ONNX 测试样例#
%cd ..
from utils.onnx_utils import (
get_input_data_shape_dict,
make_constant_node, get_onnxruntime_output,
get_tvm_output, get_tvm_output_with_vm,
verify_with_ort, verify_with_ort_with_inputs,
quantize_and_verify_with_ort
)
/media/pc/data/lxw/ai/tvm/xinetzone/tvm-book/doc/tutorials/frontend
# import torch
# import torchvision
# from torch import nn
# # from torch.nn import Linear, Module, Sequential
import numpy as np
import onnx
from onnx import TensorProto, helper, numpy_helper
import tvm
reshape#
in_shape = (4, 3, 3, 4)
ref_shape = (6, 2, 4, 3)
ref_array = np.array(ref_shape)
ref_node = onnx.helper.make_node(
"Constant",
inputs=[],
outputs=["ref_in"],
value=onnx.helper.make_tensor(
name="const_tensor",
data_type=onnx.TensorProto.INT32,
dims=ref_array.shape,
vals=ref_array.flatten().astype(int),
),
)
reshape_node = helper.make_node("Reshape", ["in", "ref_in"], ["out"])
graph = helper.make_graph(
[ref_node, reshape_node],
"reshape_test",
inputs=[helper.make_tensor_value_info("in", TensorProto.FLOAT, list(in_shape))],
outputs=[helper.make_tensor_value_info("out", TensorProto.FLOAT, list(ref_shape))],
)
model = helper.make_model(graph, producer_name="reshape_test")
target = "llvm"
dev = tvm.cpu()
x = np.random.uniform(size=in_shape).astype("int32")
tvm_out = get_tvm_output(model, x, target, dev, ref_shape, "float32")
np.testing.assert_allclose(ref_shape, tvm_out.shape)
double_reshape#
in_shape = (4, 3, 3, 4)
ref_shape = (6, 2, 4, 3)
ref_array = np.array(ref_shape)
ref_node = onnx.helper.make_node(
"Constant",
inputs=[],
outputs=["ref_in"],
value=onnx.helper.make_tensor(
name="const_tensor",
data_type=onnx.TensorProto.INT32,
dims=ref_array.shape,
vals=ref_array.flatten().astype(int),
),
)
reshape_node1 = helper.make_node("Reshape", ["in", "ref_in"], ["out1"])
reshape_node2 = helper.make_node("Reshape", ["in", "ref_in"], ["out2"])
add_node = helper.make_node("Add", ["out1", "out2"], ["out"])
graph = helper.make_graph(
[ref_node, reshape_node1, reshape_node2, add_node],
"reshape_test",
inputs=[helper.make_tensor_value_info("in", TensorProto.FLOAT, list(in_shape))],
outputs=[helper.make_tensor_value_info("out", TensorProto.FLOAT, list(ref_shape))],
)
model = helper.make_model(graph, producer_name="reshape_test")
x = np.random.uniform(size=in_shape).astype("int32")
tvm_out = get_tvm_output(model, x, target, dev, ref_shape, "float32")
np.testing.assert_allclose(ref_shape, tvm_out.shape)
expand#
def _test_expand(name, data, shape, ref_data, dtype="int32"):
shape_array = np.array(shape)
if dtype == "int32":
shape_node = onnx.helper.make_node(
"Constant",
inputs=[],
outputs=["shape"],
value=onnx.helper.make_tensor(
name="const_tensor",
data_type=onnx.TensorProto.INT32,
dims=shape_array.shape,
vals=shape_array.flatten().astype("int32"),
),
)
elif dtype == "int64":
shape_node = onnx.helper.make_node(
"Constant",
inputs=[],
outputs=["shape"],
value=onnx.helper.make_tensor(
name="const_tensor",
data_type=onnx.TensorProto.INT64,
dims=shape_array.shape,
vals=shape_array.flatten().astype("int64"),
),
)
else:
raise TypeError("Invalid dtype")
expand_node = helper.make_node("Expand", ["in", "shape"], ["out"])
graph = helper.make_graph(
[shape_node, expand_node],
"expand_test",
inputs=[helper.make_tensor_value_info("in", TensorProto.FLOAT, list(data.shape))],
outputs=[helper.make_tensor_value_info("out", TensorProto.FLOAT, list(ref_data.shape))],
)
model = helper.make_model(graph, producer_name=name)
tvm_out = get_tvm_output_with_vm(model, data, target, dev, freeze_params=True)
np.testing.assert_allclose(ref_data, tvm_out)
in_shape = (3, 1)
shape = (3, 4)
data = np.random.uniform(size=in_shape).astype(np.float32)
ref_data = np.tile(data, 4)
_test_expand("expand_with_dim_unchanged_test", data, shape, ref_data, "int32")
_test_expand("expand_with_dim_unchanged_test", data, shape, ref_data, "int64")
in_shape = (3, 1)
shape = (2, 1, 6)
data = np.random.uniform(size=in_shape).astype(np.float32)
ref_data = data * np.ones(shape, dtype=np.float32)
_test_expand("expand_larger_target_shape_test", data, shape, ref_data, "int32")
_test_expand("expand_larger_target_shape_test", data, shape, ref_data, "int64")
in_shape = (1, 1)
shape = (3,)
data = np.random.uniform(size=in_shape).astype(np.float32)
ref_data = data * np.ones(shape, dtype=np.float32)
_test_expand("expand_smaller_target_shape_test", data, shape, ref_data, "int32")
_test_expand("expand_smaller_target_shape_test", data, shape, ref_data, "int64")
depth_to_space#
def verify_depth_to_space(inshape, outshape, mode, block_size):
node = onnx.helper.make_node(
"DepthToSpace", inputs=["x"], outputs=["y"], blocksize=block_size
)
graph = helper.make_graph(
[node],
"depth_to_space_test",
inputs=[helper.make_tensor_value_info("x", TensorProto.FLOAT, list(inshape))],
outputs=[helper.make_tensor_value_info("y", TensorProto.FLOAT, list(outshape))],
)
model = helper.make_model(graph, producer_name="depth_to_space_test")
verify_with_ort(model, [inshape], [outshape], target, dev)
# current onnx.checker use OpSet-1 version of DepthToSpace, which doesn't have a mode argument.
# TO-DO, we can add mode argument to test CRD mode and DCR mode
# in the future when we update to a newer onnx version.
verify_depth_to_space((1, 8, 2, 3), (1, 2, 4, 6), mode="CRD", block_size=2)
space_to_depth#
def verify_space_to_depth(inshape, outshape, block_size):
node = onnx.helper.make_node(
"SpaceToDepth", inputs=["x"], outputs=["y"], blocksize=block_size
)
graph = helper.make_graph(
[node],
"space_to_depth_test",
inputs=[helper.make_tensor_value_info("x", TensorProto.FLOAT, list(inshape))],
outputs=[helper.make_tensor_value_info("y", TensorProto.FLOAT, list(outshape))],
)
model = helper.make_model(graph, producer_name="space_to_depth_test")
verify_with_ort(model, [inshape], [outshape], target, dev)
verify_space_to_depth((1, 1, 4, 6), (1, 4, 2, 3), 2)
shape#
in_shape = (4, 3, 3, 4)
ref_shape = (6, 2, 4, 3)
ref_array = np.array(ref_shape)
ref_node = onnx.helper.make_node(
"Constant",
inputs=[],
outputs=["ref_in"],
value=onnx.helper.make_tensor(
name="const_tensor",
data_type=onnx.TensorProto.INT32,
dims=ref_array.shape,
vals=ref_array.flatten().astype(int),
),
)
reshape_node = helper.make_node("Reshape", ["in", "ref_in"], ["out"])
shape_node = helper.make_node("Shape", ["out"], ["final_out"])
graph = helper.make_graph(
[ref_node, reshape_node, shape_node],
"shape_test",
inputs=[helper.make_tensor_value_info("in", TensorProto.FLOAT, list(in_shape))],
outputs=[helper.make_tensor_value_info("final_out", TensorProto.FLOAT, list(ref_shape))],
)
model = helper.make_model(graph, producer_name="shape_test")
x = np.random.uniform(size=in_shape).astype("int32")
tvm_out = get_tvm_output(model, x, target, dev, ref_shape, "int32")
np.testing.assert_allclose(ref_shape, tvm_out)
power#
def _test_power_iteration(x_shape, y_shape):
if isinstance(y_shape, int):
y_shape = [y_shape]
x = np.random.uniform(size=x_shape).astype(np.float32)
y = np.random.uniform(size=y_shape).astype(np.float32)
np_res = np.power(x, y).astype(np.float32)
res = helper.make_node("Pow", ["x", "y"], ["out"])
graph = helper.make_graph(
[res],
"power_test",
inputs=[
helper.make_tensor_value_info("x", TensorProto.FLOAT, list(x_shape)),
helper.make_tensor_value_info("y", TensorProto.FLOAT, list(y_shape)),
],
outputs=[helper.make_tensor_value_info("out", TensorProto.FLOAT, list(np_res.shape))],
)
model = helper.make_model(graph, producer_name="power_test")
tvm_out = get_tvm_output(model, [x, y], target, dev, np_res.shape)
tvm.testing.assert_allclose(np_res, tvm_out, rtol=1e-5, atol=1e-5)
_test_power_iteration((1, 3), (1))
_test_power_iteration((2, 3), (2, 3))
_test_power_iteration((2, 3), (1, 3))
range#
def verify_range(start, limit, delta, dtype):
dtype_map = {
"float32": TensorProto.FLOAT,
"int32": TensorProto.INT32,
"int64": TensorProto.INT64,
}
dtype_onnx = dtype_map[dtype]
y = helper.make_node("Range", ["start", "limit", "delta"], ["output"])
graph = helper.make_graph(
[y],
"range_test",
inputs=[
helper.make_tensor_value_info("start", dtype_onnx, []),
helper.make_tensor_value_info("limit", dtype_onnx, []),
helper.make_tensor_value_info("delta", dtype_onnx, []),
],
outputs=[
helper.make_tensor_value_info(
"output", dtype_onnx, np.arange(start, limit, delta).shape
)
],
)
model = helper.make_model(graph, producer_name="range_test")
inputs = [np.array(x).astype(dtype) for x in [start, limit, delta]]
verify_with_ort_with_inputs(model, inputs, target=target, dev=dev, use_vm=True)
for t in ["float32", "int32", "int64"]:
verify_range(0, 10, 1, t)
verify_range(2, 8, 2, t)
verify_range(-3, 6, 4, t)
verify_range(-2, -7, -1, t)
squeeze#
def test_squeeze_once(in_shape, out_shape, axes=None):
y = helper.make_node("Squeeze", ["in"], ["out"], axes=axes)
graph = helper.make_graph(
[y],
"squeeze_test",
inputs=[helper.make_tensor_value_info("in", TensorProto.FLOAT, list(in_shape))],
outputs=[helper.make_tensor_value_info("out", TensorProto.FLOAT, list(out_shape))],
)
model = helper.make_model(graph, producer_name="squeeze_test")
x = np.random.uniform(size=in_shape).astype("float32")
verify_with_ort_with_inputs(model, [x], [out_shape], target=target, dev=dev, opset=11)
test_squeeze_once((1, 3, 1, 3, 1, 1), (3, 3), [0, 2, 4, 5])
test_squeeze_once((1, 3, 1, 3, 1, 1), (3, 3)) # empty axis.
test_squeeze_once((), ()) # scalar testing.
flatten#
def verify_flatten(in_shape, axis, ref_shape):
flatten_node = helper.make_node("Flatten", ["in"], ["out"], axis=axis)
graph = helper.make_graph(
[flatten_node],
"flatten_test",
inputs=[helper.make_tensor_value_info("in", TensorProto.FLOAT, list(in_shape))],
outputs=[helper.make_tensor_value_info("out", TensorProto.FLOAT, list(ref_shape))],
)
model = helper.make_model(graph, producer_name="flatten_test")
verify_with_ort(model, [in_shape], target=target, dev=dev)
verify_flatten((1, 3, 4, 4), 1, (1, 48))
verify_flatten((1,), 1, (1, 1))
unsqueeze#
in_shape = (3, 3)
axis = (0, 3, 4)
out_shape = (1, 3, 3, 1, 1)
y = helper.make_node("Unsqueeze", ["in"], ["out"], axes=list(axis))
graph = helper.make_graph(
[y],
"squeeze_test",
inputs=[helper.make_tensor_value_info("in", TensorProto.FLOAT, list(in_shape))],
outputs=[helper.make_tensor_value_info("out", TensorProto.FLOAT, list(out_shape))],
)
model = helper.make_model(graph, producer_name="squeeze_test")
verify_with_ort(model, [in_shape], target=target, dev=dev, opset=11)
unsqueeze_with_neg_axes#
def verify_unsqueeze_with_neg_axes(opset=11):
in_shape = (2, 3, 4)
axis = (-2, -1)
out_shape = (2, 3, 4, 1, 1)
if opset < 13:
y = helper.make_node("Unsqueeze", ["in"], ["out"], axes=list(axis))
nodes = [y]
else:
axes = np.array(list(axis)).astype(np.int64)
axes = helper.make_node(
"Constant",
inputs=[],
outputs=["axes"],
value=onnx.helper.make_tensor(
name="const_axes",
data_type=onnx.TensorProto.INT64,
dims=axes.shape,
vals=axes.flatten().astype(int),
),
)
y = helper.make_node("Unsqueeze", ["in", "axes"], ["out"])
nodes = [axes, y]
graph = helper.make_graph(
nodes,
"squeeze_test",
inputs=[helper.make_tensor_value_info("in", TensorProto.FLOAT, list(in_shape))],
outputs=[helper.make_tensor_value_info("out", TensorProto.FLOAT, list(out_shape))],
)
model = helper.make_model(graph, producer_name="squeeze_test")
verify_with_ort(model, [in_shape], target=target, dev=dev, opset=opset)
verify_unsqueeze_with_neg_axes()
verify_unsqueeze_with_neg_axes(opset=13)
gather#
def verify_gather(in_shape, indices, axis, dtype):
x = np.random.uniform(size=in_shape).astype(dtype)
indices = np.array(indices, dtype="int64")
out_np = np.take(x, indices, axis=axis)
y = helper.make_node("Gather", ["in", "indices"], ["out"], axis=axis)
graph = helper.make_graph(
[y],
"gather_test",
inputs=[
helper.make_tensor_value_info(
"in", helper.np_dtype_to_tensor_dtype(np.dtype(dtype)), list(in_shape)
),
helper.make_tensor_value_info("indices", TensorProto.INT64, list(indices.shape)),
],
outputs=[
helper.make_tensor_value_info(
"out", helper.np_dtype_to_tensor_dtype(np.dtype(dtype)), list(out_np.shape)
)
],
)
model = helper.make_model(graph, producer_name="gather_test")
verify_with_ort_with_inputs(model, [x, indices], target=target, dev=dev, dtype=dtype)
verify_gather((4,), [1], 0, "int32")
verify_gather((1, 4), [0], 0, "int32")
verify_gather((4,), [[[1, 0], [0, 1]]], 0, "float32")
verify_gather((2, 2), [[[1, 0], [0, 1]]], 1, "int32")
verify_gather((3, 3, 3), [[[1, 0]]], -1, "int32")
verify_gather((4, 3, 5, 6), [[2, 1, 0, 0]], 0, "float32")
dynamic_gather#
from tvm import relay
dtype = "float32"
in_shape = [2, 2]
indices = 1
axis = 1
x = np.random.uniform(size=in_shape).astype(dtype)
indices = np.array(indices, dtype="int64")
out_np = np.take(x, indices, axis=axis)
indices = helper.make_node(
"Constant",
inputs=[],
outputs=["indices"],
value=onnx.helper.make_tensor(
name="const_indices",
data_type=onnx.TensorProto.INT64,
dims=[],
vals=[1],
),
)
y = helper.make_node("Gather", ["in", "indices"], ["out"], axis=axis)
graph = helper.make_graph(
[indices, y],
"gather_test",
inputs=[
helper.make_tensor_value_info(
"in", helper.np_dtype_to_tensor_dtype(np.dtype(dtype)), ["?", "?"]
),
],
outputs=[
helper.make_tensor_value_info(
"out", helper.np_dtype_to_tensor_dtype(np.dtype(dtype)), ["?"] * len(out_np.shape)
)
],
)
model = helper.make_model(graph, producer_name="dynamic_gather_test")
mod, params = relay.frontend.from_onnx(model)
result = relay.create_executor("vm", mod=mod, device=dev, target=target).evaluate()(x, **params)
np.testing.assert_allclose(out_np, result.numpy(), rtol=1e-5, atol=1e-5)
gatherelements#
def verify_gatherelements(in_shape, indices, axis):
x = np.random.uniform(size=in_shape).astype("float32")
indices = np.array(indices, dtype="int32")
y = helper.make_node("GatherElements", ["data", "indices"], ["output"], axis=axis)
graph = helper.make_graph(
[y],
"gather_elements_test",
inputs=[
helper.make_tensor_value_info("data", TensorProto.FLOAT, list(in_shape)),
helper.make_tensor_value_info("indices", TensorProto.INT32, list(indices.shape)),
],
outputs=[helper.make_tensor_value_info("output", TensorProto.FLOAT, list(in_shape))],
)
model = helper.make_model(graph, producer_name="gather_elements_test")
verify_with_ort_with_inputs(model, [x, indices], target=target, dev=dev)
verify_gatherelements((4,), [3, 0, 2, 1], 0)
verify_gatherelements((2, 2), [[1, 0], [0, 1]], 0)
verify_gatherelements((2, 2), [[0, 0], [1, 0]], 1)
verify_gatherelements((2, 2), [[1, 0], [0, 1]], 1)
indices = [
[[1, 0, 0], [1, 0, 1], [0, 1, 1]],
[[1, 1, 1], [1, 2, 1], [1, 0, 1]],
[[1, 2, 1], [1, 2, 1], [1, 2, 1]],
]
verify_gatherelements((3, 3, 3), indices, 2)
scatter#
def verify_scatter(in_shape, indices, axis):
x = np.random.uniform(size=in_shape).astype("float32")
indices = np.array(indices, dtype="int32")
updates = np.random.uniform(size=indices.shape).astype("float32")
y = helper.make_node("Scatter", ["data", "indices", "updates"], ["output"], axis=axis)
graph = helper.make_graph(
[y],
"scatter_test",
inputs=[
helper.make_tensor_value_info("data", TensorProto.FLOAT, list(in_shape)),
helper.make_tensor_value_info("indices", TensorProto.INT32, list(indices.shape)),
helper.make_tensor_value_info("updates", TensorProto.FLOAT, list(indices.shape)),
],
outputs=[helper.make_tensor_value_info("output", TensorProto.FLOAT, list(in_shape))],
)
model = helper.make_model(graph, producer_name="scatter_test")
# Scatter operator has been supported from version 9 and
# deprecated since version 11 of the default ONNX operator set
verify_with_ort_with_inputs(model, [x, indices, updates], target=target, dev=dev, opset=9)
verify_scatter((4,), [1], 0)
verify_scatter((1, 4), [[0]], 0)
verify_scatter((4,), [2, 3], 0)
verify_scatter((2, 2), [[1, 0], [0, 1]], 1)
verify_scatter((3, 3, 3), [[[-1, -3]]], -1)
verify_scatter((4, 3, 5, 6), [[[[2, 1, 0, 0]]]], 0)
scatter_elements#
def verify_scatter_elements(in_shape, indices, axis=0, reduction="update"):
x = np.random.uniform(size=in_shape).astype("float32")
indices = np.array(indices, dtype="int32")
updates = np.random.uniform(size=indices.shape).astype("float32")
scatter_elements_node = helper.make_node(
"ScatterElements",
["data", "indices", "updates"],
["output"],
axis=axis,
reduction=reduction,
)
graph = helper.make_graph(
[scatter_elements_node],
"scatter_elements_test",
inputs=[
helper.make_tensor_value_info("data", TensorProto.FLOAT, list(in_shape)),
helper.make_tensor_value_info("indices", TensorProto.INT32, list(indices.shape)),
helper.make_tensor_value_info("updates", TensorProto.FLOAT, list(indices.shape)),
],
outputs=[helper.make_tensor_value_info("output", TensorProto.FLOAT, list(in_shape))],
)
model = helper.make_model(graph, producer_name="scatter_elements_test")
verify_with_ort_with_inputs(model, [x, indices, updates], target=target, dev=dev)
# Usual scatter for 1d input
verify_scatter_elements((4,), [2, 3])
# Usual scatter with specified positive axis
verify_scatter_elements((2, 2), [[1, 0], [0, 1]], 1)
# Usual scatter for 3d input with spicified negative indices and axis
verify_scatter_elements((3, 3, 3), [[[-1, -3]]], -1)
# Usual scatter for 4d input
verify_scatter_elements((4, 3, 5, 6), [[[[2, 1, 0, 0]]]])
# Scatter elements with addition reduction of duplicates
verify_scatter_elements(
(3, 3, 3),
[[[0, 2, 1], [1, 1, 1], [2, 1, 0]], [[0, 2, 1], [1, 1, 1], [2, 1, 0]]],
0,
"add",
)
# Scatter elements with reduction and specified axis
verify_scatter_elements((3, 3, 3), [[[2, 2, 2], [1, 1, 1], [0, 0, 0]]], 2, "add")
# Scatter elements with multiplication reduction of duplicates
verify_scatter_elements(
(3, 3, 3),
[[[0, 2, 1], [1, 1, 1], [2, 1, 0]], [[0, 2, 1], [1, 1, 1], [2, 1, 0]]],
0,
"mul",
)
# TODO(vvchernov): min and max options are supported from 18 version, but CI supports 17 only
# # Scatter elements with min reduction of duplicates
# verify_scatter_elements(
# (3, 3, 3),
# [[[0, 2, 1], [1, 1, 1], [2, 1, 0]], [[0, 2, 1], [1, 1, 1], [2, 1, 0]]],
# 0,
# "min",
# )
# # Scatter elements with max reduction of duplicates
# verify_scatter_elements(
# (3, 3, 3),
# [[[0, 2, 1], [1, 1, 1], [2, 1, 0]], [[0, 2, 1], [1, 1, 1], [2, 1, 0]]],
# 0,
# "max",
# )
slice#
def _test_slice_iteration_v1(indata, outdata, starts, ends, axes=None):
if axes:
y = helper.make_node("Slice", ["in"], ["out"], axes=axes, starts=starts, ends=ends)
else:
y = helper.make_node("Slice", ["in"], ["out"], starts=starts, ends=ends)
graph = helper.make_graph(
[y],
"slice_test",
inputs=[helper.make_tensor_value_info("in", TensorProto.FLOAT, list(indata.shape))],
outputs=[helper.make_tensor_value_info("out", TensorProto.FLOAT, list(outdata.shape))],
)
model = helper.make_model(graph, producer_name="slice_test")
verify_with_ort_with_inputs(
model, [indata], [outdata.shape], opset=1, target=target, dev=dev
)
def _test_slice_iteration_v10(indata, outdata, **attrs):
starts = attrs["starts"]
ends = attrs["ends"]
axes = None if "axes" not in attrs else attrs["axes"]
steps = None if "steps" not in attrs else attrs["steps"]
starts = np.asarray(starts)
ends = np.asarray(ends)
inputs = [
helper.make_tensor_value_info("data", TensorProto.FLOAT, list(indata.shape)),
helper.make_tensor_value_info("starts", TensorProto.INT64, list(starts.shape)),
helper.make_tensor_value_info("ends", TensorProto.INT64, list(ends.shape)),
]
initializer = [
helper.make_tensor("starts", TensorProto.INT64, list(starts.shape), starts),
helper.make_tensor("ends", TensorProto.INT64, list(ends.shape), ends),
]
nodes = []
if "add_noop_to_input_attrs" in attrs:
def add_noop_to_input_attr(attr_name, attr):
output_name = attr_name + "_output"
ref_shape = list(np.array(attr).shape)
ref_shape.insert(0, 1)
ref_shape = tuple(ref_shape)
ref_array = np.array(ref_shape)
ref_node = onnx.helper.make_node(
"Constant",
inputs=[],
outputs=["ref_in_" + attr_name],
value=onnx.helper.make_tensor(
name="const_tensor__1_" + attr_name,
data_type=onnx.TensorProto.INT64,
dims=ref_array.shape,
vals=ref_array.flatten().astype(int),
),
)
in_shape = np.array(attr).shape
in_array = np.array(in_shape)
ref_node2 = onnx.helper.make_node(
"Constant",
inputs=[],
outputs=["input_shape_" + attr_name],
value=onnx.helper.make_tensor(
name="const_tensor__2_" + attr_name,
data_type=onnx.TensorProto.INT64,
dims=in_array.shape,
vals=in_array.flatten().astype(int),
),
)
reshape1_node = helper.make_node(
"Reshape", [attr_name, "ref_in_" + attr_name], ["reshape_" + attr_name]
)
reshape2_node = helper.make_node(
"Reshape", ["reshape_" + attr_name, "input_shape_" + attr_name], [output_name]
)
return [ref_node, ref_node2, reshape1_node, reshape2_node]
slice_inputs = []
for attr_name in ["starts", "ends", "axes", "steps"]:
if attr_name not in attrs:
continue
if "add_noop_to_input_attrs" in attrs and attr_name in attrs["add_noop_to_input_attrs"]:
nodes.extend(add_noop_to_input_attr(attr_name, attrs[attr_name]))
slice_inputs.append(attr_name + "_output")
else:
slice_inputs.append(attr_name)
if axes:
axes = np.asarray(axes)
inputs.append(
helper.make_tensor_value_info("axes", TensorProto.INT64, list(axes.shape))
)
initializer.append(
helper.make_tensor("axes", TensorProto.INT64, list(axes.shape), axes)
)
if steps:
assert axes is not None and len(axes) == len(steps)
steps = np.asarray(steps)
inputs.append(
helper.make_tensor_value_info("steps", TensorProto.INT64, list(axes.shape))
)
initializer.append(
helper.make_tensor("steps", TensorProto.INT64, list(steps.shape), steps)
)
y = helper.make_node("Slice", ["data", *slice_inputs], ["out"])
nodes.append(y)
graph = helper.make_graph(
nodes,
"slice_test",
inputs=inputs,
outputs=[helper.make_tensor_value_info("out", TensorProto.FLOAT, list(outdata.shape))],
initializer=initializer,
)
model = helper.make_model(graph, producer_name="slice_test")
verify_with_ort_with_inputs(
model, [indata], opset=10, freeze_params=True, use_vm=True, target=target, dev=dev
)
x = np.random.randn(20, 10, 5).astype(np.float32)
_test_slice_iteration_v1(x, x[0:3, 0:10], starts=(0, 0), ends=(3, 10), axes=(0, 1))
_test_slice_iteration_v1(x, x[0:3, 0:10], starts=(0, 0), ends=(10, 3), axes=(1, 0))
_test_slice_iteration_v1(x, x[:, :, 3:4], starts=(0, 0, 3), ends=(20, 10, 4))
_test_slice_iteration_v1(x, x[:, 1:1000], starts=(1,), ends=(1000,), axes=(1,))
_test_slice_iteration_v1(x, x[:, 0:-1], starts=(0,), ends=(-1,), axes=(1,))
_test_slice_iteration_v10(x, x[0:3, 0:10], starts=(0, 0), ends=(3, 10), axes=(0, 1))
_test_slice_iteration_v10(x, x[0:3, 0:10], starts=(0, 0), ends=(10, 3), axes=(1, 0))
_test_slice_iteration_v10(x, x[:, :, 3:4], starts=(0, 0, 3), ends=(20, 10, 4))
_test_slice_iteration_v10(x, x[:, 1:1000], starts=(1,), ends=(1000,), axes=(1,))
_test_slice_iteration_v10(x, x[:, 0:-1], starts=(0,), ends=(-1,), axes=(1,))
_test_slice_iteration_v10(x, x[:, 0:-1], starts=(0,), ends=(-1,), axes=(-1,))
_test_slice_iteration_v10(
x,
x[0:3, 0:10],
starts=(0, 0),
ends=(3, 10),
axes=(0, 1),
add_noop_to_input_attrs=["starts"],
)
_test_slice_iteration_v10(
x, x[:, :, 3:4], starts=(0, 0, 3), ends=(20, 10, 4), add_noop_to_input_attrs=["ends"]
)
_test_slice_iteration_v10(
x, x[:, 1:1000], starts=(1,), ends=(1000,), axes=(1,), add_noop_to_input_attrs=["axes"]
)
_test_slice_iteration_v10(
x,
x[:, 0:-1],
starts=(0,),
ends=(-1,),
axes=(1,),
add_noop_to_input_attrs=["starts", "ends"],
)
_test_slice_iteration_v10(
x,
x[0:3, 0:10],
starts=(0, 0),
ends=(3, 10),
axes=(0, 1),
add_noop_to_input_attrs=["ends", "axes"],
)
_test_slice_iteration_v10(
x,
x[:, :, 3:4],
starts=(0, 0, 3),
ends=(20, 10, 4),
add_noop_to_input_attrs=["starts", "axes"],
)
_test_slice_iteration_v10(
x,
x[:, 1:1000],
starts=(1,),
ends=(1000,),
axes=(1,),
add_noop_to_input_attrs=["starts", "ends", "axes"],
)
x = np.random.randn(1, 1, 1, 128).astype(np.float32)
_test_slice_iteration_v10(
x, x, starts=(0, 0), ends=(9223372036854775807, 9223372036854775807), axes=(0, 3)
)
x = np.random.randn(4, 4).astype(np.float32)
_test_slice_iteration_v10(
x, x[:, 1::2], starts=(1,), ends=(9223372036854775807,), axes=(1,), steps=(2,)
)
_test_slice_iteration_v10(
x,
x[0::1, 1::2],
starts=(0, 1),
ends=(4, 4),
axes=(0, 1),
steps=(1, 2),
)
2023-07-01 10:55:12.497323434 [W:onnxruntime:, model.cc:183 Model] ONNX Runtime only *guarantees* support for models stamped with opset version 7 or above for opset domain 'ai.onnx'. Please upgrade your model to opset 7 or higher. For now, this opset 1 model may run depending upon legacy support of some older opset version operators.
2023-07-01 10:55:12.497650501 [W:onnxruntime:, ort_transpose_optimizer.cc:24 ApplyImpl] Transpose optimizer failed: Unsupported ONNX opset: 1
2023-07-01 10:55:12.626164881 [W:onnxruntime:, model.cc:183 Model] ONNX Runtime only *guarantees* support for models stamped with opset version 7 or above for opset domain 'ai.onnx'. Please upgrade your model to opset 7 or higher. For now, this opset 1 model may run depending upon legacy support of some older opset version operators.
2023-07-01 10:55:12.626452322 [W:onnxruntime:, ort_transpose_optimizer.cc:24 ApplyImpl] Transpose optimizer failed: Unsupported ONNX opset: 1
2023-07-01 10:55:12.824904112 [W:onnxruntime:, model.cc:183 Model] ONNX Runtime only *guarantees* support for models stamped with opset version 7 or above for opset domain 'ai.onnx'. Please upgrade your model to opset 7 or higher. For now, this opset 1 model may run depending upon legacy support of some older opset version operators.
2023-07-01 10:55:12.825170974 [W:onnxruntime:, ort_transpose_optimizer.cc:24 ApplyImpl] Transpose optimizer failed: Unsupported ONNX opset: 1
2023-07-01 10:55:13.001490506 [W:onnxruntime:, model.cc:183 Model] ONNX Runtime only *guarantees* support for models stamped with opset version 7 or above for opset domain 'ai.onnx'. Please upgrade your model to opset 7 or higher. For now, this opset 1 model may run depending upon legacy support of some older opset version operators.
2023-07-01 10:55:13.001881928 [W:onnxruntime:, ort_transpose_optimizer.cc:24 ApplyImpl] Transpose optimizer failed: Unsupported ONNX opset: 1
2023-07-01 10:55:13.182445702 [W:onnxruntime:, model.cc:183 Model] ONNX Runtime only *guarantees* support for models stamped with opset version 7 or above for opset domain 'ai.onnx'. Please upgrade your model to opset 7 or higher. For now, this opset 1 model may run depending upon legacy support of some older opset version operators.
2023-07-01 10:55:13.182824473 [W:onnxruntime:, ort_transpose_optimizer.cc:24 ApplyImpl] Transpose optimizer failed: Unsupported ONNX opset: 1
2023-07-01 10:55:13.360154885 [W:onnxruntime:, graph.cc:1283 Graph] Initializer starts appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.
2023-07-01 10:55:13.360182594 [W:onnxruntime:, graph.cc:1283 Graph] Initializer ends appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.
2023-07-01 10:55:13.360192159 [W:onnxruntime:, graph.cc:1283 Graph] Initializer axes appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.
2023-07-01 10:55:13.828243801 [W:onnxruntime:, graph.cc:1283 Graph] Initializer starts appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.
2023-07-01 10:55:13.828265080 [W:onnxruntime:, graph.cc:1283 Graph] Initializer ends appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.
2023-07-01 10:55:13.828271177 [W:onnxruntime:, graph.cc:1283 Graph] Initializer axes appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.
2023-07-01 10:55:14.256960147 [W:onnxruntime:, graph.cc:1283 Graph] Initializer starts appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.
2023-07-01 10:55:14.256981384 [W:onnxruntime:, graph.cc:1283 Graph] Initializer ends appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.
2023-07-01 10:55:14.384909082 [W:onnxruntime:, graph.cc:1283 Graph] Initializer starts appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.
2023-07-01 10:55:14.384928057 [W:onnxruntime:, graph.cc:1283 Graph] Initializer ends appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.
2023-07-01 10:55:14.384933749 [W:onnxruntime:, graph.cc:1283 Graph] Initializer axes appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.
2023-07-01 10:55:14.786366893 [W:onnxruntime:, graph.cc:1283 Graph] Initializer starts appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.
2023-07-01 10:55:14.786389572 [W:onnxruntime:, graph.cc:1283 Graph] Initializer ends appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.
2023-07-01 10:55:14.786394986 [W:onnxruntime:, graph.cc:1283 Graph] Initializer axes appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.
2023-07-01 10:55:15.208355786 [W:onnxruntime:, graph.cc:1283 Graph] Initializer starts appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.
2023-07-01 10:55:15.208392578 [W:onnxruntime:, graph.cc:1283 Graph] Initializer ends appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.
2023-07-01 10:55:15.208404798 [W:onnxruntime:, graph.cc:1283 Graph] Initializer axes appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.
2023-07-01 10:55:15.209389984 [W:onnxruntime:, execution_frame.cc:857 VerifyOutputSizes] Expected shape from model of {20,9,5} does not match actual shape of {20,10,4} for output out
2023-07-01 10:55:15.624473823 [W:onnxruntime:, graph.cc:1283 Graph] Initializer starts appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.
2023-07-01 10:55:15.624496258 [W:onnxruntime:, graph.cc:1283 Graph] Initializer ends appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.
2023-07-01 10:55:15.624502027 [W:onnxruntime:, graph.cc:1283 Graph] Initializer axes appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.
2023-07-01 10:55:16.221291288 [W:onnxruntime:, graph.cc:1283 Graph] Initializer starts appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.
2023-07-01 10:55:16.221313960 [W:onnxruntime:, graph.cc:1283 Graph] Initializer ends appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.
2023-07-01 10:55:16.543084490 [W:onnxruntime:, graph.cc:1283 Graph] Initializer starts appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.
2023-07-01 10:55:16.543106694 [W:onnxruntime:, graph.cc:1283 Graph] Initializer ends appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.
2023-07-01 10:55:16.543112250 [W:onnxruntime:, graph.cc:1283 Graph] Initializer axes appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.
2023-07-01 10:55:17.133941641 [W:onnxruntime:, graph.cc:1283 Graph] Initializer starts appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.
2023-07-01 10:55:17.133962514 [W:onnxruntime:, graph.cc:1283 Graph] Initializer ends appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.
2023-07-01 10:55:17.133968120 [W:onnxruntime:, graph.cc:1283 Graph] Initializer axes appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.
2023-07-01 10:55:17.923953436 [W:onnxruntime:, graph.cc:1283 Graph] Initializer starts appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.
2023-07-01 10:55:17.923979762 [W:onnxruntime:, graph.cc:1283 Graph] Initializer ends appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.
2023-07-01 10:55:17.923988019 [W:onnxruntime:, graph.cc:1283 Graph] Initializer axes appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.
2023-07-01 10:55:18.707745956 [W:onnxruntime:, graph.cc:1283 Graph] Initializer starts appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.
2023-07-01 10:55:18.707767855 [W:onnxruntime:, graph.cc:1283 Graph] Initializer ends appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.
2023-07-01 10:55:19.035065115 [W:onnxruntime:, graph.cc:1283 Graph] Initializer starts appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.
2023-07-01 10:55:19.035086247 [W:onnxruntime:, graph.cc:1283 Graph] Initializer ends appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.
2023-07-01 10:55:19.035091746 [W:onnxruntime:, graph.cc:1283 Graph] Initializer axes appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.
2023-07-01 10:55:19.963091586 [W:onnxruntime:, graph.cc:1283 Graph] Initializer starts appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.
2023-07-01 10:55:19.963115689 [W:onnxruntime:, graph.cc:1283 Graph] Initializer ends appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.
2023-07-01 10:55:19.963120895 [W:onnxruntime:, graph.cc:1283 Graph] Initializer axes appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.
2023-07-01 10:55:20.449319403 [W:onnxruntime:, graph.cc:1283 Graph] Initializer starts appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.
2023-07-01 10:55:20.449344019 [W:onnxruntime:, graph.cc:1283 Graph] Initializer ends appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.
2023-07-01 10:55:20.449349980 [W:onnxruntime:, graph.cc:1283 Graph] Initializer axes appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.
2023-07-01 10:55:20.449354871 [W:onnxruntime:, graph.cc:1283 Graph] Initializer steps appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.
2023-07-01 10:55:20.847609594 [W:onnxruntime:, graph.cc:1283 Graph] Initializer starts appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.
2023-07-01 10:55:20.847662829 [W:onnxruntime:, graph.cc:1283 Graph] Initializer ends appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.
2023-07-01 10:55:20.847685836 [W:onnxruntime:, graph.cc:1283 Graph] Initializer axes appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.
2023-07-01 10:55:20.847705841 [W:onnxruntime:, graph.cc:1283 Graph] Initializer steps appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.