.helper.make_node(
        'Gemm',
        inputs=['1', '2', '3'],
        outputs=['y'],
        alpha = 0.5,
        beta = 0.8,
        transA = 1
    )

    return ([node], [m1,m2,m3], [y])

@onnx_test
def gemm_ex_brcst_test():
    m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 1, 5, 6])
    m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [1, 1, 5, 7])
    m3 = helper.make_tensor_value_info('3', TensorProto.FLOAT, [1, 1, 6, 1])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 1, 6, 7])

    node = onnx.helper.make_node(
        'Gemm',
        inputs=['1', '2', '3'],
        outputs=['y'],
        alpha = 0.5,
        beta = 0.8,
        transA = 1
    )

    return ([node], [m1,m2,m3], [y])

@onnx_test
def globalavgpool_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1,3,16,16])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1,3,1,1])

    node = onnx.helper.make_node(
        'GlobalAveragePool',
        inputs=['0'],
        outputs=['1'],
    )

    return ([node], [x], [y])

@onnx_test
def globalmaxpool_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1,3,16,16])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1,3,1,1])

    node = onnx.helper.make_node(
        'GlobalMaxPool',
        inputs=['0'],
        outputs=['1'],
    )

    return ([node], [x], [y])

@onnx_test
def group_conv_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 4, 16, 16])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [4, 1, 3, 3])
    z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [1, 4, 14, 14])

    node = onnx.helper.make_node(
        'Conv',
        inputs=['0', '1'],
        group=4,
        outputs=['2'],
    )

    return ([node], [x,y], [z])

@onnx_test
def imagescaler_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1,3,16,16])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1,3,16,16])

    node = onnx.helper.make_node(
        'ImageScaler',
        inputs=['0'],
        outputs=['1'],
        bias=[0.01,0.02,0.03],
        scale=0.5
    )

    return ([node], [x], [y])

@onnx_test
def implicit_add_bcast_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 4, 1])
    z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [2, 3, 4, 5])

    node = onnx.helper.make_node(
        'Add',
        inputs=['0', '1'],
        outputs=['2'],
    )

    return ([node], [x,y], [z])

@onnx_test
def implicit_pow_bcast_test():
    arg0 = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
    arg1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 4, 1])
    arg_out = helper.make_tensor_value_info('out', TensorProto.FLOAT, [2, 3, 4, 5])

    node = onnx.helper.make_node(
        'Pow',
        inputs=['0', '1'],
        outputs=['out'],
    )

    return ([node], [arg0,arg1], [arg_out])

@onnx_test
def implicit_sub_bcast_test():
    arg0 = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
    arg1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [4, 5])
    arg_out = helper.make_tensor_value_info('out', TensorProto.FLOAT, [2, 3, 4, 5])

    node = onnx.helper.make_node(
        'Sub',
        inputs=['0', '1'],
        outputs=['out'],
    )

    return ([node], [arg0,arg1], [arg_out])

@onnx_test
def leaky_relu_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3])

    node = onnx.helper.make_node(
        'LeakyRelu',
        inputs=['0'],
        outputs=['1'],
        alpha=0.01
    )

    return ([node], [x], [y])

@onnx_test
def log_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])

    node = onnx.helper.make_node(
        'Log',
        inputs=['x'],
        outputs=['y'],
    )

    return ([node], [x], [y])

@onnx_test
def logsoftmax_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 5, 6])

    node = onnx.helper.make_node(
        'LogSoftmax',
        inputs=['x'],
        outputs=['y'],
        axis = 1
    )

    return ([node], [x], [y])

@onnx_test
def lrn_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 28, 24, 24])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 28, 24, 24])

    node = onnx.helper.make_node(
        'LRN',
        inputs=['0'],
        size=5,
        alpha=0.0001,
        beta=0.75,
        bias=1.0,
        outputs=['1']
    )

    return ([node], [x], [y])

@onnx_test
def matmul_bmbm_test():
    m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 6, 7])
    m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [5, 2, 1, 7, 8])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [5, 2, 3, 6, 8])

    node = onnx.helper.make_node(
        'MatMul',
        inputs=['1', '2'],
        outputs=['y'],
    )

    return ([node], [m1,m2], [y])

@onnx_test
def matmul_bmv_test():
    m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 6, 7])
    m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [7])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 6])

    node = onnx.helper.make_node(
        'MatMul',
        inputs=['1', '2'],
        outputs=['y'],
    )

    return ([node], [m1,m2], [y])

@onnx_test
def matmul_mv_test():
    m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [6, 7])
    m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [7])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [6])

    node = onnx.helper.make_node(
        'MatMul',
        inputs=['1', '2'],
        outputs=['y'],
    )

    return ([node], [m1,m2], [y])

@onnx_test
def matmul_vbm_test():
    m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [7])
    m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [5, 7, 8])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [5, 8])

    node = onnx.helper.make_node(
        'MatMul',
        inputs=['1', '2'],
        outputs=['y'],
    )

    return ([node], [m1,m2], [y])

@onnx_test
def matmul_vm_test():
    m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [7])
    m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [7, 8])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [8])

    node = onnx.helper.make_node(
        'MatMul',
        inputs=['1', '2'],
        outputs=['y'],
    )

    return ([node], [m1,m2], [y])

@onnx_test
def matmul_vv_test():
    m1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [7])
    m2 = helper.make_tensor_value_info('2', TensorProto.FLOAT, [7])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1])

    node = onnx.helper.make_node(
        'MatMul',
        inputs=['1', '2'],
        outputs=['y'],
    )

    return ([node], [m1,m2], [y])

@onnx_test
def max_test():
    a = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
    b = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3])
    c = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3])
    y = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3])

    node = onnx.helper.make_node(
        'Max',
        inputs=['0', '1', '2'],
        outputs=['3'],
    )

    return ([node], [a,b,c], [y])

@onnx_test
def min_test():
    a = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
    b = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3])
    c = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3])
    y = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3])

    node = onnx.helper.make_node(
        'Min',
        inputs=['0', '1', '2'],
        outputs=['3'],
    )

    return ([node], [a,b,c], [y])

@onnx_test
def no_pad_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 2])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [2, 2])

    node = onnx.helper.make_node(
        'Pad',
        inputs=['0'],
        pads=[0,0,0,0],
        outputs=['1']
    )

    return ([node], [x], [y])

@onnx_test
def pad_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 2])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [4, 4])

    node = onnx.helper.make_node(
        'Pad',
        inputs=['0'],
        pads=[1,1,1,1],
        outputs=['1']
    )

    return ([node], [x], [y])

@onnx_test
def pow_test():
    arg0 = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
    arg1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [2, 3, 4, 5])
    arg_out = helper.make_tensor_value_info('out', TensorProto.FLOAT, [2, 3, 4, 5])

    node = onnx.helper.make_node(
        'Pow',
        inputs=['0', '1'],
        outputs=['out'],
    )


    return ([node], [arg0, arg1], [arg_out])

@onnx_test
def reducemean_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4])
    axes=[2, 3]

    node = onnx.helper.make_node(
        'ReduceMean',
        inputs=['x'],
        outputs=['y'],
        axes=axes,
        keepdims = 0
    )

    return ([node], [x], [y])

@onnx_test
def reducemean_keepdims_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 1, 6])
    axes=[2]

    node = onnx.helper.make_node(
        'ReduceMean',
        inputs=['x'],
        outputs=['y'],
        axes=axes,
        keepdims = 1
    )

    return ([node], [x], [y])

@onnx_test
def reducesum_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 1, 1])
    axes=[2]

    node = onnx.helper.make_node(
        'ReduceSum',
        inputs=['x'],
        outputs=['y'],
        axes=axes,
        keepdims = 0
    )

    return ([node], [x], [y])

@onnx_test
def reducesum_multiaxis_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 1, 1])
    axes=[2, 3]

    node = onnx.helper.make_node(
            'ReduceSum',
            inputs=['x'],
            outputs=['y'],
            axes=axes,
            keepdims = 0
            )

    return ([node], [x], [y])

@onnx_test
def reducesum_keepdims_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 1, 1])
    axes=[2, 3]

    node = onnx.helper.make_node(
            'ReduceSum',
            inputs=['x'],
            outputs=['y'],
            axes=axes,
            keepdims = 1
            )

    return ([node], [x], [y])

@onnx_test
def reshape_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [4, 2, 3])
    x_shape = helper.make_tensor_value_info('1', TensorProto.INT64, [2])
    x_shape_list = [3,8]
    y = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3, 8])
    y2 = helper.make_tensor_value_info('3', TensorProto.FLOAT, [3, 8])

    node = onnx.helper.make_node(
        'Reshape',
        inputs=['0', '1'],
        outputs=['2']
    )

    node2 = onnx.helper.make_node(
        'Reshape',
        inputs=['0'],
        shape=x_shape_list,
        outputs=['3']
    )

    return (
        [node,node2],
        [x, x_shape],
        [y,y2],
        [helper.make_tensor('1', TensorProto.INT64, [2], [3, 8])]
    )

@onnx_test
def reshape_non_standard_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 3, 4])
    trans_x = helper.make_tensor_value_info('trans_x', TensorProto.FLOAT, [2, 4, 3])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [4, 3, 2])

    trans = helper.make_node(
        'Transpose',
        inputs=['x'],
        outputs=['trans_x'],
        perm=[0, 2, 1],
    )

    res = onnx.helper.make_node(
        'Reshape',
        inputs=['trans_x'],
        outputs=['y'],
        shape=[4, 3, 2]
    )

    return ([trans,res], [x], [y])

@onnx_test
def shape_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
    y = helper.make_tensor_value_info('y', TensorProto.INT64, [4])

    node = onnx.helper.make_node(
        'Shape',
        inputs=['x'],
        outputs=['y'],
    )

    return ([node], [x], [y])

@onnx_test
def shape_gather_test():
    values = np.array([1])
    value = helper.make_tensor_value_info('value', TensorProto.INT32, [1])
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [7, 3, 10])
    y = helper.make_tensor_value_info('y', TensorProto.INT64, [3])
    z = helper.make_tensor_value_info('z', TensorProto.FLOAT, [1])

    value_tensor = helper.make_tensor(
            name = 'const_tensor',
            data_type = TensorProto.INT32,
            dims = values.shape,
            vals = values.flatten().astype(int))

    node_const = onnx.helper.make_node(
        'Constant',
        inputs=[],
        outputs=['value'],
        value=value_tensor,
    )

    node_shape = onnx.helper.make_node(
        'Shape',
        inputs=['x'],
        outputs=['y'],
    )

    node_gather = helper.make_node(
        'Gather',
        inputs=['y', 'value'],
        outputs=['z'],
        axis=0,
    )

    return ([node_const,node_shape,node_gather], [x], [z])

@onnx_test
def sign_test():
    x = helper.make_tensor_value_info('x', TensorProto.DOUBLE, [10, 5])
    y = helper.make_tensor_value_info('y', TensorProto.DOUBLE, [10, 5])

    node = onnx.helper.make_node(
        'Sign',
        inputs=['x'],
        outputs=['y'],
    )

    return ([node], [x], [y])

@onnx_test
def sin_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])

    node = onnx.helper.make_node(
        'Sin',
        inputs=['x'],
        outputs=['y'],
    )

    return ([node], [x], [y])

@onnx_test
def sinh_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])

    node = onnx.helper.make_node(
        'Sinh',
        inputs=['x'],
        outputs=['y'],
    )

    return ([node], [x], [y])

@onnx_test
def slice_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3, 2])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 2])

    node = onnx.helper.make_node(
        'Slice',
        inputs=['0'],
        axes=[0, 1],
        starts=[1,0],
        ends=[2, 2],
        outputs=['1']
    )

    return ([node], [x], [y])

@onnx_test
def softmax_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3])

    node = onnx.helper.make_node(
        'Softmax',
        inputs=['0'],
        outputs=['1']
    )

    return ([node], [x], [y])

@onnx_test
def sqrt_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10, 15])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10, 15])

    node = onnx.helper.make_node(
        'Sqrt',
        inputs=['x'],
        outputs=['y'],
    )

    return ([node], [x], [y])

@onnx_test
def squeeze_unsqueeze_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 1, 1, 2, 1])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 2])
    z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [1, 1, 3, 1, 2, 1])

    node = onnx.helper.make_node(
        'Squeeze',
        inputs=['0'],
        axes=[0, 2, 3, 5],
        outputs=['1']
    )

    node2 = onnx.helper.make_node(
        'Unsqueeze',
        inputs=['1'],
        axes=[0, 1, 3, 5],
        outputs=['2']
    )

    return ([node,node2], [x], [z])

@onnx_test
def sub_bcast_test():
    arg0 = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
    arg1 = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 4])
    arg_out = helper.make_tensor_value_info('out', TensorProto.FLOAT, [2, 3, 4, 5])

    node = onnx.helper.make_node(
        'Sub',
        inputs=['0', '1'],
        outputs=['out'],
        broadcast = 1,
        axis = 1,
    )

    return ([node], [arg0,arg1], [arg_out])

@onnx_test
def sub_scalar_test():
    values = np.array([1])
    arg_node = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
    arg_out = helper.make_tensor_value_info('out', TensorProto.FLOAT, [2, 3, 4, 5])

    values_tensor = helper.make_tensor(
        name = 'const',
        data_type = TensorProto.FLOAT,
        dims = values.shape,
        vals = values.flatten().astype(float)
    )

    arg_const = onnx.helper.make_node(
        'Constant',
        inputs=[],
        outputs=['arg_const'],
        value=values_tensor,
    )


    node = onnx.helper.make_node(
        'Sub',
        inputs=['0', 'arg_const'],
        outputs=['out'],
    )

    return ([arg_const,node], [arg_node], [arg_out])

@onnx_test
def sum_test():
    a = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
    b = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3])
    c = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3])

    y = helper.make_tensor_value_info('3', TensorProto.FLOAT, [3])

    node = onnx.helper.make_node(
        'Sum',
        inputs=['0', '1', '2'],
        outputs=['3'],
    )

    return ([node], [a,b,c], [y])

@onnx_test
def sum_test():
    a = helper.make_tensor_value_info('0', TensorProto.FLOAT, [3])
    b = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3])
    c = helper.make_tensor_value_info('2', TensorProto.FLOAT, [3])
    y = helper.make_tensor_value_info('3', TensorProto.FLOAT, [3])

    node = onnx.helper.make_node(
        'Sum',
        inputs=['0', '1', '2'],
        outputs=['3'],
    )

    return ([node], [a,b,c], [y])

@onnx_test
def tan_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [10])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [10])

    node = onnx.helper.make_node(
            'Tan',
            inputs=['x'],
            outputs=['y'],
            )

    return ([node], [x], [y])

@onnx_test
def tanh_test():
    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1])

    node = onnx.helper.make_node(
            'Tanh',
            inputs=['x'],
            outputs=['y'],
            )

    return ([node], [x], [y])

@onnx_test
def transpose_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 2, 2, 3])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [1, 3, 2, 2])

    node = onnx.helper.make_node(
        'Transpose',
        perm=[0, 3, 1, 2],
        inputs=['0'],
        outputs=['1'],
    )

    return ([node], [x], [y])

@onnx_test
def transpose_gather_test():
    x = helper.make_tensor_value_info('data', TensorProto.FLOAT, [3, 5, 4, 6])
    i = helper.make_tensor_value_info('indices', TensorProto.INT32, [2, 4, 3, 5])
    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 2, 3, 4, 5, 4, 5, 6])

    td = onnx.helper.make_node(
        'Transpose',
        inputs=['data'],
        outputs=['tdata'],
        perm=[0, 2, 1, 3],
    )

    ti = onnx.helper.make_node(
        'Transpose',
        inputs=['indices'],
        outputs=['tindices'],
        perm=[0, 2, 1, 3]
    )

    node = onnx.helper.make_node(
        'Gather',
        inputs=['tdata', 'tindices'],
        outputs=['y'],
        axis=1,
    )


    return ([td, ti, node], [x, i], [y])

@onnx_test
def unknown_test():
    x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [2, 3, 4, 5])
    y = helper.make_tensor_value_info('1', TensorProto.FLOAT, [3, 4])
    z = helper.make_tensor_value_info('2', TensorProto.FLOAT, [2, 3, 4, 5])
    a = helper.make_tensor_value_info('3', TensorProto.FLOAT, [2, 3, 4, 5])

    node = onnx.helper.make_node(
        'Unknown',
        inputs=['0', '1'],
        outputs=['2']
    )

    node2 = onnx.helper.make_node(
        'Unknown',
        inputs=['2'],
        outputs=['3']
    )

    return ([node,node2], [x,y], [a])