index int64 | repo_name string | branch_name string | path string | content string | import_graph string |
|---|---|---|---|---|---|
59,022 | onnx/onnx | refs/heads/main | /onnx/reference/ops/aionnxml/_op_run_aionnxml.py | # SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0913,W0221
from onnx.reference.op_run import OpRun
class OpRunAiOnnxMl(OpRun):
op_domain = "ai.onnx.ml"
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"/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/stat_coverage.py": ["/onnx/__init__.py", "/onnx/backend/test/case/__init__.py", "/onnx/backend/test/loader/__init__.py", "/onnx/backend/test/runner/__init__.py"], "/onnx/reference/ops/op_constant.py": ["/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/upsample.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_squeeze.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_einsum.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/div.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_one_hot_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_random_uniform.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/test/reference_evaluator_test.py": ["/onnx/__init__.py", "/onnx/checker.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/__init__.py", "/onnx/reference/ops/_op_common_indices.py", "/onnx/reference/ops/_op_list.py", "/onnx/reference/ops/op_celu.py", "/onnx/reference/ops/op_col2im.py", "/onnx/reference/ops/op_conv.py", "/onnx/reference/ops_optimized/__init__.py", "/onnx/reference/ops_optimized/op_conv_optimized.py"], "/onnx/backend/test/cmd_tools.py": ["/onnx/backend/test/case/model/__init__.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/__init__.py"], "/onnx/reference/ops/op_slice.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/aionnxml/op_binarizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/optionalgetelement.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/loop.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/conf.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_sequence_construct.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatterelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel2.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/bernoulli.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/constant.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/resize.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_resize.py"], "/onnx/reference/ops/aionnxml/op_svm_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/op_sequence_map.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatternd.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/numpy_helper_test.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_tfidf_vectorizer.py": ["/onnx/reference/op_run.py"], "/onnx/test/checker_test.py": ["/onnx/defs/__init__.py", "/onnx/__init__.py"], "/onnx/reference/ops/_op_common_random.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/backend/base.py": ["/onnx/checker.py", "/onnx/__init__.py"], "/onnx/backend/test/case/node/reduce_log_sum.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_linear_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_softplus.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_sub.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_quantize_linear.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_gathernd.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/qlinearmatmul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/shape_inference_test.py": ["/onnx/shape_inference.py", "/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/parser.py"], "/onnx/backend/test/case/node/mish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_expand.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": ["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/_helpers.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/tfidfvectorizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_average_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/item.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/gatherelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/slice.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_matmul.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mel_weight_matrix.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cast.py": ["/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/asin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_normalizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/unique.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather_elements.py": ["/onnx/reference/op_run.py"], "/onnx/helper.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/layernormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/groupnormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/__init__.py": ["/onnx/reference/ops/_op_list.py"], "/onnx/reference/ops/op_random_normal.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_hann_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_softmax_cross_entropy_loss.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_string_split.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/max.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/backend/test/case/utils.py"], "/onnx/backend/test/case/model/expand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/backend/test/case/node/erf.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], 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59,023 | onnx/onnx | refs/heads/main | /onnx/tools/replace_constants.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=too-many-statements,too-many-branches
from typing import List, Optional, Union
import numpy as np
from onnx import (
AttributeProto,
FunctionProto,
GraphProto,
ModelProto,
NodeProto,
SparseTensorProto,
TensorProto,
)
from onnx.helper import (
make_attribute,
make_function,
make_graph,
make_model,
make_node,
make_tensor,
make_tensor_value_info,
set_model_props,
tensor_dtype_to_np_dtype,
)
from onnx.numpy_helper import from_array
def _replace_constant(
node: NodeProto, threshold: int, value_constant_of_shape: float
) -> List[NodeProto]:
"""
Replaces a Constant node with a large tensor (with more than threshold elements)
by a sequence of nodes that produces a dummy constant of same shape as original tensor.
"""
if node.op_type != "Constant":
raise TypeError(f"Node type must be 'Constant' not {node.op_type!r}.")
for att in node.attribute:
if att.name == "sparse_value":
raise NotImplementedError(
f"This feature is not yet implemented for a sparse constant "
f"(node name={node.name!r})."
)
if att.name == "value":
value = att.t
new_name = f"{value.name}__SHAPE"
dims = value.dims
size = np.prod(dims)
if size <= threshold:
return [node]
init = from_array(np.array(list(dims), dtype=np.int64), name=new_name)
dtype = tensor_dtype_to_np_dtype(value.data_type)
node_shape = make_node(
"Constant",
[],
[new_name],
value=init,
)
new_node = make_node(
"ConstantOfShape",
[new_name],
node.output,
value=from_array(np.array([value_constant_of_shape], dtype=dtype)),
)
return [node_shape, new_node]
raise NotImplementedError(
f"Replacement of constant with attribute {att.name!r}"
)
return [node]
def _replace_constant_of_shape_with_range(
onx: Union[GraphProto, FunctionProto]
) -> Union[GraphProto, FunctionProto]:
"""
Replaces all *ConstantOfShape* by node *Range* to avoid constant tensors.
The function is not recursive. The recursivity is done by
*replace_initializer_by_constant_of_shape*.
"""
if isinstance(onx, GraphProto):
nodes = list(onx.node)
elif isinstance(onx, FunctionProto):
nodes = list(onx.node)
else:
raise TypeError(f"Not implemented for type {type(onx)}.")
existing_names = set()
for node in nodes:
existing_names |= set(node.input)
existing_names |= set(node.output)
def _find_name(prefix):
if prefix not in existing_names:
existing_names.add(prefix)
return prefix
i = 2
while True:
name = f"{prefix}_{i}"
if name not in existing_names:
existing_names.add(name)
return name
i += 1
# The function should never go through that line.
raise RuntimeError("The function should never go through that line.")
cst0 = make_node("Constant", [], [_find_name("zero")], value_int=0)
cst1 = make_node("Constant", [], [_find_name("one")], value_int=1)
update = {}
for inode, node in enumerate(nodes):
if node.op_type != "ConstantOfShape":
continue
shape = node.input[0]
n = make_node("ReduceProd", [shape], [_find_name(f"{shape}_N")])
a = make_node(
"Range",
[cst0.output[0], n.output[0], cst1.output[0]],
[_find_name(f"{shape}_RANGE")],
)
if len(node.attribute) == 1:
to = node.attribute[0].t.data_type
else:
to = TensorProto.FLOAT
ac = make_node("Cast", [a.output[0]], [_find_name(f"{shape}_RANGEf")], to=to)
cl = make_node("Cast", [n.output[0]], [_find_name(f"{shape}_Nf")], to=to)
d = make_node(
"Div", [ac.output[0], cl.output[0]], [_find_name(f"{shape}_FLAT")]
)
resh = make_node("Reshape", [d.output[0], shape], node.output)
update[inode] = [n, a, ac, cl, d, resh]
for inode, up in sorted(update.items(), reverse=True):
nodes[inode : inode + 1] = up
nodes.insert(0, cst0)
nodes.insert(1, cst1)
if isinstance(onx, GraphProto):
graph = make_graph(
nodes,
onx.name,
onx.input,
onx.output,
initializer=onx.initializer,
sparse_initializer=onx.sparse_initializer,
)
return graph
if isinstance(onx, FunctionProto):
new_onx = make_function(
onx.domain,
onx.name,
onx.input,
onx.output,
nodes,
opset_imports=onx.opset_import,
)
return new_onx
raise TypeError(f"Not implemented for type {type(onx)}.")
def _replace_constant_of_shape_value(
onx: Union[GraphProto, FunctionProto], value_constant_of_shape: float
) -> Union[GraphProto, FunctionProto]:
"""
Replaces all fill value of all nodes *ConstantOfShape*.
*replace_initializer_by_constant_of_shape*.
"""
if isinstance(onx, GraphProto):
nodes = list(onx.node)
elif isinstance(onx, FunctionProto):
nodes = list(onx.node)
else:
raise TypeError(f"Not implemented for type {type(onx)}.")
existing_names = set()
for node in nodes:
existing_names |= set(node.input)
existing_names |= set(node.output)
update = {}
for inode, node in enumerate(nodes):
if node.op_type != "ConstantOfShape":
continue
tensor = node.attribute[0].t
new_tensor = make_tensor(
tensor.name, tensor.data_type, [1], [value_constant_of_shape]
)
new_node = make_node("ConstantOfShape", node.input, node.output)
att = make_attribute(node.attribute[0].name, value=new_tensor)
new_node.attribute.append(att)
update[inode] = new_node
for inode, up in update.items():
nodes[inode] = up
if isinstance(onx, GraphProto):
graph = make_graph(
nodes,
onx.name,
onx.input,
onx.output,
initializer=onx.initializer,
sparse_initializer=onx.sparse_initializer,
)
return graph
if isinstance(onx, FunctionProto):
new_onx = make_function(
onx.domain,
onx.name,
onx.input,
onx.output,
nodes,
opset_imports=onx.opset_import,
)
return new_onx
raise TypeError(f"Not implemented for type {type(onx)}.")
def replace_initializer_by_constant_of_shape(
onx: Union[FunctionProto, GraphProto, ModelProto],
threshold: int = 128,
ir_version: Optional[int] = None,
use_range: bool = False,
value_constant_of_shape: float = 0.5,
):
"""
Replace initializers or constant node by nodes *ConstantOfShape* to reduce
the size. This reduce the cost to write a unit test about
a specific graph structure.
:param onx: ModelProto
:param threshold: every initializer under this threshold is not impacted
:param ir_version: initializer must be specified as input for `ir_version <= 3`,
this must be specified if onx is :class:`FunctionProto` or :class:`GraphProto`
:param use_range: if uses operator *Range* instead of *ConstantOfShape* to avoid
constant tensors
:param value_constant_of_shape: value to use as a value for all nodes *ConstantOfShape*,
a high value may produce nan or inf predictions
:return: onx, modified ModelProto
The function is designed so that the function can be reapplied on a modified model
and either replace *ConstantOfShape* with *Range* operators, either replace the fill value
for every *ConstantOfShape*.
"""
if isinstance(onx, FunctionProto):
modified = False
new_nodes: List[NodeProto] = []
for node in onx.node:
if node.op_type == "Constant":
cst_nodes = _replace_constant(node, threshold, value_constant_of_shape)
if len(cst_nodes) == 2:
modified = True
new_nodes.extend(cst_nodes)
continue
new_nodes.append(node)
if modified:
new_onx = make_function(
onx.domain,
onx.name,
onx.input,
onx.output,
new_nodes,
opset_imports=onx.opset_import,
)
if use_range:
return _replace_constant_of_shape_with_range(new_onx)
if value_constant_of_shape != 1:
return _replace_constant_of_shape_value(
new_onx, value_constant_of_shape
)
return new_onx
if use_range:
return _replace_constant_of_shape_with_range(onx)
if value_constant_of_shape != 1:
return _replace_constant_of_shape_value(onx, value_constant_of_shape)
return onx
if isinstance(onx, ModelProto):
new_graph = replace_initializer_by_constant_of_shape(
onx.graph,
ir_version=ir_version or onx.ir_version,
threshold=threshold,
use_range=use_range,
value_constant_of_shape=value_constant_of_shape,
)
new_functions = [
replace_initializer_by_constant_of_shape(
f,
threshold=threshold,
ir_version=ir_version or onx.ir_version,
use_range=use_range,
value_constant_of_shape=value_constant_of_shape,
)
for f in onx.functions
]
model = make_model(
new_graph,
functions=new_functions,
producer_name=onx.producer_name,
producer_version=onx.producer_version,
ir_version=ir_version or onx.ir_version,
doc_string=onx.doc_string,
domain=onx.domain,
model_version=onx.model_version,
)
if len(onx.metadata_props) > 0: # pragma: no cover
values = {p.key: p.value for p in onx.metadata_props}
set_model_props(model, values)
del model.opset_import[:] # pylint: disable=E1101
for oimp in onx.opset_import:
op_set = model.opset_import.add() # pylint: disable=E1101
if oimp.domain == "" and oimp.version < 11 and use_range:
raise RuntimeError(
f"Range was introduced in opset 11 but opset is {oimp.version}."
)
if oimp.domain == "" and oimp.version < 9:
raise RuntimeError(
f"ConstantOfShape was introduced in "
f"opset 9 but opset is {oimp.version}."
)
op_set.domain = oimp.domain
op_set.version = oimp.version
return model
if not isinstance(onx, GraphProto):
raise TypeError(f"onx should be a GraphProto at this stage not {type(onx)}.")
n_modifications = 0
new_nodes = []
removed = set()
additional_inputs = []
new_inits: List[TensorProto] = []
for init in onx.initializer:
dims = tuple(init.dims)
size = np.prod(dims)
if size <= threshold:
new_inits.append(init)
continue
n_modifications += 1
new_name = f"{init.name}__SHAPE"
new_inits.append(
from_array(np.array(list(dims), dtype=np.int64), name=new_name)
)
dtype = tensor_dtype_to_np_dtype(init.data_type)
node = make_node(
"ConstantOfShape",
[new_name],
[init.name],
value=from_array(np.array([0.5], dtype=dtype)),
)
new_nodes.append(node)
removed.add(init.name)
if ir_version is not None and ir_version <= 3:
additional_inputs.append(
make_tensor_value_info(new_name, TensorProto.INT64, [len(dims)])
)
new_sparse_inits: List[SparseTensorProto] = []
for sp_init in onx.sparse_initializer:
dims = tuple(sp_init.dims)
size = np.prod(dims)
if size <= threshold:
new_sparse_inits.append(sp_init)
continue
raise NotImplementedError(
f"This feature is not yet implemented for a sparse initializer "
f"(indices.name={sp_init.indices.name!r}, "
f"values.name={sp_init.values.name!r})."
)
for node in onx.node:
if node.op_type == "Constant":
shape_nodes = _replace_constant(node, threshold, value_constant_of_shape)
if len(shape_nodes) == 2:
n_modifications += 1
new_nodes.extend(shape_nodes)
continue
modified = False
atts = []
for att in node.attribute:
if (
att.type == AttributeProto.GRAPH
and hasattr(att, "g")
and att.g is not None
):
g = replace_initializer_by_constant_of_shape(
att.g,
threshold=threshold,
ir_version=ir_version,
use_range=use_range,
value_constant_of_shape=value_constant_of_shape,
)
if id(g) != id(att.g):
modified = True
att = make_attribute(att.name, g)
atts.append(att)
if modified:
new_node = make_node(node.op_type, node.input, node.output)
new_node.attribute.extend(atts)
new_nodes.append(new_node)
n_modifications += 1
else:
new_nodes.append(node)
if n_modifications > 0:
graph = make_graph(
new_nodes,
onx.name,
[i for i in onx.input if i.name not in removed] + additional_inputs,
onx.output,
initializer=new_inits,
sparse_initializer=new_sparse_inits,
)
if use_range:
return _replace_constant_of_shape_with_range(graph)
if value_constant_of_shape != 1:
return _replace_constant_of_shape_value(graph, value_constant_of_shape)
return graph
if use_range:
return _replace_constant_of_shape_with_range(onx)
if value_constant_of_shape != 1:
return _replace_constant_of_shape_value(onx, value_constant_of_shape)
return onx
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59,024 | onnx/onnx | refs/heads/main | /onnx/defs/__init__.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
__all__ = [
"C",
"ONNX_DOMAIN",
"ONNX_ML_DOMAIN",
"AI_ONNX_PREVIEW_TRAINING_DOMAIN",
"has",
"get_schema",
"get_all_schemas",
"get_all_schemas_with_history",
"onnx_opset_version",
"get_function_ops",
"OpSchema",
"SchemaError",
]
from typing import List
import onnx.onnx_cpp2py_export.defs as C # noqa: N812
from onnx import AttributeProto, FunctionProto
ONNX_DOMAIN = ""
ONNX_ML_DOMAIN = "ai.onnx.ml"
AI_ONNX_PREVIEW_TRAINING_DOMAIN = "ai.onnx.preview.training"
has = C.has_schema
get_schema = C.get_schema
get_all_schemas = C.get_all_schemas
get_all_schemas_with_history = C.get_all_schemas_with_history
def onnx_opset_version() -> int:
"""
Return current opset for domain `ai.onnx`.
"""
return C.schema_version_map()[ONNX_DOMAIN][1]
@property # type: ignore
def _function_proto(self): # type: ignore
func_proto = FunctionProto()
func_proto.ParseFromString(self._function_body) # pylint: disable=protected-access
return func_proto
OpSchema = C.OpSchema # type: ignore
OpSchema.function_body = _function_proto # type: ignore
@property # type: ignore
def _attribute_default_value(self): # type: ignore
attr = AttributeProto()
attr.ParseFromString(self._default_value) # pylint: disable=protected-access
return attr
OpSchema.Attribute.default_value = _attribute_default_value # type: ignore
def _op_schema_repr(self) -> str:
return f"""\
OpSchema(
name={self.name!r},
domain={self.domain!r},
since_version={self.since_version!r},
doc={self.doc!r},
type_constraints={self.type_constraints!r},
inputs={self.inputs!r},
outputs={self.outputs!r},
attributes={self.attributes!r}
)"""
OpSchema.__repr__ = _op_schema_repr # type: ignore
def _op_schema_formal_parameter_repr(self) -> str:
return (
f"OpSchema.FormalParameter(name={self.name!r}, type_str={self.type_str!r}, "
f"description={self.description!r}, param_option={self.option!r}, "
f"is_homogeneous={self.is_homogeneous!r}, min_arity={self.min_arity!r}, "
f"differentiation_category={self.differentiation_category!r})"
)
OpSchema.FormalParameter.__repr__ = _op_schema_formal_parameter_repr # type: ignore
def _op_schema_type_constraint_param_repr(self) -> str:
return (
f"OpSchema.TypeConstraintParam(type_param_str={self.type_param_str!r}, "
f"allowed_type_strs={self.allowed_type_strs!r}, description={self.description!r})"
)
OpSchema.TypeConstraintParam.__repr__ = _op_schema_type_constraint_param_repr # type: ignore
def _op_schema_attribute_repr(self) -> str:
return (
f"OpSchema.Attribute(name={self.name!r}, type={self.type!r}, description={self.description!r}, "
f"default_value={self.default_value!r}, required={self.required!r})"
)
OpSchema.Attribute.__repr__ = _op_schema_attribute_repr # type: ignore
def get_function_ops() -> List[OpSchema]:
"""
Return operators defined as functions.
"""
schemas = C.get_all_schemas()
return [schema for schema in schemas if schema.has_function or schema.has_context_dependent_function] # type: ignore
SchemaError = C.SchemaError
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"/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
59,025 | onnx/onnx | refs/heads/main | /onnx/bin/checker.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import argparse
from onnx import NodeProto, checker, load
def check_model() -> None:
parser = argparse.ArgumentParser("check-model")
parser.add_argument("model_pb", type=argparse.FileType("rb"))
args = parser.parse_args()
model = load(args.model_pb)
checker.check_model(model)
def check_node() -> None:
parser = argparse.ArgumentParser("check-node")
parser.add_argument("node_pb", type=argparse.FileType("rb"))
args = parser.parse_args()
node = NodeProto()
node.ParseFromString(args.node_pb.read())
checker.check_node(node)
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59,026 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_dft.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0913,W0221
from typing import Sequence
import numpy as np
from onnx.reference.op_run import OpRun
def _fft(x: np.ndarray, fft_length: Sequence[int], axis: int) -> np.ndarray:
if fft_length is None:
fft_length = [x.shape[axis]]
try:
ft = np.fft.fft(x, fft_length[0], axis=axis)
except TypeError:
# numpy 1.16.6, an array cannot be a key in the dictionary
# fixed in numpy 1.21.5.
ft = np.fft.fft(x, int(fft_length[0]), axis=axis)
r = np.real(ft)
i = np.imag(ft)
merged = np.vstack([r[np.newaxis, ...], i[np.newaxis, ...]])
perm = np.arange(len(merged.shape))
perm[:-1] = perm[1:]
perm[-1] = 0
tr = np.transpose(merged, list(perm))
if tr.shape[-1] != 2:
raise RuntimeError(
f"Unexpected shape {tr.shape}, x.shape={x.shape} "
f"fft_length={fft_length}."
)
return tr
def _cfft(
x: np.ndarray,
fft_length: Sequence[int],
axis: int,
onesided: bool = False,
normalize: bool = False,
) -> np.ndarray:
if x.shape[-1] == 1:
tmp = x
else:
slices = [slice(0, x) for x in x.shape]
slices[-1] = slice(0, x.shape[-1], 2)
real = x[tuple(slices)]
slices[-1] = slice(1, x.shape[-1], 2)
imag = x[tuple(slices)]
tmp = real + 1j * imag
c = np.squeeze(tmp, -1)
res = _fft(c, fft_length, axis=axis)
if onesided:
slices = [slice(0, a) for a in res.shape]
slices[axis] = slice(0, res.shape[axis] // 2 + 1)
res = res[tuple(slices)] # type: ignore
if normalize:
if len(fft_length) == 1:
res /= fft_length[0]
else:
raise NotImplementedError(
f"normalize=True not implemented for fft_length={fft_length}."
)
return res
def _ifft(
x: np.ndarray, fft_length: Sequence[int], axis: int = -1, onesided: bool = False
) -> np.ndarray:
ft = np.fft.ifft(x, fft_length[0], axis=axis)
r = np.real(ft)
i = np.imag(ft)
merged = np.vstack([r[np.newaxis, ...], i[np.newaxis, ...]])
perm = np.arange(len(merged.shape))
perm[:-1] = perm[1:]
perm[-1] = 0
tr = np.transpose(merged, list(perm))
if tr.shape[-1] != 2:
raise RuntimeError(
f"Unexpected shape {tr.shape}, x.shape={x.shape} "
f"fft_length={fft_length}."
)
if onesided:
slices = [slice(a) for a in tr.shape]
slices[axis] = slice(0, tr.shape[axis] // 2 + 1)
return tr[tuple(slices)] # type: ignore
return tr
def _cifft(
x: np.ndarray, fft_length: Sequence[int], axis: int = -1, onesided: bool = False
) -> np.ndarray:
if x.shape[-1] == 1:
tmp = x
else:
slices = [slice(0, x) for x in x.shape]
slices[-1] = slice(0, x.shape[-1], 2)
real = x[tuple(slices)]
slices[-1] = slice(1, x.shape[-1], 2)
imag = x[tuple(slices)]
tmp = real + 1j * imag
c = np.squeeze(tmp, -1)
return _ifft(c, fft_length, axis=axis, onesided=onesided)
class DFT(OpRun):
def _run(self, x, dft_length=None, axis=None, inverse=None, onesided=None): # type: ignore
if dft_length is None:
dft_length = np.array([x.shape[axis]], dtype=np.int64)
if inverse: # type: ignore
res = _cifft(x, dft_length, axis=axis, onesided=onesided)
else:
res = _cfft(x, dft_length, axis=axis, onesided=onesided)
return (res.astype(x.dtype),)
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["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/_helpers.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/tfidfvectorizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_average_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/item.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/gatherelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/slice.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_matmul.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mel_weight_matrix.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cast.py": ["/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/asin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_normalizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/unique.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather_elements.py": ["/onnx/reference/op_run.py"], "/onnx/helper.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/layernormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/groupnormalization.py": 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59,027 | onnx/onnx | refs/heads/main | /onnx/test/data_propagation_test.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import unittest
# TODO: remove the following ignore after mypy upgrade in ONNX
from shape_inference_test import TestShapeInferenceHelper
import onnx.parser
from onnx import TensorProto
from onnx.helper import make_node, make_tensor, make_tensor_value_info
class TestDataPropagation(TestShapeInferenceHelper):
def test_expand_symbolic_input(self) -> None:
graph = self._make_graph(
[("x", TensorProto.INT32, (3, 1, 2)), ("y", TensorProto.INT32, (1, 4, 2))],
[
make_node("Shape", ["y"], ["shape"]),
make_node("Expand", ["x", "shape"], ["z"]),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("shape", TensorProto.INT64, (3,)),
make_tensor_value_info("z", TensorProto.INT32, (3, 4, 2)),
],
data_prop=True,
)
def test_constantofshape_with_symbolic_shape(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (3, 4, 5))],
[
make_node("Shape", ["x"], ["shape"]),
make_node(
"ConstantOfShape",
["shape"],
["y"],
value=make_tensor("value", TensorProto.INT32, (1,), (2,)),
),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("shape", TensorProto.INT64, (3,)),
make_tensor_value_info("y", TensorProto.INT32, (3, 4, 5)),
],
data_prop=True,
) # type: ignore
def test_model_data_propagation(self) -> None:
"""Infer the shape of z by propagating the value of xshape."""
model = onnx.parser.parse_model(
"""
<ir_version: 7, opset_import: [ "" : 18]>
agraph (float[4, 1, 16] x, float[1, 8, 16] y) => () {
xshape = Shape (x)
z = Expand (y, xshape)
}
"""
)
self._assert_inferred(
model,
[
make_tensor_value_info("xshape", TensorProto.INT64, (3,)),
make_tensor_value_info("z", TensorProto.FLOAT, (4, 8, 16)),
],
data_prop=True,
)
def test_data_prop_via_function(self) -> None:
"""Test value-propagation through function calls.
Underlying core example is same as previous test_model_data_propagation.
"""
model = onnx.parser.parse_model(
"""
<ir_version: 7, opset_import: [ "" : 18, "local" : 1 ]>
agraph (float[4, 1, 16] x, float[1, 8, 16] y) => () {
xshape = local.GetShape (x)
z = Expand (y, xshape)
}
<domain: "local", opset_import: [ "" : 18 ]>
GetShape (x) => (shapeval) {
shapeval = Shape(x)
}
"""
)
self._assert_inferred(
model,
[
make_tensor_value_info("xshape", TensorProto.INT64, (3,)),
make_tensor_value_info("z", TensorProto.FLOAT, (4, 8, 16)),
],
data_prop=True,
)
def test_multiple_calls_to_function(self) -> None:
"""Test value-propagation handles multiple calls to same function correctly.
Underlying core example is same as previous test_model_data_propagation.
"""
model = onnx.parser.parse_model(
"""
<ir_version: 7, opset_import: [ "" : 18, "local" : 1 ]>
agraph (float[4, 1, 16] x, float[1, 8, 16] y) => () {
yshape = local.GetShape (y)
xshape = local.GetShape (x)
z = Expand (y, xshape)
w = Expand (y, yshape)
}
<domain: "local", opset_import: [ "" : 18 ]>
GetShape (x) => (shapeval) {
shapeval = Shape(x)
}
"""
)
self._assert_inferred(
model,
[
make_tensor_value_info("yshape", TensorProto.INT64, (3,)),
make_tensor_value_info("xshape", TensorProto.INT64, (3,)),
make_tensor_value_info("z", TensorProto.FLOAT, (4, 8, 16)),
make_tensor_value_info("w", TensorProto.FLOAT, (1, 8, 16)),
],
data_prop=True,
)
if __name__ == "__main__":
unittest.main()
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["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/__init__.py": ["/onnx/reference/ops/_op_list.py"], "/onnx/reference/ops/op_random_normal.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_hann_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_softmax_cross_entropy_loss.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_string_split.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/max.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/backend/test/case/utils.py"], "/onnx/backend/test/case/model/expand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/backend/test/case/node/erf.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
59,028 | onnx/onnx | refs/heads/main | /onnx/reference/ops/_op_list.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=C0415,R0912,R0913,R0914,R0915,W0611,W0603
"""
Every class imported in this module defines an implementation of
an operator of the main domain. Any class name uses `_` to specify a
version defined in a specific opset. The class name without `_`
defines the current implementation. If an operator has no class
with `_`, it means the implementation is valid for every opset.
The operator may have been updated to support more types but that
did not change the implementation.
"""
import textwrap
from typing import Any, Dict, List
from typing import Optional as TOptional
from typing import Union
from onnx import FunctionProto, NodeProto, TypeProto
from onnx.defs import get_schema, onnx_opset_version
from onnx.onnx_cpp2py_export.defs import SchemaError
from onnx.reference.op_run import (
OpFunction,
OpRun,
RuntimeContextError,
RuntimeImplementationError,
_split_class_name,
)
from onnx.reference.ops._helpers import build_registered_operators_any_domain
from onnx.reference.ops.op_abs import Abs
from onnx.reference.ops.op_acos import Acos
from onnx.reference.ops.op_acosh import Acosh
from onnx.reference.ops.op_add import Add
from onnx.reference.ops.op_affine_grid import AffineGrid
from onnx.reference.ops.op_and import And
from onnx.reference.ops.op_argmax import ArgMax_1, ArgMax_12
from onnx.reference.ops.op_argmin import ArgMin_1, ArgMin_12
from onnx.reference.ops.op_asin import Asin
from onnx.reference.ops.op_asinh import Asinh
from onnx.reference.ops.op_atan import Atan
from onnx.reference.ops.op_atanh import Atanh
from onnx.reference.ops.op_attribute_has_value import AttributeHasValue
from onnx.reference.ops.op_average_pool import (
AveragePool_1,
AveragePool_7,
AveragePool_11,
AveragePool_19,
)
from onnx.reference.ops.op_batch_normalization import (
BatchNormalization_6,
BatchNormalization_9,
BatchNormalization_14,
)
from onnx.reference.ops.op_bernoulli import Bernoulli
from onnx.reference.ops.op_bitshift import BitShift
from onnx.reference.ops.op_bitwise_and import BitwiseAnd
from onnx.reference.ops.op_bitwise_not import BitwiseNot
from onnx.reference.ops.op_bitwise_or import BitwiseOr
from onnx.reference.ops.op_bitwise_xor import BitwiseXor
from onnx.reference.ops.op_blackman_window import BlackmanWindow
from onnx.reference.ops.op_cast import Cast_1, Cast_19
from onnx.reference.ops.op_cast_like import CastLike_15, CastLike_19
from onnx.reference.ops.op_ceil import Ceil
from onnx.reference.ops.op_celu import Celu
from onnx.reference.ops.op_center_crop_pad import CenterCropPad
from onnx.reference.ops.op_clip import Clip_6, Clip_11
from onnx.reference.ops.op_col2im import Col2Im
from onnx.reference.ops.op_compress import Compress
from onnx.reference.ops.op_concat import Concat
from onnx.reference.ops.op_concat_from_sequence import ConcatFromSequence
from onnx.reference.ops.op_constant import (
Constant_1,
Constant_9,
Constant_11,
Constant_12,
)
from onnx.reference.ops.op_constant_of_shape import ConstantOfShape
from onnx.reference.ops.op_conv import Conv
from onnx.reference.ops.op_conv_integer import ConvInteger
from onnx.reference.ops.op_conv_transpose import ConvTranspose
from onnx.reference.ops.op_cos import Cos
from onnx.reference.ops.op_cosh import Cosh
from onnx.reference.ops.op_cum_sum import CumSum
from onnx.reference.ops.op_deform_conv import DeformConv
from onnx.reference.ops.op_depth_to_space import DepthToSpace
from onnx.reference.ops.op_dequantize_linear import DequantizeLinear
from onnx.reference.ops.op_det import Det
from onnx.reference.ops.op_dft import DFT
from onnx.reference.ops.op_div import Div
from onnx.reference.ops.op_dropout import Dropout_7, Dropout_12
from onnx.reference.ops.op_dynamic_quantize_linear import DynamicQuantizeLinear
from onnx.reference.ops.op_einsum import Einsum
from onnx.reference.ops.op_elu import Elu
from onnx.reference.ops.op_equal import Equal
from onnx.reference.ops.op_erf import Erf
from onnx.reference.ops.op_exp import Exp
from onnx.reference.ops.op_expand import Expand
from onnx.reference.ops.op_eyelike import EyeLike
from onnx.reference.ops.op_flatten import Flatten
from onnx.reference.ops.op_floor import Floor
from onnx.reference.ops.op_gather import Gather
from onnx.reference.ops.op_gather_elements import GatherElements
from onnx.reference.ops.op_gathernd import GatherND
from onnx.reference.ops.op_gemm import Gemm_6, Gemm_7
from onnx.reference.ops.op_global_average_pool import GlobalAveragePool
from onnx.reference.ops.op_global_max_pool import GlobalMaxPool
from onnx.reference.ops.op_greater import Greater
from onnx.reference.ops.op_greater_or_equal import GreaterOrEqual
from onnx.reference.ops.op_grid_sample import GridSample
from onnx.reference.ops.op_gru import GRU
from onnx.reference.ops.op_hamming_window import HammingWindow
from onnx.reference.ops.op_hann_window import HannWindow
from onnx.reference.ops.op_hard_sigmoid import HardSigmoid
from onnx.reference.ops.op_hardmax import Hardmax
from onnx.reference.ops.op_identity import Identity
from onnx.reference.ops.op_if import If
from onnx.reference.ops.op_image_decoder import ImageDecoder
from onnx.reference.ops.op_instance_normalization import InstanceNormalization
from onnx.reference.ops.op_isinf import IsInf
from onnx.reference.ops.op_isnan import IsNaN
from onnx.reference.ops.op_layer_normalization import LayerNormalization
from onnx.reference.ops.op_leaky_relu import LeakyRelu
from onnx.reference.ops.op_less import Less
from onnx.reference.ops.op_less_or_equal import LessOrEqual
from onnx.reference.ops.op_log import Log
from onnx.reference.ops.op_log_softmax import LogSoftmax
from onnx.reference.ops.op_loop import Loop
from onnx.reference.ops.op_lp_normalization import LpNormalization
from onnx.reference.ops.op_lp_pool import LpPool
from onnx.reference.ops.op_lrn import LRN
from onnx.reference.ops.op_lstm import LSTM
from onnx.reference.ops.op_matmul import MatMul
from onnx.reference.ops.op_matmul_integer import MatMulInteger
from onnx.reference.ops.op_max import Max
from onnx.reference.ops.op_max_pool import MaxPool
from onnx.reference.ops.op_max_unpool import MaxUnpool
from onnx.reference.ops.op_mean import Mean
from onnx.reference.ops.op_mel_weight_matrix import MelWeightMatrix
from onnx.reference.ops.op_min import Min
from onnx.reference.ops.op_mod import Mod
from onnx.reference.ops.op_mul import Mul
from onnx.reference.ops.op_neg import Neg
from onnx.reference.ops.op_negative_log_likelihood_loss import NegativeLogLikelihoodLoss
from onnx.reference.ops.op_non_max_suppression import NonMaxSuppression
from onnx.reference.ops.op_non_zero import NonZero
from onnx.reference.ops.op_not import Not
from onnx.reference.ops.op_one_hot import OneHot
from onnx.reference.ops.op_optional import Optional
from onnx.reference.ops.op_optional_get_element import OptionalGetElement
from onnx.reference.ops.op_optional_has_element import OptionalHasElement
from onnx.reference.ops.op_or import Or
from onnx.reference.ops.op_pad import Pad_1, Pad_2, Pad_11, Pad_18
from onnx.reference.ops.op_pow import Pow
from onnx.reference.ops.op_prelu import PRelu
from onnx.reference.ops.op_qlinear_conv import QLinearConv
from onnx.reference.ops.op_qlinear_matmul import QLinearMatMul
from onnx.reference.ops.op_quantize_linear import QuantizeLinear_10, QuantizeLinear_19
from onnx.reference.ops.op_random_normal import RandomNormal
from onnx.reference.ops.op_random_normal_like import RandomNormalLike
from onnx.reference.ops.op_random_uniform import RandomUniform
from onnx.reference.ops.op_random_uniform_like import RandomUniformLike
from onnx.reference.ops.op_range import Range
from onnx.reference.ops.op_reciprocal import Reciprocal
from onnx.reference.ops.op_reduce_l1 import ReduceL1_1, ReduceL1_18
from onnx.reference.ops.op_reduce_l2 import ReduceL2_1, ReduceL2_18
from onnx.reference.ops.op_reduce_log_sum import ReduceLogSum_1, ReduceLogSum_18
from onnx.reference.ops.op_reduce_log_sum_exp import (
ReduceLogSumExp_1,
ReduceLogSumExp_18,
)
from onnx.reference.ops.op_reduce_max import ReduceMax_1, ReduceMax_18
from onnx.reference.ops.op_reduce_mean import ReduceMean_1, ReduceMean_18
from onnx.reference.ops.op_reduce_min import ReduceMin_1, ReduceMin_18
from onnx.reference.ops.op_reduce_prod import ReduceProd_1, ReduceProd_18
from onnx.reference.ops.op_reduce_sum import ReduceSum_1, ReduceSum_13
from onnx.reference.ops.op_reduce_sum_square import (
ReduceSumSquare_1,
ReduceSumSquare_18,
)
from onnx.reference.ops.op_regex_full_match import RegexFullMatch
from onnx.reference.ops.op_relu import Relu
from onnx.reference.ops.op_reshape import Reshape_5, Reshape_14
from onnx.reference.ops.op_resize import Resize
from onnx.reference.ops.op_reverse_sequence import ReverseSequence
from onnx.reference.ops.op_rnn import RNN_7, RNN_14
from onnx.reference.ops.op_roi_align import RoiAlign
from onnx.reference.ops.op_round import Round
from onnx.reference.ops.op_scan import Scan
from onnx.reference.ops.op_scatter_elements import ScatterElements
from onnx.reference.ops.op_scatternd import ScatterND
from onnx.reference.ops.op_selu import Selu
from onnx.reference.ops.op_sequence_at import SequenceAt
from onnx.reference.ops.op_sequence_construct import SequenceConstruct
from onnx.reference.ops.op_sequence_empty import SequenceEmpty
from onnx.reference.ops.op_sequence_erase import SequenceErase
from onnx.reference.ops.op_sequence_insert import SequenceInsert
from onnx.reference.ops.op_sequence_length import SequenceLength
from onnx.reference.ops.op_sequence_map import SequenceMap
from onnx.reference.ops.op_shape import Shape_1, Shape_15
from onnx.reference.ops.op_shrink import Shrink
from onnx.reference.ops.op_sigmoid import Sigmoid
from onnx.reference.ops.op_sign import Sign
from onnx.reference.ops.op_sin import Sin
from onnx.reference.ops.op_sinh import Sinh
from onnx.reference.ops.op_size import Size
from onnx.reference.ops.op_slice import Slice_1, Slice_10
from onnx.reference.ops.op_softmax import Softmax
from onnx.reference.ops.op_softmax_cross_entropy_loss import SoftmaxCrossEntropyLoss
from onnx.reference.ops.op_softplus import Softplus
from onnx.reference.ops.op_softsign import Softsign
from onnx.reference.ops.op_space_to_depth import SpaceToDepth
from onnx.reference.ops.op_split import Split_2, Split_11, Split_13, Split_18
from onnx.reference.ops.op_split_to_sequence import SplitToSequence
from onnx.reference.ops.op_sqrt import Sqrt
from onnx.reference.ops.op_squeeze import Squeeze_1, Squeeze_11, Squeeze_13
from onnx.reference.ops.op_stft import STFT
from onnx.reference.ops.op_string_concat import StringConcat
from onnx.reference.ops.op_string_normalizer import StringNormalizer
from onnx.reference.ops.op_string_split import StringSplit
from onnx.reference.ops.op_sub import Sub
from onnx.reference.ops.op_sum import Sum
from onnx.reference.ops.op_tan import Tan
from onnx.reference.ops.op_tanh import Tanh
from onnx.reference.ops.op_tfidf_vectorizer import TfIdfVectorizer
from onnx.reference.ops.op_thresholded_relu import ThresholdedRelu
from onnx.reference.ops.op_tile import Tile
from onnx.reference.ops.op_topk import TopK_1, TopK_10, TopK_11
from onnx.reference.ops.op_transpose import Transpose
from onnx.reference.ops.op_trilu import Trilu
from onnx.reference.ops.op_unique import Unique
from onnx.reference.ops.op_unsqueeze import Unsqueeze_1, Unsqueeze_11, Unsqueeze_13
from onnx.reference.ops.op_upsample import Upsample
from onnx.reference.ops.op_where import Where
from onnx.reference.ops.op_xor import Xor
def _build_registered_operators() -> Dict[str, Dict[Union[int, None], OpRun]]:
return build_registered_operators_any_domain(globals().copy())
def load_op(
domain: str,
op_type: str,
version: Union[None, int] = None,
custom: Any = None,
node: Union[None, NodeProto] = None,
input_types: Union[None, List[TypeProto]] = None,
expand: bool = False,
) -> Any:
"""
Loads the implemented for a specified operator.
:param domain: domain
:param op_type: oprator type
:param version: requested version
:param custom: custom implementation (like a function)
:param node: used if no implementation was found and the operator defines a function
which is context dependant
:param input_types: used if no implementation was found and the operator defines a function
which is context dependant
:param expand: use the function implemented in the schema instead
of its reference implementation
:return: class
"""
global _registered_operators
schema = None
if _registered_operators is None:
_registered_operators = _build_registered_operators() # type: ignore[assignment]
if custom is not None:
return lambda *args: OpFunction(*args, impl=custom) # type: ignore
if version is None:
version = onnx_opset_version()
if domain != "":
raise ValueError(f"Domain must be '' not {domain!r}.")
if op_type in _registered_operators and not expand: # type: ignore
found = True
else:
# maybe the operator can be replacted by a function
try:
schema = get_schema(op_type, version, domain) # type: ignore
except SchemaError:
raise NotImplementedError( # pylint: disable=W0707
f"No registered schema for operator {op_type!r} "
f"and domain {domain!r}. Did you recompile the sources after updating the repository?"
) from None
if schema.has_function: # type: ignore
from onnx.reference import ReferenceEvaluator
body = schema.function_body # type: ignore
sess = ReferenceEvaluator(body)
return lambda *args, sess=sess: OpFunction(*args, impl=sess) # type: ignore
if schema.has_context_dependent_function: # type: ignore
if node is None or input_types is None:
raise RuntimeContextError(
f"No registered implementation for operator {op_type!r} "
f"and domain {domain!r}, the operator has a context dependent function. "
f"but argument node or input_types is not defined (input_types={input_types})."
)
from onnx.reference import ReferenceEvaluator
body = schema.get_context_dependent_function( # type: ignore
node.SerializeToString(), [it.SerializeToString() for it in input_types]
)
proto = FunctionProto()
proto.ParseFromString(body)
sess = ReferenceEvaluator(proto)
return lambda *args, sess=sess: OpFunction(*args, impl=sess) # type: ignore
found = False
if not found:
available = "\n".join(textwrap.wrap(", ".join(sorted(_registered_operators)))) # type: ignore
has_function = schema.has_function if schema else None # type: ignore
has_context_dependent_function = (
schema.has_context_dependent_function if schema else None # type: ignore
)
raise RuntimeImplementationError(
f"No registered implementation for operator {op_type!r} "
f"and domain {domain!r}, schema.has_function is {has_function}, "
f"schema.has_context_dependent_function is {has_context_dependent_function}. "
f"You may either add one or skip the test in "
f"'reference_evaluator_bakcend_test.py'. Available implementations:\n{available}"
)
impl = _registered_operators[op_type] # type: ignore
if None not in impl:
raise RuntimeError(
f"No default implementation for operator {op_type!r} "
f"and domain {domain!r}, found "
f"{', '.join(map(str, impl))}."
)
if version is None or len(impl) == 1:
cl = impl[None]
else:
best = -1
for v in impl:
if v is None:
continue
if best < v <= version:
best = v
if best == -1:
raise RuntimeError(
f"No implementation for operator {op_type!r} "
f"domain {domain!r} and version {version!r}, found "
f"{', '.join(map(str, impl))}."
)
cl = impl[best]
if cl is None:
available = "\n".join(textwrap.wrap(", ".join(sorted(_registered_operators)))) # type: ignore
raise ValueError(
f"Not registered implementation for operator {op_type!r}, "
f"domain {domain!r}, and {version!r} in\n{available}"
)
return cl
_registered_operators: TOptional[Dict[str, Dict[Union[int, None], OpRun]]] = None
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59,029 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/model/gradient.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.model import expect
from onnx.defs import AI_ONNX_PREVIEW_TRAINING_DOMAIN, ONNX_DOMAIN
class Gradient(Base):
@staticmethod
def export_gradient_scalar_add() -> None:
add_node = onnx.helper.make_node("Add", ["a", "b"], ["c"], name="my_add")
gradient_node = onnx.helper.make_node(
"Gradient",
["a", "b"],
["dc_da", "dc_db"],
name="my_gradient",
domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN,
xs=["a", "b"],
y="c",
)
a = np.array(1.0).astype(np.float32)
b = np.array(2.0).astype(np.float32)
c = a + b
# dc / da = d(a+b) / da = 1
dc_da = np.array(1).astype(np.float32)
# db / db = d(a+b) / db = 1
dc_db = np.array(1).astype(np.float32)
graph = onnx.helper.make_graph(
nodes=[add_node, gradient_node],
name="GradientOfAdd",
inputs=[
onnx.helper.make_tensor_value_info("a", onnx.TensorProto.FLOAT, []),
onnx.helper.make_tensor_value_info("b", onnx.TensorProto.FLOAT, []),
],
outputs=[
onnx.helper.make_tensor_value_info("c", onnx.TensorProto.FLOAT, []),
onnx.helper.make_tensor_value_info("dc_da", onnx.TensorProto.FLOAT, []),
onnx.helper.make_tensor_value_info("dc_db", onnx.TensorProto.FLOAT, []),
],
)
opsets = [
onnx.helper.make_operatorsetid(ONNX_DOMAIN, 12),
onnx.helper.make_operatorsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1),
]
model = onnx.helper.make_model_gen_version(
graph, producer_name="backend-test", opset_imports=opsets
)
expect(
model, inputs=[a, b], outputs=[c, dc_da, dc_db], name="test_gradient_of_add"
)
@staticmethod
def export_gradient_scalar_add_and_mul() -> None:
add_node = onnx.helper.make_node("Add", ["a", "b"], ["c"], name="my_add")
mul_node = onnx.helper.make_node("Mul", ["c", "a"], ["d"], name="my_mul")
gradient_node = onnx.helper.make_node(
"Gradient",
["a", "b"],
["dd_da", "dd_db"],
name="my_gradient",
domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN,
xs=["a", "b"],
y="d",
)
a = np.array(1.0).astype(np.float32)
b = np.array(2.0).astype(np.float32)
c = a + b
# d = a * c = a * (a + b)
d = a * c
# dd / da = d(a*a+a*b) / da = 2 * a + b
dd_da = (2 * a + b).astype(np.float32)
# dd / db = d(a*a+a*b) / db = a
dd_db = a
graph = onnx.helper.make_graph(
nodes=[add_node, mul_node, gradient_node],
name="GradientOfTwoOperators",
inputs=[
onnx.helper.make_tensor_value_info("a", onnx.TensorProto.FLOAT, []),
onnx.helper.make_tensor_value_info("b", onnx.TensorProto.FLOAT, []),
],
outputs=[
onnx.helper.make_tensor_value_info("d", onnx.TensorProto.FLOAT, []),
onnx.helper.make_tensor_value_info("dd_da", onnx.TensorProto.FLOAT, []),
onnx.helper.make_tensor_value_info("dd_db", onnx.TensorProto.FLOAT, []),
],
)
opsets = [
onnx.helper.make_operatorsetid(ONNX_DOMAIN, 12),
onnx.helper.make_operatorsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1),
]
model = onnx.helper.make_model_gen_version(
graph, producer_name="backend-test", opset_imports=opsets
)
expect(
model,
inputs=[a, b],
outputs=[d, dd_da, dd_db],
name="test_gradient_of_add_and_mul",
)
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"/onnx/backend/test/case/node/loop.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/conf.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_sequence_construct.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatterelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel2.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/bernoulli.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/constant.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/resize.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_resize.py"], "/onnx/reference/ops/aionnxml/op_svm_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/op_sequence_map.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatternd.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/numpy_helper_test.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_tfidf_vectorizer.py": ["/onnx/reference/op_run.py"], "/onnx/test/checker_test.py": ["/onnx/defs/__init__.py", "/onnx/__init__.py"], "/onnx/reference/ops/_op_common_random.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/backend/base.py": ["/onnx/checker.py", "/onnx/__init__.py"], "/onnx/backend/test/case/node/reduce_log_sum.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_linear_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_softplus.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_sub.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_quantize_linear.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_gathernd.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/qlinearmatmul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/shape_inference_test.py": ["/onnx/shape_inference.py", "/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/parser.py"], "/onnx/backend/test/case/node/mish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_expand.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": ["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/_helpers.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/tfidfvectorizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_average_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/item.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/gatherelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/slice.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_matmul.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mel_weight_matrix.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cast.py": ["/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/asin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_normalizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/unique.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather_elements.py": ["/onnx/reference/op_run.py"], "/onnx/helper.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/layernormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/groupnormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/__init__.py": ["/onnx/reference/ops/_op_list.py"], "/onnx/reference/ops/op_random_normal.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_hann_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_softmax_cross_entropy_loss.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_string_split.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/max.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/backend/test/case/utils.py"], "/onnx/backend/test/case/model/expand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/backend/test/case/node/erf.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
59,030 | onnx/onnx | refs/heads/main | /onnx/compose.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=unidiomatic-typecheck
from typing import Dict, List, MutableMapping, Optional, Set, Tuple
from onnx import GraphProto, ModelProto, TensorProto, checker, helper, utils
def check_overlapping_names(
g1: GraphProto, g2: GraphProto, io_map: Optional[List[Tuple[str, str]]] = None
) -> List[Tuple[str, List[str]]]:
"""Checks whether there are name collisions between two graphs
Returns a list of tuples where the first element represents the member containing overlapping names
(One of: "node", "edge", "value_info", "initializer", "sparse_initializer"), and the
second element contains a list of names that appear in both graphs on that category.
Optionally, it takes an io_map, representing the output/inputs to be connected. It provided, overlapping
present in the io_map argument will be ignored.
"""
if type(g1) is not GraphProto:
raise ValueError("g1 argument is not an ONNX graph")
if type(g2) is not GraphProto:
raise ValueError("g2 argument is not an ONNX graph")
def _overlapping(c1: List[str], c2: List[str]) -> List[str]:
return list(set(c1) & set(c2))
def _edge_names(graph: GraphProto, exclude: Optional[Set[str]] = None) -> List[str]:
if exclude is None:
exclude = set()
edges = []
for n in graph.node:
for i in n.input:
if i != "" and i not in exclude:
edges.append(i)
for o in n.output:
if o != "" and o not in exclude:
edges.append(o)
return edges
result = []
if not io_map:
io_map = []
io_map_inputs = {elem[1] for elem in io_map}
# Edges already cover input/output
overlap = _overlapping(_edge_names(g1), _edge_names(g2, exclude=io_map_inputs))
if overlap:
result.append(("edge", overlap))
overlap = _overlapping(
[e.name for e in g1.value_info], [e.name for e in g2.value_info]
)
if overlap:
result.append(("value_info", overlap))
overlap = _overlapping(
[e.name for e in g1.initializer], [e.name for e in g2.initializer]
)
if overlap:
result.append(("initializer", overlap))
overlap = _overlapping(
[e.values.name for e in g1.sparse_initializer],
[e.values.name for e in g2.sparse_initializer],
) + _overlapping(
[e.indices.name for e in g1.sparse_initializer],
[e.indices.name for e in g2.sparse_initializer],
)
if overlap:
result.append(("sparse_initializer", overlap))
return result
def merge_graphs( # pylint: disable=too-many-branches,too-many-statements
g1: GraphProto,
g2: GraphProto,
io_map: List[Tuple[str, str]],
inputs: Optional[List[str]] = None,
outputs: Optional[List[str]] = None,
prefix1: Optional[str] = None,
prefix2: Optional[str] = None,
name: Optional[str] = None,
doc_string: Optional[str] = None,
) -> GraphProto:
"""Combines two ONNX graphs into a single one.
The combined graph is defined by connecting the specified set of outputs/inputs. Those inputs/outputs
not specified in the io_map argument will remain as inputs/outputs of the combined graph.
Arguments:
g1 (GraphProto): First graph
g2 (GraphProto): Second graph
io_map (list of pairs of string): The pairs of names [(out0, in0), (out1, in1), ...]
representing outputs of the first graph and inputs of the second
to be connected
inputs (list of string): Optional list of inputs to be included in the combined graph
By default, all inputs not present in the ``io_map`` argument will be
included in the combined model
outputs (list of string): Optional list of outputs to be included in the combined graph
By default, all outputs not present in the ``io_map`` argument will be
included in the combined model
prefix1 (string): Optional prefix to be added to all names in g1
prefix2 (string): Optional prefix to be added to all names in g2
name (string): Optional name for the combined graph
By default, the name is g1.name and g2.name concatenated with an undescore delimiter
doc_string (string): Optional docstring for the combined graph
If not provided, a default docstring with the concatenation of g1 and g2 docstrings is used
Returns:
GraphProto
"""
if type(g1) is not GraphProto:
raise ValueError("g1 argument is not an ONNX graph")
if type(g2) is not GraphProto:
raise ValueError("g2 argument is not an ONNX graph")
# Prefixing names in the graph if requested, adjusting io_map accordingly
if prefix1 or prefix2:
if prefix1:
g1_copy = GraphProto()
g1_copy.CopyFrom(g1)
g1 = g1_copy
g1 = add_prefix_graph(g1, prefix=prefix1)
if prefix2:
g2_copy = GraphProto()
g2_copy.CopyFrom(g2)
g2 = g2_copy
g2 = add_prefix_graph(g2, prefix=prefix2)
io_map = [
(
prefix1 + io[0] if prefix1 else io[0],
prefix2 + io[1] if prefix2 else io[1],
)
for io in io_map
]
io_map_g1_outs = {io[0] for io in io_map}
io_map_g2_ins = {io[1] for io in io_map}
reversed_io_map = {in_name: out_name for out_name, in_name in io_map}
g1_outs = {o.name for o in g1.output}
g2_ins = {i.name for i in g2.input}
# If necessary extract subgraphs
if inputs or outputs:
if not inputs:
g1_inputs = [i.name for i in g1.input]
g2_inputs = [i.name for i in g2.input]
else:
input_set = set(inputs)
g1_inputs = [i.name for i in g1.input if i.name in input_set]
g2_inputs = [
i.name
for i in g2.input
if i.name in input_set or i.name in io_map_g2_ins
]
if not outputs:
g1_outputs = [o.name for o in g1.input]
g2_outputs = [o.name for o in g2.input]
else:
output_set = set(outputs)
g1_outputs = [
o.name
for o in g1.output
if o.name in output_set or o.name in io_map_g1_outs
]
g2_outputs = [o.name for o in g2.output if o.name in output_set]
if len(g1_inputs) < len(g1.input) or len(g1_outputs) < len(g1.output):
e1 = utils.Extractor(helper.make_model(g1))
g1 = e1.extract_model(g1_inputs, g1_outputs).graph
if len(g2_inputs) < len(g2.input) or len(g2_outputs) < len(g2.output):
e2 = utils.Extractor(helper.make_model(g2))
g2 = e2.extract_model(g2_inputs, g2_outputs).graph
# Check that input/output names specified in the io_map argument are valid input/output names
for g1_out_name, g2_in_name in io_map:
if g1_out_name not in g1_outs:
raise ValueError(f"Output {g1_out_name} is not present in g1")
if g2_in_name not in g2_ins:
raise ValueError(f"Input {g2_in_name} is not present in g2")
# Check for name collision
overlapping_names = check_overlapping_names(g1, g2, io_map)
if len(overlapping_names) > 0:
category, names = overlapping_names[0]
raise ValueError(
"Cant merge two graphs with overlapping names. "
f"Found repeated {category} names: "
+ ", ".join(names)
+ "\n"
+ "Consider using ``onnx.compose.add_prefix`` to add a prefix to names in one of the graphs."
)
g = GraphProto()
g.node.extend(g1.node)
g2_nodes_begin = len(g.node)
g.node.extend(g2.node)
g2_nodes_end = len(g.node)
# Connecting outputs of the first graph with the inputs of the second
for node_idx in range(g2_nodes_begin, g2_nodes_end):
node = g.node[node_idx]
for index, name_ in enumerate(node.input):
if name_ in reversed_io_map:
node.input[index] = reversed_io_map[name_]
if inputs:
input_set = set(inputs)
g.input.extend([i for i in g1.input if i.name in input_set])
g.input.extend([i for i in g2.input if i.name in input_set])
else:
g.input.extend(g1.input)
g.input.extend([i for i in g2.input if i.name not in io_map_g2_ins])
if outputs:
output_set = set(outputs)
g.output.extend([o for o in g1.output if o.name in output_set])
g.output.extend([o for o in g2.output if o.name in output_set])
else:
g.output.extend([o for o in g1.output if o.name not in io_map_g1_outs])
g.output.extend(g2.output)
g.initializer.extend(g1.initializer)
g.initializer.extend(
[init for init in g2.initializer if init.name not in io_map_g2_ins]
)
g.sparse_initializer.extend(g1.sparse_initializer)
g.sparse_initializer.extend(
[
init
for init in g2.sparse_initializer
if init.values.name not in io_map_g2_ins
]
)
g.value_info.extend(g1.value_info)
g.value_info.extend([vi for vi in g2.value_info if vi.name not in io_map_g2_ins])
g.name = name if name is not None else "_".join([g1.name, g2.name])
if doc_string is None:
doc_string = (
f"Graph combining {g1.name} and {g2.name}\n"
+ g1.name
+ "\n\n"
+ g1.doc_string
+ "\n\n"
+ g2.name
+ "\n\n"
+ g2.doc_string
)
g.doc_string = doc_string
return g
def merge_models( # pylint: disable=too-many-branches
m1: ModelProto,
m2: ModelProto,
io_map: List[Tuple[str, str]],
inputs: Optional[List[str]] = None,
outputs: Optional[List[str]] = None,
prefix1: Optional[str] = None,
prefix2: Optional[str] = None,
name: Optional[str] = None,
doc_string: Optional[str] = None,
producer_name: Optional[str] = "onnx.compose.merge_models",
producer_version: Optional[str] = "1.0",
domain: Optional[str] = "",
model_version: Optional[int] = 1,
) -> ModelProto:
"""Combines two ONNX models into a single one.
The combined model is defined by connecting the specified set of outputs/inputs.
Those inputs/outputs not specified in the io_map argument will remain as
inputs/outputs of the combined model.
Both models should have the same IR version, and same operator sets imported.
Arguments:
m1 (ModelProto): First model
m2 (ModelProto): Second model
io_map (list of pairs of string): The pairs of names [(out0, in0), (out1, in1), ...]
representing outputs of the first graph and inputs of the second
to be connected
inputs (list of string): Optional list of inputs to be included in the combined graph
By default, all inputs not present in the ``io_map`` argument will be
included in the combined model
outputs (list of string): Optional list of outputs to be included in the combined graph
By default, all outputs not present in the ``io_map`` argument will be
included in the combined model
prefix1 (string): Optional prefix to be added to all names in m1
prefix2 (string): Optional prefix to be added to all names in m2
name (string): Optional name for the combined graph
By default, the name is g1.name and g2.name concatenated with an undescore delimiter
doc_string (string): Optional docstring for the combined graph
If not provided, a default docstring with the concatenation of g1 and g2 docstrings is used
producer_name (string): Optional producer name for the combined model. Default: 'onnx.compose'
producer_version (string): Optional producer version for the combined model. Default: "1.0"
domain (string): Optional domain of the combined model. Default: ""
model_version (int): Optional version of the graph encoded. Default: 1
Returns:
ModelProto
"""
if type(m1) is not ModelProto:
raise ValueError("m1 argument is not an ONNX model")
if type(m2) is not ModelProto:
raise ValueError("m2 argument is not an ONNX model")
if m1.ir_version != m2.ir_version:
raise ValueError(
f"IR version mismatch {m1.ir_version} != {m2.ir_version}."
" Both models should have the same IR version"
)
ir_version = m1.ir_version
opset_import_map: MutableMapping[str, int] = {}
opset_imports = list(m1.opset_import) + list(m2.opset_import)
for entry in opset_imports:
if entry.domain in opset_import_map:
found_version = opset_import_map[entry.domain]
if entry.version != found_version:
raise ValueError(
"Can't merge two models with different operator set ids for a given domain. "
f"Got: {m1.opset_import} and {m2.opset_import}"
)
else:
opset_import_map[entry.domain] = entry.version
# Prefixing names in the graph if requested, adjusting io_map accordingly
if prefix1 or prefix2:
if prefix1:
m1_copy = ModelProto()
m1_copy.CopyFrom(m1)
m1 = m1_copy
m1 = add_prefix(m1, prefix=prefix1)
if prefix2:
m2_copy = ModelProto()
m2_copy.CopyFrom(m2)
m2 = m2_copy
m2 = add_prefix(m2, prefix=prefix2)
io_map = [
(
prefix1 + io[0] if prefix1 else io[0],
prefix2 + io[1] if prefix2 else io[1],
)
for io in io_map
]
graph = merge_graphs(
m1.graph,
m2.graph,
io_map,
inputs=inputs,
outputs=outputs,
name=name,
doc_string=doc_string,
)
model = helper.make_model(
graph,
producer_name=producer_name,
producer_version=producer_version,
domain=domain,
model_version=model_version,
opset_imports=opset_imports,
ir_version=ir_version,
)
# Merging model metadata props
model_props = {}
for meta_entry in m1.metadata_props:
model_props[meta_entry.key] = meta_entry.value
for meta_entry in m2.metadata_props:
if meta_entry.key in model_props:
value = model_props[meta_entry.key]
if value != meta_entry.value:
raise ValueError(
"Can't merge models with different values for the same model metadata property."
f" Found: property = {meta_entry.key}, with values {value} and {meta_entry.value}."
)
else:
model_props[meta_entry.key] = meta_entry.value
helper.set_model_props(model, model_props)
# Merging functions
function_overlap = list(
{f.name for f in m1.functions} & {f.name for f in m2.functions}
)
if function_overlap:
raise ValueError(
"Can't merge models with overlapping local function names."
" Found in both graphs: " + ", ".join(function_overlap)
)
model.functions.MergeFrom(m1.functions)
model.functions.MergeFrom(m2.functions)
checker.check_model(model)
return model
def add_prefix_graph( # pylint: disable=too-many-branches
graph: GraphProto,
prefix: str,
rename_nodes: Optional[bool] = True,
rename_edges: Optional[bool] = True,
rename_inputs: Optional[bool] = True,
rename_outputs: Optional[bool] = True,
rename_initializers: Optional[bool] = True,
rename_value_infos: Optional[bool] = True,
inplace: Optional[bool] = False,
name_map: Optional[Dict[str, str]] = None,
) -> GraphProto:
"""Adds a prefix to names of elements in a graph: nodes, edges, inputs, outputs,
initializers, sparse initializer, value infos.
It can be used as a utility before merging graphs that have overlapping names.
Empty names are not prefixed.
Arguments:
graph (GraphProto): Graph
prefix (str): Prefix to be added to each name in the graph
rename_nodes (bool): Whether to prefix node names
rename_edges (bool): Whether to prefix node edge names
rename_inputs (bool): Whether to prefix input names
rename_outputs (bool): Whether to prefix output names
rename_initializers (bool): Whether to prefix initializer and sparse initializer names
rename_value_infos (bool): Whether to prefix value info names
inplace (bool): If True, mutates the graph directly.
Otherwise, a copy will be created
name_map: (Dict): shared name_map in subgraph
Returns:
GraphProto
"""
if type(graph) is not GraphProto:
raise ValueError("graph argument is not an ONNX graph")
if not inplace:
g = GraphProto()
g.CopyFrom(graph)
else:
g = graph
def _prefixed(prefix: str, name: str) -> str:
return prefix + name if len(name) > 0 else name
if name_map is None:
name_map = {}
if rename_edges:
for n in g.node:
for e in n.input:
name_map[e] = _prefixed(prefix, e)
for e in n.output:
name_map[e] = _prefixed(prefix, e)
if rename_inputs:
for entry in g.input:
name_map[entry.name] = _prefixed(prefix, entry.name)
if rename_outputs:
for entry in g.output:
name_map[entry.name] = _prefixed(prefix, entry.name)
if rename_nodes:
for n in g.node:
n.name = _prefixed(prefix, n.name)
for attribute in n.attribute:
if attribute.g:
add_prefix_graph(
attribute.g, prefix, inplace=True, name_map=name_map
)
if rename_initializers:
for init in g.initializer:
name_map[init.name] = _prefixed(prefix, init.name)
for sparse_init in g.sparse_initializer:
name_map[sparse_init.values.name] = _prefixed(
prefix, sparse_init.values.name
)
name_map[sparse_init.indices.name] = _prefixed(
prefix, sparse_init.indices.name
)
if rename_value_infos:
for entry in g.value_info:
name_map[entry.name] = _prefixed(prefix, entry.name)
for n in g.node:
for i, output in enumerate(n.output):
if n.output[i] in name_map:
n.output[i] = name_map[output]
for i, input_ in enumerate(n.input):
if n.input[i] in name_map:
n.input[i] = name_map[input_]
for in_desc in g.input:
if in_desc.name in name_map:
in_desc.name = name_map[in_desc.name]
for out_desc in g.output:
if out_desc.name in name_map:
out_desc.name = name_map[out_desc.name]
for initializer in g.initializer:
if initializer.name in name_map:
initializer.name = name_map[initializer.name]
for sparse_initializer in g.sparse_initializer:
if sparse_initializer.values.name in name_map:
sparse_initializer.values.name = name_map[sparse_initializer.values.name]
if sparse_initializer.indices.name in name_map:
sparse_initializer.indices.name = name_map[sparse_initializer.indices.name]
for value_info in g.value_info:
if value_info.name in name_map:
value_info.name = name_map[value_info.name]
return g
def add_prefix(
model: ModelProto,
prefix: str,
rename_nodes: Optional[bool] = True,
rename_edges: Optional[bool] = True,
rename_inputs: Optional[bool] = True,
rename_outputs: Optional[bool] = True,
rename_initializers: Optional[bool] = True,
rename_value_infos: Optional[bool] = True,
rename_functions: Optional[bool] = True,
inplace: Optional[bool] = False,
) -> ModelProto:
"""Adds a prefix to names of elements in a graph: nodes, edges, inputs, outputs,
initializers, sparse initializer, value infos, and local functions.
It can be used as a utility before merging graphs that have overlapping names.
Empty names are not _prefixed.
Arguments:
model (ModelProto): Model
prefix (str): Prefix to be added to each name in the graph
rename_nodes (bool): Whether to prefix node names
rename_edges (bool): Whether to prefix node edge names
rename_inputs (bool): Whether to prefix input names
rename_outputs (bool): Whether to prefix output names
rename_initializers (bool): Whether to prefix initializer and sparse initializer names
rename_value_infos (bool): Whether to prefix value info nanes
rename_functions (bool): Whether to prefix local function names
inplace (bool): If True, mutates the model directly.
Otherwise, a copy will be created
Returns:
ModelProto
"""
if type(model) is not ModelProto:
raise ValueError("model argument is not an ONNX model")
if not inplace:
m = ModelProto()
m.CopyFrom(model)
model = m
add_prefix_graph(
model.graph,
prefix,
rename_nodes=rename_nodes,
rename_edges=rename_edges,
rename_inputs=rename_inputs,
rename_outputs=rename_outputs,
rename_initializers=rename_initializers,
rename_value_infos=rename_value_infos,
inplace=True, # No need to create a copy, since it's a new model
)
if rename_functions:
f_name_map = {}
for f in model.functions:
new_f_name = prefix + f.name
f_name_map[f.name] = new_f_name
f.name = new_f_name
# Adjust references to local functions in other local function
# definitions
for f in model.functions:
for n in f.node:
if n.op_type in f_name_map:
n.op_type = f_name_map[n.op_type]
# Adjust references to local functions in the graph
for n in model.graph.node:
if n.op_type in f_name_map:
n.op_type = f_name_map[n.op_type]
return model
def expand_out_dim_graph(
graph: GraphProto,
dim_idx: int,
inplace: Optional[bool] = False,
) -> GraphProto:
"""Inserts an extra dimension with extent 1 to each output in the graph.
Inserts an Unsqueeze node for each output. It can be used as a utility before merging graphs,
for example when the second one expects a batch dimension.
Arguments:
graph (GraphProto): Graph
dim_idx (int): Index of the dimension to be inserted.
A negative value means counting dimensions from the back.
inplace (bool): If True, mutates the model directly.
Otherwise, a copy will be created
Returns:
GraphProto
"""
if type(graph) is not GraphProto:
raise ValueError("graph argument is not an ONNX graph")
if not inplace:
g = GraphProto()
g.CopyFrom(graph)
else:
g = graph
orig_out_names = [output.name for output in g.output]
for n in g.node:
for i, out in enumerate(n.output):
if out in orig_out_names:
n.output[i] = out + f"_collapsed_dim_{dim_idx}"
for i, inp in enumerate(n.input):
if inp in orig_out_names:
n.input[i] = inp + f"_collapsed_dim_{dim_idx}"
expand_dim_k = g.name + "_expand_out_dim_idx"
g.node.append(
helper.make_node(
"Constant",
inputs=[],
outputs=[expand_dim_k],
name=f"{expand_dim_k}-constant",
value=helper.make_tensor(
name=f"{expand_dim_k}-value",
data_type=TensorProto.INT64,
dims=[
1,
],
vals=[
dim_idx,
],
),
)
)
for _ in range(len(g.output)):
o = g.output.pop(0)
prev_output = o.name + f"_collapsed_dim_{dim_idx}"
g.node.append(
helper.make_node(
"Unsqueeze",
inputs=[prev_output, expand_dim_k],
outputs=[o.name],
name=f"unsqueeze-{o.name}",
)
)
new_shape = [d.dim_value for d in o.type.tensor_type.shape.dim]
new_shape.insert(dim_idx, 1)
g.output.append(
helper.make_tensor_value_info(
o.name, o.type.tensor_type.elem_type, new_shape
)
)
return g
def expand_out_dim(
model: ModelProto,
dim_idx: int,
inplace: Optional[bool] = False,
) -> ModelProto:
"""Inserts an extra dimension with extent 1 to each output in the graph.
Inserts an Unsqueeze node for each output. It can be used as a utility before merging graphs,
for example when the second one expects a batch dimension.
Arguments:
model (ModelProto): Model
dim_idx (int): Index of the dimension to be inserted.
A negative value means counting dimensions from the back.
inplace (bool): If True, mutates the model directly.
Otherwise, a copy will be created
Returns:
ModelProto
"""
if type(model) is not ModelProto:
raise ValueError("model argument is not an ONNX model")
if not inplace:
m = ModelProto()
m.CopyFrom(model)
model = m
expand_out_dim_graph(
model.graph,
dim_idx,
inplace=True, # No need to create a copy, since it's a new model
)
return model
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59,031 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_det.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
import numpy as np
from onnx.reference.op_run import OpRun
class Det(OpRun):
def _run(self, x): # type: ignore
return (np.linalg.det(x),)
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59,032 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_sequence_empty.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221,W0613
from onnx.reference.op_run import OpRun
class SequenceEmpty(OpRun):
def _run(self, dtype=None): # type: ignore
return ([],)
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"/onnx/reference/ops/aionnxml/op_linear_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_softplus.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_sub.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_quantize_linear.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_gathernd.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/qlinearmatmul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/shape_inference_test.py": ["/onnx/shape_inference.py", "/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/parser.py"], "/onnx/backend/test/case/node/mish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_expand.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": 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"/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
59,033 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/topk.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
def topk_sorted_implementation(X, k, axis, largest): # type: ignore
sorted_indices = np.argsort(X, axis=axis)
sorted_values = np.sort(X, axis=axis)
if largest:
sorted_indices = np.flip(sorted_indices, axis=axis)
sorted_values = np.flip(sorted_values, axis=axis)
topk_sorted_indices = np.take(sorted_indices, np.arange(k), axis=axis)
topk_sorted_values = np.take(sorted_values, np.arange(k), axis=axis)
return topk_sorted_values, np.array(topk_sorted_indices, dtype=np.int64)
class TopK(Base):
@staticmethod
def export_top_k() -> None:
axis = 1
largest = 1
k = 3
node = onnx.helper.make_node(
"TopK", inputs=["x", "k"], outputs=["values", "indices"], axis=axis
)
X = np.array(
[
[0, 1, 2, 3],
[4, 5, 6, 7],
[8, 9, 10, 11],
],
dtype=np.float32,
)
K = np.array([k], dtype=np.int64)
values_ref, indices_ref = topk_sorted_implementation(X, k, axis, largest)
# print(values_ref)
# [[ 3. 2. 1.]
# [ 7. 6. 5.]
# [11. 10. 9.]]
# print(indices_ref)
# [[3 2 1]
# [3 2 1]
# [3 2 1]]
expect(
node, inputs=[X, K], outputs=[values_ref, indices_ref], name="test_top_k"
)
@staticmethod
def export_top_k_smallest() -> None:
axis = 1
largest = 0
sorted = 1
k = 3
node = onnx.helper.make_node(
"TopK",
inputs=["x", "k"],
outputs=["values", "indices"],
axis=axis,
largest=largest,
sorted=sorted,
)
X = np.array(
[
[0, 1, 2, 3],
[4, 5, 6, 7],
[11, 10, 9, 8],
],
dtype=np.float32,
)
K = np.array([k], dtype=np.int64)
values_ref, indices_ref = topk_sorted_implementation(X, k, axis, largest)
# print(values_ref)
# [[ 0. 1. 2.]
# [ 4. 5. 6.]
# [ 8. 9. 10.]]
# print(indices_ref)
# [[0 1 2]
# [0 1 2]
# [3 2 1]]
expect(
node,
inputs=[X, K],
outputs=[values_ref, indices_ref],
name="test_top_k_smallest",
)
@staticmethod
def export_top_k_negative_axis() -> None:
axis = -1
largest = 1
k = 3
node = onnx.helper.make_node(
"TopK", inputs=["x", "k"], outputs=["values", "indices"], axis=axis
)
X = np.array(
[
[0, 1, 2, 3],
[4, 5, 6, 7],
[8, 9, 10, 11],
],
dtype=np.float32,
)
K = np.array([k], dtype=np.int64)
values_ref, indices_ref = topk_sorted_implementation(X, k, axis, largest)
# print(values_ref)
# [[ 3. 2. 1.]
# [ 7. 6. 5.]
# [11. 10. 9.]]
# print(indices_ref)
# [[3 2 1]
# [3 2 1]
# [3 2 1]]
expect(
node,
inputs=[X, K],
outputs=[values_ref, indices_ref],
name="test_top_k_negative_axis",
)
| {"/onnx/backend/test/case/node/sign.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/dft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/parser.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/constantofshape.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/averagepool.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/__init__.py": ["/onnx/__init__.py", "/onnx/backend/base.py", "/onnx/backend/test/case/test_case.py", "/onnx/backend/test/loader/__init__.py", "/onnx/backend/test/runner/item.py"], "/onnx/reference/ops/op_topk.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_image_decoder.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_non_max_suppression.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/logsoftmax.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_affine_grid.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_lp_normalization.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_rnn.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/not.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_reduce_sum.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mean.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_roi_align.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_center_crop_pad.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/nonmaxsuppression.py": ["/onnx/__init__.py", 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59,034 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_reduce_log_sum.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
import numpy as np
from onnx.reference.ops._op import OpRunReduceNumpy
class ReduceLogSum_1(OpRunReduceNumpy):
def _run(self, data, axes=None, keepdims=True): # type: ignore
tax = tuple(axes) if axes is not None else None
res = np.sum(data, axis=tax, keepdims=keepdims) # type: ignore[arg-type]
if len(res.shape) > 0:
return (np.log(res, out=res),)
return (np.log(res),)
class ReduceLogSum_18(OpRunReduceNumpy):
def _run(self, data, axes=None, keepdims=1, noop_with_empty_axes=0): # type: ignore
if self.is_axes_empty(axes) and noop_with_empty_axes: # type: ignore
return (data,)
axes = self.handle_axes(axes)
keepdims = keepdims != 0 # type: ignore
res = np.sum(data, axis=axes, keepdims=keepdims)
if len(res.shape) > 0:
return (np.log(res, out=res),)
return (np.log(res),)
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59,035 | onnx/onnx | refs/heads/main | /onnx/reference/ops/aionnxml/op_linear_classifier.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0912,R0913,R0914,W0221
import numpy as np
from onnx.reference.ops.aionnxml._common_classifier import (
compute_probit,
compute_softmax_zero,
expit,
)
from onnx.reference.ops.aionnxml._op_run_aionnxml import OpRunAiOnnxMl
class LinearClassifier(OpRunAiOnnxMl):
@staticmethod
def _post_process_predicted_label(label, scores, classlabels_ints_string): # type: ignore
"""
Replaces int64 predicted labels by the corresponding
strings.
"""
if classlabels_ints_string is not None:
label = np.array([classlabels_ints_string[i] for i in label])
return label, scores
def _run( # type: ignore
self,
x,
classlabels_ints=None,
classlabels_strings=None,
coefficients=None,
intercepts=None,
multi_class=None, # pylint: disable=W0613
post_transform=None,
):
# multi_class is unused
dtype = x.dtype
if dtype != np.float64:
x = x.astype(np.float32)
coefficients = np.array(coefficients).astype(x.dtype)
intercepts = np.array(intercepts).astype(x.dtype)
coefficients = coefficients.reshape((-1, x.shape[1])).T
scores = np.dot(x, coefficients)
if intercepts is not None:
scores += intercepts
n_classes = max(len(classlabels_ints or []), len(classlabels_strings or []))
if coefficients.shape[1] == 1 and n_classes == 2:
new_scores = np.empty((scores.shape[0], 2), dtype=np.float32)
new_scores[:, 0] = -scores[:, 0]
new_scores[:, 1] = scores[:, 0]
scores = new_scores
if post_transform == "NONE":
pass
elif post_transform == "LOGISTIC":
scores = expit(scores)
elif post_transform == "SOFTMAX":
np.subtract(
scores,
scores.max(axis=1, keepdims=1), # pylint: disable=E1123
out=scores,
)
scores = np.exp(scores)
scores = np.divide(scores, scores.sum(axis=1, keepdims=1))
elif post_transform == "SOFTMAX_ZERO":
for i in range(scores.shape[0]):
scores[i, :] = compute_softmax_zero(scores[i, :])
elif post_transform == "PROBIT":
for i in range(scores.shape[0]):
for j in range(scores.shape[1]):
scores[i, j] = compute_probit(scores[i, j])
else:
raise NotImplementedError("Unknown post_transform: '{post_transform}'.")
if scores.shape[1] > 1:
labels = np.argmax(scores, axis=1)
if classlabels_ints is not None:
labels = np.array([classlabels_ints[i] for i in labels], dtype=np.int64)
elif classlabels_strings is not None:
labels = np.array([classlabels_strings[i] for i in labels])
else:
threshold = 0 if post_transform == "NONE" else 0.5
if classlabels_ints is not None:
labels = (
np.where(scores >= threshold, classlabels_ints[0], 0)
.astype(np.int64)
.ravel()
)
elif classlabels_strings is not None:
labels = (
np.where(scores >= threshold, classlabels_strings[0], "")
.astype(np.int64)
.ravel()
)
else:
labels = (scores >= threshold).astype(np.int64).ravel()
return (labels, scores)
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59,036 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/center_crop_pad.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class CenterCropPad(Base):
@staticmethod
def export_center_crop_pad_crop() -> None:
node = onnx.helper.make_node(
"CenterCropPad",
inputs=["x", "shape"],
outputs=["y"],
)
# First dim is even diff, second is uneven
x = np.random.randn(20, 10, 3).astype(np.float32)
shape = np.array([10, 7, 3], dtype=np.int64)
y = x[5:15, 1:8, :]
expect(node, inputs=[x, shape], outputs=[y], name="test_center_crop_pad_crop")
@staticmethod
def export_center_crop_pad_pad() -> None:
node = onnx.helper.make_node(
"CenterCropPad",
inputs=["x", "shape"],
outputs=["y"],
)
# First dim is even diff, second is uneven
x = np.random.randn(10, 7, 3).astype(np.float32)
shape = np.array([20, 10, 3], dtype=np.int64)
y = np.zeros([20, 10, 3], dtype=np.float32)
y[5:15, 1:8, :] = x
expect(node, inputs=[x, shape], outputs=[y], name="test_center_crop_pad_pad")
@staticmethod
def export_center_crop_pad_crop_and_pad() -> None:
node = onnx.helper.make_node(
"CenterCropPad",
inputs=["x", "shape"],
outputs=["y"],
)
# Cropping on first dim, padding on second, third stays the same
x = np.random.randn(20, 8, 3).astype(np.float32)
shape = np.array([10, 10, 3], dtype=np.int64)
y = np.zeros([10, 10, 3], dtype=np.float32)
y[:, 1:9, :] = x[5:15, :, :]
expect(
node,
inputs=[x, shape],
outputs=[y],
name="test_center_crop_pad_crop_and_pad",
)
@staticmethod
def export_center_crop_pad_crop_axes_hwc() -> None:
node = onnx.helper.make_node(
"CenterCropPad",
inputs=["x", "shape"],
outputs=["y"],
axes=[0, 1],
)
# Cropping on first dim, padding on second, third stays the same
x = np.random.randn(20, 8, 3).astype(np.float32)
shape = np.array([10, 9], dtype=np.int64)
y = np.zeros([10, 9, 3], dtype=np.float32)
y[:, :8, :] = x[5:15, :, :]
expect(
node,
inputs=[x, shape],
outputs=[y],
name="test_center_crop_pad_crop_axes_hwc",
)
@staticmethod
def export_center_crop_pad_crop_negative_axes_hwc() -> None:
node = onnx.helper.make_node(
"CenterCropPad",
inputs=["x", "shape"],
outputs=["y"],
axes=[-3, -2],
)
# Cropping on first dim, padding on second, third stays the same
x = np.random.randn(20, 8, 3).astype(np.float32)
shape = np.array([10, 9], dtype=np.int64)
y = np.zeros([10, 9, 3], dtype=np.float32)
y[:, :8, :] = x[5:15, :, :]
expect(
node,
inputs=[x, shape],
outputs=[y],
name="test_center_crop_pad_crop_negative_axes_hwc",
)
@staticmethod
def export_center_crop_pad_crop_axes_chw() -> None:
node = onnx.helper.make_node(
"CenterCropPad",
inputs=["x", "shape"],
outputs=["y"],
axes=[1, 2],
)
# Cropping on second dim, padding on third, first stays the same
x = np.random.randn(3, 20, 8).astype(np.float32)
shape = np.array([10, 9], dtype=np.int64)
y = np.zeros([3, 10, 9], dtype=np.float32)
y[:, :, :8] = x[:, 5:15, :]
expect(
node,
inputs=[x, shape],
outputs=[y],
name="test_center_crop_pad_crop_axes_chw",
)
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59,037 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_string_concat.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0912,R0913,W0221
import numpy as np
from onnx.reference.op_run import OpRun
_acceptable_str_dtypes = ("U", "O")
class StringConcat(OpRun):
def _run(self, x, y):
if (
x.dtype.kind not in _acceptable_str_dtypes
or y.dtype.kind not in _acceptable_str_dtypes
):
raise TypeError(
f"Inputs must be string tensors, received dtype {x.dtype} and {y.dtype}"
)
# As per onnx/mapping.py, object numpy dtype corresponds to TensorProto.STRING
return (np.char.add(x.astype(np.str_), y.astype(np.str_)).astype(object),)
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59,038 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_batch_normalization.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221,R0913,W0613
import numpy as np
from onnx.reference.op_run import OpRun
def _batchnorm_test_mode(
x: np.ndarray,
s: np.ndarray,
bias: np.ndarray,
mean: np.ndarray,
var: np.ndarray,
epsilon: float = 1e-5,
) -> np.ndarray:
dims_x = len(x.shape)
dim_ones = (1,) * (dims_x - 2)
s = s.reshape(-1, *dim_ones)
bias = bias.reshape(-1, *dim_ones)
mean = mean.reshape(-1, *dim_ones)
var = var.reshape(-1, *dim_ones)
y = s * (x - mean) / np.sqrt(var + epsilon) + bias
return y.astype(x.dtype) # type: ignore
def _batchnorm_training_mode(
x: np.ndarray,
s: np.ndarray,
bias: np.ndarray,
mean: np.ndarray,
var: np.ndarray,
momentum: float = 0.9,
epsilon: float = 1e-5,
) -> np.ndarray:
axis = tuple(np.delete(np.arange(len(x.shape)), 1))
saved_mean = x.mean(axis=axis)
saved_var = x.var(axis=axis)
output_mean = mean * momentum + saved_mean * (1 - momentum)
output_var = var * momentum + saved_var * (1 - momentum)
y = _batchnorm_test_mode(x, s, bias, saved_mean, saved_var, epsilon=epsilon)
return ( # type: ignore
y.astype(x.dtype),
saved_mean.astype(x.dtype),
saved_var.astype(x.dtype),
output_mean.astype(x.dtype),
output_var.astype(x.dtype),
)
class BatchNormalization_6(OpRun):
def _run(self, x, scale, bias, mean, var, epsilon=None, is_test=None, momentum=None, spatial=None): # type: ignore
if is_test:
res = _batchnorm_test_mode(x, scale, bias, mean, var, epsilon=epsilon)
else:
res = _batchnorm_training_mode(
x, scale, bias, mean, var, epsilon=epsilon, momentum=momentum
)
return (res,)
class BatchNormalization_9(OpRun):
def _run(self, x, scale, bias, mean, var, epsilon=None, momentum=None): # type: ignore
if momentum is None:
res = _batchnorm_test_mode(x, scale, bias, mean, var, epsilon=epsilon)
return (res,)
axis = tuple(np.delete(np.arange(len(x.shape)), 1))
saved_mean = x.mean(axis=axis)
saved_var = x.var(axis=axis)
output_mean = mean * momentum + saved_mean * (1 - momentum)
output_var = var * momentum + saved_var * (1 - momentum)
res = _batchnorm_test_mode(
x, scale, bias, output_mean, output_var, epsilon=epsilon
)
return (res,)
class BatchNormalization_14(OpRun):
def _run( # type: ignore
self, x, scale, bias, mean, var, epsilon=None, momentum=None, training_mode=None
):
if training_mode == 0: # type: ignore
res = _batchnorm_test_mode(x, scale, bias, mean, var, epsilon=epsilon)
return (res,)
res, __, _, output_mean, output_var = _batchnorm_training_mode(
x, scale, bias, mean, var, momentum, epsilon
)
return res, output_mean, output_var
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"/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
59,039 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_cum_sum.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
import numpy as np
from onnx.reference.op_run import OpRun
class CumSum(OpRun):
def _run(self, x, *axis, exclusive=None, reverse=None): # type: ignore
axis = None if not axis else axis[0] # type: ignore
if axis is None: # type: ignore
if reverse or exclusive:
raise NotImplementedError("reverse=1 or exclusive=1 not implemented")
return (np.cumsum(x),)
if not isinstance(axis, (np.int32, np.int64)):
if len(axis.shape) > 1 or (len(axis.shape) > 0 and axis.shape[0] != 1): # type: ignore
raise RuntimeError(
f"axis must be an array of one number not {axis} (shape {axis.shape})." # type: ignore
)
if len(axis.shape) > 0: # type: ignore
axis = axis[0]
if reverse:
rev_indices = [slice(0, s) for s in x.shape]
rev_indices[axis] = slice(None, None, -1) # type: ignore
x = x[tuple(rev_indices)]
if exclusive:
indices_c = [slice(0, s) for s in x.shape]
indices_d = [slice(0, s) for s in x.shape]
indices_c[axis] = slice(0, -1) # type: ignore
indices_d[axis] = slice(1, x.shape[axis]) # type: ignore
res = np.zeros(x.shape, dtype=x.dtype)
np.cumsum(x[tuple(indices_c)], axis=axis, out=res[tuple(indices_d)]) # type: ignore
else:
res = np.cumsum(x, axis=axis) # type: ignore
if reverse:
res = res[tuple(rev_indices)]
return (res,)
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59,040 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_prelu.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
import numpy as np
from onnx.reference.op_run import OpRun
class PRelu(OpRun):
def _run(self, x, slope): # type: ignore
try:
return (np.where(x > 0, x, x * slope).astype(x.dtype),)
except ValueError as e:
# Broadcast did not work according to numpy.
# The logic is then the following, if slope has d elements,
# the following code is looking for d in x.shape. If it is found
# only once, x * slope is broadcasted on any other dimension.
# Otherwise, it raises e.
if len(slope.shape) == 1:
dim = slope.shape[0]
new_shape = []
n = 0
for d in x.shape:
if d == dim:
new_shape.append(d)
n += 1
else:
new_shape.append(1)
if n == 1:
xs = x * slope.reshape(tuple(new_shape))
return (np.where(x > 0, x, xs).astype(x.dtype),)
raise e
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"/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
59,041 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_unsqueeze.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=E0203,W0221
import numpy as np
from onnx.reference.op_run import OpRun
class Unsqueeze_1(OpRun):
def _run(self, data, axes=None): # type: ignore
if isinstance(axes, np.ndarray):
axes = tuple(axes)
elif axes in ([], ()):
axes = None
elif isinstance(axes, list):
axes = tuple(axes)
if isinstance(axes, (tuple, list)):
sq = data
for a in axes:
sq = np.expand_dims(sq, axis=a)
else:
raise RuntimeError(
"axes cannot be None for operator Unsqueeze (Unsqueeze_1)."
)
return (sq,)
class Unsqueeze_11(Unsqueeze_1):
pass
class Unsqueeze_13(OpRun):
def _run(self, data, axes=None): # type: ignore
if axes is not None:
if hasattr(axes, "__iter__") and len(axes.shape) > 0:
try:
sq = np.expand_dims(data, axis=tuple(axes))
except TypeError:
# numpy 1.18 supports axes as a tuple
if len(axes) == 1:
sq = np.expand_dims(data, axis=tuple(axes)[0])
else:
sq = data
for a in reversed(axes):
sq = np.expand_dims(sq, axis=a)
else:
sq = np.expand_dims(data, axis=axes)
else:
raise RuntimeError(
"axes cannot be None for operator Unsqueeze (Unsqueeze_13)."
)
return (sq,)
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"/onnx/reference/ops/op_unique.py", "/onnx/reference/ops/op_unsqueeze.py", "/onnx/reference/ops/op_upsample.py", "/onnx/reference/ops/op_where.py"], "/onnx/backend/test/case/model/gradient.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py", "/onnx/defs/__init__.py"], "/onnx/compose.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_det.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_sequence_empty.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/topk.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_reduce_log_sum.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/aionnxml/op_linear_classifier.py": ["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/center_crop_pad.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", 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"/onnx/reference/ops/aionnxml/op_linear_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_softplus.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_sub.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_quantize_linear.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_gathernd.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/qlinearmatmul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/shape_inference_test.py": ["/onnx/shape_inference.py", "/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/parser.py"], "/onnx/backend/test/case/node/mish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_expand.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": 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59,042 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/globalaveragepool.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class GlobalAveragePool(Base):
@staticmethod
def export() -> None:
node = onnx.helper.make_node(
"GlobalAveragePool",
inputs=["x"],
outputs=["y"],
)
x = np.random.randn(1, 3, 5, 5).astype(np.float32)
y = np.mean(x, axis=tuple(range(2, np.ndim(x))), keepdims=True)
expect(node, inputs=[x], outputs=[y], name="test_globalaveragepool")
@staticmethod
def export_globalaveragepool_precomputed() -> None:
node = onnx.helper.make_node(
"GlobalAveragePool",
inputs=["x"],
outputs=["y"],
)
x = np.array(
[
[
[
[1, 2, 3],
[4, 5, 6],
[7, 8, 9],
]
]
]
).astype(np.float32)
y = np.array([[[[5]]]]).astype(np.float32)
expect(node, inputs=[x], outputs=[y], name="test_globalaveragepool_precomputed")
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59,043 | onnx/onnx | refs/heads/main | /onnx/backend/test/__init__.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
__all__ = ["BackendTest"]
# for backward compatibility
from onnx.backend.test.runner import Runner as BackendTest
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"/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": ["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/_helpers.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/tfidfvectorizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_average_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/item.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/gatherelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/slice.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_matmul.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mel_weight_matrix.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cast.py": ["/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/asin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_normalizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/unique.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather_elements.py": ["/onnx/reference/op_run.py"], "/onnx/helper.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/layernormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/groupnormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/__init__.py": ["/onnx/reference/ops/_op_list.py"], "/onnx/reference/ops/op_random_normal.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_hann_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_softmax_cross_entropy_loss.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_string_split.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/max.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/backend/test/case/utils.py"], "/onnx/backend/test/case/model/expand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/backend/test/case/node/erf.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
59,044 | onnx/onnx | refs/heads/main | /onnx/test/parser_test.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
import unittest
from parameterized import parameterized
import onnx
from onnx import GraphProto, OperatorSetIdProto, checker
class TestBasicFunctions(unittest.TestCase):
def check_graph(self, graph: GraphProto) -> None:
self.assertEqual(len(graph.node), 3)
self.assertEqual(graph.node[0].op_type, "MatMul")
self.assertEqual(graph.node[1].op_type, "Add")
self.assertEqual(graph.node[2].op_type, "Softmax")
def test_parse_graph(self) -> None:
input = """
agraph (float[N, 128] X, float[128,10] W, float[10] B) => (float[N] C)
{
T = MatMul(X, W)
S = Add(T, B)
C = Softmax(S)
}
"""
graph = onnx.parser.parse_graph(input)
self.check_graph(graph)
def test_parse_model(self) -> None:
input = """
<
ir_version: 7,
opset_import: [ "" : 10, "com.microsoft": 1]
>
agraph (float[N, 128] X, float[128,10] W, float[10] B) => (float[N] C)
{
T = MatMul(X, W)
S = Add(T, B)
C = Softmax(S)
}
"""
model = onnx.parser.parse_model(input)
self.assertEqual(model.ir_version, 7)
self.assertEqual(len(model.opset_import), 2)
self.check_graph(model.graph)
def test_parse_graph_error(self) -> None:
input = """
agraph (float[N, 128] X, float[128,10] W, float[10] B) => (float[N] C)
{
T = MatMul[X, W]
S = Add(T, B)
C = Softmax(S)
}
"""
self.assertRaises(
onnx.parser.ParseError, lambda: onnx.parser.parse_graph(input)
)
def test_parse_model_error(self) -> None:
input = """
<
ir_version: 7,
opset_import: [ "" : 10 "com.microsoft": 1]
>
agraph (float[N, 128] X, float[128,10] W, float[10] B) => (float[N] C)
{
T = MatMul(X, W)
S = Add(T, B)
C = Softmax(S)
}
"""
self.assertRaises(
onnx.parser.ParseError, lambda: onnx.parser.parse_model(input)
)
def test_parse_function_with_attributes(self) -> None:
input = """
<
ir_version: 9,
opset_import: [ "" : 15, "custom_domain" : 1],
producer_name: "FunctionProtoTest",
producer_version: "1.0",
model_version: 1,
doc_string: "A test model for model local functions."
>
agraph (float[N] x) => (float[N] out)
{
out = custom_domain.Selu<alpha=2.0, gamma=3.0>(x)
}
<
domain: "custom_domain",
opset_import: [ "" : 15],
doc_string: "Test function proto"
>
Selu
<alpha: float=1.67326319217681884765625, gamma: float=1.05070102214813232421875>
(X) => (C)
{
constant_alpha = Constant<value_float: float=@alpha>()
constant_gamma = Constant<value_float: float=@gamma>()
alpha_x = CastLike(constant_alpha, X)
gamma_x = CastLike(constant_gamma, X)
exp_x = Exp(X)
alpha_x_exp_x = Mul(alpha_x, exp_x)
alpha_x_exp_x_ = Sub(alpha_x_exp_x, alpha_x)
neg = Mul(gamma_x, alpha_x_exp_x_)
pos = Mul(gamma_x, X)
_zero = Constant<value_float=0.0>()
zero = CastLike(_zero, X)
less_eq = LessOrEqual(X, zero)
C = Where(less_eq, neg, pos)
}
"""
model = onnx.parser.parse_model(input)
checker.check_model(model)
@parameterized.expand(
[
(
"agraph (float[N] x) => (float[N] out) { out = custom_domain.Selu(x) }",
{},
),
(
"agraph (float[N] x) => (float[N] out) { out = custom_domain.Selu<alpha=2.0>(x) }",
{"alpha": 2.0},
),
(
"agraph (float[N] x) => (float[N] out) { out = custom_domain.Selu<gamma=3.0>(x) }",
{"gamma": 3.0},
),
(
"agraph (float[N] x) => (float[N] out) { out = custom_domain.Selu<alpha=2.0, gamma=3.0>(x) }",
{"alpha": 2.0, "gamma": 3.0},
),
]
)
def test_composite_parse_function_with_attributes(
self, graph_text: str, expected_attribute: dict
) -> None:
default_alpha = 1.67326319217681884765625
default_gamma = 1.05070102214813232421875
def expect_custom_node_attribute(node, attributes):
for key in attributes:
match_attr = [attr for attr in node.attribute if attr.name == key]
assert len(match_attr) == 1
assert match_attr[0].f == attributes[key]
def expect_model_function_attribute(model):
assert len(model.functions[0].attribute_proto) == 2
attr_proto_alpha = [
attr_proto
for attr_proto in model.functions[0].attribute_proto
if attr_proto.name == "alpha"
]
assert len(attr_proto_alpha) == 1 and attr_proto_alpha[0].f == default_alpha
attr_proto_gamma = [
attr_proto
for attr_proto in model.functions[0].attribute_proto
if attr_proto.name == "gamma"
]
assert len(attr_proto_gamma) == 1 and attr_proto_gamma[0].f == default_gamma
function_text = f"""
<
domain: "custom_domain",
opset_import: [ "" : 15],
doc_string: "Test function proto"
>
Selu
<alpha: float={default_alpha}, gamma: float={default_gamma}>
(X) => (C)
{{
constant_alpha = Constant<value_float: float=@alpha>()
constant_gamma = Constant<value_float: float=@gamma>()
alpha_x = CastLike(constant_alpha, X)
gamma_x = CastLike(constant_gamma, X)
exp_x = Exp(X)
alpha_x_exp_x = Mul(alpha_x, exp_x)
alpha_x_exp_x_ = Sub(alpha_x_exp_x, alpha_x)
neg = Mul(gamma_x, alpha_x_exp_x_)
pos = Mul(gamma_x, X)
_zero = Constant<value_float=0.0>()
zero = CastLike(_zero, X)
less_eq = LessOrEqual(X, zero)
C = Where(less_eq, neg, pos)
}}
"""
functions = [onnx.parser.parse_function(function_text)]
graph = onnx.parser.parse_graph(graph_text)
opset_imports = [
OperatorSetIdProto(domain="", version=15),
OperatorSetIdProto(domain="custom_domain", version=1),
]
model = onnx.helper.make_model(
graph, functions=functions, opset_imports=opset_imports
)
checker.check_model(model)
expect_model_function_attribute(model)
expect_custom_node_attribute(model.graph.node[0], expected_attribute)
def test_parse_node(self):
node = onnx.parser.parse_node(
"out1, out2 = SomeDomain.SomeOp <attr1 = 1> (in1, in2)"
)
self.assertEqual(list(node.input), ["in1", "in2"])
self.assertEqual(list(node.output), ["out1", "out2"])
self.assertEqual(len(node.attribute), 1)
attr_val = onnx.helper.get_node_attr_value(node, "attr1")
self.assertEqual(attr_val, 1)
self.assertEqual(node.domain, "SomeDomain")
self.assertEqual(node.op_type, "SomeOp")
if __name__ == "__main__":
unittest.main()
| {"/onnx/backend/test/case/node/sign.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/dft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/parser.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/constantofshape.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/averagepool.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/__init__.py": ["/onnx/__init__.py", "/onnx/backend/base.py", "/onnx/backend/test/case/test_case.py", "/onnx/backend/test/loader/__init__.py", "/onnx/backend/test/runner/item.py"], "/onnx/reference/ops/op_topk.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_image_decoder.py": 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59,045 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_regex_full_match.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0912,R0913,W0221
import numpy as np
from onnx.reference.op_run import OpRun
_acceptable_str_dtypes = ("U", "O")
class RegexFullMatch(OpRun):
def _run(self, x, pattern=None):
try:
# pylint: disable=import-outside-toplevel`
import re2
except ImportError as e:
raise ImportError(
"re2 must be installed to use the reference implementation of the RegexFullMatch operator"
) from e
# As per onnx/mapping.py, object numpy dtype corresponds to TensorProto.STRING
if x.dtype.kind not in _acceptable_str_dtypes:
raise TypeError(f"Input must be string tensor, received dtype {x.dtype}")
try:
regex = re2.compile(pattern)
except re2.error as e:
raise ValueError(f"Invalid regex pattern {pattern!r}") from e
fullmatch_func = np.vectorize(
lambda x: regex.fullmatch(x) is not None, otypes=[np.bool_]
)
return (fullmatch_func(x),)
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59,046 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/xor.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class Xor(Base):
@staticmethod
def export() -> None:
node = onnx.helper.make_node(
"Xor",
inputs=["x", "y"],
outputs=["xor"],
)
# 2d
x = (np.random.randn(3, 4) > 0).astype(bool)
y = (np.random.randn(3, 4) > 0).astype(bool)
z = np.logical_xor(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_xor2d")
# 3d
x = (np.random.randn(3, 4, 5) > 0).astype(bool)
y = (np.random.randn(3, 4, 5) > 0).astype(bool)
z = np.logical_xor(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_xor3d")
# 4d
x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
y = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
z = np.logical_xor(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_xor4d")
@staticmethod
def export_xor_broadcast() -> None:
node = onnx.helper.make_node(
"Xor",
inputs=["x", "y"],
outputs=["xor"],
)
# 3d vs 1d
x = (np.random.randn(3, 4, 5) > 0).astype(bool)
y = (np.random.randn(5) > 0).astype(bool)
z = np.logical_xor(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_xor_bcast3v1d")
# 3d vs 2d
x = (np.random.randn(3, 4, 5) > 0).astype(bool)
y = (np.random.randn(4, 5) > 0).astype(bool)
z = np.logical_xor(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_xor_bcast3v2d")
# 4d vs 2d
x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
y = (np.random.randn(5, 6) > 0).astype(bool)
z = np.logical_xor(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_xor_bcast4v2d")
# 4d vs 3d
x = (np.random.randn(3, 4, 5, 6) > 0).astype(bool)
y = (np.random.randn(4, 5, 6) > 0).astype(bool)
z = np.logical_xor(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_xor_bcast4v3d")
# 4d vs 4d
x = (np.random.randn(1, 4, 1, 6) > 0).astype(bool)
y = (np.random.randn(3, 1, 5, 6) > 0).astype(bool)
z = np.logical_xor(x, y)
expect(node, inputs=[x, y], outputs=[z], name="test_xor_bcast4v4d")
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59,047 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/shape.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
# Reference implementation of shape op
def shape_reference_impl(x, start=None, end=None): # type: ignore
dims = x.shape[start:end]
return np.array(dims).astype(np.int64)
def test_shape(testname, xval, start=None, end=None): # type: ignore
node = onnx.helper.make_node(
"Shape", inputs=["x"], outputs=["y"], start=start, end=end
)
yval = shape_reference_impl(xval, start, end)
expect(node, inputs=[xval], outputs=[yval], name="test_shape" + testname)
class Shape(Base):
@staticmethod
def export() -> None:
x = np.array(
[
[1, 2, 3],
[4, 5, 6],
]
).astype(np.float32)
test_shape("_example", x) # preserve names of original test cases
x = np.random.randn(3, 4, 5).astype(np.float32)
test_shape("", x) # preserve names of original test cases
test_shape("_start_1", x, start=1)
test_shape("_end_1", x, end=1)
test_shape("_start_negative_1", x, start=-1)
test_shape("_end_negative_1", x, end=-1)
test_shape("_start_1_end_negative_1", x, start=1, end=-1)
test_shape("_start_1_end_2", x, start=1, end=2)
test_shape("_clip_start", x, start=-10)
test_shape("_clip_end", x, end=10)
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59,048 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/dequantizelinear.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx import TensorProto
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
from onnx.helper import make_tensor
class DequantizeLinear(Base):
@staticmethod
def export() -> None:
node = onnx.helper.make_node(
"DequantizeLinear",
inputs=["x", "x_scale", "x_zero_point"],
outputs=["y"],
)
# scalar zero point and scale
x = np.array([0, 3, 128, 255]).astype(np.uint8)
x_scale = np.float32(2)
x_zero_point = np.uint8(128)
y = np.array([-256, -250, 0, 254], dtype=np.float32)
expect(
node,
inputs=[x, x_scale, x_zero_point],
outputs=[y],
name="test_dequantizelinear",
)
@staticmethod
def export_axis() -> None:
node = onnx.helper.make_node(
"DequantizeLinear",
inputs=["x", "x_scale", "x_zero_point"],
outputs=["y"],
)
# 1-D tensor zero point and scale of size equal to axis 1 of the input tensor
x = np.array(
[
[
[[3, 89], [34, 200], [74, 59]],
[[5, 24], [24, 87], [32, 13]],
[[245, 99], [4, 142], [121, 102]],
],
],
dtype=np.uint8,
)
x_scale = np.array([2, 4, 5], dtype=np.float32)
x_zero_point = np.array([84, 24, 196], dtype=np.uint8)
y = (
x.astype(np.float32) - x_zero_point.reshape(1, 3, 1, 1).astype(np.float32)
) * x_scale.reshape(1, 3, 1, 1)
expect(
node,
inputs=[x, x_scale, x_zero_point],
outputs=[y],
name="test_dequantizelinear_axis",
)
@staticmethod
def export_e4m3fn() -> None:
node = onnx.helper.make_node(
"DequantizeLinear",
inputs=["x", "x_scale"],
outputs=["y"],
axis=0,
)
# scalar zero point and scale
x = make_tensor("x", TensorProto.FLOAT8E4M3FN, [5], [0, 0.5, 1, 448, -104])
x_scale = np.float32(2)
y = np.array([0.0, 1.0, 2.0, 896.0, -208.0], dtype=np.float32)
expect(
node,
inputs=[x, x_scale],
outputs=[y],
name="test_dequantizelinear_e4m3fn",
)
@staticmethod
def export_e4m3fn_float16() -> None:
node = onnx.helper.make_node(
"DequantizeLinear",
inputs=["x", "x_scale"],
outputs=["y"],
axis=0,
)
# scalar zero point and scale
x = make_tensor("x", TensorProto.FLOAT8E4M3FN, [5], [0, 0.5, 1, 448, -104])
x_scale = np.float16(2)
y = np.array([0.0, 1.0, 2.0, 896.0, -208.0], dtype=np.float16)
expect(
node,
inputs=[x, x_scale],
outputs=[y],
name="test_dequantizelinear_e4m3fn_float16",
)
@staticmethod
def export_e4m3fn_zero_point() -> None:
node = onnx.helper.make_node(
"DequantizeLinear",
inputs=["x", "x_scale", "zero_point"],
outputs=["y"],
axis=0,
)
# scalar zero point and scale
x = make_tensor("x", TensorProto.FLOAT8E4M3FN, [5], [0, 0.5, 1, 448, -104])
zero_point = make_tensor("zero_point", TensorProto.FLOAT8E4M3FN, [1], [0])
x_scale = np.float32(2)
y = np.array([0.0, 1.0, 2.0, 896.0, -208.0], dtype=np.float32)
expect(
node,
inputs=[x, x_scale, zero_point],
outputs=[y],
name="test_dequantizelinear_e4m3fn_zero_point",
)
@staticmethod
def export_e5m2() -> None:
node = onnx.helper.make_node(
"DequantizeLinear",
inputs=["x", "x_scale"],
outputs=["y"],
axis=0,
)
# scalar zero point and scale
x = make_tensor("x", TensorProto.FLOAT8E5M2, [5], [0, 0.5, 1, 49152, -96])
x_scale = np.float32(2)
y = np.array([0.0, 1.0, 2.0, 98304.0, -192.0], dtype=np.float32)
expect(
node,
inputs=[x, x_scale],
outputs=[y],
name="test_dequantizelinear_e5m2",
)
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59,049 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_isnan.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
import numpy as np
from onnx.reference.ops._op import OpRunUnary
class IsNaN(OpRunUnary):
def _run(self, data): # type: ignore
return (np.isnan(data),)
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59,050 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/mul.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class Mul(Base):
@staticmethod
def export() -> None:
node = onnx.helper.make_node(
"Mul",
inputs=["x", "y"],
outputs=["z"],
)
x = np.array([1, 2, 3]).astype(np.float32)
y = np.array([4, 5, 6]).astype(np.float32)
z = x * y # expected output [4., 10., 18.]
expect(node, inputs=[x, y], outputs=[z], name="test_mul_example")
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.random.randn(3, 4, 5).astype(np.float32)
z = x * y
expect(node, inputs=[x, y], outputs=[z], name="test_mul")
x = np.random.randint(4, size=(3, 4, 5), dtype=np.uint8)
y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8)
z = x * y
expect(node, inputs=[x, y], outputs=[z], name="test_mul_uint8")
@staticmethod
def export_mul_broadcast() -> None:
node = onnx.helper.make_node(
"Mul",
inputs=["x", "y"],
outputs=["z"],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.random.randn(5).astype(np.float32)
z = x * y
expect(node, inputs=[x, y], outputs=[z], name="test_mul_bcast")
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59,051 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/stringnormalizer.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class StringNormalizer(Base):
@staticmethod
def export_nostopwords_nochangecase() -> None:
input = np.array(["monday", "tuesday"]).astype(object)
output = input
# No stopwords. This is a NOOP
node = onnx.helper.make_node(
"StringNormalizer",
inputs=["x"],
outputs=["y"],
is_case_sensitive=1,
)
expect(
node,
inputs=[input],
outputs=[output],
name="test_strnormalizer_nostopwords_nochangecase",
)
@staticmethod
def export_monday_casesensintive_nochangecase() -> None:
input = np.array(["monday", "tuesday", "wednesday", "thursday"]).astype(object)
output = np.array(["tuesday", "wednesday", "thursday"]).astype(object)
stopwords = ["monday"]
node = onnx.helper.make_node(
"StringNormalizer",
inputs=["x"],
outputs=["y"],
is_case_sensitive=1,
stopwords=stopwords,
)
expect(
node,
inputs=[input],
outputs=[output],
name="test_strnormalizer_export_monday_casesensintive_nochangecase",
)
@staticmethod
def export_monday_casesensintive_lower() -> None:
input = np.array(["monday", "tuesday", "wednesday", "thursday"]).astype(object)
output = np.array(["tuesday", "wednesday", "thursday"]).astype(object)
stopwords = ["monday"]
node = onnx.helper.make_node(
"StringNormalizer",
inputs=["x"],
outputs=["y"],
case_change_action="LOWER",
is_case_sensitive=1,
stopwords=stopwords,
)
expect(
node,
inputs=[input],
outputs=[output],
name="test_strnormalizer_export_monday_casesensintive_lower",
)
@staticmethod
def export_monday_casesensintive_upper() -> None:
input = np.array(["monday", "tuesday", "wednesday", "thursday"]).astype(object)
output = np.array(["TUESDAY", "WEDNESDAY", "THURSDAY"]).astype(object)
stopwords = ["monday"]
node = onnx.helper.make_node(
"StringNormalizer",
inputs=["x"],
outputs=["y"],
case_change_action="UPPER",
is_case_sensitive=1,
stopwords=stopwords,
)
expect(
node,
inputs=[input],
outputs=[output],
name="test_strnormalizer_export_monday_casesensintive_upper",
)
@staticmethod
def export_monday_empty_output() -> None:
input = np.array(["monday", "monday"]).astype(object)
output = np.array([""]).astype(object)
stopwords = ["monday"]
node = onnx.helper.make_node(
"StringNormalizer",
inputs=["x"],
outputs=["y"],
case_change_action="UPPER",
is_case_sensitive=1,
stopwords=stopwords,
)
expect(
node,
inputs=[input],
outputs=[output],
name="test_strnormalizer_export_monday_empty_output",
)
@staticmethod
def export_monday_insensintive_upper_twodim() -> None:
input = (
np.array(
["Monday", "tuesday", "wednesday", "Monday", "tuesday", "wednesday"]
)
.astype(object)
.reshape([1, 6])
)
# It does upper case cecedille, accented E
# and german umlaut but fails
# with german eszett
output = (
np.array(["TUESDAY", "WEDNESDAY", "TUESDAY", "WEDNESDAY"])
.astype(object)
.reshape([1, 4])
)
stopwords = ["monday"]
node = onnx.helper.make_node(
"StringNormalizer",
inputs=["x"],
outputs=["y"],
case_change_action="UPPER",
stopwords=stopwords,
)
expect(
node,
inputs=[input],
outputs=[output],
name="test_strnormalizer_export_monday_insensintive_upper_twodim",
)
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59,052 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/reducemin.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class ReduceMin(Base):
@staticmethod
def export_do_not_keepdims() -> None:
shape = [3, 2, 2]
axes = np.array([1], dtype=np.int64)
keepdims = 0
node = onnx.helper.make_node(
"ReduceMin",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
)
data = np.array(
[[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],
dtype=np.float32,
)
reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
# print(reduced)
# [[5., 1.]
# [30., 1.]
# [55., 1.]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_min_do_not_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_min_do_not_keepdims_random",
)
@staticmethod
def export_keepdims() -> None:
shape = [3, 2, 2]
axes = np.array([1], dtype=np.int64)
keepdims = 1
node = onnx.helper.make_node(
"ReduceMin",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
)
data = np.array(
[[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],
dtype=np.float32,
)
reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
# print(reduced)
# [[[5., 1.]]
# [[30., 1.]]
# [[55., 1.]]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_min_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_min_keepdims_random",
)
@staticmethod
def export_default_axes_keepdims() -> None:
shape = [3, 2, 2]
axes = None
keepdims = 1
node = onnx.helper.make_node(
"ReduceMin", inputs=["data"], outputs=["reduced"], keepdims=keepdims
)
data = np.array(
[[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],
dtype=np.float32,
)
reduced = np.minimum.reduce(data, axis=axes, keepdims=keepdims == 1)
# print(reduced)
# [[[1.]]]
expect(
node,
inputs=[data],
outputs=[reduced],
name="test_reduce_min_default_axes_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.minimum.reduce(data, axis=axes, keepdims=keepdims == 1)
expect(
node,
inputs=[data],
outputs=[reduced],
name="test_reduce_min_default_axes_keepdims_random",
)
@staticmethod
def export_negative_axes_keepdims() -> None:
shape = [3, 2, 2]
axes = np.array([-2], dtype=np.int64)
keepdims = 1
node = onnx.helper.make_node(
"ReduceMin",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
)
data = np.array(
[[[5, 1], [20, 2]], [[30, 1], [40, 2]], [[55, 1], [60, 2]]],
dtype=np.float32,
)
reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
# print(reduced)
# [[[5., 1.]]
# [[30., 1.]]
# [[55., 1.]]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_min_negative_axes_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.minimum.reduce(data, axis=tuple(axes), keepdims=keepdims == 1)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_min_negative_axes_keepdims_random",
)
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"/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
59,053 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/tile.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class Tile(Base):
@staticmethod
def export_tile() -> None:
node = onnx.helper.make_node("Tile", inputs=["x", "y"], outputs=["z"])
x = np.random.rand(2, 3, 4, 5).astype(np.float32)
repeats = np.random.randint(low=1, high=10, size=(np.ndim(x),)).astype(np.int64)
z = np.tile(x, repeats)
expect(node, inputs=[x, repeats], outputs=[z], name="test_tile")
@staticmethod
def export_tile_precomputed() -> None:
node = onnx.helper.make_node("Tile", inputs=["x", "y"], outputs=["z"])
x = np.array([[0, 1], [2, 3]], dtype=np.float32)
repeats = np.array([2, 2], dtype=np.int64)
z = np.array(
[[0, 1, 0, 1], [2, 3, 2, 3], [0, 1, 0, 1], [2, 3, 2, 3]], dtype=np.float32
)
expect(node, inputs=[x, repeats], outputs=[z], name="test_tile_precomputed")
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59,054 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_flatten.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
import numpy as np
from onnx.reference.ops._op import OpRunUnary
class Flatten(OpRunUnary):
def _run(self, x, axis=None): # type: ignore
i = axis or self.axis # type: ignore
shape = x.shape
new_shape = (1, -1) if i == 0 else (np.prod(shape[:i]).astype(int), -1)
return (x.reshape(new_shape),) # type: ignore
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59,055 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_scatternd.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
import numpy as np
from onnx.reference.op_run import OpRun
def _scatter_nd_impl(data, indices, updates, reduction=None): # type: ignore
output = np.copy(data)
for i in np.ndindex(indices.shape[:-1]):
if reduction == "add":
output[tuple(indices[i])] += updates[i]
elif reduction == "mul":
output[tuple(indices[i])] *= updates[i]
elif reduction == "max":
output[tuple(indices[i])] = np.maximum(output[indices[i]], updates[i])
elif reduction == "min":
output[tuple(indices[i])] = np.minimum(output[indices[i]], updates[i])
else:
output[tuple(indices[i])] = updates[i]
return output
class ScatterND(OpRun):
def _run(self, data, indices, updates, reduction=None): # type: ignore
y = _scatter_nd_impl(data, indices, updates, reduction=reduction)
return (y,)
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"/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
59,056 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_optional.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221,W0622
from onnx.helper import tensor_dtype_to_np_dtype
from onnx.reference.op_run import OpRun
class Optional(OpRun):
def _run(self, x=None, type=None): # type: ignore
if x is not None and type is not None:
dt = tensor_dtype_to_np_dtype(type)
if dt != x.dtype:
raise TypeError(
f"Input dtype {x.dtype} ({dt}) and parameter type_proto {type} disagree"
)
return ([x],)
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"/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/dequantizelinear.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py"], "/onnx/reference/ops/op_isnan.py": ["/onnx/reference/ops/_op.py"], "/onnx/backend/test/case/node/mul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stringnormalizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducemin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/tile.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_flatten.py": ["/onnx/reference/ops/_op.py"], 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"/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/identity.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/sin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gemm.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_layer_normalization.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_bernoulli.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/backend/test/case/node/deformconv.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_upsample.py": ["/onnx/reference/op_run.py"], "/onnx/test/test_with_ort.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_thresholded_relu.py": ["/onnx/reference/ops/_op.py"], "/onnx/backend/test/case/node/concat.py": ["/onnx/__init__.py", 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59,057 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/rnn.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from typing import Any, Tuple
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class RNNHelper:
def __init__(self, **params: Any) -> None:
# RNN Input Names
X = "X"
W = "W"
R = "R"
B = "B"
H_0 = "initial_h"
LAYOUT = "layout"
required_inputs = [X, W, R]
for i in required_inputs:
assert i in params, f"Missing Required Input: {i}"
self.num_directions = params[str(W)].shape[0]
if self.num_directions == 1:
for k in params:
if k != X:
params[k] = np.squeeze(params[k], axis=0)
hidden_size = params[R].shape[-1]
batch_size = params[X].shape[1]
layout = params[LAYOUT] if LAYOUT in params else 0
x = params[X]
x = x if layout == 0 else np.swapaxes(x, 0, 1)
b = (
params[B]
if B in params
else np.zeros(2 * hidden_size, dtype=np.float32)
)
h_0 = (
params[H_0]
if H_0 in params
else np.zeros((batch_size, hidden_size), dtype=np.float32)
)
self.X = x
self.W = params[W]
self.R = params[R]
self.B = b
self.H_0 = h_0
self.LAYOUT = layout
else:
raise NotImplementedError()
def f(self, x: np.ndarray) -> np.ndarray:
return np.tanh(x)
def step(self) -> Tuple[np.ndarray, np.ndarray]:
seq_length = self.X.shape[0]
hidden_size = self.H_0.shape[-1]
batch_size = self.X.shape[1]
Y = np.empty([seq_length, self.num_directions, batch_size, hidden_size])
h_list = []
H_t = self.H_0
for x in np.split(self.X, self.X.shape[0], axis=0):
H = self.f(
np.dot(x, np.transpose(self.W))
+ np.dot(H_t, np.transpose(self.R))
+ np.add(*np.split(self.B, 2))
)
h_list.append(H)
H_t = H
concatenated = np.concatenate(h_list)
if self.num_directions == 1:
Y[:, 0, :, :] = concatenated
if self.LAYOUT == 0:
Y_h = Y[-1]
else:
Y = np.transpose(Y, [2, 0, 1, 3])
Y_h = Y[:, :, -1, :]
return Y, Y_h
class RNN(Base):
@staticmethod
def export_defaults() -> None:
input = np.array([[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]]).astype(np.float32)
input_size = 2
hidden_size = 4
weight_scale = 0.1
node = onnx.helper.make_node(
"RNN", inputs=["X", "W", "R"], outputs=["", "Y_h"], hidden_size=hidden_size
)
W = weight_scale * np.ones((1, hidden_size, input_size)).astype(np.float32)
R = weight_scale * np.ones((1, hidden_size, hidden_size)).astype(np.float32)
rnn = RNNHelper(X=input, W=W, R=R)
_, Y_h = rnn.step()
expect(
node,
inputs=[input, W, R],
outputs=[Y_h.astype(np.float32)],
name="test_simple_rnn_defaults",
)
@staticmethod
def export_initial_bias() -> None:
input = np.array([[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]]).astype(
np.float32
)
input_size = 3
hidden_size = 5
custom_bias = 0.1
weight_scale = 0.1
node = onnx.helper.make_node(
"RNN",
inputs=["X", "W", "R", "B"],
outputs=["", "Y_h"],
hidden_size=hidden_size,
)
W = weight_scale * np.ones((1, hidden_size, input_size)).astype(np.float32)
R = weight_scale * np.ones((1, hidden_size, hidden_size)).astype(np.float32)
# Adding custom bias
W_B = custom_bias * np.ones((1, hidden_size)).astype(np.float32)
R_B = np.zeros((1, hidden_size)).astype(np.float32)
B = np.concatenate((W_B, R_B), axis=1)
rnn = RNNHelper(X=input, W=W, R=R, B=B)
_, Y_h = rnn.step()
expect(
node,
inputs=[input, W, R, B],
outputs=[Y_h.astype(np.float32)],
name="test_simple_rnn_with_initial_bias",
)
@staticmethod
def export_seq_length() -> None:
input = np.array(
[
[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]],
[[10.0, 11.0, 12.0], [13.0, 14.0, 15.0], [16.0, 17.0, 18.0]],
]
).astype(np.float32)
input_size = 3
hidden_size = 5
node = onnx.helper.make_node(
"RNN",
inputs=["X", "W", "R", "B"],
outputs=["", "Y_h"],
hidden_size=hidden_size,
)
W = np.random.randn(1, hidden_size, input_size).astype(np.float32)
R = np.random.randn(1, hidden_size, hidden_size).astype(np.float32)
# Adding custom bias
W_B = np.random.randn(1, hidden_size).astype(np.float32)
R_B = np.random.randn(1, hidden_size).astype(np.float32)
B = np.concatenate((W_B, R_B), axis=1)
rnn = RNNHelper(X=input, W=W, R=R, B=B)
_, Y_h = rnn.step()
expect(
node,
inputs=[input, W, R, B],
outputs=[Y_h.astype(np.float32)],
name="test_rnn_seq_length",
)
@staticmethod
def export_batchwise() -> None:
input = np.array([[[1.0, 2.0]], [[3.0, 4.0]], [[5.0, 6.0]]]).astype(np.float32)
input_size = 2
hidden_size = 4
weight_scale = 0.5
layout = 1
node = onnx.helper.make_node(
"RNN",
inputs=["X", "W", "R"],
outputs=["Y", "Y_h"],
hidden_size=hidden_size,
layout=layout,
)
W = weight_scale * np.ones((1, hidden_size, input_size)).astype(np.float32)
R = weight_scale * np.ones((1, hidden_size, hidden_size)).astype(np.float32)
rnn = RNNHelper(X=input, W=W, R=R, layout=layout)
Y, Y_h = rnn.step()
expect(
node,
inputs=[input, W, R],
outputs=[Y.astype(np.float32), Y_h.astype(np.float32)],
name="test_simple_rnn_batchwise",
)
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59,058 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/image_decoder.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import cv2
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
def generate_checkerboard(width, height, square_size):
# Create an empty RGB image
image = np.zeros((height, width, 3), dtype=np.uint8)
# Calculate the number of squares in each dimension
num_squares_x = width // square_size
num_squares_y = height // square_size
# Generate a random color for each square
colors = np.random.randint(
0, 256, size=(num_squares_y, num_squares_x, 3), dtype=np.uint8
)
# Iterate over each square
for i in range(num_squares_y):
for j in range(num_squares_x):
# Calculate the position of the current square
x = j * square_size
y = i * square_size
# Get the color for the current square
color = colors[i, j]
# Fill the square with the corresponding color
image[y : y + square_size, x : x + square_size, :] = color
return image
def generate_test_data(extension, pixel_format="RGB", h=40, w=40, tile_sz=5):
data, output = None, None
np.random.seed(12345)
image = generate_checkerboard(h, w, tile_sz)
image_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
_, encoded_image = cv2.imencode(extension, image_bgr)
data = np.frombuffer(encoded_image, dtype=np.uint8)
if pixel_format == "BGR":
output = cv2.imdecode(data, cv2.IMREAD_COLOR)
elif pixel_format == "RGB":
output_bgr = cv2.imdecode(data, cv2.IMREAD_COLOR)
output = cv2.cvtColor(output_bgr, cv2.COLOR_BGR2RGB)
elif pixel_format == "Grayscale":
output = cv2.imdecode(data, cv2.IMREAD_GRAYSCALE)
output = np.expand_dims(output, axis=2) # (H, W) to (H, W, 1)
return data, output
class ImageDecoder(Base):
@staticmethod
def export_image_decoder_decode_jpeg_rgb() -> None:
node = onnx.helper.make_node(
"ImageDecoder",
inputs=["data"],
outputs=["output"],
pixel_format="RGB",
)
data, output = generate_test_data(".jpg", "RGB")
expect(
node,
inputs=[data],
outputs=[output],
name="test_image_decoder_decode_jpeg_rgb",
)
@staticmethod
def export_image_decoder_decode_jpeg_grayscale() -> None:
node = onnx.helper.make_node(
"ImageDecoder",
inputs=["data"],
outputs=["output"],
pixel_format="Grayscale",
)
data, output = generate_test_data(".jpg", "Grayscale")
expect(
node,
inputs=[data],
outputs=[output],
name="test_image_decoder_decode_jpeg_grayscale",
)
@staticmethod
def export_image_decoder_decode_jpeg_bgr() -> None:
node = onnx.helper.make_node(
"ImageDecoder",
inputs=["data"],
outputs=["output"],
pixel_format="BGR",
)
data, output = generate_test_data(".jpg", "BGR")
expect(
node,
inputs=[data],
outputs=[output],
name="test_image_decoder_decode_jpeg_bgr",
)
@staticmethod
def export_image_decoder_decode_jpeg2k_rgb() -> None:
node = onnx.helper.make_node(
"ImageDecoder",
inputs=["data"],
outputs=["output"],
pixel_format="RGB",
)
data, output = generate_test_data(".jp2", "RGB")
expect(
node,
inputs=[data],
outputs=[output],
name="test_image_decoder_decode_jpeg2k_rgb",
)
@staticmethod
def export_image_decoder_decode_bmp_rgb() -> None:
node = onnx.helper.make_node(
"ImageDecoder",
inputs=["data"],
outputs=["output"],
pixel_format="RGB",
)
data, output = generate_test_data(".bmp", "RGB")
expect(
node,
inputs=[data],
outputs=[output],
name="test_image_decoder_decode_bmp_rgb",
)
@staticmethod
def export_image_decoder_decode_png_rgb() -> None:
node = onnx.helper.make_node(
"ImageDecoder",
inputs=["data"],
outputs=["output"],
pixel_format="RGB",
)
data, output = generate_test_data(".png", "RGB")
expect(
node,
inputs=[data],
outputs=[output],
name="test_image_decoder_decode_png_rgb",
)
@staticmethod
def export_image_decoder_decode_tiff_rgb() -> None:
node = onnx.helper.make_node(
"ImageDecoder",
inputs=["data"],
outputs=["output"],
pixel_format="RGB",
)
data, output = generate_test_data(".tiff", "RGB")
expect(
node,
inputs=[data],
outputs=[output],
name="test_image_decoder_decode_tiff_rgb",
)
@staticmethod
def export_image_decoder_decode_webp_rgb() -> None:
node = onnx.helper.make_node(
"ImageDecoder",
inputs=["data"],
outputs=["output"],
pixel_format="RGB",
)
data, output = generate_test_data(".webp", "RGB")
expect(
node,
inputs=[data],
outputs=[output],
name="test_image_decoder_decode_webp_rgb",
)
@staticmethod
def export_image_decoder_decode_pnm_rgb() -> None:
node = onnx.helper.make_node(
"ImageDecoder",
inputs=["data"],
outputs=["output"],
pixel_format="RGB",
)
data, output = generate_test_data(".pnm", "RGB")
expect(
node,
inputs=[data],
outputs=[output],
name="test_image_decoder_decode_pnm_rgb",
)
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59,059 | onnx/onnx | refs/heads/main | /onnx/reference/ops/aionnxml/op_tree_ensemble_regressor.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0912,R0913,R0914,W0221
import numpy as np
from onnx.reference.ops.aionnxml._op_run_aionnxml import OpRunAiOnnxMl
from onnx.reference.ops.aionnxml.op_tree_ensemble_helper import TreeEnsemble
class TreeEnsembleRegressor(OpRunAiOnnxMl):
"""
`nodes_hitrates` and `nodes_hitrates_as_tensor` are not used.
"""
def _run( # type: ignore
self,
X,
aggregate_function=None,
base_values=None,
base_values_as_tensor=None,
n_targets=None,
nodes_falsenodeids=None,
nodes_featureids=None,
nodes_hitrates=None,
nodes_hitrates_as_tensor=None,
nodes_missing_value_tracks_true=None,
nodes_modes=None,
nodes_nodeids=None,
nodes_treeids=None,
nodes_truenodeids=None,
nodes_values=None,
nodes_values_as_tensor=None,
post_transform=None,
target_ids=None,
target_nodeids=None,
target_treeids=None,
target_weights=None,
target_weights_as_tensor=None,
):
nmv = nodes_missing_value_tracks_true
tr = TreeEnsemble(
base_values=base_values,
base_values_as_tensor=base_values_as_tensor,
nodes_falsenodeids=nodes_falsenodeids,
nodes_featureids=nodes_featureids,
nodes_hitrates=nodes_hitrates,
nodes_hitrates_as_tensor=nodes_hitrates_as_tensor,
nodes_missing_value_tracks_true=nmv,
nodes_modes=nodes_modes,
nodes_nodeids=nodes_nodeids,
nodes_treeids=nodes_treeids,
nodes_truenodeids=nodes_truenodeids,
nodes_values=nodes_values,
nodes_values_as_tensor=nodes_values_as_tensor,
target_weights=target_weights,
target_weights_as_tensor=target_weights_as_tensor,
)
# unused unless for debugging purposes
self._tree = tr # pylint: disable=W0201
leaves_index = tr.leave_index_tree(X)
res = np.zeros((leaves_index.shape[0], n_targets), dtype=X.dtype)
n_trees = len(set(tr.atts.nodes_treeids)) # type: ignore
target_index = {} # type: ignore
for i, (tid, nid) in enumerate(zip(target_treeids, target_nodeids)):
if (tid, nid) not in target_index:
target_index[tid, nid] = []
target_index[tid, nid].append(i)
for i in range(res.shape[0]):
indices = leaves_index[i]
t_index = [
target_index[nodes_treeids[i], nodes_nodeids[i]] for i in indices
]
if aggregate_function in ("SUM", "AVERAGE"):
for its in t_index:
for it in its:
res[i, target_ids[it]] += tr.atts.target_weights[it] # type: ignore
elif aggregate_function == "MIN":
res[i, :] = np.finfo(res.dtype).max
for its in t_index:
for it in its:
res[i, target_ids[it]] = min(
res[i, target_ids[it]], tr.atts.target_weights[it] # type: ignore
)
elif aggregate_function == "MAX":
res[i, :] = np.finfo(res.dtype).min
for its in t_index:
for it in its:
res[i, target_ids[it]] = max(
res[i, target_ids[it]], tr.atts.target_weights[it] # type: ignore
)
else:
raise NotImplementedError(
f"aggregate_transform={aggregate_function!r} not supported yet."
)
if aggregate_function == "AVERAGE":
res /= n_trees
if base_values is not None:
res[:, :] = np.array(base_values).reshape((1, -1))
if post_transform in (None, "NONE"):
return (res,)
raise NotImplementedError(f"post_transform={post_transform!r} not implemented.")
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"/onnx/reference/ops/aionnxml/op_linear_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_softplus.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_sub.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_quantize_linear.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_gathernd.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/qlinearmatmul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/shape_inference_test.py": ["/onnx/shape_inference.py", "/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/parser.py"], "/onnx/backend/test/case/node/mish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_expand.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": 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59,060 | onnx/onnx | refs/heads/main | /workflow_scripts/test_model_zoo.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
import argparse
import gc
import os
import sys
import time
from typing import List
import config
import onnx
from onnx import hub, version_converter
def skip_model(error_message: str, skip_list: List[str], model_name: str):
print(error_message)
skip_list.append(model_name)
def main():
parser = argparse.ArgumentParser(description="Test settings")
# default: test all models in the repo
# if test_dir is specified, only test files under that specified path
parser.add_argument(
"--test_dir",
required=False,
default="",
type=str,
help="Directory path for testing. e.g., text, vision",
)
model_list = hub.list_models()
print(f"=== Running ONNX Checker on {len(model_list)} models ===")
# run checker on each model
failed_models = []
failed_messages = []
skip_models: List[str] = []
for m in model_list:
start = time.time()
model_name = m.model
model_path = m.model_path
print(f"-----------------Testing: {model_name}-----------------")
try:
model = hub.load(model_name)
# 1) Test onnx checker and shape inference
if model.opset_import[0].version < 4:
# Ancient opset version does not have defined shape inference function
onnx.checker.check_model(model)
print(f"[PASS]: {model_name} is checked by onnx checker. ")
else:
# stricter onnx.checker with onnx.shape_inference
onnx.checker.check_model(model, True)
print(
f"[PASS]: {model_name} is checked by onnx checker with shape_inference. "
)
# 2) Test onnx version converter with upgrade functionality
original_version = model.opset_import[0].version
latest_opset_version = onnx.helper.VERSION_TABLE[-1][2]
if original_version < latest_opset_version:
if model_path in config.SKIP_VERSION_CONVERTER_MODELS:
skip_model(
f"[SKIP]: model {model_name} is in the skip list for version converter. ",
skip_models,
model_name,
)
elif model_path.endswith("-int8.onnx"):
skip_model(
f"[SKIP]: model {model_name} is a quantized model using non-official ONNX domain. ",
skip_models,
model_name,
)
else:
converted = version_converter.convert_version(
model, original_version + 1
)
onnx.checker.check_model(converted, True)
print(
f"[PASS]: {model_name} can be version converted by original_version+1. "
)
elif original_version == latest_opset_version:
skip_model(
f"[SKIP]: {model_name} is already the latest opset version. ",
skip_models,
model_name,
)
else:
raise RuntimeError(
f"{model_name} has unsupported opset_version {original_version}. "
)
# remove the model to save space in CIs
if os.path.exists(model_name):
os.remove(model_name)
except Exception as e:
print(f"[FAIL]: {e}")
failed_models.append(model_name)
failed_messages.append((model_name, e))
end = time.time()
print(f"--------------Time used: {end - start} secs-------------")
# enable gc collection to prevent MemoryError by loading too many large models
gc.collect()
if len(failed_models) == 0:
print(
f"{len(model_list)} models have been checked. {len(skip_models)} models were skipped."
)
else:
print(
f"In all {len(model_list)} models, {len(failed_models)} models failed, {len(skip_models)} models were skipped"
)
for model_name, error in failed_messages:
print(f"{model_name} failed because: {error}")
sys.exit(1)
if __name__ == "__main__":
main()
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["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_non_max_suppression.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/logsoftmax.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_affine_grid.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_lp_normalization.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_rnn.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/not.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_reduce_sum.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mean.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_roi_align.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_center_crop_pad.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/nonmaxsuppression.py": ["/onnx/__init__.py", 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59,061 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/gelu.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import math
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class Gelu(Base):
@staticmethod
def export_gelu_tanh() -> None:
node = onnx.helper.make_node(
"Gelu", inputs=["x"], outputs=["y"], approximate="tanh"
)
x = np.array([-1, 0, 1]).astype(np.float32)
# expected output [-0.158808, 0., 0.841192]
y = (
0.5
* x
* (1 + np.tanh(np.sqrt(2 / np.pi) * (x + 0.044715 * np.power(x, 3))))
).astype(np.float32)
expect(node, inputs=[x], outputs=[y], name="test_gelu_tanh_1")
x = np.random.randn(3, 4, 5).astype(np.float32)
# expected output [2.9963627, 3.99993, 4.9999995]
y = (
0.5
* x
* (1 + np.tanh(np.sqrt(2 / np.pi) * (x + 0.044715 * np.power(x, 3))))
).astype(np.float32)
expect(node, inputs=[x], outputs=[y], name="test_gelu_tanh_2")
@staticmethod
def export_gelu_default() -> None:
node = onnx.helper.make_node("Gelu", inputs=["x"], outputs=["y"])
x = np.array([-1, 0, 1]).astype(np.float32)
# expected output [-0.15865526, 0., 0.84134474]
y = (0.5 * x * (1 + np.vectorize(math.erf)(x / np.sqrt(2)))).astype(np.float32)
expect(node, inputs=[x], outputs=[y], name="test_gelu_default_1")
x = np.random.randn(3, 4, 5).astype(np.float32)
# expected output [2.99595031, 3.99987331, 4.99999857]
y = (0.5 * x * (1 + np.vectorize(math.erf)(x / np.sqrt(2)))).astype(np.float32)
expect(node, inputs=[x], outputs=[y], name="test_gelu_default_2")
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"/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
59,062 | onnx/onnx | refs/heads/main | /onnx/printer.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from typing import Union
import onnx
import onnx.onnx_cpp2py_export.printer as C # noqa: N812
def to_text(proto: Union[onnx.ModelProto, onnx.FunctionProto, onnx.GraphProto]) -> str:
if isinstance(proto, onnx.ModelProto):
return C.model_to_text(proto.SerializeToString())
if isinstance(proto, onnx.FunctionProto):
return C.function_to_text(proto.SerializeToString())
if isinstance(proto, onnx.GraphProto):
return C.graph_to_text(proto.SerializeToString())
raise TypeError("Unsupported argument type.")
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59,063 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/nonzero.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class NonZero(Base):
@staticmethod
def export() -> None:
node = onnx.helper.make_node(
"NonZero",
inputs=["condition"],
outputs=["result"],
)
condition = np.array([[1, 0], [1, 1]], dtype=bool)
result = np.array(
np.nonzero(condition), dtype=np.int64
) # expected output [[0, 1, 1], [0, 0, 1]]
expect(node, inputs=[condition], outputs=[result], name="test_nonzero_example")
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"/onnx/backend/test/case/node/loop.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/conf.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_sequence_construct.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatterelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel2.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/bernoulli.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/constant.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/resize.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", 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"/onnx/reference/ops/aionnxml/op_linear_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_softplus.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_sub.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_quantize_linear.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_gathernd.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/qlinearmatmul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/shape_inference_test.py": ["/onnx/shape_inference.py", "/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/parser.py"], "/onnx/backend/test/case/node/mish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_expand.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": ["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/_helpers.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/tfidfvectorizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_average_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/item.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/gatherelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/slice.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_matmul.py": 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"/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
59,064 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/identity.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class Identity(Base):
@staticmethod
def export() -> None:
node = onnx.helper.make_node(
"Identity",
inputs=["x"],
outputs=["y"],
)
data = np.array(
[
[
[
[1, 2],
[3, 4],
]
]
],
dtype=np.float32,
)
expect(node, inputs=[data], outputs=[data], name="test_identity")
@staticmethod
def export_sequence() -> None:
node = onnx.helper.make_node(
"Identity",
inputs=["x"],
outputs=["y"],
)
data = [
np.array(
[
[
[
[1, 2],
[3, 4],
]
]
],
dtype=np.float32,
),
np.array(
[
[
[
[2, 3],
[1, 5],
]
]
],
dtype=np.float32,
),
]
expect(node, inputs=[data], outputs=[data], name="test_identity_sequence")
@staticmethod
def export_identity_opt() -> None:
ten_in_tp = onnx.helper.make_tensor_type_proto(
onnx.TensorProto.FLOAT, shape=[5]
)
seq_in_tp = onnx.helper.make_sequence_type_proto(ten_in_tp)
opt_in_tp = onnx.helper.make_optional_type_proto(seq_in_tp)
identity_node = onnx.helper.make_node(
"Identity", inputs=["opt_in"], outputs=["opt_out"]
)
x = [np.array([1, 2, 3, 4, 5]).astype(np.float32)]
expect(
identity_node,
inputs=[x],
outputs=[x],
name="test_identity_opt",
opset_imports=[onnx.helper.make_opsetid("", 16)],
input_type_protos=[opt_in_tp],
output_type_protos=[opt_in_tp],
)
| {"/onnx/backend/test/case/node/sign.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/dft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/parser.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/constantofshape.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/averagepool.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/__init__.py": ["/onnx/__init__.py", "/onnx/backend/base.py", "/onnx/backend/test/case/test_case.py", "/onnx/backend/test/loader/__init__.py", "/onnx/backend/test/runner/item.py"], "/onnx/reference/ops/op_topk.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_image_decoder.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_non_max_suppression.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/logsoftmax.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_affine_grid.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_lp_normalization.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_rnn.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/not.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_reduce_sum.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mean.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_roi_align.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_center_crop_pad.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/nonmaxsuppression.py": ["/onnx/__init__.py", 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59,065 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/sin.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class Sin(Base):
@staticmethod
def export() -> None:
node = onnx.helper.make_node(
"Sin",
inputs=["x"],
outputs=["y"],
)
x = np.array([-1, 0, 1]).astype(np.float32)
y = np.sin(x)
expect(node, inputs=[x], outputs=[y], name="test_sin_example")
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.sin(x)
expect(node, inputs=[x], outputs=[y], name="test_sin")
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59,066 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_gemm.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0913,W0221
import numpy as np
from onnx.reference.op_run import OpRun
def _gemm00(a, b, c, alpha, beta): # type: ignore
o = np.dot(a, b) * alpha
if c is not None and beta != 0:
o += c * beta
return o
def _gemm01(a, b, c, alpha, beta): # type: ignore
o = np.dot(a, b.T) * alpha
if c is not None and beta != 0:
o += c * beta
return o
def _gemm10(a, b, c, alpha, beta): # type: ignore
o = np.dot(a.T, b) * alpha
if c is not None and beta != 0:
o += c * beta
return o
def _gemm11(a, b, c, alpha, beta): # type: ignore
o = np.dot(a.T, b.T) * alpha
if c is not None and beta != 0:
o += c * beta
return o
class Gemm_6(OpRun):
def _run(self, a, b, c=None, alpha=None, beta=None, transA=None, transB=None, broadcast=None): # type: ignore
if broadcast == 0:
if transA:
_meth = _gemm11 if transB else _gemm10
else:
_meth = _gemm01 if transB else _gemm00
res = _meth(a, b, None, alpha, beta)
if c is None:
return (res.astype(a.dtype),)
if c.shape != res.shape:
raise ValueError(
f"Unable to add shape {c.shape} to shape {res.shape} without broadcast."
)
return (res + c,)
if transA:
_meth = _gemm11 if transB else _gemm10
else:
_meth = _gemm01 if transB else _gemm00
return (_meth(a, b, c, alpha, beta).astype(a.dtype),)
class Gemm_7(OpRun):
def _run(self, a, b, c=None, alpha=None, beta=None, transA=None, transB=None): # type: ignore
if transA:
_meth = _gemm11 if transB else _gemm10
else:
_meth = _gemm01 if transB else _gemm00
return (_meth(a, b, c, alpha, beta).astype(a.dtype),)
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59,067 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_layer_normalization.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0913,R0914,W0221
from typing import Tuple
import numpy as np
from onnx.reference.op_run import OpRun
def _layer_normalization(
X: np.ndarray,
W: np.ndarray,
B: np.ndarray,
axis: int = -1,
epsilon: float = 1e-5,
) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
X_shape = X.shape
X_rank = len(X_shape)
if axis < 0:
# If axis = -1 and rank of X is 4,
# the axis is changed to -1 + 4 = 3,
# which means the last axis.
axis = axis + X_rank
unsqueezed_rank = X_rank - axis
reduction_shape = X_shape[0:axis] + (1,) * unsqueezed_rank
# Parameter used to convert N-D tensor layer
# normalization to equivalent 2-D matirx operations.
row_number = 1
col_number = 1
for i in range(X_rank):
if i < axis:
row_number *= X_shape[i]
else:
col_number *= X_shape[i]
# After reshaping input tensor X into a matrix,
# layer normalization is equivalent to conducting
# standardization on each column vector (s.t. each
# column has zero mean and unit variance).
x_mat = np.reshape(X, (row_number, col_number))
# This computes mean for every x_mat's column.
x_mean = np.sum(x_mat, axis=1, keepdims=True) / col_number
x_diff = x_mat - x_mean
x_squared_diff = x_diff * x_diff
# This computes variance for every x_mat's column.
variance = np.sum(x_squared_diff, axis=1, keepdims=True) / col_number
variance_eps = variance + epsilon
std_dev = np.sqrt(variance_eps)
inv_std_dev = np.reciprocal(std_dev)
# Standardization step. y_mat is zero-mean and unit-variance.
y_mat = x_diff * inv_std_dev
# Apply affine transform on normalization outcome.
# W is linear coefficient while B is bias.
Y = np.reshape(y_mat, X_shape) * W
if B is not None:
Y = Y + B
# Matrix-level operations' outputs should be reshaped
# to compensate the initial tensor-to-matrix reshape.
X_mean = np.reshape(x_mean, reduction_shape)
X_inv_std_dev = np.reshape(inv_std_dev, reduction_shape)
return (Y.astype(X.dtype), X_mean.astype(X.dtype), X_inv_std_dev.astype(X.dtype))
class LayerNormalization(OpRun):
def _run(self, X, Scale, B=None, axis=None, epsilon=None, stash_type=None): # type: ignore
if stash_type != 1:
raise NotImplementedError(
f"LayerNormalization not implemented for stash_type={stash_type} != 1."
)
res = _layer_normalization(X, Scale, B, axis=axis, epsilon=epsilon)
return res
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59,068 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_bernoulli.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
from onnx.helper import np_dtype_to_tensor_dtype
from onnx.reference.ops._op_common_random import _CommonRandom
class Bernoulli(_CommonRandom):
def _run(self, x, dtype=None, seed=None): # type: ignore
if dtype is None:
dtype = np_dtype_to_tensor_dtype(x.dtype)
dtype = self._dtype(x, dtype=dtype, dtype_first=True)
state = self._get_state(seed)
res = state.binomial(1, p=x).astype(dtype)
return (res.astype(dtype),)
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59,069 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/deformconv.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class DeformConv(Base):
@staticmethod
def export() -> None:
X = np.arange(9).astype(np.float32)
X.shape = (1, 1, 3, 3)
W = np.ones((1, 1, 2, 2), dtype=np.float32)
# Convolution with padding
offset_with_padding = np.zeros((1, 8, 4, 4), dtype=np.float32)
offset_with_padding[
0, 0, 0, 0
] = 0.5 # h-coord of [0, 0] element of kernel, at output position [0, 0]
offset_with_padding[
0, 5, 1, 2
] = -0.1 # w-coord of [1, 0] element of kernel, at output position [1, 2]
node_with_padding = onnx.helper.make_node(
"DeformConv",
inputs=["X", "W", "offset_with_padding"],
outputs=["Y_with_padding"],
kernel_shape=[2, 2],
pads=[1, 1, 1, 1],
)
Y_with_padding = np.array(
[
[
[
[0.0, 1.0, 3.0, 2.0], # (1, 1, 4, 4) output tensor
[3.0, 8.0, 11.9, 7.0],
[9.0, 20.0, 24.0, 13.0],
[6.0, 13.0, 15.0, 8.0],
]
]
]
).astype(np.float32)
expect(
node_with_padding,
inputs=[X, W, offset_with_padding],
outputs=[Y_with_padding],
name="test_basic_deform_conv_with_padding",
)
# Convolution without padding
offset_without_padding = np.zeros((1, 8, 2, 2), dtype=np.float32)
offset_without_padding[
0, 0, 0, 0
] = 0.5 # h-coord of [0, 0] element of kernel, at output position [0, 0]
offset_without_padding[
0, 5, 0, 1
] = -0.1 # w-coord of [1, 0] element of kernel, at output position [0, 1]
node_without_padding = onnx.helper.make_node(
"DeformConv",
inputs=["X", "W", "offset_without_padding"],
outputs=["Y_without_padding"],
kernel_shape=[2, 2],
pads=[0, 0, 0, 0],
)
Y_without_padding = np.array(
[
[
[
[9.5, 11.9], # (1, 1, 2, 2) output tensor
[20.0, 24.0],
]
]
]
).astype(np.float32)
expect(
node_without_padding,
inputs=[X, W, offset_without_padding],
outputs=[Y_without_padding],
name="test_basic_deform_conv_without_padding",
)
@staticmethod
def export_deformconv_with_mask_bias() -> None:
X = np.arange(9).astype(np.float32)
X.shape = (1, 1, 3, 3)
W = np.ones((1, 1, 2, 2), dtype=np.float32)
B = np.ones((1,), dtype=np.float32)
offset = np.zeros((1, 8, 2, 2), dtype=np.float32)
offset[
0, 0, 0, 0
] = 0.5 # h-coord of [0, 0] element of kernel, at output position [0, 0]
offset[
0, 5, 0, 1
] = -0.1 # w-coord of [1, 0] element of kernel, at output position [0, 1]
mask = np.ones((1, 4, 2, 2), dtype=np.float32)
mask[0, 2, 1, 1] = 0.2 # [1, 0] element of kernel at output position [1, 1]
node = onnx.helper.make_node(
"DeformConv",
inputs=["X", "W", "offset", "B", "mask"],
outputs=["Y"],
kernel_shape=[2, 2],
pads=[0, 0, 0, 0],
)
Y = np.array(
[
[
[
[10.5, 12.9], # (1, 1, 2, 2) output tensor
[21.0, 19.4],
]
]
]
).astype(np.float32)
expect(
node,
inputs=[X, W, offset, B, mask],
outputs=[Y],
name="test_deform_conv_with_mask_bias",
)
@staticmethod
def export_deformconv_with_multiple_offset_groups() -> None:
X = np.zeros((1, 2, 3, 3), dtype=np.float32)
X[0, 0] = np.reshape(np.arange(9).astype(np.float32), (3, 3))
X[0, 1] = np.reshape(np.arange(8, -1, -1).astype(np.float32), (3, 3))
X.shape = (1, 2, 3, 3)
W = np.ones((1, 2, 2, 2), dtype=np.float32)
offset = np.zeros((1, 16, 2, 2), dtype=np.float32)
offset[
0, 0, 0, 0
] = 0.5 # h-coord of [0, 0] element of kernel in channel 0, at output position [0, 0]
offset[
0, 13, 0, 1
] = (
-0.1
) # w-coord of [1, 0] element of kernel in channel 1, at output position [0, 1]
node = onnx.helper.make_node(
"DeformConv",
inputs=["X", "W", "offset"],
outputs=["Y"],
kernel_shape=[2, 2],
pads=[0, 0, 0, 0],
offset_group=2,
)
Y = np.array(
[
[
[
[33.5, 32.1], # (1, 1, 2, 2) output tensor
[32.0, 32.0],
]
]
]
).astype(np.float32)
expect(
node,
inputs=[X, W, offset],
outputs=[Y],
name="test_deform_conv_with_multiple_offset_groups",
)
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59,070 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_upsample.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221,R0913
import numpy as np
from onnx.reference.op_run import OpRun
class Upsample(OpRun):
def _run(self, x, scale, mode=None): # type: ignore
if mode == "nearest" and scale.astype(np.int64).tolist() == scale.tolist():
r = x
for axis, s in enumerate(scale):
if s == 1:
continue
r = np.repeat(r, int(s), axis=axis)
return (r,)
raise RuntimeError(f"Not implemented for mode={mode!r} and scale={scale!r}.")
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59,071 | onnx/onnx | refs/heads/main | /onnx/test/test_with_ort.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# This file is for testing ONNX with ONNX Runtime
# Create a general scenario to use ONNX Runtime with ONNX
# pylint: disable=C0415
import unittest
class TestONNXRuntime(unittest.TestCase):
def test_with_ort_example(self) -> None:
try:
import onnxruntime # pylint: disable=W0611
del onnxruntime
except ImportError:
raise unittest.SkipTest("onnxruntime not installed") from None
from numpy import float32, random
from onnxruntime import InferenceSession
from onnxruntime.datasets import get_example
from onnx import checker, load, shape_inference, version_converter
# get certain example model from ORT using opset 9
example1 = get_example("sigmoid.onnx")
# test ONNX functions
model = load(example1)
checker.check_model(model)
checker.check_model(model, full_check=True)
inferred_model = shape_inference.infer_shapes(
model, check_type=True, strict_mode=True, data_prop=True
)
converted_model = version_converter.convert_version(inferred_model, 10)
# test ONNX Runtime functions
sess = InferenceSession(converted_model.SerializeToString())
input_name = sess.get_inputs()[0].name
output_name = sess.get_outputs()[0].name
x = random.random((3, 4, 5))
x = x.astype(float32)
sess.run([output_name], {input_name: x})
if __name__ == "__main__":
unittest.main()
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59,072 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_thresholded_relu.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
import numpy as np
from onnx.reference.ops._op import OpRunUnaryNum
class ThresholdedRelu(OpRunUnaryNum):
def _run(self, x, alpha=None): # type: ignore
alpha = alpha or self.alpha # type: ignore
return (np.where(x > alpha, x, 0).astype(x.dtype),) # type: ignore
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"/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/dequantizelinear.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py"], "/onnx/reference/ops/op_isnan.py": ["/onnx/reference/ops/_op.py"], "/onnx/backend/test/case/node/mul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stringnormalizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducemin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/tile.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_flatten.py": ["/onnx/reference/ops/_op.py"], 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"/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
59,073 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/concat.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from typing import Any, Dict, Sequence
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class Concat(Base):
@staticmethod
def export() -> None:
test_cases: Dict[str, Sequence[Any]] = {
"1d": ([1, 2], [3, 4]),
"2d": ([[1, 2], [3, 4]], [[5, 6], [7, 8]]),
"3d": (
[[[1, 2], [3, 4]], [[5, 6], [7, 8]]],
[[[9, 10], [11, 12]], [[13, 14], [15, 16]]],
),
}
for test_case, values_ in test_cases.items():
values = [np.asarray(v, dtype=np.float32) for v in values_]
for i in range(len(values[0].shape)):
in_args = ["value" + str(k) for k in range(len(values))]
node = onnx.helper.make_node(
"Concat", inputs=list(in_args), outputs=["output"], axis=i
)
output = np.concatenate(values, i)
expect(
node,
inputs=list(values),
outputs=[output],
name="test_concat_" + test_case + "_axis_" + str(i),
)
for i in range(-len(values[0].shape), 0):
in_args = ["value" + str(k) for k in range(len(values))]
node = onnx.helper.make_node(
"Concat", inputs=list(in_args), outputs=["output"], axis=i
)
output = np.concatenate(values, i)
expect(
node,
inputs=list(values),
outputs=[output],
name="test_concat_" + test_case + "_axis_negative_" + str(abs(i)),
)
| {"/onnx/backend/test/case/node/sign.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/dft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/parser.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/constantofshape.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/averagepool.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/__init__.py": ["/onnx/__init__.py", "/onnx/backend/base.py", "/onnx/backend/test/case/test_case.py", "/onnx/backend/test/loader/__init__.py", "/onnx/backend/test/runner/item.py"], "/onnx/reference/ops/op_topk.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_image_decoder.py": 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"/onnx/test/reference_evaluator_test.py": ["/onnx/__init__.py", "/onnx/checker.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/__init__.py", "/onnx/reference/ops/_op_common_indices.py", "/onnx/reference/ops/_op_list.py", "/onnx/reference/ops/op_celu.py", "/onnx/reference/ops/op_col2im.py", "/onnx/reference/ops/op_conv.py", "/onnx/reference/ops_optimized/__init__.py", "/onnx/reference/ops_optimized/op_conv_optimized.py"], "/onnx/backend/test/cmd_tools.py": ["/onnx/backend/test/case/model/__init__.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/__init__.py"], "/onnx/reference/ops/op_slice.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/aionnxml/op_binarizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/optionalgetelement.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/loop.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/conf.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_sequence_construct.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatterelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel2.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/bernoulli.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/constant.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/resize.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_resize.py"], "/onnx/reference/ops/aionnxml/op_svm_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/op_sequence_map.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatternd.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/numpy_helper_test.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_tfidf_vectorizer.py": ["/onnx/reference/op_run.py"], "/onnx/test/checker_test.py": ["/onnx/defs/__init__.py", "/onnx/__init__.py"], "/onnx/reference/ops/_op_common_random.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/backend/base.py": ["/onnx/checker.py", "/onnx/__init__.py"], "/onnx/backend/test/case/node/reduce_log_sum.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_linear_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_softplus.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_sub.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_quantize_linear.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_gathernd.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/qlinearmatmul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/shape_inference_test.py": ["/onnx/shape_inference.py", "/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/parser.py"], "/onnx/backend/test/case/node/mish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_expand.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": ["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/_helpers.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/tfidfvectorizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_average_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/item.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/gatherelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/slice.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_matmul.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mel_weight_matrix.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cast.py": ["/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/asin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_normalizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/unique.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather_elements.py": ["/onnx/reference/op_run.py"], "/onnx/helper.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/layernormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/groupnormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/__init__.py": ["/onnx/reference/ops/_op_list.py"], "/onnx/reference/ops/op_random_normal.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_hann_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_softmax_cross_entropy_loss.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_string_split.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/max.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/backend/test/case/utils.py"], "/onnx/backend/test/case/model/expand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/backend/test/case/node/erf.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
59,074 | onnx/onnx | refs/heads/main | /onnx/backend/test/stat_coverage.py | #!/usr/bin/env python
# Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import os
from typing import IO, Any, Dict, List, Sequence
from onnx import AttributeProto, defs, load
from onnx.backend.test.case import collect_snippets
from onnx.backend.test.loader import load_model_tests
from onnx.backend.test.runner import Runner
def is_ml(schemas: Sequence[defs.OpSchema]) -> bool:
return any(s.domain == "ai.onnx.ml" for s in schemas)
def gen_outlines(f: IO[Any], ml: bool) -> None:
f.write("# Test Coverage Report")
if ml:
f.write(" (ONNX-ML Operators)\n")
else:
f.write(" (ONNX Core Operators)\n")
f.write("## Outlines\n")
f.write("* [Node Test Coverage](#node-test-coverage)\n")
f.write("* [Model Test Coverage](#model-test-coverage)\n")
f.write("* [Overall Test Coverage](#overall-test-coverage)\n")
common_covered: Sequence[str] = []
experimental_covered: Sequence[str] = []
def gen_node_test_coverage(
schemas: Sequence[defs.OpSchema], f: IO[Any], ml: bool
) -> None:
global common_covered
global experimental_covered
generators = set(
{
"Multinomial",
"RandomNormal",
"RandomNormalLike",
"RandomUniform",
"RandomUniformLike",
}
)
node_tests = collect_snippets()
common_covered = sorted(
s.name
for s in schemas
if s.name in node_tests
and s.support_level == defs.OpSchema.SupportType.COMMON
and (s.domain == "ai.onnx.ml") == ml
)
common_no_cover = sorted(
s.name
for s in schemas
if s.name not in node_tests
and s.support_level == defs.OpSchema.SupportType.COMMON
and (s.domain == "ai.onnx.ml") == ml
)
common_generator = sorted(name for name in common_no_cover if name in generators)
experimental_covered = sorted(
s.name
for s in schemas
if s.name in node_tests
and s.support_level == defs.OpSchema.SupportType.EXPERIMENTAL
and (s.domain == "ai.onnx.ml") == ml
)
experimental_no_cover = sorted(
s.name
for s in schemas
if s.name not in node_tests
and s.support_level == defs.OpSchema.SupportType.EXPERIMENTAL
and (s.domain == "ai.onnx.ml") == ml
)
experimental_generator = sorted(
name for name in experimental_no_cover if name in generators
)
num_common = len(common_covered) + len(common_no_cover) - len(common_generator)
num_experimental = (
len(experimental_covered)
+ len(experimental_no_cover)
- len(experimental_generator)
)
f.write("# Node Test Coverage\n")
f.write("## Summary\n")
if num_common:
f.write(
f"Node tests have covered {len(common_covered)}/{num_common} "
f"({len(common_covered) / float(num_common) * 100:.2f}%, {len(common_generator)} "
f"generators excluded) common operators.\n\n"
)
else:
f.write("Node tests have covered 0/0 (N/A) common operators. \n\n")
if num_experimental:
f.write(
"Node tests have covered {}/{} ({:.2f}%, {} generators excluded) "
"experimental operators.\n\n".format(
len(experimental_covered),
num_experimental,
(len(experimental_covered) / float(num_experimental) * 100),
len(experimental_generator),
)
)
else:
f.write("Node tests have covered 0/0 (N/A) experimental operators.\n\n")
titles = [
"💚Covered Common Operators",
"💔No Cover Common Operators",
"💚Covered Experimental Operators",
"💔No Cover Experimental Operators",
]
all_lists = [
common_covered,
common_no_cover,
experimental_covered,
experimental_no_cover,
]
for t in titles:
f.write(f"* [{t[9:]}](#{t[9:].lower().replace(' ', '-')})\n")
f.write("\n")
for t, l in zip(titles, all_lists): # noqa: E741
f.write(f"## {t}\n")
for s in l:
f.write(f"### {s}")
if s in node_tests:
f.write(
f"\nThere are {len(node_tests[s])} test cases, listed as following:\n"
)
for summary, code in sorted(node_tests[s]):
f.write("<details>\n")
f.write(f"<summary>{summary}</summary>\n\n")
f.write(f"```python\n{code}\n```\n\n")
f.write("</details>\n")
else:
if s in generators:
f.write(" (random generator operator)\n")
else:
f.write(" (call for test cases)\n")
f.write("\n\n")
f.write("<br/>\n\n")
def gen_model_test_coverage(
schemas: Sequence[defs.OpSchema], f: IO[Any], ml: bool
) -> None:
f.write("# Model Test Coverage\n")
# Process schemas
schema_dict = {}
for schema in schemas:
schema_dict[schema.name] = schema
# Load models from each model test using Runner.prepare_model_data
# Need to grab associated nodes
attrs: Dict[str, Dict[str, List[Any]]] = {}
model_paths: List[Any] = []
for rt in load_model_tests(kind="real"):
if rt.url.startswith("onnx/backend/test/data/light/"):
# testing local files
model_name = os.path.normpath(
os.path.join(os.path.dirname(__file__), "..", "..", "..", rt.url)
)
if not os.path.exists(model_name):
raise FileNotFoundError(f"Unable to find model {model_name!r}.")
model_paths.append(model_name)
else:
model_dir = Runner.prepare_model_data(rt)
model_paths.append(os.path.join(model_dir, "model.onnx"))
model_paths.sort()
model_written = False
for model_pb_path in model_paths:
model = load(model_pb_path)
if ml:
ml_present = False
for opset in model.opset_import:
if opset.domain == "ai.onnx.ml":
ml_present = True
if not ml_present:
continue
else:
model_written = True
f.write(f"## {model.graph.name}\n")
# Deconstruct model
num_covered = 0
for node in model.graph.node:
if node.op_type in common_covered or node.op_type in experimental_covered:
num_covered += 1
# Add details of which nodes are/aren't covered
# Iterate through and store each node's attributes
for attr in node.attribute:
if node.op_type not in attrs:
attrs[node.op_type] = {}
if attr.name not in attrs[node.op_type]:
attrs[node.op_type][attr.name] = []
if attr.type == AttributeProto.FLOAT:
if attr.f not in attrs[node.op_type][attr.name]:
attrs[node.op_type][attr.name].append(attr.f)
elif attr.type == AttributeProto.INT:
if attr.i not in attrs[node.op_type][attr.name]:
attrs[node.op_type][attr.name].append(attr.i)
elif attr.type == AttributeProto.STRING:
if attr.s not in attrs[node.op_type][attr.name]:
attrs[node.op_type][attr.name].append(attr.s)
elif attr.type == AttributeProto.TENSOR:
if attr.t not in attrs[node.op_type][attr.name]:
attrs[node.op_type][attr.name].append(attr.t)
elif attr.type == AttributeProto.GRAPH:
if attr.g not in attrs[node.op_type][attr.name]:
attrs[node.op_type][attr.name].append(attr.g)
elif attr.type == AttributeProto.FLOATS:
if attr.floats not in attrs[node.op_type][attr.name]:
attrs[node.op_type][attr.name].append(attr.floats)
elif attr.type == AttributeProto.INTS:
if attr.ints not in attrs[node.op_type][attr.name]:
attrs[node.op_type][attr.name].append(attr.ints)
elif attr.type == AttributeProto.STRINGS:
if attr.strings not in attrs[node.op_type][attr.name]:
attrs[node.op_type][attr.name].append(attr.strings)
elif attr.type == AttributeProto.TENSORS:
if attr.tensors not in attrs[node.op_type][attr.name]:
attrs[node.op_type][attr.name].append(attr.tensors)
elif attr.type == AttributeProto.GRAPHS:
if attr.graphs not in attrs[node.op_type][attr.name]:
attrs[node.op_type][attr.name].append(attr.graphs)
f.write(
f"\n{model.graph.name} has {num_covered} nodes. "
f"Of these, {len(model.graph.node)} are covered by node tests "
f"({100.0 * float(num_covered) / float(len(model.graph.node))}%)\n\n\n"
)
# Iterate through attrs, print
f.write("<details>\n")
f.write("<summary>nodes</summary>\n\n")
for op in sorted(attrs):
f.write("<details>\n")
# Get total number of attributes for node schema
f.write(
f"<summary>{op}: {len(attrs[op])} out of {len(schema_dict[op].attributes)} attributes covered</summary>\n\n"
)
for attribute in sorted(schema_dict[op].attributes):
if attribute in attrs[op]:
f.write(f"{attribute}: {len(attrs[op][attribute])}\n")
else:
f.write(f"{attribute}: 0\n")
f.write("</details>\n")
f.write("</details>\n\n\n")
if not model_written and ml:
f.write("No model tests present for selected domain\n")
def gen_overall_test_coverage(
schemas: Sequence[defs.OpSchema], f: IO[Any], ml: bool
) -> None:
f.write("# Overall Test Coverage\n")
f.write("## To be filled.\n")
def gen_spdx(f: IO[Any]) -> None:
f.write("<!--- SPDX-License-Identifier: Apache-2.0 -->\n")
def main() -> None:
base_dir = os.path.dirname(
os.path.dirname(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
)
docs_dir = os.path.join(base_dir, "docs")
schemas = defs.get_all_schemas()
has_ml = is_ml(schemas)
fname = os.path.join(docs_dir, "TestCoverage.md")
with open(fname, "w+", newline="", encoding="utf-8") as f: # type: ignore
gen_spdx(f)
gen_outlines(f, False)
gen_node_test_coverage(schemas, f, False)
gen_model_test_coverage(schemas, f, False)
gen_overall_test_coverage(schemas, f, False)
if has_ml:
fname = os.path.join(docs_dir, "TestCoverage-ml.md")
with open(fname, "w+", newline="", encoding="utf-8") as f: # type: ignore
gen_spdx(f)
gen_outlines(f, True)
gen_node_test_coverage(schemas, f, True)
gen_model_test_coverage(schemas, f, True)
gen_overall_test_coverage(schemas, f, True)
if __name__ == "__main__":
main()
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59,075 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_constant.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
import numpy as np
from onnx.reference.custom_element_types import (
bfloat16,
float8e4m3fn,
float8e4m3fnuz,
float8e5m2,
float8e5m2fnuz,
)
from onnx.reference.op_run import OpRun, RefAttrName
def _check_dtype(val): # type: ignore
a = val.dtype
if not isinstance(a, np.dtype) and a not in {
bfloat16,
float8e4m3fn,
float8e4m3fnuz,
float8e5m2,
float8e5m2fnuz,
np.int8,
np.uint8,
np.float16,
np.float32,
np.float64,
np.int32,
np.int64,
np.int16,
np.uint16,
np.uint32,
np.bool_,
np.str_,
np.uint64,
bool,
str,
}:
raise TypeError(
f"Type ({a}, {type(a)}) is not a numpy type (operator 'Constant')"
)
class ConstantCommon(OpRun):
def _check(self, cst): # type: ignore
if isinstance(cst, tuple):
raise TypeError(f"Unexpected type {type(cst)} for a constant.")
return cst
class Constant_1(ConstantCommon):
def __init__(self, onnx_node, run_params): # type: ignore
ConstantCommon.__init__(self, onnx_node, run_params)
self.cst = self.value # type: ignore
_check_dtype(self.cst)
def _run(self, **overridden_attributes): # type: ignore
if overridden_attributes and (
len(overridden_attributes) > 1
or "value" not in overridden_attributes
or id(overridden_attributes["value"]) != id(self.value)
):
raise RuntimeError(
"Function attributes are not implemented for opset <= 11. Use opset > 12."
)
return (self._check(self.cst),)
class Constant_9(Constant_1):
def __init__(self, onnx_node, run_params): # type: ignore
Constant_1.__init__(self, onnx_node, run_params)
class Constant_11(ConstantCommon):
def __init__(self, onnx_node, run_params): # type: ignore
ConstantCommon.__init__(self, onnx_node, run_params)
if getattr(self, "sparse_value", None) is None:
self.cst = self.value # type: ignore
else:
self.cst = self.sparse_value # type: ignore
_check_dtype(self.cst)
def _run(self, **overridden_attributes): # type: ignore
if overridden_attributes and (
len(overridden_attributes) > 1
or "value" not in overridden_attributes
or id(overridden_attributes["value"]) != id(self.value)
):
raise RuntimeError(
"Function attributes are not implemented for opset <= 11. Use opset > 12."
)
return (self._check(self.cst),)
class Constant_12(ConstantCommon):
def __init__(self, onnx_node, run_params): # type: ignore
ConstantCommon.__init__(self, onnx_node, run_params)
if hasattr(self, "sparse_value") and self.sparse_value is not None: # type: ignore
self.cst_name = "sparse_value"
self.cst = self.sparse_value # type: ignore
self.cst_convert = lambda v: v
elif hasattr(self, "value") and self.value is not None: # type: ignore
self.cst_name = "value" # type: ignore
self.cst = self.value if isinstance(self.value, RefAttrName) else self.value # type: ignore
self.cst_convert = lambda v: v
else:
for attr, np_dtype in {
"value_float": np.float32,
"value_floats": np.float32,
"value_int": np.int64,
"value_ints": np.int64,
"value_string": np.str_,
"value_strings": np.str_,
}.items():
if hasattr(self, attr) and getattr(self, attr) is not None: # type: ignore
self.cst_name = attr
v = getattr(self, attr)
self.cst = (
v # type: ignore
if isinstance(v, RefAttrName) # type: ignore
else np.array(v, dtype=np_dtype) # type: ignore
)
self.cst_convert = lambda v, np_dtype=np_dtype: np.array( # type: ignore
v, dtype=np_dtype
)
break
if not hasattr(self, "cst_name"):
raise AttributeError("No constant is defined for operator 'Constant'.")
def _run(self, **overridden_attributes): # type: ignore
if self.has_linked_attribute:
if overridden_attributes is None:
raise RuntimeError(
f"Attributes are empty, cannot retrieve value for {self.cst!r}."
)
if self.cst_name not in overridden_attributes:
raise RuntimeError(
f"Cannot find attribute {self.cst_name!r} in {list(overridden_attributes)!r}."
)
value = overridden_attributes[self.cst_name]
if isinstance(value, np.ndarray):
return (value,)
return (self.cst_convert(value),)
return (self._check(self.cst),)
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59,076 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/upsample.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx import helper
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class Upsample(Base):
@staticmethod
def export_nearest() -> None:
node = onnx.helper.make_node(
"Upsample",
inputs=["X", "scales"],
outputs=["Y"],
mode="nearest",
)
data = np.array(
[
[
[
[1, 2],
[3, 4],
]
]
],
dtype=np.float32,
)
scales = np.array([1.0, 1.0, 2.0, 3.0], dtype=np.float32)
output = np.array(
[
[
[
[1, 1, 1, 2, 2, 2],
[1, 1, 1, 2, 2, 2],
[3, 3, 3, 4, 4, 4],
[3, 3, 3, 4, 4, 4],
]
]
],
dtype=np.float32,
)
expect(
node,
inputs=[data, scales],
outputs=[output],
name="test_upsample_nearest",
opset_imports=[helper.make_opsetid("", 9)],
)
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"/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
59,077 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_squeeze.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=E0203,W0221
import numpy as np
from onnx.reference.op_run import OpRun
class Squeeze_1(OpRun):
def _run(self, data, axes=None): # type: ignore
if isinstance(axes, np.ndarray):
axes = tuple(axes)
elif axes in [[], ()]:
axes = None
elif isinstance(axes, list):
axes = tuple(axes)
if isinstance(axes, (tuple, list)):
sq = data
for a in reversed(axes):
sq = np.squeeze(sq, axis=a)
else:
sq = np.squeeze(data, axis=axes)
return (sq,)
class Squeeze_11(Squeeze_1):
pass
class Squeeze_13(OpRun):
def __init__(self, onnx_node, run_params): # type: ignore
OpRun.__init__(self, onnx_node, run_params)
self.axes = None
def _run(self, data, axes=None): # type: ignore
if axes is not None:
if hasattr(axes, "__iter__"):
sq = np.squeeze(data, axis=tuple(axes))
else:
sq = np.squeeze(data, axis=axes)
else:
sq = np.squeeze(data)
return (sq,)
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"/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
59,078 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_einsum.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=E0203,W0221
import numpy as np
from onnx.reference.op_run import OpRun
class Einsum(OpRun):
def _run(self, *args, equation=None): # type: ignore
if not isinstance(equation, str):
raise TypeError(f"equation must be string but is {type(equation)!r}.")
equation = equation.strip()
if not equation:
raise TypeError("equation is empty.")
try:
return (np.einsum(equation, *args, optimize=True),)
except TypeError:
return (np.einsum(equation, *args),)
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59,079 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/div.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class Div(Base):
@staticmethod
def export() -> None:
node = onnx.helper.make_node(
"Div",
inputs=["x", "y"],
outputs=["z"],
)
x = np.array([3, 4]).astype(np.float32)
y = np.array([1, 2]).astype(np.float32)
z = x / y # expected output [3., 2.]
expect(node, inputs=[x, y], outputs=[z], name="test_div_example")
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.random.rand(3, 4, 5).astype(np.float32) + 1.0
z = x / y
expect(node, inputs=[x, y], outputs=[z], name="test_div")
x = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8)
y = np.random.randint(24, size=(3, 4, 5), dtype=np.uint8) + 1
z = x // y
expect(node, inputs=[x, y], outputs=[z], name="test_div_uint8")
@staticmethod
def export_div_broadcast() -> None:
node = onnx.helper.make_node(
"Div",
inputs=["x", "y"],
outputs=["z"],
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = np.random.rand(5).astype(np.float32) + 1.0
z = x / y
expect(node, inputs=[x, y], outputs=[z], name="test_div_bcast")
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59,080 | onnx/onnx | refs/heads/main | /onnx/reference/ops/aionnxml/op_one_hot_encoder.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0912,R0913,R0914,W0221
import numpy as np
from onnx.reference.ops.aionnxml._op_run_aionnxml import OpRunAiOnnxMl
class OneHotEncoder(OpRunAiOnnxMl):
def _run(self, x, cats_int64s=None, cats_strings=None, zeros=None): # type: ignore
if cats_int64s is not None and len(cats_int64s) > 0:
classes = {v: i for i, v in enumerate(cats_int64s)}
elif len(cats_strings) > 0:
classes = {v: i for i, v in enumerate(cats_strings)}
else:
raise RuntimeError("No encoding was defined.")
shape = x.shape
new_shape = (*shape, len(classes))
res = np.zeros(new_shape, dtype=np.float32)
if len(x.shape) == 1:
for i, v in enumerate(x):
j = classes.get(v, -1)
if j >= 0:
res[i, j] = 1.0
elif len(x.shape) == 2:
for a, row in enumerate(x):
for i, v in enumerate(row):
j = classes.get(v, -1)
if j >= 0:
res[a, i, j] = 1.0
else:
raise RuntimeError(f"This operator is not implemented shape {x.shape}.")
if not zeros:
red = res.sum(axis=len(res.shape) - 1)
if np.min(red) == 0:
rows = []
for i, val in enumerate(red):
if val == 0:
rows.append({"row": i, "value": x[i]})
if len(rows) > 5:
break
msg = "\n".join(str(_) for _ in rows)
raise RuntimeError(
f"One observation did not have any defined category.\n"
f"classes: {classes}\nfirst rows:\n"
f"{msg}\nres:\n{res[:5]}\nx:\n{x[:5]}"
)
return (res,)
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59,081 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_random_uniform.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0913,W0221
from onnx.reference.ops._op_common_random import _CommonRandom
class RandomUniform(_CommonRandom):
def _run(self, dtype=None, high=None, low=None, seed=None, shape=None): # type: ignore
dtype = self._dtype(dtype=dtype)
state = self._get_state(seed)
res = state.rand(*shape).astype(dtype) # type: ignore
res *= high - low # type: ignore
res += low # type: ignore
return (res.astype(dtype),)
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59,082 | onnx/onnx | refs/heads/main | /onnx/reference/ops/aionnxml/op_svm_helper.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0911,R0913,R0914,W0221
from typing import Any
import numpy as np
class SVMAttributes:
def __init__(self):
self._names = []
def add(self, name: str, value: Any) -> None:
if isinstance(value, list) and name not in {"kernel_params"}:
if name in {"vectors_per_class"}:
value = np.array(value, dtype=np.int64)
else:
value = np.array(value, dtype=np.float32)
setattr(self, name, value)
def __str__(self) -> str:
rows = ["Attributes"]
for name in self._names:
rows.append(f" {name}={getattr(self, name)}")
return "\n".join(rows)
class SVMCommon:
"""
Base class for SVM.
"""
def __init__(self, **kwargs): # type: ignore
self.atts = SVMAttributes()
for name, value in kwargs.items():
self.atts.add(name, value)
if self.atts.kernel_params: # type: ignore
self.gamma_ = self.atts.kernel_params[0] # type: ignore
self.coef0_ = self.atts.kernel_params[1] # type: ignore
self.degree_ = int(self.atts.kernel_params[2]) # type: ignore
else:
self.gamma_ = 0.0
self.coef0_ = 0.0
self.degree_ = 0
def __str__(self) -> str:
rows = ["TreeEnsemble", f"root_index={self.root_index}", str(self.atts)] # type: ignore
return "\n".join(rows)
def kernel_dot(self, pA: np.ndarray, pB: np.ndarray, kernel: str) -> np.ndarray:
k = kernel.lower()
if k == "poly":
s = np.dot(pA, pB)
s = s * self.gamma_ + self.coef0_
return s**self.degree_ # type: ignore
if k == "sigmoid":
s = np.dot(pA, pB)
s = s * self.gamma_ + self.coef0_
return np.tanh(s) # type: ignore
if k == "rbf":
diff = pA - pB
s = (diff * diff).sum()
return np.exp(-self.gamma_ * s) # type: ignore
if k == "linear":
return np.dot(pA, pB) # type: ignore
raise ValueError(f"Unexpected kernel={kernel!r}.")
def run_reg(self, X: np.ndarray) -> np.ndarray:
if self.atts.n_supports > 0: # type: ignore
# length of each support vector
mode_ = "SVM_SVC"
kernel_type_ = self.atts.kernel_type # type: ignore
sv = self.atts.support_vectors.reshape((self.atts.n_supports, -1)) # type: ignore
else:
mode_ = "SVM_LINEAR"
kernel_type_ = "LINEAR"
z = np.empty((X.shape[0], 1), dtype=X.dtype)
for n in range(X.shape[0]):
s = 0.0
if mode_ == "SVM_SVC":
for j in range(self.atts.n_supports): # type: ignore
d = self.kernel_dot(X[n], sv[j], kernel_type_)
s += self.atts.coefficients[j] * d # type: ignore
s += self.atts.rho[0] # type: ignore
elif mode_ == "SVM_LINEAR":
s = self.kernel_dot(X[n], self.atts.coefficients, kernel_type_) # type: ignore
s += self.atts.rho[0] # type: ignore
if self.atts.one_class: # type: ignore
z[n, 0] = 1 if s > 0 else -1
else:
z[n, 0] = s
return z
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"/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_resize.py"], "/onnx/reference/ops/aionnxml/op_svm_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/op_sequence_map.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatternd.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/numpy_helper_test.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_tfidf_vectorizer.py": ["/onnx/reference/op_run.py"], "/onnx/test/checker_test.py": ["/onnx/defs/__init__.py", "/onnx/__init__.py"], "/onnx/reference/ops/_op_common_random.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/backend/base.py": ["/onnx/checker.py", "/onnx/__init__.py"], "/onnx/backend/test/case/node/reduce_log_sum.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_linear_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_softplus.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_sub.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_quantize_linear.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_gathernd.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/qlinearmatmul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/shape_inference_test.py": ["/onnx/shape_inference.py", "/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/parser.py"], "/onnx/backend/test/case/node/mish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_expand.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": ["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/_helpers.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/tfidfvectorizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_average_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/item.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/gatherelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/slice.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_matmul.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mel_weight_matrix.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cast.py": ["/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/asin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_normalizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/unique.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather_elements.py": ["/onnx/reference/op_run.py"], "/onnx/helper.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/layernormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/groupnormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/__init__.py": ["/onnx/reference/ops/_op_list.py"], "/onnx/reference/ops/op_random_normal.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_hann_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_softmax_cross_entropy_loss.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_string_split.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/max.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/backend/test/case/utils.py"], "/onnx/backend/test/case/model/expand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/backend/test/case/node/erf.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
59,083 | onnx/onnx | refs/heads/main | /onnx/reference/ops/aionnxml/__init__.py | # SPDX-License-Identifier: Apache-2.0
from onnx.reference.ops.aionnxml._op_list import load_op
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"/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/stat_coverage.py": ["/onnx/__init__.py", "/onnx/backend/test/case/__init__.py", "/onnx/backend/test/loader/__init__.py", "/onnx/backend/test/runner/__init__.py"], "/onnx/reference/ops/op_constant.py": ["/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/upsample.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_squeeze.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_einsum.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/div.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_one_hot_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_random_uniform.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/test/reference_evaluator_test.py": ["/onnx/__init__.py", "/onnx/checker.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/__init__.py", "/onnx/reference/ops/_op_common_indices.py", "/onnx/reference/ops/_op_list.py", "/onnx/reference/ops/op_celu.py", "/onnx/reference/ops/op_col2im.py", "/onnx/reference/ops/op_conv.py", "/onnx/reference/ops_optimized/__init__.py", "/onnx/reference/ops_optimized/op_conv_optimized.py"], "/onnx/backend/test/cmd_tools.py": ["/onnx/backend/test/case/model/__init__.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/__init__.py"], "/onnx/reference/ops/op_slice.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/aionnxml/op_binarizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/optionalgetelement.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/loop.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/conf.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_sequence_construct.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatterelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel2.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/bernoulli.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/constant.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/resize.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_resize.py"], "/onnx/reference/ops/aionnxml/op_svm_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/op_sequence_map.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatternd.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/numpy_helper_test.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_tfidf_vectorizer.py": ["/onnx/reference/op_run.py"], "/onnx/test/checker_test.py": ["/onnx/defs/__init__.py", "/onnx/__init__.py"], "/onnx/reference/ops/_op_common_random.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/backend/base.py": ["/onnx/checker.py", "/onnx/__init__.py"], "/onnx/backend/test/case/node/reduce_log_sum.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_linear_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_softplus.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_sub.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_quantize_linear.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_gathernd.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/qlinearmatmul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/shape_inference_test.py": ["/onnx/shape_inference.py", "/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/parser.py"], "/onnx/backend/test/case/node/mish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_expand.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": ["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/_helpers.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/tfidfvectorizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_average_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/item.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/gatherelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/slice.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_matmul.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mel_weight_matrix.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cast.py": ["/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/asin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_normalizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/unique.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather_elements.py": ["/onnx/reference/op_run.py"], "/onnx/helper.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/layernormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/groupnormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/__init__.py": ["/onnx/reference/ops/_op_list.py"], "/onnx/reference/ops/op_random_normal.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_hann_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_softmax_cross_entropy_loss.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_string_split.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/max.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/backend/test/case/utils.py"], "/onnx/backend/test/case/model/expand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/backend/test/case/node/erf.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
59,084 | onnx/onnx | refs/heads/main | /onnx/test/reference_evaluator_test.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# type: ignore
# pylint: disable=C3001,C0302,C0415,R0904,R0913,R0914,R0915,W0221,W0707
"""
You can run a specific test by using the following syntax.
::
python onnx/test/reference_evaluator_test.py TestReferenceEvaluator.test_function_attribute_nested_graph
"""
import itertools
import math
import sys
import unittest
from contextlib import redirect_stdout
from functools import wraps
from io import StringIO
from os import getenv
from textwrap import dedent
from typing import Sequence, Tuple
import numpy as np
import parameterized
from numpy.testing import assert_allclose
from onnx import AttributeProto, FunctionProto, ModelProto, TensorProto, checker, parser
from onnx.backend.test.case.node.roialign import get_roi_align_input_values
from onnx.checker import check_model
from onnx.defs import onnx_opset_version
from onnx.helper import (
float32_to_bfloat16,
float32_to_float8e4m3,
float32_to_float8e5m2,
make_function,
make_graph,
make_model,
make_model_gen_version,
make_node,
make_opsetid,
make_sequence_type_proto,
make_tensor,
make_tensor_sequence_value_info,
make_tensor_value_info,
make_value_info,
)
from onnx.numpy_helper import float8e4m3_to_float32, float8e5m2_to_float32, from_array
from onnx.reference import ReferenceEvaluator
from onnx.reference.op_run import OpRun, OpRunExpand
from onnx.reference.ops import load_op
from onnx.reference.ops._op_common_indices import _get_indices, _is_out
from onnx.reference.ops._op_list import Cast_19, Celu
from onnx.reference.ops.aionnx_preview_training._op_list import Adam
from onnx.reference.ops.op_celu import _vcelu1
from onnx.reference.ops.op_col2im import (
_col2im_naive_implementation_2d,
col2im_naive_implementation,
)
from onnx.reference.ops.op_conv import Conv, _conv_implementation
from onnx.reference.ops_optimized import Conv as ConvOptimized
from onnx.reference.ops_optimized.op_conv_optimized import _conv_implementation_im2col
# TODO (https://github.com/microsoft/onnxruntime/issues/14932): Get max supported version from onnxruntime directly
# For now, bump the version in CIs whenever there is a new onnxruntime release
ORT_MAX_IR_SUPPORTED_VERSION = int(getenv("ORT_MAX_IR_SUPPORTED_VERSION", "8"))
ORT_MAX_ONNX_OPSET_SUPPORTED_VERSION = int(
getenv("ORT_MAX_ONNX_OPSET_SUPPORTED_VERSION", "18")
)
def skip_if_no_onnxruntime(fn):
@wraps(fn)
def wrapper(*args, **kwargs):
try:
import onnxruntime # pylint: disable=W0611
del onnxruntime
except ImportError:
raise unittest.SkipTest("onnxruntime not installed") from None
fn(*args, **kwargs)
return wrapper
def skip_if_no_torch(fn):
@wraps(fn)
def wrapper(*args, **kwargs):
try:
import torch # pylint: disable=W0611
del torch
except ImportError:
raise unittest.SkipTest("torch not installed") from None
fn(*args, **kwargs)
return wrapper
def skip_if_no_torchvision(fn):
@wraps(fn)
def wrapper(*args, **kwargs):
try:
import torchvision # pylint: disable=W0611
del torchvision
except ImportError:
raise unittest.SkipTest("torchvision not installed") from None
fn(*args, **kwargs)
return wrapper
def make_sequence_value_info(name, elem_type, shape):
if isinstance(elem_type, int):
return make_tensor_sequence_value_info(name, elem_type, shape)
s_type = make_sequence_type_proto(elem_type)
return make_value_info(name, s_type, shape)
def run_ort_inference(onnx_model):
import onnxruntime as ort
onnx_domain_opset = ORT_MAX_ONNX_OPSET_SUPPORTED_VERSION
for opset in onnx_model.opset_import:
if opset.domain in ("", "ai.onnx"):
onnx_domain_opset = opset.version
break
# The new IR or opset version is not supported by onnxruntime yet
if (
onnx_model.ir_version > ORT_MAX_IR_SUPPORTED_VERSION
or onnx_domain_opset > ORT_MAX_ONNX_OPSET_SUPPORTED_VERSION
):
return None
return ort.InferenceSession(
onnx_model.SerializeToString(), providers=["CPUExecutionProvider"]
)
def im2col_naive_implementation(data, kernel_shape, dilations, pads, strides): # type: ignore
"""
Naive implementation for `im2col`.
:param image: image (float)
:param kernel_shape: kernel shape
:param dilations: dilations
:param pads: pads
:param strides: strides
:return: result
"""
if not isinstance(kernel_shape, tuple):
raise TypeError(f"Unexpected type {type(kernel_shape)!r} for kernel_shape.")
if len(data.shape) != len(kernel_shape):
raise ValueError(f"Shape mismatch {data.shape!r} and {kernel_shape!r}.")
n_dims = len(pads) // 2
new_pads = np.array([(pads[i], pads[i + n_dims]) for i in range(n_dims)])
list_output_shape = list(data.shape + kernel_shape)
for d in range(n_dims):
kd = kernel_shape[d] + (kernel_shape[d] - 1) * (dilations[d] - 1)
nd = int(
((list_output_shape[d] - kd + new_pads[d][0] + new_pads[d][1]) / strides[d])
+ 1
)
list_output_shape[d] = nd
output_shape = tuple(list_output_shape)
res = np.zeros(output_shape, dtype=data.dtype)
kernel_size = np.prod(kernel_shape)
res_size = np.prod(res.shape[:-n_dims])
for i in range(res_size):
i_res = _get_indices(i, res.shape[:-n_dims])
t_res = tuple(i_res)
for j in range(kernel_size):
i_kernel = _get_indices(j, kernel_shape)
t_kernel = tuple(i_kernel)
i_img = i_res * strides - new_pads[:, 0] + i_kernel * dilations
t_img = tuple(i_img)
if _is_out(t_img, data.shape):
res[t_res + t_kernel] = 0
else:
res[t_res + t_kernel] = data[tuple(t_img)]
return res
def im2col(
img: np.ndarray,
kernel_shape: Tuple[int, ...],
dilations: Sequence[int],
pads: Sequence[int],
strides: Sequence[int],
) -> np.ndarray:
res = None
for n in range(img.shape[0]):
for c in range(img.shape[1]):
out = im2col_naive_implementation(
img[n, c, ...], kernel_shape, dilations, pads, strides
)
if res is None:
new_shape = img.shape[:2] + out.shape
res = np.empty(new_shape, dtype=img.dtype)
res[n, c, ...] = out
new_shape = res.shape[: -len(kernel_shape)] + (-1,) # type: ignore
return res.reshape(new_shape) # type: ignore
class TestReferenceEvaluator(unittest.TestCase):
m2_def = """
<
ir_version: 7,
opset_import: [ "": 10, "com.microsoft": 1]
>
agraph (float[N, M] B01, float[N, M] B11, float[N, M] B21) => (float[N, M] D0)
{
C0 = Add(B01, B11)
C1 = Sub(B11, B21)
D0 = Mul(C0, C1)
}
"""
@staticmethod
def _load_model(m_def: str) -> ModelProto:
"""
Parses a model from a string representation, including checking
the model for correctness
"""
m = parser.parse_model(m_def)
checker.check_model(m)
return m
@staticmethod
def _linear_regression(clip=False, opset=None, min_value=-1.0, max_value=1.0):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
A = make_tensor_value_info("A", TensorProto.FLOAT, [None, None])
B = make_tensor_value_info("B", TensorProto.FLOAT, [None, None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None])
node1 = make_node("MatMul", ["X", "A"], ["XA"])
if clip:
node2 = make_node("Add", ["XA", "B"], ["Y_clip"])
if opset is not None and opset < 11:
if min_value:
if max_value:
node3 = make_node(
"Clip", ["Y_clip"], ["Y"], min=min_value, max=max_value
)
else:
node3 = make_node("Clip", ["Y_clip"], ["Y"], min=min_value)
elif max_value:
node3 = make_node("Clip", ["Y_clip"], ["Y"], max=max_value)
else:
node3 = make_node("Clip", ["Y_clip"], ["Y"])
graph = make_graph([node1, node2, node3], "lr", [X, A, B], [Y])
else:
mi = (
from_array(np.array([min_value], dtype=np.float32), name="mi")
if min_value
else None
)
ma = (
from_array(np.array([max_value], dtype=np.float32), name="ma")
if max_value
else None
)
inputs = ["Y_clip", "mi" if mi else "", "ma" if ma else ""]
node3 = make_node("Clip", inputs, ["Y"])
initializer = [_ for _ in [mi, ma] if _]
graph = make_graph(
[node1, node2, node3], "lr", [X, A, B], [Y], initializer=initializer
)
f = lambda x, a, b: np.clip(a @ a + b, min_value, max_value) # noqa: E731
else:
node2 = make_node("Add", ["XA", "B"], ["Y"])
graph = make_graph([node1, node2], "lr", [X, A, B], [Y])
f = lambda x, a, b: a @ a + b # noqa: E731
if opset is None:
onnx_model = make_model(graph)
else:
onnx_model = make_model(graph, opset_imports=[make_opsetid("", opset)])
try:
check_model(onnx_model)
except Exception as e:
raise AssertionError(f"checker fails for\n{onnx_model}") from e
return onnx_model, f
def test_reference_evaluator_exceptions(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
with self.assertRaises(TypeError):
ReferenceEvaluator(X)
def test_reference_evaluator_no_attribute(self):
m = TestReferenceEvaluator._load_model(TestReferenceEvaluator.m2_def)
checker.check_model(m)
sess = ReferenceEvaluator(m)
self.assertEqual(sess.input_names, ["B01", "B11", "B21"])
self.assertEqual(sess.output_names, ["D0"])
self.assertEqual(sess.opsets, {"": 10, "com.microsoft": 1})
x = np.array([[0, 1], [2, 3]], dtype=np.float32)
y = np.array([[4, 5], [6, 7]], dtype=np.float32)
z = np.array([[-4, -5], [-6, -7]], dtype=np.float32)
res = sess.run(None, {"B01": x, "B11": y, "B21": z})[0]
expected = (x + y) * (y - z)
assert_allclose(expected, res)
def test_reference_evaluator_no_attribute_bytes(self):
m = TestReferenceEvaluator._load_model(TestReferenceEvaluator.m2_def)
checker.check_model(m)
sess = ReferenceEvaluator(m.SerializeToString())
self.assertEqual(sess.input_names, ["B01", "B11", "B21"])
self.assertEqual(sess.output_names, ["D0"])
self.assertEqual(sess.opsets, {"": 10, "com.microsoft": 1})
x = np.array([[0, 1], [2, 3]], dtype=np.float32)
y = np.array([[4, 5], [6, 7]], dtype=np.float32)
z = np.array([[-4, -5], [-6, -7]], dtype=np.float32)
res = sess.run(None, {"B01": x, "B11": y, "B21": z})[0]
expected = (x + y) * (y - z)
assert_allclose(expected, res)
def test_reference_evaluator_no_attribute_verbose(self):
m = TestReferenceEvaluator._load_model(TestReferenceEvaluator.m2_def)
x = np.array([[0, 1], [2, 3]], dtype=np.float32)
y = np.array([[4, 5], [6, 7]], dtype=np.float32)
z = np.array([[-4, -5], [-6, -7]], dtype=np.float32)
with self.subTest(level=2):
sess = ReferenceEvaluator(m, verbose=2)
stdout = StringIO()
with redirect_stdout(stdout):
sess.run(None, {"B01": x, "B11": y, "B21": z})
out = stdout.getvalue()
log = "Add(B01, B11) -> C0\nSub(B11, B21) -> C1\nMul(C0, C1) -> D0\n"
self.assertEqual(log, out)
with self.subTest(level=3):
sess = ReferenceEvaluator(m, verbose=3)
stdout = StringIO()
with redirect_stdout(stdout):
sess.run(None, {"B01": x, "B11": y, "B21": z})
out = stdout.getvalue()
log = dedent(
"""
+I B01: float32:(2, 2) in [0.0, 3.0]
+I B11: float32:(2, 2) in [4.0, 7.0]
+I B21: float32:(2, 2) in [-7.0, -4.0]
Add(B01, B11) -> C0
+ C0: float32:(2, 2) in [4.0, 10.0]
Sub(B11, B21) -> C1
+ C1: float32:(2, 2) in [8.0, 14.0]
Mul(C0, C1) -> D0
+ D0: float32:(2, 2) in [32.0, 140.0]
"""
).lstrip("\n")
self.assertEqual(log, out)
with self.subTest(level=4):
sess = ReferenceEvaluator(m, verbose=4)
stdout = StringIO()
with redirect_stdout(stdout):
sess.run(None, {"B01": x, "B11": y, "B21": z})
out = stdout.getvalue()
log = dedent(
"""
+I B01: float32:(2, 2):[0.0, 1.0, 2.0, 3.0]
+I B11: float32:(2, 2):[4.0, 5.0, 6.0, 7.0]
+I B21: float32:(2, 2):[-4.0, -5.0, -6.0, -7.0]
Add(B01, B11) -> C0
+ C0: float32:(2, 2):[4.0, 6.0, 8.0, 10.0]
Sub(B11, B21) -> C1
+ C1: float32:(2, 2):[8.0, 10.0, 12.0, 14.0]
Mul(C0, C1) -> D0
+ D0: float32:(2, 2):[32.0, 60.0, 96.0, 140.0]
"""
).lstrip("\n")
self.assertEqual(log, out)
with self.subTest(level=15):
sess = ReferenceEvaluator(m, verbose=15)
stdout = StringIO()
with redirect_stdout(stdout):
sess.run(None, {"B01": x, "B11": y, "B21": z})
out = stdout.getvalue()
log = dedent(
"""
+I B01: float32:(2, 2):[0.0, 1.0, 2.0, 3.0]
+I B11: float32:(2, 2):[4.0, 5.0, 6.0, 7.0]
+I B21: float32:(2, 2):[-4.0, -5.0, -6.0, -7.0]
Add(B01, B11) -> C0
-- begin Add.run(2 inputs)
-- done Add.run -> 1 outputs
+ C0: float32:(2, 2):[4.0, 6.0, 8.0, 10.0]
Sub(B11, B21) -> C1
-- begin Sub.run(2 inputs)
-- done Sub.run -> 1 outputs
+ C1: float32:(2, 2):[8.0, 10.0, 12.0, 14.0]
Mul(C0, C1) -> D0
-- begin Mul.run(2 inputs)
-- done Mul.run -> 1 outputs
+ D0: float32:(2, 2):[32.0, 60.0, 96.0, 140.0]
"""
).lstrip("\n")
self.assertEqual(log, out)
def test_reference_evaluator_lr(self):
lr, f = TestReferenceEvaluator._linear_regression()
x = np.array([[0, 1], [2, 3]], dtype=np.float32)
a = np.array([1, 1], dtype=np.float32)
b = np.array([11], dtype=np.float32)
expected = f(x, a, b)
sess = ReferenceEvaluator(lr)
got = sess.run(None, {"X": a, "A": a, "B": b})[0]
assert_allclose(expected, got)
def test_reference_evaluator_lr_clip(self):
with self.subTest(opt="min+max"):
lr, f = TestReferenceEvaluator._linear_regression(clip=True)
x = np.array([[0, 1], [2, 3]], dtype=np.float32)
a = np.array([1, 1], dtype=np.float32)
b = np.array([11], dtype=np.float32)
expected = f(x, a, b)
sess = ReferenceEvaluator(lr)
last_node = sess.rt_nodes_[-1]
self.assertEqual(last_node.__class__.__name__, "Clip_11")
got = sess.run(None, {"X": a, "A": a, "B": b})[0]
assert_allclose(expected, got)
with self.subTest(opt="max"):
lr, f = TestReferenceEvaluator._linear_regression(clip=True, min_value=None)
x = np.array([[0, 1], [2, 3]], dtype=np.float32)
a = np.array([1, 1], dtype=np.float32)
b = np.array([11], dtype=np.float32)
expected = f(x, a, b)
sess = ReferenceEvaluator(lr)
last_node = sess.rt_nodes_[-1]
self.assertEqual(last_node.__class__.__name__, "Clip_11")
got = sess.run(None, {"X": a, "A": a, "B": b})[0]
assert_allclose(expected, got)
with self.subTest(opt="min"):
lr, f = TestReferenceEvaluator._linear_regression(clip=True, max_value=None)
x = np.array([[0, 1], [2, 3]], dtype=np.float32)
a = np.array([1, 1], dtype=np.float32)
b = np.array([11], dtype=np.float32)
expected = f(x, a, b)
sess = ReferenceEvaluator(lr)
last_node = sess.rt_nodes_[-1]
self.assertEqual(last_node.__class__.__name__, "Clip_11")
got = sess.run(None, {"X": a, "A": a, "B": b})[0]
assert_allclose(expected, got)
def test_reference_evaluator_lr_clip_6(self):
with self.subTest(opt="min+max"):
lr, f = TestReferenceEvaluator._linear_regression(clip=True, opset=10)
x = np.array([[0, 1], [2, 3]], dtype=np.float32)
a = np.array([1, 1], dtype=np.float32)
b = np.array([11], dtype=np.float32)
expected = f(x, a, b)
sess = ReferenceEvaluator(lr)
last_node = sess.rt_nodes_[-1]
self.assertEqual(last_node.__class__.__name__, "Clip_6")
self.assertEqual(last_node.min, -1)
self.assertEqual(last_node.max, 1)
got = sess.run(None, {"X": a, "A": a, "B": b})[0]
assert_allclose(expected, got)
with self.subTest(opt="max"):
lr, f = TestReferenceEvaluator._linear_regression(
clip=True, opset=10, min_value=None
)
x = np.array([[0, 1], [2, 3]], dtype=np.float32)
a = np.array([1, 1], dtype=np.float32)
b = np.array([11], dtype=np.float32)
expected = f(x, a, b)
sess = ReferenceEvaluator(lr)
last_node = sess.rt_nodes_[-1]
self.assertEqual(last_node.__class__.__name__, "Clip_6")
self.assertEqual(last_node.max, 1)
self.assertEqual(last_node.min, -3.4028234663852886e38)
got = sess.run(None, {"X": a, "A": a, "B": b})[0]
assert_allclose(expected, got)
with self.subTest(opt="min"):
lr, f = TestReferenceEvaluator._linear_regression(
clip=True, opset=10, max_value=None
)
x = np.array([[0, 1], [2, 3]], dtype=np.float32)
a = np.array([1, 1], dtype=np.float32)
b = np.array([11], dtype=np.float32)
expected = f(x, a, b)
sess = ReferenceEvaluator(lr)
last_node = sess.rt_nodes_[-1]
self.assertEqual(last_node.__class__.__name__, "Clip_6")
self.assertEqual(last_node.min, -1)
self.assertEqual(last_node.max, 3.4028234663852886e38)
got = sess.run(None, {"X": a, "A": a, "B": b})[0]
assert_allclose(expected, got)
def test_nested_local_functions(self):
m = parser.parse_model(
"""
<
ir_version: 8,
opset_import: [ "" : 14, "local" : 1],
producer_name: "test",
producer_version: "1.0",
model_version: 1,
doc_string: "Test preprocessing model"
>
agraph (uint8[H, W, C] x) => (uint8[H, W, C] x_processed)
{
x_processed = local.func(x)
}
<
opset_import: [ "" : 14 ],
domain: "local",
doc_string: "function 1"
>
f1 (x) => (y) {
y = Identity(x)
}
<
opset_import: [ "" : 14 ],
domain: "local",
doc_string: "function 2"
>
f2 (x) => (y) {
y = Identity(x)
}
<
opset_import: [ "" : 14, "local" : 1 ],
domain: "local",
doc_string: "Preprocessing function."
>
func (x) => (y) {
x1 = local.f1(x)
y = local.f2(x1)
}
"""
)
sess = ReferenceEvaluator(m)
x = np.array([0, 1, 3], dtype=np.uint8).reshape((1, 1, 3))
result = sess.run(None, {"x": x})[0]
expected = x
assert_allclose(expected, result)
def test_reduce_sum_11(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None])
node1 = make_node("ReduceSum", ["X"], ["Y"], axes=[1], keepdims=1)
graph = make_graph([node1], "rs", [X], [Y])
onnx_model = make_model(graph, opset_imports=[make_opsetid("", 11)])
check_model(onnx_model)
x = np.arange(60).reshape((3, 4, 5)).astype(np.float32)
expected = x.sum(axis=1, keepdims=1)
sess = ReferenceEvaluator(onnx_model)
got = sess.run(None, {"X": x})[0]
assert_allclose(expected, got)
def test_reduce_sum_square_11(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None])
node1 = make_node("ReduceSumSquare", ["X"], ["Y"], axes=[1], keepdims=1)
graph = make_graph([node1], "rs", [X], [Y])
onnx_model = make_model(graph, opset_imports=[make_opsetid("", 11)])
check_model(onnx_model)
x = np.arange(60).reshape((3, 4, 5)).astype(np.float32)
expected = (x * x).sum(axis=1, keepdims=1)
sess = ReferenceEvaluator(onnx_model)
got = sess.run(None, {"X": x})[0]
assert_allclose(expected, got)
def test_reduce_sum_13(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
A = make_tensor_value_info("A", TensorProto.INT64, [None, None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None])
node1 = make_node("ReduceSum", ["X", "A"], ["Y"], keepdims=1)
graph = make_graph([node1], "rs", [X, A], [Y])
onnx_model = make_model(graph, opset_imports=[make_opsetid("", 13)])
check_model(onnx_model)
x = np.arange(60).reshape((3, 4, 5)).astype(np.float32)
a = np.array([1], dtype=np.int64)
expected = x.sum(axis=1, keepdims=1)
sess = ReferenceEvaluator(onnx_model)
got = sess.run(None, {"X": x, "A": a})[0]
assert_allclose(expected, got)
def test_reduce_sum_attribute(self):
opset = onnx_opset_version()
new_domain = "custom"
opset_imports = [make_opsetid("", opset), make_opsetid(new_domain, 1)]
node = make_node("ReduceSum", ["X", "axis"], ["Y"])
att = AttributeProto()
att.name = "keepdims"
att.ref_attr_name = "keepdims"
att.type = AttributeProto.INT
node.attribute.append(att)
my_reduce_sum = make_function(
new_domain,
"MyReduceSum",
["X", "axis"],
["Y"],
[node],
opset_imports,
["keepdims"],
)
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
axis = make_tensor_value_info("axis", TensorProto.INT64, [None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None])
graph = make_graph(
[
make_node(
"MyReduceSum",
["X", "axis"],
["Y"],
domain=new_domain,
keepdims=1,
),
],
"example",
[X, axis],
[Y],
)
onnx_model = make_model(
graph, opset_imports=opset_imports, functions=[my_reduce_sum]
)
sess = ReferenceEvaluator(onnx_model)
x = np.arange(6).reshape((3, 2)).astype(np.float32)
a = np.array([-1], dtype=np.int64)
result = sess.run(None, {"X": x, "axis": a})[0]
expected = x.sum(axis=-1, keepdims=1)
assert_allclose(expected, result)
def test_reduce_sum_square_18(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
A = make_tensor_value_info("A", TensorProto.INT64, [None, None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None])
node1 = make_node("ReduceSumSquare", ["X", "A"], ["Y"], keepdims=1)
graph = make_graph([node1], "rs", [X, A], [Y])
onnx_model = make_model(graph, opset_imports=[make_opsetid("", 18)])
check_model(onnx_model)
x = np.arange(60).reshape((3, 4, 5)).astype(np.float32)
a = np.array([1], dtype=np.int64)
expected = (x * x).sum(axis=1, keepdims=1)
sess = ReferenceEvaluator(onnx_model)
got = sess.run(None, {"X": x, "A": a})[0]
assert_allclose(expected, got)
def test_reduce_sum_13_empty_axes(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
A = make_tensor_value_info("A", TensorProto.INT64, [None, None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None])
node1 = make_node("ReduceSum", ["X", "A"], ["Y"], keepdims=1)
graph = make_graph([node1], "rs", [X, A], [Y])
onnx_model = make_model(graph, opset_imports=[make_opsetid("", 13)])
check_model(onnx_model)
x = np.arange(60).reshape((3, 4, 5)).astype(np.float32)
a = np.array([], dtype=np.int64)
expected = x.sum(keepdims=1)
sess = ReferenceEvaluator(onnx_model)
got = sess.run(None, {"X": x, "A": a})[0]
assert_allclose(expected, got)
def test_reduce_sum_square_18_empty_axes(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
A = make_tensor_value_info("A", TensorProto.INT64, [None, None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None])
node1 = make_node("ReduceSumSquare", ["X", "A"], ["Y"], keepdims=1)
graph = make_graph([node1], "rs", [X, A], [Y])
onnx_model = make_model(graph, opset_imports=[make_opsetid("", 18)])
check_model(onnx_model)
x = np.arange(60).reshape((3, 4, 5)).astype(np.float32)
a = np.array([], dtype=np.int64)
expected = (x * x).sum(keepdims=1)
sess = ReferenceEvaluator(onnx_model)
got = sess.run(None, {"X": x, "A": a})[0]
assert_allclose(expected, got)
def test_reduce_sum_13_empty_axes_noop(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None])
node1 = make_node("ReduceSum", ["X"], ["Y"], keepdims=1, noop_with_empty_axes=1)
graph = make_graph([node1], "rs", [X], [Y])
onnx_model = make_model(graph, opset_imports=[make_opsetid("", 13)])
check_model(onnx_model)
x = np.arange(60).reshape((3, 4, 5)).astype(np.float32)
sess = ReferenceEvaluator(onnx_model)
got = sess.run(None, {"X": x})[0]
assert_allclose(x, got)
def test_reduce_sum_square_18_empty_axes_noop(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None])
node1 = make_node(
"ReduceSumSquare", ["X"], ["Y"], keepdims=1, noop_with_empty_axes=1
)
graph = make_graph([node1], "rs", [X], [Y])
onnx_model = make_model(graph, opset_imports=[make_opsetid("", 18)])
check_model(onnx_model)
x = np.arange(60).reshape((3, 4, 5)).astype(np.float32)
sess = ReferenceEvaluator(onnx_model)
got = sess.run(None, {"X": x})[0]
assert_allclose(x * x, got)
def test_greater(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None])
Z = make_tensor_value_info("Z", TensorProto.FLOAT, [None])
node1 = make_node("Greater", ["X", "Y"], ["Z"])
graph = make_graph([node1], "g", [X, Y], [Z])
onnx_model = make_model(graph, opset_imports=[make_opsetid("", 13)])
check_model(onnx_model)
x = np.arange(4).reshape((2, 2)).astype(np.float32)
y = np.array([2], dtype=np.float32)
expected = x > y
sess = ReferenceEvaluator(onnx_model)
got = sess.run(None, {"X": x, "Y": y})[0]
assert_allclose(expected, got)
def test_node_proto(self):
node1 = make_node("Greater", ["X", "Y"], ["Z"])
x = np.arange(4).reshape((2, 2)).astype(np.float32)
y = np.array([2], dtype=np.float32)
expected = x > y
sess = ReferenceEvaluator(node1)
got = sess.run(None, {"X": x, "Y": y})[0]
assert_allclose(expected, got)
def test_greater_or_equal(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None])
Z = make_tensor_value_info("Z", TensorProto.FLOAT, [None])
node1 = make_node("GreaterOrEqual", ["X", "Y"], ["Z"])
graph = make_graph([node1], "g", [X, Y], [Z])
onnx_model = make_model(graph, opset_imports=[make_opsetid("", 13)])
check_model(onnx_model)
x = np.arange(4).reshape((2, 2)).astype(np.float32)
y = np.array([2], dtype=np.float32)
expected = x >= y
sess = ReferenceEvaluator(onnx_model)
got = sess.run(None, {"X": x, "Y": y})[0]
assert_allclose(expected, got)
def test_if(self):
C = make_tensor_value_info("C", TensorProto.FLOAT, [None])
bthen = make_node(
"Constant",
[],
["C"],
value_floats=from_array(np.array([1], dtype=np.float32)),
)
bthen_body = make_graph([bthen], "gthen", [], [C])
C = make_tensor_value_info("C", TensorProto.FLOAT, [None])
belse = make_node(
"Constant",
[],
["C"],
value_floats=from_array(np.array([0], dtype=np.float32)),
)
belse_body = make_graph([belse], "gelse", [], [C])
zero = from_array(np.array([0], dtype=np.float32), name="zero")
greater = make_node("Greater", ["X", "zero"], ["G"])
node_if = make_node(
"If",
["G"],
["Z"],
then_branch=bthen_body,
else_branch=belse_body,
)
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
Z = make_tensor_value_info("Z", TensorProto.FLOAT, [None])
graph = make_graph([greater, node_if], "g", [X], [Z], initializer=[zero])
model_def = make_model(graph)
sess = ReferenceEvaluator(model_def)
self.assertEqual(str(sess), "ReferenceEvaluator(X) -> Z")
x = np.array([1, 2], dtype=np.float32)
got = sess.run(None, {"X": x})[0]
assert_allclose(np.array([1], dtype=np.float32), got)
x = np.array([-1, -2], dtype=np.float32)
got = sess.run(None, {"X": x})[0]
assert_allclose(np.array([0], dtype=np.float32), got)
def test_if_function(self):
then_out = make_tensor_value_info("then_out", TensorProto.FLOAT, [5])
else_out = make_tensor_value_info("else_out", TensorProto.FLOAT, [5])
x = np.array([1, 2, 3, 4, 5]).astype(np.float32)
y = np.array([5, 4, 3, 2, 1]).astype(np.float32)
then_const_node = make_node(
"Constant", inputs=[], outputs=["then_out"], value=from_array(x)
)
else_const_node = make_node(
"Constant", inputs=[], outputs=["else_out"], value=from_array(y)
)
then_body = make_graph([then_const_node], "then_body", [], [then_out])
else_body = make_graph([else_const_node], "else_body", [], [else_out])
if_node = make_node(
"If",
inputs=["f_cond"],
outputs=["f_res"],
then_branch=then_body,
else_branch=else_body,
)
f = FunctionProto()
f.domain = "custom"
f.name = "fn"
f.input.extend(["f_cond"])
f.output.extend(["f_res"])
f.node.extend([if_node])
opset = onnx_opset_version()
f.opset_import.extend([make_opsetid("", opset)])
graph = make_graph(
nodes=[make_node("fn", domain="custom", inputs=["cond"], outputs=["res"])],
name="graph",
inputs=[make_tensor_value_info("cond", TensorProto.BOOL, [])],
outputs=[make_tensor_value_info("res", TensorProto.FLOAT, [5])],
)
m = make_model(
graph,
producer_name="test",
opset_imports=[make_opsetid("", opset), make_opsetid("custom", 1)],
)
m.functions.extend([f])
sess = ReferenceEvaluator(m)
result = sess.run(None, {"cond": np.array(True)})
expected = np.array([1, 2, 3, 4, 5], dtype=np.float32)
assert_allclose(expected, result[0])
def test_function_attribute(self):
opset = onnx_opset_version()
new_domain = "custom"
opset_imports = [make_opsetid("", opset), make_opsetid(new_domain, 1)]
cst = make_node("Constant", [], ["B"])
att = AttributeProto()
att.name = "value"
att.ref_attr_name = "bias"
att.type = AttributeProto.TENSOR
cst.attribute.append(att)
node1 = make_node("MatMul", ["X", "A"], ["XA"])
node2 = make_node("Add", ["XA", "B"], ["Y"])
linear_regression = make_function(
new_domain,
"LinearRegression",
["X", "A"],
["Y"],
[cst, node1, node2],
opset_imports,
["bias"],
)
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
A = make_tensor_value_info("A", TensorProto.FLOAT, [None, None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None])
graph = make_graph(
[
make_node(
"LinearRegression",
["X", "A"],
["Y1"],
domain=new_domain,
bias=make_tensor("former_B", TensorProto.FLOAT, [1], [0.67]),
),
make_node("Abs", ["Y1"], ["Y"]),
],
"example",
[X, A],
[Y],
)
onnx_model = make_model(
graph, opset_imports=opset_imports, functions=[linear_regression]
)
sess = ReferenceEvaluator(onnx_model)
x = np.arange(6).reshape((3, 2)).astype(np.float32)
a = np.array([1, -1], dtype=np.float32)
result = sess.run(None, {"X": x, "A": a})[0]
expected = np.abs(x @ a + 0.67)
assert_allclose(expected, result)
def test_function_attribute_nested_graph(self):
opset = onnx_opset_version()
new_domain = "custom"
opset_imports = [make_opsetid("", opset), make_opsetid(new_domain, 1)]
cst1 = make_node("Constant", [], ["B1"])
att = AttributeProto()
att.name = "value"
att.ref_attr_name = "bias1"
att.type = AttributeProto.TENSOR
cst1.attribute.append(att)
cst2 = make_node("Constant", [], ["B2"])
att = AttributeProto()
att.name = "value"
att.ref_attr_name = "bias2"
att.type = AttributeProto.TENSOR
cst2.attribute.append(att)
then_out = make_tensor_value_info("B1", TensorProto.FLOAT, [None])
else_out = make_tensor_value_info("B2", TensorProto.FLOAT, [None])
then_body = make_graph([cst1], "then_body", [], [then_out])
else_body = make_graph([cst2], "else_body", [], [else_out])
zero = make_node(
"Constant",
inputs=[],
outputs=["zero"],
value=from_array(np.array([0], dtype=np.float32)),
)
mini = make_node("ReduceMin", ["X"], ["Xmin"])
f_cond = make_node("Greater", ["Xmin", "zero"], ["f_cond"])
if_node = make_node(
"If",
inputs=["f_cond"],
outputs=["B"],
then_branch=then_body,
else_branch=else_body,
)
node1 = make_node("MatMul", ["X", "A"], ["XA"])
node2 = make_node("Add", ["XA", "B"], ["Y"])
linear_regression = make_function(
new_domain,
"LinearRegression",
["X", "A"],
["Y"],
[zero, mini, f_cond, if_node, node1, node2],
opset_imports,
["bias1", "bias2"],
)
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
A = make_tensor_value_info("A", TensorProto.FLOAT, [None, None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None])
graph = make_graph(
[
make_node(
"LinearRegression",
["X", "A"],
["Y1"],
domain=new_domain,
bias1=make_tensor("former_B1", TensorProto.FLOAT, [1], [0.67]),
bias2=make_tensor("former_B2", TensorProto.FLOAT, [1], [777]),
),
make_node("Abs", ["Y1"], ["Y"]),
],
"example",
[X, A],
[Y],
)
onnx_model = make_model(
graph, opset_imports=opset_imports, functions=[linear_regression]
)
check_model(onnx_model)
sess = ReferenceEvaluator(onnx_model)
self.assertEqual(sess.rt_nodes_[0].__class__.__name__, "OpFunction")
self.assertEqual(
sess.rt_nodes_[0].impl_.__class__.__name__, "ReferenceEvaluator"
)
fct = sess.rt_nodes_[0].impl_
checked = False
for node in fct.rt_nodes_:
if node.__class__.__name__.startswith("If"):
if not node.has_linked_attribute:
raise AssertionError(
f"Nested node {type(node)} declares no linked attribute "
f"but a subgraph does."
)
checked = True
if not checked:
raise AssertionError(
"No node 'If' was found, has_linked_attribute could not be checked."
)
x = np.arange(6).reshape((3, 2)).astype(np.float32)
a = np.array([1, -1], dtype=np.float32)
result = sess.run(None, {"X": x + 1, "A": a})[0]
expected = np.abs(x @ a + 0.67)
assert_allclose(expected, result)
result = sess.run(None, {"X": x - 10, "A": a})[0]
expected = np.abs(x @ a + 777)
assert_allclose(expected, result)
def test_function_attribute_nested_nested_graph(self):
opset = onnx_opset_version()
new_domain = "custom"
opset_imports = [make_opsetid("", opset), make_opsetid(new_domain, 1)]
# first If
cst1 = make_node("Constant", [], ["B1"])
att = AttributeProto()
att.name = "value"
att.ref_attr_name = "bias1"
att.type = AttributeProto.TENSOR
cst1.attribute.append(att)
cst2 = make_node("Constant", [], ["B2"])
att = AttributeProto()
att.name = "value"
att.ref_attr_name = "bias2"
att.type = AttributeProto.TENSOR
cst2.attribute.append(att)
then_out = make_tensor_value_info("B1", TensorProto.FLOAT, [None])
else_out = make_tensor_value_info("B2", TensorProto.FLOAT, [None])
then_body1 = make_graph([cst1], "then_body", [], [then_out])
else_body1 = make_graph([cst2], "else_body", [], [else_out])
# sub graph 2
c100 = make_node(
"Constant",
inputs=[],
outputs=["c100"],
value=from_array(np.array([100], dtype=np.float32)),
)
f_cond = make_node("Greater", ["Xmin", "c100"], ["f_cond_100"])
if_node = make_node(
"If",
inputs=["f_cond_100"],
outputs=["B4"],
then_branch=then_body1,
else_branch=else_body1,
)
# second If
cst3 = make_node("Constant", [], ["B3"])
att = AttributeProto()
att.name = "value"
att.ref_attr_name = "bias3"
att.type = AttributeProto.TENSOR
cst3.attribute.append(att)
then_out = make_tensor_value_info("B3", TensorProto.FLOAT, [None])
then_body2 = make_graph([cst3], "then_body", [], [then_out])
else_out = make_tensor_value_info("B4", TensorProto.FLOAT, [None])
else_body2 = make_graph([c100, f_cond, if_node], "else_body", [], [else_out])
# function
zero = make_node(
"Constant",
inputs=[],
outputs=["zero"],
value=from_array(np.array([0], dtype=np.float32)),
)
mini = make_node("ReduceMin", ["X"], ["Xmin"])
f_cond = make_node("Less", ["Xmin", "zero"], ["f_cond_zero"])
if_node = make_node(
"If",
inputs=["f_cond_zero"],
outputs=["B"],
then_branch=then_body2,
else_branch=else_body2,
)
node1 = make_node("MatMul", ["X", "A"], ["XA"])
node2 = make_node("Add", ["XA", "B"], ["Y"])
linear_regression = make_function(
new_domain,
"LinearRegression",
["X", "A"],
["Y"],
[zero, mini, f_cond, if_node, node1, node2],
opset_imports,
["bias1", "bias2", "bias3"],
)
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
A = make_tensor_value_info("A", TensorProto.FLOAT, [None, None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None])
graph = make_graph(
[
make_node(
"LinearRegression",
["X", "A"],
["Y1"],
domain=new_domain,
bias1=make_tensor("former_B1", TensorProto.FLOAT, [1], [0.67]),
bias2=make_tensor("former_B2", TensorProto.FLOAT, [1], [777]),
bias3=make_tensor("former_B3", TensorProto.FLOAT, [1], [-888]),
),
make_node("Abs", ["Y1"], ["Y"]),
],
"example",
[X, A],
[Y],
)
onnx_model = make_model(
graph, opset_imports=opset_imports, functions=[linear_regression]
)
check_model(onnx_model)
sess = ReferenceEvaluator(onnx_model)
x = np.arange(6).reshape((3, 2)).astype(np.float32)
a = np.array([1, -1], dtype=np.float32)
result = sess.run(None, {"X": x + 1, "A": a})[0]
expected = np.abs(x @ a + 777)
assert_allclose(expected, result)
result = sess.run(None, {"X": x - 10, "A": a})[0]
expected = np.abs(x @ a - 888)
assert_allclose(expected, result)
result = sess.run(None, {"X": x + 1000, "A": a})[0]
expected = np.abs(x @ a + 0.67)
assert_allclose(expected, result)
def test_custom_node(self):
class _InvAlpha:
op_domain = "custom"
def __init__(self, onnx_node, run_params): # type: ignore
self.onnx_node = onnx_node
self.run_params = run_params
def _run(self, x): # type: ignore
return (1 / (x + self.alpha),)
class InvAlpha2(OpRun):
def _run(self, x): # type: ignore
return (1 / (x + self.alpha),)
class InvAlpha(OpRun):
op_domain = "custom"
def _run(self, x, alpha=None): # type: ignore
alpha = alpha or self.alpha # type: ignore
return (1 / (x + alpha),)
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None])
node1 = make_node("InvAlpha", ["X"], ["Y"], alpha=0.5, domain="custom")
graph = make_graph([node1], "rs", [X], [Y])
onnx_model = make_model(graph, opset_imports=[make_opsetid("custom", 1)])
x = np.arange(60).reshape((3, 4, 5)).astype(np.float32) + 1
with self.assertRaises(NotImplementedError):
ReferenceEvaluator(onnx_model)
node1 = make_node("_InvAlpha", ["X"], ["Y"], alpha=0.5, domain="custom")
graph = make_graph([node1], "rs", [X], [Y])
onnx_model = make_model(graph, opset_imports=[make_opsetid("custom", 1)])
with self.assertRaises(TypeError):
ReferenceEvaluator(onnx_model, new_ops=[_InvAlpha])
node1 = make_node("InvAlpha2", ["X"], ["Y"], alpha=0.5, domain="custom")
graph = make_graph([node1], "rs", [X], [Y])
onnx_model = make_model(graph, opset_imports=[make_opsetid("custom", 1)])
with self.assertRaises(NotImplementedError):
ReferenceEvaluator(onnx_model, new_ops=[InvAlpha2])
node1 = make_node("InvAlpha", ["X"], ["Y"], alpha=0.5, domain="custom")
graph = make_graph([node1], "rs", [X], [Y])
onnx_model = make_model(graph, opset_imports=[make_opsetid("custom", 1)])
sess = ReferenceEvaluator(onnx_model, new_ops=[InvAlpha, InvAlpha])
got = sess.run(None, {"X": x})[0]
expected = 1 / (x + 0.5)
assert_allclose(expected, got)
def test_loop(self):
# Given a tensor x of values [x1, ..., xN],
# Return a sequence of tensors of
# [[x1], [x1, x2], ..., [x1, ..., xN]]
cond_in = make_tensor_value_info("cond_in", TensorProto.BOOL, [])
cond_out = make_tensor_value_info("cond_out", TensorProto.BOOL, [])
iter_count = make_tensor_value_info("iter_count", TensorProto.INT64, [])
seq_in = make_tensor_sequence_value_info("seq_in", TensorProto.FLOAT, None)
seq_out = make_tensor_sequence_value_info("seq_out", TensorProto.FLOAT, None)
x = np.array([1, 2, 3, 4, 5]).astype(np.float32)
x_const_node = make_node(
"Constant",
inputs=[],
outputs=["x"],
value=make_tensor(
name="const_tensor_x",
data_type=TensorProto.FLOAT,
dims=x.shape,
vals=x.flatten().astype(float),
),
)
one_const_node = make_node(
"Constant",
inputs=[],
outputs=["one"],
value=make_tensor(
name="const_tensor_one",
data_type=TensorProto.INT64,
dims=(),
vals=[1],
),
)
zero_const_node = make_node(
"Constant",
inputs=[],
outputs=["slice_start"],
value=make_tensor(
name="const_tensor_zero",
data_type=TensorProto.INT64,
dims=(1,),
vals=[0],
),
)
axes_node = make_node(
"Constant",
inputs=[],
outputs=["axes"],
value=make_tensor(
name="const_tensor_axes",
data_type=TensorProto.INT64,
dims=(),
vals=[0],
),
)
add_node = make_node("Add", inputs=["iter_count", "one"], outputs=["end"])
end_unsqueeze_node = make_node(
"Unsqueeze", inputs=["end", "axes"], outputs=["slice_end"]
)
slice_node = make_node(
"Slice", inputs=["x", "slice_start", "slice_end"], outputs=["slice_out"]
)
insert_node = make_node(
"SequenceInsert", inputs=["seq_in", "slice_out"], outputs=["seq_out"]
)
identity_node = make_node("Identity", inputs=["cond_in"], outputs=["cond_out"])
loop_body = make_graph(
[
identity_node,
x_const_node,
one_const_node,
zero_const_node,
add_node,
axes_node,
end_unsqueeze_node,
slice_node,
insert_node,
],
"loop_body",
[iter_count, cond_in, seq_in],
[cond_out, seq_out],
)
node = make_node(
"Loop",
inputs=["trip_count", "cond", "seq_empty"],
outputs=["seq_res"],
body=loop_body,
)
node_concat = make_node(
"ConcatFromSequence",
inputs=["seq_res"],
outputs=["res"],
axis=0,
new_axis=0,
)
trip_count = np.array(5).astype(np.int64)
seq_empty = [] # type: List[Any]
# seq_res = [x[:int(i)] for i in x]
cond = np.array(1).astype(np.bool_)
model_def = make_model(
graph=make_graph(
name="loop_test",
inputs=[
make_tensor_value_info(
"trip_count", TensorProto.INT64, trip_count.shape
),
make_tensor_value_info("cond", TensorProto.BOOL, cond.shape),
make_sequence_value_info("seq_empty", TensorProto.FLOAT, []),
],
outputs=[make_tensor_value_info("res", TensorProto.FLOAT, None)],
nodes=[node, node_concat],
)
)
expected = np.array(
[1.0, 1.0, 2.0, 1.0, 2.0, 3.0, 1.0, 2.0, 3.0, 4.0, 1.0, 2.0, 3.0, 4.0, 5.0],
dtype=np.float32,
)
oinf = ReferenceEvaluator(model_def)
inputs = {"trip_count": trip_count, "cond": cond, "seq_empty": seq_empty}
got = oinf.run(None, inputs)
assert_allclose(expected, got[0])
def test_onnxt_runtime_bernoulli(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None])
node1 = make_node("Bernoulli", ["X"], ["Y"], seed=0.0)
graph = make_graph([node1], "g", [X], [Y])
onnx_model = make_model(graph)
check_model(onnx_model)
sess = ReferenceEvaluator(onnx_model)
got = sess.run(None, {"X": np.zeros((2, 4), dtype=np.float32) + 0.5})[0]
self.assertEqual(got.shape, (2, 4))
self.assertEqual(got.dtype, np.float32)
self.assertGreater(got.min(), -1e-5)
self.assertLess(got.max(), 1 + 1e-5)
def test_onnxt_runtime_random_uniform(self):
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None])
node1 = make_node("RandomUniform", [], ["Y"], seed=0.0, shape=[2, 4])
graph = make_graph([node1], "g", [], [Y])
onnx_model = make_model(graph)
check_model(onnx_model)
sess = ReferenceEvaluator(onnx_model)
got = sess.run(None, {})[0]
self.assertEqual(got.shape, (2, 4))
self.assertEqual(got.dtype, np.float32)
self.assertGreater(got.min(), 0)
self.assertLess(got.max(), 1)
def test_onnxt_runtime_random_uniform_like(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None])
node1 = make_node("RandomUniformLike", ["X"], ["Y"], seed=0.0)
graph = make_graph([node1], "g", [X], [Y])
onnx_model = make_model(graph)
check_model(onnx_model)
sess = ReferenceEvaluator(onnx_model)
got = sess.run(None, {"X": np.zeros((2, 4), dtype=np.float32)})[0]
self.assertEqual(got.shape, (2, 4))
self.assertEqual(got.dtype, np.float32)
self.assertGreater(got.min(), 0)
self.assertLess(got.max(), 1)
def test_onnxt_runtime_random_normal(self):
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None])
node1 = make_node("RandomNormal", [], ["Y"], seed=0.0, shape=[2, 4])
graph = make_graph([node1], "g", [], [Y])
onnx_model = make_model(graph)
check_model(onnx_model)
sess = ReferenceEvaluator(onnx_model)
got = sess.run(None, {})[0]
self.assertEqual(got.shape, (2, 4))
self.assertEqual(got.dtype, np.float32)
def test_onnxt_runtime_random_normal_like(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None])
node1 = make_node("RandomNormalLike", ["X"], ["Y"], seed=0.0)
graph = make_graph([node1], "g", [X], [Y])
onnx_model = make_model(graph)
check_model(onnx_model)
sess = ReferenceEvaluator(onnx_model)
got = sess.run(None, {"X": np.zeros((2, 4), dtype=np.float32)})[0]
self.assertEqual(got.shape, (2, 4))
self.assertEqual(got.dtype, np.float32)
def test_eval_celu(self):
inst = Celu.create(alpha=0.5)
self.assertEqual(inst.alpha, 0.5)
x = np.array([[0, 1], [-1, 2]], dtype=np.float32)
y = Celu.eval(x, alpha=0.5)
expected = _vcelu1(x, alpha=0.5)
assert_allclose(expected, y)
def test_eval_cast(self):
x = np.array([[0, 1], [-1, 2]], dtype=np.float32)
y = Cast_19.eval(x, to=TensorProto.FLOAT8E4M3FN)
dy = Cast_19.eval(y, to=TensorProto.FLOAT)
expected = x
assert_allclose(expected, dy)
def test_eval_celu_load_op(self):
celu = load_op("", "Celu")
self.assertEqual(celu.op_domain, "")
inst = celu.create(alpha=0.5)
self.assertEqual(inst.alpha, 0.5)
x = np.array([[0, 1], [-1, 2]], dtype=np.float32)
y = celu.eval(x, alpha=0.5)
expected = _vcelu1(x, alpha=0.5)
assert_allclose(expected, y)
def test_create_adam(self):
inst = Adam.create(alpha=0.5)
self.assertEqual(inst.alpha, 0.5)
@skip_if_no_onnxruntime
def test_conv(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None, None, None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None, None, None, None])
B = make_tensor_value_info("B", TensorProto.FLOAT, [None, None, None, None])
W = make_tensor_value_info("W", TensorProto.FLOAT, [None, None, None, None])
node = make_node(
"Conv",
["X", "W", "B"],
["Y"],
pads=[1, 1, 1, 1],
dilations=[1, 1],
strides=[2, 2],
)
graph = make_graph([node], "g", [X, W, B], [Y])
onnx_model = make_model_gen_version(graph, opset_imports=[make_opsetid("", 16)])
sess1 = run_ort_inference(onnx_model)
if sess1 is None:
return
sess2 = ReferenceEvaluator(onnx_model, optimized=False)
self.assertIsInstance(sess2.rt_nodes_[0], Conv)
sess3 = ReferenceEvaluator(onnx_model, new_ops=[ConvOptimized], optimized=False)
self.assertIsInstance(sess3.rt_nodes_[0], ConvOptimized)
sess4 = ReferenceEvaluator(onnx_model, optimized=True)
self.assertIsInstance(sess4.rt_nodes_[0], ConvOptimized)
sH, sW = 5, 6
for i in range(sH):
for j in range(sW):
X = np.zeros((1, 1, sH, sW), dtype=np.float32)
X[0, 0, i, j] = 1.0
W = np.zeros((1, 1, 3, 3), dtype=np.float32)
W[0, 0, :, :] = np.minimum(2 ** np.arange(9).reshape((3, -1)), 256)
B = np.array([[[[0]]]], dtype=np.float32)
expected = sess1.run(None, {"X": X, "W": W, "B": B})[0]
got = sess2.run(None, {"X": X, "W": W, "B": B})[0]
assert_allclose(expected, got)
got3 = sess3.run(None, {"X": X, "W": W, "B": B})[0]
assert_allclose(expected, got3)
got4 = sess4.run(None, {"X": X, "W": W, "B": B})[0]
assert_allclose(expected, got4)
@skip_if_no_onnxruntime
def test_qlinearconv(self):
x = make_tensor_value_info("x", TensorProto.UINT8, [None, None, None, None])
w = make_tensor_value_info("w", TensorProto.UINT8, [None, None, None, None])
y = make_tensor_value_info("y", TensorProto.UINT8, [None, None, None, None])
x_scale = make_tensor_value_info("x_scale", TensorProto.FLOAT, [None])
w_scale = make_tensor_value_info("w_scale", TensorProto.FLOAT, [None])
y_scale = make_tensor_value_info("y_scale", TensorProto.FLOAT, [None])
x_zero_point = make_tensor_value_info("x_zero_point", TensorProto.UINT8, [None])
w_zero_point = make_tensor_value_info("w_zero_point", TensorProto.UINT8, [None])
y_zero_point = make_tensor_value_info("y_zero_point", TensorProto.UINT8, [None])
node = make_node(
"QLinearConv",
[
"x",
"x_scale",
"x_zero_point",
"w",
"w_scale",
"w_zero_point",
"y_scale",
"y_zero_point",
],
["y"],
)
graph = make_graph(
[node],
"g",
[x, x_scale, x_zero_point, w, w_scale, w_zero_point, y_scale, y_zero_point],
[y],
)
onnx_model = make_model_gen_version(graph, opset_imports=[make_opsetid("", 16)])
sess1 = run_ort_inference(onnx_model)
if sess1 is None:
return
sess2 = ReferenceEvaluator(onnx_model)
sH, sW = 3, 3
for i in range(sH):
for j in range(sW):
x = np.zeros((1, 1, sH, sW), dtype=np.uint8)
x[0, 0, i, j] = 1.0
with self.subTest(w="1x1", i=i, j=j):
w = np.zeros((1, 1, 1, 1), dtype=np.uint8)
w[0, 0, :, :] = 1
feeds = {
"x": x,
"x_scale": np.array([1], dtype=np.float32),
"x_zero_point": np.array([0], dtype=np.uint8),
"w": w,
"w_scale": np.array([1], dtype=np.float32),
"w_zero_point": np.array([0], dtype=np.uint8),
"y_scale": np.array([1], dtype=np.float32),
"y_zero_point": np.array([0], np.uint8),
}
expected = sess1.run(None, feeds)[0]
got = sess2.run(None, feeds)[0]
try:
assert_allclose(expected, got)
except AssertionError as e:
raise e
with self.subTest(w="3x3", i=i, j=j):
w = np.zeros((1, 1, 3, 3), dtype=np.uint8)
w[0, 0, :, :] = np.minimum(2 ** np.arange(9).reshape((3, -1)), 128)
feeds = {
"x": x,
"x_scale": np.array([1], dtype=np.float32),
"x_zero_point": np.array([0], dtype=np.uint8),
"w": w,
"w_scale": np.array([1], dtype=np.float32),
"w_zero_point": np.array([0], dtype=np.uint8),
"y_scale": np.array([1], dtype=np.float32),
"y_zero_point": np.array([0], np.uint8),
}
expected = sess1.run(None, feeds)[0]
got = sess2.run(None, feeds)[0]
assert_allclose(expected, got)
with self.subTest(w="1x1", i=i, j=j):
w = np.zeros((1, 1, 1, 1), dtype=np.uint8)
w[0, 0, :, :] = 0
feeds = {
"x": x,
"x_scale": np.array([0.00369204697], dtype=np.float32),
"x_zero_point": np.array([132], dtype=np.uint8),
"w": w,
"w_scale": np.array([100.001727945750], dtype=np.float32),
"w_zero_point": np.array([255], dtype=np.uint8),
"y_scale": np.array([0.00162681262], dtype=np.float32),
"y_zero_point": np.array([132], np.uint8),
}
expected = sess1.run(None, feeds)[0]
got = sess2.run(None, feeds)[0]
assert_allclose(expected, got)
x = np.array(
[
[255, 174, 162, 25, 203, 168, 58],
[15, 59, 237, 95, 129, 0, 64],
[56, 242, 153, 221, 168, 12, 166],
[232, 178, 186, 195, 237, 162, 237],
[188, 39, 124, 77, 80, 102, 43],
[127, 230, 21, 83, 41, 40, 134],
[255, 154, 92, 141, 42, 148, 247],
],
dtype=np.uint8,
).reshape((1, 1, 7, 7))
x_scale = np.array([0.00369204697], dtype=np.float32)
x_zero_point = np.array([132], dtype=np.uint8)
w = np.array([0], dtype=np.uint8).reshape((1, 1, 1, 1))
w_scale = np.array([0.00172794575], dtype=np.float32)
w_zero_point = np.array([255], dtype=np.uint8)
y_scale = np.array([0.00162681262], dtype=np.float32)
y_zero_point = np.array([123], dtype=np.uint8)
feeds = {
"x": x,
"x_scale": x_scale,
"x_zero_point": x_zero_point,
"w": w,
"w_scale": w_scale,
"w_zero_point": w_zero_point,
"y_scale": y_scale,
"y_zero_point": y_zero_point,
}
expected = sess1.run(None, feeds)[0]
got = sess2.run(None, feeds)[0]
assert_allclose(expected, got)
def common_test_im2col(self, kernel_shape, pads, strides, dilations):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None, None, None])
Y1 = make_tensor_value_info("Y1", TensorProto.FLOAT, [None, None, None, None])
Y2 = make_tensor_value_info("Y2", TensorProto.FLOAT, [None, None, None, None])
W = make_tensor_value_info("W", TensorProto.FLOAT, [None, None, None, None])
node = make_node(
"Conv", ["X", "W"], ["Y1"], pads=pads, strides=strides, dilations=dilations
)
node_shape = make_node("Shape", ["W"], ["shape"])
node_im = make_node(
"Im2Col",
["X", "shape"],
["xim"],
pads=pads,
strides=strides,
dilations=dilations,
domain="experimental",
)
node_flat = make_node("Flatten", ["W"], ["wflat"])
node_gem = make_node("MatMul", ["wflat", "xim"], ["Y2"])
graph = make_graph(
[node, node_shape, node_im, node_flat, node_gem],
"g",
[X, W],
[Y1, Y2],
)
onnx_model = make_model(
graph, opset_imports=[make_opsetid("", 16), make_opsetid("experimental", 1)]
)
graph_conv = make_graph([node], "g", [X, W], [Y1])
onnx_model_conv = make_model_gen_version(
graph_conv, opset_imports=[make_opsetid("", 16)]
)
sess = ReferenceEvaluator(onnx_model)
try:
sess_conv = run_ort_inference(onnx_model_conv)
if sess_conv is None:
return
except ImportError:
sess_conv = None
sH, sW = 7, 7
nker = np.prod(kernel_shape)
for i in range(sH):
for j in range(sW):
X = np.zeros((1, 1, sH, sW), dtype=np.float32)
X[0, 0, i, j] = 1.0
W = np.zeros(
(1, 1, *kernel_shape),
dtype=np.float32,
)
W[0, 0, :, :] = np.minimum(
2 ** np.arange(nker).reshape((kernel_shape[0], -1)), 256
)
got = sess.run(None, {"X": X, "W": W})
if sess_conv is not None:
ort_res = sess_conv.run(None, {"X": X, "W": W})[0]
assert_allclose(got[1].ravel(), ort_res.ravel())
try:
assert_allclose(got[0].ravel(), got[1].ravel())
except AssertionError as e:
raise AssertionError(
f"Discrepancies: pads={pads}, dilations={dilations}, strides={strides}, "
f"kernel_shape={kernel_shape}"
f"\n{got[0]}\n!=\n{got[1]}"
) from e
def test_im2col_1x1(self):
self.common_test_im2col(
(1, 1), pads=[1, 1, 1, 2], strides=[1, 1], dilations=[1, 1]
)
def test_im2col_2x2(self):
self.common_test_im2col(
(2, 2), pads=[1, 1, 1, 2], strides=[1, 1], dilations=[1, 1]
)
def test_im2col_3x3(self):
self.common_test_im2col(
(3, 3), pads=[1, 1, 1, 2], strides=[1, 1], dilations=[1, 1]
)
def test_im2col_3x3_pads(self):
self.common_test_im2col(
(3, 3), pads=[0, 1, 2, 3], strides=[1, 1], dilations=[1, 1]
)
def test_im2col_3x3_strides(self):
self.common_test_im2col(
(3, 3), pads=[0, 1, 1, 1], strides=[1, 2], dilations=[1, 1]
)
def test_im2col_5x5(self):
self.common_test_im2col(
(5, 5), pads=[1, 1, 1, 2], strides=[1, 1], dilations=[1, 1]
)
@skip_if_no_torch
def test_col2im(self):
import torch
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None, None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None, None, None])
IS = make_tensor_value_info("I", TensorProto.INT64, [None])
BS = make_tensor_value_info("B", TensorProto.INT64, [None])
node = make_node(
"Col2Im",
["X", "I", "B"],
["Y"],
pads=[0, 0, 0, 0],
strides=[1, 1],
dilations=[1, 1],
)
graph = make_graph([node], "g", [X, IS, BS], [Y])
onnx_model = make_model(graph, opset_imports=[make_opsetid("", 16)])
sess = ReferenceEvaluator(onnx_model)
X = np.array(
[
[
[1.0, 6.0, 11.0, 16.0, 21.0],
[2.0, 7.0, 12.0, 17.0, 22.0],
[3.0, 8.0, 13.0, 18.0, 23.0],
[4.0, 9.0, 14.0, 19.0, 24.0],
[5.0, 0.0, 15.0, 20.0, 25.0],
]
]
).astype(np.float32)
image_shape = np.array([5, 5]).astype(np.int64)
block_shape = np.array([1, 5]).astype(np.int64)
fold = torch.nn.Fold(output_size=tuple(image_shape), kernel_size=block_shape)
got = sess.run(None, {"X": X, "B": block_shape, "I": image_shape})
output = fold(torch.from_numpy(X)).numpy()
assert_allclose(output, got[0])
def common_test_col2im(
self, size, image_shape, block_shape, pads, strides, dilations
):
import torch
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None, None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None, None, None])
IS = make_tensor_value_info("I", TensorProto.INT64, [None])
BS = make_tensor_value_info("B", TensorProto.INT64, [None])
node = make_node(
"Col2Im",
["X", "I", "B"],
["Y"],
pads=pads,
strides=strides,
dilations=dilations,
)
graph = make_graph([node], "g", [X, IS, BS], [Y])
onnx_model = make_model(graph, opset_imports=[make_opsetid("", 16)])
sess = ReferenceEvaluator(onnx_model)
fold = torch.nn.Fold(
output_size=tuple(image_shape),
kernel_size=tuple(block_shape),
dilation=tuple(dilations),
padding=min(pads),
stride=tuple(strides),
)
nker = np.prod(block_shape)
for i in range(nker):
for j in range(size):
X = np.zeros((1, nker, size), dtype=np.float32)
X[0, i, j] = 1.0
i_shape = np.array(image_shape, dtype=np.int64)
b_shape = np.array(block_shape, dtype=np.int64)
output = fold(torch.from_numpy(X)).numpy()
got = sess.run(None, {"X": X, "B": b_shape, "I": i_shape})
# print(output)
# print(got)
assert_allclose(output, got[0])
@skip_if_no_torch
def test_col2im_2x3(self):
self.common_test_col2im(
10, (6, 4), (2, 3), pads=[0, 0, 0, 0], strides=[1, 1], dilations=[1, 1]
)
@skip_if_no_torch
def test_col2im_2x3_pads(self):
self.common_test_col2im(
28, (6, 4), (2, 3), pads=[1, 1, 1, 1], strides=[1, 1], dilations=[1, 1]
)
def test_col2im_2d(self):
data = np.zeros([6, 28], dtype=np.float32)
data[0][0] = 1.0
image_shape, kernel_shape, dilations, pads, stride = (
np.array([6, 4]),
(2, 3),
np.array([1, 1]),
np.array([1, 1, 1, 1]),
np.array([1, 1]),
)
r1 = _col2im_naive_implementation_2d(
data, image_shape, kernel_shape, dilations, pads, stride
)
r2 = col2im_naive_implementation(
data, image_shape, kernel_shape, dilations, pads, stride
)
assert_allclose(r1, r2)
def test_conv_im2col_group4(self):
# model 1
X = make_tensor_value_info("X", TensorProto.FLOAT, [2, 4, 6, 6])
W = make_tensor_value_info("W", TensorProto.FLOAT, [4, 1, 3, 3])
B = make_tensor_value_info("B", TensorProto.FLOAT, [4])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [2, 4, 6, 6])
node = make_node(
"Conv",
["X", "W", "B"],
["Y"],
group=4,
dilations=[1, 1],
kernel_shape=[3, 3],
pads=[1, 1, 1, 1],
strides=[1, 1],
)
graph = make_graph([node], "g", [X, W, B], [Y])
onnx_model = make_model(graph, opset_imports=[make_opsetid("", 16)])
feeds = {
"X": np.arange(2 * 4 * 6 * 6).reshape((2, 4, 6, 6)).astype(np.float32),
"W": np.array(
[
[
[
[
-0.026239916682243347,
0.07565222680568695,
-0.03209298849105835,
],
[
-0.08708783239126205,
0.0961190015077591,
0.13418219983577728,
],
[
0.1598859578371048,
0.03840477764606476,
-0.13170936703681946,
],
]
],
[
[
[
-0.0689004510641098,
0.1408083587884903,
-0.03717087209224701,
],
[
0.030967697501182556,
0.0263785719871521,
-0.0899493545293808,
],
[
0.07828782498836517,
-0.06266771256923676,
0.10750330984592438,
],
]
],
[
[
[
0.020227551460266113,
-0.04353883117437363,
-0.10938453674316406,
],
[
-0.14101561903953552,
-0.03393106162548065,
0.12139306962490082,
],
[
0.02838282287120819,
0.13864465057849884,
-0.06065710633993149,
],
]
],
[
[
[
-0.06511610746383667,
-0.05987360328435898,
-0.008047685027122498,
],
[
0.07340313494205475,
0.0326494425535202,
0.012516498565673828,
],
[
0.13260947167873383,
-0.022225692868232727,
-0.11167611926794052,
],
]
],
],
dtype=np.float32,
),
"B": np.array(
[
-0.1457933485507965,
-0.07481209933757782,
-0.05890338122844696,
-0.11964251846075058,
],
dtype=np.float32,
),
}
feeds["B"][:] = 0
# model 2
X = feeds["X"]
W = feeds["W"]
B = feeds["B"]
Y = np.empty((2, 4, 6, 6), dtype=X.dtype)
for b in range(X.shape[0]):
for g in range(4):
x = X[b : b + 1, g : g + 1]
w = W[g]
c2 = im2col(x, (3, 3), [1, 1], [1, 1, 1, 1], [1, 1])
mul = np.matmul(c2, w.flatten())
mul = mul + B[g]
Y[b, g, :, :] = mul
ref1 = ReferenceEvaluator(onnx_model)
got1 = ref1.run(None, feeds)
assert_allclose(Y, got1[0], atol=1e-5)
def test_conv_strides(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [1, 3, 6, 6])
W = make_tensor_value_info("W", TensorProto.FLOAT, [2, 3, 3, 3])
B = make_tensor_value_info("B", TensorProto.FLOAT, [2])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None, None, None, None])
node = make_node(
"Conv",
["X", "W", "B"],
["Y"],
group=1,
dilations=[1, 1],
kernel_shape=[3, 3],
pads=[1, 1, 1, 1],
strides=[2, 2],
)
graph = make_graph([node], "g", [X, W, B], [Y])
onnx_model = make_model(graph, opset_imports=[make_opsetid("", 16)])
feeds = {
"X": np.arange(1 * 3 * 6 * 6).reshape((1, 3, 6, 6)).astype(np.float32) + 1,
"W": np.zeros((2, 3, 3, 3), dtype=np.float32),
"B": np.zeros((2,), dtype=np.float32),
}
feeds["W"][0, 0, 0, 1] = 1
ref1 = ReferenceEvaluator(onnx_model)
got1 = ref1.run(None, feeds)
expected = np.array(
[
[
[[0.0, 0.0, 0.0], [7.0, 9.0, 11.0], [19.0, 21.0, 23.0]],
[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
]
],
dtype=np.float32,
)
assert_allclose(expected, got1[0])
def test_max_pool_2d_1(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None, None, None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None, None, None, None])
node = make_node(
"MaxPool",
["X"],
["Y"],
kernel_shape=[3, 3],
pads=[1, 1, 1, 1],
strides=[2, 2],
)
graph = make_graph([node], "g", [X], [Y])
onnx_model = make_model(graph, opset_imports=[make_opsetid("", 16)])
feeds = {"X": np.arange(49)[::-1].reshape((1, 1, 7, 7)).astype(np.float32)}
expected = np.array(
[
[
[
[48.0, 47.0, 45.0, 43.0],
[41.0, 40.0, 38.0, 36.0],
[27.0, 26.0, 24.0, 22.0],
[13.0, 12.0, 10.0, 8.0],
]
]
],
dtype=np.float32,
)
ref1 = ReferenceEvaluator(onnx_model)
got1 = ref1.run(None, feeds)
assert_allclose(expected, got1[0])
def test_max_pool_2d_2(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None, None, None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None, None, None, None])
node = make_node(
"MaxPool",
["X"],
["Y"],
kernel_shape=[3, 3],
pads=[1, 1, 1, 1],
strides=[2, 2],
)
graph = make_graph([node], "g", [X], [Y])
onnx_model = make_model(graph, opset_imports=[make_opsetid("", 16)])
feeds = {
"X": np.array(
[
[
[
[683, 358, 726, 578, 650, 946, 200],
[679, 260, 264, 5, 240, 255, 582],
[322, 66, 687, 632, 852, 698, 428],
[111, 452, 627, 332, 751, 842, 685],
[472, 52, 956, 81, 807, 827, 360],
[972, 574, 81, 799, 646, 499, 486],
[892, 758, 75, 833, 972, 415, 736],
]
]
],
dtype=np.float32,
)
}
expected = np.array(
[
[
[
[683.0, 726.0, 946.0, 946.0],
[679.0, 687.0, 852.0, 842.0],
[972.0, 956.0, 842.0, 842.0],
[972.0, 833.0, 972.0, 736.0],
]
]
],
dtype=np.float32,
)
ref1 = ReferenceEvaluator(onnx_model)
got1 = ref1.run(None, feeds)
assert_allclose(expected, got1[0])
def test_scatter_elements(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
Ind = make_tensor_value_info("I", TensorProto.INT64, [None, None])
U = make_tensor_value_info("U", TensorProto.FLOAT, [None, None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None, None])
node = make_node(
"ScatterElements",
["X", "I", "U"],
["Y"],
axis=1,
reduction="min",
)
graph = make_graph([node], "g", [X, Ind, U], [Y])
onnx_model = make_model(graph, opset_imports=[make_opsetid("", 16)])
feeds = {
"X": np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32),
"I": np.array([[1, 1]]),
"U": np.array([[1.1, 2.1]], dtype=np.float32),
}
ref1 = ReferenceEvaluator(onnx_model)
got1 = ref1.run(None, feeds)
expected = np.array([[1.0, 1.1, 3.0, 4.0, 5.0]], dtype=np.float32)
assert_allclose(expected, got1[0])
def test_scatternd(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
Ind = make_tensor_value_info("I", TensorProto.INT64, [None, None])
U = make_tensor_value_info("U", TensorProto.FLOAT, [None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None, None])
node = make_node(
"ScatterND",
["X", "I", "U"],
["Y"],
)
graph = make_graph([node], "g", [X, Ind, U], [Y])
onnx_model = make_model(graph, opset_imports=[make_opsetid("", 16)])
feeds = {
"X": np.array([[1.0, 2.0]], dtype=np.float32),
"I": np.array([[0, 0]]),
"U": np.array([3.0], dtype=np.float32),
}
ref1 = ReferenceEvaluator(onnx_model)
got1 = ref1.run(None, feeds)
expected = np.array([[3.0, 2.0]], dtype=np.float32)
assert_allclose(expected, got1[0])
def test_col2im_impl(self):
def get_im2col_indices(
x_shape, field_height, field_width, padding=None, stride=1
):
# source: https://stackoverflow.com/questions/51703367/col2im-implementation-in-convnet
N, C, H, W = x_shape
del N # Unused
assert (H + padding[0] + padding[2] - field_height) % stride == 0
assert (W + padding[1] + padding[3] - field_height) % stride == 0
out_height = (H + padding[0] + padding[2] - field_height) // stride + 1
out_width = (W + padding[1] + padding[3] - field_width) // stride + 1
i0 = np.repeat(np.arange(field_height), field_width)
i0 = np.tile(i0, C)
i1 = stride * np.repeat(np.arange(out_height), out_width)
j0 = np.tile(np.arange(field_width), field_height * C)
j1 = stride * np.tile(np.arange(out_width), out_height)
i = i0.reshape(-1, 1) + i1.reshape(1, -1)
j = j0.reshape(-1, 1) + j1.reshape(1, -1)
k = np.repeat(np.arange(C), field_height * field_width).reshape(-1, 1)
return (k, i, j)
def col2im_indices(
cols, x_shape, field_height=3, field_width=3, padding=None, stride=1
):
# source: https://stackoverflow.com/questions/51703367/col2im-implementation-in-convnet
N, C, H, W = x_shape
H_padded, W_padded = (
H + padding[0] + padding[2],
W + padding[1] + padding[3],
)
x_padded = np.zeros((N, C, H_padded, W_padded), dtype=cols.dtype)
k, i, j = get_im2col_indices(
x_shape, field_height, field_width, padding, stride
)
cols_reshaped = cols.reshape(C * field_height * field_width, -1, N)
cols_reshaped = cols_reshaped.transpose(2, 0, 1)
np.add.at(x_padded, (slice(None), k, i, j), cols_reshaped)
padding = padding.copy()
if padding[2] == 0:
padding[2] += x_padded.shape[2]
elif padding[2] > 0:
padding[2] *= -1
if padding[3] == 0:
padding[3] += x_padded.shape[3]
elif padding[3] > 0:
padding[3] *= -1
res = x_padded[:, :, padding[0] : padding[2], padding[1] : padding[3]]
return res
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None, None])
IS = make_tensor_value_info("IS", TensorProto.INT64, [None])
BS = make_tensor_value_info("BS", TensorProto.INT64, [None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None, None, None, None])
node = make_node("Col2Im", ["X", "IS", "BS"], ["Y"], pads=[0, 1, 0, 1])
graph = make_graph([node], "g", [X, IS, BS], [Y])
onnx_model = make_model(graph, opset_imports=[make_opsetid("", 16)])
feeds = {
"X": np.arange(5 * 15).astype(np.float32).reshape((1, 5, 15)),
"IS": np.array([5, 5]),
"BS": np.array([1, 5]),
}
ref1 = ReferenceEvaluator(onnx_model)
got1 = ref1.run(None, feeds)
expected = col2im_indices(
feeds["X"],
(1, 1, 5, 5),
field_height=1,
field_width=5,
padding=[0, 1, 0, 1],
)
assert_allclose(expected, got1[0])
def test_conv_transpose_2d(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None, None, None])
W = make_tensor_value_info("W", TensorProto.FLOAT, [None, None, None, None])
B = make_tensor_value_info("B", TensorProto.FLOAT, [None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None, None, None, None])
node = make_node(
"ConvTranspose",
["X", "W", "B"],
["Y"],
dilations=[1, 1],
kernel_shape=[3, 3],
output_padding=[0, 0],
pads=[1, 1, 1, 1],
strides=[1, 1],
)
graph = make_graph([node], "g", [X, W, B], [Y])
onnx_model = make_model(graph, opset_imports=[make_opsetid("", 16)])
feeds = {
"X": np.arange(1 * 3 * 5 * 4).reshape((1, 3, 5, 4)).astype(np.float32),
"W": np.arange(3 * 1 * 3 * 3).reshape((3, 1, 3, 3)).astype(np.float32),
"B": np.array([0, 0, 0, 0], dtype=np.float32),
}
# import torch
# ex = torch.nn.functional.conv_transpose2d(
# torch.Tensor(feeds["X"]), torch.Tensor(feeds["W"]),
# bias=None, stride=1, padding=1, output_padding=0, groups=1, dilation=1)
# print(ex)
ref1 = ReferenceEvaluator(onnx_model)
got1 = ref1.run(None, feeds)
expected = np.array(
[
[
[
[4371, 6855, 7062, 4929],
[7524, 11781, 12132, 8451],
[8424, 13185, 13536, 9423],
[9324, 14589, 14940, 10395],
[7197, 11229, 11490, 7971],
],
]
],
dtype=np.float32,
)
assert_allclose(expected, got1[0])
feeds["X"] *= 0
feeds["X"][0, 0, 0, 0] = 1
ref1 = ReferenceEvaluator(onnx_model)
got1 = ref1.run(None, feeds)
expected = np.array(
[
[
[
[4, 5, 0, 0],
[7, 8, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
]
]
],
dtype=np.float32,
)
assert_allclose(expected, got1[0])
def test_conv_transpose_2d_upper(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None, None, None])
W = make_tensor_value_info("W", TensorProto.FLOAT, [None, None, None, None])
B = make_tensor_value_info("B", TensorProto.FLOAT, [None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None, None, None, None])
node = make_node(
"ConvTranspose",
["X", "W", "B"],
["Y"],
auto_pad="SAME_UPPER",
strides=[2, 2],
# output_shape=[6, 6],
)
graph = make_graph([node], "g", [X, W, B], [Y])
onnx_model = make_model(graph, opset_imports=[make_opsetid("", 16)])
feeds = {
"X": np.arange(1 * 1 * 3 * 3).reshape((1, 1, 3, 3)).astype(np.float32),
"W": np.arange(1 * 2 * 3 * 3).reshape((1, 2, 3, 3)).astype(np.float32),
"B": np.array([0, 0, 0, 0], dtype=np.float32),
}
expected = np.array(
[
[
[
[0, 0, 0, 1, 2, 2],
[0, 0, 3, 4, 11, 8],
[0, 3, 12, 11, 28, 19],
[9, 12, 27, 16, 35, 20],
[18, 27, 60, 35, 76, 43],
[18, 24, 51, 28, 59, 32],
],
[
[0, 0, 9, 10, 29, 20],
[0, 0, 12, 13, 38, 26],
[27, 30, 84, 56, 136, 82],
[36, 39, 90, 52, 116, 65],
[99, 108, 240, 134, 292, 160],
[72, 78, 168, 91, 194, 104],
],
]
],
dtype=np.float32,
)
# import onnxruntime
# ref0 = onnxruntime.InferenceSession(onnx_model.SerializeToString(), providers=["CPUExecutionProvider"])
# got0 = ref0.run(None, feeds)
ref1 = ReferenceEvaluator(onnx_model)
got1 = ref1.run(None, feeds)
assert_allclose(expected, got1[0])
def test_stft(self):
signal = make_tensor_value_info("signal", TensorProto.FLOAT, [None, None, None])
frame_step = make_tensor_value_info("frame_step", TensorProto.INT64, [None])
frame_length = make_tensor_value_info("frame_length", TensorProto.INT64, [None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None, None, None, None])
node = make_node(
"STFT",
["signal", "frame_step", "", "frame_length"],
["Y"],
)
graph = make_graph([node], "g", [signal, frame_step, frame_length], [Y])
onnx_model = make_model(graph, opset_imports=[make_opsetid("", 17)])
feeds = {
"signal": np.arange(128).reshape((1, 128, 1)).astype(np.float32),
"frame_step": np.array(8, dtype=np.int64),
"frame_length": np.array(16, dtype=np.int64),
}
signal = feeds["signal"]
frame_length = int(feeds["frame_length"])
frame_step = int(feeds["frame_step"])
onesided_length = (frame_length // 2) + 1
nstfts = ((feeds["signal"].shape[1] - frame_length) // frame_step) + 1
# [batch_size][frames][frame_length][2]
expected = np.empty([1, nstfts, onesided_length, 2], dtype=np.float32)
for i in range(nstfts):
start = i * frame_step
stop = i * frame_step + frame_length
complex_out = np.fft.fft(signal[0, start:stop, 0])
c_out = complex_out[0:onesided_length]
expected[0, i] = np.stack((c_out.real, c_out.imag), axis=1)
# import torch
# correspondance with torch
# hop_length = frame_step
# window = np.ones((frame_length,), dtype=np.float32)
# ex = torch.stft(
# torch.Tensor(feeds["signal"][:, :, 0]),
# n_fft=frame_length, window=torch.Tensor(window),
# hop_length=hop_length, win_length=frame_length,
# onesided=True, return_complex=True, center=False,
# normalized=False)
# ex = np.transpose(ex.numpy(), [0, 2, 1])
ref1 = ReferenceEvaluator(onnx_model)
got1 = ref1.run(None, feeds)
assert_allclose(expected, got1[0])
def test_stft_with_window(self):
signal = make_tensor_value_info("signal", TensorProto.FLOAT, [None, None, None])
frame_step = make_tensor_value_info("frame_step", TensorProto.INT64, [None])
window = make_tensor_value_info("window", TensorProto.FLOAT, [None])
frame_length = make_tensor_value_info("frame_length", TensorProto.INT64, [None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None, None, None, None])
node = make_node(
"STFT",
["signal", "frame_step", "window", "frame_length"],
["Y"],
)
graph = make_graph([node], "g", [signal, frame_step, window, frame_length], [Y])
onnx_model = make_model(graph, opset_imports=[make_opsetid("", 17)])
feeds = {
"signal": np.arange(128).reshape((1, 128, 1)).astype(np.float32),
"frame_step": np.array(8, dtype=np.int64),
"window": 0.5
+ 0.5 * np.cos(2 * np.pi * np.arange(0, 16, 1, dtype=np.float32) / 16),
"frame_length": np.array(16, dtype=np.int64),
}
signal = feeds["signal"]
frame_length = int(feeds["frame_length"])
window = feeds["window"]
frame_step = int(feeds["frame_step"])
onesided_length = (frame_length // 2) + 1
nstfts = 1 + (signal.shape[1] - window.shape[0]) // 8
# [batch_size][frames][frame_length][2]
expected = np.empty([1, nstfts, onesided_length, 2], dtype=np.float32)
for i in range(nstfts):
start = i * frame_step
stop = i * frame_step + frame_length
complex_out = np.fft.fft(signal[0, start:stop, 0] * window)[
0:onesided_length
]
c_out = complex_out[0:onesided_length]
expected[0, i] = np.stack((c_out.real, c_out.imag), axis=1)
# import torch
# hop_length = frame_step
# ex = torch.stft(
# torch.Tensor(feeds["signal"][:, :, 0]),
# n_fft=frame_length, window=torch.Tensor(window),
# hop_length=hop_length, win_length=frame_length,
# onesided=True, return_complex=True, center=False,
# normalized=False)
# ex = np.transpose(ex.numpy(), [0, 2, 1])
ref1 = ReferenceEvaluator(onnx_model)
got1 = ref1.run(None, feeds)
assert_allclose(expected, got1[0])
def get_roi_align_model(self, mode):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None, None, None])
rois = make_tensor_value_info("rois", TensorProto.FLOAT, [None, None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None, None, None, None])
IS = make_tensor_value_info("I", TensorProto.INT64, [None])
node = make_node(
"RoiAlign",
["X", "rois", "I"],
["Y"],
output_height=5,
output_width=5,
sampling_ratio=2,
spatial_scale=1.0,
coordinate_transformation_mode="output_half_pixel",
mode=mode,
)
graph = make_graph([node], "g", [X, rois, IS], [Y])
return make_model_gen_version(graph, opset_imports=[make_opsetid("", 17)])
def common_test_roi_align(self, mode):
onnx_model = self.get_roi_align_model(mode)
X, batch_indices, rois = get_roi_align_input_values()
feeds = {"X": X, "rois": rois, "I": batch_indices}
sess = run_ort_inference(onnx_model)
if sess is None:
return
expected = sess.run(None, feeds)
ref = ReferenceEvaluator(onnx_model)
got = ref.run(None, feeds)
assert_allclose(expected[0], got[0], atol=1e-5)
@skip_if_no_onnxruntime
def test_roi_align(self):
with self.subTest(mode="avg"):
self.common_test_roi_align("avg")
# max does not have example in the backend
with self.subTest(mode="max"):
self.common_test_roi_align("max")
def common_test_roi_align_torch(self, mode):
import torch
from torchvision.ops import RoIAlign
onnx_model = self.get_roi_align_model(mode)
sess = ReferenceEvaluator(onnx_model)
X, batch_indices, rois = get_roi_align_input_values()
got = sess.run(None, {"X": X, "rois": rois, "I": batch_indices})
a = RoIAlign((5, 5), spatial_scale=1.0, sampling_ratio=2)
expected = a(torch.from_numpy(X), [torch.from_numpy(rois)])
assert_allclose(expected, got[0], atol=1e-5)
@skip_if_no_torch
@skip_if_no_torchvision
def test_roi_align_torch(self):
with self.subTest(mode="avg"):
self.common_test_roi_align_torch("avg")
# not implemented in torch
# with self.subTest(mode="max"):
# self.common_test_roi_align_torch("max")
def test_split(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None])
Y1 = make_tensor_value_info("Y1", TensorProto.FLOAT, [None])
Y2 = make_tensor_value_info("Y2", TensorProto.FLOAT, [None])
Y3 = make_tensor_value_info("Y3", TensorProto.FLOAT, [None])
Y4 = make_tensor_value_info("Y4", TensorProto.FLOAT, [None])
node = make_node("Split", ["X"], ["Y1", "Y2", "Y3", "Y4"], num_outputs=4)
graph = make_graph([node], "g", [X], [Y1, Y2, Y3, Y4])
onnx_model = make_model(graph, opset_imports=[make_opsetid("", 18)])
feeds = {"X": np.arange(10).astype(np.float32)}
expected = [
np.array([0, 1, 2], dtype=np.float32),
np.array([3, 4, 5], dtype=np.float32),
np.array([6, 7, 8], dtype=np.float32),
np.array([9], dtype=np.float32),
]
ref1 = ReferenceEvaluator(onnx_model)
got1 = ref1.run(None, feeds)
for i in range(4):
assert_allclose(expected[i], got1[i])
def test_split_2(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None])
Y1 = make_tensor_value_info("Y1", TensorProto.FLOAT, [None])
Y2 = make_tensor_value_info("Y2", TensorProto.FLOAT, [None])
Y3 = make_tensor_value_info("Y3", TensorProto.FLOAT, [None])
Y4 = make_tensor_value_info("Y4", TensorProto.FLOAT, [None])
node = make_node("Split", ["X", "split"], ["Y1", "Y2", "Y3", "Y4"])
graph = make_graph([node], "g", [X], [Y1, Y2, Y3, Y4])
onnx_model = make_model(graph, opset_imports=[make_opsetid("", 18)])
feeds = {
"X": np.arange(10).astype(np.float32),
"split": np.array([3, 3, 2, 2], dtype=np.int64),
}
expected = [
np.array([0, 1, 2], dtype=np.float32),
np.array([3, 4, 5], dtype=np.float32),
np.array([6, 7], dtype=np.float32),
np.array([8, 9], dtype=np.float32),
]
ref1 = ReferenceEvaluator(onnx_model)
got1 = ref1.run(None, feeds)
for i in range(4):
assert_allclose(expected[i], got1[i])
def test_split_num_outputs_4(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None])
Y1 = make_tensor_value_info("Y1", TensorProto.FLOAT, [None])
Y2 = make_tensor_value_info("Y2", TensorProto.FLOAT, [None])
Y3 = make_tensor_value_info("Y3", TensorProto.FLOAT, [None])
Y4 = make_tensor_value_info("Y4", TensorProto.FLOAT, [None])
node = make_node("Split", ["X"], ["Y1", "Y2", "Y3", "Y4"], num_outputs=4)
graph = make_graph([node], "g", [X], [Y1, Y2, Y3, Y4])
onnx_model = make_model(graph, opset_imports=[make_opsetid("", 18)])
# case 1
feeds = {"X": np.arange(10).astype(np.float32)}
expected = [
np.array([0, 1, 2], dtype=np.float32),
np.array([3, 4, 5], dtype=np.float32),
np.array([6, 7, 8], dtype=np.float32),
np.array([9], dtype=np.float32),
]
ref1 = ReferenceEvaluator(onnx_model)
got1 = ref1.run(None, feeds)
for i in range(4):
assert_allclose(expected[i], got1[i])
# case 2
feeds = {"X": np.arange(9).astype(np.float32)}
expected = [
np.array([0, 1, 2], dtype=np.float32),
np.array([3, 4, 5], dtype=np.float32),
np.array([6, 7, 8], dtype=np.float32),
np.array([], dtype=np.float32),
]
ref1 = ReferenceEvaluator(onnx_model)
got1 = ref1.run(None, feeds)
for i in range(4):
assert_allclose(expected[i], got1[i])
def test_argmin(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
Y = make_tensor_value_info("Y", TensorProto.INT64, [None])
node = make_node("ArgMin", ["X"], ["Y"], axis=1)
graph = make_graph([node], "g", [X], [Y])
onnx_model = make_model(graph, opset_imports=[make_opsetid("", 18)])
feeds = {"X": np.arange(12).reshape((3, 4)).astype(np.float32)}
ref1 = ReferenceEvaluator(onnx_model)
got1 = ref1.run(None, feeds)
expected = np.array([0, 0, 0], dtype=np.int64).reshape((-1, 1))
self.assertEqual(expected.tolist(), got1[0].tolist())
def test_argmax(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
Y = make_tensor_value_info("Y", TensorProto.INT64, [None])
node = make_node("ArgMax", ["X"], ["Y"], axis=1)
graph = make_graph([node], "g", [X], [Y])
onnx_model = make_model(graph, opset_imports=[make_opsetid("", 18)])
feeds = {"X": np.arange(12).reshape((3, 4)).astype(np.float32)}
ref1 = ReferenceEvaluator(onnx_model)
got1 = ref1.run(None, feeds)
expected = np.array([3, 3, 3], dtype=np.int64).reshape((-1, 1))
self.assertEqual(expected.tolist(), got1[0].tolist())
def test_slice_squeeze(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
starts = make_tensor_value_info("starts", TensorProto.INT64, [None])
ends = make_tensor_value_info("ends", TensorProto.INT64, [None])
axes = make_tensor_value_info("axes", TensorProto.INT64, [None])
Y = make_tensor_value_info("Y", TensorProto.INT64, [None])
nodes = [
make_node("Slice", ["X", "starts", "ends", "axes"], ["T"]),
make_node("Squeeze", ["T", "axes"], ["Y"]),
]
graph = make_graph(nodes, "g", [X, starts, ends, axes], [Y])
onnx_model = make_model(graph, opset_imports=[make_opsetid("", 18)])
feeds = {
"X": np.array([[0]], dtype=np.int64),
"starts": np.array([0], dtype=np.int64),
"ends": np.array([1], dtype=np.int64),
"axes": np.array([0], dtype=np.int64),
}
ref1 = ReferenceEvaluator(onnx_model)
got1 = ref1.run(None, feeds)
expected = np.array([0], dtype=np.int64)
self.assertEqual(expected.tolist(), got1[0].tolist())
def test_slice_squeeze_6(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
Y = make_tensor_value_info("Y", TensorProto.INT64, [None])
nodes = [
make_node("Slice", ["X"], ["T"], axes=[0], starts=[0], ends=[1]),
make_node("Squeeze", ["T"], ["Y"], axes=[0]),
]
graph = make_graph(nodes, "g", [X], [Y])
onnx_model = make_model(graph, opset_imports=[make_opsetid("", 6)])
feeds = {"X": np.array([[0]], dtype=np.int64)}
ref1 = ReferenceEvaluator(onnx_model)
got1 = ref1.run(None, feeds)
expected = np.array([0], dtype=np.int64)
self.assertEqual(expected.tolist(), got1[0].tolist())
def test_onnxrt_reduce_mean(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None, None])
node1 = make_node("ReduceMean", ["X"], ["Y"])
graph = make_graph([node1], "g", [X], [Y])
onnx_model = make_model(graph, opset_imports=[make_opsetid("", 17)])
check_model(onnx_model)
sess = ReferenceEvaluator(onnx_model)
cls = sess.rt_nodes_[0]
self.assertEqual(cls.__class__.__name__, "ReduceMean_1")
got = sess.run(None, {"X": np.ones((2, 4), dtype=np.float32)})[0]
self.assertEqual(got.shape, (1, 1))
self.assertEqual(got[0, 0], 1)
onnx_model = make_model(graph, opset_imports=[make_opsetid("", 18)])
check_model(onnx_model)
sess = ReferenceEvaluator(onnx_model)
cls = sess.rt_nodes_[0]
self.assertEqual(cls.__class__.__name__, "ReduceMean_18")
got = sess.run(None, {"X": np.ones((2, 4), dtype=np.float32)})[0]
self.assertEqual(got.shape, (1, 1))
self.assertEqual(got[0, 0], 1)
@staticmethod
def _cdist_model(opset, reduce_op="ReduceSumSquare"):
# subgraph
initializers = []
inputs = [
make_tensor_value_info("next_in", TensorProto.FLOAT, [None, 4]),
make_tensor_value_info("next", TensorProto.FLOAT, [None]),
]
outputs = [
make_tensor_value_info("next_out", TensorProto.FLOAT, [None, None]),
make_tensor_value_info("scan_out", TensorProto.FLOAT, [None]),
]
if opset >= 18:
initializers.append(
from_array(np.array([1], dtype=np.int64), name="axis_red")
)
node_reduce = make_node(
reduce_op,
["cdistdf_17_C0", "axis_red"],
["cdistdf_17_reduced0"],
name="cdistdf_17_ReduceSumSquare",
keepdims=0,
)
else:
node_reduce = make_node(
reduce_op,
["cdistdf_17_C0"],
["cdistdf_17_reduced0"],
name="cdistdf_17_ReduceSumSquare",
axes=[1],
keepdims=0,
)
nodes = [
make_node("Identity", ["next_in"], ["next_out"], name="cdistd_17_Identity"),
make_node(
"Sub", ["next_in", "next"], ["cdistdf_17_C0"], name="cdistdf_17_Sub"
),
node_reduce,
make_node(
"Identity",
["cdistdf_17_reduced0"],
["scan_out"],
name="cdistdf_17_Identity",
),
]
graph = make_graph(nodes, "OnnxIdentity", inputs, outputs, initializers)
# main graph
initializers = []
list_value = [
1.1394007205963135,
-0.6848101019859314,
-1.234825849533081,
0.4023416340351105,
0.17742614448070526,
0.46278226375579834,
-0.4017809331417084,
-1.630198359489441,
-0.5096521973609924,
0.7774903774261475,
-0.4380742907524109,
-1.2527953386306763,
-1.0485529899597168,
1.950775384902954,
-1.420017957687378,
-1.7062702178955078,
1.8675580024719238,
-0.15135720372200012,
-0.9772778749465942,
0.9500884413719177,
-2.5529897212982178,
-0.7421650290489197,
0.653618574142456,
0.8644362092018127,
1.5327792167663574,
0.37816253304481506,
1.4693588018417358,
0.154947429895401,
-0.6724604368209839,
-1.7262825965881348,
-0.35955315828323364,
-0.8131462931632996,
-0.8707971572875977,
0.056165341287851334,
-0.5788496732711792,
-0.3115525245666504,
1.2302906513214111,
-0.302302747964859,
1.202379822731018,
-0.38732680678367615,
2.269754648208618,
-0.18718385696411133,
-1.4543657302856445,
0.04575851559638977,
-0.9072983860969543,
0.12898291647434235,
0.05194539576768875,
0.7290905714035034,
1.4940791130065918,
-0.8540957570075989,
-0.2051582634449005,
0.3130677044391632,
1.764052391052246,
2.2408931255340576,
0.40015721321105957,
0.978738009929657,
0.06651721894741058,
-0.3627411723136902,
0.30247190594673157,
-0.6343221068382263,
-0.5108051300048828,
0.4283318817615509,
-1.18063223361969,
-0.02818222902715206,
-1.6138978004455566,
0.38690251111984253,
-0.21274028718471527,
-0.8954665660858154,
0.7610377073287964,
0.3336743414402008,
0.12167501449584961,
0.44386324286460876,
-0.10321885347366333,
1.4542734622955322,
0.4105985164642334,
0.14404356479644775,
-0.8877857327461243,
0.15634897351264954,
-1.980796456336975,
-0.34791216254234314,
]
initializers.append(
from_array(
np.array(list_value, dtype=np.float32).reshape((20, 4)),
name="Sc_Scancst",
)
)
initializers.append(
from_array(np.array([2], dtype=np.int64), name="To_TopKcst")
)
inputs = [make_tensor_value_info("input", TensorProto.FLOAT, [None, 4])]
outputs = [
make_tensor_value_info("values", TensorProto.FLOAT, [None, 2]),
make_tensor_value_info("indices", TensorProto.INT64, [None, 2]),
]
# nodes
nodes = [
make_node(
"Scan",
["input", "Sc_Scancst"],
["UU032UU", "UU033UU"],
name="Sc_Scan",
body=graph,
num_scan_inputs=1,
),
make_node(
"Transpose",
["UU033UU"],
["Tr_transposed0"],
name="Tr_Transpose",
perm=[1, 0],
),
make_node("Sqrt", ["Tr_transposed0"], ["Sq_Y0"], name="Sq_Sqrt"),
make_node(
"TopK",
["Sq_Y0", "To_TopKcst"],
["values", "indices"],
name="To_TopK",
largest=0,
sorted=1,
),
]
graph = make_graph(nodes, "dummy", inputs, outputs, initializers)
# model
onnx_model = make_model(graph, opset_imports=[make_opsetid("", opset)])
return onnx_model
@parameterized.parameterized.expand(
itertools.product(
[
(
"ReduceMin",
[
np.array(
[[np.nan, np.nan], [14.422706, 18.80527]], dtype=np.float32
),
np.array([[2, 15], [10, 4]], dtype=np.int64),
],
),
(
"ReduceL1",
[
np.array(
[[2.2367053, 2.3516612], [4.076292, 4.2970634]],
dtype=np.float32,
),
np.array([[18, 6], [13, 6]], dtype=np.int64),
],
),
(
"ReduceL2",
[
np.array(
[[1.80155, 1.8169948], [2.9928076, 3.1205883]],
dtype=np.float32,
),
np.array([[11, 18], [13, 6]], dtype=np.int64),
],
),
(
"ReduceLogSum",
[
np.array(
[[0.9497848, 1.1872643], [1.6764175, 1.70759]],
dtype=np.float32,
),
np.array([[6, 18], [13, 6]], dtype=np.int64),
],
),
(
"ReduceLogSumExp",
[
np.array(
[[1.6005973, 1.7445935], [2.5616229, 2.6539795]],
dtype=np.float32,
),
np.array([[13, 6], [13, 6]], dtype=np.int64),
],
),
(
"ReduceMax",
[
np.array(
[[1.4217108, 1.5069536], [2.453826, 2.5041783]],
dtype=np.float32,
),
np.array([[13, 11], [13, 11]], dtype=np.int64),
],
),
(
"ReduceMean",
[
np.array(
[[0.39247903, 0.78497636], [2.038146, 2.1485317]],
dtype=np.float32,
),
np.array([[13, 6], [13, 6]], dtype=np.int64),
],
),
(
"ReduceSumSquare",
[
np.array(
[[3.2455828, 3.3014696], [8.956896, 9.7380705]],
dtype=np.float32,
),
np.array([[11, 18], [13, 6]], dtype=np.int64),
],
),
(
"ReduceProd",
[
np.array(
[[np.nan, np.nan], [14.422706, 18.80527]], dtype=np.float32
),
np.array([[2, 15], [13, 6]], dtype=np.int64),
],
),
],
[17, 18],
)
)
def test_op_reduce(self, reduce_op_expected, opset: int):
reduce_op, expected = reduce_op_expected
X = np.arange(8).reshape((-1, 4)).astype(np.float32)
results = {}
model = self._cdist_model(opset, reduce_op)
sess = ReferenceEvaluator(model)
got = sess.run(None, {"input": X})
results["ref", opset] = got
cl = [
n
for n in sess.rt_nodes_[0].body.rt_nodes_
if n.__class__.__name__.startswith(reduce_op)
]
schema = cl[0]._schema # pylint: disable=protected-access
new_cl = type(reduce_op, (cl[0].__class__,), {"op_schema": schema})
sess = ReferenceEvaluator(model, new_ops=[new_cl])
got = sess.run(None, {"input": X})
results["ref_cl", opset] = got
baseline = "constant"
for k, v in results.items():
for a, b in zip(reversed(expected), reversed(v)):
if a.shape != b.shape:
raise AssertionError(
f"Shape mismatch for {reduce_op!r}, {baseline}:{a.shape} != {k}:{b.shape}."
)
diff = np.abs(a - b).max()
if diff > 1e-6:
raise AssertionError(
f"Discrepancies (max={diff}) for {reduce_op!r}, {baseline} != {k}\n{a}\n!=\n{b}"
)
@parameterized.parameterized.expand(
[
(13,),
(17,),
(18,),
]
)
def test_mvn(self, opset: int, ref_opset: int = 13):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None, None, None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None, None, None, None])
nodes = [
make_node("MeanVarianceNormalization", ["X"], ["Y"]),
]
graph = make_graph(nodes, "g", [X], [Y])
x = np.random.rand(3, 3, 3, 1).astype(np.float32)
onnx_model = make_model(graph, opset_imports=[make_opsetid("", opset)])
ref = ReferenceEvaluator(onnx_model)
got = ref.run(None, {"X": x})[0]
ref_onnx_model = make_model(graph, opset_imports=[make_opsetid("", ref_opset)])
ref_expected = ReferenceEvaluator(ref_onnx_model)
expected = ref_expected.run(None, {"X": x})[0]
self.assertEqual(expected.shape, got.shape)
assert_allclose(expected, got)
def test_concat_in_a_function(self):
def create_model():
nodes = []
inputs = []
outputs = []
functions = []
opsets = {"": onnx_opset_version(), "custom_domain": 1}
nodes_fct = []
node = make_node("Concat", ["x:0", "x:1"], ["r__0"], axis=0, domain="")
nodes_fct.append(node)
opset_imports_fct = [
make_opsetid(domain, 1 if version is None else version)
for domain, version in opsets.items()
]
fct = make_function(
"custom_domain",
"concat_2",
["x:0", "x:1"],
["r__0"],
nodes_fct,
opset_imports_fct,
)
functions.append(fct)
inputs.append(make_tensor_value_info("I__0", TensorProto.DOUBLE, []))
inputs.append(make_tensor_value_info("I__1", TensorProto.DOUBLE, []))
inputs.append(make_tensor_value_info("I__2", TensorProto.DOUBLE, []))
outputs.append(make_tensor_value_info("r__4", TensorProto.DOUBLE, []))
node = make_node(
"concat_2", ["I__0", "I__1"], ["r__3"], axis=0, domain="custom_domain"
)
nodes.append(node)
node = make_node(
"concat_2", ["I__2", "r__3"], ["r__4"], axis=0, domain="custom_domain"
)
nodes.append(node)
opset_imports = [
make_opsetid(domain, 1 if version is None else version)
for domain, version in opsets.items()
]
graph = make_graph(nodes, "numpyx", inputs, outputs)
onnx_model = make_model(
graph, opset_imports=opset_imports, functions=functions
)
return onnx_model
onnx_model = create_model()
x1 = np.array([[-5, 6], [15, 3]], dtype=np.float64)
x2 = np.array([[1, 2]], dtype=np.float64)
x3 = np.array([[-1, -2]], dtype=np.float64)
z = np.vstack([x1, x2, x3])
ref = ReferenceEvaluator(onnx_model)
feeds = {"I__2": x1, "I__0": x2, "I__1": x3}
got = ref.run(None, feeds)
assert_allclose(z, got[0])
def test_cast_float_to_string(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None])
Y = make_tensor_value_info("Y", TensorProto.STRING, [None])
model = make_model(
make_graph(
[
make_node("Cast", ["X"], ["Y"], to=TensorProto.STRING),
],
"g",
[X],
[Y],
)
)
ref = ReferenceEvaluator(model)
data = np.array([1.152512, -0.152612, 0.0, np.nan])
got = ref.run(None, {"X": data})[0]
self.assertTrue(
(got == np.array([1.152512, -0.152612, 0.0, np.nan]).astype(np.str_)).all()
)
def test_cast_float_to_string_and_back(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None])
model = make_model(
make_graph(
[
make_node("Cast", ["X"], ["Z"], to=TensorProto.STRING),
make_node("Cast", ["Z"], ["Y"], to=TensorProto.FLOAT),
],
"g",
[X],
[Y],
)
)
ref = ReferenceEvaluator(model)
data = np.array([1.152512, -0.152612, 0.0, np.nan])
got = ref.run(None, {"X": data})[0]
assert_allclose(got, np.array([1.152512, -0.152612, 0.0, np.nan]))
def test_split_to_sequence(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, None)
Y = make_tensor_value_info("Y", TensorProto.INT64, None)
Z = make_tensor_value_info("Z", TensorProto.UNDEFINED, None)
nodes = [make_node("SplitToSequence", ["X", "Y"], ["Z"], axis=2)]
model = make_model(make_graph(nodes, "g", [X, Y], [Z]))
ref = ReferenceEvaluator(model)
data = np.arange(18).reshape((1, 3, 6)).astype(np.float32)
indices = np.array(2, dtype=np.int64)
got = ref.run(None, {"X": data, "Y": indices})
expected = [
[
np.array([[[0.0, 1.0], [6.0, 7.0], [12.0, 13.0]]], dtype=np.float32),
np.array([[[2.0, 3.0], [8.0, 9.0], [14.0, 15.0]]], dtype=np.float32),
np.array([[[4.0, 5.0], [10.0, 11.0], [16.0, 17.0]]], dtype=np.float32),
]
]
self.assertEqual(len(expected[0]), len(got[0]))
for a, b in zip(expected[0], got[0]):
assert_allclose(a, b)
def test_split_to_sequence_1d(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, None)
Y = make_tensor_value_info("Y", TensorProto.INT64, None)
Z = make_tensor_value_info("Z", TensorProto.UNDEFINED, None)
nodes = [make_node("SplitToSequence", ["X", "Y"], ["Z"], axis=2)]
model = make_model(make_graph(nodes, "g", [X, Y], [Z]))
ref = ReferenceEvaluator(model)
data = np.arange(18).reshape((1, 3, 6)).astype(np.float32)
indices = np.array([2, 2, 2], dtype=np.int64)
got = ref.run(None, {"X": data, "Y": indices})
expected = [
[
np.array([[[0.0, 1.0], [6.0, 7.0], [12.0, 13.0]]], dtype=np.float32),
np.array([[[2.0, 3.0], [8.0, 9.0], [14.0, 15.0]]], dtype=np.float32),
np.array([[[4.0, 5.0], [10.0, 11.0], [16.0, 17.0]]], dtype=np.float32),
]
]
self.assertEqual(len(expected[0]), len(got[0]))
for a, b in zip(expected[0], got[0]):
assert_allclose(a, b)
def test_split_to_sequence_nokeepdims_noinput(self):
# keepdims is ignored in that case
X = make_tensor_value_info("X", TensorProto.FLOAT, None)
Z = make_tensor_value_info("Z", TensorProto.UNDEFINED, None)
nodes = [make_node("SplitToSequence", ["X"], ["Z"], axis=2, keepdims=0)]
model = make_model(make_graph(nodes, "g", [X], [Z]))
ref = ReferenceEvaluator(model)
data = np.arange(18).reshape((1, 3, 6)).astype(np.float32)
got = ref.run(None, {"X": data})
expected = [[data[:, :, i] for i in range(data.shape[2])]]
self.assertEqual(len(expected[0]), len(got[0]))
for a, b in zip(expected[0], got[0]):
assert_allclose(a, b)
def test_cast_float8(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None])
F1 = make_tensor_value_info("F1", TensorProto.FLOAT, [None])
F2 = make_tensor_value_info("F2", TensorProto.FLOAT, [None])
F3 = make_tensor_value_info("F3", TensorProto.FLOAT, [None])
F4 = make_tensor_value_info("F4", TensorProto.FLOAT, [None])
model = make_model(
make_graph(
[
make_node("Cast", ["X"], ["f81"], to=TensorProto.FLOAT8E4M3FN),
make_node("Cast", ["X"], ["f82"], to=TensorProto.FLOAT8E5M2),
make_node(
"Constant",
[],
["C1"],
value=make_tensor(
"C1", TensorProto.FLOAT8E4M3FN, [5], [0, 1, 2, 5e-2, 200]
),
),
make_node(
"Constant",
[],
["C2"],
value=make_tensor(
"C2", TensorProto.FLOAT8E5M2, [5], [0, 1, 2, 5e-2, 200]
),
),
make_node("Cast", ["f81"], ["F1"], to=TensorProto.FLOAT),
make_node("Cast", ["f82"], ["F2"], to=TensorProto.FLOAT),
make_node("Cast", ["C1"], ["F3"], to=TensorProto.FLOAT),
make_node("Cast", ["C2"], ["F4"], to=TensorProto.FLOAT),
],
"g",
[X],
[F1, F2, F3, F4],
)
)
ref = ReferenceEvaluator(model)
data = np.array([0, 1, 2, 5e-2, 200], dtype=np.float32)
expected1 = np.array(
[float8e4m3_to_float32(float32_to_float8e4m3(x)) for x in data]
)
expected2 = np.array(
[float8e5m2_to_float32(float32_to_float8e5m2(x)) for x in data]
)
got = ref.run(None, {"X": data})
assert_allclose(got[0], expected1)
assert_allclose(got[1], expected2)
assert_allclose(got[2], expected1)
assert_allclose(got[3], expected2)
def test_cast_like_float8(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None])
model = make_model(
make_graph(
[
make_node("Cast", ["X"], ["f8"], to=TensorProto.FLOAT8E4M3FNUZ),
make_node("CastLike", ["X", "f8"], ["f32"], saturate=0),
make_node("Cast", ["f32"], ["Y"], to=TensorProto.FLOAT),
],
"g",
[X],
[Y],
)
)
data = np.array([0, 1e7], dtype=np.float32)
expected = np.array(
[
float8e4m3_to_float32(
float32_to_float8e4m3(x, uz=True, saturate=False), uz=True
)
for x in data
]
)
ref = ReferenceEvaluator(model)
got = ref.run(None, {"X": data})
assert_allclose(got[0], expected)
# Forces ReferenceEvaluator to not use the associated implementation for CastLike
# but its implementation as a function instead.
class CastLike(OpRunExpand):
op_domain = ""
ref = ReferenceEvaluator(model, new_ops=[CastLike])
got = ref.run(None, {"X": data})
assert_allclose(got[0], expected)
def test_cast_float8_output(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None])
F1 = make_tensor_value_info("F1", TensorProto.FLOAT8E4M3FN, [None])
F2 = make_tensor_value_info("F2", TensorProto.FLOAT8E5M2, [None])
model = make_model(
make_graph(
[
make_node("Cast", ["X"], ["F1"], to=TensorProto.FLOAT8E4M3FN),
make_node("Cast", ["X"], ["F2"], to=TensorProto.FLOAT8E5M2),
],
"g",
[X],
[F1, F2],
)
)
ref = ReferenceEvaluator(model)
data = np.array([0, 1, 2, 5e-2, 200], dtype=np.float32)
expected1 = np.array([float32_to_float8e4m3(x) for x in data])
expected2 = np.array([float32_to_float8e5m2(x) for x in data])
got = ref.run(None, {"X": data})
self.assertEqual(expected1.tolist(), got[0].tolist())
self.assertEqual(expected2.tolist(), got[1].tolist())
def test_float8_4_types(self):
x = np.array(
[
0.4068359375,
352,
416,
336,
304,
272,
-248,
-100,
1e-4,
1e-2,
416,
432,
1e5,
np.inf,
-np.inf,
np.nan,
],
dtype=np.float32,
)
expected = {
TensorProto.FLOAT8E4M3FN: np.array(
[
0.40625,
352.0,
416.0,
320.0,
320.0,
256.0,
-256.0,
-96.0,
0.0,
0.009765625,
416.0,
448.0,
448.0,
448.0,
-448.0,
np.nan,
],
dtype=np.float32,
),
TensorProto.FLOAT8E4M3FNUZ: np.array(
[
0.40625,
240.0,
240.0,
240.0,
240.0,
240.0,
-240.0,
-96.0,
0.0,
0.009765625,
240.0,
240.0,
240.0,
240.0,
-240.0,
np.nan,
],
dtype=np.float32,
),
TensorProto.FLOAT8E5M2: np.array(
[
0.4375,
384.0,
384.0,
320.0,
320.0,
256.0,
-256.0,
-96.0,
0.0001068115234375,
0.009765625,
384.0,
448.0,
57344.0,
57344.0,
-57344.0,
np.nan,
],
dtype=np.float32,
),
TensorProto.FLOAT8E5M2FNUZ: np.array(
[
4.3750000e-01,
3.8400000e02,
3.8400000e02,
3.2000000e02,
3.2000000e02,
2.5600000e02,
-2.5600000e02,
-9.6000000e01,
1.0681152e-04,
9.7656250e-03,
3.8400000e02,
4.4800000e02,
5.7344000e04,
5.7344000e04,
-5.7344000e04,
np.nan,
],
dtype=np.float32,
),
}
def model_cast_cast(to):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None])
node1 = make_node("Cast", ["X"], ["T"], to=to)
node2 = make_node("Cast", ["T"], ["Y"], to=TensorProto.FLOAT)
graph = make_graph([node1, node2], "lr", [X], [Y])
onnx_model = make_model(graph)
check_model(onnx_model)
return onnx_model
for to, expect in expected.items():
with self.subTest(to=to):
onnx_model = model_cast_cast(to)
ref = ReferenceEvaluator(onnx_model)
y = ref.run(None, {"X": x})[0]
assert_allclose(expect, y)
self.assertEqual(expect.shape, y.shape)
self.assertEqual(expect.dtype, y.dtype)
def test_cast_bfloat16_output(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None])
Y = make_tensor_value_info("Y", TensorProto.BFLOAT16, [None])
model = make_model(
make_graph(
[
make_node("Cast", ["X"], ["Y"], to=TensorProto.BFLOAT16),
],
"g",
[X],
[Y],
)
)
ref = ReferenceEvaluator(model)
data = np.array([0, 1, 2, 1e5, 200], dtype=np.float32)
expected1 = np.array([float32_to_bfloat16(x) for x in data])
got = ref.run(None, {"X": data})
self.assertEqual(expected1.tolist(), got[0].tolist())
def test_quantize_linear_e4m3(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None])
model = make_model(
make_graph(
[
make_node(
"Constant",
[],
["scale"],
value=make_tensor("scale", TensorProto.FLOAT, [1], [2.0]),
),
make_node(
"Constant",
[],
["zero"],
value=make_tensor("zero", TensorProto.FLOAT8E4M3FN, [1], [0.0]),
),
make_node("QuantizeLinear", ["X", "scale", "zero"], ["T"]),
make_node("DequantizeLinear", ["T", "scale"], ["Y"], axis=0),
],
"g",
[X],
[Y],
)
)
ref = ReferenceEvaluator(model)
data = np.array([0, 1, 2, 1e5, 200], dtype=np.float32)
expected = np.array([0, 1, 2, 896, 192], dtype=np.float32)
got = ref.run(None, {"X": data})
assert_allclose(expected, got[0])
def test_quantize_linear_e4m3_initializer(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None])
model = make_model(
make_graph(
[
make_node("QuantizeLinear", ["X", "scale", "zero"], ["T"]),
make_node("DequantizeLinear", ["T", "scale"], ["Y"], axis=0),
],
"g",
[X],
[Y],
[
make_tensor("scale", TensorProto.FLOAT, [1], [2.0]),
make_tensor("zero", TensorProto.FLOAT8E4M3FN, [1], [0.0]),
],
)
)
ref = ReferenceEvaluator(model)
data = np.array([0, 1, 2, 1e5, 200], dtype=np.float32)
expected = np.array([0, 1, 2, 896, 192], dtype=np.float32)
got = ref.run(None, {"X": data})
assert_allclose(expected, got[0])
def test_quantize_linear_e5m2(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, [None])
Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None])
model = make_model(
make_graph(
[
make_node(
"Constant",
[],
["scale"],
value=make_tensor("scale", TensorProto.FLOAT, [1], [2.0]),
),
make_node(
"Constant",
[],
["zero"],
value=make_tensor("zero", TensorProto.FLOAT8E5M2, [1], [0.0]),
),
make_node("QuantizeLinear", ["X", "scale", "zero"], ["T"]),
make_node("DequantizeLinear", ["T", "scale"], ["Y"], axis=0),
],
"g",
[X],
[Y],
)
)
ref = ReferenceEvaluator(model)
data = np.array([0, 1, 2, 1e5, 200], dtype=np.float32)
expected = np.array([0, 1, 2, 98304, 192], dtype=np.float32)
got = ref.run(None, {"X": data})
assert_allclose(expected, got[0])
def test_lrn(self):
def _expected(x, alpha, beta, bias, size):
square_sum = np.zeros((5, 5, 5, 5)).astype(np.float32)
for n, c, h, w in np.ndindex(x.shape):
square_sum[n, c, h, w] = sum(
x[
n,
max(0, c - int(math.floor((size - 1) / 2))) : min(
5, c + int(math.ceil((size - 1) / 2)) + 1
),
h,
w,
]
** 2
)
y = x / ((bias + (alpha / size) * square_sum) ** beta)
return y
# keepdims is ignored in that case
alpha = 0.0002
beta = 0.5
bias = 2.0
size = 3
X = make_tensor_value_info("X", TensorProto.FLOAT, [5, 5, 50, 50])
Z = make_tensor_value_info("Z", TensorProto.UNDEFINED, None)
nodes = [
make_node("LRN", ["X"], ["Z"], alpha=alpha, beta=beta, bias=bias, size=size)
]
model = make_model(make_graph(nodes, "g", [X], [Z]))
ref = ReferenceEvaluator(model)
data = np.random.rand(5, 5, 5, 5).astype(np.float32)
got = ref.run(None, {"X": data})
expected = _expected(data, alpha, beta, bias, size)
self.assertEqual(len(expected), len(got[0]))
def test_conv_im2col_1d(self):
feeds = {
"X": np.arange(1 * 1 * 11).reshape((1, 1, 11)).astype(np.float32) + 1,
"W": np.arange(3).reshape((1, 1, 3)).astype(np.float32),
"B": np.zeros((1,), dtype=np.float32),
}
kwargs = dict(
group=1,
dilations=[1],
kernel_shape=[3],
pads=[1, 1],
strides=[1],
auto_pad="NOTSET",
)
expected = _conv_implementation(**feeds, **kwargs)
got = _conv_implementation_im2col(**feeds, **kwargs)
assert_allclose(expected, got)
def test_conv_im2col_1d_pad0(self):
feeds = {
"X": np.arange(2 * 4 * 3).reshape((2, 4, -1)).astype(np.float32) + 1,
"W": np.arange(2 * 4 * 3).reshape((-1, 4, 3)).astype(np.float32),
"B": np.zeros((1,), dtype=np.float32),
}
kwargs = dict(
group=1,
dilations=[1],
kernel_shape=[3],
pads=[0, 0],
strides=[1],
auto_pad="NOTSET",
)
expected = _conv_implementation(**feeds, **kwargs)
got = _conv_implementation_im2col(**feeds, **kwargs)
assert_allclose(expected, got)
def test_conv_im2col_2d(self):
feeds = {
"X": np.arange(1 * 1 * 11 * 23).reshape((1, 1, 11, 23)).astype(np.float32)
+ 1,
"W": np.arange(9).reshape((1, 1, 3, 3)).astype(np.float32),
"B": np.zeros((1,), dtype=np.float32),
}
kwargs = dict(
group=1,
dilations=[1, 1],
kernel_shape=[3, 3],
pads=[1, 1, 1, 1],
strides=[1, 1],
auto_pad="NOTSET",
)
expected = _conv_implementation(**feeds, **kwargs)
got = _conv_implementation_im2col(**feeds, **kwargs)
assert_allclose(expected, got)
def test_conv_im2col_2d_pad0(self):
feeds = {
"X": np.arange(2 * 3 * 5 * 2).reshape((2, 3, 5, -1)).astype(np.float32) + 1,
"W": 2
** np.arange(3 * 3 * 1 * 2).reshape((-1, 3, 1, 2)).astype(np.float32),
"B": np.zeros((1,), dtype=np.float32),
}
kwargs = dict(
group=1,
dilations=[1, 1],
kernel_shape=[1, 2],
pads=[0, 0, 0, 0],
strides=[1, 1],
auto_pad="NOTSET",
)
expected = _conv_implementation(**feeds, **kwargs)
got = _conv_implementation_im2col(**feeds, **kwargs)
assert_allclose(expected, got)
def test_conv_im2col_2d_autopad(self):
feeds = {
"X": np.arange(5 * 5).reshape((1, 1, 5, -1)).astype(np.float32) + 1,
"W": 2 ** np.arange(3 * 3).reshape((1, 1, 3, 3)).astype(np.float32),
"B": np.zeros((1,), dtype=np.float32),
}
kwargs = dict(
group=1,
dilations=[1, 1],
kernel_shape=[3, 3],
strides=[2, 2],
pads=None,
auto_pad="SAME_LOWER",
)
expected = _conv_implementation(**feeds, **kwargs)
got = _conv_implementation_im2col(**feeds, **kwargs)
assert_allclose(expected, got)
def test_conv_im2col_3d(self):
feeds = {
"X": np.arange(1 * 1 * 11 * 5 * 13)
.reshape((1, 1, 11, 5, 13))
.astype(np.float32)
+ 1,
"W": np.arange(27).reshape((1, 1, 3, 3, 3)).astype(np.float32),
"B": np.zeros((1,), dtype=np.float32),
}
kwargs = dict(
group=1,
dilations=[1, 1, 1],
kernel_shape=[3, 3, 3],
pads=[1, 1, 1, 1, 1, 1],
strides=[1, 1, 1],
auto_pad="NOTSET",
)
expected = _conv_implementation(**feeds, **kwargs)
got = _conv_implementation_im2col(**feeds, **kwargs)
assert_allclose(expected, got)
def test_conv_im2col_2d_strides(self):
feeds = {
"X": np.arange(1 * 3 * 6 * 6).reshape((1, 3, 6, 6)).astype(np.float32) + 1,
"W": np.arange(2 * 3 * 3 * 3).reshape((2, 3, 3, 3)).astype(np.float32),
"B": np.zeros((2,), dtype=np.float32),
}
kwargs = dict(
group=1,
dilations=[1, 1],
kernel_shape=[3, 3],
pads=[1, 1, 1, 1],
strides=[2, 2],
auto_pad="NOTSET",
)
expected = _conv_implementation(**feeds, **kwargs)
got = _conv_implementation_im2col(**feeds, **kwargs)
assert_allclose(expected, got)
def test_conv_im2col_2d_dilations(self):
feeds = {
"X": np.arange(1 * 3 * 6 * 6).reshape((1, 3, 6, 6)).astype(np.float32) + 1,
"W": np.arange(2 * 3 * 3 * 3).reshape((2, 3, 3, 3)).astype(np.float32),
"B": np.zeros((2,), dtype=np.float32),
}
kwargs = dict(
group=1,
dilations=[2, 1],
kernel_shape=[3, 3],
pads=[1, 1, 1, 1],
strides=[2, 2],
auto_pad="NOTSET",
)
expected = _conv_implementation(**feeds, **kwargs)
got = _conv_implementation_im2col(**feeds, **kwargs)
assert_allclose(expected, got)
@parameterized.parameterized.expand(
[
("ReduceSum",),
("ReduceL1",),
("ReduceL2",),
("ReduceMin",),
("ReduceMax",),
("ReduceProd",),
("ReduceSumSquare",),
]
)
def test_reduce_op_no_axis(self, op):
X = make_tensor_value_info("X", TensorProto.FLOAT, None)
Y = make_tensor_value_info("Y", TensorProto.FLOAT, None)
data = np.arange(6).reshape((1, 3, 2)).astype(np.float32)
nodes = [make_node(op, ["X"], ["Y"], keepdims=0)]
model = make_model(make_graph(nodes, "g", [X], [Y]))
ref = ReferenceEvaluator(model)
got = ref.run(None, {"X": data})
r = got[0]
self.assertIsInstance(r, np.ndarray)
self.assertEqual(r.shape, ())
@parameterized.parameterized.expand([(1,), (2,), (3,), (4,), (5,), (6,)])
def test_pad(self, dim):
X = make_tensor_value_info("X", TensorProto.FLOAT, None)
P = make_tensor_value_info("P", TensorProto.INT64, None)
V = make_tensor_value_info("V", TensorProto.FLOAT, None)
Y = make_tensor_value_info("Y", TensorProto.FLOAT, None)
value = np.array([-5], dtype=np.float32)
node = make_node("Pad", inputs=["X", "P", "V"], outputs=["Y"], mode="constant")
model = make_model(make_graph([node], "g", [X, P, V], [Y]))
ref = ReferenceEvaluator(model)
x = np.array([1], dtype=np.float32).reshape((1,) * dim)
p = np.array([1, 1] * dim, dtype=np.int64)
got = ref.run(None, {"X": x, "P": p, "V": value})[0]
self.assertEqual(got.shape, (3,) * dim)
self.assertEqual(got.dtype, np.float32)
p = np.repeat([7, 3], dim).astype(np.int64)
got = ref.run(None, {"X": x, "P": p, "V": value})[0]
self.assertEqual(got.shape, (11,) * dim)
self.assertEqual(got.dtype, np.float32)
def test_constant_of_shape(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, None)
Y = make_tensor_value_info("Y", TensorProto.FLOAT, None)
nodes = [
make_node("Shape", inputs=["X"], outputs=["shape"]),
make_node(
"ConstantOfShape",
inputs=["shape"],
outputs=["Y"],
value=make_tensor("value", TensorProto.UINT16, [1], [1]),
),
]
model = make_model(make_graph(nodes, "g", [X], [Y]))
ref = ReferenceEvaluator(model)
x = np.array(1, dtype=np.float32)
got = ref.run(None, {"X": x})[0]
self.assertEqual(got.shape, tuple())
self.assertEqual(got.dtype, np.uint16)
assert_allclose(np.array(1, dtype=np.uint16), got)
def test_constant_of_shape_castlike(self):
X = make_tensor_value_info("X", TensorProto.FLOAT, None)
Y = make_tensor_value_info("Y", TensorProto.FLOAT, None)
nodes = [
make_node(
"Constant",
[],
["like"],
value=make_tensor("c", TensorProto.UINT16, [1], [2]),
),
make_node("Shape", inputs=["X"], outputs=["shape"]),
make_node(
"ConstantOfShape",
inputs=["shape"],
outputs=["cst"],
value=make_tensor("value", TensorProto.INT64, [1], [1]),
),
make_node("CastLike", ["cst", "like"], ["Y"]),
]
model = make_model(make_graph(nodes, "g", [X], [Y]))
ref = ReferenceEvaluator(model)
x = np.array(1, dtype=np.float32)
got = ref.run(None, {"X": x})[0]
self.assertEqual(got.shape, tuple())
self.assertEqual(got.dtype, np.uint16)
assert_allclose(np.array(1, dtype=np.uint16), got)
def test_dynamic_quantize_linear(self):
feeds = {
"X": np.array(
[
[
-7.80749545e-02,
-3.80597055e-01,
1.33831516e-01,
-8.20474699e-02,
7.56645501e-02,
5.65112457e-02,
2.56818235e-01,
9.42316353e-02,
1.88027292e-01,
1.44878656e-01,
1.34825557e-01,
-2.04576910e-01,
1.68852255e-01,
6.23253360e-02,
4.30482924e-01,
-5.50433956e-02,
9.10681635e-02,
1.55332625e-01,
-4.53630984e-02,
3.99910688e-01,
-1.28678545e-01,
3.77916731e-02,
1.29872710e-01,
-1.12420328e-01,
-2.97306702e-02,
2.20508516e-01,
-5.88933006e-03,
4.81076002e-01,
-1.18835129e-01,
-4.45004404e-02,
-7.53675848e-02,
1.41112670e-01,
1.97793499e-01,
-7.71476865e-01,
8.64694864e-02,
1.73293594e-02,
1.28247693e-01,
7.58144110e-02,
-2.71435380e-01,
1.75212905e-01,
-2.47283235e-01,
-3.02810557e-02,
8.45039487e-02,
6.02229357e-01,
-1.04913145e-01,
-2.46705681e-01,
2.92073280e-01,
-3.88464853e-02,
4.26557302e-01,
-3.71325493e-01,
-3.11283618e-01,
7.85303488e-02,
3.18069518e-01,
-1.51467413e-01,
-1.02828763e-01,
9.29131880e-02,
2.55233884e-01,
5.00160515e-01,
-1.49993747e-01,
4.29408073e-01,
-1.91787735e-01,
3.16187665e-02,
-1.84284449e-02,
-1.62873864e-01,
-2.73632705e-01,
2.84725696e-01,
-2.87029266e-01,
-7.15534389e-02,
2.24836454e-01,
-1.70527741e-01,
-2.65601039e-01,
-2.68008932e-03,
1.44260898e-01,
7.80707747e-02,
2.73875445e-02,
-1.18391573e-01,
-6.44972250e-02,
-5.22445887e-03,
-2.96754301e-01,
1.05800219e-01,
2.62558222e-01,
3.62841524e-02,
9.44730639e-03,
1.75837606e-01,
2.69956529e-01,
3.02758247e-01,
-1.13724738e-01,
2.98936248e-01,
8.54668319e-02,
-6.74555600e-01,
4.38643873e-01,
1.27896462e-02,
9.20789093e-02,
1.93946883e-01,
1.97548166e-01,
2.82558739e-01,
-2.48879120e-01,
-3.93428057e-01,
6.45540953e-02,
-9.66283306e-03,
],
[
-2.42438495e-01,
-3.58334243e-01,
1.22619808e-01,
-1.21529415e-01,
-5.23974374e-02,
-6.74922541e-02,
1.09727375e-01,
-3.56835872e-03,
1.51029706e-01,
1.18043356e-01,
8.16475376e-02,
-2.36466587e-01,
9.44180191e-02,
5.61679937e-02,
3.67988586e-01,
1.29441261e-01,
1.15772486e-01,
5.30351102e-02,
-1.04345076e-01,
1.29062623e-01,
-2.17205048e-01,
2.58089490e-02,
2.18848974e-01,
-8.36039707e-02,
5.43577969e-03,
7.87076280e-02,
1.19966723e-01,
2.81631678e-01,
-3.58020402e-02,
-9.65647772e-02,
3.17915753e-02,
2.49396205e-01,
2.23600790e-01,
-3.82718384e-01,
1.16506606e-01,
-3.19400802e-02,
1.60812005e-01,
9.81735960e-02,
-1.99046463e-01,
8.81427377e-02,
-1.65171087e-01,
-1.10251531e-01,
1.15387037e-01,
3.19312930e-01,
-1.14400804e-01,
-2.02447772e-01,
2.33669549e-01,
9.20853689e-02,
3.91551465e-01,
-3.58036369e-01,
-2.35071778e-01,
-5.00670634e-02,
1.65313914e-01,
-1.60922945e-01,
-1.95520848e-01,
1.82456985e-01,
3.80704433e-01,
4.19890225e-01,
-2.98131555e-02,
3.66110623e-01,
-2.81170905e-01,
-1.23942450e-01,
9.58625227e-02,
-5.90450205e-02,
-1.56236291e-01,
2.11865619e-01,
-1.75286919e-01,
-8.36539492e-02,
1.29381597e-01,
-1.66115880e-01,
-1.66922957e-01,
1.65688396e-01,
8.41224194e-02,
1.67839468e-01,
-4.03967649e-02,
-8.34071711e-02,
-5.65301552e-02,
1.67074010e-01,
-3.19734544e-01,
7.71123618e-02,
5.57036921e-02,
1.20709330e-01,
-6.63790107e-02,
1.36002287e-01,
3.42324018e-01,
3.42968374e-01,
-1.42380476e-01,
2.89662749e-01,
1.82179566e-02,
-4.96056974e-01,
2.64364302e-01,
7.38918930e-02,
1.11150607e-01,
-5.95749579e-02,
1.18562862e-01,
6.90007359e-02,
-2.08283514e-01,
-1.70682222e-01,
7.82715827e-02,
1.35489792e-01,
],
[
-6.35757595e-02,
-3.20629895e-01,
9.38569903e-02,
-1.15190029e-01,
1.03646070e-02,
-4.31734361e-02,
2.31717676e-01,
4.01005745e-02,
1.18915148e-01,
2.10071519e-01,
-2.99234912e-02,
-2.93135524e-01,
-2.39588290e-01,
6.71441257e-02,
3.15238893e-01,
-5.08778207e-02,
1.16147280e-01,
-6.72954097e-02,
-1.63787514e-01,
1.60288364e-01,
-2.30847582e-01,
-2.22037435e-01,
4.50191796e-02,
-1.09636143e-01,
-6.00997508e-02,
2.14693844e-01,
-8.51289369e-03,
3.88416052e-01,
-1.18085533e-01,
-8.57385695e-02,
-1.18666515e-02,
3.29741478e-01,
2.03779504e-01,
-1.69492334e-01,
1.94324687e-01,
4.16374728e-02,
6.83876276e-02,
-1.85160581e-02,
-3.73600274e-02,
7.14804307e-02,
-3.01446438e-01,
1.74035393e-02,
3.35123807e-01,
4.17102218e-01,
-1.58562332e-01,
-2.54483074e-02,
1.99573949e-01,
7.95029774e-02,
4.82958555e-01,
-4.58213627e-01,
-2.67229170e-01,
1.27542794e-01,
4.47799414e-02,
-1.68686539e-01,
-8.92183557e-02,
9.79782715e-02,
2.77656261e-02,
2.96871901e-01,
6.29860088e-02,
1.77246213e-01,
-3.15523535e-01,
-1.74582571e-01,
1.25724282e-02,
-4.54988703e-02,
-1.29154682e-01,
2.13568255e-01,
-1.40891463e-01,
-2.66211092e-01,
2.62144595e-01,
-1.42889306e-01,
-6.67349845e-02,
1.63380653e-02,
-1.92995071e-02,
1.14811368e-01,
1.43584609e-01,
-2.65347548e-02,
9.32592154e-02,
2.23325342e-01,
-1.87100068e-01,
5.71197420e-02,
2.71253467e-01,
1.02890521e-01,
-3.04941833e-03,
1.10537663e-01,
2.75728375e-01,
2.11693868e-01,
-1.80009842e-01,
2.99300496e-02,
1.77923322e-01,
-4.53491032e-01,
1.94149211e-01,
2.47100577e-01,
-9.95091349e-03,
1.99301094e-01,
2.06564650e-01,
-1.99648179e-02,
-1.89629450e-01,
1.61689930e-02,
1.04817756e-01,
2.06400946e-01,
],
[
-3.86251360e-02,
-3.07115853e-01,
3.74236181e-02,
-1.71237886e-01,
-2.77359486e-02,
-4.08068746e-02,
6.91853091e-02,
2.65322998e-03,
1.23958819e-01,
2.20951259e-01,
8.36500078e-02,
-3.14413190e-01,
1.34745941e-01,
-8.25274512e-02,
3.36270213e-01,
-2.49634441e-02,
-1.06189884e-02,
7.18819201e-02,
-1.73392966e-01,
2.98084319e-01,
-1.25626653e-01,
-2.17043106e-02,
2.87982523e-01,
-3.41932476e-03,
-6.89984411e-02,
-7.14176893e-03,
4.49542440e-02,
5.16424477e-01,
-1.02622584e-01,
2.02640779e-02,
2.30106711e-02,
1.93037808e-01,
2.03393996e-01,
-4.34632808e-01,
5.74068353e-02,
9.66466218e-02,
-7.19890296e-02,
4.23505977e-02,
-1.60067812e-01,
3.88947129e-02,
-3.32329512e-01,
1.91072702e-01,
3.79437394e-02,
4.33839470e-01,
-1.29231960e-01,
-1.46881789e-01,
2.47269243e-01,
2.86379829e-02,
2.92926908e-01,
-2.97341049e-01,
-3.40239167e-01,
1.52589783e-01,
8.81168991e-02,
1.40633471e-02,
-8.83188471e-02,
2.48367310e-01,
3.41982782e-01,
3.18781316e-01,
-1.15148552e-01,
2.54325420e-01,
-1.82771236e-01,
5.33889830e-02,
2.12034155e-02,
-9.78844613e-02,
-1.61611915e-01,
3.54084134e-01,
-1.25332132e-01,
-2.07989410e-01,
2.35610008e-02,
-1.35993093e-01,
-1.97377697e-01,
-1.21356212e-02,
7.86775351e-03,
4.71337497e-01,
9.49376822e-03,
4.25345525e-02,
1.14162050e-01,
6.27847165e-02,
-2.31957644e-01,
-8.33211765e-02,
2.02719584e-01,
-4.64919358e-02,
7.57966787e-02,
1.01521172e-01,
3.16580981e-01,
1.49488643e-01,
-1.20770879e-01,
2.56563038e-01,
1.66572407e-01,
-6.11343801e-01,
2.09183827e-01,
6.66101649e-02,
1.77328646e-01,
1.77777156e-01,
2.03266457e-01,
1.37545317e-01,
-1.38004154e-01,
-2.57656008e-01,
1.83920860e-01,
2.87696868e-02,
],
[
5.14136627e-04,
-2.88997203e-01,
-3.34554128e-02,
-6.80408552e-02,
-4.61972654e-02,
1.75428241e-01,
1.86710209e-01,
-1.51355267e-01,
1.28381401e-01,
2.87129283e-01,
5.22154570e-03,
-3.53413224e-01,
-3.87947261e-02,
5.81918843e-02,
3.17016482e-01,
-2.51671404e-01,
7.01867491e-02,
-4.92945537e-02,
-2.73323953e-01,
1.27938241e-01,
-4.11552131e-01,
-2.23789401e-02,
1.95418939e-01,
-3.25946212e-01,
4.60528135e-02,
1.86884090e-01,
6.98191971e-02,
2.95638293e-01,
-1.80322871e-01,
-2.98620313e-02,
2.11789399e-01,
3.15145910e-01,
3.11763227e-01,
-5.78147054e-01,
6.43244758e-02,
-3.14367823e-02,
1.86190963e-01,
1.71108633e-01,
-6.29745722e-02,
7.48428777e-02,
-4.58003521e-01,
-3.01471800e-01,
2.17973694e-01,
5.73273778e-01,
-1.01379365e-01,
-2.46951967e-01,
1.58989042e-01,
-1.79126799e-01,
5.24153829e-01,
-4.64852840e-01,
-2.94867605e-01,
1.83558539e-01,
2.50552952e-01,
-8.56962949e-02,
-2.57554710e-01,
2.30709136e-01,
3.53280157e-01,
3.20184112e-01,
2.99184099e-02,
3.09098989e-01,
-2.02217728e-01,
-9.29201543e-02,
-1.20569356e-02,
-1.37087986e-01,
-2.16524690e-01,
1.39787242e-01,
-1.27902150e-01,
-2.64347821e-01,
9.29943919e-02,
-1.57217175e-01,
-3.86638314e-01,
7.90465698e-02,
4.07211930e-02,
-3.07695866e-02,
1.27393469e-01,
-1.18648581e-01,
-7.21216127e-02,
-3.71141285e-02,
-3.37082207e-01,
-6.23112544e-02,
3.52166295e-01,
1.51260465e-01,
5.03427610e-02,
1.90433189e-01,
5.21304548e-01,
3.85341585e-01,
-1.26457050e-01,
1.54961571e-01,
5.29025272e-02,
-5.06486952e-01,
2.28533953e-01,
1.78438187e-01,
-4.14765030e-02,
2.01903239e-01,
-3.89365852e-02,
8.84043872e-02,
-1.55351788e-01,
-9.06582028e-02,
9.95599255e-02,
-4.79989760e-02,
],
],
dtype=np.float32,
)
}
expected = [
np.array(
[
[
129,
72,
168,
128,
157,
153,
191,
160,
178,
170,
168,
105,
174,
155,
223,
133,
160,
172,
135,
217,
119,
150,
167,
122,
137,
184,
142,
232,
121,
135,
129,
169,
180,
0,
159,
146,
167,
157,
93,
176,
97,
137,
159,
255,
124,
97,
197,
136,
222,
74,
85,
158,
202,
115,
124,
160,
190,
236,
115,
223,
107,
149,
140,
113,
92,
196,
90,
130,
185,
111,
94,
143,
170,
157,
148,
121,
131,
142,
88,
163,
192,
150,
145,
176,
193,
199,
122,
198,
159,
18,
224,
145,
160,
179,
180,
195,
97,
70,
155,
141,
],
[
98,
76,
166,
120,
133,
130,
163,
142,
171,
165,
158,
99,
161,
153,
211,
167,
164,
153,
124,
167,
103,
148,
184,
127,
144,
158,
165,
195,
136,
125,
149,
189,
185,
72,
165,
137,
173,
161,
106,
159,
112,
123,
164,
202,
122,
105,
186,
160,
216,
77,
99,
134,
174,
113,
107,
177,
214,
221,
137,
211,
91,
120,
161,
132,
114,
182,
110,
127,
167,
112,
112,
174,
159,
174,
136,
128,
133,
174,
84,
157,
153,
165,
131,
168,
207,
207,
117,
197,
146,
51,
192,
157,
164,
132,
165,
156,
104,
111,
158,
168,
],
[
131,
83,
160,
122,
145,
135,
186,
150,
165,
182,
137,
89,
99,
155,
202,
134,
165,
131,
113,
173,
100,
102,
151,
123,
132,
183,
141,
215,
121,
127,
141,
204,
181,
112,
179,
151,
156,
140,
136,
156,
87,
146,
205,
220,
114,
138,
180,
158,
233,
58,
93,
167,
151,
112,
126,
161,
148,
198,
155,
176,
84,
111,
145,
135,
119,
183,
117,
94,
192,
116,
131,
146,
139,
164,
170,
138,
160,
184,
108,
154,
193,
162,
142,
164,
194,
182,
110,
149,
176,
59,
179,
189,
141,
180,
181,
139,
108,
146,
162,
181,
],
[
136,
86,
150,
111,
138,
135,
156,
143,
166,
184,
159,
85,
168,
128,
205,
138,
141,
156,
111,
198,
120,
139,
196,
142,
130,
142,
151,
239,
124,
147,
147,
179,
181,
62,
154,
161,
130,
151,
113,
150,
81,
178,
150,
224,
119,
116,
189,
148,
197,
88,
80,
171,
159,
146,
127,
189,
206,
202,
122,
190,
109,
153,
147,
125,
113,
209,
120,
104,
147,
118,
106,
141,
144,
230,
145,
151,
164,
155,
100,
128,
181,
134,
157,
162,
202,
171,
121,
191,
174,
30,
182,
155,
176,
176,
181,
169,
117,
95,
177,
148,
],
[
143,
89,
137,
130,
134,
176,
178,
115,
167,
196,
144,
77,
136,
154,
202,
96,
156,
134,
92,
167,
67,
139,
179,
82,
152,
178,
156,
198,
110,
137,
182,
202,
201,
36,
155,
137,
178,
175,
131,
157,
58,
87,
183,
249,
124,
97,
173,
110,
240,
57,
88,
177,
190,
127,
95,
186,
209,
202,
149,
200,
105,
126,
141,
118,
103,
169,
119,
94,
160,
114,
71,
158,
151,
137,
167,
121,
130,
136,
80,
131,
208,
171,
152,
178,
240,
215,
120,
172,
153,
49,
185,
176,
135,
180,
136,
159,
114,
126,
161,
134,
],
],
dtype=np.uint8,
),
np.array(0.005387083161622286, dtype=np.float32),
np.array(143, dtype=np.uint8),
]
X = make_tensor_value_info("X", TensorProto.FLOAT, None)
Y = make_tensor_value_info("Y", TensorProto.UINT8, None)
Scale = make_tensor_value_info("scale", TensorProto.FLOAT, None)
Zp = make_tensor_value_info("zp", TensorProto.UINT8, None)
nodes = [
make_node(
"DynamicQuantizeLinear",
["X"],
["Y", "scale", "zp"],
),
]
model = make_model(
make_graph(nodes, "g", [X], [Y, Scale, Zp]),
opset_imports=[make_opsetid("", onnx_opset_version() - 1)],
)
ref = ReferenceEvaluator(model)
got = ref.run(None, feeds)
self.assertEqual(len(got), 3)
for i in range(2, -1, -1):
assert_allclose(expected[i], got[i])
@parameterized.parameterized.expand(
[
(["abc", "def"], [".com", ".net"], ["abc.com", "def.net"], (2,)),
(["cat", "dog", "snake"], ["s"], ["cats", "dogs", "snakes"], (3,)),
("cat", "s", "cats", ()),
(["a", "ß", "y"], ["a", "ß", "y"], ["aa", "ßß", "yy"], (3,)),
]
)
def test_string_concat(self, a, b, expected, expected_shape):
A = make_tensor_value_info("A", TensorProto.STRING, None)
B = make_tensor_value_info("B", TensorProto.STRING, None)
Y = make_tensor_value_info("Y", TensorProto.STRING, None)
node = make_node("StringConcat", inputs=["A", "B"], outputs=["Y"])
model = make_model(make_graph([node], "g", [A, B], [Y]))
ref = ReferenceEvaluator(model)
result, *_ = ref.run(None, {"A": np.array(a), "B": np.array(b)})
np.testing.assert_array_equal(result, expected)
self.assertEqual(result.dtype.kind, "O")
self.assertEqual(result.shape, expected_shape)
@parameterized.parameterized.expand(
[
(
["1,2,3", "4,5,6"],
",",
None,
[["1", "2", "3"], ["4", "5", "6"]],
[3, 3],
),
(
["1,", "4,6", ""],
",",
None,
[["1", ""], ["4", "6"], ["", ""]],
[2, 2, 1],
),
(
["1", "4,6", "4,5,6"],
",",
1,
[["1", ""], ["4", "6"], ["4", "5,6"]],
[1, 2, 2],
),
(
[["1,", "4,6", "4,5,6"], ["1,", "4,6", "4,5,6"]],
",",
None,
[
[["1", "", ""], ["4", "6", ""], ["4", "5", "6"]],
[["1", "", ""], ["4", "6", ""], ["4", "5", "6"]],
],
[[2, 2, 3], [2, 2, 3]],
),
(
["hello world !", " hello world !", " hello world ! "],
None,
None,
[
["hello", "world", "!"],
["hello", "world", "!"],
["hello", "world", "!"],
],
[3, 3, 3],
),
(
["hello world !", " hello world !", " hello world ! "],
"",
None,
[
["hello", "world", "!"],
["hello", "world", "!"],
["hello", "world", "!"],
],
[3, 3, 3],
),
(
["o-n-n--x-", "o-n----nx"],
"-",
None,
[["o", "n", "n", "", "x", ""], ["o", "n", "", "", "", "nx"]],
[6, 6],
),
(
[],
" ",
2,
np.array([]).reshape((0, 0)),
[],
),
]
)
def test_string_split(
self,
x,
delimiter,
maxsplit,
expected_split,
expected_num_splits,
):
X = make_tensor_value_info("X", TensorProto.STRING, (None))
Splits = make_tensor_value_info("Splits", TensorProto.STRING, (None))
MaxSplits = make_tensor_value_info("MaxSplits", TensorProto.INT32, (None))
node = make_node(
"StringSplit",
inputs=["X"],
outputs=["Splits", "MaxSplits"],
delimiter=delimiter,
maxsplit=maxsplit,
)
model = make_model(make_graph([node], "g", [X], [Splits, MaxSplits]))
ref = ReferenceEvaluator(model)
x = np.array(x, dtype=object)
result, num_splits, *_ = ref.run(None, {"X": x})
np.testing.assert_array_equal(result, np.array(expected_split, dtype=object))
np.testing.assert_array_equal(
num_splits, np.array(expected_num_splits, dtype=np.int64)
)
@parameterized.parameterized.expand(
[
(
["www.google.com", "www.facebook.com", "www.bbc.co.uk"],
r"www\.[\w.-]+\.\bcom\b",
[True, True, False],
(3,),
),
(
[["Onnx", "tensorflow", "Numpy"], ["Pytorch", "Cython", "numba"]],
r"^[A-Z][a-z]*$",
[[True, False, True], [True, True, False]],
(2, 3),
),
(
[
"account@gmail.com",
"account@hotmail.com",
"not email",
"account2@yahoo.com",
],
r"(\W|^)[\w.\-]{0,25}@(yahoo|gmail)\.com(\W|$)",
[True, False, False, True],
(4,),
),
]
)
@unittest.skipIf(
sys.platform == "win32", "google-re2 package is not built for win32"
)
def test_regex_full_match(self, x, pattern, expected, expected_shape):
X = make_tensor_value_info("X", TensorProto.STRING, None)
Y = make_tensor_value_info("Y", TensorProto.BOOL, None)
node = make_node("RegexFullMatch", inputs=["X"], outputs=["Y"], pattern=pattern)
model = make_model(make_graph([node], "g", [X], [Y]))
ref = ReferenceEvaluator(model)
result, *_ = ref.run(None, {"X": np.array(x)})
np.testing.assert_array_equal(result, expected)
self.assertEqual(result.dtype.kind, "b")
self.assertEqual(result.shape, expected_shape)
@unittest.skipIf(
sys.platform == "win32", "google-re2 package is not built for win32"
)
def test_regex_invalid_pattern(self):
X = make_tensor_value_info("X", TensorProto.STRING, None)
Y = make_tensor_value_info("Y", TensorProto.BOOL, None)
node = make_node("RegexFullMatch", inputs=["X"], outputs=["Y"], pattern="x)")
model = make_model(make_graph([node], "g", [X], [Y]))
ref = ReferenceEvaluator(model)
with self.assertRaises(ValueError):
ref.run(None, {"X": np.array(["x"])})
if __name__ == "__main__":
unittest.main(verbosity=2)
| {"/onnx/backend/test/case/node/sign.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/dft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/parser.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/constantofshape.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/averagepool.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/__init__.py": ["/onnx/__init__.py", "/onnx/backend/base.py", "/onnx/backend/test/case/test_case.py", "/onnx/backend/test/loader/__init__.py", "/onnx/backend/test/runner/item.py"], "/onnx/reference/ops/op_topk.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_image_decoder.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_non_max_suppression.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/logsoftmax.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_affine_grid.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_lp_normalization.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_rnn.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/not.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_reduce_sum.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mean.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_roi_align.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_center_crop_pad.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/nonmaxsuppression.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/dropout.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/model_inference_test.py": ["/onnx/__init__.py", "/onnx/parser.py", "/onnx/shape_inference.py"], "/onnx/test/inliner_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/argmin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/op_run.py": ["/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/reference_evaluator.py"], "/onnx/reference/ops/op_max_unpool.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/reversesequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/celu.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_where.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/test_case.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_erf.py": ["/onnx/reference/ops/_op.py"], "/onnx/test/function_inference_test.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/parser.py", "/onnx/shape_inference.py"], "/onnx/reference/ops/aionnxml/op_dict_vectorizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/inliner.py": ["/onnx/__init__.py"], "/onnx/test/reference_evaluator_ml_test.py": ["/onnx/__init__.py", "/onnx/checker.py", "/onnx/defs/__init__.py", "/onnx/helper.py"], "/onnx/reference/ops/op_softmax.py": ["/onnx/reference/ops/_op.py"], "/onnx/backend/test/case/node/reduce_log_sum_exp.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/utils.py": ["/onnx/__init__.py"], 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"/onnx/reference/ops/aionnxml/op_linear_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_softplus.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_sub.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_quantize_linear.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_gathernd.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/qlinearmatmul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/shape_inference_test.py": ["/onnx/shape_inference.py", "/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/parser.py"], "/onnx/backend/test/case/node/mish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_expand.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": ["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/_helpers.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/tfidfvectorizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_average_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/item.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/gatherelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/slice.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_matmul.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mel_weight_matrix.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cast.py": ["/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/asin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_normalizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/unique.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather_elements.py": ["/onnx/reference/op_run.py"], "/onnx/helper.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/layernormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/groupnormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/__init__.py": ["/onnx/reference/ops/_op_list.py"], "/onnx/reference/ops/op_random_normal.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_hann_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_softmax_cross_entropy_loss.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_string_split.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/max.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/backend/test/case/utils.py"], "/onnx/backend/test/case/model/expand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/backend/test/case/node/erf.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
59,085 | onnx/onnx | refs/heads/main | /onnx/backend/test/cmd_tools.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import argparse
import json
import os
import shutil
import warnings
import onnx.backend.test.case.model as model_test
import onnx.backend.test.case.node as node_test
from onnx import ONNX_ML, TensorProto, numpy_helper
TOP_DIR = os.path.realpath(os.path.dirname(__file__))
DATA_DIR = os.path.join(TOP_DIR, "data")
def generate_data(args: argparse.Namespace) -> None:
def prepare_dir(path: str) -> None:
if os.path.exists(path):
shutil.rmtree(path)
os.makedirs(path)
# Clean the output directory before generating data for node testcases
# It is used to check new generated data is correct in CIs
node_root = os.path.join(args.output, "node")
original_dir_number = len(
[name for name in os.listdir(node_root) if os.path.isfile(name)]
)
if args.clean and os.path.exists(node_root):
for sub_dir in os.listdir(node_root):
if ONNX_ML or not sub_dir.startswith("test_ai_onnx_ml_"):
shutil.rmtree(os.path.join(node_root, sub_dir))
cases = model_test.collect_testcases()
# If op_type is specified, only include those testcases including the given operator
# Otherwise, include all of the testcases
cases += node_test.collect_testcases(args.op_type)
node_number = 0
for case in cases:
output_dir = os.path.join(args.output, case.kind, case.name)
prepare_dir(output_dir)
if case.kind == "node":
node_number += 1
if case.kind == "real":
with open(os.path.join(output_dir, "data.json"), "w") as fi:
json.dump(
{
"url": case.url,
"model_name": case.model_name,
"rtol": case.rtol,
"atol": case.atol,
},
fi,
sort_keys=True,
)
else:
assert case.model
with open(os.path.join(output_dir, "model.onnx"), "wb") as f:
f.write(case.model.SerializeToString())
assert case.data_sets
for i, (inputs, outputs) in enumerate(case.data_sets):
data_set_dir = os.path.join(output_dir, f"test_data_set_{i}")
prepare_dir(data_set_dir)
for j, input in enumerate(inputs):
with open(os.path.join(data_set_dir, f"input_{j}.pb"), "wb") as f:
if case.model.graph.input[j].type.HasField("map_type"):
f.write(
numpy_helper.from_dict(
input, case.model.graph.input[j].name
).SerializeToString()
)
elif case.model.graph.input[j].type.HasField("sequence_type"):
f.write(
numpy_helper.from_list(
input, case.model.graph.input[j].name
).SerializeToString()
)
elif case.model.graph.input[j].type.HasField("optional_type"):
f.write(
numpy_helper.from_optional(
input, case.model.graph.input[j].name
).SerializeToString()
)
else:
assert case.model.graph.input[j].type.HasField(
"tensor_type"
)
if isinstance(input, TensorProto):
f.write(input.SerializeToString())
else:
f.write(
numpy_helper.from_array(
input, case.model.graph.input[j].name
).SerializeToString()
)
for j, output in enumerate(outputs):
with open(os.path.join(data_set_dir, f"output_{j}.pb"), "wb") as f:
if case.model.graph.output[j].type.HasField("map_type"):
f.write(
numpy_helper.from_dict(
output, case.model.graph.output[j].name
).SerializeToString()
)
elif case.model.graph.output[j].type.HasField("sequence_type"):
f.write(
numpy_helper.from_list(
output, case.model.graph.output[j].name
).SerializeToString()
)
elif case.model.graph.output[j].type.HasField("optional_type"):
f.write(
numpy_helper.from_optional(
output, case.model.graph.output[j].name
).SerializeToString()
)
else:
assert case.model.graph.output[j].type.HasField(
"tensor_type"
)
if isinstance(output, TensorProto):
f.write(output.SerializeToString())
else:
f.write(
numpy_helper.from_array(
output, case.model.graph.output[j].name
).SerializeToString()
)
if not args.clean and node_number != original_dir_number:
warnings.warn(
"There are some models under 'onnx/backend/test/data/node' which cannot not"
" be generated by the script from 'onnx/backend/test/case/node'. Please add"
" '--clean' option for 'python onnx/backend/test/cmd_tools.py generate-data'"
" to cleanup the existing directories and regenerate them.",
Warning,
stacklevel=2,
)
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser("backend-test-tools")
subparsers = parser.add_subparsers()
subparser = subparsers.add_parser(
"generate-data", help="convert testcases to test data."
)
subparser.add_argument(
"-c",
"--clean",
default=False,
action="store_true",
help="Clean the output directory before generating data for node testcases.",
)
subparser.add_argument(
"-o",
"--output",
default=DATA_DIR,
help="output directory (default: %(default)s)",
)
subparser.add_argument(
"-t",
"--op_type",
default=None,
help="op_type for test case generation. (generates test data for the specified op_type only.)",
)
subparser.set_defaults(func=generate_data)
return parser.parse_args()
def main() -> None:
args = parse_args()
args.func(args)
if __name__ == "__main__":
main()
| {"/onnx/backend/test/case/node/sign.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/dft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/parser.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/constantofshape.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/averagepool.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/__init__.py": ["/onnx/__init__.py", "/onnx/backend/base.py", "/onnx/backend/test/case/test_case.py", "/onnx/backend/test/loader/__init__.py", "/onnx/backend/test/runner/item.py"], "/onnx/reference/ops/op_topk.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_image_decoder.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_non_max_suppression.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/logsoftmax.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_affine_grid.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_lp_normalization.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_rnn.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/not.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_reduce_sum.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mean.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_roi_align.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_center_crop_pad.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/nonmaxsuppression.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/dropout.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/model_inference_test.py": ["/onnx/__init__.py", "/onnx/parser.py", "/onnx/shape_inference.py"], "/onnx/test/inliner_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/argmin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/op_run.py": ["/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/reference_evaluator.py"], "/onnx/reference/ops/op_max_unpool.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/reversesequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/celu.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_where.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/test_case.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_erf.py": ["/onnx/reference/ops/_op.py"], "/onnx/test/function_inference_test.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/parser.py", "/onnx/shape_inference.py"], "/onnx/reference/ops/aionnxml/op_dict_vectorizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/inliner.py": ["/onnx/__init__.py"], "/onnx/test/reference_evaluator_ml_test.py": ["/onnx/__init__.py", "/onnx/checker.py", "/onnx/defs/__init__.py", "/onnx/helper.py"], "/onnx/reference/ops/op_softmax.py": ["/onnx/reference/ops/_op.py"], "/onnx/backend/test/case/node/reduce_log_sum_exp.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/utils.py": ["/onnx/__init__.py"], 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59,086 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_slice.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0912,R0913,W0221
from typing import Optional
import numpy as np
from onnx.reference.ops._op import OpRun
def _slice(
data: np.ndarray,
starts: np.ndarray,
ends: np.ndarray,
axes: Optional[np.ndarray] = None,
steps: Optional[np.ndarray] = None,
) -> np.ndarray:
if isinstance(starts, list):
starts = np.array(starts)
if isinstance(ends, list):
ends = np.array(ends)
if isinstance(axes, list):
axes = np.array(axes)
if isinstance(steps, list):
steps = np.array(steps)
if len(starts.shape) == 0:
starts = np.array([starts])
if len(ends.shape) == 0:
ends = np.array([ends])
if axes is None:
if steps is None:
slices = [slice(s, e) for s, e in zip(starts, ends)]
else:
slices = [slice(s, e, d) for s, e, d in zip(starts, ends, steps)]
else:
if steps is None:
slices = [slice(0, a) for a in data.shape]
for s, e, a in zip(starts, ends, axes):
slices[a] = slice(s, e)
else:
slices = [slice(0, a) for a in data.shape]
for s, e, a, d in zip(starts, ends, axes, steps):
slices[a] = slice(s, e, d)
try:
return data[tuple(slices)] # type: ignore
except TypeError as e: # pragma: no cover
raise TypeError(
f"Unable to extract slice {slices!r} for shape {data.shape!r}."
) from e
class SliceCommon(OpRun):
def _run(self, data, starts, ends, axes=None, steps=None): # type: ignore
res = _slice(data, starts, ends, axes, steps)
return (res,)
class Slice_10(SliceCommon):
def __init__(self, onnx_node, run_params): # type: ignore
SliceCommon.__init__(self, onnx_node, run_params)
class Slice_1(SliceCommon):
def __init__(self, onnx_node, run_params): # type: ignore
SliceCommon.__init__(self, onnx_node, run_params)
for f in ["starts", "ends", "steps", "axes"]:
if not hasattr(self, f):
continue
if getattr(self, f) is not None and len(getattr(self, f)) == 0:
setattr(self, f, None)
def _run(self, data, axes=None, ends=None, starts=None): # type: ignore
return SliceCommon._run(self, data, starts, ends, axes)
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59,087 | onnx/onnx | refs/heads/main | /onnx/reference/ops/aionnxml/op_binarizer.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0913,R0914,W0221
from onnx.reference.ops.aionnxml._op_run_aionnxml import OpRunAiOnnxMl
def compute_binarizer(x, threshold=None):
return ((x > threshold).astype(x.dtype),)
class Binarizer(OpRunAiOnnxMl):
def _run(self, x, threshold=None): # type: ignore
return compute_binarizer(x, threshold)
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"/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/identity.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/sin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gemm.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_layer_normalization.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_bernoulli.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/backend/test/case/node/deformconv.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_upsample.py": ["/onnx/reference/op_run.py"], "/onnx/test/test_with_ort.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_thresholded_relu.py": ["/onnx/reference/ops/_op.py"], "/onnx/backend/test/case/node/concat.py": ["/onnx/__init__.py", 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"/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_resize.py"], "/onnx/reference/ops/aionnxml/op_svm_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/op_sequence_map.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatternd.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/numpy_helper_test.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_tfidf_vectorizer.py": ["/onnx/reference/op_run.py"], "/onnx/test/checker_test.py": ["/onnx/defs/__init__.py", "/onnx/__init__.py"], "/onnx/reference/ops/_op_common_random.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/backend/base.py": ["/onnx/checker.py", "/onnx/__init__.py"], "/onnx/backend/test/case/node/reduce_log_sum.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_linear_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_softplus.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_sub.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_quantize_linear.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_gathernd.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/qlinearmatmul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/shape_inference_test.py": ["/onnx/shape_inference.py", "/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/parser.py"], "/onnx/backend/test/case/node/mish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_expand.py": ["/onnx/reference/op_run.py"], 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59,088 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/optionalgetelement.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from typing import Any, Optional
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
def optional_get_element_reference_implementation(optional: Optional[Any]) -> Any:
assert optional is not None
return optional
class OptionalHasElement(Base):
@staticmethod
def export_get_element_tensor() -> None:
optional = np.array([1, 2, 3, 4]).astype(np.float32)
tensor_type_proto = onnx.helper.make_tensor_type_proto(
elem_type=onnx.TensorProto.FLOAT,
shape=[
4,
],
)
optional_type_proto = onnx.helper.make_optional_type_proto(tensor_type_proto)
node = onnx.helper.make_node(
"OptionalGetElement", inputs=["optional_input"], outputs=["output"]
)
output = optional_get_element_reference_implementation(optional)
expect(
node,
inputs=[optional],
outputs=[output],
input_type_protos=[optional_type_proto],
name="test_optional_get_element_optional_tensor",
)
expect(
node,
inputs=[optional],
outputs=[output],
input_type_protos=[tensor_type_proto],
name="test_optional_get_element_tensor",
)
@staticmethod
def export_get_element_sequence() -> None:
optional = [np.array([1, 2, 3, 4]).astype(np.int32)]
tensor_type_proto = onnx.helper.make_tensor_type_proto(
elem_type=onnx.TensorProto.INT32,
shape=[
4,
],
)
seq_type_proto = onnx.helper.make_sequence_type_proto(tensor_type_proto)
optional_type_proto = onnx.helper.make_optional_type_proto(seq_type_proto)
node = onnx.helper.make_node(
"OptionalGetElement", inputs=["optional_input"], outputs=["output"]
)
output = optional_get_element_reference_implementation(optional)
expect(
node,
inputs=[optional],
outputs=[output],
input_type_protos=[optional_type_proto],
name="test_optional_get_element_optional_sequence",
)
expect(
node,
inputs=[optional],
outputs=[output],
input_type_protos=[seq_type_proto],
name="test_optional_get_element_sequence",
)
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"/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/stat_coverage.py": ["/onnx/__init__.py", "/onnx/backend/test/case/__init__.py", "/onnx/backend/test/loader/__init__.py", "/onnx/backend/test/runner/__init__.py"], "/onnx/reference/ops/op_constant.py": ["/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/upsample.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_squeeze.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_einsum.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/div.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_one_hot_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_random_uniform.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/test/reference_evaluator_test.py": ["/onnx/__init__.py", "/onnx/checker.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/__init__.py", "/onnx/reference/ops/_op_common_indices.py", "/onnx/reference/ops/_op_list.py", "/onnx/reference/ops/op_celu.py", "/onnx/reference/ops/op_col2im.py", "/onnx/reference/ops/op_conv.py", "/onnx/reference/ops_optimized/__init__.py", "/onnx/reference/ops_optimized/op_conv_optimized.py"], "/onnx/backend/test/cmd_tools.py": ["/onnx/backend/test/case/model/__init__.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/__init__.py"], "/onnx/reference/ops/op_slice.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/aionnxml/op_binarizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/optionalgetelement.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/loop.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/conf.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_sequence_construct.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatterelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel2.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/bernoulli.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/constant.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/resize.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_resize.py"], "/onnx/reference/ops/aionnxml/op_svm_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/op_sequence_map.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatternd.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/numpy_helper_test.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_tfidf_vectorizer.py": ["/onnx/reference/op_run.py"], "/onnx/test/checker_test.py": ["/onnx/defs/__init__.py", "/onnx/__init__.py"], "/onnx/reference/ops/_op_common_random.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/backend/base.py": ["/onnx/checker.py", "/onnx/__init__.py"], "/onnx/backend/test/case/node/reduce_log_sum.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_linear_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_softplus.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_sub.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_quantize_linear.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_gathernd.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/qlinearmatmul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/shape_inference_test.py": ["/onnx/shape_inference.py", "/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/parser.py"], "/onnx/backend/test/case/node/mish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_expand.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": ["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/_helpers.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/tfidfvectorizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_average_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/item.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/gatherelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/slice.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_matmul.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mel_weight_matrix.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cast.py": ["/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/asin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_normalizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/unique.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather_elements.py": ["/onnx/reference/op_run.py"], "/onnx/helper.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/layernormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/groupnormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/__init__.py": ["/onnx/reference/ops/_op_list.py"], "/onnx/reference/ops/op_random_normal.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_hann_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_softmax_cross_entropy_loss.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_string_split.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/max.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/backend/test/case/utils.py"], "/onnx/backend/test/case/model/expand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/backend/test/case/node/erf.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
59,089 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/loop.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from typing import Any, List
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
def compute_loop_outputs(x, seq, trip_count): # type: ignore
for i in range(trip_count):
if seq is None:
seq = []
seq += [x[: int(i + 1)]]
return seq
class Loop(Base):
@staticmethod
def export_loop_11() -> None:
# Given a tensor x of values [x1, ..., xN], and initial tensor y
# sum up its elements using a scan
# returning the final state (y+x1+x2+...+xN) as well the scan_output
# [y+x1, y+x1+x2, ..., y+x1+x2+...+xN]
y_in = onnx.helper.make_tensor_value_info("y_in", onnx.TensorProto.FLOAT, [1])
y_out = onnx.helper.make_tensor_value_info("y_out", onnx.TensorProto.FLOAT, [1])
scan_out = onnx.helper.make_tensor_value_info(
"scan_out", onnx.TensorProto.FLOAT, [1]
)
cond_in = onnx.helper.make_tensor_value_info(
"cond_in", onnx.TensorProto.BOOL, []
)
cond_out = onnx.helper.make_tensor_value_info(
"cond_out", onnx.TensorProto.BOOL, []
)
iter_count = onnx.helper.make_tensor_value_info(
"iter_count", onnx.TensorProto.INT64, []
)
x = np.array([1, 2, 3, 4, 5]).astype(np.float32)
y = np.array([-2]).astype(np.float32)
x_const_node = onnx.helper.make_node(
"Constant",
inputs=[],
outputs=["x"],
value=onnx.helper.make_tensor(
name="const_tensor_x",
data_type=onnx.TensorProto.FLOAT,
dims=x.shape,
vals=x.flatten().astype(float),
),
)
one_const_node = onnx.helper.make_node(
"Constant",
inputs=[],
outputs=["one"],
value=onnx.helper.make_tensor(
name="const_tensor_one",
data_type=onnx.TensorProto.INT64,
dims=(),
vals=[1],
),
)
i_add_node = onnx.helper.make_node(
"Add", inputs=["iter_count", "one"], outputs=["end"]
)
start_unsqueeze_node = onnx.helper.make_node(
"Unsqueeze", inputs=["iter_count"], outputs=["slice_start"], axes=[0]
)
end_unsqueeze_node = onnx.helper.make_node(
"Unsqueeze", inputs=["end"], outputs=["slice_end"], axes=[0]
)
slice_node = onnx.helper.make_node(
"Slice", inputs=["x", "slice_start", "slice_end"], outputs=["slice_out"]
)
y_add_node = onnx.helper.make_node(
"Add", inputs=["y_in", "slice_out"], outputs=["y_out"]
)
identity_node = onnx.helper.make_node(
"Identity", inputs=["cond_in"], outputs=["cond_out"]
)
scan_identity_node = onnx.helper.make_node(
"Identity", inputs=["y_out"], outputs=["scan_out"]
)
loop_body = onnx.helper.make_graph(
[
identity_node,
x_const_node,
one_const_node,
i_add_node,
start_unsqueeze_node,
end_unsqueeze_node,
slice_node,
y_add_node,
scan_identity_node,
],
"loop_body",
[iter_count, cond_in, y_in],
[cond_out, y_out, scan_out],
)
node = onnx.helper.make_node(
"Loop",
inputs=["trip_count", "cond", "y"],
outputs=["res_y", "res_scan"],
body=loop_body,
)
trip_count = np.array(5).astype(np.int64)
res_y = np.array([13]).astype(np.float32)
cond = np.array(1).astype(bool)
res_scan = np.array([-1, 1, 4, 8, 13]).astype(np.float32).reshape((5, 1))
expect(
node,
inputs=[trip_count, cond, y],
outputs=[res_y, res_scan],
name="test_loop11",
opset_imports=[onnx.helper.make_opsetid("", 11)],
)
@staticmethod
def export_loop_13() -> None:
# Given a tensor x of values [x1, ..., xN],
# Return a sequence of tensors of
# [[x1], [x1, x2], ..., [x1, ..., xN]]
seq_in = onnx.helper.make_tensor_sequence_value_info(
"seq_in", onnx.TensorProto.FLOAT, None
)
seq_out = onnx.helper.make_tensor_sequence_value_info(
"seq_out", onnx.TensorProto.FLOAT, None
)
cond_in = onnx.helper.make_tensor_value_info(
"cond_in", onnx.TensorProto.BOOL, []
)
cond_out = onnx.helper.make_tensor_value_info(
"cond_out", onnx.TensorProto.BOOL, []
)
iter_count = onnx.helper.make_tensor_value_info(
"iter_count", onnx.TensorProto.INT64, []
)
x = np.array([1, 2, 3, 4, 5]).astype(np.float32)
x_const_node = onnx.helper.make_node(
"Constant",
inputs=[],
outputs=["x"],
value=onnx.helper.make_tensor(
name="const_tensor_x",
data_type=onnx.TensorProto.FLOAT,
dims=x.shape,
vals=x.flatten().astype(float),
),
)
one_const_node = onnx.helper.make_node(
"Constant",
inputs=[],
outputs=["one"],
value=onnx.helper.make_tensor(
name="const_tensor_one",
data_type=onnx.TensorProto.INT64,
dims=(),
vals=[1],
),
)
zero_const_node = onnx.helper.make_node(
"Constant",
inputs=[],
outputs=["slice_start"],
value=onnx.helper.make_tensor(
name="const_tensor_zero",
data_type=onnx.TensorProto.INT64,
dims=(1,),
vals=[0],
),
)
axes_node = onnx.helper.make_node(
"Constant",
inputs=[],
outputs=["axes"],
value=onnx.helper.make_tensor(
name="const_tensor_axes",
data_type=onnx.TensorProto.INT64,
dims=(),
vals=[0],
),
)
add_node = onnx.helper.make_node(
"Add", inputs=["iter_count", "one"], outputs=["end"]
)
end_unsqueeze_node = onnx.helper.make_node(
"Unsqueeze", inputs=["end", "axes"], outputs=["slice_end"]
)
slice_node = onnx.helper.make_node(
"Slice", inputs=["x", "slice_start", "slice_end"], outputs=["slice_out"]
)
insert_node = onnx.helper.make_node(
"SequenceInsert", inputs=["seq_in", "slice_out"], outputs=["seq_out"]
)
identity_node = onnx.helper.make_node(
"Identity", inputs=["cond_in"], outputs=["cond_out"]
)
loop_body = onnx.helper.make_graph(
[
identity_node,
x_const_node,
one_const_node,
zero_const_node,
add_node,
axes_node,
end_unsqueeze_node,
slice_node,
insert_node,
],
"loop_body",
[iter_count, cond_in, seq_in],
[cond_out, seq_out],
)
node = onnx.helper.make_node(
"Loop",
inputs=["trip_count", "cond", "seq_empty"],
outputs=["seq_res"],
body=loop_body,
)
trip_count = np.array(5).astype(np.int64)
seq_empty: List[Any] = []
seq_res = [x[: int(i)] for i in x]
cond = np.array(1).astype(bool)
expect(
node,
inputs=[trip_count, cond, seq_empty],
outputs=[seq_res],
name="test_loop13_seq",
opset_imports=[onnx.helper.make_opsetid("", 13)],
input_type_protos=[
onnx.helper.make_tensor_type_proto(
onnx.TensorProto.INT64, trip_count.shape
),
onnx.helper.make_tensor_type_proto(onnx.TensorProto.BOOL, cond.shape),
onnx.helper.make_sequence_type_proto(
onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, [])
),
],
)
@staticmethod
def export_loop_16_none() -> None:
# Given a tensor sequence of values [x1, ..., xN], and an initial optional sequence of tensors [x0],
# Return a concatenated sequence of tensors of
# [x0, [x1], [x1, x2], ..., [x1, ..., xN]]
ten_in_tp = onnx.helper.make_tensor_type_proto(onnx.TensorProto.FLOAT, [])
seq_in_tp = onnx.helper.make_sequence_type_proto(ten_in_tp)
opt_in_tp = onnx.helper.make_optional_type_proto(seq_in_tp)
opt_in = onnx.helper.make_value_info("opt_seq_in", opt_in_tp)
seq_out = onnx.helper.make_tensor_sequence_value_info(
"seq_out", onnx.TensorProto.FLOAT, []
)
cond_in = onnx.helper.make_tensor_value_info(
"cond_in", onnx.TensorProto.BOOL, []
)
cond_out = onnx.helper.make_tensor_value_info(
"cond_out", onnx.TensorProto.BOOL, []
)
iter_count = onnx.helper.make_tensor_value_info(
"iter_count", onnx.TensorProto.INT64, []
)
x0 = np.array(0).astype(np.float32)
x = np.array([1, 2, 3, 4, 5]).astype(np.float32)
optional_has_elem_node = onnx.helper.make_node(
"OptionalHasElement", inputs=["opt_seq_in"], outputs=["optional_has_elem"]
)
optional_is_none = onnx.helper.make_node(
"Not", inputs=["optional_has_elem"], outputs=["optional_is_none"]
)
optional_get_elem = onnx.helper.make_node(
"OptionalGetElement", inputs=["opt_seq_in"], outputs=["seq_in"]
)
constant_in = onnx.helper.make_node(
"Constant",
inputs=[],
outputs=["constant_in"],
value=onnx.helper.make_tensor(
name="const_tensor", data_type=onnx.TensorProto.FLOAT, dims=(), vals=[0]
),
)
seq_const_in = onnx.helper.make_node(
"SequenceConstruct", inputs=["constant_in"], outputs=["init_seq_in"]
)
then_seq_out = onnx.helper.make_tensor_sequence_value_info(
"init_seq_in", onnx.TensorProto.FLOAT, []
)
then_body = onnx.helper.make_graph(
[constant_in, seq_const_in], "then_body", [], [then_seq_out]
)
else_seq_out = onnx.helper.make_tensor_sequence_value_info(
"seq_in", onnx.TensorProto.FLOAT, []
)
else_body = onnx.helper.make_graph(
[optional_get_elem], "else_body", [], [else_seq_out]
)
if_node = onnx.helper.make_node(
"If",
inputs=["optional_is_none"],
outputs=["sequence"],
then_branch=then_body,
else_branch=else_body,
)
x_const_node = onnx.helper.make_node(
"Constant",
inputs=[],
outputs=["x"],
value=onnx.helper.make_tensor(
name="const_tensor_x",
data_type=onnx.TensorProto.FLOAT,
dims=x.shape,
vals=x.flatten().astype(float),
),
)
one_const_node = onnx.helper.make_node(
"Constant",
inputs=[],
outputs=["one"],
value=onnx.helper.make_tensor(
name="const_tensor_one",
data_type=onnx.TensorProto.INT64,
dims=(),
vals=[1],
),
)
zero_const_node = onnx.helper.make_node(
"Constant",
inputs=[],
outputs=["slice_start"],
value=onnx.helper.make_tensor(
name="const_tensor_zero",
data_type=onnx.TensorProto.INT64,
dims=(1,),
vals=[0],
),
)
axes_node = onnx.helper.make_node(
"Constant",
inputs=[],
outputs=["axes"],
value=onnx.helper.make_tensor(
name="const_tensor_axes",
data_type=onnx.TensorProto.INT64,
dims=(),
vals=[0],
),
)
add_node = onnx.helper.make_node(
"Add", inputs=["iter_count", "one"], outputs=["end"]
)
end_unsqueeze_node = onnx.helper.make_node(
"Unsqueeze", inputs=["end", "axes"], outputs=["slice_end"]
)
slice_node = onnx.helper.make_node(
"Slice", inputs=["x", "slice_start", "slice_end"], outputs=["slice_out"]
)
insert_node = onnx.helper.make_node(
"SequenceInsert", inputs=["sequence", "slice_out"], outputs=["seq_out"]
)
identity_node = onnx.helper.make_node(
"Identity", inputs=["cond_in"], outputs=["cond_out"]
)
loop_body = onnx.helper.make_graph(
[
identity_node,
optional_has_elem_node,
optional_is_none,
if_node,
x_const_node,
one_const_node,
zero_const_node,
add_node,
axes_node,
end_unsqueeze_node,
slice_node,
insert_node,
],
"loop_body",
[iter_count, cond_in, opt_in],
[cond_out, seq_out],
)
node = onnx.helper.make_node(
"Loop",
inputs=["trip_count", "cond", "opt_seq"],
outputs=["seq_res"],
body=loop_body,
)
trip_count = np.array(5).astype(np.int64)
cond = np.array(1).astype(bool)
seq_res = compute_loop_outputs(x, [x0], trip_count)
opt_seq_in: List[Any] = [x0]
expect(
node,
inputs=[trip_count, cond, opt_seq_in],
outputs=[seq_res],
name="test_loop16_seq_none",
opset_imports=[onnx.helper.make_opsetid("", 16)],
input_type_protos=[
onnx.helper.make_tensor_type_proto(
onnx.TensorProto.INT64, trip_count.shape
),
onnx.helper.make_tensor_type_proto(onnx.TensorProto.BOOL, cond.shape),
opt_in_tp,
],
)
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"/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_string_concat.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_batch_normalization.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cum_sum.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_prelu.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_unsqueeze.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/globalaveragepool.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/__init__.py": ["/onnx/backend/test/runner/__init__.py"], "/onnx/test/parser_test.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_regex_full_match.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/xor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/shape.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/dequantizelinear.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py"], "/onnx/reference/ops/op_isnan.py": ["/onnx/reference/ops/_op.py"], "/onnx/backend/test/case/node/mul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stringnormalizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducemin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/tile.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_flatten.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_scatternd.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_optional.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/rnn.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/image_decoder.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_tree_ensemble_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_tree_ensemble_helper.py"], "/workflow_scripts/test_model_zoo.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/gelu.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/printer.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/nonzero.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/identity.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/sin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gemm.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_layer_normalization.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_bernoulli.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/backend/test/case/node/deformconv.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_upsample.py": ["/onnx/reference/op_run.py"], "/onnx/test/test_with_ort.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_thresholded_relu.py": ["/onnx/reference/ops/_op.py"], "/onnx/backend/test/case/node/concat.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/stat_coverage.py": ["/onnx/__init__.py", "/onnx/backend/test/case/__init__.py", "/onnx/backend/test/loader/__init__.py", "/onnx/backend/test/runner/__init__.py"], "/onnx/reference/ops/op_constant.py": ["/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/upsample.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_squeeze.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_einsum.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/div.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_one_hot_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_random_uniform.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/test/reference_evaluator_test.py": ["/onnx/__init__.py", "/onnx/checker.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/__init__.py", "/onnx/reference/ops/_op_common_indices.py", "/onnx/reference/ops/_op_list.py", "/onnx/reference/ops/op_celu.py", "/onnx/reference/ops/op_col2im.py", "/onnx/reference/ops/op_conv.py", "/onnx/reference/ops_optimized/__init__.py", "/onnx/reference/ops_optimized/op_conv_optimized.py"], "/onnx/backend/test/cmd_tools.py": ["/onnx/backend/test/case/model/__init__.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/__init__.py"], "/onnx/reference/ops/op_slice.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/aionnxml/op_binarizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/optionalgetelement.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/loop.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/conf.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_sequence_construct.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatterelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel2.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/bernoulli.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/constant.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/resize.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_resize.py"], "/onnx/reference/ops/aionnxml/op_svm_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/op_sequence_map.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatternd.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/numpy_helper_test.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_tfidf_vectorizer.py": ["/onnx/reference/op_run.py"], "/onnx/test/checker_test.py": ["/onnx/defs/__init__.py", "/onnx/__init__.py"], "/onnx/reference/ops/_op_common_random.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/backend/base.py": ["/onnx/checker.py", "/onnx/__init__.py"], "/onnx/backend/test/case/node/reduce_log_sum.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_linear_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_softplus.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_sub.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_quantize_linear.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_gathernd.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/qlinearmatmul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/shape_inference_test.py": ["/onnx/shape_inference.py", "/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/parser.py"], "/onnx/backend/test/case/node/mish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_expand.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": ["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/_helpers.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/tfidfvectorizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_average_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/item.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/gatherelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/slice.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_matmul.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mel_weight_matrix.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cast.py": ["/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/asin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_normalizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/unique.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather_elements.py": ["/onnx/reference/op_run.py"], "/onnx/helper.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/layernormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/groupnormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/__init__.py": ["/onnx/reference/ops/_op_list.py"], "/onnx/reference/ops/op_random_normal.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_hann_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_softmax_cross_entropy_loss.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_string_split.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/max.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/backend/test/case/utils.py"], "/onnx/backend/test/case/model/expand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/backend/test/case/node/erf.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
59,090 | onnx/onnx | refs/heads/main | /docs/docsgen/source/conf.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0622
# type: ignore
import os
import sys
import warnings
import onnx
sys.path.append(os.path.abspath(os.path.dirname(__file__)))
# -- Project information -----------------------------------------------------
author = "ONNX"
copyright = "2023"
project = "ONNX"
release = onnx.__version__
version = onnx.__version__
# define the latest opset to document,
# this is meant to avoid documenting opset not released yet
max_opset = onnx.helper.VERSION_TABLE[-1][2]
# define the latest opset to document for every opset
_opsets = [t for t in onnx.helper.VERSION_TABLE if t[2] == max_opset][-1]
max_opsets = {
'': max_opset,
'ai.onnx.ml': _opsets[3],
'ai.onnx.training': _opsets[4],
}
# -- General configuration ---------------------------------------------------
extensions = [
"myst_parser",
"onnx_sphinx",
"sphinx_copybutton",
"sphinx_exec_code",
"sphinx_tabs.tabs",
"sphinx.ext.autodoc",
"sphinx.ext.autosummary",
"sphinx.ext.coverage",
"sphinx.ext.doctest",
"sphinx.ext.githubpages",
"sphinx.ext.graphviz",
"sphinx.ext.ifconfig",
"sphinx.ext.intersphinx",
"sphinx.ext.mathjax",
"sphinx.ext.napoleon",
"sphinx.ext.viewcode",
]
myst_enable_extensions = [
"amsmath",
"attrs_inline",
"colon_fence",
"deflist",
"dollarmath",
"fieldlist",
"html_admonition",
"html_image",
"linkify",
"replacements",
"smartquotes",
"strikethrough",
"substitution",
"tasklist",
]
coverage_show_missing_items = True
exclude_patterns = []
graphviz_output_format = "svg"
html_css_files = ["css/custom.css"]
html_favicon = "onnx-favicon.png"
html_sidebars = {}
html_static_path = ["_static"]
html_theme = "furo"
language = "en"
mathdef_link_only = True
master_doc = "index"
onnx_doc_folder = os.path.join(os.path.abspath(os.path.dirname(__file__)), "operators")
pygments_style = "default"
source_suffix = [".rst", ".md"]
templates_path = ["_templates"]
html_context = {
"default_mode": "auto", # auto: the documentation theme will follow the system default that you have set (light or dark)
}
html_theme_options = {
"light_logo": "onnx-horizontal-color.png",
"dark_logo": "onnx-horizontal-white.png",
}
intersphinx_mapping = {
"numpy": ("https://numpy.org/doc/stable/", None),
"python": (f"https://docs.python.org/{sys.version_info.major}/", None),
"scipy": ("https://docs.scipy.org/doc/scipy/", None),
"torch": ("https://pytorch.org/docs/stable/", None),
}
sphinx_gallery_conf = {
"examples_dirs": ["examples"],
"gallery_dirs": ["auto_examples", "auto_tutorial"],
"capture_repr": ("_repr_html_", "__repr__"),
"ignore_repr_types": r"matplotlib.text|matplotlib.axes",
"binder": {
"org": "onnx",
"repo": ".",
"notebooks_dir": "auto_examples",
"binderhub_url": "https://mybinder.org",
"branch": "master",
"dependencies": "./requirements.txt",
},
}
warnings.filterwarnings("ignore", category=FutureWarning)
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59,091 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_sequence_construct.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
from onnx.reference.op_run import OpRun
class SequenceConstruct(OpRun):
def _run(self, *data): # type: ignore
return (list(data),)
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59,092 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/scatterelements.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
# The below ScatterElements' numpy implementation is from https://stackoverflow.com/a/46204790/11767360
def scatter_elements(data, indices, updates, axis=0, reduction="none"): # type: ignore
if axis < 0:
axis = data.ndim + axis
idx_xsection_shape = indices.shape[:axis] + indices.shape[axis + 1 :]
def make_slice(arr, axis, i): # type: ignore
slc = [slice(None)] * arr.ndim
slc[axis] = i
return slc
def unpack(packed): # type: ignore
unpacked = packed[0]
for i in range(1, len(packed)):
unpacked = unpacked, packed[i]
return unpacked
def make_indices_for_duplicate(idx): # type: ignore
final_idx = []
for i in range(len(idx[0])):
final_idx.append(tuple(idx_element[i] for idx_element in idx))
return list(final_idx)
# We use indices and axis parameters to create idx
# idx is in a form that can be used as a NumPy advanced indices for scattering of updates param. in data
idx = [
[
unpack(np.indices(idx_xsection_shape).reshape(indices.ndim - 1, -1)),
indices[tuple(make_slice(indices, axis, i))].reshape(1, -1)[0],
]
for i in range(indices.shape[axis])
]
idx = list(np.concatenate(idx, axis=1))
idx.insert(axis, idx.pop())
# updates_idx is a NumPy advanced indices for indexing of elements in the updates
updates_idx = list(idx)
updates_idx.pop(axis)
updates_idx.insert(
axis, np.repeat(np.arange(indices.shape[axis]), np.prod(idx_xsection_shape))
)
scattered = np.copy(data)
if reduction == "none":
scattered[tuple(idx)] = updates[tuple(updates_idx)]
else:
idx, updates_idx = make_indices_for_duplicate(idx), make_indices_for_duplicate(
updates_idx
)
for iter, idx_set in enumerate(idx):
if reduction == "add":
scattered[idx_set] += updates[updates_idx[iter]]
elif reduction == "mul":
scattered[idx_set] *= updates[updates_idx[iter]]
elif reduction == "max":
scattered[idx_set] = np.maximum(
scattered[idx_set], updates[updates_idx[iter]]
)
elif reduction == "min":
scattered[idx_set] = np.minimum(
scattered[idx_set], updates[updates_idx[iter]]
)
return scattered
class ScatterElements(Base):
@staticmethod
def export_scatter_elements_without_axis() -> None:
node = onnx.helper.make_node(
"ScatterElements",
inputs=["data", "indices", "updates"],
outputs=["y"],
)
data = np.zeros((3, 3), dtype=np.float32)
indices = np.array([[1, 0, 2], [0, 2, 1]], dtype=np.int64)
updates = np.array([[1.0, 1.1, 1.2], [2.0, 2.1, 2.2]], dtype=np.float32)
y = scatter_elements(data, indices, updates)
# print(y) produces
# [[2.0, 1.1, 0.0],
# [1.0, 0.0, 2.2],
# [0.0, 2.1, 1.2]]
expect(
node,
inputs=[data, indices, updates],
outputs=[y],
name="test_scatter_elements_without_axis",
)
@staticmethod
def export_scatter_elements_with_axis() -> None:
axis = 1
node = onnx.helper.make_node(
"ScatterElements",
inputs=["data", "indices", "updates"],
outputs=["y"],
axis=axis,
)
data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32)
indices = np.array([[1, 3]], dtype=np.int64)
updates = np.array([[1.1, 2.1]], dtype=np.float32)
y = scatter_elements(data, indices, updates, axis)
# print(y) produces
# [[1.0, 1.1, 3.0, 2.1, 5.0]]
expect(
node,
inputs=[data, indices, updates],
outputs=[y],
name="test_scatter_elements_with_axis",
)
@staticmethod
def export_scatter_elements_with_negative_indices() -> None:
axis = 1
node = onnx.helper.make_node(
"ScatterElements",
inputs=["data", "indices", "updates"],
outputs=["y"],
axis=axis,
)
data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32)
indices = np.array([[1, -3]], dtype=np.int64)
updates = np.array([[1.1, 2.1]], dtype=np.float32)
y = scatter_elements(data, indices, updates, axis)
# print(y) produces
# [[1.0, 1.1, 2.1, 4.0, 5.0]]
expect(
node,
inputs=[data, indices, updates],
outputs=[y],
name="test_scatter_elements_with_negative_indices",
)
@staticmethod
def export_scatter_elements_with_duplicate_indices() -> None:
axis = 1
node = onnx.helper.make_node(
"ScatterElements",
inputs=["data", "indices", "updates"],
outputs=["y"],
axis=axis,
reduction="add",
)
data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32)
indices = np.array([[1, 1]], dtype=np.int64)
updates = np.array([[1.1, 2.1]], dtype=np.float32)
y = scatter_elements(data, indices, updates, axis, reduction="add")
# print(y) produces
# [[1.0, 5.2, 3.0, 4.0, 5.0]]
expect(
node,
inputs=[data, indices, updates],
outputs=[y],
name="test_scatter_elements_with_duplicate_indices",
)
@staticmethod
def export_scatter_elements_with_reduction_max() -> None:
axis = 1
node = onnx.helper.make_node(
"ScatterElements",
inputs=["data", "indices", "updates"],
outputs=["y"],
axis=axis,
reduction="max",
)
data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32)
indices = np.array([[1, 1]], dtype=np.int64)
updates = np.array([[1.1, 2.1]], dtype=np.float32)
y = scatter_elements(data, indices, updates, axis, reduction="max")
# print(y) produces
# [[1.0, 2.1, 3.0, 4.0, 5.0]]
expect(
node,
inputs=[data, indices, updates],
outputs=[y],
name="test_scatter_elements_with_reduction_max",
)
@staticmethod
def export_scatter_elements_with_reduction_min() -> None:
axis = 1
node = onnx.helper.make_node(
"ScatterElements",
inputs=["data", "indices", "updates"],
outputs=["y"],
axis=axis,
reduction="min",
)
data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32)
indices = np.array([[1, 1]], dtype=np.int64)
updates = np.array([[1.1, 2.1]], dtype=np.float32)
y = scatter_elements(data, indices, updates, axis, reduction="min")
# print(y) produces
# [[1.0, 1.1, 3.0, 4.0, 5.0]]
expect(
node,
inputs=[data, indices, updates],
outputs=[y],
name="test_scatter_elements_with_reduction_min",
)
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"/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": 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59,093 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/reducel2.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class ReduceL2(Base):
@staticmethod
def export_do_not_keepdims() -> None:
shape = [3, 2, 2]
axes = np.array([2], dtype=np.int64)
keepdims = 0
node = onnx.helper.make_node(
"ReduceL2",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
)
data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
# print(data)
# [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
reduced = np.sqrt(
np.sum(a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1)
)
# print(reduced)
# [[2.23606798, 5.],
# [7.81024968, 10.63014581],
# [13.45362405, 16.2788206]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_l2_do_not_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.sqrt(
np.sum(a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1)
)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_l2_do_not_keepdims_random",
)
@staticmethod
def export_keepdims() -> None:
shape = [3, 2, 2]
axes = np.array([2], dtype=np.int64)
keepdims = 1
node = onnx.helper.make_node(
"ReduceL2",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
)
data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
# print(data)
# [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
reduced = np.sqrt(
np.sum(a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1)
)
# print(reduced)
# [[[2.23606798], [5.]]
# [[7.81024968], [10.63014581]]
# [[13.45362405], [16.2788206 ]]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_l2_keep_dims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.sqrt(
np.sum(a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1)
)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_l2_keep_dims_random",
)
@staticmethod
def export_default_axes_keepdims() -> None:
shape = [3, 2, 2]
axes = np.array([], dtype=np.int64)
keepdims = 1
node = onnx.helper.make_node(
"ReduceL2", inputs=["data", "axes"], outputs=["reduced"], keepdims=keepdims
)
data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
# print(data)
# [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
reduced = np.sqrt(np.sum(a=np.square(data), axis=None, keepdims=keepdims == 1))
# print(reduced)
# [[[25.49509757]]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_l2_default_axes_keepdims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.sqrt(np.sum(a=np.square(data), axis=None, keepdims=keepdims == 1))
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_l2_default_axes_keepdims_random",
)
@staticmethod
def export_negative_axes_keepdims() -> None:
shape = [3, 2, 2]
axes = np.array([-1], dtype=np.int64)
keepdims = 1
node = onnx.helper.make_node(
"ReduceL2",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=keepdims,
)
data = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
# print(data)
# [[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], [[9., 10.], [11., 12.]]]
reduced = np.sqrt(
np.sum(a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1)
)
# print(reduced)
# [[[2.23606798], [5.]]
# [[7.81024968], [10.63014581]]
# [[13.45362405], [16.2788206 ]]]
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_l2_negative_axes_keep_dims_example",
)
np.random.seed(0)
data = np.random.uniform(-10, 10, shape).astype(np.float32)
reduced = np.sqrt(
np.sum(a=np.square(data), axis=tuple(axes), keepdims=keepdims == 1)
)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_l2_negative_axes_keep_dims_random",
)
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59,094 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/bernoulli.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
def bernoulli_reference_implementation(x, dtype): # type: ignore
# binomial n = 1 equal bernoulli
# This example and test-case is for informational purpose. The generator operator is
# non-deterministic and may not produce the same values in different implementations
# even if a seed is specified.
return np.random.binomial(1, p=x).astype(dtype)
class Bernoulli(Base):
@staticmethod
def export_bernoulli_without_dtype() -> None:
node = onnx.helper.make_node(
"Bernoulli",
inputs=["x"],
outputs=["y"],
)
x = np.random.uniform(0.0, 1.0, 10).astype(float)
y = bernoulli_reference_implementation(x, float)
expect(node, inputs=[x], outputs=[y], name="test_bernoulli")
@staticmethod
def export_bernoulli_with_dtype() -> None:
node = onnx.helper.make_node(
"Bernoulli",
inputs=["x"],
outputs=["y"],
dtype=onnx.TensorProto.DOUBLE,
)
x = np.random.uniform(0.0, 1.0, 10).astype(np.float32)
y = bernoulli_reference_implementation(x, float)
expect(node, inputs=[x], outputs=[y], name="test_bernoulli_double")
@staticmethod
def export_bernoulli_with_seed() -> None:
seed = float(0)
node = onnx.helper.make_node(
"Bernoulli",
inputs=["x"],
outputs=["y"],
seed=seed,
)
x = np.random.uniform(0.0, 1.0, 10).astype(np.float32)
y = bernoulli_reference_implementation(x, np.float32)
expect(node, inputs=[x], outputs=[y], name="test_bernoulli_seed")
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59,095 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/constant.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class Constant(Base):
@staticmethod
def export() -> None:
values = np.random.randn(5, 5).astype(np.float32)
node = onnx.helper.make_node(
"Constant",
inputs=[],
outputs=["values"],
value=onnx.helper.make_tensor(
name="const_tensor",
data_type=onnx.TensorProto.FLOAT,
dims=values.shape,
vals=values.flatten().astype(float),
),
)
expect(node, inputs=[], outputs=[values], name="test_constant")
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"/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/dequantizelinear.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py"], "/onnx/reference/ops/op_isnan.py": ["/onnx/reference/ops/_op.py"], "/onnx/backend/test/case/node/mul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stringnormalizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducemin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/tile.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_flatten.py": ["/onnx/reference/ops/_op.py"], 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"/onnx/reference/ops/aionnxml/op_linear_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_softplus.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_sub.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_quantize_linear.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_gathernd.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/qlinearmatmul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/shape_inference_test.py": ["/onnx/shape_inference.py", "/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/parser.py"], "/onnx/backend/test/case/node/mish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_expand.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": 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59,096 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/resize.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
from onnx.reference.ops.op_resize import _cubic_coeffs as cubic_coeffs
from onnx.reference.ops.op_resize import (
_cubic_coeffs_antialias as cubic_coeffs_antialias,
)
from onnx.reference.ops.op_resize import _interpolate_nd as interpolate_nd
from onnx.reference.ops.op_resize import _linear_coeffs as linear_coeffs
from onnx.reference.ops.op_resize import (
_linear_coeffs_antialias as linear_coeffs_antialias,
)
from onnx.reference.ops.op_resize import _nearest_coeffs as nearest_coeffs
class Resize(Base):
@staticmethod
def export_resize_upsample_scales_nearest() -> None:
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "scales"],
outputs=["Y"],
mode="nearest",
)
data = np.array(
[
[
[
[1, 2],
[3, 4],
]
]
],
dtype=np.float32,
)
scales = np.array([1.0, 1.0, 2.0, 3.0], dtype=np.float32)
# [[[[1. 1. 1. 2. 2. 2.]
# [1. 1. 1. 2. 2. 2.]
# [3. 3. 3. 4. 4. 4.]
# [3. 3. 3. 4. 4. 4.]]]]
output = interpolate_nd(
data, lambda x, _: nearest_coeffs(x), scale_factors=scales
).astype(np.float32)
expect(
node,
inputs=[data, scales],
outputs=[output],
name="test_resize_upsample_scales_nearest",
)
@staticmethod
def export_resize_downsample_scales_nearest() -> None:
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "scales"],
outputs=["Y"],
mode="nearest",
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
]
]
],
dtype=np.float32,
)
scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32)
# [[[[1. 3.]]]]
output = interpolate_nd(
data, lambda x, _: nearest_coeffs(x), scale_factors=scales
).astype(np.float32)
expect(
node,
inputs=[data, scales],
outputs=[output],
name="test_resize_downsample_scales_nearest",
)
@staticmethod
def export_resize_upsample_sizes_nearest() -> None:
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "", "sizes"],
outputs=["Y"],
mode="nearest",
)
data = np.array(
[
[
[
[1, 2],
[3, 4],
]
]
],
dtype=np.float32,
)
sizes = np.array([1, 1, 7, 8], dtype=np.int64)
# [[[[1. 1. 1. 1. 2. 2. 2. 2.]
# [1. 1. 1. 1. 2. 2. 2. 2.]
# [1. 1. 1. 1. 2. 2. 2. 2.]
# [1. 1. 1. 1. 2. 2. 2. 2.]
# [3. 3. 3. 3. 4. 4. 4. 4.]
# [3. 3. 3. 3. 4. 4. 4. 4.]
# [3. 3. 3. 3. 4. 4. 4. 4.]]]]
output = interpolate_nd(
data, lambda x, _: nearest_coeffs(x), output_size=sizes
).astype(np.float32)
expect(
node,
inputs=[data, sizes],
outputs=[output],
name="test_resize_upsample_sizes_nearest",
)
@staticmethod
def export_resize_downsample_sizes_nearest() -> None:
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "", "sizes"],
outputs=["Y"],
mode="nearest",
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
]
]
],
dtype=np.float32,
)
sizes = np.array([1, 1, 1, 3], dtype=np.int64)
# [[[[1. 2. 4.]]]]
output = interpolate_nd(
data, lambda x, _: nearest_coeffs(x), output_size=sizes
).astype(np.float32)
expect(
node,
inputs=[data, sizes],
outputs=[output],
name="test_resize_downsample_sizes_nearest",
)
@staticmethod
def export_resize_upsample_scales_linear() -> None:
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "scales"],
outputs=["Y"],
mode="linear",
)
data = np.array(
[
[
[
[1, 2],
[3, 4],
]
]
],
dtype=np.float32,
)
scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
# [[[[1. 1.25 1.75 2. ]
# [1.5 1.75 2.25 2.5 ]
# [2.5 2.75 3.25 3.5 ]
# [3. 3.25 3.75 4. ]]]]
output = interpolate_nd(
data, lambda x, _: linear_coeffs(x), scale_factors=scales
).astype(np.float32)
expect(
node,
inputs=[data, scales],
outputs=[output],
name="test_resize_upsample_scales_linear",
)
@staticmethod
def export_resize_upsample_scales_linear_align_corners() -> None:
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "scales"],
outputs=["Y"],
mode="linear",
coordinate_transformation_mode="align_corners",
)
data = np.array(
[
[
[
[1, 2],
[3, 4],
]
]
],
dtype=np.float32,
)
scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
# [[[[1. 1.33333333 1.66666667 2. ]
# [1.66666667 2. 2.33333333 2.66666667]
# [2.33333333 2.66666667 3. 3.33333333]
# [3. 3.33333333 3.66666667 4. ]]]]
output = interpolate_nd(
data,
lambda x, _: linear_coeffs(x),
scale_factors=scales,
coordinate_transformation_mode="align_corners",
).astype(np.float32)
expect(
node,
inputs=[data, scales],
outputs=[output],
name="test_resize_upsample_scales_linear_align_corners",
)
@staticmethod
def export_resize_downsample_scales_linear() -> None:
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "scales"],
outputs=["Y"],
mode="linear",
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
]
]
],
dtype=np.float32,
)
scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32)
# [[[[2.6666665 4.3333331]]]]
output = interpolate_nd(
data, lambda x, _: linear_coeffs(x), scale_factors=scales
).astype(np.float32)
expect(
node,
inputs=[data, scales],
outputs=[output],
name="test_resize_downsample_scales_linear",
)
@staticmethod
def export_resize_downsample_scales_linear_align_corners() -> None:
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "scales"],
outputs=["Y"],
mode="linear",
coordinate_transformation_mode="align_corners",
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
]
]
],
dtype=np.float32,
)
scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32)
# [[[[1. 3.142857]]]]
output = interpolate_nd(
data,
lambda x, _: linear_coeffs(x),
scale_factors=scales,
coordinate_transformation_mode="align_corners",
).astype(np.float32)
expect(
node,
inputs=[data, scales],
outputs=[output],
name="test_resize_downsample_scales_linear_align_corners",
)
@staticmethod
def export_resize_upsample_scales_cubic() -> None:
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "scales"],
outputs=["Y"],
mode="cubic",
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
# [[[[ 0.47265625 0.76953125 1.24609375 1.875 2.28125
# 2.91015625 3.38671875 3.68359375]
# [ 1.66015625 1.95703125 2.43359375 3.0625 3.46875
# 4.09765625 4.57421875 4.87109375]
# [ 3.56640625 3.86328125 4.33984375 4.96875 5.375
# 6.00390625 6.48046875 6.77734375]
# [ 6.08203125 6.37890625 6.85546875 7.484375 7.890625
# 8.51953125 8.99609375 9.29296875]
# [ 7.70703125 8.00390625 8.48046875 9.109375 9.515625
# 10.14453125 10.62109375 10.91796875]
# [10.22265625 10.51953125 10.99609375 11.625 12.03125
# 12.66015625 13.13671875 13.43359375]
# [12.12890625 12.42578125 12.90234375 13.53125 13.9375
# 14.56640625 15.04296875 15.33984375]
# [13.31640625 13.61328125 14.08984375 14.71875 15.125
# 15.75390625 16.23046875 16.52734375]]]]
output = interpolate_nd(
data, lambda x, _: cubic_coeffs(x), scale_factors=scales
).astype(np.float32)
expect(
node,
inputs=[data, scales],
outputs=[output],
name="test_resize_upsample_scales_cubic",
)
@staticmethod
def export_resize_upsample_scales_cubic_align_corners() -> None:
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "scales"],
outputs=["Y"],
mode="cubic",
coordinate_transformation_mode="align_corners",
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
# [[[[ 1. 1.34110787 1.80029155 2.32944606 2.67055394
# 3.19970845 3.65889213 4. ]
# [ 2.36443149 2.70553936 3.16472303 3.69387755 4.03498542
# 4.56413994 5.02332362 5.36443149]
# [ 4.20116618 4.54227405 5.00145773 5.53061224 5.87172012
# 6.40087464 6.86005831 7.20116618]
# [ 6.31778426 6.65889213 7.1180758 7.64723032 7.98833819
# 8.51749271 8.97667638 9.31778426]
# [ 7.68221574 8.02332362 8.48250729 9.01166181 9.35276968
# 9.8819242 10.34110787 10.68221574]
# [ 9.79883382 10.13994169 10.59912536 11.12827988 11.46938776
# 11.99854227 12.45772595 12.79883382]
# [11.63556851 11.97667638 12.43586006 12.96501458 13.30612245
# 13.83527697 14.29446064 14.63556851]
# [13. 13.34110787 13.80029155 14.32944606 14.67055394
# 15.19970845 15.65889213 16. ]]]]
output = interpolate_nd(
data,
lambda x, _: cubic_coeffs(x),
scale_factors=scales,
coordinate_transformation_mode="align_corners",
).astype(np.float32)
expect(
node,
inputs=[data, scales],
outputs=[output],
name="test_resize_upsample_scales_cubic_align_corners",
)
@staticmethod
def export_resize_downsample_scales_cubic() -> None:
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "scales"],
outputs=["Y"],
mode="cubic",
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
scales = np.array([1.0, 1.0, 0.8, 0.8], dtype=np.float32)
# [[[[ 1.47119141 2.78125 4.08251953]
# [ 6.71142578 8.02148438 9.32275391]
# [11.91650391 13.2265625 14.52783203]]]]
output = interpolate_nd(
data, lambda x, _: cubic_coeffs(x), scale_factors=scales
).astype(np.float32)
expect(
node,
inputs=[data, scales],
outputs=[output],
name="test_resize_downsample_scales_cubic",
)
@staticmethod
def export_resize_downsample_scales_cubic_align_corners() -> None:
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "scales"],
outputs=["Y"],
mode="cubic",
coordinate_transformation_mode="align_corners",
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
scales = np.array([1.0, 1.0, 0.8, 0.8], dtype=np.float32)
# [[[[ 1. 2.39519159 3.79038317]
# [ 6.58076634 7.97595793 9.37114951]
# [12.16153268 13.55672427 14.95191585]]]]
output = interpolate_nd(
data,
lambda x, _: cubic_coeffs(x),
scale_factors=scales,
coordinate_transformation_mode="align_corners",
).astype(np.float32)
expect(
node,
inputs=[data, scales],
outputs=[output],
name="test_resize_downsample_scales_cubic_align_corners",
)
@staticmethod
def export_resize_upsample_sizes_cubic() -> None:
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "", "sizes"],
outputs=["Y"],
mode="cubic",
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
sizes = np.array([1, 1, 9, 10], dtype=np.int64)
# [[[[ 0.45507922 0.64057922 0.97157922 1.42257922 1.90732922
# 2.22332922 2.70807922 3.15907922 3.49007922 3.67557922]
# [ 1.39437963 1.57987963 1.91087963 2.36187963 2.84662963
# 3.16262963 3.64737963 4.09837963 4.42937963 4.61487963]
# [ 2.95130693 3.13680693 3.46780693 3.91880693 4.40355693
# 4.71955693 5.20430693 5.65530693 5.98630693 6.17180693]
# [ 5.20525069 5.39075069 5.72175069 6.17275069 6.65750069
# 6.97350069 7.45825069 7.90925069 8.24025069 8.42575069]
# [ 6.88975 7.07525 7.40625 7.85725 8.342
# 8.658 9.14275 9.59375 9.92475 10.11025 ]
# [ 8.57424931 8.75974931 9.09074931 9.54174931 10.02649931
# 10.34249931 10.82724931 11.27824931 11.60924931 11.79474931]
# [10.82819307 11.01369307 11.34469307 11.79569307 12.28044307
# 12.59644307 13.08119307 13.53219307 13.86319307 14.04869307]
# [12.38512037 12.57062037 12.90162037 13.35262037 13.83737037
# 14.15337037 14.63812037 15.08912037 15.42012037 15.60562037]
# [13.32442078 13.50992078 13.84092078 14.29192078 14.77667078
# 15.09267078 15.57742078 16.02842078 16.35942078 16.54492078]]]]
output = interpolate_nd(
data, lambda x, _: cubic_coeffs(x), output_size=sizes
).astype(np.float32)
expect(
node,
inputs=[data, sizes],
outputs=[output],
name="test_resize_upsample_sizes_cubic",
)
@staticmethod
def export_resize_downsample_sizes_cubic() -> None:
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "", "sizes"],
outputs=["Y"],
mode="cubic",
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
sizes = np.array([1, 1, 3, 3], dtype=np.int64)
# [[[[ 1.63078704 3.00462963 4.37847222]
# [ 7.12615741 8.5 9.87384259]
# [12.62152778 13.99537037 15.36921296]]]]
output = interpolate_nd(
data, lambda x, _: cubic_coeffs(x), output_size=sizes
).astype(np.float32)
expect(
node,
inputs=[data, sizes],
outputs=[output],
name="test_resize_downsample_sizes_cubic",
)
# TensorFlow v1 bicubic with half_pixel_centers=True
@staticmethod
def export_resize_upsample_scales_cubic_A_n0p5_exclude_outside() -> None:
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "scales"],
outputs=["Y"],
mode="cubic",
cubic_coeff_a=-0.5,
exclude_outside=True,
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
# [[[[ 0.55882353 0.81494204 1.35698249 1.89705882 2.39705882
# 2.93713516 3.47917561 3.73529412]
# [ 1.58329755 1.83941606 2.38145651 2.92153285 3.42153285
# 3.96160918 4.50364964 4.75976814]
# [ 3.75145936 4.00757787 4.54961832 5.08969466 5.58969466
# 6.12977099 6.67181144 6.92792995]
# [ 5.91176471 6.16788321 6.70992366 7.25 7.75
# 8.29007634 8.83211679 9.08823529]
# [ 7.91176471 8.16788321 8.70992366 9.25 9.75
# 10.29007634 10.83211679 11.08823529]
# [10.07207005 10.32818856 10.87022901 11.41030534 11.91030534
# 12.45038168 12.99242213 13.24854064]
# [12.24023186 12.49635036 13.03839082 13.57846715 14.07846715
# 14.61854349 15.16058394 15.41670245]
# [13.26470588 13.52082439 14.06286484 14.60294118 15.10294118
# 15.64301751 16.18505796 16.44117647]]]]
output = interpolate_nd(
data,
lambda x, _: cubic_coeffs(x, A=-0.5),
scale_factors=scales,
exclude_outside=True,
).astype(np.float32)
expect(
node,
inputs=[data, scales],
outputs=[output],
name="test_resize_upsample_scales_cubic_A_n0p5_exclude_outside",
)
@staticmethod
def export_resize_downsample_scales_cubic_A_n0p5_exclude_outside() -> None:
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "scales"],
outputs=["Y"],
mode="cubic",
cubic_coeff_a=-0.5,
exclude_outside=True,
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
scales = np.array([1.0, 1.0, 0.8, 0.8], dtype=np.float32)
# [[[[ 1.36812675 2.6695014 4.0133367 ]
# [ 6.57362535 7.875 9.2188353 ]
# [11.94896657 13.25034122 14.59417652]]]]
output = interpolate_nd(
data,
lambda x, _: cubic_coeffs(x, A=-0.5),
scale_factors=scales,
exclude_outside=True,
).astype(np.float32)
expect(
node,
inputs=[data, scales],
outputs=[output],
name="test_resize_downsample_scales_cubic_A_n0p5_exclude_outside",
)
# TensorFlow v1 bicubic with half_pixel_centers=False
@staticmethod
def export_resize_upsample_scales_cubic_asymmetric() -> None:
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "scales"],
outputs=["Y"],
mode="cubic",
coordinate_transformation_mode="asymmetric",
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
# [[[[ 1. 1.40625 2. 2.5 3. 3.59375 4.
# 4.09375]
# [ 2.625 3.03125 3.625 4.125 4.625 5.21875 5.625
# 5.71875]
# [ 5. 5.40625 6. 6.5 7. 7.59375 8.
# 8.09375]
# [ 7. 7.40625 8. 8.5 9. 9.59375 10.
# 10.09375]
# [ 9. 9.40625 10. 10.5 11. 11.59375 12.
# 12.09375]
# [11.375 11.78125 12.375 12.875 13.375 13.96875 14.375
# 14.46875]
# [13. 13.40625 14. 14.5 15. 15.59375 16.
# 16.09375]
# [13.375 13.78125 14.375 14.875 15.375 15.96875 16.375
# 16.46875]]]]
output = interpolate_nd(
data,
lambda x, _: cubic_coeffs(x, A=-0.75),
scale_factors=scales,
coordinate_transformation_mode="asymmetric",
).astype(np.float32)
expect(
node,
inputs=[data, scales],
outputs=[output],
name="test_resize_upsample_scales_cubic_asymmetric",
)
@staticmethod
def export_resize_tf_crop_and_resize() -> None:
node = onnx.helper.make_node(
"Resize",
inputs=["X", "roi", "", "sizes"],
outputs=["Y"],
mode="linear",
coordinate_transformation_mode="tf_crop_and_resize",
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
# Note: for some rois, the result may be different with that of TF for inaccurate floating point
roi = np.array([0, 0, 0.4, 0.6, 1, 1, 0.6, 0.8], dtype=np.float32)
sizes = np.array([1, 1, 3, 3], dtype=np.int64)
# [[[[ 7.6000004 7.9 8.2 ]
# [ 8.8 9.1 9.400001 ]
# [10. 10.3 10.6 ]]]]
output = interpolate_nd(
data,
lambda x, _: linear_coeffs(x),
output_size=sizes,
roi=roi,
coordinate_transformation_mode="tf_crop_and_resize",
).astype(np.float32)
expect(
node,
inputs=[data, roi, sizes],
outputs=[output],
name="test_resize_tf_crop_and_resize",
)
@staticmethod
def export_resize_tf_crop_and_resize_extrapolation_value() -> None:
node = onnx.helper.make_node(
"Resize",
inputs=["X", "roi", "", "sizes"],
outputs=["Y"],
mode="linear",
coordinate_transformation_mode="tf_crop_and_resize",
extrapolation_value=10.0,
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
# Note: for some rois, the result may be different with that of TF for inaccurate floating point
roi = np.array([0, 0, 0.4, 0.6, 1, 1, 1.2, 1.7], dtype=np.float32)
sizes = np.array([1, 1, 3, 3], dtype=np.int64)
# [[[[ 7.6000004 10. 10. ]
# [12.400001 10. 10. ]
# [10. 10. 10. ]]]]
output = interpolate_nd(
data,
lambda x, _: linear_coeffs(x),
output_size=sizes,
roi=roi,
coordinate_transformation_mode="tf_crop_and_resize",
extrapolation_value=10.0,
).astype(np.float32)
expect(
node,
inputs=[data, roi, sizes],
outputs=[output],
name="test_resize_tf_crop_and_resize",
)
@staticmethod
def export_resize_downsample_sizes_linear_pytorch_half_pixel() -> None:
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "", "sizes"],
outputs=["Y"],
mode="linear",
coordinate_transformation_mode="pytorch_half_pixel",
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
sizes = np.array([1, 1, 3, 1], dtype=np.int64)
# [[[[ 1.6666666]
# [ 7. ]
# [12.333333 ]]]]
output = interpolate_nd(
data,
lambda x, _: linear_coeffs(x),
output_size=sizes,
coordinate_transformation_mode="pytorch_half_pixel",
).astype(np.float32)
expect(
node,
inputs=[data, sizes],
outputs=[output],
name="test_resize_downsample_sizes_linear_pytorch_half_pixel",
)
@staticmethod
def export_resize_upsample_sizes_nearest_floor_align_corners() -> None:
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "", "sizes"],
outputs=["Y"],
mode="nearest",
coordinate_transformation_mode="align_corners",
nearest_mode="floor",
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
sizes = np.array([1, 1, 8, 8], dtype=np.int64)
# [[[[ 1. 1. 1. 2. 2. 3. 3. 4.]
# [ 1. 1. 1. 2. 2. 3. 3. 4.]
# [ 1. 1. 1. 2. 2. 3. 3. 4.]
# [ 5. 5. 5. 6. 6. 7. 7. 8.]
# [ 5. 5. 5. 6. 6. 7. 7. 8.]
# [ 9. 9. 9. 10. 10. 11. 11. 12.]
# [ 9. 9. 9. 10. 10. 11. 11. 12.]
# [13. 13. 13. 14. 14. 15. 15. 16.]]]]
output = interpolate_nd(
data,
lambda x, _: nearest_coeffs(x, mode="floor"),
output_size=sizes,
coordinate_transformation_mode="align_corners",
).astype(np.float32)
expect(
node,
inputs=[data, sizes],
outputs=[output],
name="test_resize_upsample_sizes_nearest_floor_align_corners",
)
@staticmethod
def export_resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric() -> None:
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "", "sizes"],
outputs=["Y"],
mode="nearest",
coordinate_transformation_mode="asymmetric",
nearest_mode="round_prefer_ceil",
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
sizes = np.array([1, 1, 8, 8], dtype=np.int64)
# [[[[ 1. 2. 2. 3. 3. 4. 4. 4.]
# [ 5. 6. 6. 7. 7. 8. 8. 8.]
# [ 5. 6. 6. 7. 7. 8. 8. 8.]
# [ 9. 10. 10. 11. 11. 12. 12. 12.]
# [ 9. 10. 10. 11. 11. 12. 12. 12.]
# [13. 14. 14. 15. 15. 16. 16. 16.]
# [13. 14. 14. 15. 15. 16. 16. 16.]
# [13. 14. 14. 15. 15. 16. 16. 16.]]]]
output = interpolate_nd(
data,
lambda x, _: nearest_coeffs(x, mode="round_prefer_ceil"),
output_size=sizes,
coordinate_transformation_mode="asymmetric",
).astype(np.float32)
expect(
node,
inputs=[data, sizes],
outputs=[output],
name="test_resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric",
)
@staticmethod
def export_resize_upsample_sizes_nearest_ceil_half_pixel() -> None:
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "", "sizes"],
outputs=["Y"],
mode="nearest",
coordinate_transformation_mode="half_pixel",
nearest_mode="ceil",
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
sizes = np.array([1, 1, 8, 8], dtype=np.int64)
# [[[[ 1. 2. 2. 3. 3. 4. 4. 4.]
# [ 5. 6. 6. 7. 7. 8. 8. 8.]
# [ 5. 6. 6. 7. 7. 8. 8. 8.]
# [ 9. 10. 10. 11. 11. 12. 12. 12.]
# [ 9. 10. 10. 11. 11. 12. 12. 12.]
# [13. 14. 14. 15. 15. 16. 16. 16.]
# [13. 14. 14. 15. 15. 16. 16. 16.]
# [13. 14. 14. 15. 15. 16. 16. 16.]]]]
output = interpolate_nd(
data, lambda x, _: nearest_coeffs(x, mode="ceil"), output_size=sizes
).astype(np.float32)
expect(
node,
inputs=[data, sizes],
outputs=[output],
name="test_resize_upsample_sizes_nearest_ceil_half_pixel",
)
@staticmethod
def export_resize_downsample_scales_linear_antialias() -> None:
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "scales"],
outputs=["Y"],
mode="linear",
antialias=1,
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32)
# [[[[ 2.875 4.5 ]
# [ 9.375 11. ]]]]
output = interpolate_nd(
data, linear_coeffs_antialias, scale_factors=scales
).astype(np.float32)
expect(
node,
inputs=[data, scales],
outputs=[output],
name="test_resize_downsample_scales_linear_antialias",
)
@staticmethod
def export_resize_downsample_sizes_linear_antialias() -> None:
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "", "sizes"],
outputs=["Y"],
mode="linear",
antialias=1,
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
sizes = np.array([1, 1, 3, 3], dtype=np.int64)
# [[[[ 2.3636363 3.590909 4.818182 ]
# [ 7.2727275 8.5 9.727273 ]
# [12.181818 13.409091 14.636364 ]]]]
output = interpolate_nd(
data, linear_coeffs_antialias, output_size=sizes
).astype(np.float32)
expect(
node,
inputs=[data, sizes],
outputs=[output],
name="test_resize_downsample_sizes_linear_antialias",
)
@staticmethod
def export_resize_downsample_scales_cubic_antialias() -> None:
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "scales"],
outputs=["Y"],
mode="cubic",
antialias=1,
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32)
# [[[[ 2.5180721 4.2858863]
# [ 9.589329 11.357142 ]]]]
output = interpolate_nd(
data, cubic_coeffs_antialias, scale_factors=scales
).astype(np.float32)
expect(
node,
inputs=[data, scales],
outputs=[output],
name="test_resize_downsample_scales_cubic_antialias",
)
@staticmethod
def export_resize_downsample_sizes_cubic_antialias() -> None:
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "", "sizes"],
outputs=["Y"],
mode="cubic",
antialias=1,
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
sizes = np.array([1, 1, 3, 3], dtype=np.int64)
# [[[[ 1.7750092 3.1200073 4.4650054]
# [ 7.1550016 8.5 9.844998 ]
# [12.534994 13.8799925 15.224991 ]]]]
output = interpolate_nd(data, cubic_coeffs_antialias, output_size=sizes).astype(
np.float32
)
expect(
node,
inputs=[data, sizes],
outputs=[output],
name="test_resize_downsample_sizes_cubic_antialias",
)
@staticmethod
def export_resize_upsample_scales_nearest_axes_2_3() -> None:
axes = [2, 3]
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "scales"],
outputs=["Y"],
mode="nearest",
axes=axes,
)
data = np.array(
[
[
[
[1, 2],
[3, 4],
]
]
],
dtype=np.float32,
)
scales = np.array([2.0, 3.0], dtype=np.float32)
# [[[[1. 1. 1. 2. 2. 2.]
# [1. 1. 1. 2. 2. 2.]
# [3. 3. 3. 4. 4. 4.]
# [3. 3. 3. 4. 4. 4.]]]]
output = interpolate_nd(
data, lambda x, _: nearest_coeffs(x), scale_factors=scales, axes=axes
).astype(np.float32)
expect(
node,
inputs=[data, scales],
outputs=[output],
name="test_resize_upsample_scales_nearest_axes_2_3",
)
@staticmethod
def export_resize_upsample_scales_nearest_axes_3_2() -> None:
axes = [3, 2]
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "scales"],
outputs=["Y"],
mode="nearest",
axes=axes,
)
data = np.array(
[
[
[
[1, 2],
[3, 4],
]
]
],
dtype=np.float32,
)
scales = np.array([3.0, 2.0], dtype=np.float32)
# [[[[1. 1. 1. 2. 2. 2.]
# [1. 1. 1. 2. 2. 2.]
# [3. 3. 3. 4. 4. 4.]
# [3. 3. 3. 4. 4. 4.]]]]
output = interpolate_nd(
data, lambda x, _: nearest_coeffs(x), scale_factors=scales, axes=axes
).astype(np.float32)
expect(
node,
inputs=[data, scales],
outputs=[output],
name="test_resize_upsample_scales_nearest_axes_3_2",
)
@staticmethod
def export_resize_upsample_sizes_nearest_axes_2_3() -> None:
axes = [2, 3]
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "", "sizes"],
outputs=["Y"],
mode="nearest",
axes=axes,
)
data = np.array(
[
[
[
[1, 2],
[3, 4],
]
]
],
dtype=np.float32,
)
sizes = np.array([7, 8], dtype=np.int64)
# [[[[1. 1. 1. 1. 2. 2. 2. 2.]
# [1. 1. 1. 1. 2. 2. 2. 2.]
# [1. 1. 1. 1. 2. 2. 2. 2.]
# [1. 1. 1. 1. 2. 2. 2. 2.]
# [3. 3. 3. 3. 4. 4. 4. 4.]
# [3. 3. 3. 3. 4. 4. 4. 4.]
# [3. 3. 3. 3. 4. 4. 4. 4.]]]]
output = interpolate_nd(
data, lambda x, _: nearest_coeffs(x), output_size=sizes, axes=axes
).astype(np.float32)
expect(
node,
inputs=[data, sizes],
outputs=[output],
name="test_resize_upsample_sizes_nearest_axes_2_3",
)
@staticmethod
def export_resize_upsample_sizes_nearest_axes_3_2() -> None:
axes = [3, 2]
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "", "sizes"],
outputs=["Y"],
mode="nearest",
axes=axes,
)
data = np.array(
[
[
[
[1, 2],
[3, 4],
]
]
],
dtype=np.float32,
)
sizes = np.array([8, 7], dtype=np.int64)
# [[[[1. 1. 1. 1. 2. 2. 2. 2.]
# [1. 1. 1. 1. 2. 2. 2. 2.]
# [1. 1. 1. 1. 2. 2. 2. 2.]
# [1. 1. 1. 1. 2. 2. 2. 2.]
# [3. 3. 3. 3. 4. 4. 4. 4.]
# [3. 3. 3. 3. 4. 4. 4. 4.]
# [3. 3. 3. 3. 4. 4. 4. 4.]]]]
output = interpolate_nd(
data, lambda x, _: nearest_coeffs(x), output_size=sizes, axes=axes
).astype(np.float32)
expect(
node,
inputs=[data, sizes],
outputs=[output],
name="test_resize_upsample_sizes_nearest_axes_3_2",
)
@staticmethod
def export_resize_tf_crop_and_resize_axes_2_3() -> None:
axes = [2, 3]
node = onnx.helper.make_node(
"Resize",
inputs=["X", "roi", "", "sizes"],
outputs=["Y"],
mode="linear",
coordinate_transformation_mode="tf_crop_and_resize",
axes=axes,
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
# Note: for some rois, the result may be different with that of TF for inaccurate floating point
roi = np.array([0.4, 0.6, 0.6, 0.8], dtype=np.float32)
sizes = np.array([3, 3], dtype=np.int64)
# [[[[ 7.6000004 7.9 8.2 ]
# [ 8.8 9.1 9.400001 ]
# [10. 10.3 10.6 ]]]]
output = interpolate_nd(
data,
lambda x, _: linear_coeffs(x),
output_size=sizes,
roi=roi,
axes=axes,
coordinate_transformation_mode="tf_crop_and_resize",
).astype(np.float32)
expect(
node,
inputs=[data, roi, sizes],
outputs=[output],
name="test_resize_tf_crop_and_resize_axes_2_3",
)
@staticmethod
def export_resize_tf_crop_and_resize_axes_3_2() -> None:
axes = [3, 2]
node = onnx.helper.make_node(
"Resize",
inputs=["X", "roi", "", "sizes"],
outputs=["Y"],
mode="linear",
coordinate_transformation_mode="tf_crop_and_resize",
axes=axes,
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
# Note: for some rois, the result may be different with that of TF for inaccurate floating point
roi = np.array([0.6, 0.4, 0.8, 0.6], dtype=np.float32)
sizes = np.array([3, 3], dtype=np.int64)
# [[[[ 7.6000004 7.9 8.2 ]
# [ 8.8 9.1 9.400001 ]
# [10. 10.3 10.6 ]]]]
output = interpolate_nd(
data,
lambda x, _: linear_coeffs(x),
output_size=sizes,
roi=roi,
axes=axes,
coordinate_transformation_mode="tf_crop_and_resize",
).astype(np.float32)
expect(
node,
inputs=[data, roi, sizes],
outputs=[output],
name="test_resize_tf_crop_and_resize_axes_3_2",
)
@staticmethod
def export_resize_upsample_sizes_nearest_not_larger() -> None:
keep_aspect_ratio_policy = "not_larger"
axes = [2, 3]
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "", "sizes"],
outputs=["Y"],
mode="nearest",
axes=axes,
keep_aspect_ratio_policy=keep_aspect_ratio_policy,
)
data = np.array(
[
[
[
[1, 2],
[3, 4],
]
]
],
dtype=np.float32,
)
sizes = np.array([7, 8], dtype=np.int64) # Results in 7x7
# [[[[1. 1. 1. 1. 2. 2. 2.]
# [1. 1. 1. 1. 2. 2. 2.]
# [1. 1. 1. 1. 2. 2. 2.]
# [1. 1. 1. 1. 2. 2. 2.]
# [3. 3. 3. 3. 4. 4. 4.]
# [3. 3. 3. 3. 4. 4. 4.]
# [3. 3. 3. 3. 4. 4. 4.]]]]
output = interpolate_nd(
data,
lambda x, _: nearest_coeffs(x),
output_size=sizes,
axes=axes,
keep_aspect_ratio_policy=keep_aspect_ratio_policy,
).astype(np.float32)
expect(
node,
inputs=[data, sizes],
outputs=[output],
name="test_resize_upsample_sizes_nearest_not_larger",
)
@staticmethod
def export_resize_upsample_sizes_nearest_not_smaller() -> None:
keep_aspect_ratio_policy = "not_smaller"
axes = [2, 3]
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "", "sizes"],
outputs=["Y"],
mode="nearest",
axes=axes,
keep_aspect_ratio_policy=keep_aspect_ratio_policy,
)
data = np.array(
[
[
[
[1, 2],
[3, 4],
]
]
],
dtype=np.float32,
)
sizes = np.array([7, 8], dtype=np.int64) # Results in 8x8
# [[[[1. 1. 1. 1. 2. 2. 2. 2.]
# [1. 1. 1. 1. 2. 2. 2. 2.]
# [1. 1. 1. 1. 2. 2. 2. 2.]
# [1. 1. 1. 1. 2. 2. 2. 2.]
# [3. 3. 3. 3. 4. 4. 4. 4.]
# [3. 3. 3. 3. 4. 4. 4. 4.]
# [3. 3. 3. 3. 4. 4. 4. 4.]]]]
output = interpolate_nd(
data,
lambda x, _: nearest_coeffs(x),
output_size=sizes,
axes=axes,
keep_aspect_ratio_policy=keep_aspect_ratio_policy,
).astype(np.float32)
expect(
node,
inputs=[data, sizes],
outputs=[output],
name="test_resize_upsample_sizes_nearest_not_larger",
)
@staticmethod
def export_resize_downsample_sizes_nearest_not_larger() -> None:
keep_aspect_ratio_policy = "not_larger"
axes = [2, 3]
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "", "sizes"],
outputs=["Y"],
mode="nearest",
axes=axes,
keep_aspect_ratio_policy=keep_aspect_ratio_policy,
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
]
]
],
dtype=np.float32,
)
sizes = np.array([1, 3], dtype=np.int64) # Results in 1x2
# [[[[1. 3.]]]]
output = interpolate_nd(
data,
lambda x, _: nearest_coeffs(x),
output_size=sizes,
axes=axes,
keep_aspect_ratio_policy=keep_aspect_ratio_policy,
).astype(np.float32)
expect(
node,
inputs=[data, sizes],
outputs=[output],
name="test_resize_downsample_sizes_nearest_not_larger",
)
@staticmethod
def export_resize_downsample_sizes_nearest_not_smaller() -> None:
keep_aspect_ratio_policy = "not_smaller"
axes = [2, 3]
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "", "sizes"],
outputs=["Y"],
mode="nearest",
axes=axes,
keep_aspect_ratio_policy=keep_aspect_ratio_policy,
)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
]
]
],
dtype=np.float32,
)
sizes = np.array([1, 3], dtype=np.int64) # Results in 2x3
# [[[[1. 2. 4.]
# [5. 6. 8.]]]]
output = interpolate_nd(
data,
lambda x, _: nearest_coeffs(x),
output_size=sizes,
axes=axes,
keep_aspect_ratio_policy=keep_aspect_ratio_policy,
).astype(np.float32)
expect(
node,
inputs=[data, sizes],
outputs=[output],
name="test_resize_downsample_sizes_nearest_not_smaller",
)
@staticmethod
def export_resize_downsample_scales_linear_half_pixel_symmetric() -> None:
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "scales"],
outputs=["Y"],
mode="linear",
coordinate_transformation_mode="half_pixel_symmetric",
)
data = np.array([[[[1, 2, 3, 4]]]], dtype=np.float32)
scales = np.array([1.0, 1.0, 1.0, 0.6], dtype=np.float32)
# [[[[1.6666667, 3.3333333]]]]
output = interpolate_nd(
data,
lambda x, _: linear_coeffs(x),
scale_factors=scales,
coordinate_transformation_mode="half_pixel_symmetric",
).astype(np.float32)
expect(
node,
inputs=[data, scales],
outputs=[output],
name="test_resize_downsample_scales_linear_half_pixel_symmetric",
)
@staticmethod
def export_resize_upsample_scales_linear_half_pixel_symmetric() -> None:
node = onnx.helper.make_node(
"Resize",
inputs=["X", "", "scales"],
outputs=["Y"],
mode="linear",
coordinate_transformation_mode="half_pixel_symmetric",
)
data = np.array([[[[1, 2], [3, 4]]]], dtype=np.float32)
scales = np.array([1.0, 1.0, 2.3, 2.94], dtype=np.float32)
# [[[[1. , 1.15986395, 1.5 , 1.84013605, 2. ],
# [1.56521738, 1.72508133, 2.06521738, 2.40535343, 2.56521738],
# [2.43478262, 2.59464657, 2.93478262, 3.27491867, 3.43478262],
# [3. , 3.15986395, 3.5 , 3.84013605, 4. ]]]]
output = interpolate_nd(
data,
lambda x, _: linear_coeffs(x),
scale_factors=scales,
coordinate_transformation_mode="half_pixel_symmetric",
).astype(np.float32)
expect(
node,
inputs=[data, scales],
outputs=[output],
name="test_resize_upsample_scales_linear_half_pixel_symmetric",
)
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59,097 | onnx/onnx | refs/heads/main | /onnx/reference/ops/aionnxml/op_svm_regressor.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0912,R0913,R0914,W0221
from onnx.reference.ops.aionnxml._op_run_aionnxml import OpRunAiOnnxMl
from onnx.reference.ops.aionnxml.op_svm_helper import SVMCommon
class SVMRegressor(OpRunAiOnnxMl):
"""
The class only implements `POST_TRANSFORM="NONE"`.
"""
def _run( # type: ignore
self,
X,
coefficients=None,
kernel_params=None,
kernel_type=None,
n_targets=None,
n_supports=None,
one_class=None,
post_transform=None,
rho=None,
support_vectors=None,
):
svm = SVMCommon(
coefficients=coefficients,
kernel_params=kernel_params,
kernel_type=kernel_type,
n_targets=n_targets,
n_supports=n_supports,
one_class=one_class,
post_transform=post_transform,
rho=rho,
support_vectors=support_vectors,
)
# adding an attribute for debugging purpose
self._svm = svm # pylint: disable=W0201
res = svm.run_reg(X)
if post_transform in (None, "NONE"):
return (res,)
raise NotImplementedError(f"post_transform={post_transform!r} not implemented.")
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"/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": 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"/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
59,098 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_sequence_map.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
from onnx.reference.op_run import OpRun
class SequenceMap(OpRun):
def _run(self, input_sequence, *additional_inputs, body=None, attributes=None): # type: ignore
if len(additional_inputs) == 1 and isinstance(additional_inputs[0], list):
res = None
for obj1, obj2 in zip(input_sequence, additional_inputs[0]):
feeds = {body.input_names[0]: obj1, body.input_names[1]: obj2}
r = body.run(None, feeds)
if res is None:
res = [[i] for i in r]
else:
for s, i in zip(res, r):
s.append(i)
return tuple(res) # type: ignore
feeds = dict(zip(body.input_names[1:], additional_inputs))
res = None
for obj in input_sequence:
feeds[body.input_names[0]] = obj
r = body.run(None, feeds, attributes=attributes)
if res is None:
res = [[i] for i in r]
else:
for s, i in zip(res, r):
s.append(i)
return tuple(res) # type: ignore
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["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/__init__.py": ["/onnx/reference/ops/_op_list.py"], "/onnx/reference/ops/op_random_normal.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_hann_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_softmax_cross_entropy_loss.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_string_split.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/max.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/backend/test/case/utils.py"], "/onnx/backend/test/case/model/expand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/backend/test/case/node/erf.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
59,099 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/scatternd.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
def scatter_nd_impl(data, indices, updates, reduction="none"): # type: ignore
# Check tensor shapes
assert indices.shape[-1] <= len(data.shape)
assert updates.shape == indices.shape[:-1] + data.shape[indices.shape[-1] :]
# Compute output
output = np.copy(data)
for i in np.ndindex(indices.shape[:-1]):
# NOTE: The order of iteration in this loop is not specified.
if reduction == "add":
output[tuple(indices[i])] += updates[i]
elif reduction == "mul":
output[tuple(indices[i])] *= updates[i]
elif reduction == "max":
output[tuple(indices[i])] = np.maximum(output[indices[i]], updates[i])
elif reduction == "min":
output[tuple(indices[i])] = np.minimum(output[indices[i]], updates[i])
else:
output[tuple(indices[i])] = updates[i]
return output
class ScatterND(Base):
@staticmethod
def export_scatternd() -> None:
node = onnx.helper.make_node(
"ScatterND",
inputs=["data", "indices", "updates"],
outputs=["y"],
)
data = np.array(
[
[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
[[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
[[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
],
dtype=np.float32,
)
indices = np.array([[0], [2]], dtype=np.int64)
updates = np.array(
[
[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
[[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
],
dtype=np.float32,
)
# Expecting output as np.array(
# [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
# [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
# [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
# [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32)
output = scatter_nd_impl(data, indices, updates)
expect(
node,
inputs=[data, indices, updates],
outputs=[output],
name="test_scatternd",
)
@staticmethod
def export_scatternd_add() -> None:
node = onnx.helper.make_node(
"ScatterND",
inputs=["data", "indices", "updates"],
outputs=["y"],
reduction="add",
)
data = np.array(
[
[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
[[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
[[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
],
dtype=np.float32,
)
indices = np.array([[0], [0]], dtype=np.int64)
updates = np.array(
[
[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
[[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
],
dtype=np.float32,
)
# Expecting output as np.array(
# [[[7, 8, 9, 10], [13, 14, 15, 16], [18, 17, 16, 15], [16, 15, 14, 13]],
# [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
# [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
# [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32)
output = scatter_nd_impl(data, indices, updates, reduction="add")
expect(
node,
inputs=[data, indices, updates],
outputs=[output],
name="test_scatternd_add",
)
@staticmethod
def export_scatternd_multiply() -> None:
node = onnx.helper.make_node(
"ScatterND",
inputs=["data", "indices", "updates"],
outputs=["y"],
reduction="mul",
)
data = np.array(
[
[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
[[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
[[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
],
dtype=np.float32,
)
indices = np.array([[0], [0]], dtype=np.int64)
updates = np.array(
[
[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
[[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
],
dtype=np.float32,
)
# Expecting output as np.array(
# [[[5, 10, 15, 20], [60, 72, 84, 96], [168, 147, 126, 105], [128, 96, 64, 32]],
# [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
# [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
# [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32)
output = scatter_nd_impl(data, indices, updates, reduction="mul")
expect(
node,
inputs=[data, indices, updates],
outputs=[output],
name="test_scatternd_multiply",
)
@staticmethod
def export_scatternd_max() -> None:
node = onnx.helper.make_node(
"ScatterND",
inputs=["data", "indices", "updates"],
outputs=["y"],
reduction="max",
)
data = np.array(
[
[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
[[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
[[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
],
dtype=np.float32,
)
indices = np.array([[0], [0]], dtype=np.int64)
updates = np.array(
[
[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
[[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
],
dtype=np.float32,
)
# Expecting output as np.array(
# [[[5, 5, 5, 5], [6, 6, 7, 8], [8, 7, 7, 7], [8, 8 ,8, 8]],
# [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
# [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
# [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32)
output = scatter_nd_impl(data, indices, updates, reduction="max")
expect(
node,
inputs=[data, indices, updates],
outputs=[output],
name="test_scatternd_max",
)
@staticmethod
def export_scatternd_min() -> None:
node = onnx.helper.make_node(
"ScatterND",
inputs=["data", "indices", "updates"],
outputs=["y"],
reduction="min",
)
data = np.array(
[
[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
[[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
[[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
],
dtype=np.float32,
)
indices = np.array([[0], [0]], dtype=np.int64)
updates = np.array(
[
[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
[[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
],
dtype=np.float32,
)
# Expecting output as np.array(
# [[[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 3, 2, 1]],
# [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
# [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
# [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32)
output = scatter_nd_impl(data, indices, updates, reduction="min")
expect(
node,
inputs=[data, indices, updates],
outputs=[output],
name="test_scatternd_min",
)
| {"/onnx/backend/test/case/node/sign.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/dft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/parser.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/constantofshape.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/averagepool.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/__init__.py": ["/onnx/__init__.py", "/onnx/backend/base.py", "/onnx/backend/test/case/test_case.py", "/onnx/backend/test/loader/__init__.py", "/onnx/backend/test/runner/item.py"], "/onnx/reference/ops/op_topk.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_image_decoder.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_non_max_suppression.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/logsoftmax.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_affine_grid.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_lp_normalization.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_rnn.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/not.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_reduce_sum.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mean.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_roi_align.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_center_crop_pad.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/nonmaxsuppression.py": ["/onnx/__init__.py", 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["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_where.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/test_case.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_erf.py": ["/onnx/reference/ops/_op.py"], "/onnx/test/function_inference_test.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/parser.py", "/onnx/shape_inference.py"], "/onnx/reference/ops/aionnxml/op_dict_vectorizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/inliner.py": ["/onnx/__init__.py"], "/onnx/test/reference_evaluator_ml_test.py": ["/onnx/__init__.py", "/onnx/checker.py", "/onnx/defs/__init__.py", "/onnx/helper.py"], "/onnx/reference/ops/op_softmax.py": ["/onnx/reference/ops/_op.py"], "/onnx/backend/test/case/node/reduce_log_sum_exp.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/utils.py": ["/onnx/__init__.py"], 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"/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": ["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/_helpers.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/tfidfvectorizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_average_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/item.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/gatherelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/slice.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_matmul.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mel_weight_matrix.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cast.py": ["/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/asin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_normalizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/unique.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather_elements.py": ["/onnx/reference/op_run.py"], "/onnx/helper.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/layernormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/groupnormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/__init__.py": ["/onnx/reference/ops/_op_list.py"], "/onnx/reference/ops/op_random_normal.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_hann_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_softmax_cross_entropy_loss.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_string_split.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/max.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/backend/test/case/utils.py"], "/onnx/backend/test/case/model/expand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/backend/test/case/node/erf.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
59,100 | onnx/onnx | refs/heads/main | /onnx/test/numpy_helper_test.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
import unittest
from typing import Any
import numpy as np
import parameterized
from onnx import helper, numpy_helper
def bfloat16_to_float32(ival: int) -> Any:
if ival == 0x7FC0:
return np.float32(np.nan)
expo = ival >> 7
prec = ival - (expo << 7)
sign = expo & 256
powe = expo & 255
fval = float(prec * 2 ** (-7) + 1) * 2.0 ** (powe - 127)
if sign:
fval = -fval
return np.float32(fval)
def float8e4m3_to_float32(ival: int) -> Any:
if ival < 0 or ival > 255:
raise ValueError(f"{ival} is not a float8.")
if ival == 255:
return np.float32(-np.nan)
if ival == 127:
return np.float32(np.nan)
if (ival & 0x7F) == 0:
return np.float32(0)
sign = ival & 0x80
ival &= 0x7F
expo = ival >> 3
mant = ival & 0x07
powe = expo & 0x0F
if expo == 0:
powe -= 6
fraction = 0
else:
powe -= 7
fraction = 1
fval = float(mant / 8 + fraction) * 2.0**powe
if sign:
fval = -fval
return np.float32(fval)
def float8e5m2_to_float32(ival: int) -> Any:
if ival < 0 or ival > 255:
raise ValueError(f"{ival} is not a float8.")
if ival in (255, 254, 253):
return np.float32(-np.nan)
if ival in (127, 126, 125):
return np.float32(np.nan)
if ival == 252:
return -np.float32(np.inf)
if ival == 124:
return np.float32(np.inf)
if (ival & 0x7F) == 0:
return np.float32(0)
sign = ival & 0x80
ival &= 0x7F
expo = ival >> 2
mant = ival & 0x03
powe = expo & 0x1F
if expo == 0:
powe -= 14
fraction = 0
else:
powe -= 15
fraction = 1
fval = float(mant / 4 + fraction) * 2.0**powe
if sign:
fval = -fval
return np.float32(fval)
class TestNumpyHelper(unittest.TestCase):
def _test_numpy_helper_float_type(self, dtype: np.number) -> None:
a = np.random.rand(13, 37).astype(dtype)
tensor_def = numpy_helper.from_array(a, "test")
self.assertEqual(tensor_def.name, "test")
a_recover = numpy_helper.to_array(tensor_def)
np.testing.assert_equal(a, a_recover)
def _test_numpy_helper_int_type(self, dtype: np.number) -> None:
a = np.random.randint(
np.iinfo(dtype).min, np.iinfo(dtype).max, dtype=dtype, size=(13, 37)
)
tensor_def = numpy_helper.from_array(a, "test")
self.assertEqual(tensor_def.name, "test")
a_recover = numpy_helper.to_array(tensor_def)
np.testing.assert_equal(a, a_recover)
def test_float(self) -> None:
self._test_numpy_helper_float_type(np.float32)
def test_uint8(self) -> None:
self._test_numpy_helper_int_type(np.uint8)
def test_int8(self) -> None:
self._test_numpy_helper_int_type(np.int8)
def test_uint16(self) -> None:
self._test_numpy_helper_int_type(np.uint16)
def test_int16(self) -> None:
self._test_numpy_helper_int_type(np.int16)
def test_int32(self) -> None:
self._test_numpy_helper_int_type(np.int32)
def test_int64(self) -> None:
self._test_numpy_helper_int_type(np.int64)
def test_string(self) -> None:
a = np.array(["Amy", "Billy", "Cindy", "David"]).astype(object)
tensor_def = numpy_helper.from_array(a, "test")
self.assertEqual(tensor_def.name, "test")
a_recover = numpy_helper.to_array(tensor_def)
np.testing.assert_equal(a, a_recover)
def test_bool(self) -> None:
a = np.random.randint(2, size=(13, 37)).astype(bool)
tensor_def = numpy_helper.from_array(a, "test")
self.assertEqual(tensor_def.name, "test")
a_recover = numpy_helper.to_array(tensor_def)
np.testing.assert_equal(a, a_recover)
def test_float16(self) -> None:
self._test_numpy_helper_float_type(np.float32)
def test_complex64(self) -> None:
self._test_numpy_helper_float_type(np.complex64)
def test_complex128(self) -> None:
self._test_numpy_helper_float_type(np.complex128)
@parameterized.parameterized.expand(
[
(1,),
(0.100097656,),
(130048,),
(1.2993813e-5,),
(np.nan,),
(np.inf,),
]
)
def test_bfloat16_to_float32(self, f):
f32 = np.float32(f)
bf16 = helper.float32_to_bfloat16(f32)
assert isinstance(bf16, int)
f32_1 = numpy_helper.bfloat16_to_float32(np.array([bf16]))[0]
f32_2 = bfloat16_to_float32(bf16)
if np.isnan(f32):
assert np.isnan(f32_1)
assert np.isnan(f32_2)
else:
self.assertEqual(f32, f32_1)
self.assertEqual(f32, f32_2)
def test_float8e4m3_to_float32(self):
self.assertEqual(numpy_helper.float8e4m3_to_float32(int("1111110", 2)), 448)
self.assertEqual(numpy_helper.float8e4m3_to_float32(int("1000", 2)), 2 ** (-6))
self.assertEqual(numpy_helper.float8e4m3_to_float32(int("1", 2)), 2 ** (-9))
self.assertEqual(
numpy_helper.float8e4m3_to_float32(int("111", 2)), 0.875 * 2 ** (-6)
)
for f in [
0,
1,
-1,
0.5,
-0.5,
0.1015625,
-0.1015625,
2,
3,
-2,
-3,
448,
2 ** (-6),
2 ** (-9),
0.875 * 2 ** (-6),
np.nan,
]:
with self.subTest(f=f):
f32 = np.float32(f)
f8 = helper.float32_to_float8e4m3(f32)
assert isinstance(f8, int)
f32_1 = numpy_helper.float8e4m3_to_float32(np.array([f8]))[0]
f32_2 = float8e4m3_to_float32(f8)
if np.isnan(f32):
assert np.isnan(f32_1)
assert np.isnan(f32_2)
else:
self.assertEqual(f32, f32_1)
self.assertEqual(f32, f32_2)
@parameterized.parameterized.expand(
[
(0.00439453125, 0.00390625),
(0.005859375, 0.005859375),
(0.005759375, 0.005859375),
(0.0046875, 0.00390625),
(0.001953125, 0.001953125),
(0.0029296875, 0.00390625),
(0.002053125, 0.001953125),
(0.00234375, 0.001953125),
(0.0087890625, 0.0078125),
(0.001171875, 0.001953125),
(1.8131605, 1.875),
]
)
def test_float8e4m3_to_float32_round(self, val, expected):
f8 = helper.float32_to_float8e4m3(val)
f32 = numpy_helper.float8e4m3_to_float32(f8)
self.assertEqual(f32, expected)
def test_float8e5m2_to_float32(self):
self.assertEqual(numpy_helper.float8e5m2_to_float32(int("1111011", 2)), 57344)
self.assertEqual(numpy_helper.float8e5m2_to_float32(int("100", 2)), 2 ** (-14))
self.assertEqual(
numpy_helper.float8e5m2_to_float32(int("11", 2)), 0.75 * 2 ** (-14)
)
self.assertEqual(numpy_helper.float8e5m2_to_float32(int("1", 2)), 2 ** (-16))
self.assertTrue(np.isnan(numpy_helper.float8e5m2_to_float32(int("1111101", 2))))
self.assertTrue(np.isnan(numpy_helper.float8e5m2_to_float32(int("1111110", 2))))
self.assertTrue(np.isnan(numpy_helper.float8e5m2_to_float32(int("1111111", 2))))
self.assertTrue(
np.isnan(numpy_helper.float8e5m2_to_float32(int("11111101", 2)))
)
self.assertTrue(
np.isnan(numpy_helper.float8e5m2_to_float32(int("11111110", 2)))
)
self.assertTrue(
np.isnan(numpy_helper.float8e5m2_to_float32(int("11111111", 2)))
)
self.assertEqual(numpy_helper.float8e5m2_to_float32(int("1111100", 2)), np.inf)
self.assertEqual(
numpy_helper.float8e5m2_to_float32(int("11111100", 2)), -np.inf
)
for f in [
0,
0.0017089844,
20480,
14,
-3584,
np.nan,
]:
with self.subTest(f=f):
f32 = np.float32(f)
f8 = helper.float32_to_float8e5m2(f32)
assert isinstance(f8, int)
f32_1 = numpy_helper.float8e5m2_to_float32(np.array([f8]))[0]
f32_2 = float8e5m2_to_float32(f8)
if np.isnan(f32):
assert np.isnan(f32_1)
assert np.isnan(f32_2)
else:
self.assertEqual(f32, f32_1)
self.assertEqual(f32, f32_2)
def test_float8_e4m3fn_inf(self):
x = np.float32(np.inf)
to = helper.float32_to_float8e4m3(x)
back = numpy_helper.float8e4m3_to_float32(to)
self.assertEqual(back, 448)
x = np.float32(np.inf)
to = helper.float32_to_float8e4m3(x, saturate=False)
back = numpy_helper.float8e4m3_to_float32(to)
self.assertTrue(np.isnan(back))
x = np.float32(-np.inf)
to = helper.float32_to_float8e4m3(x)
self.assertEqual(to & 0x80, 0x80)
back = numpy_helper.float8e4m3_to_float32(to)
self.assertEqual(back, -448)
x = np.float32(-np.inf)
to = helper.float32_to_float8e4m3(x, saturate=False)
self.assertEqual(to & 0x80, 0x80)
back = numpy_helper.float8e4m3_to_float32(to)
self.assertTrue(np.isnan(back))
def test_float8_e4m3fnuz_inf(self):
x = np.float32(np.inf)
to = helper.float32_to_float8e4m3(x, uz=True)
back = numpy_helper.float8e4m3_to_float32(to, uz=True)
self.assertEqual(back, 240)
x = np.float32(np.inf)
to = helper.float32_to_float8e4m3(x, uz=True, saturate=False)
back = numpy_helper.float8e4m3_to_float32(to, uz=True)
self.assertTrue(np.isnan(back))
x = np.float32(-np.inf)
to = helper.float32_to_float8e4m3(x, uz=True)
back = numpy_helper.float8e4m3_to_float32(to, uz=True)
self.assertEqual(back, -240)
x = np.float32(-np.inf)
to = helper.float32_to_float8e4m3(x, uz=True, saturate=False)
back = numpy_helper.float8e4m3_to_float32(to, uz=True)
self.assertTrue(np.isnan(back))
def test_float8_e5m2_inf(self):
x = np.float32(np.inf)
to = helper.float32_to_float8e5m2(x)
back = numpy_helper.float8e5m2_to_float32(to)
self.assertEqual(back, 57344)
x = np.float32(np.inf)
to = helper.float32_to_float8e5m2(x, saturate=False)
back = numpy_helper.float8e5m2_to_float32(to)
self.assertTrue(np.isinf(back))
x = np.float32(-np.inf)
to = helper.float32_to_float8e5m2(x)
self.assertEqual(to & 0x80, 0x80)
back = numpy_helper.float8e5m2_to_float32(to)
self.assertEqual(back, -57344)
x = np.float32(-np.inf)
to = helper.float32_to_float8e5m2(x, saturate=False)
self.assertEqual(to & 0x80, 0x80)
back = numpy_helper.float8e5m2_to_float32(to)
self.assertTrue(np.isinf(back))
self.assertLess(back, 0)
def test_float8_e5m2fnuz_inf(self):
x = np.float32(np.inf)
to = helper.float32_to_float8e5m2(x, fn=True, uz=True)
back = numpy_helper.float8e5m2_to_float32(to, fn=True, uz=True)
self.assertEqual(back, 57344)
x = np.float32(np.inf)
to = helper.float32_to_float8e5m2(x, fn=True, uz=True, saturate=False)
back = numpy_helper.float8e5m2_to_float32(to, fn=True, uz=True)
self.assertTrue(np.isnan(back))
x = np.float32(-np.inf)
to = helper.float32_to_float8e5m2(x, fn=True, uz=True)
back = numpy_helper.float8e5m2_to_float32(to, fn=True, uz=True)
self.assertEqual(back, -57344)
x = np.float32(-np.inf)
to = helper.float32_to_float8e5m2(x, fn=True, uz=True, saturate=False)
back = numpy_helper.float8e5m2_to_float32(to, fn=True, uz=True)
self.assertTrue(np.isnan(back))
def test_float8_e4m3fn_out_of_range(self):
x = np.float32(1000000)
to = helper.float32_to_float8e4m3(x)
back = numpy_helper.float8e4m3_to_float32(to)
self.assertEqual(back, 448)
x = np.float32(1000000)
to = helper.float32_to_float8e4m3(x, saturate=False)
back = numpy_helper.float8e4m3_to_float32(to)
self.assertTrue(np.isnan(back))
x = np.float32(-1000000)
to = helper.float32_to_float8e4m3(x)
back = numpy_helper.float8e4m3_to_float32(to)
self.assertEqual(back, -448)
x = np.float32(-1000000)
to = helper.float32_to_float8e4m3(x, saturate=False)
back = numpy_helper.float8e4m3_to_float32(to)
self.assertTrue(np.isnan(back))
def test_float8_e4m3fnuz_out_of_range(self):
x = np.float32(1000000)
to = helper.float32_to_float8e4m3(x, uz=True)
back = numpy_helper.float8e4m3_to_float32(to, uz=True)
self.assertEqual(back, 240)
x = np.float32(1000000)
to = helper.float32_to_float8e4m3(x, uz=True, saturate=False)
back = numpy_helper.float8e4m3_to_float32(to, uz=True)
self.assertTrue(np.isnan(back))
x = np.float32(-1000000)
to = helper.float32_to_float8e4m3(x, uz=True)
back = numpy_helper.float8e4m3_to_float32(to, uz=True)
self.assertEqual(back, -240)
x = np.float32(-1000000)
to = helper.float32_to_float8e4m3(x, uz=True, saturate=False)
back = numpy_helper.float8e4m3_to_float32(to, uz=True)
self.assertTrue(np.isnan(back))
def test_float8_e5m2_out_of_range(self):
x = np.float32(1000000)
to = helper.float32_to_float8e5m2(x)
back = numpy_helper.float8e5m2_to_float32(to)
self.assertEqual(back, 57344)
x = np.float32(1000000)
to = helper.float32_to_float8e5m2(x, saturate=False)
back = numpy_helper.float8e5m2_to_float32(to)
self.assertTrue(np.isinf(back))
x = np.float32(-1000000)
to = helper.float32_to_float8e5m2(x)
back = numpy_helper.float8e5m2_to_float32(to)
self.assertEqual(back, -57344)
x = np.float32(-1000000)
to = helper.float32_to_float8e5m2(x, saturate=False)
back = numpy_helper.float8e5m2_to_float32(to)
self.assertTrue(np.isinf(back))
def test_float8_e5m2fnuz_out_of_range(self):
x = np.float32(1000000)
to = helper.float32_to_float8e5m2(x, fn=True, uz=True)
back = numpy_helper.float8e5m2_to_float32(to, fn=True, uz=True)
self.assertEqual(back, 57344)
x = np.float32(1000000)
to = helper.float32_to_float8e5m2(x, fn=True, uz=True, saturate=False)
back = numpy_helper.float8e5m2_to_float32(to, fn=True, uz=True)
self.assertTrue(np.isnan(back))
x = np.float32(-1000000)
to = helper.float32_to_float8e5m2(x, fn=True, uz=True)
back = numpy_helper.float8e5m2_to_float32(to, fn=True, uz=True)
self.assertEqual(back, -57344)
x = np.float32(-1000000)
to = helper.float32_to_float8e5m2(x, fn=True, uz=True, saturate=False)
back = numpy_helper.float8e5m2_to_float32(to, fn=True, uz=True)
self.assertTrue(np.isnan(back))
def test_float8_e4m3fn_negative_zero(self):
x = numpy_helper.float8e5m2_to_float32(0x80) # -0
to = helper.float32_to_float8e4m3(x)
self.assertEqual(to, 0x80)
back = numpy_helper.float8e4m3_to_float32(to)
self.assertEqual(back, 0)
x = numpy_helper.float8e5m2_to_float32(0x80) # -0
to = helper.float32_to_float8e4m3(x, saturate=False)
self.assertEqual(to, 0x80)
back = numpy_helper.float8e4m3_to_float32(to)
self.assertEqual(back, 0)
def test_float8_e4m3fnuz_negative_zero(self):
x = numpy_helper.float8e5m2_to_float32(0x80) # -0
to = helper.float32_to_float8e4m3(x, uz=True)
self.assertEqual(to, 0)
back = numpy_helper.float8e4m3_to_float32(to, uz=True)
self.assertEqual(back, 0)
x = numpy_helper.float8e5m2_to_float32(0x80) # -0
to = helper.float32_to_float8e4m3(x, uz=True, saturate=False)
back = numpy_helper.float8e4m3_to_float32(to, uz=True)
self.assertEqual(back, 0)
self.assertEqual(to, 0)
def test_float8_e5m2_negative_zero(self):
x = numpy_helper.float8e5m2_to_float32(0x80) # -0
to = helper.float32_to_float8e5m2(x)
self.assertEqual(to, 0x80)
back = numpy_helper.float8e4m3_to_float32(to)
self.assertEqual(back, 0)
x = numpy_helper.float8e5m2_to_float32(0x80) # -0
to = helper.float32_to_float8e5m2(x, saturate=False)
self.assertEqual(to, 0x80)
back = numpy_helper.float8e4m3_to_float32(to)
self.assertEqual(back, 0)
def test_float8_e5m2fnuz_negative_zero(self):
x = numpy_helper.float8e5m2_to_float32(0x80) # -0
to = helper.float32_to_float8e5m2(x, fn=True, uz=True)
self.assertEqual(to, 0)
back = numpy_helper.float8e4m3_to_float32(to, fn=True, uz=True)
self.assertEqual(back, 0)
x = numpy_helper.float8e5m2_to_float32(0x80) # -0
to = helper.float32_to_float8e5m2(x, fn=True, uz=True, saturate=False)
self.assertEqual(to, 0)
back = numpy_helper.float8e4m3_to_float32(to, fn=True, uz=True)
self.assertEqual(back, 0)
def test_float8_e4m3fn_negative_nan(self):
x = numpy_helper.float8e5m2_to_float32(255) # -nan
to = helper.float32_to_float8e4m3(x)
self.assertEqual(to, 255)
back = numpy_helper.float8e4m3_to_float32(to)
self.assertTrue(np.isnan(back))
x = numpy_helper.float8e5m2_to_float32(255) # -nan
to = helper.float32_to_float8e4m3(x, saturate=False)
self.assertEqual(to, 255)
back = numpy_helper.float8e4m3_to_float32(to)
self.assertTrue(np.isnan(back))
def test_float8_e4m3fnuz_negative_nan(self):
x = numpy_helper.float8e5m2_to_float32(255) # -nan
to = helper.float32_to_float8e4m3(x, uz=True)
self.assertEqual(to, 0x80)
back = numpy_helper.float8e4m3_to_float32(to, uz=True)
self.assertTrue(np.isnan(back))
x = numpy_helper.float8e5m2_to_float32(255) # -nan
to = helper.float32_to_float8e4m3(x, uz=True, saturate=False)
self.assertEqual(to, 0x80)
back = numpy_helper.float8e4m3_to_float32(to, uz=True)
self.assertTrue(np.isnan(back))
def test_float8_e5m2_negative_nan(self):
x = numpy_helper.float8e5m2_to_float32(255) # -nan
to = helper.float32_to_float8e5m2(x)
self.assertEqual(to, 255)
back = numpy_helper.float8e4m3_to_float32(to)
self.assertTrue(np.isnan(back))
x = numpy_helper.float8e5m2_to_float32(255) # -nan
to = helper.float32_to_float8e5m2(x, saturate=False)
self.assertEqual(to, 255)
back = numpy_helper.float8e4m3_to_float32(to)
self.assertTrue(np.isnan(back))
def test_float8_e5m2fnuz_negative_nan(self):
x = numpy_helper.float8e5m2_to_float32(255) # -nan
to = helper.float32_to_float8e5m2(x, fn=True, uz=True)
self.assertEqual(to, 0x80)
back = numpy_helper.float8e4m3_to_float32(to, fn=True, uz=True)
self.assertTrue(np.isnan(back))
x = numpy_helper.float8e5m2_to_float32(255) # -nan
to = helper.float32_to_float8e5m2(x, fn=True, uz=True, saturate=False)
self.assertEqual(to, 0x80)
back = numpy_helper.float8e4m3_to_float32(to, fn=True, uz=True)
self.assertTrue(np.isnan(back))
if __name__ == "__main__":
unittest.main()
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"/onnx/reference/ops/op_random_normal.py", "/onnx/reference/ops/op_random_normal_like.py", "/onnx/reference/ops/op_random_uniform.py", "/onnx/reference/ops/op_reduce_l1.py", "/onnx/reference/ops/op_reduce_log_sum.py", "/onnx/reference/ops/op_reduce_log_sum_exp.py", "/onnx/reference/ops/op_reduce_mean.py", "/onnx/reference/ops/op_reduce_sum.py", "/onnx/reference/ops/op_regex_full_match.py", "/onnx/reference/ops/op_reshape.py", "/onnx/reference/ops/op_resize.py", "/onnx/reference/ops/op_reverse_sequence.py", "/onnx/reference/ops/op_rnn.py", "/onnx/reference/ops/op_roi_align.py", "/onnx/reference/ops/op_scan.py", "/onnx/reference/ops/op_scatter_elements.py", "/onnx/reference/ops/op_scatternd.py", "/onnx/reference/ops/op_selu.py", "/onnx/reference/ops/op_sequence_construct.py", "/onnx/reference/ops/op_sequence_empty.py", "/onnx/reference/ops/op_sequence_erase.py", "/onnx/reference/ops/op_sequence_insert.py", "/onnx/reference/ops/op_sequence_length.py", "/onnx/reference/ops/op_sequence_map.py", "/onnx/reference/ops/op_shape.py", "/onnx/reference/ops/op_shrink.py", "/onnx/reference/ops/op_sigmoid.py", "/onnx/reference/ops/op_slice.py", "/onnx/reference/ops/op_softmax.py", "/onnx/reference/ops/op_softmax_cross_entropy_loss.py", "/onnx/reference/ops/op_softplus.py", "/onnx/reference/ops/op_space_to_depth.py", "/onnx/reference/ops/op_split.py", "/onnx/reference/ops/op_split_to_sequence.py", "/onnx/reference/ops/op_sqrt.py", "/onnx/reference/ops/op_squeeze.py", "/onnx/reference/ops/op_stft.py", "/onnx/reference/ops/op_string_concat.py", "/onnx/reference/ops/op_string_normalizer.py", "/onnx/reference/ops/op_string_split.py", "/onnx/reference/ops/op_sub.py", "/onnx/reference/ops/op_sum.py", "/onnx/reference/ops/op_tfidf_vectorizer.py", "/onnx/reference/ops/op_thresholded_relu.py", "/onnx/reference/ops/op_tile.py", "/onnx/reference/ops/op_topk.py", "/onnx/reference/ops/op_transpose.py", "/onnx/reference/ops/op_trilu.py", "/onnx/reference/ops/op_unique.py", "/onnx/reference/ops/op_unsqueeze.py", "/onnx/reference/ops/op_upsample.py", "/onnx/reference/ops/op_where.py"], "/onnx/backend/test/case/model/gradient.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py", "/onnx/defs/__init__.py"], "/onnx/compose.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_det.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_sequence_empty.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/topk.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_reduce_log_sum.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/aionnxml/op_linear_classifier.py": ["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/center_crop_pad.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_string_concat.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_batch_normalization.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cum_sum.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_prelu.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_unsqueeze.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/globalaveragepool.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/__init__.py": ["/onnx/backend/test/runner/__init__.py"], "/onnx/test/parser_test.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_regex_full_match.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/xor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/shape.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/dequantizelinear.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py"], "/onnx/reference/ops/op_isnan.py": ["/onnx/reference/ops/_op.py"], "/onnx/backend/test/case/node/mul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stringnormalizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducemin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/tile.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_flatten.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_scatternd.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_optional.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/rnn.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/image_decoder.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_tree_ensemble_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_tree_ensemble_helper.py"], "/workflow_scripts/test_model_zoo.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/gelu.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/printer.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/nonzero.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/identity.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/sin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gemm.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_layer_normalization.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_bernoulli.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/backend/test/case/node/deformconv.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_upsample.py": ["/onnx/reference/op_run.py"], "/onnx/test/test_with_ort.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_thresholded_relu.py": ["/onnx/reference/ops/_op.py"], "/onnx/backend/test/case/node/concat.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/stat_coverage.py": ["/onnx/__init__.py", "/onnx/backend/test/case/__init__.py", "/onnx/backend/test/loader/__init__.py", "/onnx/backend/test/runner/__init__.py"], "/onnx/reference/ops/op_constant.py": ["/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/upsample.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_squeeze.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_einsum.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/div.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_one_hot_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_random_uniform.py": ["/onnx/reference/ops/_op_common_random.py"], 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"/onnx/backend/test/case/node/loop.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/conf.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_sequence_construct.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatterelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel2.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/bernoulli.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/constant.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/resize.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_resize.py"], "/onnx/reference/ops/aionnxml/op_svm_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/op_sequence_map.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatternd.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/numpy_helper_test.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_tfidf_vectorizer.py": ["/onnx/reference/op_run.py"], "/onnx/test/checker_test.py": ["/onnx/defs/__init__.py", "/onnx/__init__.py"], "/onnx/reference/ops/_op_common_random.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/backend/base.py": ["/onnx/checker.py", "/onnx/__init__.py"], "/onnx/backend/test/case/node/reduce_log_sum.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_linear_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_softplus.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_sub.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_quantize_linear.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_gathernd.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/qlinearmatmul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/shape_inference_test.py": ["/onnx/shape_inference.py", "/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/parser.py"], "/onnx/backend/test/case/node/mish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_expand.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": ["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/_helpers.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/tfidfvectorizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_average_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/item.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/gatherelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/slice.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_matmul.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mel_weight_matrix.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cast.py": ["/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/asin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_normalizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/unique.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather_elements.py": ["/onnx/reference/op_run.py"], "/onnx/helper.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/layernormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/groupnormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/__init__.py": ["/onnx/reference/ops/_op_list.py"], "/onnx/reference/ops/op_random_normal.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_hann_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_softmax_cross_entropy_loss.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_string_split.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/max.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/backend/test/case/utils.py"], "/onnx/backend/test/case/model/expand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/backend/test/case/node/erf.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
59,101 | onnx/onnx | refs/heads/main | /onnx/backend/sample/ops/__init__.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
import importlib
import inspect
import pkgutil
import sys
from types import ModuleType
from typing import Dict
def collect_sample_implementations() -> Dict[str, str]:
dict_: Dict[str, str] = {}
_recursive_scan(sys.modules[__name__], dict_)
return dict_
def _recursive_scan(package: ModuleType, dict_: Dict[str, str]) -> None:
pkg_dir = package.__path__ # type: ignore
module_location = package.__name__
for _module_loader, name, ispkg in pkgutil.iter_modules(pkg_dir): # type: ignore
module_name = f"{module_location}.{name}" # Module/package
module = importlib.import_module(module_name)
dict_[name] = inspect.getsource(module)
if ispkg:
_recursive_scan(module, dict_)
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"/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/stat_coverage.py": ["/onnx/__init__.py", "/onnx/backend/test/case/__init__.py", "/onnx/backend/test/loader/__init__.py", "/onnx/backend/test/runner/__init__.py"], "/onnx/reference/ops/op_constant.py": ["/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/upsample.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_squeeze.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_einsum.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/div.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_one_hot_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_random_uniform.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/test/reference_evaluator_test.py": ["/onnx/__init__.py", "/onnx/checker.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/__init__.py", "/onnx/reference/ops/_op_common_indices.py", "/onnx/reference/ops/_op_list.py", "/onnx/reference/ops/op_celu.py", "/onnx/reference/ops/op_col2im.py", "/onnx/reference/ops/op_conv.py", "/onnx/reference/ops_optimized/__init__.py", "/onnx/reference/ops_optimized/op_conv_optimized.py"], "/onnx/backend/test/cmd_tools.py": ["/onnx/backend/test/case/model/__init__.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/__init__.py"], "/onnx/reference/ops/op_slice.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/aionnxml/op_binarizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/optionalgetelement.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/loop.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/conf.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_sequence_construct.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatterelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel2.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/bernoulli.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/constant.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/resize.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_resize.py"], "/onnx/reference/ops/aionnxml/op_svm_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/op_sequence_map.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatternd.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/numpy_helper_test.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_tfidf_vectorizer.py": ["/onnx/reference/op_run.py"], "/onnx/test/checker_test.py": ["/onnx/defs/__init__.py", "/onnx/__init__.py"], "/onnx/reference/ops/_op_common_random.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/backend/base.py": ["/onnx/checker.py", "/onnx/__init__.py"], "/onnx/backend/test/case/node/reduce_log_sum.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_linear_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_softplus.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_sub.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_quantize_linear.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_gathernd.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/qlinearmatmul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/shape_inference_test.py": ["/onnx/shape_inference.py", "/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/parser.py"], "/onnx/backend/test/case/node/mish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_expand.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": ["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/_helpers.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/tfidfvectorizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_average_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/item.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/gatherelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/slice.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_matmul.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mel_weight_matrix.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cast.py": ["/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/asin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_normalizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/unique.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather_elements.py": ["/onnx/reference/op_run.py"], "/onnx/helper.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/layernormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/groupnormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/__init__.py": ["/onnx/reference/ops/_op_list.py"], "/onnx/reference/ops/op_random_normal.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_hann_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_softmax_cross_entropy_loss.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_string_split.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/max.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/backend/test/case/utils.py"], "/onnx/backend/test/case/model/expand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/backend/test/case/node/erf.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
59,102 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_tfidf_vectorizer.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=C0200,R0902,R0912,R0913,R0914,R0915,R1716,W0611,W0612,W0613,W0221
from enum import IntEnum
from typing import List
import numpy as np
from onnx.reference.op_run import OpRun
class IntMap(dict): # type: ignore
def __init__(self):
dict.__init__(self)
self.added_keys = []
def emplace(self, key, value):
if not isinstance(key, int):
raise TypeError(f"key must be a NGramPart not {type(key)}.")
if not isinstance(value, NgramPart):
raise TypeError(f"value must be a NGramPart not {type(value)}.")
if key not in self:
self.added_keys.append(key)
self[key] = value
return self[key]
def __repr__(self):
vals = {k: repr(v) for k, v in self.items()}
rows = ["{"]
for k, v in sorted(vals.items()):
if "\n" in v:
vs = v.split("\n")
for i, line in enumerate(vs):
if i == 0:
if line == "{":
rows.append(f" {k}={line}")
else:
rows.append(f" {k}={line},")
elif i == len(vs) - 1:
rows.append(f" {line}")
else:
rows.append(f" {line}")
else:
rows.append(f" {k}={v},")
rows.append("}")
return "\n".join(rows)
@property
def first_key(self):
if len(self) == 0:
raise ValueError("IntMap is empty.")
return self.added_keys[0]
class NgramPart:
def __init__(self, nid: int):
self.id_ = nid # 0 - means no entry, search for a bigger N
self._leafs_ = None
def init(self):
self._leafs_ = IntMap() # type: ignore
def __repr__(self):
if self.empty():
return f"NgramPart({self.id_})"
return f"NgramPart({self.id_}, {self.leafs_!r})"
def empty(self):
return self._leafs_ is None
def has_leaves(self):
return self._leafs_ is not None and len(self._leafs_) > 0
@property
def leafs_(self):
if self._leafs_ is None:
raise RuntimeError("NgramPart was not initialized.")
return self._leafs_
def find(self, key):
if not self.has_leaves():
return None
if key in self._leafs_: # type: ignore
return key
return None
def emplace(self, key, value):
return self.leafs_.emplace(key, value)
def __getitem__(self, key):
return self._leafs_[key] # type: ignore
class WeightingCriteria(IntEnum):
NONE = 0
TF = 1
IDF = 2
TFIDF = 3
def populate_grams(
els,
els_index,
n_ngrams: int,
ngram_size: int,
ngram_id: int,
c, # : ForwardIter , # Map
):
for _ngrams in range(n_ngrams, 0, -1):
n = 1
m = c
while els_index < len(els):
p = m.emplace(els[els_index], NgramPart(0))
if n == ngram_size:
p.id_ = ngram_id
ngram_id += 1
els_index += 1
break
if p.empty():
p.init()
m = p.leafs_
n += 1
els_index += 1
return ngram_id
class TfIdfVectorizer(OpRun):
def __init__(self, onnx_node, run_params): # type: ignore
OpRun.__init__(self, onnx_node, run_params)
mode = self.mode # type: ignore
if mode == "TF":
self.weighting_criteria_ = WeightingCriteria.TF
elif mode == "IDF":
self.weighting_criteria_ = WeightingCriteria.IDF
elif mode == "TFIDF":
self.weighting_criteria_ = WeightingCriteria.TFIDF
self.min_gram_length_ = self.min_gram_length # type: ignore
self.max_gram_length_ = self.max_gram_length # type: ignore
self.max_skip_count_ = self.max_skip_count # type: ignore
self.ngram_counts_ = self.ngram_counts # type: ignore
self.max_gram_length_ = self.max_gram_length # type: ignore
self.ngram_indexes_ = self.ngram_indexes # type: ignore
self.output_size_ = max(self.ngram_indexes_) + 1
self.weights_ = self.weights # type: ignore
self.pool_int64s_ = self.pool_int64s # type: ignore
self.int64_map_ = NgramPart(-10)
self.int64_map_.init()
total_items = len(self.pool_int64s_)
ngram_id = 1 # start with 1, 0 - means no n-gram
# Load into dictionary only required gram sizes
ngram_size = 1
for i in range(len(self.ngram_counts_)):
start_idx = self.ngram_counts_[i]
end_idx = (
self.ngram_counts_[i + 1]
if (i + 1) < len(self.ngram_counts_)
else total_items
)
items = end_idx - start_idx
if items > 0:
ngrams = items // ngram_size
if (
ngram_size >= self.min_gram_length_
and ngram_size <= self.max_gram_length_
):
ngram_id = populate_grams(
self.pool_int64s_,
start_idx,
ngrams,
ngram_size,
ngram_id,
self.int64_map_,
)
else:
ngram_id += ngrams
ngram_size += 1
def increment_count(
self, ngram_id: int, row_num: int, frequencies: List[int]
) -> None:
ngram_id -= 1
# assert(ngram_id < ngram_indexes_.size());
output_idx = row_num * self.output_size_ + self.ngram_indexes_[ngram_id]
# assert(static_cast<size_t>(output_idx) < frequencies.size());
frequencies[output_idx] += 1
def output_result(self, B: int, frequencies: List[int]) -> np.ndarray:
l_output_dims: List[int] = []
if B == 0:
l_output_dims.append(self.output_size_)
B = 1
else:
l_output_dims.append(B)
l_output_dims.append(self.output_size_)
output_dims = tuple(l_output_dims)
row_size = self.output_size_
total_dims = np.prod(output_dims)
Y = np.empty((total_dims,), dtype=np.float32)
w = self.weights_
if self.weighting_criteria_ == WeightingCriteria.TF:
i = 0
for f in frequencies:
Y[i] = f
i += 1
elif self.weighting_criteria_ == WeightingCriteria.IDF:
if len(w) > 0:
p = 0
for _batch in range(B):
for i in range(row_size):
Y[p] = w[i] if frequencies[p] > 0 else 0
p += 1
else:
p = 0
for f in frequencies:
Y[p] = 1 if f > 0 else 0
p += 1
elif self.weighting_criteria_ == WeightingCriteria.TFIDF:
if len(w) > 0:
p = 0
for _batch in range(B):
for i in range(row_size):
Y[p] = w[i] * frequencies[p]
p += 1
else:
p = 0
for f in frequencies:
Y[p] = f
p += 1
else:
raise RuntimeError("Unexpected weighting_criteria.")
return Y.reshape(output_dims)
def compute_impl( # type: ignore
self,
X: np.ndarray,
row_num: int,
row_size: int,
frequencies: List[int],
max_gram_length=None,
max_skip_count=None,
min_gram_length=None,
mode=None,
ngram_counts=None,
ngram_indexes=None,
pool_int64s=None,
pool_strings=None,
weights=None,
) -> None:
if len(X.shape) > 1:
X_flat = X[row_num]
else:
X_flat = X
row_begin = 0
row_end = row_begin + row_size
max_skip_distance = max_skip_count + 1
start_ngram_size = min_gram_length
for skip_distance in range(1, max_skip_distance + 1):
ngram_start = row_begin
ngram_row_end = row_end
while ngram_start < ngram_row_end:
# We went far enough so no n-grams of any size can be gathered
at_least_this = ngram_start + skip_distance * (start_ngram_size - 1)
if at_least_this >= ngram_row_end:
break
ngram_item = ngram_start
int_map = self.int64_map_
ngram_size = 1
while (
int_map.has_leaves()
and ngram_size <= max_gram_length
and ngram_item < ngram_row_end
):
val = X_flat[ngram_item]
hit = int_map.find(val)
if hit is None:
break
hit = int_map[val].id_
if ngram_size >= start_ngram_size and hit != 0:
self.increment_count(hit, row_num, frequencies)
int_map = int_map[val]
ngram_size += 1
ngram_item += skip_distance
ngram_start += 1
# We count UniGrams only once since they are not affected by skip_distance
if start_ngram_size == 1:
start_ngram_size += 1
if start_ngram_size > max_gram_length:
break
def _run( # type: ignore
self,
X,
max_gram_length=None,
max_skip_count=None,
min_gram_length=None,
mode=None,
ngram_counts=None,
ngram_indexes=None,
pool_int64s=None,
pool_strings=None,
weights=None,
):
# weights should be identical to self.weights as well as
# pool_strings, pool_int64s, ngram_indexes, ngram_counts, mode.
# This means none of those attributes can be used in one function.
total_items = np.prod(X.shape)
num_rows = 0
B = 0
C = 0
input_dims = X.shape
if len(input_dims) == 0:
num_rows = 1
C = 1
if total_items != 1:
raise ValueError(f"Unexpected total of items {total_items}.")
elif len(input_dims) == 1:
num_rows = 1
C = input_dims[0]
elif len(input_dims) == 2:
B = input_dims[0]
C = input_dims[1]
num_rows = B
if B < 1:
raise ValueError(
f"Input shape must have either [C] or [B,C] dimensions with B > 0, B={B}, C={C}."
)
else:
raise ValueError(
f"Input shape must have either [C] or [B,C] dimensions with B > 0, B={B}, C={C}."
)
if num_rows * C != total_items:
raise ValueError(
f"Unexpected total of items, num_rows * C = {num_rows * C} != total_items = {total_items}."
)
# Frequency holder allocate [B..output_size_] and init all to zero
frequencies = np.zeros((num_rows * self.output_size_,), dtype=np.int64)
if total_items == 0 or self.int64_map_.empty():
# TfidfVectorizer may receive an empty input when it follows a Tokenizer
# (for example for a string containing only stopwords).
# TfidfVectorizer returns a zero tensor of shape
# {b_dim, output_size} when b_dim is the number of received observations
# and output_size the is the maximum value in ngram_indexes attribute plus 1.
return self.output_result(B, frequencies) # type: ignore[arg-type]
def fn(row_num):
self.compute_impl(
X,
row_num,
C,
frequencies, # type: ignore[arg-type]
max_gram_length=max_gram_length,
max_skip_count=max_skip_count,
min_gram_length=min_gram_length,
mode=mode,
ngram_counts=ngram_counts,
ngram_indexes=ngram_indexes,
pool_int64s=pool_int64s,
pool_strings=pool_strings,
weights=weights,
)
# can be parallelized.
for i in range(num_rows):
fn(i)
return (self.output_result(B, frequencies),) # type: ignore[arg-type]
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"/onnx/reference/ops/op_scatternd.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_optional.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/rnn.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/image_decoder.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_tree_ensemble_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_tree_ensemble_helper.py"], "/workflow_scripts/test_model_zoo.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/gelu.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/printer.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/nonzero.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/identity.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/sin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gemm.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_layer_normalization.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_bernoulli.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/backend/test/case/node/deformconv.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_upsample.py": ["/onnx/reference/op_run.py"], "/onnx/test/test_with_ort.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_thresholded_relu.py": ["/onnx/reference/ops/_op.py"], "/onnx/backend/test/case/node/concat.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/stat_coverage.py": ["/onnx/__init__.py", "/onnx/backend/test/case/__init__.py", "/onnx/backend/test/loader/__init__.py", "/onnx/backend/test/runner/__init__.py"], "/onnx/reference/ops/op_constant.py": ["/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/upsample.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_squeeze.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_einsum.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/div.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_one_hot_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_random_uniform.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/test/reference_evaluator_test.py": ["/onnx/__init__.py", "/onnx/checker.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/__init__.py", "/onnx/reference/ops/_op_common_indices.py", "/onnx/reference/ops/_op_list.py", "/onnx/reference/ops/op_celu.py", "/onnx/reference/ops/op_col2im.py", "/onnx/reference/ops/op_conv.py", "/onnx/reference/ops_optimized/__init__.py", "/onnx/reference/ops_optimized/op_conv_optimized.py"], "/onnx/backend/test/cmd_tools.py": ["/onnx/backend/test/case/model/__init__.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/__init__.py"], "/onnx/reference/ops/op_slice.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/aionnxml/op_binarizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/optionalgetelement.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/loop.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/conf.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_sequence_construct.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatterelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel2.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/bernoulli.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/constant.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/resize.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_resize.py"], "/onnx/reference/ops/aionnxml/op_svm_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/op_sequence_map.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatternd.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/numpy_helper_test.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_tfidf_vectorizer.py": ["/onnx/reference/op_run.py"], "/onnx/test/checker_test.py": ["/onnx/defs/__init__.py", "/onnx/__init__.py"], "/onnx/reference/ops/_op_common_random.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/backend/base.py": ["/onnx/checker.py", "/onnx/__init__.py"], "/onnx/backend/test/case/node/reduce_log_sum.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_linear_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_softplus.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_sub.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_quantize_linear.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_gathernd.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/qlinearmatmul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/shape_inference_test.py": ["/onnx/shape_inference.py", "/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/parser.py"], "/onnx/backend/test/case/node/mish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_expand.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": ["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/_helpers.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/tfidfvectorizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_average_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/item.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/gatherelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/slice.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_matmul.py": 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["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/__init__.py": ["/onnx/reference/ops/_op_list.py"], "/onnx/reference/ops/op_random_normal.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_hann_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_softmax_cross_entropy_loss.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_string_split.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/max.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/backend/test/case/utils.py"], "/onnx/backend/test/case/model/expand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/backend/test/case/node/erf.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], 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59,103 | onnx/onnx | refs/heads/main | /onnx/test/checker_test.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
import unittest
from typing import Sequence
import numpy as np
import onnx.defs
from onnx import (
GraphProto,
SparseTensorProto,
TensorProto,
checker,
helper,
numpy_helper,
shape_inference,
)
class TestChecker(unittest.TestCase):
@property
def _sample_float_tensor(self) -> TensorProto:
np_array = np.random.randn(2, 3).astype(np.float32)
return helper.make_tensor(
name="test",
data_type=TensorProto.FLOAT,
dims=(2, 3),
vals=np_array.reshape(6).tolist(),
)
def make_sparse(
self,
shape: Sequence[int],
values: Sequence[int],
indices_shape: Sequence[int],
indices: Sequence[int],
name: str = "spval",
) -> SparseTensorProto:
sparse = SparseTensorProto()
sparse.dims.extend(shape)
nnz = len(values)
sparse.values.CopyFrom(
helper.make_tensor(name, TensorProto.INT64, (nnz,), values)
)
sparse.indices.CopyFrom(
helper.make_tensor("spind", TensorProto.INT64, indices_shape, indices)
)
return sparse
def test_check_node(self) -> None:
node = helper.make_node("Relu", ["X"], ["Y"], name="test")
checker.check_node(node)
def test_check_node_input_marked_optional(self) -> None:
# GivenTensorFill's input is marked optional, hence it is used in this test.
node = helper.make_node("GivenTensorFill", [], ["Y"], name="test")
checker.check_node(node)
# Explicitly pass the empty string as optional
node = helper.make_node("GivenTensorFill", [""], ["Y"], name="test")
checker.check_node(node)
# Input of RELU is not optional
node = helper.make_node("Relu", [""], ["Y"], name="test")
self.assertRaises(checker.ValidationError, checker.check_node, node)
def test_check_function_nested(self) -> None:
func_domain = "local"
func_nested_opset_imports = [
helper.make_opsetid("", 14),
helper.make_opsetid(func_domain, 1),
]
# nested identity/add function
func_nested_identity_add_name = "func_nested_identity_add"
func_nested_identity_add_inputs = ["a", "b"]
func_nested_identity_add_outputs = ["c"]
func_nested_identity_add_nodes = [
helper.make_node("func_identity", ["a"], ["a1"], domain=func_domain),
helper.make_node("func_identity", ["b"], ["b1"], domain=func_domain),
helper.make_node("func_add", ["a1", "b1"], ["c"], domain=func_domain),
]
func_nested_identity_add = helper.make_function(
func_domain,
func_nested_identity_add_name,
func_nested_identity_add_inputs,
func_nested_identity_add_outputs,
func_nested_identity_add_nodes,
func_nested_opset_imports,
)
checker.check_function(func_nested_identity_add)
def test_check_graph_ir_version_3(self) -> None:
ctx = checker.C.CheckerContext()
ctx.ir_version = 3
ctx.opset_imports = {"": onnx.defs.onnx_opset_version()}
def check_ir_version_3(g: GraphProto) -> None:
checker.check_graph(g, ctx)
node = helper.make_node("Relu", ["X"], ["Y"], name="test")
graph = helper.make_graph(
[node],
"test",
[helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 2])],
[helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 2])],
)
check_ir_version_3(graph)
graph.initializer.extend([self._sample_float_tensor])
graph.initializer[0].name = "no-exist"
self.assertRaises(checker.ValidationError, check_ir_version_3, graph)
graph.initializer[0].name = "X"
check_ir_version_3(graph)
def test_check_graph(self) -> None:
node = helper.make_node("Relu", ["X"], ["Y"], name="test")
graph = helper.make_graph(
[node],
"test",
[helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 2])],
[helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 2])],
)
checker.check_graph(graph)
graph.initializer.extend([self._sample_float_tensor])
graph.initializer[0].name = "no-exist"
checker.check_graph(graph)
graph.initializer[0].name = "X"
checker.check_graph(graph)
def test_check_graph_types(self) -> None:
# This is for https://github.com/onnx/onnx/issues/3849.
# It confirms that type checking is performed
# when checker.check_model is called with full_check=True
node_div = helper.make_node("Div", ["X", "Y"], ["Z"], name="test_div")
node_identity = helper.make_node("Identity", ["Z"], ["W"], name="test_identity")
graph = helper.make_graph(
[node_div, node_identity],
"test",
[
helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 2]),
# intentionally use a BOOL type which is not supported by the Div op.
helper.make_tensor_value_info("Y", TensorProto.BOOL, [1, 2]),
],
[helper.make_tensor_value_info("W", TensorProto.FLOAT, [1, 2])],
)
model = helper.make_model(graph, producer_name="test")
self.assertRaises(
shape_inference.InferenceError, checker.check_model, model, True
)
checker.check_graph(graph)
graph = helper.make_graph(
[node_div, node_identity],
"test",
[
helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 2]),
# intentionally use a Int32 type which is in conflict with Div's other input X.
helper.make_tensor_value_info("Y", TensorProto.INT32, [1, 2]),
],
[helper.make_tensor_value_info("W", TensorProto.FLOAT, [1, 2])],
)
model = helper.make_model(graph, producer_name="test")
self.assertRaises(
shape_inference.InferenceError, checker.check_model, model, True
)
checker.check_graph(graph)
def test_check_graph_empty_initializer_name(self) -> None:
node = helper.make_node("Relu", ["X"], ["Y"], name="test")
graph = helper.make_graph(
[node],
"test",
[helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 2])],
[helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 2])],
)
checker.check_graph(graph)
# Supply no name for the initializer
graph.initializer.extend([self._sample_float_tensor])
graph.initializer[0].name = ""
self.assertRaises(checker.ValidationError, checker.check_graph, graph)
def test_check_graph_empty_sparse_initializer_name(self) -> None:
node = helper.make_node("Relu", ["X"], ["Y"], name="test")
graph = helper.make_graph(
[node],
"test",
[helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 2])],
[helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 2])],
)
checker.check_graph(graph)
# Supply no name for the sparse_initializer
sparse = self.make_sparse([100], [13, 17, 19], [3], [9, 27, 81], "")
graph.sparse_initializer.extend([sparse])
self.assertRaises(checker.ValidationError, checker.check_graph, graph)
def test_check_graph_duplicate_init_names(self) -> None:
node = helper.make_node("Relu", ["X"], ["Y"], name="test")
graph = helper.make_graph(
[node],
"test",
[helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 2])],
[helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 2])],
)
checker.check_graph(graph)
graph.initializer.extend([self._sample_float_tensor])
graph.initializer[0].name = "X"
# Add sparse initializer with the same name as above
sparse = self.make_sparse([100], [13, 17, 19], [3], [9, 27, 81], "X")
graph.sparse_initializer.extend([sparse])
self.assertRaises(checker.ValidationError, checker.check_graph, graph)
def test_check_graph_optional_input(self) -> None:
# GivenTensorFill's input is marked optional, hence it is used in this test.
node = helper.make_node("GivenTensorFill", [""], ["Y"], name="test")
graph = helper.make_graph(
[node],
"test",
[],
[helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 2])],
)
checker.check_graph(graph)
def test_check_graph_ssa(self) -> None:
relu1 = helper.make_node("Relu", ["X"], ["Z"], name="relu1")
relu2 = helper.make_node("Relu", ["Y"], ["Z"], name="relu2")
graph = helper.make_graph(
[relu1, relu2],
"test",
inputs=[
helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 2]),
helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 2]),
],
outputs=[helper.make_tensor_value_info("Z", TensorProto.FLOAT, [1, 2])],
)
self.assertRaises(checker.ValidationError, checker.check_graph, graph)
def test_check_graph_topologically_sorted(self) -> None:
n1 = helper.make_node("Scale", ["X"], ["Y"], scale=2.0, name="n1")
n2 = helper.make_node("Scale", ["Y"], ["Z"], scale=3.0, name="n2")
graph = helper.make_graph(
[n2, n1],
"test",
inputs=[helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 2])],
outputs=[helper.make_tensor_value_info("Z", TensorProto.FLOAT, [1, 2])],
)
self.assertRaises(checker.ValidationError, checker.check_graph, graph)
def test_check_model(self) -> None:
node = helper.make_node("Relu", ["X"], ["Y"], name="test")
graph = helper.make_graph(
[node],
"test",
[helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 2])],
[helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 2])],
)
model = helper.make_model(graph, producer_name="test")
checker.check_model(model)
def test_check_serialized_model(self) -> None:
node = helper.make_node("Relu", ["X"], ["Y"], name="test")
graph = helper.make_graph(
[node],
"test",
[helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 2])],
[helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 2])],
)
model = helper.make_model(graph, producer_name="test")
checker.check_model(model.SerializeToString())
def test_check_old_model(self) -> None:
node = helper.make_node("Pad", ["X"], ["Y"], paddings=(0, 0, 0, 0))
graph = helper.make_graph(
[node],
"test",
[helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 2])],
[helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 2])],
)
onnx_id = helper.make_opsetid("", 1)
model = helper.make_model(graph, producer_name="test", opset_imports=[onnx_id])
checker.check_model(model)
def test_check_tensor(self) -> None:
tensor = self._sample_float_tensor
checker.check_tensor(tensor)
tensor.raw_data = np.random.randn(2, 3).astype(np.float32).tobytes()
self.assertRaises(checker.ValidationError, checker.check_tensor, tensor)
def test_check_string_tensor(self) -> None:
tensor = TensorProto()
tensor.data_type = TensorProto.STRING
tensor.dims.append(1)
tensor.string_data.append(b"Test")
checker.check_tensor(tensor)
del tensor.string_data[:]
tensor.raw_data = b"Test"
# string data should not be stored in raw_data field
self.assertRaises(checker.ValidationError, checker.check_tensor, tensor)
def test_check_tensor_mismatched_field(self) -> None:
tensor = self._sample_float_tensor
tensor.data_type = TensorProto.INT32
self.assertRaises(checker.ValidationError, checker.check_tensor, tensor)
def test_nested_graph(self) -> None:
n1 = helper.make_node("Scale", ["X"], ["Y"], scale=2.0, name="n1")
n2 = helper.make_node("Scale", ["Y"], ["Z"], scale=3.0, name="n2")
graph = helper.make_graph(
[n1, n2],
"nested",
inputs=[],
outputs=[helper.make_tensor_value_info("Z", TensorProto.FLOAT, [1, 2])],
)
i1 = helper.make_node(
"If", ["cond"], ["Z"], then_branch=graph, else_branch=graph
)
graph = helper.make_graph(
[i1],
"test",
inputs=[
helper.make_tensor_value_info("cond", TensorProto.BOOL, [1]),
helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 2]),
],
outputs=[helper.make_tensor_value_info("Z", TensorProto.FLOAT, [1, 2])],
)
checker.check_graph(graph)
def test_nested_graph_without_subgraph_input_shape(self) -> None:
n1 = helper.make_node("Scale", ["X"], ["Y"], scale=2.0, name="n1")
n2 = helper.make_node("Scale", ["Y"], ["Z"], scale=3.0, name="n2")
input_x = onnx.ValueInfoProto()
input_x.name = "X"
graph = helper.make_graph(
[n1, n2],
"nested",
inputs=[],
outputs=[helper.make_tensor_value_info("Z", TensorProto.FLOAT, [1, 2])],
)
i1 = helper.make_node(
"If", ["cond"], ["Z"], then_branch=graph, else_branch=graph
)
graph = helper.make_graph(
[i1],
"test",
inputs=[
helper.make_tensor_value_info("cond", TensorProto.BOOL, [1]),
helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 2]),
],
outputs=[helper.make_tensor_value_info("Z", TensorProto.FLOAT, [1, 2])],
)
checker.check_graph(graph)
@property
def _sample_0_elem_tensor(self) -> TensorProto:
np_array = np.random.randn(0, 3).astype(np.float32)
return helper.make_tensor(
name="test",
data_type=TensorProto.FLOAT,
dims=(0, 3),
vals=np_array.reshape(0).tolist(),
)
def test_check_tensor_zero_elem(self) -> None:
tensor = self._sample_0_elem_tensor
checker.check_tensor(tensor)
def test_check_removed_experimental_op(self) -> None:
node = helper.make_node("ConstantFill", [], ["Y"], name="test", shape=[1, 2])
checker.check_node(node)
def test_skip_schema_check_on_non_standard_domain(self) -> None:
node = helper.make_node(
"NonExistOp", ["X"], ["Y"], name="test", domain="test.domain"
)
graph = helper.make_graph(
[node],
"test",
[helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 2])],
[helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 2])],
)
onnx_id = helper.make_opsetid("test.domain", 1)
model = helper.make_model(graph, producer_name="test", opset_imports=[onnx_id])
checker.check_model(model)
def test_check_sparse_tensor(self) -> None:
sparse = self.make_sparse([100], [13, 17, 19], [3], [9, 27, 81])
checker.check_sparse_tensor(sparse)
def test_check_sparse_tensor_invalid_index(self) -> None:
# index value 181 is out-of-range
sparse = self.make_sparse([100], [13, 17, 19], [3], [9, 27, 181])
self.assertRaises(checker.ValidationError, checker.check_sparse_tensor, sparse)
def test_check_sparse_tensor_unordered(self) -> None:
# index values are not in sorted order
sparse = self.make_sparse([100], [13, 17, 19], [3], [27, 9, 81])
self.assertRaises(checker.ValidationError, checker.check_sparse_tensor, sparse)
def test_check_sparse_tensor_coo_format(self) -> None:
sparse = self.make_sparse([10, 10], [13, 17, 19], [3, 2], [0, 9, 2, 7, 8, 1])
checker.check_sparse_tensor(sparse)
def test_check_sparse_tensor_coo_format_invalid_index(self) -> None:
sparse = self.make_sparse([10, 10], [13, 17, 19], [3, 2], [0, 9, 0, 27, 8, 1])
self.assertRaises(checker.ValidationError, checker.check_sparse_tensor, sparse)
def test_check_sparse_tensor_coo_format_invalid_shape(self) -> None:
sparse = self.make_sparse([10, 10], [13, 17, 19], [2, 3], [0, 9, 2, 7, 8, 1])
self.assertRaises(checker.ValidationError, checker.check_sparse_tensor, sparse)
def test_check_sparse_tensor_coo_format_invalid_dim2(self) -> None:
sparse = self.make_sparse([10, 10], [13, 17, 19], [3, 1], [0, 1, 2])
self.assertRaises(checker.ValidationError, checker.check_sparse_tensor, sparse)
def test_check_sparse_matmul(self) -> None:
M = 5
N = 10
# Create ValueInfoProto for input X of shape [N]
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [N])
# Create a [M,N] sparse-matrix constant C
sparse_tensor = self.make_sparse([M, N], [2, 3, 1], [3], [3, 11, 37])
node1 = helper.make_node("Constant", [], ["C"], sparse_value=sparse_tensor)
# Create ValueInfoProto for output Y of shape [M]
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [M])
# Compute Y = C X
node2 = helper.make_node("MatMul", ["C", "X"], ["Y"])
# create graph
graph = helper.make_graph([node1, node2], "sparse_matmul", [X], [Y])
# check graph
checker.check_graph(graph)
def test_check_model_unsupported_input_type(self) -> None:
N = 10
X = helper.make_tensor_value_info("X", TensorProto.BOOL, [N])
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [N])
Z = helper.make_tensor_value_info("Z", TensorProto.FLOAT, [N])
onnx_id = helper.make_opsetid("", 6)
node = helper.make_node("Add", ["X", "Y"], ["Z"])
graph = helper.make_graph([node], "test_add_input", [X, Y], [Z])
model = helper.make_model(graph, producer_name="test", opset_imports=[onnx_id])
self.assertRaises(
shape_inference.InferenceError, checker.check_model, model, True
)
def test_check_model_inconsistent_type(self) -> None:
N = 10
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [N])
Y = helper.make_tensor_value_info("Y", TensorProto.INT32, [N])
Z = helper.make_tensor_value_info("Z", TensorProto.FLOAT, [N])
onnx_id = helper.make_opsetid("", 6)
node = helper.make_node("Add", ["X", "Y"], ["Z"])
graph = helper.make_graph([node], "test_add_input", [X, Y], [Z])
model = helper.make_model(graph, producer_name="test", opset_imports=[onnx_id])
self.assertRaises(
shape_inference.InferenceError, checker.check_model, model, True
)
def test_check_model_unsupported_output_type(self) -> None:
N = 10
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [N])
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [N])
Z = helper.make_tensor_value_info("Z", TensorProto.BOOL, [N])
onnx_id = helper.make_opsetid("", 6)
node = helper.make_node("Add", ["X", "Y"], ["Z"])
graph = helper.make_graph([node], "test_add_input", [X, Y], [Z])
model = helper.make_model(graph, producer_name="test", opset_imports=[onnx_id])
self.assertRaises(
shape_inference.InferenceError, checker.check_model, model, True
)
def test_loop_with_same_initializer_input_below_ir4(self) -> None:
# This is for testing IR<4: tensors must exist both in initializer and input
# shape_inference should allow different number of graph input and node input for Loop
# Comes from a tf2onnx model
model = helper.make_model(
opset_imports=[helper.make_operatorsetid("", 8)],
ir_version=3,
graph=helper.make_graph(
name="test-loop",
inputs=[
helper.make_tensor_value_info(
"input_0", TensorProto.INT32, shape=[1]
),
helper.make_tensor_value_info(
"while_maximum_iterations_0", TensorProto.INT64, shape=[]
),
helper.make_tensor_value_info(
"const_fold_opt__18", TensorProto.INT64, shape=[1]
),
helper.make_tensor_value_info(
"const_fold_opt__17", TensorProto.FLOAT, shape=[]
),
helper.make_tensor_value_info(
"Const_0", TensorProto.INT32, shape=[1]
),
],
outputs=[
helper.make_tensor_value_info(
"output_0", TensorProto.INT32, shape=[1]
)
],
initializer=[
numpy_helper.from_array(
np.array(9223372036854775807, dtype=np.int64),
name="while_maximum_iterations_0",
),
numpy_helper.from_array(
np.array([-1], dtype=np.int64), name="const_fold_opt__18"
),
numpy_helper.from_array(
np.array(10.0, dtype=np.float32), name="const_fold_opt__17"
),
numpy_helper.from_array(
np.array([1], dtype=np.int32), name="Const_0"
),
],
nodes=[
helper.make_node(
"Cast",
inputs=["input_0"],
outputs=["while_cond_158_while_Less__13_0"],
name="while_cond_158_while_Less__13",
domain="",
to=TensorProto.FLOAT,
),
helper.make_node(
"Less",
inputs=[
"while_cond_158_while_Less__13_0",
"const_fold_opt__17",
],
outputs=["while_cond_158_while_Less_0"],
name="while_cond_158_while_Less",
domain="",
),
helper.make_node(
"Squeeze",
inputs=["while_cond_158_while_Less_0"],
outputs=["while_cond_158_while_Squeeze_0"],
name="while_cond_158_while_Squeeze",
domain="",
),
helper.make_node(
"Loop",
inputs=[
"while_maximum_iterations_0",
"while_cond_158_while_Squeeze_0",
"input_0",
"Const_0",
],
outputs=["while_loop_0", "while_loop_1"],
name="while_loop",
body=helper.make_graph(
name="while_body",
inputs=[
helper.make_tensor_value_info(
"while_while_loop_counter_0",
TensorProto.INT64,
shape=[],
),
helper.make_tensor_value_info(
"cond__15_0", TensorProto.BOOL, shape=[]
),
helper.make_tensor_value_info(
"while_placeholder_0", TensorProto.INT32, shape=[1]
),
helper.make_tensor_value_info(
"while_add_const_0_0", TensorProto.INT32, shape=[1]
),
helper.make_tensor_value_info(
"const_fold_opt__19", TensorProto.FLOAT, shape=[]
),
],
outputs=[
helper.make_tensor_value_info(
"cond___while_Identity_graph_outputs_Identity__3_0",
TensorProto.BOOL,
shape=[],
),
helper.make_tensor_value_info(
"while_Identity_2_0", TensorProto.INT32, shape=[1]
),
helper.make_tensor_value_info(
"while_add_const_0_0", TensorProto.INT32, shape=[1]
),
],
initializer=[
numpy_helper.from_array(
np.array(10.0, dtype=np.float32),
name="const_fold_opt__19",
)
],
nodes=[
helper.make_node(
"Add",
inputs=[
"while_placeholder_0",
"while_add_const_0_0",
],
outputs=["while_Identity_2_0"],
name="while_Add",
),
helper.make_node(
"Cast",
inputs=["while_Identity_2_0"],
outputs=["cond___while_Less__13_0"],
name="cond___while_Less__13",
domain="",
to=TensorProto.FLOAT,
),
helper.make_node(
"Less",
inputs=[
"cond___while_Less__13_0",
"const_fold_opt__19",
],
outputs=["cond___while_Less_0"],
name="cond___while_Less",
domain="",
),
helper.make_node(
"Squeeze",
inputs=["cond___while_Less_0"],
outputs=[
"cond___while_Identity_graph_outputs_Identity__3_0"
],
name="cond___while_Squeeze",
domain="",
),
],
),
),
helper.make_node(
"Unsqueeze",
inputs=["while_loop_0"],
outputs=["Reshape_tensor_0"],
name="Reshape_tensor",
axes=[0],
),
helper.make_node(
"Reshape",
inputs=["Reshape_tensor_0", "const_fold_opt__18"],
outputs=["output_0"],
name="Reshape",
),
],
),
)
# Should not throw an error
checker.check_model(model, full_check=True)
def test_loop_with_different_initializer_input_below_ir4(self) -> None:
# This is for testing IR<4: tensors must exist both in initializer and input
# Testing an optional input which does not exist in initializers
# Checker should throw an error said the missing input is not in initializers
model = helper.make_model(
opset_imports=[helper.make_operatorsetid("", 8)],
ir_version=3,
graph=helper.make_graph(
name="test-loop",
inputs=[
helper.make_tensor_value_info(
"input_0", TensorProto.INT32, shape=[1]
),
helper.make_tensor_value_info(
"while_maximum_iterations_0", TensorProto.INT64, shape=[]
),
helper.make_tensor_value_info(
"const_fold_opt__18", TensorProto.INT64, shape=[1]
),
helper.make_tensor_value_info(
"const_fold_opt__17", TensorProto.FLOAT, shape=[]
),
helper.make_tensor_value_info(
"Const_0", TensorProto.INT32, shape=[1]
),
],
outputs=[
helper.make_tensor_value_info(
"output_0", TensorProto.INT32, shape=[1]
)
],
initializer=[
numpy_helper.from_array(
np.array(9223372036854775807, dtype=np.int64),
name="while_maximum_iterations_0",
),
numpy_helper.from_array(
np.array([-1], dtype=np.int64), name="const_fold_opt__18"
),
numpy_helper.from_array(
np.array(10.0, dtype=np.float32), name="const_fold_opt__17"
),
numpy_helper.from_array(
np.array([1], dtype=np.int32), name="Const_0"
),
],
nodes=[
helper.make_node(
"Cast",
inputs=["input_0"],
outputs=["while_cond_158_while_Less__13_0"],
name="while_cond_158_while_Less__13",
domain="",
to=TensorProto.FLOAT,
),
helper.make_node(
"Less",
inputs=[
"while_cond_158_while_Less__13_0",
"const_fold_opt__17",
],
outputs=["while_cond_158_while_Less_0"],
name="while_cond_158_while_Less",
domain="",
),
helper.make_node(
"Squeeze",
inputs=["while_cond_158_while_Less_0"],
outputs=["while_cond_158_while_Squeeze_0"],
name="while_cond_158_while_Squeeze",
domain="",
),
helper.make_node(
"Loop",
inputs=[
"while_maximum_iterations_0",
"while_cond_158_while_Squeeze_0",
"input_0",
"Const_0",
],
outputs=["while_loop_0", "while_loop_1"],
name="while_loop",
body=helper.make_graph(
name="while_body",
inputs=[
helper.make_tensor_value_info(
"while_while_loop_counter_0",
TensorProto.INT64,
shape=[],
),
helper.make_tensor_value_info(
"cond__15_0", TensorProto.BOOL, shape=[]
),
helper.make_tensor_value_info(
"while_placeholder_0", TensorProto.INT32, shape=[1]
),
helper.make_tensor_value_info(
"while_add_const_0_0", TensorProto.INT32, shape=[1]
),
# The following input cannot be found in initializer and checker should throw an error
helper.make_tensor_value_info(
"const_fold_opt__18", TensorProto.FLOAT, shape=[]
),
],
outputs=[
helper.make_tensor_value_info(
"cond___while_Identity_graph_outputs_Identity__3_0",
TensorProto.BOOL,
shape=[],
),
helper.make_tensor_value_info(
"while_Identity_2_0", TensorProto.INT32, shape=[1]
),
helper.make_tensor_value_info(
"while_add_const_0_0", TensorProto.INT32, shape=[1]
),
],
initializer=[],
nodes=[
helper.make_node(
"Add",
inputs=[
"while_placeholder_0",
"while_add_const_0_0",
],
outputs=["while_Identity_2_0"],
name="while_Add",
),
helper.make_node(
"Cast",
inputs=["while_Identity_2_0"],
outputs=["cond___while_Less__13_0"],
name="cond___while_Less__13",
domain="",
to=TensorProto.FLOAT,
),
],
),
),
helper.make_node(
"Unsqueeze",
inputs=["while_loop_0"],
outputs=["Reshape_tensor_0"],
name="Reshape_tensor",
axes=[0],
),
helper.make_node(
"Reshape",
inputs=["Reshape_tensor_0", "const_fold_opt__18"],
outputs=["output_0"],
name="Reshape",
),
],
),
)
self.assertRaises(
shape_inference.InferenceError, checker.check_model, model, True
)
def test_loop_with_same_initializer_input_above_ir4(self) -> None:
# This is for testing IR>=4:
# Cannot use the same name as both a subgraph initializer and subgraph input
model = helper.make_model(
opset_imports=[helper.make_operatorsetid("", 11)],
ir_version=6,
graph=helper.make_graph(
name="test-loop",
inputs=[
helper.make_tensor_value_info(
"input_0", TensorProto.INT32, shape=[1]
),
helper.make_tensor_value_info(
"while_maximum_iterations_0", TensorProto.INT64, shape=[]
),
helper.make_tensor_value_info(
"const_fold_opt__18", TensorProto.INT64, shape=[1]
),
helper.make_tensor_value_info(
"const_fold_opt__17", TensorProto.FLOAT, shape=[]
),
helper.make_tensor_value_info(
"Const_0", TensorProto.INT32, shape=[1]
),
],
outputs=[
helper.make_tensor_value_info(
"output_0", TensorProto.INT32, shape=[1]
)
],
initializer=[
numpy_helper.from_array(
np.array(9223372036854775807, dtype=np.int64),
name="while_maximum_iterations_0",
),
numpy_helper.from_array(
np.array([-1], dtype=np.int64), name="const_fold_opt__18"
),
numpy_helper.from_array(
np.array(10.0, dtype=np.float32), name="const_fold_opt__17"
),
numpy_helper.from_array(
np.array([1], dtype=np.int32), name="Const_0"
),
],
nodes=[
helper.make_node(
"Cast",
inputs=["input_0"],
outputs=["while_cond_158_while_Less__13_0"],
name="while_cond_158_while_Less__13",
domain="",
to=TensorProto.FLOAT,
),
helper.make_node(
"Less",
inputs=[
"while_cond_158_while_Less__13_0",
"const_fold_opt__17",
],
outputs=["while_cond_158_while_Less_0"],
name="while_cond_158_while_Less",
domain="",
),
helper.make_node(
"Squeeze",
inputs=["while_cond_158_while_Less_0"],
outputs=["while_cond_158_while_Squeeze_0"],
name="while_cond_158_while_Squeeze",
domain="",
),
helper.make_node(
"Loop",
inputs=[
"while_maximum_iterations_0",
"while_cond_158_while_Squeeze_0",
"input_0",
"Const_0",
],
outputs=["while_loop_0", "while_loop_1"],
name="while_loop",
body=helper.make_graph(
name="while_body",
inputs=[
helper.make_tensor_value_info(
"while_while_loop_counter_0",
TensorProto.INT64,
shape=[],
),
helper.make_tensor_value_info(
"cond__15_0", TensorProto.BOOL, shape=[]
),
helper.make_tensor_value_info(
"while_placeholder_0", TensorProto.INT32, shape=[1]
),
helper.make_tensor_value_info(
"while_add_const_0_0", TensorProto.INT32, shape=[1]
),
],
outputs=[
helper.make_tensor_value_info(
"cond___while_Identity_graph_outputs_Identity__3_0",
TensorProto.BOOL,
shape=[],
),
helper.make_tensor_value_info(
"while_Identity_2_0", TensorProto.INT32, shape=[1]
),
helper.make_tensor_value_info(
"while_add_const_0_0", TensorProto.INT32, shape=[1]
),
],
# Cannot use the same name as both a subgraph initializer and subgraph input: while_while_loop_counter_0
initializer=[
numpy_helper.from_array(
np.array(10, dtype=np.int64),
name="while_while_loop_counter_0",
)
],
nodes=[
helper.make_node(
"Add",
inputs=[
"while_placeholder_0",
"while_add_const_0_0",
],
outputs=["while_Identity_2_0"],
name="while_Add",
),
helper.make_node(
"Cast",
inputs=["while_Identity_2_0"],
outputs=["cond___while_Less__13_0"],
name="cond___while_Less__13",
domain="",
to=TensorProto.FLOAT,
),
helper.make_node(
"Less",
inputs=[
"cond___while_Less__13_0",
"while_while_loop_counter_0",
],
outputs=["cond___while_Less_0"],
name="cond___while_Less",
domain="",
),
helper.make_node(
"Squeeze",
inputs=["cond___while_Less_0"],
outputs=[
"cond___while_Identity_graph_outputs_Identity__3_0"
],
name="cond___while_Squeeze",
domain="",
),
],
),
),
helper.make_node(
"Unsqueeze",
inputs=["while_loop_0"],
outputs=["Reshape_tensor_0"],
name="Reshape_tensor",
axes=[0],
),
helper.make_node(
"Reshape",
inputs=["Reshape_tensor_0", "const_fold_opt__18"],
outputs=["output_0"],
name="Reshape",
),
],
),
)
self.assertRaises(
shape_inference.InferenceError, checker.check_model, model, True
)
if __name__ == "__main__":
unittest.main()
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"/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
59,104 | onnx/onnx | refs/heads/main | /onnx/reference/ops/_op_common_random.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
import numpy as np
from onnx.helper import tensor_dtype_to_np_dtype
from onnx.reference.op_run import OpRun
class _CommonRandom(OpRun):
def __init__(self, onnx_node, run_params): # type: ignore
OpRun.__init__(self, onnx_node, run_params)
if hasattr(self, "shape") and len(self.shape) == 0: # type: ignore
raise ValueError( # pragma: no cover
f"shape cannot be empty for operator {self.__class__.__name__}."
)
@staticmethod
def numpy_type(dtype): # type: ignore
return tensor_dtype_to_np_dtype(dtype)
@staticmethod
def _dtype(*data, dtype=None, dtype_first=False): # type: ignore
numpy_type = _CommonRandom.numpy_type(dtype)
if dtype_first and numpy_type is not None:
if dtype != 0: # type: ignore
return numpy_type
if data:
return data[0].dtype
raise RuntimeError(
f"dtype cannot be None for a random operator {_CommonRandom.__name__!r}, "
f"numpy_type={numpy_type}, len(data)={len(data)}."
)
res = None
if not data:
res = numpy_type
elif numpy_type is not None:
res = numpy_type
elif hasattr(data[0], "dtype"):
res = data[0].dtype
if res is None:
raise RuntimeError(
f"dtype cannot be None, numpy_type={numpy_type}, type(data[0])={type(data[0])}."
)
return res
@staticmethod
def _get_state(seed): # type: ignore
if seed is None or np.isnan(seed): # type: ignore
state = np.random.RandomState()
else:
state = np.random.RandomState(seed=int(seed)) # type: ignore
return state
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59,105 | onnx/onnx | refs/heads/main | /onnx/backend/base.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0613
from collections import namedtuple
from typing import Any, Dict, NewType, Optional, Sequence, Tuple, Type
import numpy
import onnx.checker
import onnx.onnx_cpp2py_export.checker as c_checker
from onnx import IR_VERSION, ModelProto, NodeProto
class DeviceType:
"""
Describes device type.
"""
_Type = NewType("_Type", int)
CPU: _Type = _Type(0)
CUDA: _Type = _Type(1)
class Device:
"""
Describes device type and device id
syntax: device_type:device_id(optional)
example: 'CPU', 'CUDA', 'CUDA:1'
"""
def __init__(self, device: str) -> None:
options = device.split(":")
self.type = getattr(DeviceType, options[0])
self.device_id = 0
if len(options) > 1:
self.device_id = int(options[1])
def namedtupledict(
typename: str, field_names: Sequence[str], *args: Any, **kwargs: Any
) -> Type[Tuple[Any, ...]]:
field_names_map = {n: i for i, n in enumerate(field_names)}
# Some output names are invalid python identifier, e.g. "0"
kwargs.setdefault("rename", True)
data = namedtuple(typename, field_names, *args, **kwargs) # type: ignore
def getitem(self: Any, key: Any) -> Any:
if isinstance(key, str):
key = field_names_map[key]
return super(type(self), self).__getitem__(key) # type: ignore
data.__getitem__ = getitem # type: ignore[assignment]
return data
class BackendRep:
"""
BackendRep is the handle that a Backend returns after preparing to execute
a model repeatedly. Users will then pass inputs to the run function of
BackendRep to retrieve the corresponding results.
"""
def run(self, inputs: Any, **kwargs: Any) -> Tuple[Any, ...]:
"""Abstract function."""
return (None,)
class Backend:
"""
Backend is the entity that will take an ONNX model with inputs,
perform a computation, and then return the output.
For one-off execution, users can use run_node and run_model to obtain results quickly.
For repeated execution, users should use prepare, in which the Backend
does all of the preparation work for executing the model repeatedly
(e.g., loading initializers), and returns a BackendRep handle.
"""
@classmethod
def is_compatible(
cls, model: ModelProto, device: str = "CPU", **kwargs: Any
) -> bool:
# Return whether the model is compatible with the backend.
return True
@classmethod
def prepare(
cls, model: ModelProto, device: str = "CPU", **kwargs: Any
) -> Optional[BackendRep]:
# TODO Remove Optional from return type
onnx.checker.check_model(model)
return None
@classmethod
def run_model(
cls, model: ModelProto, inputs: Any, device: str = "CPU", **kwargs: Any
) -> Tuple[Any, ...]:
backend = cls.prepare(model, device, **kwargs)
assert backend is not None
return backend.run(inputs)
@classmethod
def run_node(
cls,
node: NodeProto,
inputs: Any,
device: str = "CPU",
outputs_info: Optional[Sequence[Tuple[numpy.dtype, Tuple[int, ...]]]] = None,
**kwargs: Dict[str, Any],
) -> Optional[Tuple[Any, ...]]:
"""Simple run one operator and return the results.
Args:
outputs_info: a list of tuples, which contains the element type and
shape of each output. First element of the tuple is the dtype, and
the second element is the shape. More use case can be found in
https://github.com/onnx/onnx/blob/main/onnx/backend/test/runner/__init__.py
"""
# TODO Remove Optional from return type
if "opset_version" in kwargs:
special_context = c_checker.CheckerContext()
special_context.ir_version = IR_VERSION
special_context.opset_imports = {"": kwargs["opset_version"]} # type: ignore
onnx.checker.check_node(node, special_context)
else:
onnx.checker.check_node(node)
return None
@classmethod
def supports_device(cls, device: str) -> bool:
"""
Checks whether the backend is compiled with particular device support.
In particular it's used in the testing suite.
"""
return True
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59,106 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/reduce_log_sum.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class ReduceLogSum(Base):
@staticmethod
def export_nokeepdims() -> None:
shape = [3, 4, 5]
axes = np.array([2, 1], dtype=np.int64)
node = onnx.helper.make_node(
"ReduceLogSum",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=0,
)
data = np.random.ranf(shape).astype(np.float32)
reduced = np.log(np.sum(data, axis=tuple(axes), keepdims=False))
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_log_sum_desc_axes",
)
axes = np.array([0, 1], dtype=np.int64)
node = onnx.helper.make_node(
"ReduceLogSum",
inputs=["data", "axes"],
outputs=["reduced"],
keepdims=0,
)
data = np.random.ranf(shape).astype(np.float32)
reduced = np.log(np.sum(data, axis=tuple(axes), keepdims=False))
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_log_sum_asc_axes",
)
@staticmethod
def export_keepdims() -> None:
node = onnx.helper.make_node(
"ReduceLogSum", inputs=["data", "axes"], outputs=["reduced"]
)
data = np.random.ranf([3, 4, 5]).astype(np.float32)
reduced = np.log(np.sum(data, keepdims=True))
axes = np.array([], dtype=np.int64)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_log_sum_default",
)
@staticmethod
def export_negative_axes_keepdims() -> None:
axes = np.array([-2], dtype=np.int64)
node = onnx.helper.make_node(
"ReduceLogSum", inputs=["data", "axes"], outputs=["reduced"]
)
data = np.random.ranf([3, 4, 5]).astype(np.float32)
reduced = np.log(np.sum(data, axis=tuple(axes), keepdims=True))
# print(reduced)
expect(
node,
inputs=[data, axes],
outputs=[reduced],
name="test_reduce_log_sum_negative_axes",
)
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59,107 | onnx/onnx | refs/heads/main | /onnx/reference/ops/aionnxml/op_linear_regressor.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0913,R0914,W0221
import numpy as np
from onnx.reference.ops.aionnxml._op_run_aionnxml import OpRunAiOnnxMl
class LinearRegressor(OpRunAiOnnxMl):
def _run( # type: ignore
self, x, coefficients=None, intercepts=None, targets=1, post_transform=None
):
coefficients = np.array(coefficients).astype(x.dtype)
intercepts = np.array(intercepts).astype(x.dtype)
n = coefficients.shape[0] // targets
coefficients = coefficients.reshape(targets, n).T
score = np.dot(x, coefficients)
if intercepts is not None:
score += intercepts
if post_transform == "NONE":
return (score,)
raise NotImplementedError(
f"post_transform: {post_transform!r} is not implemented."
)
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"/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
59,108 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_softplus.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
import numpy as np
from onnx.reference.ops._op import OpRunUnaryNum
class Softplus(OpRunUnaryNum):
def _run(self, X): # type: ignore
tmp = np.exp(X).astype(X.dtype)
tmp += 1
np.log(tmp, out=tmp)
return (tmp,)
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59,109 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_sub.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
import numpy as np
from onnx.reference.ops._op import OpRunBinaryNumpy
class Sub(OpRunBinaryNumpy):
def __init__(self, onnx_node, run_params): # type: ignore
OpRunBinaryNumpy.__init__(self, np.subtract, onnx_node, run_params)
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59,110 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_quantize_linear.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
from typing import Optional, Tuple
import numpy as np
from onnx import TensorProto
from onnx.helper import (
float32_to_float8e4m3,
float32_to_float8e5m2,
np_dtype_to_tensor_dtype,
tensor_dtype_to_np_dtype,
)
from onnx.reference.custom_element_types import (
float8e4m3fn,
float8e4m3fnuz,
float8e5m2,
float8e5m2fnuz,
)
from onnx.reference.op_run import OpRun
class _CommonQuantizeLinear(OpRun):
float32_to_float8e4m3 = np.vectorize(float32_to_float8e4m3)
float32_to_float8e5m2 = np.vectorize(float32_to_float8e5m2)
def get_zero_point_type(self, zero_point: np.ndarray) -> int:
if (
zero_point.dtype == float8e4m3fn
and zero_point.dtype.descr[0][0] == "e4m3fn"
):
return TensorProto.FLOAT8E4M3FN
if (
zero_point.dtype == float8e4m3fnuz
and zero_point.dtype.descr[0][0] == "e4m3fnuz"
):
return TensorProto.FLOAT8E4M3FNUZ
if zero_point.dtype == float8e5m2 and zero_point.dtype.descr[0][0] == "e5m2":
return TensorProto.FLOAT8E5M2
if (
zero_point.dtype == float8e5m2fnuz
and zero_point.dtype.descr[0][0] == "e5m2fnuz"
):
return TensorProto.FLOAT8E5M2FNUZ
return np_dtype_to_tensor_dtype(zero_point.dtype)
def common_run( # pylint: disable=too-many-branches
self,
x: np.ndarray,
y_scale: np.ndarray,
zero_point: Optional[np.ndarray] = None,
axis: int = 1,
saturate: bool = True,
) -> Tuple[np.ndarray]:
if len(y_scale.shape) > 1:
raise RuntimeError("Input 2 must be a vector or a number.")
if len(y_scale.shape) > 0 and y_scale.size == 1:
y_scale = y_scale[0]
if len(y_scale.shape) > 0:
new_shape = [1 for s in x.shape]
new_shape[axis] = len(y_scale)
x = x / y_scale.reshape(new_shape)
else:
x = x / y_scale
new_shape = x.shape # unused
if zero_point is not None:
tensor_type = self.get_zero_point_type(zero_point)
if tensor_type == TensorProto.UINT8:
xi = np.rint(x).astype(np.int32)
if len(y_scale.shape) > 0:
xi += zero_point.reshape(new_shape)
else:
xi += zero_point
dtype = tensor_dtype_to_np_dtype(tensor_type)
return (np.clip(xi, 0, 255).astype(dtype),)
if tensor_type == TensorProto.INT8:
xi = np.rint(x).astype(np.int32)
if len(y_scale.shape) > 0:
xi += zero_point.reshape(new_shape)
else:
xi += zero_point
dtype = tensor_dtype_to_np_dtype(tensor_type)
return (np.clip(xi, -128, 127).astype(dtype),)
if tensor_type == TensorProto.FLOAT8E4M3FN:
f8 = _CommonQuantizeLinear.float32_to_float8e4m3(x, saturate=saturate)
return (f8.astype(float8e4m3fn),) # type: ignore[attr-defined]
if tensor_type == TensorProto.FLOAT8E4M3FNUZ:
f8 = _CommonQuantizeLinear.float32_to_float8e4m3(
x, uz=True, saturate=saturate
)
return (f8.astype(float8e4m3fnuz),) # type: ignore[attr-defined]
if tensor_type == TensorProto.FLOAT8E5M2:
f8 = _CommonQuantizeLinear.float32_to_float8e5m2(x, saturate=saturate)
return (f8.astype(float8e5m2),) # type: ignore[attr-defined]
if tensor_type == TensorProto.FLOAT8E5M2FNUZ:
f8 = _CommonQuantizeLinear.float32_to_float8e5m2(
x, fn=True, uz=True, saturate=saturate
)
return (f8.astype(float8e5m2fnuz),) # type: ignore[attr-defined]
raise RuntimeError(
f"Unexpected tensor_type for input 2: tensor_type={tensor_type}, "
f"zero_point.dtype={zero_point.dtype}."
)
dtype = np.uint8 # type: ignore[assignment]
xi = np.rint(x).astype(np.int32)
return (np.clip(xi, 0, 255).astype(dtype),)
class QuantizeLinear_10(_CommonQuantizeLinear):
def _run(self, *args, axis=None): # type: ignore
# args: x, y_scale, zero_point
return self.common_run(*args, axis=axis) # type: ignore
class QuantizeLinear_19(_CommonQuantizeLinear):
def _run(self, *args, axis=None, saturate=None): # type: ignore
# args: x, y_scale, zero_point
return self.common_run(*args, axis=axis, saturate=saturate) # type: ignore
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59,111 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_gathernd.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
from typing import Tuple
import numpy as np
from onnx.reference.op_run import OpRun
def _gather_nd_impl(
data: np.ndarray, indices: np.ndarray, batch_dims: int
) -> Tuple[np.ndarray]:
# Note the data rank - will be reused multiple times later
data_rank = len(data.shape)
# The list of data/indice shape of batch_dims.
batch_dims_shape = []
# The number of elements in the batch_dims for data/indice array.
batch_dims_size = 1
# Check the shape of indice and data are identical for batch dims.
for i in range(batch_dims):
batch_dims_shape.append(indices.shape[i])
batch_dims_size *= indices.shape[i]
# Compute output of the op as below.
# Compute shape of output array.
output_shape = (
batch_dims_shape + list(indices.shape)[batch_dims:-1]
if (indices.shape[-1] == data_rank - batch_dims)
else batch_dims_shape
+ list(indices.shape)[batch_dims:-1]
+ list(data.shape)[batch_dims + indices.shape[-1] :]
)
# Placeholder for output data.
output_data_buffer = []
# Flatten 'indices' to 2D array.
reshaped_indices = indices.reshape(batch_dims_size, -1, indices.shape[-1])
# Flatten 'data' to array of shape
# (batch_dim_size, data.shape[batch_dimes:]).
reshaped_data = data.reshape((batch_dims_size,) + data.shape[batch_dims:])
# Gather each scalar value from 'data'.
for batch_dim in range(reshaped_indices.shape[0]):
for outer_dim in range(reshaped_indices.shape[1]):
gather_index = tuple(reshaped_indices[batch_dim][outer_dim])
output_data_buffer.append(reshaped_data[(batch_dim, *gather_index)])
return (np.asarray(output_data_buffer, dtype=data.dtype).reshape(output_shape),)
class GatherND(OpRun):
def _run(self, data, indices, batch_dims=None): # type: ignore
return _gather_nd_impl(data, indices, batch_dims) # type: ignore
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59,112 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/qlinearmatmul.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class QLinearMatMul(Base):
@staticmethod
def export() -> None:
node = onnx.helper.make_node(
"QLinearMatMul",
inputs=[
"a",
"a_scale",
"a_zero_point",
"b",
"b_scale",
"b_zero_point",
"y_scale",
"y_zero_point",
],
outputs=["y"],
)
# 2D
a = np.array(
[
[208, 236, 0, 238],
[3, 214, 255, 29],
],
dtype=np.uint8,
)
a_scale = np.array([0.0066], dtype=np.float32)
a_zero_point = np.array([113], dtype=np.uint8)
b = np.array(
[[152, 51, 244], [60, 26, 255], [0, 127, 246], [127, 254, 247]],
dtype=np.uint8,
)
b_scale = np.array([0.00705], dtype=np.float32)
b_zero_point = np.array([114], dtype=np.uint8)
y_scale = np.array([0.0107], dtype=np.float32)
y_zero_point = np.array([118], dtype=np.uint8)
output = np.array(
[
[168, 115, 255],
[1, 66, 151],
],
dtype=np.uint8,
)
expect(
node,
inputs=[
a,
a_scale,
a_zero_point,
b,
b_scale,
b_zero_point,
y_scale,
y_zero_point,
],
outputs=[output],
name="test_qlinearmatmul_2D",
)
# 3D
a = np.array(
[
[[208, 236, 0, 238], [3, 214, 255, 29]],
[[208, 236, 0, 238], [3, 214, 255, 29]],
],
dtype=np.uint8,
)
a_scale = np.array([0.0066], dtype=np.float32)
a_zero_point = np.array([113], dtype=np.uint8)
b = np.array(
[
[[152, 51, 244], [60, 26, 255], [0, 127, 246], [127, 254, 247]],
[[152, 51, 244], [60, 26, 255], [0, 127, 246], [127, 254, 247]],
],
dtype=np.uint8,
)
b_scale = np.array([0.00705], dtype=np.float32)
b_zero_point = np.array([114], dtype=np.uint8)
y_scale = np.array([0.0107], dtype=np.float32)
y_zero_point = np.array([118], dtype=np.uint8)
output = np.array(
[[[168, 115, 255], [1, 66, 151]], [[168, 115, 255], [1, 66, 151]]],
dtype=np.uint8,
)
expect(
node,
inputs=[
a,
a_scale,
a_zero_point,
b,
b_scale,
b_zero_point,
y_scale,
y_zero_point,
],
outputs=[output],
name="test_qlinearmatmul_3D",
)
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"/onnx/reference/ops/op_unique.py", "/onnx/reference/ops/op_unsqueeze.py", "/onnx/reference/ops/op_upsample.py", "/onnx/reference/ops/op_where.py"], "/onnx/backend/test/case/model/gradient.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py", "/onnx/defs/__init__.py"], "/onnx/compose.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_det.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_sequence_empty.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/topk.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_reduce_log_sum.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/aionnxml/op_linear_classifier.py": ["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/center_crop_pad.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_string_concat.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_batch_normalization.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cum_sum.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_prelu.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_unsqueeze.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/globalaveragepool.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/__init__.py": ["/onnx/backend/test/runner/__init__.py"], "/onnx/test/parser_test.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_regex_full_match.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/xor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/shape.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/dequantizelinear.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py"], "/onnx/reference/ops/op_isnan.py": ["/onnx/reference/ops/_op.py"], "/onnx/backend/test/case/node/mul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stringnormalizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducemin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/tile.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_flatten.py": ["/onnx/reference/ops/_op.py"], 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"/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_resize.py"], "/onnx/reference/ops/aionnxml/op_svm_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/op_sequence_map.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatternd.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/numpy_helper_test.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_tfidf_vectorizer.py": ["/onnx/reference/op_run.py"], "/onnx/test/checker_test.py": ["/onnx/defs/__init__.py", "/onnx/__init__.py"], "/onnx/reference/ops/_op_common_random.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/backend/base.py": ["/onnx/checker.py", "/onnx/__init__.py"], "/onnx/backend/test/case/node/reduce_log_sum.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_linear_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_softplus.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_sub.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_quantize_linear.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_gathernd.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/qlinearmatmul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/shape_inference_test.py": ["/onnx/shape_inference.py", "/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/parser.py"], "/onnx/backend/test/case/node/mish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_expand.py": ["/onnx/reference/op_run.py"], 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59,113 | onnx/onnx | refs/heads/main | /onnx/test/shape_inference_test.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import unittest
from typing import Any, Sequence
import numpy as np
import pytest
from parameterized import parameterized
import onnx.shape_inference
from onnx import (
ONNX_ML,
GraphProto,
ModelProto,
NodeProto,
OperatorSetIdProto,
SparseTensorProto,
TensorProto,
TypeProto,
ValueInfoProto,
checker,
defs,
helper,
numpy_helper,
)
from onnx.defs import (
AI_ONNX_PREVIEW_TRAINING_DOMAIN,
ONNX_DOMAIN,
ONNX_ML_DOMAIN,
OpSchema,
SchemaError,
)
from onnx.helper import (
make_empty_tensor_value_info,
make_node,
make_opsetid,
make_tensor,
make_tensor_sequence_value_info,
make_tensor_value_info,
)
from onnx.parser import parse_graph
def get_available_versions(schema: OpSchema) -> set[int]:
versions: set[int] = set()
for version in range(schema.since_version, 0, -1):
try:
versions.add(defs.get_schema(schema.name, version).since_version)
except SchemaError:
break
return versions
ALL_OP_VERSIONS: dict[str, frozenset[int]] = {
schema.name: frozenset(get_available_versions(schema))
for schema in defs.get_all_schemas()
}
def all_versions_for(op_name: str) -> list[tuple[str, int]]:
versions_set = ALL_OP_VERSIONS[op_name]
assert versions_set
versions = sorted(versions_set)
return [
(
f"version{version}",
version,
)
for version in versions
# FIXME(#5289): Reshape errors in self._make_graph when version <= 5.
# Issue reference: https://github.com/onnx/onnx/issues/5289.
if version > 5
]
class TestShapeInferenceHelper(unittest.TestCase):
def _make_graph(
self,
seed_values: Sequence[str | tuple[str, TensorProto.DataType, Any]],
nodes: list[NodeProto],
value_info: list[ValueInfoProto],
initializer: Sequence[TensorProto] | None = None,
) -> GraphProto:
if initializer is None:
initializer = []
names_in_initializer = {x.name for x in initializer}
input_value_infos = []
# If the starting values are not also initializers,
# introduce the starting values as the output of reshape,
# so that the sizes are guaranteed to be unknown
for seed_value in seed_values:
if isinstance(seed_value, tuple):
seed_name, proto_type = seed_value[:2]
seed_value_info = make_tensor_value_info(*seed_value)
else:
seed_name, proto_type = seed_value, TensorProto.UNDEFINED
seed_value_info = make_empty_tensor_value_info(seed_value)
if seed_name in names_in_initializer:
input_value_infos.append(seed_value_info)
else:
value_info.append(seed_value_info)
input_value_infos.append(
make_tensor_value_info("SEED_" + seed_name, proto_type, ())
)
input_value_infos.append(
make_tensor_value_info(
"UNKNOWN_SHAPE_" + seed_name, TensorProto.INT64, (None,)
)
)
nodes[:0] = [
make_node(
"Reshape",
["SEED_" + seed_name, "UNKNOWN_SHAPE_" + seed_name],
[seed_name],
)
]
return helper.make_graph(
nodes,
"test",
input_value_infos,
[],
initializer=initializer,
value_info=value_info,
)
def _inferred(
self, graph_or_model: GraphProto | ModelProto, **kwargs: Any
) -> ModelProto:
data_prop = kwargs.pop("data_prop", False)
if isinstance(graph_or_model, GraphProto):
kwargs["producer_name"] = "onnx-test"
orig_model = helper.make_model(graph_or_model, **kwargs)
else:
orig_model = graph_or_model
inferred_model = onnx.shape_inference.infer_shapes(
orig_model, strict_mode=True, data_prop=data_prop
)
checker.check_model(inferred_model)
return inferred_model
def _assert_inferred(
self,
graph_or_model: GraphProto | ModelProto,
vis: list[ValueInfoProto],
**kwargs: Any,
) -> None:
graph = (
graph_or_model
if isinstance(graph_or_model, GraphProto)
else graph_or_model.graph
)
names_in_vis = {x.name for x in vis}
vis = [x for x in graph.value_info if x.name not in names_in_vis] + vis
inferred_model = self._inferred(graph_or_model, **kwargs)
inferred_vis = list(inferred_model.graph.value_info)
vis = sorted(vis, key=lambda x: x.name) # type: ignore[no-any-return]
inferred_vis = sorted(inferred_vis, key=lambda x: x.name) # type: ignore
assert len(vis) == len(inferred_vis)
for v, inferred_v in zip(vis, inferred_vis):
self._compare_value_infos(v.type, inferred_v.type)
def _compare_value_infos(
self, vi_type: TypeProto, inferred_vi_type: TypeProto
) -> None:
if vi_type.HasField("tensor_type"):
assert inferred_vi_type.HasField("tensor_type")
assert vi_type.tensor_type.HasField("elem_type")
assert inferred_vi_type.tensor_type.HasField("elem_type")
assert (
vi_type.tensor_type.elem_type == inferred_vi_type.tensor_type.elem_type
)
assert vi_type.tensor_type.HasField(
"shape"
) == inferred_vi_type.tensor_type.HasField("shape")
if vi_type.tensor_type.HasField("shape"):
assert len(vi_type.tensor_type.shape.dim) == len(
inferred_vi_type.tensor_type.shape.dim
)
for dim_i, dim in enumerate(vi_type.tensor_type.shape.dim):
inferred_dim = inferred_vi_type.tensor_type.shape.dim[dim_i]
# if it is a symbolic shape, make sure the inferred symbol has generated (dim_param)
if dim.dim_param:
assert (
dim.dim_param == inferred_dim.dim_param
), f"\n{vi_type}\n{inferred_vi_type}\n"
else:
assert (
dim.dim_value == inferred_dim.dim_value
), f"\n{vi_type}\n{inferred_vi_type}\n"
elif vi_type.HasField("sequence_type"):
assert inferred_vi_type.HasField("sequence_type")
vi = vi_type.sequence_type.elem_type
inferred_vi = inferred_vi_type.sequence_type.elem_type
self._compare_value_infos(vi, inferred_vi)
elif vi_type.HasField("optional_type"):
assert inferred_vi_type.HasField("optional_type")
vi = vi_type.optional_type.elem_type
inferred_vi = inferred_vi_type.optional_type.elem_type
self._compare_value_infos(vi, inferred_vi)
elif vi_type.HasField("map_type"):
assert inferred_vi_type.HasField("map_type")
assert vi_type.map_type.key_type == vi_type.map_type.key_type
self._compare_value_infos(
vi_type.map_type.value_type, inferred_vi_type.map_type.value_type
)
elif vi_type == onnx.TypeProto():
assert inferred_vi_type == onnx.TypeProto()
else:
raise NotImplementedError(
"Unrecognized value info type in _compare_value_infos: ", str(vi_type)
)
def skipIf(self, condition, reason):
if condition:
pytest.skip(reason)
class TestShapeInference(TestShapeInferenceHelper):
def test_empty_graph(self) -> None:
graph = self._make_graph(["y"], [], [])
self.assertRaises(onnx.shape_inference.InferenceError, self._inferred, graph)
def _identity_prop(self, op: str, **kwargs: Any) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (30, 4, 5))],
[make_node(op, "x", "y", **kwargs)],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("y", TensorProto.FLOAT, (30, 4, 5))]
)
@parameterized.expand(all_versions_for("Transpose"))
def test_transpose(self, _, version) -> None:
graph = self._make_graph(
[("X", TensorProto.FLOAT, (2, 3, 4))],
[make_node("Transpose", ["X"], ["Y"], perm=[1, 0, 2])],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("Y", TensorProto.FLOAT, (3, 2, 4))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Transpose"))
def test_transpose_preexisting(self, _, version) -> None:
graph = self._make_graph(
[("X", TensorProto.FLOAT, (2, 3, 4))],
[make_node("Transpose", ["X"], ["Y"], perm=[1, 0, 2])],
[make_tensor_value_info("Y", TensorProto.FLOAT, None)],
)
self._assert_inferred(
graph,
[make_tensor_value_info("Y", TensorProto.FLOAT, (3, 2, 4))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Transpose"))
def test_transpose_scalar(self, _, version) -> None:
graph = self._make_graph(
[("X", TensorProto.FLOAT, ())],
[make_node("Transpose", ["X"], ["Y"])],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("Y", TensorProto.FLOAT, ())],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Transpose"))
def test_transpose_partial(self, _, version) -> None:
graph = self._make_graph(
[("X", TensorProto.FLOAT, (2, 3, 4))],
[make_node("Transpose", ["X"], ["Y"], perm=[1, 0, 2])],
[make_tensor_value_info("Y", TensorProto.UNDEFINED, (3, "a", "b"))],
) # type: ignore
self._assert_inferred(
graph,
[make_tensor_value_info("Y", TensorProto.FLOAT, (3, 2, 4))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Transpose"))
def test_transpose_preexisting_incorrect_shape(self, *_) -> None:
graph = self._make_graph(
[("X", TensorProto.FLOAT, (2, 3, 4))],
[make_node("Transpose", ["X"], ["Y"], perm=[1, 0, 2])],
[make_tensor_value_info("Y", TensorProto.FLOAT, (5, 5, 5))],
)
self.assertRaises(onnx.shape_inference.InferenceError, self._inferred, graph)
@parameterized.expand(all_versions_for("Transpose"))
def test_transpose_preexisting_incorrect_type(self, *_) -> None:
graph = self._make_graph(
[("X", TensorProto.FLOAT, (2, 3, 4))],
[make_node("Transpose", ["X"], ["Y"], perm=[1, 0, 2])],
[make_tensor_value_info("Y", TensorProto.STRING, (3, 2, 4))],
)
self.assertRaises(onnx.shape_inference.InferenceError, self._inferred, graph)
@parameterized.expand(all_versions_for("Transpose"))
def test_transpose_incorrect_repeated_perm(self, *_) -> None:
graph = self._make_graph(
[("X", TensorProto.FLOAT, (2, 3, 4))],
[make_node("Transpose", ["X"], ["Y"], perm=[1, 0, 1])],
[],
)
self.assertRaises(onnx.shape_inference.InferenceError, self._inferred, graph)
def _make_matmul_test_all_dims_known(
self, version, shape1: Sequence[int], shape2: Sequence[int]
) -> None:
expected_out_shape = np.matmul(
np.arange(np.prod(shape1)).reshape(shape1),
np.arange(np.prod(shape2)).reshape(shape2),
).shape
graph = self._make_graph(
[("x", TensorProto.FLOAT, shape1), ("y", TensorProto.FLOAT, shape2)],
[make_node("MatMul", ["x", "y"], ["z"])],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("z", TensorProto.FLOAT, expected_out_shape)],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("MatMul"))
def test_matmul_all_dims_known(self, _, version) -> None:
self._make_matmul_test_all_dims_known(version, (2,), (2,))
self._make_matmul_test_all_dims_known(version, (4, 2), (2, 4))
self._make_matmul_test_all_dims_known(version, (5, 2), (2, 4))
self._make_matmul_test_all_dims_known(version, (5, 2), (2, 1))
self._make_matmul_test_all_dims_known(version, (1, 2), (2, 3))
self._make_matmul_test_all_dims_known(version, (2,), (2, 3))
self._make_matmul_test_all_dims_known(version, (4, 2), (2,))
self._make_matmul_test_all_dims_known(version, (1, 4, 2), (3, 2, 3))
self._make_matmul_test_all_dims_known(version, (3, 4, 2), (3, 2, 3))
self._make_matmul_test_all_dims_known(version, (5, 1, 4, 2), (1, 3, 2, 3))
self._make_matmul_test_all_dims_known(version, (4, 2), (3, 2, 3))
def _make_matmul_test_allow_unknown(
self, version, shape1: Any, shape2: Any, expected_out_shape: Any
) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, shape1), ("y", TensorProto.FLOAT, shape2)],
[make_node("MatMul", ["x", "y"], ["z"])],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("z", TensorProto.FLOAT, expected_out_shape)],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("MatMul"))
def test_matmul_allow_unknown(self, _, version) -> None:
self._make_matmul_test_allow_unknown(version, (None,), (None,), ())
self._make_matmul_test_allow_unknown(version, (3,), (None,), ())
self._make_matmul_test_allow_unknown(version, (2,), (2, "a"), ("a",))
self._make_matmul_test_allow_unknown(version, (4, 2), (2, "a"), (4, "a"))
self._make_matmul_test_allow_unknown(version, (4, None), (2, "a"), (4, "a"))
self._make_matmul_test_allow_unknown(version, (4, None), (None, "a"), (4, "a"))
self._make_matmul_test_allow_unknown(
version, (1, 4, 2), ("a", 2, 5), ("a", 4, 5)
)
self._make_matmul_test_allow_unknown(
version, (1, 3, 4, 2), ("a", 2, 5), (1, 3, 4, 5)
)
self._make_matmul_test_allow_unknown(version, (3,), None, None)
self._make_matmul_test_allow_unknown(version, None, None, None)
@parameterized.expand(all_versions_for("Cast"))
def test_cast(self, _, version) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (2, 4, 3))],
[make_node("Cast", ["x"], ["y"], to=TensorProto.UINT8)],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.UINT8, (2, 4, 3))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("CastLike"))
def test_cast_like(self, _, version) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (2, 4, 3)), ("t", TensorProto.FLOAT16, ("N",))],
[make_node("CastLike", ["x", "t"], ["y"])],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.FLOAT16, (2, 4, 3))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Col2Im"))
def test_col2im(self, _, version) -> None:
graph = self._make_graph(
[
("input", TensorProto.FLOAT, (1, 5, 5)),
("output_shape", TensorProto.INT64, (2,)),
("kernel_shape", TensorProto.INT64, (2,)),
],
[
make_node(
"Col2Im", ["input", "output_shape", "kernel_shape"], ["output"]
)
],
[],
initializer=[
make_tensor("output_shape", TensorProto.INT64, (2,), (5, 5)),
make_tensor("kernel_shape", TensorProto.INT64, (2,), (1, 5)),
],
)
self._assert_inferred(
graph,
[make_tensor_value_info("output", TensorProto.FLOAT, (1, 1, 5, 5))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Col2Im"))
def test_col2im_strides(self, _, version) -> None:
graph = self._make_graph(
[
("input", TensorProto.FLOAT, (1, 9, 4)),
("output_shape", TensorProto.INT64, (2,)),
("kernel_shape", TensorProto.INT64, (2,)),
],
[
make_node(
"Col2Im",
["input", "output_shape", "kernel_shape"],
["output"],
strides=[2, 2],
)
],
[],
initializer=[
make_tensor("output_shape", TensorProto.INT64, (2,), (5, 5)),
make_tensor("kernel_shape", TensorProto.INT64, (2,), (3, 3)),
],
)
self._assert_inferred(
graph,
[make_tensor_value_info("output", TensorProto.FLOAT, (1, 1, 5, 5))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Col2Im"))
def test_col2im_pads(self, _, version) -> None:
graph = self._make_graph(
[
("input", TensorProto.FLOAT, (1, 5, 15)),
("output_shape", TensorProto.INT64, (2,)),
("kernel_shape", TensorProto.INT64, (2,)),
],
[
make_node(
"Col2Im",
["input", "output_shape", "kernel_shape"],
["output"],
pads=[0, 1, 0, 1],
)
],
[],
initializer=[
make_tensor("output_shape", TensorProto.INT64, (2,), (5, 5)),
make_tensor("kernel_shape", TensorProto.INT64, (2,), (1, 5)),
],
)
self._assert_inferred(
graph,
[make_tensor_value_info("output", TensorProto.FLOAT, (1, 1, 5, 5))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Col2Im"))
def test_col2im_dilations(self, _, version) -> None:
graph = self._make_graph(
[
("input", TensorProto.FLOAT, (1, 4, 5)),
("output_shape", TensorProto.INT64, (2,)),
("kernel_shape", TensorProto.INT64, (2,)),
],
[
make_node(
"Col2Im",
["input", "output_shape", "kernel_shape"],
["output"],
dilations=[1, 5],
)
],
[],
initializer=[
make_tensor("output_shape", TensorProto.INT64, (2,), (6, 6)),
make_tensor("kernel_shape", TensorProto.INT64, (2,), (2, 2)),
],
)
self._assert_inferred(
graph,
[make_tensor_value_info("output", TensorProto.FLOAT, (1, 1, 6, 6))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Col2Im"))
def test_col2im_5d(self, _, version) -> None:
graph = self._make_graph(
[
("input", TensorProto.FLOAT, (1, 10, 12)),
("output_shape", TensorProto.INT64, (3,)),
("kernel_shape", TensorProto.INT64, (3,)),
],
[
make_node(
"Col2Im", ["input", "output_shape", "kernel_shape"], ["output"]
)
],
[],
initializer=[
make_tensor("output_shape", TensorProto.INT64, (3,), (3, 4, 5)),
make_tensor("kernel_shape", TensorProto.INT64, (3,), (1, 1, 5)),
],
)
self._assert_inferred(
graph,
[make_tensor_value_info("output", TensorProto.FLOAT, (1, 2, 3, 4, 5))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Concat"))
def test_concat(self, _, version) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (2, 4, 3)), ("y", TensorProto.FLOAT, (7, 4, 3))],
[make_node("Concat", ["x", "y"], ["z"], axis=0)],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("z", TensorProto.FLOAT, (9, 4, 3))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Concat"))
def test_concat_missing_shape(self, *_) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (2, 4, 3)),
"y",
("z", TensorProto.FLOAT, (None, None, None)),
],
[make_node("Concat", ["x", "y", "z"], ["out"], axis=0)],
[],
)
self.assertRaises(onnx.shape_inference.InferenceError, self._inferred, graph)
@parameterized.expand(all_versions_for("Concat"))
def test_concat_3d_axis_2(self, _, version) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (2, 2, 2)), ("y", TensorProto.FLOAT, (2, 2, 2))],
[make_node("Concat", ["x", "y"], ["z"], axis=2)],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("z", TensorProto.FLOAT, (2, 2, 4))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Concat"))
def test_concat_param(self, _, version) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, ("a", 2)), ("y", TensorProto.FLOAT, ("a", 3))],
[make_node("Concat", ["x", "y"], ["z"], axis=1)],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("z", TensorProto.FLOAT, ("a", 5))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Concat"))
def test_concat_param_single_input(self, _, version) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, ("a", 2))],
[make_node("Concat", ["x"], ["z"], axis=0)],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("z", TensorProto.FLOAT, ("a", 2))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Reshape"))
def test_reshape_dynamic_shape_known_rank(self, _, version) -> None:
self.skipIf(version < 14, "Rank inference is added from Version 14")
graph = self._make_graph(
[("x", TensorProto.UINT8, (2, 4, 3)), ("shape", TensorProto.INT64, (2,))],
[make_node("Reshape", ["x", "shape"], ["y"])],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.UINT8, (None, None))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Reshape"))
def test_reshape_dynamic_shape_symbolic(self, _, version) -> None:
graph = self._make_graph(
[("x", TensorProto.UINT8, (2, 4, 3)), ("shape", TensorProto.INT64, ("M",))],
[make_node("Reshape", ["x", "shape"], ["y"])],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.UINT8, None)],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Reshape"))
def test_reshape_dynamic_unknown_shape(self, _, version) -> None:
graph = self._make_graph(
[("x", TensorProto.UINT8, (2, 4, 3)), ("shape", TensorProto.INT64, None)],
[make_node("Reshape", ["x", "shape"], ["y"])],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.UINT8, None)],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Reshape"))
def test_reshape_static_shape(self, _, version) -> None:
graph = self._make_graph(
[("x", TensorProto.UINT8, (2, 4, 3)), ("shape", TensorProto.INT64, (2,))],
[make_node("Reshape", ["x", "shape"], ["y"])],
[],
initializer=[make_tensor("shape", TensorProto.INT64, (2,), (3, 8))],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.UINT8, (3, 8))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Reshape"))
def test_reshape_static_shape_inferred(self, _, version) -> None:
graph = self._make_graph(
[("x", TensorProto.UINT8, (2, 4, 3)), ("shape", TensorProto.INT64, (3,))],
[make_node("Reshape", ["x", "shape"], ["y"])],
[],
initializer=[make_tensor("shape", TensorProto.INT64, (3,), (0, 3, -1))],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.UINT8, (2, 3, 4))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Reshape"))
def test_reshape_static_shape_zero(self, _, version) -> None:
graph = self._make_graph(
[("x", TensorProto.UINT8, (1, 1, 1)), ("shape", TensorProto.INT64, (3,))],
[make_node("Reshape", ["x", "shape"], ["y"])],
[],
initializer=[make_tensor("shape", TensorProto.INT64, (3,), (0, 1, 1))],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.UINT8, (1, 1, 1))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Reshape"))
def test_reshape_static_shape_allowzero(self, _, version) -> None:
self.skipIf(version < 14, "allowzero is added from Version 14")
graph = self._make_graph(
[
("x", TensorProto.UINT8, (1, 0, 0)),
("shape", TensorProto.INT64, (3,)),
],
[make_node("Reshape", ["x", "shape"], ["y"], allowzero=1)],
[],
initializer=[make_tensor("shape", TensorProto.INT64, (3,), (0, 1, 1))],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.UINT8, (0, 1, 1))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Reshape"))
def test_reshape_static_shape_constant(self, _, version) -> None:
graph = self._make_graph(
[("x", TensorProto.UINT8, (2, 4, 3))],
[
make_node(
"Constant",
[],
["shape"],
value=make_tensor("shape", TensorProto.INT64, (2,), (3, 8)),
),
make_node("Reshape", ["x", "shape"], ["y"]),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("shape", TensorProto.INT64, (2,)),
make_tensor_value_info("y", TensorProto.UINT8, (3, 8)),
],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Upsample"))
def test_upsample(self, _, version) -> None:
if version == 7:
graph = self._make_graph(
[("x", TensorProto.INT32, (2, 4, 3, 5))],
[make_node("Upsample", ["x"], ["y"], scales=[1.0, 1.1, 1.3, 1.9])],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.INT32, (2, 4, 3, 9))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
else:
graph = self._make_graph(
[
("x", TensorProto.INT32, (2, 4, 3, 5)),
("scales", TensorProto.FLOAT, (4,)),
],
[make_node("Upsample", ["x", "scales"], ["y"])],
[],
initializer=[
make_tensor("scales", TensorProto.FLOAT, (4,), (1.0, 1.1, 1.3, 1.9))
],
)
def call_inference():
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.INT32, (2, 4, 3, 9))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
if version == 9:
call_inference()
else:
# Upsample is deprecated since Version 10.
with self.assertRaises(onnx.checker.ValidationError) as cm:
call_inference()
exception = cm.exception
assert "Upsample is deprecated" in str(exception)
@parameterized.expand(all_versions_for("Upsample"))
def test_upsample_raw_data(self, _, version) -> None:
if version == 7:
graph = self._make_graph(
[("x", TensorProto.INT32, (1, 3, 4, 5))],
[make_node("Upsample", ["x"], ["y"], scales=[2.0, 1.1, 2.3, 1.9])],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.INT32, (2, 3, 9, 9))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
else:
graph = self._make_graph(
[
("x", TensorProto.INT32, (2, 4, 3, 5)),
("scales", TensorProto.FLOAT, (4,)),
],
[make_node("Upsample", ["x", "scales"], ["y"])],
[],
initializer=[
make_tensor(
"scales",
TensorProto.FLOAT,
(4,),
vals=np.array([1.0, 1.1, 1.3, 1.9], dtype="<f4").tobytes(),
raw=True,
)
],
) # Feed raw bytes (force little endian ordering like onnx standard) for test purpose
def call_inference():
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.INT32, (2, 4, 3, 9))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
if version == 9:
call_inference()
else:
# Upsample is deprecated since Version 10.
with self.assertRaises(onnx.checker.ValidationError) as cm:
call_inference()
exception = cm.exception
assert "Upsample is deprecated" in str(exception)
@parameterized.expand(all_versions_for("Expand"))
def test_expand(self, _, version) -> None:
graph = self._make_graph(
[("x", TensorProto.INT32, (3, 1)), ("shape", TensorProto.INT64, (3,))],
[make_node("Expand", ["x", "shape"], ["y"])],
[],
initializer=[make_tensor("shape", TensorProto.INT64, (3,), (2, 1, 6))],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.INT32, (2, 3, 6))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Expand"))
def test_expand_scalar_input(self, _, version) -> None:
graph = self._make_graph(
[("x", TensorProto.INT32, ()), ("shape", TensorProto.INT64, (2,))],
[make_node("Expand", ["x", "shape"], ["y"])],
[],
initializer=[make_tensor("shape", TensorProto.INT64, (2,), (4, 8))],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.INT32, (4, 8))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Expand"))
def test_expand_raw_data(self, _, version) -> None:
graph = self._make_graph(
[("x", TensorProto.INT32, (3, 1)), ("shape", TensorProto.INT64, (2,))],
[make_node("Expand", ["x", "shape"], ["y"])],
[],
initializer=[
make_tensor(
"shape",
TensorProto.INT64,
(2,),
vals=np.array([3, 4], dtype="<i8").tobytes(),
raw=True,
)
],
) # Feed raw bytes (force little endian ordering like onnx standard) for test purpose
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.INT32, (3, 4))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Expand"))
def test_expand_dynamic_shape(self, _, version) -> None:
graph = self._make_graph(
[
("x", TensorProto.INT32, (1, 2, None)),
("shape", TensorProto.INT64, (3,)),
],
[make_node("Expand", ["x", "shape"], ["y"])],
[],
initializer=[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.INT32, (None, 2, None))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Expand"))
def test_expand_symbolic_shape(self, _, version) -> None:
graph = self._make_graph(
[
("x", TensorProto.INT32, (1, 2, None)),
("shape", TensorProto.INT64, ("unk__0",)),
],
[make_node("Expand", ["x", "shape"], ["y"])],
[],
initializer=[],
)
# if giving a symbolic shape, Expand should not infer any shape or rank inference
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.INT32, None)],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Resize"))
def test_resize_size(self, _, version) -> None:
if version == 10:
graph = self._make_graph(
[
("x", TensorProto.INT32, (2, 4, 3, 5)),
("scales", TensorProto.FLOAT, (4,)),
],
[make_node("Resize", ["x", "scales"], ["y"])],
[],
initializer=[
make_tensor("scales", TensorProto.FLOAT, (4,), (1.0, 1.1, 1.3, 1.9))
],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.INT32, (2, 4, 3, 9))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
elif version == 11:
graph = self._make_graph(
[
("x", TensorProto.INT32, (2, 4, 3, 5)),
("roi", TensorProto.FLOAT, (8,)),
("scales", TensorProto.FLOAT, (4,)),
("sizes", TensorProto.INT64, (4,)),
],
[make_node("Resize", ["x", "roi", "scales", "sizes"], ["y"])],
[],
initializer=[
make_tensor("sizes", TensorProto.INT64, (4,), (3, 5, 6, 7))
],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.INT32, (3, 5, 6, 7))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
else:
graph = self._make_graph(
[
("x", TensorProto.INT32, (2, 4, 3, 5)),
("roi", TensorProto.FLOAT, (8,)),
("sizes", TensorProto.INT64, (4,)),
],
[make_node("Resize", ["x", "roi", "", "sizes"], ["y"])],
[],
initializer=[
make_tensor("sizes", TensorProto.INT64, (4,), (3, 5, 6, 7))
],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.INT32, (3, 5, 6, 7))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Resize"))
def test_resize_size_axes_2_3(self, _, version) -> None:
self.skipIf(version < 18, "axes is from Version 18")
graph = self._make_graph(
[
("x", TensorProto.INT32, (2, 4, 3, 5)),
("roi", TensorProto.FLOAT, (4,)),
("sizes", TensorProto.INT64, (2,)),
],
[make_node("Resize", ["x", "roi", "", "sizes"], ["y"], axes=(2, 3))],
[],
initializer=[make_tensor("sizes", TensorProto.INT64, (2,), (6, 7))],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.INT32, (2, 4, 6, 7))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Resize"))
def test_resize_size_axes_3_2(self, _, version) -> None:
self.skipIf(version < 18, "axes is from Version 18")
graph = self._make_graph(
[
("x", TensorProto.INT32, (2, 4, 3, 5)),
("roi", TensorProto.FLOAT, (4,)),
("sizes", TensorProto.INT64, (2,)),
],
[make_node("Resize", ["x", "roi", "", "sizes"], ["y"], axes=(3, 2))],
[],
initializer=[make_tensor("sizes", TensorProto.INT64, (2,), (6, 7))],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.INT32, (2, 4, 7, 6))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Resize"))
def test_resize_size_not_larger(self, _, version) -> None:
self.skipIf(
version < 18,
"keep_aspect_ratio_policy is from Version 18",
)
graph = self._make_graph(
[
("x", TensorProto.INT32, (3, 5)),
("roi", TensorProto.FLOAT, (4,)),
("sizes", TensorProto.INT64, (2,)),
],
[
make_node(
"Resize",
["x", "roi", "", "sizes"],
["y"],
keep_aspect_ratio_policy="not_larger",
)
],
[],
initializer=[make_tensor("sizes", TensorProto.INT64, (2,), (6, 6))],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.INT32, (4, 6))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Resize"))
def test_resize_size_axes_2_3_not_larger(self, _, version) -> None:
self.skipIf(
version < 18,
"axes & keep_aspect_ratio_policy are from Version 18",
)
graph = self._make_graph(
[
("x", TensorProto.INT32, (2, 4, 3, 5)),
("roi", TensorProto.FLOAT, (4,)),
("sizes", TensorProto.INT64, (2,)),
],
[
make_node(
"Resize",
["x", "roi", "", "sizes"],
["y"],
axes=(2, 3),
keep_aspect_ratio_policy="not_larger",
)
],
[],
initializer=[make_tensor("sizes", TensorProto.INT64, (2,), (6, 6))],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.INT32, (2, 4, 4, 6))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Resize"))
def test_resize_size_not_smaller(self, _, version) -> None:
self.skipIf(
version < 18,
"keep_aspect_ratio_policy is from Version 18",
)
graph = self._make_graph(
[
("x", TensorProto.INT32, (3, 5)),
("roi", TensorProto.FLOAT, (4,)),
("sizes", TensorProto.INT64, (2,)),
],
[
make_node(
"Resize",
["x", "roi", "", "sizes"],
["y"],
keep_aspect_ratio_policy="not_smaller",
)
],
[],
initializer=[make_tensor("sizes", TensorProto.INT64, (2,), (6, 6))],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.INT32, (6, 10))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Resize"))
def test_resize_size_axes_2_3_not_smaller(self, _, version) -> None:
self.skipIf(
version < 18,
"axes & keep_aspect_ratio_policy are from Version 18",
)
graph = self._make_graph(
[
("x", TensorProto.INT32, (2, 4, 3, 5)),
("roi", TensorProto.FLOAT, (4,)),
("sizes", TensorProto.INT64, (2,)),
],
[
make_node(
"Resize",
["x", "roi", "", "sizes"],
["y"],
axes=(2, 3),
keep_aspect_ratio_policy="not_smaller",
)
],
[],
initializer=[make_tensor("sizes", TensorProto.INT64, (2,), (6, 6))],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.INT32, (2, 4, 6, 10))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Resize"))
def test_resize_scale(self, _, version) -> None:
self.skipIf(version < 11, "roi input is from Version 11")
graph = self._make_graph(
[
("x", TensorProto.INT32, (2, 4, 3, 5)),
("roi", TensorProto.FLOAT, (8,)),
("scales", TensorProto.FLOAT, (4,)),
],
[make_node("Resize", ["x", "roi", "scales"], ["y"])],
[],
initializer=[
make_tensor("scales", TensorProto.FLOAT, (4,), (1.0, 1.1, 1.3, 1.9))
],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.INT32, (2, 4, 3, 9))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Resize"))
def test_resize_scale_axes_2_3(self, _, version) -> None:
self.skipIf(version < 18, "axes is from Version 18")
graph = self._make_graph(
[
("x", TensorProto.INT32, (2, 4, 3, 5)),
("roi", TensorProto.FLOAT, (8,)),
("scales", TensorProto.FLOAT, (2,)),
],
[make_node("Resize", ["x", "roi", "scales"], ["y"], axes=(2, 3))],
[],
initializer=[make_tensor("scales", TensorProto.FLOAT, (2,), (1.3, 1.9))],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.INT32, (2, 4, 3, 9))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Resize"))
def test_resize_scale_axes_3_2(self, _, version) -> None:
self.skipIf(version < 18, "axes is from Version 18")
graph = self._make_graph(
[
("x", TensorProto.INT32, (2, 4, 3, 5)),
("roi", TensorProto.FLOAT, (8,)),
("scales", TensorProto.FLOAT, (2,)),
],
[make_node("Resize", ["x", "roi", "scales"], ["y"], axes=(3, 2))],
[],
initializer=[make_tensor("scales", TensorProto.FLOAT, (2,), (1.9, 1.3))],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.INT32, (2, 4, 3, 9))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Resize"))
def test_resize_scale_raw_data(self, _, version) -> None:
self.skipIf(version < 11, "roi input is from Version 11")
graph = self._make_graph(
[
("x", TensorProto.INT32, (1, 3, 4, 5)),
("roi", TensorProto.FLOAT, (8,)),
("scales", TensorProto.FLOAT, (4,)),
],
[make_node("Resize", ["x", "roi", "scales"], ["y"])],
[],
initializer=[
make_tensor(
"scales",
TensorProto.FLOAT,
(4,),
vals=np.array([2.0, 1.1, 2.3, 1.9], dtype="<f4").tobytes(),
raw=True,
)
],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.INT32, (2, 3, 9, 9))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Resize"))
def test_resize_scale_and_size_but_one_is_empty(self, _, version) -> None:
self.skipIf(version < 11, "roi input is from Version 11")
graph = self._make_graph(
[
("x", TensorProto.INT32, (1, 3, 4, 5)),
("roi", TensorProto.FLOAT, (8,)),
("scales", TensorProto.FLOAT, (4,)),
("sizes", TensorProto.INT64, (0,)),
],
[make_node("Resize", ["x", "roi", "scales", "sizes"], ["y"])],
[],
initializer=[
make_tensor(
"scales",
TensorProto.FLOAT,
(4,),
vals=np.array([2.0, 1.1, 2.3, 1.9], dtype="<f4").tobytes(),
raw=True,
),
make_tensor(
"sizes",
TensorProto.INT64,
(0,),
vals=np.array([], dtype="<i8").tobytes(),
raw=True,
),
],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.INT32, (2, 3, 9, 9))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Resize"))
def test_resize_opset11_scales_is_empty(self, _, version) -> None:
self.skipIf(version != 11, "This test only works for Version 11")
# "scales" input in Resize in opset11 is not optional. It must be an empty tensor
# if sizes is needed. Shape inference for Resize shall handle this case.
graph = self._make_graph(
[
("x", TensorProto.INT32, (1, 3, 4, 5)),
("roi", TensorProto.FLOAT, (8,)),
("scales", TensorProto.FLOAT, (0,)),
("sizes", TensorProto.INT64, (4,)),
],
[make_node("Resize", ["x", "roi", "scales", "sizes"], ["y"])],
[],
initializer=[
make_tensor(
"sizes",
TensorProto.INT64,
(4,),
vals=np.array(
[2, 6, 8, 10], dtype="<i8"
).tobytes(), # double in all dimensions
raw=True,
),
],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.INT32, (2, 6, 8, 10))],
opset_imports=[helper.make_opsetid("", version)],
)
@parameterized.expand(all_versions_for("Shape"))
def test_shape(self, _, version) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (2, 4, 3))],
[make_node("Shape", ["x"], ["y"])],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.INT64, (3,))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Shape"))
def test_shape_start_1(self, _, version) -> None:
self.skipIf(version < 15, "start and end are from Version 15")
graph = self._make_graph(
[("x", TensorProto.FLOAT, (2, 4, 3))],
[make_node("Shape", ["x"], ["y"], start=1)],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.INT64, (2,))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Shape"))
def test_shape_end_1(self, _, version) -> None:
self.skipIf(version < 15, "start and end are from Version 15")
graph = self._make_graph(
[("x", TensorProto.FLOAT, (2, 4, 3))],
[make_node("Shape", ["x"], ["y"], end=1)],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.INT64, (1,))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Shape"))
def test_shape_negative_start(self, _, version) -> None:
self.skipIf(version < 15, "start and end are from Version 15")
graph = self._make_graph(
[("x", TensorProto.FLOAT, (2, 4, 3))],
[make_node("Shape", ["x"], ["y"], start=-1)],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.INT64, (1,))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Shape"))
def test_shape_clip1(self, _, version) -> None:
self.skipIf(version < 15, "start and end are from Version 15")
graph = self._make_graph(
[("x", TensorProto.FLOAT, (2, 4, 3))],
[make_node("Shape", ["x"], ["y"], start=-5)],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.INT64, (3,))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Shape"))
def test_shape_clip2(self, _, version) -> None:
self.skipIf(version < 15, "start and end are from Version 15")
graph = self._make_graph(
[("x", TensorProto.FLOAT, (2, 4, 3))],
[make_node("Shape", ["x"], ["y"], end=10)],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.INT64, (3,))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Size"))
def test_size(self, _, version) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (2, 4, 3))], [make_node("Size", ["x"], ["y"])], []
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.INT64, ())],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Gather"))
def test_gather(self, _, version) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (4, 3)), ("i", TensorProto.INT64, (2,))],
[make_node("Gather", ["x", "i"], ["y"])],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.FLOAT, (2, 3))], # type: ignore
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Gather"))
def test_gather_axis1(self, _, version) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (4, 3, 5)), ("i", TensorProto.INT64, (1, 2))],
[make_node("Gather", ["x", "i"], ["y"], axis=1)],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.FLOAT, (4, 1, 2, 5))], # type: ignore
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Gather"))
def test_gather_into_scalar(self, _, version) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (3,)), ("i", TensorProto.INT64, ())],
[make_node("Gather", ["x", "i"], ["y"])],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.FLOAT, ())],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("GatherElements"))
def test_gather_elements(self, _, version) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (2, 2)), ("i", TensorProto.INT64, (2, 2))],
[make_node("GatherElements", ["x", "i"], ["y"], axis=1)],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.FLOAT, (2, 2))], # type: ignore
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("GatherElements"))
def test_gather_elements_axis0(self, _, version) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (3, 3)), ("i", TensorProto.INT64, (2, 3))],
[make_node("GatherElements", ["x", "i"], ["y"], axis=0)],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.FLOAT, (2, 3))], # type: ignore
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("Scatter"))
def test_scatter(self, _, version) -> None:
if version >= 11:
# Scatter is deprecated in domain_version of 11.
with self.assertRaises(onnx.checker.ValidationError) as cm:
self._test_scatter(version)
exception = cm.exception
assert "Scatter is deprecated" in str(exception)
else:
self._test_scatter(version)
def _test_scatter(self, version) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (3, 3)),
("i", TensorProto.INT64, (2, 3)),
("u", TensorProto.FLOAT, (2, 3)),
],
[make_node("Scatter", ["x", "i", "u"], ["y"])],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.FLOAT, (3, 3))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
) # type: ignore
@parameterized.expand(all_versions_for("Scatter"))
def test_scatter_axis1(self, _, version) -> None:
if version >= 11:
# Scatter is deprecated in domain_version of 11.
with self.assertRaises(onnx.checker.ValidationError) as cm:
self._test_scatter_axis1(version)
exception = cm.exception
assert "Scatter is deprecated" in str(exception)
else:
self._test_scatter_axis1(version)
def _test_scatter_axis1(self, version) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (1, 5)),
("i", TensorProto.INT64, (1, 2)),
("u", TensorProto.FLOAT, (1, 2)),
],
[make_node("Scatter", ["x", "i", "u"], ["y"], axis=1)],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.FLOAT, (1, 5))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
) # type: ignore
@parameterized.expand(all_versions_for("ScatterElements"))
def test_scatter_elements(self, _, version) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (3, 3)),
("i", TensorProto.INT64, (2, 3)),
("u", TensorProto.FLOAT, (2, 3)),
],
[make_node("ScatterElements", ["x", "i", "u"], ["y"])],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.FLOAT, (3, 3))], # type: ignore
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("ScatterElements"))
def test_scatter_elements_axis1(self, _, version) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (1, 5)),
("i", TensorProto.INT64, (1, 2)),
("u", TensorProto.FLOAT, (1, 2)),
],
[make_node("ScatterElements", ["x", "i", "u"], ["y"], axis=1)],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.FLOAT, (1, 5))], # type: ignore
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("ScatterND"))
def test_scatternd(self, _, version) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (4, 5, 6)),
("indices", TensorProto.INT64, (3, 3, 2)),
("updates", TensorProto.FLOAT, (3, 3, 6)),
],
[make_node("ScatterND", ["x", "indices", "updates"], ["y"])],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.FLOAT, (4, 5, 6))], # type: ignore
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("ScatterND"))
def test_scatternd_noshape(self, _, version) -> None:
# The shape of 'x_reshaped' cannot be inferred, since it is the output of a dynamic reshape.
# Thus the shape of 'y' is also None.
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (4, 5, 6)),
("indices", TensorProto.INT64, (3, 3, 2)),
("updates", TensorProto.FLOAT, (3, 3, 6)),
("shape", TensorProto.INT64, ("M",)),
],
[
make_node("Reshape", ["x", "shape"], ["x_reshaped"]),
make_node("ScatterND", ["x_reshaped", "indices", "updates"], ["y"]),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("x_reshaped", TensorProto.FLOAT, None),
make_tensor_value_info("y", TensorProto.FLOAT, None),
],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
) # type: ignore
@parameterized.expand(all_versions_for("Squeeze"))
def test_squeeze(self, _, version) -> None:
if version == 11:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (1, 3, 1, 1, 2, 1))],
[make_node("Squeeze", "x", "y", axes=[0, 2, 3, 5])],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.FLOAT, (3, 2))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
else:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (1, 3, 1, 1, 2, 1)),
("axes", TensorProto.INT64, (4,)),
],
[make_node("Squeeze", ["x", "axes"], "y")],
[],
initializer=[
make_tensor("axes", TensorProto.INT64, (4,), (0, 2, 3, 5))
],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.FLOAT, (3, 2))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("StringConcat"))
def test_stringconcat(self, _, version) -> None:
graph = self._make_graph(
[
("x", TensorProto.STRING, (2, 3, 4)),
("y", TensorProto.STRING, (2, 3, 4)),
],
[make_node("StringConcat", ["x", "y"], "z")],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("z", TensorProto.STRING, (2, 3, 4))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("StringConcat"))
def test_stringconcat_broadcasting(self, _, version) -> None:
graph = self._make_graph(
[
("x", TensorProto.STRING, (2, 3, 4)),
("y", TensorProto.STRING, (1, 3, 1)),
],
[make_node("StringConcat", ["x", "y"], "z")],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("z", TensorProto.STRING, (2, 3, 4))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("RegexFullMatch"))
def test_regex_full_match(self, _, version) -> None:
graph = self._make_graph(
[("x", TensorProto.STRING, (2, 4, 3, 9))],
[make_node("RegexFullMatch", ["x"], ["y"], pattern=r"^[A-Z][a-z]*$")],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.BOOL, (2, 4, 3, 9))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("RegexFullMatch"))
def test_regex_full_match_empty_shape(self, _, version) -> None:
graph = self._make_graph(
[("x", TensorProto.STRING, ())],
[make_node("RegexFullMatch", ["x"], ["y"], pattern=r"^[A-Z][a-z]*$")],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.BOOL, ())],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
def test_unsqueeze_regular(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (3, 2)), ("axes", TensorProto.INT64, (4,))],
[make_node("Unsqueeze", ["x", "axes"], "y")],
[],
initializer=[make_tensor("axes", TensorProto.INT64, (4,), (0, 1, 3, 5))],
)
self._assert_inferred(
graph, [make_tensor_value_info("y", TensorProto.FLOAT, (1, 1, 3, 1, 2, 1))]
)
def test_unsqueeze_unsorted_axes(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (3, 4, 5)), ("axes", TensorProto.INT64, (2,))],
[make_node("Unsqueeze", ["x", "axes"], "y")],
[],
initializer=[make_tensor("axes", TensorProto.INT64, (2,), (4, 0))],
)
self._assert_inferred(
graph, [make_tensor_value_info("y", TensorProto.FLOAT, (1, 3, 4, 5, 1))]
)
def test_unsqueeze_negative_axes(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (3, 4, 5)), ("axes", TensorProto.INT64, (2,))],
[make_node("Unsqueeze", ["x", "axes"], "y")],
[],
initializer=[make_tensor("axes", TensorProto.INT64, (2,), (0, -1))],
)
self._assert_inferred(
graph, [make_tensor_value_info("y", TensorProto.FLOAT, (1, 3, 4, 5, 1))]
)
def test_unsqueeze_scalar(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, ()), ("axes", TensorProto.INT64, ())],
[make_node("Unsqueeze", ["x", "axes"], "y")],
[],
initializer=[make_tensor("axes", TensorProto.INT64, (), (-1,))],
)
self._assert_inferred(
graph, [make_tensor_value_info("y", TensorProto.FLOAT, (1,))]
)
def test_slice_without_input_shape(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (3, 2)),
("starts", TensorProto.INT64, (1,)),
("ends", TensorProto.INT64, (1,)),
],
[make_node("Slice", ["x", "starts", "ends"], ["y"])],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("y", TensorProto.FLOAT, None)]
)
def test_slice_with_input_shape(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (3, 2)),
("starts", TensorProto.INT64, (2,)),
("ends", TensorProto.INT64, (2,)),
],
[make_node("Slice", ["x", "starts", "ends"], ["y"])],
[],
initializer=[
make_tensor(
"starts",
TensorProto.INT64,
(2,),
vals=np.array([1, 0], dtype="<i8").tobytes(),
raw=True,
), # Feed raw bytes (force little endian ordering like onnx standard) for test purpose
make_tensor("ends", TensorProto.INT64, (2,), (2, 2)),
],
)
self._assert_inferred(
graph, [make_tensor_value_info("y", TensorProto.FLOAT, (1, 2))]
)
def test_slice_with_input_shape_containing_dim_params(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (1, "a", 1)),
("starts", TensorProto.INT64, (3,)),
("ends", TensorProto.INT64, (3,)),
],
[make_node("Slice", ["x", "starts", "ends"], ["y"])],
[],
initializer=[
make_tensor("starts", TensorProto.INT64, (3,), (0, 0, 0)),
make_tensor("ends", TensorProto.INT64, (3,), (1, 1, 1)),
],
)
self._assert_inferred(graph, [make_tensor_value_info("y", TensorProto.FLOAT, (1, None, 1))]) # type: ignore
def test_slice_with_input_shape_steps(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (5, 6, 7)),
("starts", TensorProto.INT64, (3,)),
("ends", TensorProto.INT64, (3,)),
("axes", TensorProto.INT64, (None)),
("steps", TensorProto.INT64, (3,)),
],
[make_node("Slice", ["x", "starts", "ends", "axes", "steps"], ["y"])],
[],
initializer=[
make_tensor("starts", TensorProto.INT64, (3,), (1, 0, 0)),
make_tensor("ends", TensorProto.INT64, (3,), (2, 6, 6)),
make_tensor("steps", TensorProto.INT64, (3,), (1, 4, 3)),
],
)
self._assert_inferred(
graph, [make_tensor_value_info("y", TensorProto.FLOAT, (1, 2, 2))]
)
def test_slice_with_input_shape_axes(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (3, 6, 2)),
("starts", TensorProto.INT64, (2,)),
("ends", TensorProto.INT64, (2,)),
("axes", TensorProto.INT64, (2,)),
("steps", TensorProto.INT64, (None)),
],
[make_node("Slice", ["x", "starts", "ends", "axes", "steps"], ["y"])],
[],
initializer=[
make_tensor("starts", TensorProto.INT64, (2,), (1, 0)),
make_tensor("ends", TensorProto.INT64, (2,), (2, 2)),
make_tensor("axes", TensorProto.INT64, (2,), (0, 2)),
],
)
self._assert_inferred(
graph, [make_tensor_value_info("y", TensorProto.FLOAT, (1, 6, 2))]
)
def test_slice_unsorted_axes(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (3, 2)),
("starts", TensorProto.INT64, (2,)),
("ends", TensorProto.INT64, (2,)),
("axes", TensorProto.INT64, (2,)),
],
[make_node("Slice", ["x", "starts", "ends", "axes"], "y")],
[],
initializer=[
make_tensor("starts", TensorProto.INT64, (2,), (1, 0)),
make_tensor("ends", TensorProto.INT64, (2,), (2, 2)),
make_tensor("axes", TensorProto.INT64, (2,), (1, 0)),
],
)
self._assert_inferred(
graph, [make_tensor_value_info("y", TensorProto.FLOAT, (2, 1))]
) # can handle unsorted axes
def test_slice_giant_number(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (3, 2)),
("starts", TensorProto.INT64, (2,)),
("ends", TensorProto.INT64, (2,)),
("axes", TensorProto.INT64, (2,)),
],
[make_node("Slice", ["x", "starts", "ends", "axes"], "y")],
[],
initializer=[
make_tensor("starts", TensorProto.INT64, (2,), (1, 0)),
make_tensor("ends", TensorProto.INT64, (2,), (200, 22000)),
make_tensor("axes", TensorProto.INT64, (2,), (0, 1)),
],
)
self._assert_inferred(
graph, [make_tensor_value_info("y", TensorProto.FLOAT, (2, 2))]
)
def test_slice_giant_step(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (3, 2)),
("starts", TensorProto.INT64, (2,)),
("ends", TensorProto.INT64, (2,)),
("axes", TensorProto.INT64, (2,)),
("steps", TensorProto.INT64, (2,)),
],
[make_node("Slice", ["x", "starts", "ends", "axes", "steps"], "y")],
[],
initializer=[
make_tensor("starts", TensorProto.INT64, (2,), (1, 0)),
make_tensor("ends", TensorProto.INT64, (2,), (200, 200)),
make_tensor("axes", TensorProto.INT64, (2,), (0, 1)),
make_tensor("steps", TensorProto.INT64, (2,), (1, 200)),
],
)
self._assert_inferred(
graph, [make_tensor_value_info("y", TensorProto.FLOAT, (2, 1))]
)
def test_slice_negative_end(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (3, 2)),
("starts", TensorProto.INT64, (2,)),
("ends", TensorProto.INT64, (2,)),
("axes", TensorProto.INT64, (2,)),
],
[make_node("Slice", ["x", "starts", "ends", "axes"], "y")],
[],
initializer=[
make_tensor("starts", TensorProto.INT64, (2,), (1, 0)),
make_tensor(
"ends", TensorProto.INT64, (2,), (200, -1)
), # negative end means begin from end of a dimension (here end = 2 - 1 = 1)
make_tensor("axes", TensorProto.INT64, (2,), (0, 1)),
],
)
self._assert_inferred(graph, [make_tensor_value_info("y", TensorProto.FLOAT, (2, 1))]) # type: ignore
def test_slice_negative_start(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (3, 2)),
("starts", TensorProto.INT64, (2,)),
("ends", TensorProto.INT64, (2,)),
("axes", TensorProto.INT64, (2,)),
],
[make_node("Slice", ["x", "starts", "ends", "axes"], "y")],
[],
initializer=[
make_tensor(
"starts", TensorProto.INT64, (2,), (1, -2)
), # negative start means begin from end of a dimension (here end = 2 - 2 = 0)
make_tensor("ends", TensorProto.INT64, (2,), (200, 3)),
make_tensor("axes", TensorProto.INT64, (2,), (0, 1)),
],
)
self._assert_inferred(graph, [make_tensor_value_info("y", TensorProto.FLOAT, (2, 2))]) # type: ignore
def test_slice_negative_step(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (3, 4)),
("starts", TensorProto.INT64, (2,)),
("ends", TensorProto.INT64, (2,)),
("axes", TensorProto.INT64, (2,)),
("steps", TensorProto.INT64, (2,)),
],
[make_node("Slice", ["x", "starts", "ends", "axes", "steps"], "y")],
[],
initializer=[
make_tensor(
"starts", TensorProto.INT64, (2,), (1, 4)
), # 4 will be clamped to 3 since we are negative stepping
make_tensor("ends", TensorProto.INT64, (2,), (200, 0)),
make_tensor("axes", TensorProto.INT64, (2,), (0, 1)),
make_tensor("steps", TensorProto.INT64, (2,), (1, -1)),
],
)
self._assert_inferred(graph, [make_tensor_value_info("y", TensorProto.FLOAT, (2, 3))]) # type: ignore
def test_slice_variable_copy(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, ("a", 2)),
("starts", TensorProto.INT64, (1,)),
("ends", TensorProto.INT64, (1,)),
("axes", TensorProto.INT64, (1,)),
],
[make_node("Slice", ["x", "starts", "ends", "axes"], "y")],
[],
initializer=[
make_tensor("starts", TensorProto.INT64, (1,), (1,)),
make_tensor("ends", TensorProto.INT64, (1,), (200,)),
make_tensor("axes", TensorProto.INT64, (1,), (1,)),
],
)
self._assert_inferred(graph, [make_tensor_value_info("y", TensorProto.FLOAT, ("a", 1))]) # type: ignore
def test_slice_variable_input_types(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.DOUBLE, (3, 2)),
("starts", TensorProto.INT32, (2,)),
("ends", TensorProto.INT32, (2,)),
("axes", TensorProto.INT32, (2,)),
],
[make_node("Slice", ["x", "starts", "ends", "axes"], "y")],
[],
initializer=[
make_tensor("starts", TensorProto.INT32, (2,), (1, 0)),
make_tensor("ends", TensorProto.INT32, (2,), (200, 22000)),
make_tensor("axes", TensorProto.INT32, (2,), (0, 1)),
],
)
self._assert_inferred(
graph, [make_tensor_value_info("y", TensorProto.DOUBLE, (2, 2))]
)
def test_conv(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (3, 4, 5, 6, 7)),
("y", TensorProto.FLOAT, (5, 4, 2, 4, 3)),
],
[
make_node(
"Conv",
["x", "y"],
"z",
pads=[0, 1, 1, 0, 0, 1],
dilations=[1, 2, 2],
strides=[1, 1, 2],
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("z", TensorProto.FLOAT, (3, 5, 4, 1, 3))]
)
def test_conv_1d_simple(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (30, 4, 5)),
("y", TensorProto.FLOAT, (50, 4, 2)),
],
[make_node("Conv", ["x", "y"], "z", dilations=[1])],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("z", TensorProto.FLOAT, (30, 50, 4))]
)
def test_conv_dilations(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (30, 4, 8, 8, 8)),
("y", TensorProto.FLOAT, (50, 4, 3, 3, 3)),
],
[make_node("Conv", ["x", "y"], "z", dilations=[1, 2, 3])],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("z", TensorProto.FLOAT, (30, 50, 6, 4, 2))]
)
def test_conv_strides(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (30, 4, 8, 8, 8)),
("y", TensorProto.FLOAT, (50, 4, 3, 3, 3)),
],
[make_node("Conv", ["x", "y"], "z", strides=[1, 2, 3])],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("z", TensorProto.FLOAT, (30, 50, 6, 3, 2))]
)
def test_conv_pads(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (30, 4, 7, 6, 4)),
("y", TensorProto.FLOAT, (50, 4, 3, 3, 3)),
],
[make_node("Conv", ["x", "y"], "z", pads=[1, 1, 2, 0, 1, 2])],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("z", TensorProto.FLOAT, (30, 50, 6, 6, 6))]
)
def test_conv_auto_pad(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (30, 4, 7, 6, 4)),
("y", TensorProto.FLOAT, (50, 4, 4, 3, 2)),
],
[make_node("Conv", ["x", "y"], "z", auto_pad="SAME_UPPER")],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("z", TensorProto.FLOAT, (30, 50, 7, 6, 4))]
)
def test_conv_auto_pads(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (30, 4, 7, 6, 4)),
("y", TensorProto.FLOAT, (50, 4, 4, 3, 2)),
],
[
make_node(
"Conv", ["x", "y"], "z", auto_pad="SAME_UPPER", strides=[2, 2, 1]
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("z", TensorProto.FLOAT, (30, 50, 4, 3, 4))]
)
def test_conv_auto_pad_dilation(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (30, 4, 65, 64, 63)),
("y", TensorProto.FLOAT, (50, 4, 4, 3, 2)),
],
[
make_node(
"Conv", ["x", "y"], "z", auto_pad="SAME_UPPER", dilations=[2, 3, 4]
)
],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("z", TensorProto.FLOAT, (30, 50, 65, 64, 63))],
)
def test_conv_group(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (30, 4, 8, 8, 8)),
("y", TensorProto.FLOAT, (4, 1, 8, 8, 8)),
],
[make_node("Conv", ["x", "y"], "z", group=4)],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("z", TensorProto.FLOAT, (30, 4, 1, 1, 1))]
)
def test_conv_only_one_pos(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (30, 4, 5)),
("y", TensorProto.FLOAT, (50, 4, 5)),
],
[make_node("Conv", ["x", "y"], "z", strides=[2])],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("z", TensorProto.FLOAT, (30, 50, 1))]
)
def test_conv_partial_missing_shape(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (30, 4, None, 6, 4)),
("y", TensorProto.FLOAT, (50, 4, 3, 3, 3)),
],
[make_node("Conv", ["x", "y"], "z", pads=[1, 1, 2, 0, 1, 2])],
[],
)
self._assert_inferred(graph, [make_tensor_value_info("z", TensorProto.FLOAT, (30, 50, None, 6, 6))]) # type: ignore
def test_conv_partial_missing_weight_shape(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (30, 4, 7, 6, 4)),
("y", TensorProto.FLOAT, (50, 4, None, 3, 3)),
],
[make_node("Conv", ["x", "y"], "z", pads=[1, 1, 2, 0, 1, 2])],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("z", TensorProto.FLOAT, None)]
)
def test_average_pool_auto_pads(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (30, 4, 7, 6, 4))],
[
make_node(
"AveragePool",
["x"],
"z",
auto_pad="SAME_UPPER",
kernel_shape=[4, 3, 2],
strides=[2, 2, 1],
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("z", TensorProto.FLOAT, (30, 4, 4, 3, 4))]
)
def test_average_pool_with_dilations(self) -> None:
graph = self._make_graph(
[("X", TensorProto.FLOAT, (5, 3, 4, 4))],
[
make_node(
"AveragePool", ["X"], ["Y"], kernel_shape=[2, 2], dilations=[2, 2]
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 2, 2))]
)
def test_average_pool_with_same_upper_padding_and_stride_and_dilation(self) -> None:
graph = self._make_graph(
[("X", TensorProto.FLOAT, (5, 3, 4, 4))],
[
make_node(
"AveragePool",
["X"],
["Y"],
auto_pad="SAME_UPPER",
kernel_shape=[2, 2],
strides=[2, 2],
dilations=[2, 3],
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 2, 2))]
)
def test_relu(self) -> None:
self._identity_prop("Relu")
def test_identity(self) -> None:
self._identity_prop("Identity")
def test_identity_sequence(self) -> None:
graph = self._make_graph(
[
("input1", TensorProto.FLOAT, (2, 3, 4)),
("input2", TensorProto.FLOAT, (2, 3, 4)),
("input3", TensorProto.FLOAT, (2, 5, 4)),
],
[
make_node(
"SequenceConstruct", ["input1", "input2", "input3"], ["in_sequence"]
),
make_node("Identity", ["in_sequence"], ["output_sequence"]),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_sequence_value_info("in_sequence", TensorProto.FLOAT, (2, None, 4)), # type: ignore
make_tensor_sequence_value_info(
"output_sequence", TensorProto.FLOAT, (2, None, 4)
),
],
) # type: ignore
def test_identity_optional(self) -> None:
graph = self._make_graph(
[("in_tensor", TensorProto.FLOAT, (2, 3, 4))],
[
make_node("Optional", ["in_tensor"], ["in_optional"]),
make_node("Identity", ["in_optional"], ["output_optional"]),
],
[],
)
tensor_type_proto = helper.make_tensor_type_proto(TensorProto.FLOAT, (2, 3, 4))
optional_type_proto = helper.make_optional_type_proto(tensor_type_proto)
self._assert_inferred(
graph,
[
helper.make_value_info("in_optional", optional_type_proto), # type: ignore
helper.make_value_info("output_optional", optional_type_proto),
],
) # type: ignore
def test_identity_optional_sequence(self) -> None:
graph = self._make_graph(
[
("input1", TensorProto.FLOAT, (2, 3, 4)),
("input2", TensorProto.FLOAT, (2, 3, 4)),
("input3", TensorProto.FLOAT, (2, 5, 4)),
],
[
make_node(
"SequenceConstruct", ["input1", "input2", "input3"], ["in_sequence"]
),
make_node("Optional", ["in_sequence"], ["in_optional"]),
make_node("Identity", ["in_optional"], ["output_optional"]),
],
[],
)
tensor_type_proto = helper.make_tensor_type_proto(
TensorProto.FLOAT, (2, None, 4)
)
sequence_type_proto = helper.make_sequence_type_proto(tensor_type_proto)
optional_type_proto = helper.make_optional_type_proto(sequence_type_proto)
self._assert_inferred(
graph,
[
helper.make_value_info("in_sequence", sequence_type_proto), # type: ignore
helper.make_value_info("in_optional", optional_type_proto), # type: ignore
helper.make_value_info("output_optional", optional_type_proto),
],
) # type: ignore
def test_add(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (30, 4, 5)),
("y", TensorProto.FLOAT, (30, 4, 5)),
],
[make_node("Add", ["x", "y"], "z")],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("z", TensorProto.FLOAT, (30, 4, 5))]
)
def test_pow(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (30, 4, 5)),
("y", TensorProto.FLOAT, (30, 4, 5)),
],
[make_node("Pow", ["x", "y"], "z")],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("z", TensorProto.FLOAT, (30, 4, 5))]
)
def test_bitshift(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.UINT32, (2, 3, 1)),
("y", TensorProto.UINT32, (2, 3, 1)),
],
[make_node("BitShift", ["x", "y"], "z", direction="RIGHT")],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("z", TensorProto.UINT32, (2, 3, 1))]
)
def test_bitshift_broadcast_to_first(self) -> None:
graph = self._make_graph(
[("x", TensorProto.UINT32, (16, 4, 1)), ("y", TensorProto.UINT32, (1,))],
[make_node("BitShift", ["x", "y"], "z", direction="RIGHT")],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("z", TensorProto.UINT32, (16, 4, 1))]
)
def test_bitshift_broadcast_to_second(self) -> None:
graph = self._make_graph(
[("x", TensorProto.UINT32, (1,)), ("y", TensorProto.UINT32, (2, 3, 1))],
[make_node("BitShift", ["x", "y"], "z", direction="RIGHT")],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("z", TensorProto.UINT32, (2, 3, 1))]
)
def test_sum_single(self) -> None:
self._identity_prop("Sum")
def test_sum_multi(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (30, 4, 5)),
("y", TensorProto.FLOAT, (30, 4, 5)),
("z", TensorProto.FLOAT, (30, 4, 5)),
],
[make_node("Sum", ["x", "y", "z"], ["out"])],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("out", TensorProto.FLOAT, (30, 4, 5))]
)
def test_sum_multi_broadcasting(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (30, 1, 5)),
("y", TensorProto.FLOAT, ("a", 4, 1)),
("z", TensorProto.FLOAT, (4, "b")),
],
[make_node("Sum", ["x", "y", "z"], ["out"])],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("out", TensorProto.FLOAT, (30, 4, 5))]
)
def test_sum_broadcasting_param(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, ("a", 1, 5)),
("y", TensorProto.FLOAT, ("a", 4, 1)),
],
[make_node("Sum", ["x", "y"], ["out"])],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("out", TensorProto.FLOAT, ("a", 4, 5))]
)
def test_random_normal(self) -> None:
graph = self._make_graph(
[],
[
make_node(
"RandomNormal",
[],
["out"],
dtype=TensorProto.DOUBLE,
shape=(3, 4, 5),
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("out", TensorProto.DOUBLE, (3, 4, 5))]
)
def test_random_normal_like(self) -> None:
graph = self._make_graph(
[("X", TensorProto.FLOAT, (2, 3, 4))],
[make_node("RandomNormalLike", ["X"], ["out"])],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("out", TensorProto.FLOAT, (2, 3, 4))]
)
def test_random_normal_like_with_dtype(self) -> None:
graph = self._make_graph(
[("X", TensorProto.FLOAT, (2, 3, 4))],
[
make_node(
"RandomNormalLike",
["X"],
["out"],
dtype=TensorProto.DOUBLE,
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("out", TensorProto.DOUBLE, (2, 3, 4))]
)
def test_bernoulli(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (3, 4))],
[make_node("Bernoulli", ["x"], ["out"])],
[],
)
self._assert_inferred(graph, [make_tensor_value_info("out", TensorProto.FLOAT, (3, 4))]) # type: ignore
def test_bernoulli_with_dtype(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (2, 3, 4))],
[
make_node(
"Bernoulli",
["x"],
["out"],
dtype=TensorProto.DOUBLE,
)
],
[],
)
self._assert_inferred(graph, [make_tensor_value_info("out", TensorProto.DOUBLE, (2, 3, 4))]) # type: ignore
def _logical_binary_op(self, op: str, input_type: TensorProto.DataType) -> None:
graph = self._make_graph(
[("x", input_type, (30, 4, 5)), ("y", input_type, (30, 4, 5))],
[make_node(op, ["x", "y"], "z")],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("z", TensorProto.BOOL, (30, 4, 5))]
)
def _logical_binary_op_with_broadcasting(
self, op: str, input_type: TensorProto.DataType
) -> None:
graph = self._make_graph(
[("x", input_type, (1, 5)), ("y", input_type, (30, 4, 5))],
[make_node(op, ["x", "y"], "z")],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("z", TensorProto.BOOL, (30, 4, 5))]
)
def test_logical_and(self) -> None:
self._logical_binary_op("And", TensorProto.BOOL)
self._logical_binary_op_with_broadcasting("And", TensorProto.BOOL)
def test_logical_or(self) -> None:
self._logical_binary_op("Or", TensorProto.BOOL)
self._logical_binary_op_with_broadcasting("Or", TensorProto.BOOL)
def test_logical_xor(self) -> None:
self._logical_binary_op("Xor", TensorProto.BOOL)
self._logical_binary_op_with_broadcasting("Xor", TensorProto.BOOL)
def test_greater(self) -> None:
self._logical_binary_op("Greater", TensorProto.BOOL)
self._logical_binary_op_with_broadcasting("Greater", TensorProto.BOOL)
def test_less(self) -> None:
self._logical_binary_op("Less", TensorProto.BOOL)
self._logical_binary_op_with_broadcasting("Less", TensorProto.BOOL)
def test_equal(self) -> None:
self._logical_binary_op("Equal", TensorProto.BOOL)
self._logical_binary_op_with_broadcasting("Equal", TensorProto.BOOL)
def test_equal_string(self) -> None:
self._logical_binary_op("Equal", TensorProto.STRING)
self._logical_binary_op_with_broadcasting("Equal", TensorProto.STRING)
def test_logical_not(self) -> None:
graph = self._make_graph(
[("x", TensorProto.BOOL, (30, 4, 5))], [make_node("Not", ["x"], "z")], []
)
self._assert_inferred(
graph, [make_tensor_value_info("z", TensorProto.BOOL, (30, 4, 5))]
)
def test_less_or_equal(self) -> None:
self._logical_binary_op("LessOrEqual", TensorProto.BOOL)
self._logical_binary_op_with_broadcasting("LessOrEqual", TensorProto.BOOL)
def test_greater_or_equal(self) -> None:
self._logical_binary_op("GreaterOrEqual", TensorProto.BOOL)
self._logical_binary_op_with_broadcasting("GreaterOrEqual", TensorProto.BOOL)
def test_flatten(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (2, 3, 4, 5))],
[make_node("Flatten", ["x"], ["z"], axis=2)],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("z", TensorProto.FLOAT, (6, 20))]
)
def test_flatten_default_axis(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (2, 3, 4, 5))],
[make_node("Flatten", ["x"], ["z"])],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("z", TensorProto.FLOAT, (2, 60))]
)
def test_flatten_zero_axis(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (2, 3, 4, 5))],
[make_node("Flatten", ["x"], ["z"], axis=0)],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("z", TensorProto.FLOAT, (1, 120))]
)
def test_flatten_unknown_dim(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (2, "N", 4, 5))],
[make_node("Flatten", ["x"], ["z"], axis=2)],
[],
)
self._assert_inferred(graph, [make_tensor_value_info("z", TensorProto.FLOAT, (None, 20))]) # type: ignore
def test_space_to_depth(self) -> None:
b = 10
graph = self._make_graph(
[("x", TensorProto.FLOAT, (2, 3, 100, 100))],
[make_node("SpaceToDepth", ["x"], ["z"], blocksize=b)],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("z", TensorProto.FLOAT, (2, 300, 10, 10))]
)
def test_space_to_depth_unknown_dim(self) -> None:
b = 10
graph = self._make_graph(
[("x", TensorProto.FLOAT, (2, "N", 100, 100))],
[make_node("SpaceToDepth", ["x"], ["z"], blocksize=b)],
[],
)
self._assert_inferred(graph, [make_tensor_value_info("z", TensorProto.FLOAT, (2, None, 10, 10))]) # type: ignore
def test_depth_to_space(self) -> None:
b = 10
graph = self._make_graph(
[("x", TensorProto.FLOAT, (2, 300, 10, 10))],
[make_node("DepthToSpace", ["x"], ["z"], blocksize=b, mode="DCR")],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("z", TensorProto.FLOAT, (2, 3, 100, 100))]
)
def _rnn_forward(
self, seqlen: int, batchsize: int, inpsize: int, hiddensize: int
) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (seqlen, batchsize, inpsize)),
("w", TensorProto.FLOAT, (1, hiddensize, inpsize)),
("r", TensorProto.FLOAT, (1, hiddensize, hiddensize)),
],
[
make_node(
"RNN", ["x", "w", "r"], ["all", "last"], hidden_size=hiddensize
)
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info(
"all", TensorProto.FLOAT, (seqlen, 1, batchsize, hiddensize)
),
make_tensor_value_info(
"last", TensorProto.FLOAT, (1, batchsize, hiddensize)
),
],
)
def test_rnn_forward(self) -> None:
self._rnn_forward(64, 32, 10, 4)
def _rnn_bidirectional(
self, seqlen: int, batchsize: int, inpsize: int, hiddensize: int
) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (seqlen, batchsize, inpsize)),
("w", TensorProto.FLOAT, (2, hiddensize, inpsize)),
("r", TensorProto.FLOAT, (2, hiddensize, hiddensize)),
],
[
make_node(
"RNN",
["x", "w", "r"],
["all", "last"],
hidden_size=hiddensize,
direction="bidirectional",
)
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info(
"all", TensorProto.FLOAT, (seqlen, 2, batchsize, hiddensize)
),
make_tensor_value_info(
"last", TensorProto.FLOAT, (2, batchsize, hiddensize)
),
],
)
def test_rnn_layout(self) -> None:
self._rnn_layout(64, 32, 10, 4)
self._rnn_layout(64, 32, 10, 4, "bidirectional")
def _rnn_layout(
self,
seqlen: int,
batchsize: int,
inpsize: int,
hiddensize: int,
direction: str = "forward",
) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (batchsize, seqlen, inpsize)),
("w", TensorProto.FLOAT, (1, hiddensize, inpsize)),
("r", TensorProto.FLOAT, (1, hiddensize, hiddensize)),
],
[
make_node(
"RNN",
["x", "w", "r"],
["all", "last"],
hidden_size=hiddensize,
layout=1,
direction=direction,
)
],
[],
)
if direction == "bidirectional":
num_directions = 2
else:
num_directions = 1
self._assert_inferred(
graph,
[
make_tensor_value_info(
"all",
TensorProto.FLOAT,
(batchsize, seqlen, num_directions, hiddensize),
),
make_tensor_value_info(
"last", TensorProto.FLOAT, (batchsize, num_directions, hiddensize)
),
],
)
def test_rnn_bidirectional(self) -> None:
self._rnn_bidirectional(64, 32, 10, 4)
def _lstm_forward(
self, seqlen: int, batchsize: int, inpsize: int, hiddensize: int
) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (seqlen, batchsize, inpsize)),
("w", TensorProto.FLOAT, (1, 4 * hiddensize, inpsize)),
("r", TensorProto.FLOAT, (1, 4 * hiddensize, hiddensize)),
],
[
make_node(
"LSTM",
["x", "w", "r"],
["all", "hidden", "last"],
hidden_size=hiddensize,
)
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info(
"all", TensorProto.FLOAT, (seqlen, 1, batchsize, hiddensize)
),
make_tensor_value_info(
"hidden", TensorProto.FLOAT, (1, batchsize, hiddensize)
),
make_tensor_value_info(
"last", TensorProto.FLOAT, (1, batchsize, hiddensize)
),
],
)
def test_lstm_forward(self) -> None:
self._lstm_forward(64, 32, 10, 4)
def test_topk_default_axis(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (3, 4, 5, 10))],
[make_node("TopK", ["x", "k"], ["y", "z"])],
[],
initializer=[make_tensor("k", TensorProto.INT64, (1,), (2,))],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("y", TensorProto.FLOAT, (3, 4, 5, 2)),
make_tensor_value_info("z", TensorProto.INT64, (3, 4, 5, 2)),
],
)
def test_topk(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (3, 4, 5, 10))],
[make_node("TopK", ["x", "k"], ["y", "z"], axis=2)],
[],
initializer=[make_tensor("k", TensorProto.INT64, (1,), (2,))],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("y", TensorProto.FLOAT, (3, 4, 2, 10)),
make_tensor_value_info("z", TensorProto.INT64, (3, 4, 2, 10)),
],
)
def test_topk_raw_data(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (3, 4, 5, 10))],
[make_node("TopK", ["x", "k"], ["y", "z"], axis=2)],
[],
initializer=[
make_tensor(
"k",
TensorProto.INT64,
(1,),
vals=np.array([3], dtype="<i8").tobytes(),
raw=True,
)
],
) # Feed raw bytes (force little endian ordering like onnx standard) for test purpose
self._assert_inferred(
graph,
[
make_tensor_value_info("y", TensorProto.FLOAT, (3, 4, 3, 10)),
make_tensor_value_info("z", TensorProto.INT64, (3, 4, 3, 10)),
],
)
def test_topk_missing_k_value_output_rank_check(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (3, 4, 5, 10)), ("k", TensorProto.INT64, (1,))],
[make_node("TopK", ["x", "k"], ["y", "z"], axis=2)],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("y", TensorProto.FLOAT, (None, None, None, None)), # type: ignore
make_tensor_value_info(
"z", TensorProto.INT64, (None, None, None, None)
),
],
) # type: ignore
def test_gemm(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (7, 5)),
("y", TensorProto.FLOAT, (5, 11)),
("z", TensorProto.FLOAT, None),
],
[make_node("Gemm", ["x", "y", "z"], ["out"])],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("out", TensorProto.FLOAT, (7, 11))]
)
def test_gemm_transA(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (5, 7)),
("y", TensorProto.FLOAT, (5, 11)),
("z", TensorProto.FLOAT, None),
],
[make_node("Gemm", ["x", "y", "z"], ["out"], transA=1)],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("out", TensorProto.FLOAT, (7, 11))]
)
def test_gemm_transB(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (7, 5)),
("y", TensorProto.FLOAT, (11, 5)),
("z", TensorProto.FLOAT, None),
],
[make_node("Gemm", ["x", "y", "z"], ["out"], transB=1)],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("out", TensorProto.FLOAT, (7, 11))]
)
def test_gemm_transA_and_transB(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (5, 7)),
("y", TensorProto.FLOAT, (11, 5)),
("z", TensorProto.FLOAT, None),
],
[make_node("Gemm", ["x", "y", "z"], ["out"], transA=1, transB=1)],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("out", TensorProto.FLOAT, (7, 11))]
)
def test_gemm_no_bias(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (13, 7)), ("y", TensorProto.FLOAT, (7, 17))],
[make_node("Gemm", ["x", "y"], ["out"])],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("out", TensorProto.FLOAT, (13, 17))]
)
def test_reduce_op_shape_2_axis_opset13(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (24, 4, 11))],
[make_node("ReduceL1", "x", "y", axes=(1, 2), keepdims=0)],
[],
initializer=[make_tensor("axes", TensorProto.INT64, (2,), (1, 2))],
)
operatorsetid = OperatorSetIdProto()
operatorsetid.domain = ""
operatorsetid.version = 13
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.FLOAT, (24,))],
opset_imports=[operatorsetid],
)
def test_reduce_op_shape_2_axis_opset18(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (24, 4, 11)), ("axes", TensorProto.INT64, (2,))],
[make_node("ReduceL1", ["x", "axes"], "y", keepdims=0)],
[],
initializer=[make_tensor("axes", TensorProto.INT64, (2,), (1, 2))],
)
operatorsetid = OperatorSetIdProto()
operatorsetid.domain = ""
operatorsetid.version = 18
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.FLOAT, (24,))],
opset_imports=[operatorsetid],
)
def test_reduce_op_shape_keep_dims_opset13(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (24, 4, 11))],
[make_node("ReduceL1", "x", "y", axes=(1, 2), keepdims=1)],
[],
initializer=[make_tensor("axes", TensorProto.INT64, (2,), (1, 2))],
)
operatorsetid = OperatorSetIdProto()
operatorsetid.domain = ""
operatorsetid.version = 13
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.FLOAT, (24, 1, 1))],
opset_imports=[operatorsetid],
)
def test_reduce_op_shape_keep_dims_opset18(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (24, 4, 11)), ("axes", TensorProto.INT64, (2,))],
[make_node("ReduceL1", ["x", "axes"], "y", keepdims=1)],
[],
initializer=[make_tensor("axes", TensorProto.INT64, (2,), (1, 2))],
)
operatorsetid = OperatorSetIdProto()
operatorsetid.domain = ""
operatorsetid.version = 18
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.FLOAT, (24, 1, 1))],
opset_imports=[operatorsetid],
)
def test_reduce_op_shape_default_value(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (24, 4, 11))],
[make_node("ReduceL1", "x", "y")],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("y", TensorProto.FLOAT, (1, 1, 1))]
)
def test_reduce_op_shape_no_axes_do_not_keep_dims(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (24, 4, 11))],
[make_node("ReduceL1", "x", "y", keepdims=0)],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("y", TensorProto.FLOAT, ())]
)
def test_reduce_op_shape_negative_axis(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (24, 4, 11)), ("axes", TensorProto.INT64, (2,))],
[make_node("ReduceL1", ["x", "axes"], "y")],
[],
initializer=[make_tensor("axes", TensorProto.INT64, (2,), (-1, -2))],
)
self._assert_inferred(
graph, [make_tensor_value_info("y", TensorProto.FLOAT, (24, 1, 1))]
)
def test_argmax_shape(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (24, 4, 11))],
[make_node("ArgMax", "x", "y", axis=1, keepdims=1)],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("y", TensorProto.INT64, (24, 1, 11))]
)
def test_argmax_shape_keepdims(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (24, 4, 11))],
[make_node("ArgMax", "x", "y", axis=0, keepdims=0)],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("y", TensorProto.INT64, (4, 11))]
)
def test_argmax_shape_default_value(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (24, 4, 11))], [make_node("ArgMax", "x", "y")], []
)
self._assert_inferred(
graph, [make_tensor_value_info("y", TensorProto.INT64, (1, 4, 11))]
)
def test_argmax_shape_negative_axis(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (24, 4, 11))],
[make_node("ArgMax", "x", "y", axis=-2)],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("y", TensorProto.INT64, (24, 1, 11))]
)
def test_dropout(self) -> None:
graph = self._make_graph(
[
(
"data",
TensorProto.FLOAT,
(
3,
4,
5,
),
),
("ratio", TensorProto.FLOAT, ()),
],
[make_node("Dropout", ["data", "ratio"], ["out"])],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info(
"out",
TensorProto.FLOAT,
(
3,
4,
5,
),
)
],
)
def test_LRN(self) -> None:
self._identity_prop("LRN", alpha=0.5, beta=0.5, size=1)
def test_batch_norm(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (3, 4, 5, 6, 7)),
("scale", TensorProto.FLOAT, (4,)),
("b", TensorProto.FLOAT, (4,)),
("mean", TensorProto.FLOAT, (4,)),
("var", TensorProto.FLOAT, (4,)),
],
[
make_node(
"BatchNormalization", ["x", "scale", "b", "mean", "var"], ["out"]
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("out", TensorProto.FLOAT, (3, 4, 5, 6, 7))]
)
def test_batch_norm_rank1(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (128,)), # 1-dimensional permitted
("scale", TensorProto.FLOAT, (1,)),
("b", TensorProto.FLOAT, (1,)),
("mean", TensorProto.FLOAT, (1,)),
("var", TensorProto.FLOAT, (1,)),
],
[
make_node(
"BatchNormalization", ["x", "scale", "b", "mean", "var"], ["out"]
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("out", TensorProto.FLOAT, (128,))]
)
def test_batch_norm_invalid(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (128,)),
("scale", TensorProto.FLOAT, (1, 2)), # invalid rank
("b", TensorProto.FLOAT, (1,)),
("mean", TensorProto.FLOAT, (1,)),
("var", TensorProto.FLOAT, (1,)),
],
[
make_node(
"BatchNormalization", ["x", "scale", "b", "mean", "var"], ["out"]
)
],
[],
)
self.assertRaises(onnx.shape_inference.InferenceError, self._inferred, graph)
def test_split_negative_axis(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (2, 4))],
[make_node("Split", ["x"], ["y", "z"], axis=-1, num_outputs=2)],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("y", TensorProto.FLOAT, (2, 2)),
make_tensor_value_info("z", TensorProto.FLOAT, (2, 2)),
],
)
def test_split_with_split_attribute(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (2, 4)), ("split", TensorProto.INT64, (2,))],
[make_node("Split", ["x", "split"], ["y", "z"], axis=1)],
[],
initializer=[make_tensor("split", TensorProto.INT64, (2,), (3, 1))],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("y", TensorProto.FLOAT, (2, 3)),
make_tensor_value_info("z", TensorProto.FLOAT, (2, 1)),
],
)
def test_split_with_split_attribute_unknown_split_dim(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (2, "a", "b")),
("split", TensorProto.INT64, (2,)),
],
[make_node("Split", ["x", "split"], ["y", "z"], axis=1)],
[],
initializer=[make_tensor("split", TensorProto.INT64, (2,), (3, 1))],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("y", TensorProto.FLOAT, (2, None, "b")), # type: ignore
make_tensor_value_info("z", TensorProto.FLOAT, (2, None, "b")),
],
) # type: ignore
def test_split_from_GLU(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (5, 6, 7))],
[make_node("Split", ["x"], ["y", "z"], axis=1, num_outputs=2)],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("y", TensorProto.FLOAT, (5, 3, 7)),
make_tensor_value_info("z", TensorProto.FLOAT, (5, 3, 7)),
],
)
def test_split_uneven_split_2d(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (8, 2))],
[make_node("Split", ["x"], ["y", "z", "a"], axis=0, num_outputs=3)],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("y", TensorProto.FLOAT, (3, 2)),
make_tensor_value_info("z", TensorProto.FLOAT, (3, 2)),
make_tensor_value_info("a", TensorProto.FLOAT, (2, 2)),
],
)
def test_split_uneven_split_3d(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (2, 7, 3))],
[make_node("Split", ["x"], ["y", "z", "a"], axis=1, num_outputs=3)],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("y", TensorProto.FLOAT, (2, 3, 3)),
make_tensor_value_info("z", TensorProto.FLOAT, (2, 3, 3)),
make_tensor_value_info("a", TensorProto.FLOAT, (2, 1, 3)),
],
)
def test_GLU_partial(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (5, 6, 7))],
[
make_node("Split", ["x"], ["y", "z"], axis=1, num_outputs=2),
make_node("Sigmoid", ["z"], ["a"]),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("y", TensorProto.FLOAT, (5, 3, 7)),
make_tensor_value_info("z", TensorProto.FLOAT, (5, 3, 7)),
make_tensor_value_info("a", TensorProto.FLOAT, (5, 3, 7)),
],
)
def test_GLU(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (5, 6, 7))],
[
make_node("Split", ["x"], ["y", "z"], axis=1, num_outputs=2),
make_node("Sigmoid", ["z"], ["a"]),
make_node("Mul", ["y", "a"], ["b"]),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("y", TensorProto.FLOAT, (5, 3, 7)),
make_tensor_value_info("z", TensorProto.FLOAT, (5, 3, 7)),
make_tensor_value_info("a", TensorProto.FLOAT, (5, 3, 7)),
make_tensor_value_info("b", TensorProto.FLOAT, (5, 3, 7)),
],
)
def test_softmax_2d(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (4, 5))], [make_node("Softmax", ["x"], "z")], []
)
self._assert_inferred(
graph, [make_tensor_value_info("z", TensorProto.FLOAT, (4, 5))]
)
def test_softmax_3d(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (4, 5, 6))],
[make_node("Softmax", ["x"], "z")],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("z", TensorProto.FLOAT, (4, 5, 6))]
)
def test_hardmax_2d(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (4, 5))], [make_node("Hardmax", ["x"], "z")], []
)
self._assert_inferred(
graph, [make_tensor_value_info("z", TensorProto.FLOAT, (4, 5))]
)
def test_hardmax_3d(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (4, 5, 6))],
[make_node("Hardmax", ["x"], "z")],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("z", TensorProto.FLOAT, (4, 5, 6))]
)
def test_logsoftmax_2d(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (4, 5))],
[make_node("LogSoftmax", ["x"], "z")],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("z", TensorProto.FLOAT, (4, 5))]
)
def test_logsoftmax_3d(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (4, 5, 6))],
[make_node("LogSoftmax", ["x"], "z")],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("z", TensorProto.FLOAT, (4, 5, 6))]
)
def test_logsoftmax_3d_negative_axis(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (4, 5, 6))],
[make_node("LogSoftmax", ["x"], "z", axis=-1)],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("z", TensorProto.FLOAT, (4, 5, 6))]
)
def test_maxpool(self) -> None:
graph = self._make_graph(
[("X", TensorProto.FLOAT, (5, 3, 4, 4))],
[make_node("MaxPool", ["X"], ["Y"], kernel_shape=[2, 2])],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 3, 3))]
)
def test_maxpool_with_indices(self) -> None:
graph = self._make_graph(
[("X", TensorProto.FLOAT, (5, 3, 4, 4))],
[make_node("MaxPool", ["X"], ["Y", "Z"], kernel_shape=[2, 2])],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 3, 3)),
make_tensor_value_info("Z", TensorProto.INT64, (5, 3, 3, 3)),
],
)
def test_maxpool_3D(self) -> None:
graph = self._make_graph(
[("X", TensorProto.FLOAT, (5, 3, 4, 4, 4))],
[make_node("MaxPool", ["X"], ["Y"], kernel_shape=[2, 2, 2])],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 3, 3, 3))]
)
def test_maxpool_with_padding(self) -> None:
graph = self._make_graph(
[("X", TensorProto.FLOAT, (5, 3, 4, 4))],
[
make_node(
"MaxPool", ["X"], ["Y"], kernel_shape=[2, 2], pads=[1, 1, 2, 2]
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 6, 6))]
)
def test_maxpool_with_padding_and_stride(self) -> None:
graph = self._make_graph(
[("X", TensorProto.FLOAT, (5, 3, 4, 4))],
[
make_node(
"MaxPool",
["X"],
["Y"],
kernel_shape=[2, 2],
pads=[1, 1, 2, 2],
strides=[2, 2],
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 3, 3))]
)
def test_maxpool_with_floor_mode(self) -> None:
graph = self._make_graph(
[("X", TensorProto.FLOAT, (32, 288, 35, 35))],
[
make_node(
"MaxPool",
["X"],
["Y"],
kernel_shape=[2, 2],
strides=[2, 2],
ceil_mode=False,
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (32, 288, 17, 17))]
)
def test_maxpool_with_ceil_mode(self) -> None:
graph = self._make_graph(
[("X", TensorProto.FLOAT, (32, 288, 35, 35))],
[
make_node(
"MaxPool",
["X"],
["Y"],
kernel_shape=[2, 2],
strides=[2, 2],
ceil_mode=True,
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (32, 288, 18, 18))]
)
def test_maxpool_ceil(self) -> None:
graph = self._make_graph(
[("X", TensorProto.FLOAT, (1, 1, 4, 4))],
[
make_node(
"MaxPool",
["X"],
["Y"],
kernel_shape=[3, 3],
strides=[2, 2],
ceil_mode=True,
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (1, 1, 2, 2))]
)
def test_maxpool_with_dilations(self) -> None:
graph = self._make_graph(
[("X", TensorProto.FLOAT, (5, 3, 4, 4))],
[make_node("MaxPool", ["X"], ["Y"], kernel_shape=[2, 2], dilations=[2, 2])],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 2, 2))]
)
def test_maxpool_with_same_upper_padding_and_stride(self) -> None:
graph = self._make_graph(
[("X", TensorProto.FLOAT, (5, 3, 4, 4))],
[
make_node(
"MaxPool",
["X"],
["Y"],
auto_pad="SAME_UPPER",
kernel_shape=[2, 2],
strides=[2, 2],
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 2, 2))]
)
def test_maxpool_with_same_upper_padding_and_stride_and_dilation(self) -> None:
graph = self._make_graph(
[("X", TensorProto.FLOAT, (5, 3, 4, 4))],
[
make_node(
"MaxPool",
["X"],
["Y"],
auto_pad="SAME_UPPER",
kernel_shape=[2, 2],
strides=[2, 2],
dilations=[2, 3],
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 2, 2))]
)
def test_maxpool_with_same_upper_padding_and_stride_one(self) -> None:
graph = self._make_graph(
[("X", TensorProto.FLOAT, (5, 3, 4, 4))],
[
make_node(
"MaxPool",
["X"],
["Y"],
auto_pad="SAME_UPPER",
kernel_shape=[2, 2],
strides=[1, 1],
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 4, 4))]
)
def test_maxpool_with_same_lower_padding_and_stride(self) -> None:
graph = self._make_graph(
[("X", TensorProto.FLOAT, (5, 3, 9, 9))],
[
make_node(
"MaxPool",
["X"],
["Y"],
auto_pad="SAME_LOWER",
kernel_shape=[2, 2],
strides=[2, 2],
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 5, 5))]
)
def test_maxpool_with_same_lower_padding_and_stride_and_dilation(self) -> None:
graph = self._make_graph(
[("X", TensorProto.FLOAT, (5, 3, 9, 9))],
[
make_node(
"MaxPool",
["X"],
["Y"],
auto_pad="SAME_LOWER",
kernel_shape=[2, 2],
strides=[2, 2],
dilations=[2, 3],
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 5, 5))]
)
def test_maxpool_with_same_lower_padding_and_big_stride(self) -> None:
graph = self._make_graph(
[("X", TensorProto.FLOAT, (5, 3, 4, 4))],
[
make_node(
"MaxPool",
["X"],
["Y"],
auto_pad="SAME_LOWER",
kernel_shape=[2, 2],
strides=[4, 4],
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 1, 1))]
)
def test_averagepool(self) -> None:
graph = self._make_graph(
[("X", TensorProto.FLOAT, (5, 3, 4, 4))],
[make_node("AveragePool", ["X"], ["Y"], kernel_shape=[2, 2])],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 3, 3))]
)
def test_averagepool_3D(self) -> None:
graph = self._make_graph(
[("X", TensorProto.FLOAT, (5, 3, 4, 4, 4))],
[make_node("AveragePool", ["X"], ["Y"], kernel_shape=[2, 2, 2])],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 3, 3, 3))]
)
def test_averagepool_with_padding(self) -> None:
graph = self._make_graph(
[("X", TensorProto.FLOAT, (5, 3, 4, 4))],
[
make_node(
"AveragePool", ["X"], ["Y"], kernel_shape=[2, 2], pads=[1, 1, 2, 2]
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 6, 6))]
)
def test_averagepool_with_padding_and_stride(self) -> None:
graph = self._make_graph(
[("X", TensorProto.FLOAT, (5, 3, 4, 4))],
[
make_node(
"AveragePool",
["X"],
["Y"],
kernel_shape=[2, 2],
pads=[1, 1, 2, 2],
strides=[2, 2],
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 3, 3))]
)
def test_averagepool_ceil(self) -> None:
graph = self._make_graph(
[("X", TensorProto.FLOAT, (1, 1, 4, 4))],
[
make_node(
"AveragePool",
["X"],
["Y"],
kernel_shape=[3, 3],
strides=[2, 2],
ceil_mode=True,
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (1, 1, 2, 2))]
)
def test_lppool(self) -> None:
graph = self._make_graph(
[("X", TensorProto.FLOAT, (5, 3, 4, 4))],
[make_node("LpPool", ["X"], ["Y"], kernel_shape=[2, 2])],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 3, 3))]
)
def test_lppool_3D(self) -> None:
graph = self._make_graph(
[("X", TensorProto.FLOAT, (5, 3, 4, 4, 4))],
[make_node("LpPool", ["X"], ["Y"], kernel_shape=[2, 2, 2])],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 3, 3, 3))]
)
def test_lppool_with_padding(self) -> None:
graph = self._make_graph(
[("X", TensorProto.FLOAT, (5, 3, 4, 4))],
[make_node("LpPool", ["X"], ["Y"], kernel_shape=[2, 2], pads=[1, 1, 2, 2])],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 6, 6))]
)
def test_lppool_with_padding_and_stride(self) -> None:
graph = self._make_graph(
[("X", TensorProto.FLOAT, (5, 3, 4, 4))],
[
make_node(
"LpPool",
["X"],
["Y"],
kernel_shape=[2, 2],
pads=[1, 1, 2, 2],
strides=[2, 2],
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 3, 3))]
)
def test_lppool_with_dilations(self) -> None:
graph = self._make_graph(
[("X", TensorProto.FLOAT, (5, 3, 4, 4))],
[make_node("LpPool", ["X"], ["Y"], kernel_shape=[2, 2], dilations=[2, 2])],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 2, 2))]
)
def test_lppool_with_same_upper_padding_and_stride_and_dilation(self) -> None:
graph = self._make_graph(
[("X", TensorProto.FLOAT, (5, 3, 4, 4))],
[
make_node(
"LpPool",
["X"],
["Y"],
auto_pad="SAME_UPPER",
kernel_shape=[2, 2],
strides=[2, 2],
dilations=[2, 3],
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 2, 2))]
)
def test_roipool(self) -> None:
graph = self._make_graph(
[
("X", TensorProto.FLOAT, (5, 3, 4, 4)),
("rois", TensorProto.INT64, (2, 5)),
],
[make_node("MaxRoiPool", ["X", "rois"], ["Y"], pooled_shape=[2, 2])],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (2, 3, 2, 2))]
)
def test_lp_norm(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (3, 4, 5, 6, 7))],
[make_node("LpNormalization", ["x"], ["out"])],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("out", TensorProto.FLOAT, (3, 4, 5, 6, 7))]
)
def test_instance_norm(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (3, 4, 5, 6, 7)),
("scale", TensorProto.FLOAT, (4,)),
("b", TensorProto.FLOAT, (4,)),
],
[make_node("InstanceNormalization", ["x", "scale", "b"], ["out"])],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("out", TensorProto.FLOAT, (3, 4, 5, 6, 7))]
)
def test_global_maxpool(self) -> None:
graph = self._make_graph(
[("X", TensorProto.FLOAT, (5, 3, 4, 4))],
[make_node("GlobalMaxPool", ["X"], ["Y"])],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 1, 1))]
)
def test_global_averagepool(self) -> None:
graph = self._make_graph(
[("X", TensorProto.FLOAT, (5, 3, 4, 4))],
[make_node("GlobalAveragePool", ["X"], ["Y"])],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 1, 1))]
)
def test_global_lppool(self) -> None:
graph = self._make_graph(
[("X", TensorProto.FLOAT, (5, 3, 4, 4))],
[make_node("GlobalLpPool", ["X"], ["Y"])],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (5, 3, 1, 1))]
)
def test_conv_transpose(self) -> None:
graph = self._make_graph(
[
("X", TensorProto.FLOAT, (25, 48, 16, 16)),
("W", TensorProto.FLOAT, (48, 32, 3, 3)),
],
[make_node("ConvTranspose", ["X", "W"], "Y", strides=[2, 2])],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (25, 32, 33, 33))]
)
def test_conv_transpose_with_pads(self) -> None:
graph = self._make_graph(
[
("X", TensorProto.FLOAT, (25, 48, 16, 16)),
("W", TensorProto.FLOAT, (48, 32, 3, 3)),
],
[
make_node(
"ConvTranspose", ["X", "W"], "Y", strides=[2, 2], pads=[1, 1, 2, 2]
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (25, 32, 30, 30))]
)
def test_conv_transpose_with_output_shape(self) -> None:
graph = self._make_graph(
[
("X", TensorProto.FLOAT, (25, 48, 16, 16)),
("W", TensorProto.FLOAT, (48, 32, 3, 3)),
],
[
make_node(
"ConvTranspose",
["X", "W"],
"Y",
strides=[2, 2],
pads=[1, 1, 2, 2],
output_shape=[36, 36],
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (25, 32, 36, 36))]
)
def test_conv_transpose_with_kernel_shape(self) -> None:
graph = self._make_graph(
[
("X", TensorProto.FLOAT, (25, 48, 16, 16)),
("W", TensorProto.FLOAT, (48, 32, None, None)),
],
[
make_node(
"ConvTranspose",
["X", "W"],
"Y",
kernel_shape=[3, 3],
strides=[2, 2],
pads=[1, 1, 2, 2],
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (25, 32, 30, 30))]
)
def test_conv_transpose_with_dilations(self) -> None:
graph = self._make_graph(
[
("X", TensorProto.FLOAT, (25, 48, 16, 16)),
("W", TensorProto.FLOAT, (48, 32, 3, 3)),
],
[
make_node(
"ConvTranspose",
["X", "W"],
"Y",
strides=[2, 2],
pads=[1, 1, 2, 2],
dilations=[3, 3],
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (25, 32, 34, 34))]
)
def test_conv_transpose_with_group(self) -> None:
graph = self._make_graph(
[
("X", TensorProto.FLOAT, (25, 48, 16, 16)),
("W", TensorProto.FLOAT, (48, 32, 3, 3)),
],
[
make_node(
"ConvTranspose",
["X", "W"],
"Y",
strides=[2, 2],
pads=[1, 1, 2, 2],
group=2,
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (25, 64, 30, 30))]
)
def test_conv_transpose_with_group_and_output_shape(self) -> None:
graph = self._make_graph(
[
("X", TensorProto.FLOAT, (25, 48, 16, 16)),
("W", TensorProto.FLOAT, (48, 32, 3, 3)),
],
[
make_node(
"ConvTranspose",
["X", "W"],
"Y",
strides=[2, 2],
pads=[1, 1, 2, 2],
group=2,
output_shape=[36, 36],
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (25, 64, 36, 36))]
)
def test_conv_transpose_with_pads_and_auto_pads(self) -> None:
# This test should fail because pads cannot be used simultaneously with auto_pad
graph = self._make_graph(
[
("X", TensorProto.FLOAT, (1, 1, 2, 2)),
("W", TensorProto.FLOAT, (1, 1, 3, 3)),
("B", TensorProto.FLOAT, (1,)),
],
[
make_node(
"ConvTranspose",
["X", "W", "B"],
"Y",
auto_pad="SAME_UPPER",
strides=[1, 1],
pads=[0, 1, 1, 0],
)
],
[],
)
self.assertRaises(
onnx.shape_inference.InferenceError,
onnx.shape_inference.infer_shapes,
helper.make_model(graph),
strict_mode=True,
)
def test_conv_transpose_auto_pads(self) -> None:
graph = self._make_graph(
[
("X", TensorProto.FLOAT, (25, 48, 16, 16)),
("W", TensorProto.FLOAT, (48, 32, 3, 3)),
],
[
make_node(
"ConvTranspose",
["X", "W"],
"Y",
auto_pad="SAME_UPPER",
strides=[2, 2],
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (25, 32, 32, 32))]
)
def test_mvn_function_output_shape(self) -> None:
graph = self._make_graph(
[("X", TensorProto.FLOAT, (25, 48, 16, 16))],
[make_node("MeanVarianceNormalization", "X", "Y", axes=[0, 2, 3])],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (25, 48, 16, 16))]
)
def test_scan(self) -> None:
batch_size = 1
seq_len = "sequence"
input_size = 2
loop_state_size = 3
# can't use self._make_graph for the subgraph as it add more inputs for the Reshape operations it inserts.
# this breaks the subgraph inferencing as it expects the number of inputs passed from Scan to match
# the GraphProto, but Scan knows nothing about the additional inputs.
input_value_infos = [
make_tensor_value_info("loop_state_in", TensorProto.UNDEFINED, None),
make_tensor_value_info("input", TensorProto.UNDEFINED, None),
]
output_value_infos = [
make_tensor_value_info("loop_state_out", TensorProto.UNDEFINED, None),
make_tensor_value_info("output", TensorProto.UNDEFINED, None),
]
subgraph = helper.make_graph(
[
make_node("Identity", ["loop_state_in"], ["loop_state_out"]),
make_node("Identity", ["input"], ["output"]),
],
"subgraph",
input_value_infos,
output_value_infos,
)
graph = self._make_graph(
[
("loop_state_orig", TensorProto.FLOAT, (batch_size, loop_state_size)),
("scan_input", TensorProto.FLOAT, (batch_size, seq_len, input_size)),
],
[
make_node(
"Scan",
["", "loop_state_orig", "scan_input"],
["loop_state_final", "scan_output"],
num_scan_inputs=1,
body=subgraph,
)
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info(
"loop_state_final", TensorProto.FLOAT, (batch_size, loop_state_size)
),
make_tensor_value_info(
"scan_output", TensorProto.FLOAT, (batch_size, seq_len, input_size)
),
],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 8)],
)
def test_scan_opset9(self) -> None:
seq_len = "sequence"
input_size = 2
loop_state_size = 3
# can't use self._make_graph for the subgraph as it add more inputs for the Reshape operations it inserts.
# this breaks the subgraph inferencing as it expects the number of inputs passed from Scan to match
# the GraphProto, but Scan knows nothing about the additional inputs.
input_value_infos = [
make_tensor_value_info("loop_state_in", TensorProto.UNDEFINED, None),
make_tensor_value_info("input", TensorProto.UNDEFINED, None),
]
output_value_infos = [
make_tensor_value_info("loop_state_out", TensorProto.UNDEFINED, None),
make_tensor_value_info("output", TensorProto.UNDEFINED, None),
]
subgraph = helper.make_graph(
[
make_node("Identity", ["loop_state_in"], ["loop_state_out"]),
make_node("Identity", ["input"], ["output"]),
],
"subgraph",
input_value_infos,
output_value_infos,
)
graph = self._make_graph(
[
("loop_state_orig", TensorProto.FLOAT, (loop_state_size,)),
("scan_input", TensorProto.FLOAT, (seq_len, input_size)),
],
[
make_node(
"Scan",
["loop_state_orig", "scan_input"],
["loop_state_final", "scan_output"],
num_scan_inputs=1,
body=subgraph,
)
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info(
"loop_state_final", TensorProto.FLOAT, (loop_state_size,)
),
make_tensor_value_info(
"scan_output", TensorProto.FLOAT, (seq_len, input_size)
),
],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 9)],
)
def test_scan_opset9_axes(self) -> None:
axis_0_len = "axis0"
seq_len = "sequence"
input_size = 2
loop_state_size = 3
# can't use self._make_graph for the subgraph as it add more inputs for the Reshape operations it inserts.
# this breaks the subgraph inferencing as it expects the number of inputs passed from Scan to match
# the GraphProto, but Scan knows nothing about the additional inputs.
input_value_infos = [
make_tensor_value_info("loop_state_in", TensorProto.UNDEFINED, None),
make_tensor_value_info("input", TensorProto.UNDEFINED, None),
]
output_value_infos = [
make_tensor_value_info("loop_state_out", TensorProto.UNDEFINED, None),
make_tensor_value_info("output", TensorProto.UNDEFINED, None),
]
subgraph = helper.make_graph(
[
make_node("Identity", ["loop_state_in"], ["loop_state_out"]),
make_node("Identity", ["input"], ["output"]),
],
"subgraph",
input_value_infos,
output_value_infos,
)
graph = self._make_graph(
[
("loop_state_orig", TensorProto.FLOAT, (loop_state_size,)),
("scan_input", TensorProto.FLOAT, (axis_0_len, seq_len, input_size)),
],
[
make_node(
"Scan",
["loop_state_orig", "scan_input"],
["loop_state_final", "scan_output"],
num_scan_inputs=1,
body=subgraph,
scan_input_axes=[1],
)
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info(
"loop_state_final", TensorProto.FLOAT, (loop_state_size,)
),
make_tensor_value_info(
"scan_output", TensorProto.FLOAT, (seq_len, axis_0_len, input_size)
),
],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 9)],
)
def test_scan_opset9_output_axes(self) -> None:
axis_0_len = "axis0"
seq_len = "sequence"
input_size = 2
loop_state_size = 3
input_value_infos = [
make_tensor_value_info("loop_state_in", TensorProto.UNDEFINED, None),
make_tensor_value_info("input", TensorProto.UNDEFINED, None),
]
output_value_infos = [
make_tensor_value_info("loop_state_out", TensorProto.UNDEFINED, None),
make_tensor_value_info("output", TensorProto.UNDEFINED, None),
]
subgraph = helper.make_graph(
[
make_node("Identity", ["loop_state_in"], ["loop_state_out"]),
make_node("Identity", ["input"], ["output"]),
],
"subgraph",
input_value_infos,
output_value_infos,
)
graph = self._make_graph(
[
("loop_state_orig", TensorProto.FLOAT, (loop_state_size,)),
("scan_input", TensorProto.FLOAT, (axis_0_len, seq_len, input_size)),
],
[
make_node(
"Scan",
["loop_state_orig", "scan_input"],
["loop_state_final", "scan_output"],
num_scan_inputs=1,
body=subgraph,
scan_input_axes=[1],
scan_output_axes=[1],
)
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info(
"loop_state_final", TensorProto.FLOAT, (loop_state_size,)
),
make_tensor_value_info(
"scan_output", TensorProto.FLOAT, (axis_0_len, seq_len, input_size)
),
],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 9)],
)
def test_scan_opset9_negative_axes(self) -> None:
axis_0_len = "axis0"
seq_len = "sequence"
input_size = 2
loop_state_size = 3
input_value_infos = [
make_tensor_value_info("loop_state_in", TensorProto.UNDEFINED, None),
make_tensor_value_info("input", TensorProto.UNDEFINED, None),
]
output_value_infos = [
make_tensor_value_info("loop_state_out", TensorProto.UNDEFINED, None),
make_tensor_value_info("output", TensorProto.UNDEFINED, None),
]
subgraph = helper.make_graph(
[
make_node("Identity", ["loop_state_in"], ["loop_state_out"]),
make_node("Identity", ["input"], ["output"]),
],
"subgraph",
input_value_infos,
output_value_infos,
)
graph = self._make_graph(
[
("loop_state_orig", TensorProto.FLOAT, (loop_state_size,)),
("scan_input", TensorProto.FLOAT, (axis_0_len, seq_len, input_size)),
],
[
make_node(
"Scan",
["loop_state_orig", "scan_input"],
["loop_state_final", "scan_output"],
num_scan_inputs=1,
body=subgraph,
scan_input_axes=[-2],
scan_output_axes=[-2],
)
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info(
"loop_state_final", TensorProto.FLOAT, (loop_state_size,)
),
make_tensor_value_info(
"scan_output", TensorProto.FLOAT, (axis_0_len, seq_len, input_size)
),
],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 9)],
)
def test_if_ver1(self) -> None:
# Create a simple If node where the 'then' subgraph adds to the current value, and the 'else' subgraph
# subtracts.
# can't use self._make_graph for the subgraphs as that add more inputs for the Reshape operations it inserts.
# this breaks the subgraph inferencing as it expects the subgraphs to have zero inputs
then_subgraph = helper.make_graph(
[make_node("Add", ["current_value", "add_value"], ["then_output"])],
"then_subgraph",
[], # no inputs
[make_tensor_value_info("then_output", TensorProto.UNDEFINED, None)],
)
else_subgraph = helper.make_graph(
[make_node("Sub", ["current_value", "sub_value"], ["else_output"])],
"else_subgraph",
[], # no inputs
[make_tensor_value_info("else_output", TensorProto.UNDEFINED, None)],
)
graph = self._make_graph(
[
("cond", TensorProto.BOOL, (1,)),
("current_value", TensorProto.FLOAT, (1,)),
("add_value", TensorProto.FLOAT, (1,)),
("sub_value", TensorProto.FLOAT, (1,)),
],
[
make_node(
"If",
["cond"],
["if_output"],
then_branch=then_subgraph,
else_branch=else_subgraph,
)
],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("if_output", TensorProto.FLOAT, (1,))],
opset_imports=[make_opsetid(ONNX_DOMAIN, 10)],
)
def test_if(self) -> None:
# Create a simple If node where the 'then' subgraph adds to the current value, and the 'else' subgraph
# subtracts.
# can't use self._make_graph for the subgraphs as that add more inputs for the Reshape operations it inserts.
# this breaks the subgraph inferencing as it expects the subgraphs to have zero inputs
then_subgraph = helper.make_graph(
[make_node("Add", ["current_value", "add_value"], ["then_output"])],
"then_subgraph",
[], # no inputs
[make_tensor_value_info("then_output", TensorProto.UNDEFINED, None)],
)
else_subgraph = helper.make_graph(
[make_node("Sub", ["current_value", "sub_value"], ["else_output"])],
"else_subgraph",
[], # no inputs
[make_tensor_value_info("else_output", TensorProto.UNDEFINED, None)],
)
graph = self._make_graph(
[
("cond", TensorProto.BOOL, (1,)),
("current_value", TensorProto.FLOAT, (1,)),
("add_value", TensorProto.FLOAT, (1,)),
("sub_value", TensorProto.FLOAT, (1,)),
],
[
make_node(
"If",
["cond"],
["if_output"],
then_branch=then_subgraph,
else_branch=else_subgraph,
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("if_output", TensorProto.FLOAT, (1,))]
)
def test_if_with_different_shapes_in_then_else_branches(self) -> None:
# Create a simple If node where the 'then' subgraph adds to the current value, and the 'else' subgraph
# subtracts.
# can't use self._make_graph for the subgraphs as that add more inputs for the Reshape operations it inserts.
# this breaks the subgraph inferencing as it expects the subgraphs to have zero inputs
then_subgraph = helper.make_graph(
[make_node("Add", ["current_value", "add_value"], ["then_output"])],
"then_subgraph",
[], # no inputs
[make_tensor_value_info("then_output", TensorProto.UNDEFINED, (1,))],
)
else_subgraph = helper.make_graph(
[make_node("Sub", ["current_value", "sub_value"], ["else_output"])],
"else_subgraph",
[], # no inputs
[make_tensor_value_info("else_output", TensorProto.UNDEFINED, (5,))],
)
graph = self._make_graph(
[
("cond", TensorProto.BOOL, (1,)),
("current_value", TensorProto.FLOAT, (1,)),
("add_value", TensorProto.FLOAT, (1,)),
("sub_value", TensorProto.FLOAT, (5,)),
],
[
make_node(
"If",
["cond"],
["if_output"],
then_branch=then_subgraph,
else_branch=else_subgraph,
)
],
[],
)
self._assert_inferred(graph, [make_tensor_value_info("if_output", TensorProto.FLOAT, (None,))]) # type: ignore
def test_if_no_shape_in_then_branch(self) -> None:
then_graph = parse_graph(
"then_graph () => (then_output) { then_output = ReduceSum <keepdims=0> (X, axes) }"
)
else_graph = parse_graph(
"else_graph () => (else_output) { else_output = ReduceSum <keepdims=0> (X) }"
)
graph = self._make_graph(
[
("cond", TensorProto.BOOL, (1,)),
("X", TensorProto.FLOAT, (4, 8, 16)),
("axes", TensorProto.INT64, (1,)),
],
[
make_node(
"If",
["cond"],
["if_output"],
then_branch=then_graph,
else_branch=else_graph,
)
],
[],
)
self._assert_inferred(graph, [make_tensor_value_info("if_output", TensorProto.FLOAT, None)]) # type: ignore
def test_if_no_shape_in_else_branch(self) -> None:
then_graph = parse_graph(
"then_graph () => (then_output) { then_output = ReduceSum <keepdims=0> (X) }"
)
else_graph = parse_graph(
"else_graph () => (else_output) { else_output = ReduceSum <keepdims=0> (X, axes) }"
)
graph = self._make_graph(
[
("cond", TensorProto.BOOL, (1,)),
("X", TensorProto.FLOAT, (4, 8, 16)),
("axes", TensorProto.INT64, (1,)),
],
[
make_node(
"If",
["cond"],
["if_output"],
then_branch=then_graph,
else_branch=else_graph,
)
],
[],
)
self._assert_inferred(graph, [make_tensor_value_info("if_output", TensorProto.FLOAT, None)]) # type: ignore
def test_if_with_different_optional_shapes_in_then_else_branches(self) -> None:
# Create a simple If node where the 'then' subgraph adds to the current value, and the 'else' subgraph
# subtracts.
# can't use self._make_graph for the subgraphs as that add more inputs for the Reshape operations it inserts.
# this breaks the subgraph inferencing as it expects the subgraphs to have zero inputs
then_tensor_proto = helper.make_tensor_type_proto(
elem_type=TensorProto.UNDEFINED,
shape=[
1,
],
)
then_optional_type_proto = helper.make_optional_type_proto(then_tensor_proto)
then_optional_vi = helper.make_value_info(
"then_optional_output", then_optional_type_proto
)
then_subgraph = helper.make_graph(
[make_node("Optional", ["then_tensor_value"], ["then_optional_output"])],
"then_subgraph",
[], # no inputs
[then_optional_vi],
)
else_tensor_proto = helper.make_tensor_type_proto(
elem_type=TensorProto.UNDEFINED,
shape=[
5,
],
)
else_optional_type_proto = helper.make_optional_type_proto(else_tensor_proto)
else_optional_vi = helper.make_value_info(
"else_optional_output", else_optional_type_proto
)
else_subgraph = helper.make_graph(
[make_node("Optional", ["else_tensor_value"], ["else_optional_output"])],
"else_subgraph",
[], # no inputs
[else_optional_vi],
)
graph = self._make_graph(
[
("cond", TensorProto.BOOL, (1,)),
("then_tensor_value", TensorProto.FLOAT, (1,)),
("else_tensor_value", TensorProto.FLOAT, (5,)),
],
[
make_node(
"If",
["cond"],
["if_output"],
then_branch=then_subgraph,
else_branch=else_subgraph,
)
],
[],
)
output_tensor_proto = helper.make_tensor_type_proto(
elem_type=TensorProto.FLOAT, shape=(None,)
)
output_optional_type_proto = helper.make_optional_type_proto(
output_tensor_proto
)
output_optional_vi = helper.make_value_info(
"if_output", output_optional_type_proto
)
self._assert_inferred(graph, [output_optional_vi]) # type: ignore
def test_maxunpool_shape_without_output_shape(self) -> None:
graph = self._make_graph(
[
("xT", TensorProto.FLOAT, (1, 1, 2, 2)),
("xI", TensorProto.FLOAT, (1, 1, 2, 2)),
],
[
make_node(
"MaxUnpool", ["xT", "xI"], "Y", kernel_shape=[2, 2], strides=[2, 2]
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (1, 1, 4, 4))]
)
def test_maxunpool_shape_with_output_shape(self) -> None:
graph = self._make_graph(
[
("xT", TensorProto.FLOAT, (1, 1, 2, 2)),
("xI", TensorProto.FLOAT, (1, 1, 2, 2)),
("output_shape", TensorProto.FLOAT, (4,)),
],
[
make_node(
"MaxUnpool",
["xT", "xI", "output_shape"],
"Y",
kernel_shape=[2, 2],
strides=[2, 2],
)
],
[make_tensor_value_info("Y", TensorProto.FLOAT, None)],
)
self._assert_inferred(
graph, [make_tensor_value_info("Y", TensorProto.FLOAT, None)]
)
def test_onehot_without_axis(self) -> None:
graph = self._make_graph(
[
("indices", TensorProto.INT64, (2, 2)),
("depth", TensorProto.INT64, ()),
("values", TensorProto.FLOAT, (2,)),
],
[make_node("OneHot", ["indices", "depth", "values"], "Y")],
[],
)
self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (2, 2, None))]) # type: ignore
def test_onehot_with_axis(self) -> None:
graph = self._make_graph(
[
("indices", TensorProto.INT64, (2, 3, 5)),
("depth", TensorProto.INT64, (1,)),
("values", TensorProto.FLOAT, (2,)),
],
[make_node("OneHot", ["indices", "depth", "values"], "Y", axis=1)],
[],
)
self._assert_inferred(graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (2, None, 3, 5))]) # type: ignore
def test_loop(self) -> None:
# can't use self._make_graph for the subgraph as it add more inputs for the Reshape operations it inserts.
# this breaks the subgraph inferencing as it expects the number of inputs passed from Loop to match
# the GraphProto, but Loop knows nothing about the additional inputs.
input_value_infos = [
make_tensor_value_info("iter_num_in", TensorProto.INT64, (1,)),
make_tensor_value_info("cond_in", TensorProto.UNDEFINED, None),
make_tensor_value_info("loop_state_in", TensorProto.UNDEFINED, ()),
]
output_value_infos = [
make_tensor_value_info("cond_out", TensorProto.UNDEFINED, None),
make_tensor_value_info("loop_state_out", TensorProto.UNDEFINED, None),
make_tensor_value_info("output", TensorProto.FLOAT, (3,)),
]
subgraph = helper.make_graph(
[
make_node("Identity", ["cond_in"], ["cond_out"]),
make_node("Identity", ["loop_state_in"], ["loop_state_out"]),
make_node("Identity", ["outer_scope_input"], ["output"]),
],
"subgraph",
input_value_infos,
output_value_infos,
)
graph = self._make_graph(
[
("max_trip_count", TensorProto.INT64, (1,)),
("cond_orig", TensorProto.FLOAT, (1,)),
("loop_state_orig", TensorProto.FLOAT, (2,)),
("outer_scope_input", TensorProto.FLOAT, (3,)),
],
[
make_node(
"Loop",
["max_trip_count", "cond_orig", "loop_state_orig"],
["loop_state_final", "loop_output"],
body=subgraph,
)
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info(
"loop_state_final", TensorProto.FLOAT, None
), # shape may change between iterations
make_tensor_value_info("loop_output", TensorProto.FLOAT, (None, 3)),
],
) # type: ignore
def test_loop_no_state(self) -> None:
input_value_infos = [
make_tensor_value_info("iter_num_in", TensorProto.INT64, (1,)),
make_tensor_value_info("cond_in", TensorProto.UNDEFINED, None),
]
output_value_infos = [
make_tensor_value_info("cond_out", TensorProto.UNDEFINED, None),
make_tensor_value_info("output", TensorProto.FLOAT, (3,)),
]
subgraph = helper.make_graph(
[
make_node("Identity", ["cond_in"], ["cond_out"]),
make_node("Identity", ["outer_scope_input"], ["output"]),
],
"subgraph",
input_value_infos,
output_value_infos,
)
graph = self._make_graph(
[
("max_trip_count", TensorProto.INT64, (1,)),
("cond_orig", TensorProto.FLOAT, (1,)),
("outer_scope_input", TensorProto.FLOAT, (3,)),
],
[
make_node(
"Loop",
["max_trip_count", "cond_orig"],
["loop_output"],
body=subgraph,
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("loop_output", TensorProto.FLOAT, (None, 3))]
) # type: ignore
def test_constantofshape_with_input_shape(self) -> None:
graph = self._make_graph(
[],
[
make_node(
"Constant",
[],
["shape"],
value=make_tensor("shape", TensorProto.INT64, (3,), (3, 4, 5)),
),
make_node(
"ConstantOfShape",
["shape"],
["y"],
value=make_tensor("value", TensorProto.INT32, (1,), (2,)),
),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("shape", TensorProto.INT64, (3,)),
make_tensor_value_info("y", TensorProto.INT32, (3, 4, 5)),
],
) # type: ignore
def test_constantofshape_without_input_shape(self) -> None:
graph = self._make_graph(
[("shape", TensorProto.INT64, (3,))],
[
make_node(
"ConstantOfShape",
["shape"],
["y"],
value=make_tensor("value", TensorProto.UINT8, (1,), (2,)),
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("y", TensorProto.UINT8, (None, None, None))]
) # type: ignore
def test_constantofshape_without_input_shape_scalar(self) -> None:
graph = self._make_graph(
[("shape", TensorProto.INT64, (0,))],
[
make_node(
"ConstantOfShape",
["shape"],
["y"],
value=make_tensor("value", TensorProto.UINT8, (1,), (2,)),
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("y", TensorProto.UINT8, ())]
) # type: ignore
def test_constantofshape_with_shape_zero(self) -> None:
graph = self._make_graph(
[],
[
make_node(
"Constant",
[],
["shape"],
value=make_tensor("shape", TensorProto.INT64, (1,), (0,)),
),
make_node(
"ConstantOfShape",
["shape"],
["y"],
value=make_tensor("value", TensorProto.INT32, (1,), (2,)),
),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("shape", TensorProto.INT64, (1,)),
make_tensor_value_info("y", TensorProto.INT32, (0,)),
],
) # type: ignore
def test_convinteger(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.UINT8, (3, 4, 5, 6, 7)),
("y", TensorProto.UINT8, (5, 4, 2, 4, 3)),
],
[
make_node(
"ConvInteger",
["x", "y"],
"z",
pads=[0, 1, 1, 0, 0, 1],
dilations=[1, 2, 2],
strides=[1, 1, 2],
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("z", TensorProto.INT32, (3, 5, 4, 1, 3))]
)
def test_convinetger_dilations(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.UINT8, (30, 4, 8, 8, 8)),
("y", TensorProto.INT8, (50, 4, 3, 3, 3)),
("x_zero_point", TensorProto.UINT8, ()),
("y_zero_point", TensorProto.UINT8, ()),
],
[
make_node(
"ConvInteger",
["x", "y", "x_zero_point", "y_zero_point"],
"z",
dilations=[1, 2, 3],
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("z", TensorProto.INT32, (30, 50, 6, 4, 2))]
)
def test_convinteger_strides(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.INT8, (30, 4, 8, 8, 8)),
("y", TensorProto.INT8, (50, 4, 3, 3, 3)),
("x_zero_point", TensorProto.UINT8, ()),
("y_zero_point", TensorProto.UINT8, ()),
],
[
make_node(
"ConvInteger",
["x", "y", "x_zero_point", "y_zero_point"],
"z",
strides=[1, 2, 3],
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("z", TensorProto.INT32, (30, 50, 6, 3, 2))]
)
def test_convineteger_pads(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.UINT8, (30, 4, 7, 6, 4)),
("y", TensorProto.INT8, (50, 4, 3, 3, 3)),
],
[make_node("ConvInteger", ["x", "y"], "z", pads=[1, 1, 2, 0, 1, 2])],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("z", TensorProto.INT32, (30, 50, 6, 6, 6))]
)
def test_convineteger_group(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.INT8, (30, 4, 8, 8, 8)),
("y", TensorProto.INT8, (4, 1, 8, 8, 8)),
],
[make_node("ConvInteger", ["x", "y"], "z", group=4)],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("z", TensorProto.INT32, (30, 4, 1, 1, 1))]
)
def test_convineteger_partial_missing_shape(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.UINT8, (30, 4, None, 6, 4)),
("y", TensorProto.UINT8, (50, 4, 3, 3, 3)),
("x_zero_point", TensorProto.UINT8, ()),
("y_zero_point", TensorProto.UINT8, ()),
],
[
make_node(
"ConvInteger",
["x", "y", "x_zero_point", "y_zero_point"],
"z",
pads=[1, 1, 2, 0, 1, 2],
)
],
[],
)
self._assert_inferred(graph, [make_tensor_value_info("z", TensorProto.INT32, (30, 50, None, 6, 6))]) # type: ignore
def test_convineteger_partial_missing_weight_shape(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.UINT8, (30, 4, 7, 6, 4)),
("y", TensorProto.UINT8, (50, 4, None, 3, 3)),
],
[make_node("ConvInteger", ["x", "y"], "z", pads=[1, 1, 2, 0, 1, 2])],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("z", TensorProto.INT32, None)]
)
def test_qlinearconv(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.UINT8, (3, 4, 5, 6, 7)),
("x_scale", TensorProto.FLOAT, ()),
("x_zero_point", TensorProto.UINT8, ()),
("w", TensorProto.UINT8, (5, 4, 2, 4, 3)),
("w_scale", TensorProto.FLOAT, ()),
("w_zero_point", TensorProto.UINT8, ()),
("y_scale", TensorProto.FLOAT, ()),
("y_zero_point", TensorProto.UINT8, ()),
],
[
make_node(
"QLinearConv",
[
"x",
"x_scale",
"x_zero_point",
"w",
"w_scale",
"w_zero_point",
"y_scale",
"y_zero_point",
],
"y",
pads=[0, 1, 1, 0, 0, 1],
dilations=[1, 2, 2],
strides=[1, 1, 2],
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("y", TensorProto.UINT8, (3, 5, 4, 1, 3))]
)
def test_qlinearconv_dilations(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.UINT8, (30, 4, 8, 8, 8)),
("x_scale", TensorProto.FLOAT, ()),
("x_zero_point", TensorProto.UINT8, ()),
("w", TensorProto.UINT8, (50, 4, 3, 3, 3)),
("w_scale", TensorProto.FLOAT, ()),
("w_zero_point", TensorProto.UINT8, ()),
("y_scale", TensorProto.FLOAT, ()),
("y_zero_point", TensorProto.UINT8, ()),
],
[
make_node(
"QLinearConv",
[
"x",
"x_scale",
"x_zero_point",
"w",
"w_scale",
"w_zero_point",
"y_scale",
"y_zero_point",
],
"y",
dilations=[1, 2, 3],
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("y", TensorProto.UINT8, (30, 50, 6, 4, 2))]
)
def test_qlinearconv_strides(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.INT8, (30, 4, 8, 8, 8)),
("x_scale", TensorProto.FLOAT, ()),
("x_zero_point", TensorProto.INT8, ()),
("w", TensorProto.INT8, (50, 4, 3, 3, 3)),
("w_scale", TensorProto.FLOAT, ()),
("w_zero_point", TensorProto.INT8, ()),
("y_scale", TensorProto.FLOAT, ()),
("y_zero_point", TensorProto.INT8, ()),
],
[
make_node(
"QLinearConv",
[
"x",
"x_scale",
"x_zero_point",
"w",
"w_scale",
"w_zero_point",
"y_scale",
"y_zero_point",
],
"y",
strides=[1, 2, 3],
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("y", TensorProto.INT8, (30, 50, 6, 3, 2))]
)
def test_qlinearconv_pads(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.UINT8, (30, 4, 7, 6, 4)),
("x_scale", TensorProto.FLOAT, ()),
("x_zero_point", TensorProto.UINT8, ()),
("w", TensorProto.INT8, (50, 4, 3, 3, 3)),
("w_scale", TensorProto.FLOAT, ()),
("w_zero_point", TensorProto.INT8, ()),
("y_scale", TensorProto.FLOAT, ()),
("y_zero_point", TensorProto.UINT8, ()),
],
[
make_node(
"QLinearConv",
[
"x",
"x_scale",
"x_zero_point",
"w",
"w_scale",
"w_zero_point",
"y_scale",
"y_zero_point",
],
"y",
pads=[1, 1, 2, 0, 1, 2],
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("y", TensorProto.UINT8, (30, 50, 6, 6, 6))]
)
def test_qlinearconv_group(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.INT8, (30, 4, 8, 8, 8)),
("x_scale", TensorProto.FLOAT, ()),
("x_zero_point", TensorProto.INT8, ()),
("w", TensorProto.INT8, (4, 1, 8, 8, 8)),
("w_scale", TensorProto.FLOAT, ()),
("w_zero_point", TensorProto.INT8, ()),
("y_scale", TensorProto.FLOAT, ()),
("y_zero_point", TensorProto.INT8, ()),
],
[
make_node(
"QLinearConv",
[
"x",
"x_scale",
"x_zero_point",
"w",
"w_scale",
"w_zero_point",
"y_scale",
"y_zero_point",
],
"y",
group=4,
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("y", TensorProto.INT8, (30, 4, 1, 1, 1))]
)
def test_qlinearconv_partial_missing_shape(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.UINT8, (30, 4, None, 6, 4)),
("x_scale", TensorProto.FLOAT, ()),
("x_zero_point", TensorProto.UINT8, ()),
("w", TensorProto.UINT8, (50, 4, 3, 3, 3)),
("w_scale", TensorProto.FLOAT, ()),
("w_zero_point", TensorProto.UINT8, ()),
("y_scale", TensorProto.FLOAT, ()),
("y_zero_point", TensorProto.UINT8, ()),
],
[
make_node(
"QLinearConv",
[
"x",
"x_scale",
"x_zero_point",
"w",
"w_scale",
"w_zero_point",
"y_scale",
"y_zero_point",
],
"y",
pads=[1, 1, 2, 0, 1, 2],
)
],
[],
)
self._assert_inferred(graph, [make_tensor_value_info("y", TensorProto.UINT8, (30, 50, None, 6, 6))]) # type: ignore
def test_qlinearconv_partial_missing_weight_shape(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.UINT8, (30, 4, 7, 6, 4)),
("x_scale", TensorProto.FLOAT, ()),
("x_zero_point", TensorProto.UINT8, ()),
("w", TensorProto.UINT8, (50, 4, None, 3, 3)),
("w_scale", TensorProto.FLOAT, ()),
("w_zero_point", TensorProto.UINT8, ()),
("y_scale", TensorProto.FLOAT, ()),
("y_zero_point", TensorProto.UINT8, ()),
],
[
make_node(
"QLinearConv",
[
"x",
"x_scale",
"x_zero_point",
"w",
"w_scale",
"w_zero_point",
"y_scale",
"y_zero_point",
],
"y",
pads=[1, 1, 2, 0, 1, 2],
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("y", TensorProto.UINT8, None)]
)
def _make_qlinearmatmul_test(
self, shape1: Sequence[int], shape2: Sequence[int]
) -> None:
expected_out_shape = np.matmul(
np.arange(np.prod(shape1)).reshape(shape1),
np.arange(np.prod(shape2)).reshape(shape2),
).shape
graph = self._make_graph(
[
("a", TensorProto.UINT8, shape1),
("a_scale", TensorProto.FLOAT, ()),
("a_zero_point", TensorProto.UINT8, ()),
("b", TensorProto.UINT8, shape2),
("b_scale", TensorProto.FLOAT, ()),
("b_zero_point", TensorProto.UINT8, ()),
("y_scale", TensorProto.FLOAT, ()),
("y_zero_point", TensorProto.UINT8, ()),
],
[
make_node(
"QLinearMatMul",
[
"a",
"a_scale",
"a_zero_point",
"b",
"b_scale",
"b_zero_point",
"y_scale",
"y_zero_point",
],
["y"],
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("y", TensorProto.UINT8, expected_out_shape)]
)
def test_qlinearmatmul(self) -> None:
self._make_qlinearmatmul_test((3,), (3,))
self._make_qlinearmatmul_test((4, 2), (2, 4))
self._make_qlinearmatmul_test((2,), (2, 3))
self._make_qlinearmatmul_test((4, 2), (2,))
self._make_qlinearmatmul_test((5, 1, 4, 2), (1, 3, 2, 3))
self._make_qlinearmatmul_test((4, 2), (3, 2, 3))
def _make_qlinearmatmul_test_allow_unknown(
self, shape1: Any, shape2: Any, expected_out_shape: Any
) -> None:
graph = self._make_graph(
[
("a", TensorProto.UINT8, shape1),
("a_scale", TensorProto.FLOAT, ()),
("a_zero_point", TensorProto.UINT8, ()),
("b", TensorProto.UINT8, shape2),
("b_scale", TensorProto.FLOAT, ()),
("b_zero_point", TensorProto.UINT8, ()),
("y_scale", TensorProto.FLOAT, ()),
("y_zero_point", TensorProto.UINT8, ()),
],
[
make_node(
"QLinearMatMul",
[
"a",
"a_scale",
"a_zero_point",
"b",
"b_scale",
"b_zero_point",
"y_scale",
"y_zero_point",
],
["y"],
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("y", TensorProto.UINT8, expected_out_shape)]
)
def test_qlinearmatmul_allow_unknown(self) -> None:
self._make_qlinearmatmul_test_allow_unknown((None,), (None,), ())
self._make_qlinearmatmul_test_allow_unknown((3,), (None,), ())
self._make_qlinearmatmul_test_allow_unknown((2,), (2, "a"), ("a",))
self._make_qlinearmatmul_test_allow_unknown((4, 2), (2, "a"), (4, "a"))
self._make_qlinearmatmul_test_allow_unknown((4, None), (2, "a"), (4, "a"))
self._make_qlinearmatmul_test_allow_unknown((4, None), (None, "a"), (4, "a"))
self._make_qlinearmatmul_test_allow_unknown((1, 4, 2), ("a", 2, 5), ("a", 4, 5))
self._make_qlinearmatmul_test_allow_unknown(
(1, 3, 4, 2), ("a", 2, 5), (1, 3, 4, 5)
)
self._make_qlinearmatmul_test_allow_unknown(None, ("a", 2, 5), None)
self._make_qlinearmatmul_test_allow_unknown(None, None, None)
def _make_matmulinteger_test(
self, shape1: Sequence[int], shape2: Sequence[int]
) -> None:
expected_out_shape = np.matmul(
np.arange(np.prod(shape1)).reshape(shape1),
np.arange(np.prod(shape2)).reshape(shape2),
).shape
graph = self._make_graph(
[
("A", TensorProto.UINT8, shape1),
("B", TensorProto.UINT8, shape2),
("a_zero_point", TensorProto.UINT8, ()),
("b_zero_point", TensorProto.UINT8, ()),
],
[
make_node(
"MatMulInteger", ["A", "B", "a_zero_point", "b_zero_point"], ["Y"]
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("Y", TensorProto.INT32, expected_out_shape)]
)
def test_matmulinteger(self) -> None:
self._make_matmulinteger_test((2,), (2,))
self._make_matmulinteger_test((1, 2), (2, 3))
self._make_matmulinteger_test((2,), (2, 3))
self._make_matmulinteger_test((4, 2), (2,))
self._make_matmulinteger_test((5, 1, 4, 2), (1, 3, 2, 3))
self._make_matmulinteger_test((4, 2), (3, 2, 3))
def test_quantizelinear(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (30, 4, 5)),
("y_scale", TensorProto.FLOAT, ()),
("y_zero_point", TensorProto.UINT8, ()),
],
[make_node("QuantizeLinear", ["x", "y_scale", "y_zero_point"], ["y"])],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("y", TensorProto.UINT8, (30, 4, 5))]
)
def test_quantizelinear_default_zp(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (30, 4, 5)), ("y_scale", TensorProto.FLOAT, ())],
[make_node("QuantizeLinear", ["x", "y_scale"], ["y"])],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("y", TensorProto.UINT8, (30, 4, 5))]
)
def test_quantizelinear_optional_input(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (30, 4, 5)), ("y_scale", TensorProto.FLOAT, ())],
[make_node("QuantizeLinear", ["x", "y_scale", ""], ["y"])],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("y", TensorProto.UINT8, (30, 4, 5))]
)
def test_dequantizelinear(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.UINT8, (30, 4, 5)),
("x_scale", TensorProto.FLOAT, ()),
("x_zero_point", TensorProto.UINT8, ()),
],
[make_node("DequantizeLinear", ["x", "x_scale", "x_zero_point"], ["y"])],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("y", TensorProto.FLOAT, (30, 4, 5))]
)
def test_dynamicquantizelinear(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (30, 4, 5))],
[
make_node(
"DynamicQuantizeLinear", ["x"], ["y", "y_scale", "y_zero_point"]
)
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("y", TensorProto.UINT8, (30, 4, 5)),
make_tensor_value_info("y_scale", TensorProto.FLOAT, ()),
make_tensor_value_info("y_zero_point", TensorProto.UINT8, ()),
],
)
def test_reversesequence(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (4, 5, 6)),
("sequence_lens", TensorProto.INT64, (5,)),
],
[make_node("ReverseSequence", ["x", "sequence_lens"], ["y"])],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("y", TensorProto.FLOAT, (4, 5, 6))]
)
def test_unique_without_axis(self) -> None:
graph = self._make_graph(
[("X", TensorProto.FLOAT, (2, 4, 2))],
[make_node("Unique", ["X"], ["Y", "indices", "inverse_indices", "counts"])],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("Y", TensorProto.FLOAT, (None,)), # type: ignore
make_tensor_value_info("indices", TensorProto.INT64, (None,)), # type: ignore
make_tensor_value_info("inverse_indices", TensorProto.INT64, (None,)), # type: ignore
make_tensor_value_info("counts", TensorProto.INT64, (None,)),
],
) # type: ignore
def test_unique_with_axis(self) -> None:
graph = self._make_graph(
[("X", TensorProto.FLOAT, (2, 4, 2))],
[
make_node(
"Unique",
["X"],
["Y", "indices", "inverse_indices", "counts"],
axis=1,
)
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("Y", TensorProto.FLOAT, (2, None, 2)), # type: ignore
make_tensor_value_info("indices", TensorProto.INT64, (None,)), # type: ignore
make_tensor_value_info("inverse_indices", TensorProto.INT64, (None,)), # type: ignore
make_tensor_value_info("counts", TensorProto.INT64, (None,)),
],
) # type: ignore
def test_det(self) -> None:
graph = self._make_graph(
[("X", TensorProto.FLOAT, (3, 3))], [make_node("Det", ["X"], ["Y"])], []
)
self._assert_inferred(
graph, [make_tensor_value_info("Y", TensorProto.FLOAT, ())]
)
graph = self._make_graph(
[("X", TensorProto.FLOAT, (4, 5, 6, 7, 7))],
[make_node("Det", ["X"], ["Y"])],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (4, 5, 6))]
)
def test_tile(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (4, 5, 6)), ("repeats", TensorProto.INT64, (3,))],
[make_node("Tile", ["x", "repeats"], ["y"])],
[],
initializer=[make_tensor("repeats", TensorProto.INT64, (3,), (1, 2, 3))],
)
self._assert_inferred(
graph, [make_tensor_value_info("y", TensorProto.FLOAT, (4, 10, 18))]
)
def test_tile_raw_input_data(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (4, 5, 6)), ("repeats", TensorProto.INT64, (3,))],
[make_node("Tile", ["x", "repeats"], ["y"])],
[],
initializer=[
make_tensor(
"repeats",
TensorProto.INT64,
(3,),
vals=np.array([1, 2, 3], dtype="<i8").tobytes(),
raw=True,
)
],
) # Feed raw bytes (force little endian ordering like onnx standard) for test purpose
self._assert_inferred(
graph, [make_tensor_value_info("y", TensorProto.FLOAT, (4, 10, 18))]
)
def test_tile_rank_inference(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (4, 5, 6)), ("repeats", TensorProto.INT64, (3,))],
[make_node("Tile", ["x", "repeats"], ["y"])],
[],
)
self._assert_inferred(graph, [make_tensor_value_info("y", TensorProto.FLOAT, (None, None, None))]) # type: ignore
@unittest.skipUnless(ONNX_ML, "ONNX_ML required to test ai.onnx.ml operators")
def test_linearclassifier_1D_input(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (5,))],
[
make_node(
"LinearClassifier",
["x"],
["y", "z"],
domain=ONNX_ML_DOMAIN,
coefficients=[0.0008, -0.0008],
intercepts=[2.0, 2.0],
classlabels_ints=[1, 2],
)
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("y", TensorProto.INT64, (1,)),
make_tensor_value_info("z", TensorProto.FLOAT, (1, 2)),
],
opset_imports=[
make_opsetid(ONNX_ML_DOMAIN, 1),
make_opsetid(ONNX_DOMAIN, 11),
],
)
@unittest.skipUnless(ONNX_ML, "ONNX_ML required to test ai.onnx.ml operators")
def test_linearclassifier_2D_input(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (4, 5))],
[
make_node(
"LinearClassifier",
["x"],
["y", "z"],
domain=ONNX_ML_DOMAIN,
coefficients=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6],
intercepts=[2.0, 2.0, 3.0],
classlabels_ints=[1, 2, 3],
)
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("y", TensorProto.INT64, (4,)),
make_tensor_value_info("z", TensorProto.FLOAT, (4, 3)),
],
opset_imports=[
make_opsetid(ONNX_ML_DOMAIN, 1),
make_opsetid(ONNX_DOMAIN, 11),
],
)
def test_roialign_symbolic(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, ("N", "C", "H", "W")),
("rois", TensorProto.FLOAT, ("num_rois", 4)),
("batch_indices", TensorProto.INT64, ("num_rois",)),
],
[
make_node(
"RoiAlign",
["x", "rois", "batch_indices"],
["y"],
output_height=10,
output_width=5,
)
],
[],
)
self._assert_inferred(graph, [make_tensor_value_info("y", TensorProto.FLOAT, ("num_rois", "C", 10, 5))]) # type: ignore
def test_roialign_symbolic_defaults(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, ("N", "C", "H", "W")),
("rois", TensorProto.FLOAT, ("num_rois", 4)),
("batch_indices", TensorProto.INT64, ("num_rois",)),
],
[make_node("RoiAlign", ["x", "rois", "batch_indices"], ["y"])],
[],
)
self._assert_inferred(graph, [make_tensor_value_info("y", TensorProto.FLOAT, ("num_rois", "C", 1, 1))]) # type: ignore
def test_roialign_num_rois(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, ("N", "C", "H", "W")),
("rois", TensorProto.FLOAT, ("num_rois", 4)),
("batch_indices", TensorProto.INT64, (15,)),
],
[make_node("RoiAlign", ["x", "rois", "batch_indices"], ["y"])],
[],
)
self._assert_inferred(graph, [make_tensor_value_info("y", TensorProto.FLOAT, (15, "C", 1, 1))]) # type: ignore
@unittest.skipUnless(ONNX_ML, "ONNX_ML required to test ai.onnx.ml operators")
def test_label_encoder_string_int64(self) -> None:
string_list = ["A", "m", "y"]
float_list = [94.17, 36.00]
int64_list = [12, 28, 86]
graph = self._make_graph(
[("x", TensorProto.STRING, (6, 1))],
[
make_node(
"LabelEncoder",
["x"],
["y"],
domain=ONNX_ML_DOMAIN,
keys_strings=string_list,
values_int64s=int64_list,
)
],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.INT64, (6, 1))],
opset_imports=[
make_opsetid(ONNX_ML_DOMAIN, 2),
make_opsetid(ONNX_DOMAIN, 11),
],
)
graph = self._make_graph(
[("x", TensorProto.INT64, (2, 3))],
[
make_node(
"LabelEncoder",
["x"],
["y"],
domain=ONNX_ML_DOMAIN,
keys_int64s=int64_list,
values_strings=string_list,
)
],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.STRING, (2, 3))],
opset_imports=[
make_opsetid(ONNX_ML_DOMAIN, 2),
make_opsetid(ONNX_DOMAIN, 11),
],
)
graph = self._make_graph(
[("x", TensorProto.FLOAT, (2,))],
[
make_node(
"LabelEncoder",
["x"],
["y"],
domain=ONNX_ML_DOMAIN,
keys_floats=float_list,
values_int64s=int64_list,
)
],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.INT64, (2,))],
opset_imports=[
make_opsetid(ONNX_ML_DOMAIN, 2),
make_opsetid(ONNX_DOMAIN, 11),
],
)
graph = self._make_graph(
[("x", TensorProto.INT64, (8,))],
[
make_node(
"LabelEncoder",
["x"],
["y"],
domain=ONNX_ML_DOMAIN,
keys_int64s=int64_list,
values_floats=float_list,
)
],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.FLOAT, (8,))],
opset_imports=[
make_opsetid(ONNX_ML_DOMAIN, 2),
make_opsetid(ONNX_DOMAIN, 11),
],
)
graph = self._make_graph(
[("x", TensorProto.FLOAT, ())],
[
make_node(
"LabelEncoder",
["x"],
["y"],
domain=ONNX_ML_DOMAIN,
keys_floats=float_list,
values_strings=string_list,
)
],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.STRING, ())],
opset_imports=[
make_opsetid(ONNX_ML_DOMAIN, 2),
make_opsetid(ONNX_DOMAIN, 11),
],
)
graph = self._make_graph(
[("x", TensorProto.STRING, (1, 2))],
[
make_node(
"LabelEncoder",
["x"],
["y"],
domain=ONNX_ML_DOMAIN,
keys_strings=string_list,
values_floats=float_list,
)
],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.FLOAT, (1, 2))],
opset_imports=[
make_opsetid(ONNX_ML_DOMAIN, 2),
make_opsetid(ONNX_DOMAIN, 11),
],
)
def make_sparse(
self,
shape: Sequence[int],
values: Sequence[int],
indices_shape: Sequence[int],
indices: Sequence[int],
) -> SparseTensorProto:
sparse = SparseTensorProto()
sparse.dims.extend(shape)
nnz = len(values)
sparse.values.CopyFrom(
helper.make_tensor("spval", TensorProto.INT64, (nnz,), values)
)
sparse.indices.CopyFrom(
helper.make_tensor("spind", TensorProto.INT64, indices_shape, indices)
)
return sparse
def test_constant_sparse(self) -> None:
y_shape = [100]
y_value = self.make_sparse(y_shape, [13, 17, 19], [3], [9, 27, 81])
graph = self._make_graph(
[], [make_node("Constant", [], ["y"], sparse_value=y_value)], []
)
self._assert_inferred(graph, [make_tensor_value_info("y", TensorProto.INT64, y_shape)]) # type: ignore
def test_constant_value_int(self) -> None:
graph = self._make_graph(
[], [make_node("Constant", [], ["y"], value_int=42)], []
)
self._assert_inferred(
graph, [make_tensor_value_info("y", TensorProto.INT64, [])]
)
def test_constant_value_ints(self) -> None:
value_ints = [1, 2, 3]
graph = self._make_graph(
[], [make_node("Constant", [], ["y"], value_ints=value_ints)], []
)
self._assert_inferred(
graph, [make_tensor_value_info("y", TensorProto.INT64, [len(value_ints)])]
)
def test_constant_value_float(self) -> None:
graph = self._make_graph(
[], [make_node("Constant", [], ["y"], value_float=1.42)], []
)
self._assert_inferred(
graph, [make_tensor_value_info("y", TensorProto.FLOAT, [])]
)
def test_constant_value_floats(self) -> None:
value_floats = [1.0, 1.1, 1.2]
graph = self._make_graph(
[], [make_node("Constant", [], ["y"], value_floats=value_floats)], []
)
self._assert_inferred(
graph, [make_tensor_value_info("y", TensorProto.FLOAT, [len(value_floats)])]
)
def test_constant_value_string(self) -> None:
graph = self._make_graph(
[], [make_node("Constant", [], ["y"], value_string="String value")], []
)
self._assert_inferred(
graph, [make_tensor_value_info("y", TensorProto.STRING, [])]
)
def test_constant_value_strings(self) -> None:
value_strings = ["o", "n", "n", "x"]
graph = self._make_graph(
[], [make_node("Constant", [], ["y"], value_strings=value_strings)], []
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.STRING, [len(value_strings)])],
)
def test_range(self) -> None:
graph = self._make_graph(
[
("start", TensorProto.FLOAT, ()),
("limit", TensorProto.FLOAT, ()),
("delta", TensorProto.FLOAT, ()),
],
[make_node("Range", ["start", "limit", "delta"], ["output"])],
[],
initializer=[
make_tensor("start", TensorProto.FLOAT, (), (1,)),
make_tensor("limit", TensorProto.FLOAT, (), (5,)),
make_tensor("delta", TensorProto.FLOAT, (), (2,)),
],
)
self._assert_inferred(
graph, [make_tensor_value_info("output", TensorProto.FLOAT, (2,))]
)
def test_range_rank_inference(self) -> None:
graph = self._make_graph(
[
("start", TensorProto.INT32, ()),
("limit", TensorProto.INT32, ()),
("delta", TensorProto.INT32, ()),
],
[make_node("Range", ["start", "limit", "delta"], ["output"])],
[],
initializer=[
make_tensor("start", TensorProto.INT32, (), (1,)),
make_tensor("limit", TensorProto.INT32, (), (5,)),
],
) # Missing 'delta' initializer
self._assert_inferred(graph, [make_tensor_value_info("output", TensorProto.INT32, (None,))]) # type: ignore
def test_gathernd(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (4, 5, 6)), ("indices", TensorProto.INT64, (2,))],
[make_node("GatherND", ["x", "indices"], ["y"])],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("y", TensorProto.FLOAT, (6,))]
)
def test_gathernd_batchdim_1(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (2, 2, 2)),
("indices", TensorProto.INT64, (2, 1)),
],
[make_node("GatherND", ["x", "indices"], ["y"], batch_dims=1)],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("y", TensorProto.FLOAT, (2, 2))]
)
def test_cumsum(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (2, 3)), ("axis", TensorProto.FLOAT, (1,))],
[make_node("CumSum", ["x", "axis"], "z")],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("z", TensorProto.FLOAT, (2, 3))]
)
def test_nonmaxsuppression(self) -> None:
graph = self._make_graph(
[
("boxes", TensorProto.FLOAT, (1, 3, 4)),
("scores", TensorProto.FLOAT, (1, 5, 3)),
],
[make_node("NonMaxSuppression", ["boxes", "scores"], ["y"])],
[],
)
self._assert_inferred(graph, [make_tensor_value_info("y", TensorProto.INT64, (None, 3))]) # type: ignore
def test_sequence_empty(self) -> None:
graph = self._make_graph([], [make_node("SequenceEmpty", [], ["output"])], [])
self._assert_inferred(graph, [make_tensor_sequence_value_info("output", TensorProto.FLOAT, None)]) # type: ignore
def test_sequence_construct(self) -> None:
graph = self._make_graph(
[
("input1", TensorProto.FLOAT, (2, 3, 4)),
("input2", TensorProto.FLOAT, (2, 3, 4)),
("input3", TensorProto.FLOAT, (2, 3, 4)),
],
[
make_node(
"SequenceConstruct",
["input1", "input2", "input3"],
["output_sequence"],
)
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_sequence_value_info(
"output_sequence", TensorProto.FLOAT, (2, 3, 4)
)
],
) # type: ignore
def test_sequence_construct_one_input(self) -> None:
graph = self._make_graph(
[("input1", TensorProto.FLOAT, (2, 3, 4))],
[make_node("SequenceConstruct", ["input1"], ["output_sequence"])],
[],
)
self._assert_inferred(
graph,
[
make_tensor_sequence_value_info(
"output_sequence", TensorProto.FLOAT, (2, 3, 4)
)
],
) # type: ignore
def test_sequence_construct_diff_rank(self) -> None:
graph = self._make_graph(
[
("input1", TensorProto.FLOAT, (2, 3, 4)),
("input2", TensorProto.FLOAT, (2, 3)),
("input3", TensorProto.FLOAT, (2, 3)),
],
[
make_node(
"SequenceConstruct",
["input1", "input2", "input3"],
["output_sequence"],
)
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_sequence_value_info(
"output_sequence", TensorProto.FLOAT, None
)
],
) # type: ignore
def test_sequence_construct_diff_dim_size(self) -> None:
graph = self._make_graph(
[
("input1", TensorProto.FLOAT, (2, 3, 4)),
("input2", TensorProto.FLOAT, (2, 3, 5)),
("input3", TensorProto.FLOAT, (2, 3, 6)),
],
[
make_node(
"SequenceConstruct",
["input1", "input2", "input3"],
["output_sequence"],
)
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_sequence_value_info(
"output_sequence", TensorProto.FLOAT, (2, 3, None)
)
],
) # type: ignore
def test_sequence_insert(self) -> None:
graph = self._make_graph(
[
("input1", TensorProto.FLOAT, (2, 3, 4)),
("input2", TensorProto.FLOAT, (2, 3, 4)),
("input3", TensorProto.FLOAT, (2, 3, 4)),
("input4", TensorProto.FLOAT, (2, 3, 4)),
],
[
make_node(
"SequenceConstruct", ["input1", "input2", "input3"], ["in_sequence"]
),
make_node(
"SequenceInsert", ["in_sequence", "input4"], ["output_sequence"]
),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_sequence_value_info(
"in_sequence", TensorProto.FLOAT, (2, 3, 4)
),
make_tensor_sequence_value_info(
"output_sequence", TensorProto.FLOAT, (2, 3, 4)
),
],
) # type: ignore
def test_sequence_insert_diff_rank(self) -> None:
graph = self._make_graph(
[
("input1", TensorProto.FLOAT, (2, 3, 4)),
("input2", TensorProto.FLOAT, (2, 3, 4)),
("input3", TensorProto.FLOAT, (2, 3, 4)),
("input4", TensorProto.FLOAT, (2, 3)),
],
[
make_node(
"SequenceConstruct", ["input1", "input2", "input3"], ["in_sequence"]
),
make_node(
"SequenceInsert", ["in_sequence", "input4"], ["output_sequence"]
),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_sequence_value_info(
"in_sequence", TensorProto.FLOAT, (2, 3, 4)
),
make_tensor_sequence_value_info(
"output_sequence", TensorProto.FLOAT, None
),
],
) # type: ignore
def test_sequence_insert_diff_shape(self) -> None:
graph = self._make_graph(
[
("input1", TensorProto.FLOAT, (2, 3, 4)),
("input2", TensorProto.FLOAT, (2, 3, 4)),
("input3", TensorProto.FLOAT, (2, 5, 4)),
("input4", TensorProto.FLOAT, (2, 5, 2)),
],
[
make_node(
"SequenceConstruct", ["input1", "input2", "input3"], ["in_sequence"]
),
make_node(
"SequenceInsert", ["in_sequence", "input4"], ["output_sequence"]
),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_sequence_value_info("in_sequence", TensorProto.FLOAT, (2, None, 4)), # type: ignore
make_tensor_sequence_value_info(
"output_sequence", TensorProto.FLOAT, (2, None, None)
),
],
) # type: ignore
def test_sequence_at(self) -> None:
graph = self._make_graph(
[
("input1", TensorProto.FLOAT, (2, 3, 4)),
("input2", TensorProto.FLOAT, (2, 3, 4)),
("input3", TensorProto.FLOAT, (2, 3, 4)),
("ind", TensorProto.INT64, ()),
],
[
make_node(
"SequenceConstruct", ["input1", "input2", "input3"], ["in_sequence"]
),
make_node("SequenceAt", ["in_sequence", "ind"], ["output"]),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_sequence_value_info(
"in_sequence", TensorProto.FLOAT, (2, 3, 4)
),
make_tensor_value_info("output", TensorProto.FLOAT, (2, 3, 4)),
],
) # type: ignore
def test_sequence_at_unknown_shape(self) -> None:
graph = self._make_graph(
[
("input1", TensorProto.FLOAT, (2, 3, 4)),
("input2", TensorProto.FLOAT, (2, 3)),
("input3", TensorProto.FLOAT, (2, 3, 4)),
("ind", TensorProto.INT64, ()),
],
[
make_node(
"SequenceConstruct", ["input1", "input2", "input3"], ["in_sequence"]
),
make_node("SequenceAt", ["in_sequence", "ind"], ["output"]),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_sequence_value_info("in_sequence", TensorProto.FLOAT, None),
make_tensor_value_info("output", TensorProto.FLOAT, None),
],
) # type: ignore
def test_sequence_at_unknown_dim_size(self) -> None:
graph = self._make_graph(
[
("input1", TensorProto.FLOAT, (2, 3, 4)),
("input2", TensorProto.FLOAT, (2, 3, 5)),
("input3", TensorProto.FLOAT, (2, 3, 4)),
("ind", TensorProto.INT64, ()),
],
[
make_node(
"SequenceConstruct", ["input1", "input2", "input3"], ["in_sequence"]
),
make_node("SequenceAt", ["in_sequence", "ind"], ["output"]),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_sequence_value_info("in_sequence", TensorProto.FLOAT, (2, 3, None)), # type: ignore
make_tensor_value_info("output", TensorProto.FLOAT, (2, 3, None)),
],
) # type: ignore
def test_sequence_erase(self) -> None:
graph = self._make_graph(
[
("input1", TensorProto.FLOAT, (2, 3, 4)),
("input2", TensorProto.FLOAT, (2, 3, 4)),
("input3", TensorProto.FLOAT, (2, 3, 4)),
("ind", TensorProto.INT64, ()),
],
[
make_node(
"SequenceConstruct", ["input1", "input2", "input3"], ["in_sequence"]
),
make_node("SequenceErase", ["in_sequence", "ind"], ["output_sequence"]),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_sequence_value_info(
"in_sequence", TensorProto.FLOAT, (2, 3, 4)
),
make_tensor_sequence_value_info(
"output_sequence", TensorProto.FLOAT, (2, 3, 4)
),
],
) # type: ignore
def test_sequence_erase_diff_dim_size(self) -> None:
graph = self._make_graph(
[
("input1", TensorProto.FLOAT, (2, 3, "x")),
("input2", TensorProto.FLOAT, (2, 3, "x")),
("input3", TensorProto.FLOAT, (2, 5, "x")),
("ind", TensorProto.INT64, ()),
],
[
make_node(
"SequenceConstruct", ["input1", "input2", "input3"], ["in_sequence"]
),
make_node("SequenceErase", ["in_sequence", "ind"], ["output_sequence"]),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_sequence_value_info("in_sequence", TensorProto.FLOAT, (2, None, "x")), # type: ignore
make_tensor_sequence_value_info(
"output_sequence", TensorProto.FLOAT, (2, None, "x")
),
],
) # type: ignore
def test_sequence_length(self) -> None:
graph = self._make_graph(
[
("input1", TensorProto.FLOAT, (2, 3, "x")),
("input2", TensorProto.FLOAT, (2, 3, "x")),
("input3", TensorProto.FLOAT, (2, 3, "x")),
],
[
make_node(
"SequenceConstruct", ["input1", "input2", "input3"], ["in_sequence"]
),
make_node("SequenceLength", ["in_sequence"], ["len"]),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_sequence_value_info(
"in_sequence", TensorProto.FLOAT, (2, 3, "x")
),
make_tensor_value_info("len", TensorProto.INT64, ()),
],
) # type: ignore
def test_split_to_sequence(self) -> None:
graph = self._make_graph(
[("input", TensorProto.FLOAT, (6, 4)), ("split", TensorProto.INT32, (2,))],
[make_node("SplitToSequence", ["input", "split"], ["output_sequence"])],
[],
initializer=[make_tensor("split", TensorProto.INT32, (2,), (3, 3))],
)
self._assert_inferred(
graph,
[
make_tensor_sequence_value_info(
"output_sequence", TensorProto.FLOAT, (3, 4)
)
],
) # type: ignore
def test_split_to_sequence_scalar(self) -> None:
graph = self._make_graph(
[("input", TensorProto.FLOAT, (6, 4)), ("split", TensorProto.INT32, ())],
[make_node("SplitToSequence", ["input", "split"], ["output_sequence"])],
[],
initializer=[make_tensor("split", TensorProto.INT32, (), (2,))],
)
self._assert_inferred(
graph,
[
make_tensor_sequence_value_info(
"output_sequence", TensorProto.FLOAT, (2, 4)
)
],
) # type: ignore
def test_split_to_sequence_keepdims(self) -> None:
graph = self._make_graph(
[("input", TensorProto.FLOAT, (6, 4))],
[make_node("SplitToSequence", ["input"], ["output_sequence"], keepdims=1)],
[],
)
self._assert_inferred(
graph,
[
make_tensor_sequence_value_info(
"output_sequence", TensorProto.FLOAT, (1, 4)
)
],
) # type: ignore
def test_split_to_sequence_not_keepdims(self) -> None:
graph = self._make_graph(
[("input", TensorProto.FLOAT, (6, 4))],
[make_node("SplitToSequence", ["input"], ["output_sequence"], keepdims=0)],
[],
)
self._assert_inferred(
graph,
[
make_tensor_sequence_value_info(
"output_sequence", TensorProto.FLOAT, (4,)
)
],
) # type: ignore
def test_split_to_sequence_ignore_keepdims(self) -> None:
graph = self._make_graph(
[("input", TensorProto.FLOAT, (6, 4)), ("split", TensorProto.INT32, (2,))],
[
make_node(
"SplitToSequence",
["input", "split"],
["output_sequence"],
keepdims=0,
)
],
[],
initializer=[make_tensor("split", TensorProto.INT32, (2,), (3, 3))],
)
self._assert_inferred(
graph,
[
make_tensor_sequence_value_info(
"output_sequence", TensorProto.FLOAT, (3, 4)
)
],
) # type: ignore
def test_split_to_sequence_axis(self) -> None:
graph = self._make_graph(
[("input", TensorProto.FLOAT, (6, 4))],
[make_node("SplitToSequence", ["input"], ["output_sequence"], axis=1)],
[],
)
self._assert_inferred(
graph,
[
make_tensor_sequence_value_info(
"output_sequence", TensorProto.FLOAT, (6, 1)
)
],
) # type: ignore
def test_split_to_sequence_neg_axis(self) -> None:
graph = self._make_graph(
[("input", TensorProto.FLOAT, (6, 4))],
[make_node("SplitToSequence", ["input"], ["output_sequence"], axis=-2)],
[],
)
self._assert_inferred(
graph,
[
make_tensor_sequence_value_info(
"output_sequence", TensorProto.FLOAT, (1, 4)
)
],
) # type: ignore
def test_split_to_sequence_split_sizes(self) -> None:
graph = self._make_graph(
[("input", TensorProto.FLOAT, (6, 4)), ("split", TensorProto.INT32, (3,))],
[make_node("SplitToSequence", ["input", "split"], ["output_sequence"])],
[],
initializer=[make_tensor("split", TensorProto.INT32, (3,), (2, 1, 3))],
)
self._assert_inferred(
graph,
[
make_tensor_sequence_value_info(
"output_sequence", TensorProto.FLOAT, (None, 4)
)
],
) # type: ignore
def test_split_to_sequence_non_divisible(self) -> None:
graph = self._make_graph(
[("input", TensorProto.FLOAT, (6, 4)), ("split", TensorProto.INT32, ())],
[make_node("SplitToSequence", ["input", "split"], ["output_sequence"])],
[],
initializer=[make_tensor("split", TensorProto.INT32, (), (4,))],
)
self._assert_inferred(
graph,
[
make_tensor_sequence_value_info(
"output_sequence", TensorProto.FLOAT, (None, 4)
)
],
) # type: ignore
def test_concat_from_sequence(self) -> None:
graph = self._make_graph(
[
("input1", TensorProto.FLOAT, (2, 3, "x")),
("input2", TensorProto.FLOAT, (2, 3, "x")),
("input3", TensorProto.FLOAT, (2, 3, "x")),
],
[
make_node(
"SequenceConstruct", ["input1", "input2", "input3"], ["in_sequence"]
),
make_node("ConcatFromSequence", ["in_sequence"], ["out"], axis=0),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_sequence_value_info(
"in_sequence", TensorProto.FLOAT, (2, 3, "x")
),
make_tensor_value_info("out", TensorProto.FLOAT, (None, 3, "x")),
],
) # type: ignore
def test_concat_from_sequence_unknown_shape(self) -> None:
graph = self._make_graph(
[
("input1", TensorProto.FLOAT, (2, 3, "x")),
("input2", TensorProto.FLOAT, (2, 3)),
("input3", TensorProto.FLOAT, (2, 3, "x")),
],
[
make_node(
"SequenceConstruct", ["input1", "input2", "input3"], ["in_sequence"]
),
make_node("ConcatFromSequence", ["in_sequence"], ["out"], axis=0),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_sequence_value_info("in_sequence", TensorProto.FLOAT, None),
make_tensor_value_info("out", TensorProto.FLOAT, None),
],
) # type: ignore
def test_concat_from_sequence_unknown_dim_size(self) -> None:
graph = self._make_graph(
[
("input1", TensorProto.FLOAT, (2, 3, "x")),
("input2", TensorProto.FLOAT, (2, 4, "x")),
("input3", TensorProto.FLOAT, (2, 3, "x")),
],
[
make_node(
"SequenceConstruct", ["input1", "input2", "input3"], ["in_sequence"]
),
make_node("ConcatFromSequence", ["in_sequence"], ["out"], axis=0),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_sequence_value_info("in_sequence", TensorProto.FLOAT, (2, None, "x")), # type: ignore
make_tensor_value_info("out", TensorProto.FLOAT, (None, None, "x")),
],
) # type: ignore
def test_concat_from_sequence_axis(self) -> None:
graph = self._make_graph(
[
("input1", TensorProto.FLOAT, (2, 3, "x")),
("input2", TensorProto.FLOAT, (2, 4, "x")),
("input3", TensorProto.FLOAT, (2, 3, "x")),
],
[
make_node(
"SequenceConstruct", ["input1", "input2", "input3"], ["in_sequence"]
),
make_node("ConcatFromSequence", ["in_sequence"], ["out"], axis=2),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_sequence_value_info("in_sequence", TensorProto.FLOAT, (2, None, "x")), # type: ignore
make_tensor_value_info("out", TensorProto.FLOAT, (2, None, None)),
],
) # type: ignore
def test_concat_from_sequence_neg_axis(self) -> None:
graph = self._make_graph(
[
("input1", TensorProto.FLOAT, (2, 3, "x")),
("input2", TensorProto.FLOAT, (2, 4, "x")),
("input3", TensorProto.FLOAT, (2, 3, "x")),
],
[
make_node(
"SequenceConstruct", ["input1", "input2", "input3"], ["in_sequence"]
),
make_node("ConcatFromSequence", ["in_sequence"], ["out"], axis=-3),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_sequence_value_info("in_sequence", TensorProto.FLOAT, (2, None, "x")), # type: ignore
make_tensor_value_info("out", TensorProto.FLOAT, (None, None, "x")),
],
) # type: ignore
def test_concat_from_sequence_new_axis(self) -> None:
graph = self._make_graph(
[
("input1", TensorProto.FLOAT, (2, 3, "x")),
("input2", TensorProto.FLOAT, (2, 3, "x")),
("input3", TensorProto.FLOAT, (2, 3, "x")),
],
[
make_node(
"SequenceConstruct", ["input1", "input2", "input3"], ["in_sequence"]
),
make_node(
"ConcatFromSequence", ["in_sequence"], ["out"], axis=2, new_axis=1
),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_sequence_value_info(
"in_sequence", TensorProto.FLOAT, (2, 3, "x")
),
make_tensor_value_info("out", TensorProto.FLOAT, (2, 3, None, "x")),
],
) # type: ignore
def test_concat_from_sequence_neg_new_axis(self) -> None:
graph = self._make_graph(
[
("input1", TensorProto.FLOAT, (2, 3, "x")),
("input2", TensorProto.FLOAT, (2, 3, "x")),
("input3", TensorProto.FLOAT, (2, 3, "x")),
],
[
make_node(
"SequenceConstruct", ["input1", "input2", "input3"], ["in_sequence"]
),
make_node(
"ConcatFromSequence", ["in_sequence"], ["out"], axis=-1, new_axis=1
),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_sequence_value_info(
"in_sequence", TensorProto.FLOAT, (2, 3, "x")
),
make_tensor_value_info("out", TensorProto.FLOAT, (2, 3, "x", None)),
],
) # type: ignore
def test_adagrad(self) -> None:
graph = self._make_graph(
[
("R", TensorProto.FLOAT, ()), # scalar's shape is ()
("T", TensorProto.INT64, ()), # scalar's shape is ()
("X", TensorProto.FLOAT, (1, 2)),
("G", TensorProto.FLOAT, (1, 2)),
("H", TensorProto.FLOAT, (1, 2)),
],
[
make_node(
"Adagrad",
["R", "T", "X", "G", "H"],
["X_new", "H_new"],
domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN,
)
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("X_new", TensorProto.FLOAT, (1, 2)),
make_tensor_value_info("H_new", TensorProto.FLOAT, (1, 2)),
],
opset_imports=[
helper.make_opsetid(ONNX_DOMAIN, 12),
helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1),
],
)
def test_adagrad_multiple(self) -> None:
graph = self._make_graph(
[
("R", TensorProto.FLOAT, ()), # scalar's shape is ()
("T", TensorProto.INT64, ()), # scalar's shape is ()
("X1", TensorProto.FLOAT, (1, 2)),
("X2", TensorProto.FLOAT, (3, 4)),
("G1", TensorProto.FLOAT, (1, 2)),
("G2", TensorProto.FLOAT, (3, 4)),
("H1", TensorProto.FLOAT, (1, 2)),
("H2", TensorProto.FLOAT, (3, 4)),
],
[
make_node(
"Adagrad",
["R", "T", "X1", "X2", "G1", "G2", "H1", "H2"],
["X1_new", "X2_new", "H1_new", "H2_new"],
domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN,
)
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("X1_new", TensorProto.FLOAT, (1, 2)),
make_tensor_value_info("X2_new", TensorProto.FLOAT, (3, 4)),
make_tensor_value_info("H1_new", TensorProto.FLOAT, (1, 2)),
make_tensor_value_info("H2_new", TensorProto.FLOAT, (3, 4)),
],
opset_imports=[
helper.make_opsetid(ONNX_DOMAIN, 12),
helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1),
],
)
def test_momentum(self) -> None:
graph = self._make_graph(
[
("R", TensorProto.FLOAT, ()), # scalar's shape is ()
("T", TensorProto.INT64, ()), # scalar's shape is ()
("X", TensorProto.FLOAT, (1, 2)),
("G", TensorProto.FLOAT, (1, 2)),
("V", TensorProto.FLOAT, (1, 2)),
],
[
make_node(
"Momentum",
["R", "T", "X", "G", "V"],
["X_new", "V_new"],
alpha=0.9,
beta=1.0,
norm_coefficient=0.02,
mode="standard",
domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN,
)
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("X_new", TensorProto.FLOAT, (1, 2)),
make_tensor_value_info("V_new", TensorProto.FLOAT, (1, 2)),
],
opset_imports=[
helper.make_opsetid(ONNX_DOMAIN, 12),
helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1),
],
)
def test_momentum_multiple(self) -> None:
graph = self._make_graph(
[
("R", TensorProto.FLOAT, ()), # scalar's shape is ()
("T", TensorProto.INT64, ()), # scalar's shape is ()
("X1", TensorProto.FLOAT, (1, 2)),
("X2", TensorProto.FLOAT, (3, 4)),
("G1", TensorProto.FLOAT, (1, 2)),
("G2", TensorProto.FLOAT, (3, 4)),
("V1", TensorProto.FLOAT, (1, 2)),
("V2", TensorProto.FLOAT, (3, 4)),
],
[
make_node(
"Momentum",
["R", "T", "X1", "X2", "G1", "G2", "V1", "V2"],
["X1_new", "X2_new", "V1_new", "V2_new"],
alpha=0.9,
beta=1.0,
norm_coefficient=0.02,
mode="nesterov",
domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN,
)
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("X1_new", TensorProto.FLOAT, (1, 2)),
make_tensor_value_info("X2_new", TensorProto.FLOAT, (3, 4)),
make_tensor_value_info("V1_new", TensorProto.FLOAT, (1, 2)),
make_tensor_value_info("V2_new", TensorProto.FLOAT, (3, 4)),
],
opset_imports=[
helper.make_opsetid(ONNX_DOMAIN, 12),
helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1),
],
)
def test_adam(self) -> None:
graph = self._make_graph(
[
("R", TensorProto.FLOAT, ()), # scalar's shape is ()
("T", TensorProto.INT64, ()), # scalar's shape is ()
("X", TensorProto.FLOAT, (1, 2)),
("G", TensorProto.FLOAT, (1, 2)),
("V", TensorProto.FLOAT, (1, 2)),
("H", TensorProto.FLOAT, (1, 2)),
],
[
make_node(
"Adam",
["R", "T", "X", "G", "V", "H"],
["X_new", "V_new", "H_new"],
domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN,
alpha=0.9,
beta=1.0,
norm_coefficient=0.02,
)
],
[],
)
infos = [
make_tensor_value_info("X_new", TensorProto.FLOAT, (1, 2)),
make_tensor_value_info("V_new", TensorProto.FLOAT, (1, 2)),
make_tensor_value_info("H_new", TensorProto.FLOAT, (1, 2)),
]
self._assert_inferred(
graph,
infos,
opset_imports=[
make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1),
make_opsetid(ONNX_DOMAIN, 12),
],
)
def test_adam_multiple(self) -> None:
graph = self._make_graph(
[
("R", TensorProto.FLOAT, ()), # scalar's shape is ()
("T", TensorProto.INT64, ()), # scalar's shape is ()
("X1", TensorProto.FLOAT, (1, 2)),
("X2", TensorProto.FLOAT, (3, 4)),
("G1", TensorProto.FLOAT, (1, 2)),
("G2", TensorProto.FLOAT, (3, 4)),
("V1", TensorProto.FLOAT, (1, 2)),
("V2", TensorProto.FLOAT, (3, 4)),
("H1", TensorProto.FLOAT, (1, 2)),
("H2", TensorProto.FLOAT, (3, 4)),
],
[
make_node(
"Adam",
["R", "T", "X1", "X2", "G1", "G2", "V1", "V2", "H1", "H2"],
["X1_new", "X2_new", "V1_new", "V2_new", "H1_new", "H2_new"],
domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN,
alpha=0.9,
beta=1.0,
norm_coefficient=0.02,
)
],
[],
)
infos = [
make_tensor_value_info("X1_new", TensorProto.FLOAT, (1, 2)),
make_tensor_value_info("X2_new", TensorProto.FLOAT, (3, 4)),
make_tensor_value_info("V1_new", TensorProto.FLOAT, (1, 2)),
make_tensor_value_info("V2_new", TensorProto.FLOAT, (3, 4)),
make_tensor_value_info("H1_new", TensorProto.FLOAT, (1, 2)),
make_tensor_value_info("H2_new", TensorProto.FLOAT, (3, 4)),
]
self._assert_inferred(
graph,
infos,
opset_imports=[
make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1),
make_opsetid(ONNX_DOMAIN, 12),
],
)
def test_pad_opset10(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (1, None, 2))],
[make_node("Pad", "x", "y", pads=[1, 3, 1, 1, 0, 1])],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.FLOAT, (3, None, 4))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 10)],
) # type: ignore
def test_constant_pad_2d_opset10(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (2, 3, 4, 4))],
[
make_node(
"Pad",
"x",
"y",
pads=[0, 0, 3, 1, 0, 0, 4, 2],
mode="constant",
value=2.0,
)
],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.FLOAT, (2, 3, 11, 7))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 10)],
)
def test_pad(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (1, None, 2)), ("pads", TensorProto.INT64, (6,))],
[make_node("Pad", ["x", "pads"], "y")],
[],
initializer=[
make_tensor(
"pads",
TensorProto.INT64,
(6,),
(
1,
3,
1,
1,
0,
1,
),
)
],
)
self._assert_inferred(graph, [make_tensor_value_info("y", TensorProto.FLOAT, (3, None, 4))]) # type: ignore
def test_gatherelements_basic(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (6,)), ("indices", TensorProto.INT64, (2,))],
[make_node("GatherElements", ["x", "indices"], ["y"])],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("y", TensorProto.FLOAT, (2,))]
)
def test_gatherelements_indices_missing_shape(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (6,)),
("indices", TensorProto.INT64, None),
], # type: ignore
[make_node("GatherElements", ["x", "indices"], ["y"])],
[],
)
self._assert_inferred(graph, [make_tensor_value_info("y", TensorProto.FLOAT, None)]) # type: ignore
def test_einsum_transpose(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (3, 4))],
[make_node("Einsum", ["x"], ["y"], equation="ij->ji")],
[],
)
self._assert_inferred(graph, [make_tensor_value_info("y", TensorProto.FLOAT, (None, None))]) # type: ignore
def test_einsum_dot(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (1,)), ("y", TensorProto.FLOAT, (1,))],
[make_node("Einsum", ["x", "y"], ["z"], equation="i,i->")],
[],
)
self._assert_inferred(graph, [make_tensor_value_info("z", TensorProto.FLOAT, ())]) # type: ignore
def test_einsum_scalar(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, ()), ("y", TensorProto.FLOAT, ())],
[make_node("Einsum", ["x", "y"], ["z"], equation=",->")],
[],
)
self._assert_inferred(graph, [make_tensor_value_info("z", TensorProto.FLOAT, ())]) # type: ignore
def test_einsum_outer_prod(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (3, 5)), ("y", TensorProto.FLOAT, (7, 9))],
[make_node("Einsum", ["x", "y"], ["z"], equation="ij,ab->ijab")],
[],
)
self._assert_inferred(graph, [make_tensor_value_info("z", TensorProto.FLOAT, (None, None, None, None))]) # type: ignore
def test_einsum_sum_along_dim(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (3, 4))],
[make_node("Einsum", ["x"], ["y"], equation="i j->i ")],
[],
)
self._assert_inferred(graph, [make_tensor_value_info("y", TensorProto.FLOAT, (None,))]) # type: ignore
def test_einsum_ellipsis(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (3, 4, 4))],
[make_node("Einsum", ["x"], ["y"], equation="... ii ->... i")],
[],
)
self._assert_inferred(graph, [make_tensor_value_info("y", TensorProto.FLOAT, (None, None))]) # type: ignore
def test_einsum_ellipsis_2(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (2, 2, 2)), ("y", TensorProto.FLOAT, (2, 2, 2))],
[make_node("Einsum", ["x", "y"], ["z"], equation="...ij,...jk->...ik")],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("z", TensorProto.FLOAT, (None, None, None))]
) # type: ignore
def test_einsum_ellipsis_3(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (2, 2, 2)), ("y", TensorProto.FLOAT, (2, 2, 2))],
[make_node("Einsum", ["x", "y"], ["z"], equation="...ij,...jk")],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("z", TensorProto.FLOAT, (None, None, None))]
) # type: ignore
def test_einsum_contraction(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (5, 6, 7, 8)),
("y", TensorProto.FLOAT, (8, 9, 10)),
],
[make_node("Einsum", ["x", "y"], ["z"], equation="abcd,dfg->abcfg")],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info(
"z", TensorProto.FLOAT, (None, None, None, None, None)
)
],
) # type: ignore
def test_einsum_contraction_2(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (3, 4, 5)), ("y", TensorProto.FLOAT, (3, 5))],
[make_node("Einsum", ["x", "y"], ["z"], equation="ijk,ik->jk")],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("z", TensorProto.FLOAT, (None, None))]
) # type: ignore
def test_einsum_batch_matmul(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (5, 2, 3)), ("y", TensorProto.FLOAT, (5, 3, 4))],
[make_node("Einsum", ["x", "y"], ["z"], equation="bij , b jk-> bik")],
[],
)
self._assert_inferred(graph, [make_tensor_value_info("z", TensorProto.FLOAT, (None, None, None))]) # type: ignore
def test_einsum_left_hand_eqn(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (2, 3)), ("y", TensorProto.FLOAT, (3, 4))],
[make_node("Einsum", ["x", "y"], ["z"], equation="ij,kl")],
[],
)
self._assert_inferred(graph, [make_tensor_value_info("z", TensorProto.FLOAT, (None, None, None, None))]) # type: ignore
def test_einsum_incorrect_num_inputs(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (2, 3)),
("y", TensorProto.FLOAT, (2, 3)),
("z", TensorProto.FLOAT, (2, 3)),
],
[make_node("Einsum", ["x", "y"], ["z"], equation="i,...j, k, l-> i")],
[],
)
self.assertRaises(onnx.shape_inference.InferenceError, self._inferred, graph)
def test_negative_log_likehood_shape_is_NCdd(self) -> None:
N, C = 3, 4
graph = self._make_graph(
[("input", TensorProto.FLOAT, (N, C)), ("target", TensorProto.INT64, (N,))],
[
make_node(
"NegativeLogLikelihoodLoss",
["input", "target"],
["loss"],
reduction="none",
)
],
[],
)
self._assert_inferred(graph, [make_tensor_value_info("loss", TensorProto.FLOAT, (N,))]) # type: ignore
def test_negative_log_likehood_shape_is_NC_with_weight(self) -> None:
N, C = 3, 4
graph = self._make_graph(
[
("input", TensorProto.FLOAT, (N, C)),
("target", TensorProto.INT64, (N,)),
("weight", TensorProto.FLOAT, (C,)),
],
[
make_node(
"NegativeLogLikelihoodLoss",
["input", "target", "weight"],
["loss"],
reduction="none",
)
],
[],
)
self._assert_inferred(graph, [make_tensor_value_info("loss", TensorProto.FLOAT, (N,))]) # type: ignore
def test_negative_log_likehood_shape_is_NC_reduction_mean(self) -> None:
N, C = 3, 4
graph = self._make_graph(
[("input", TensorProto.FLOAT, (N, C)), ("target", TensorProto.INT64, (N,))],
[
make_node(
"NegativeLogLikelihoodLoss",
["input", "target"],
["loss"],
reduction="mean",
)
],
[],
)
self._assert_inferred(graph, [make_tensor_value_info("loss", TensorProto.FLOAT, ())]) # type: ignore
def test_negative_log_likehood_shape_is_NC_with_weight_reduction_mean(self) -> None:
N, C = 3, 4
graph = self._make_graph(
[
("input", TensorProto.FLOAT, (N, C)),
("target", TensorProto.INT64, (N,)),
("weight", TensorProto.FLOAT, (C,)),
],
[
make_node(
"NegativeLogLikelihoodLoss",
["input", "target", "weight"],
["loss"],
reduction="mean",
)
],
[],
)
self._assert_inferred(graph, [make_tensor_value_info("loss", TensorProto.FLOAT, ())]) # type: ignore
def test_negative_log_likehood_shape_is_NCd1d2(self) -> None:
N, C, d1, d2 = 3, 4, 5, 6
graph = self._make_graph(
[
("input", TensorProto.FLOAT, (N, C, d1, d2)),
("target", TensorProto.INT64, (N, d1, d2)),
],
[
make_node(
"NegativeLogLikelihoodLoss",
["input", "target"],
["loss"],
reduction="none",
)
],
[],
)
self._assert_inferred(graph, [make_tensor_value_info("loss", TensorProto.FLOAT, (N, d1, d2))]) # type: ignore
def test_negative_log_likehood_shape_is_NCd1d2_with_weight(self) -> None:
N, C, d1, d2 = 3, 4, 5, 6
graph = self._make_graph(
[
("input", TensorProto.FLOAT, (N, C, d1, d2)),
("target", TensorProto.INT64, (N, d1, d2)),
("weight", TensorProto.FLOAT, (C,)),
],
[
make_node(
"NegativeLogLikelihoodLoss",
["input", "target", "weight"],
["loss"],
reduction="none",
)
],
[],
)
self._assert_inferred(graph, [make_tensor_value_info("loss", TensorProto.FLOAT, (N, d1, d2))]) # type: ignore
def test_negative_log_likehood_shape_is_NCd1d2_reduction_sum(self) -> None:
N, C, d1, d2 = 3, 4, 5, 6
graph = self._make_graph(
[
("input", TensorProto.FLOAT, (N, C, d1, d2)),
("target", TensorProto.INT64, (N, d1, d2)),
],
[
make_node(
"NegativeLogLikelihoodLoss",
["input", "target"],
["loss"],
reduction="sum",
)
],
[],
)
self._assert_inferred(graph, [make_tensor_value_info("loss", TensorProto.FLOAT, ())]) # type: ignore
def test_negative_log_likehood_shape_is_NCd1d2_with_weight_reduction_mean(
self,
) -> None:
N, C, d1, d2 = 3, 4, 5, 6
graph = self._make_graph(
[
("input", TensorProto.FLOAT, (N, C, d1, d2)),
("target", TensorProto.INT64, (N, d1, d2)),
("weight", TensorProto.FLOAT, (C,)),
],
[
make_node(
"NegativeLogLikelihoodLoss",
["input", "target", "weight"],
["loss"],
reduction="mean",
)
],
[],
)
self._assert_inferred(graph, [make_tensor_value_info("loss", TensorProto.FLOAT, ())]) # type: ignore
def test_negative_log_likehood_input_target_shape_mismatch(self) -> None:
N, C, d1, d2 = 3, 4, 5, 6
graph = self._make_graph(
[
("input", TensorProto.FLOAT, (N, d1, d2)),
("target", TensorProto.INT64, (N, d1 + 1, d2)),
("weight", TensorProto.FLOAT, (C,)),
("loss", TensorProto.FLOAT, ()),
],
[
make_node(
"NegativeLogLikelihoodLoss",
["input", "target", "weight"],
["loss"],
reduction="mean",
)
],
[],
)
self.assertRaises(onnx.shape_inference.InferenceError, self._inferred, graph)
def test_negative_log_likehood_input_weight_shape_mismatch(self) -> None:
N, C, d1, d2 = 3, 4, 5, 6
graph = self._make_graph(
[
("input", TensorProto.FLOAT, (N, C, d1, d2)),
("target", TensorProto.INT64, (N, d1, d2)),
("weight", TensorProto.FLOAT, (C + 1,)),
("loss", TensorProto.FLOAT, (N, d1, d2)),
],
[
make_node(
"NegativeLogLikelihoodLoss",
["input", "target", "weight"],
["loss"],
reduction="none",
)
],
[],
)
self.assertRaises(checker.ValidationError, self._inferred, graph)
def test_softmax_cross_entropy_none(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (2, 3)), ("y", TensorProto.FLOAT, (2,))],
[make_node("SoftmaxCrossEntropyLoss", ["x", "y"], ["z"], reduction="none")],
[],
)
self._assert_inferred(graph, [make_tensor_value_info("z", TensorProto.FLOAT, (2,))]) # type: ignore
def test_softmax_cross_entropy_mean(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (2, 3)), ("y", TensorProto.FLOAT, (2,))],
[make_node("SoftmaxCrossEntropyLoss", ["x", "y"], ["z"], reduction="mean")],
[],
)
self._assert_inferred(graph, [make_tensor_value_info("z", TensorProto.FLOAT, ())]) # type: ignore
def test_softmax_cross_entropy_none_NCD1D2(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (2, 3, 5, 8)),
("y", TensorProto.FLOAT, (2, 5, 8)),
],
[make_node("SoftmaxCrossEntropyLoss", ["x", "y"], ["z"], reduction="none")],
[],
)
self._assert_inferred(graph, [make_tensor_value_info("z", TensorProto.FLOAT, (2, 5, 8))]) # type: ignore
def test_softmax_cross_entropy_mean_NCD1D2(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (2, 3, 4, 5)),
("y", TensorProto.FLOAT, (2, 4, 5)),
],
[make_node("SoftmaxCrossEntropyLoss", ["x", "y"], ["z"], reduction="mean")],
[],
)
self._assert_inferred(graph, [make_tensor_value_info("z", TensorProto.FLOAT, ())]) # type: ignore
def test_celu_function_output_shape(self) -> None:
graph = self._make_graph(
[("X", TensorProto.FLOAT, (25, 48, 16, 16))],
[make_node("Celu", ["X"], ["Y"], alpha=2.0)],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("Y", TensorProto.FLOAT, (25, 48, 16, 16))]
)
def prepare_input_initializer_tensors(self, initializer_shape, input_shape): # type: ignore
nodes = [make_node("Add", ["x", "y"], "z")]
if initializer_shape is None:
initializer = [] # type: ignore
else:
size = 1
for d in initializer_shape:
size = size * d
vals = [0.0 for i in range(size)]
initializer = [
make_tensor("x", TensorProto.FLOAT, initializer_shape, vals), # type: ignore
make_tensor("y", TensorProto.FLOAT, initializer_shape, vals),
]
if input_shape is None:
inputs = [] # type: ignore
else:
inputs = [
helper.make_tensor_value_info("x", TensorProto.FLOAT, input_shape), # type: ignore
helper.make_tensor_value_info("y", TensorProto.FLOAT, input_shape),
]
graph = helper.make_graph(
nodes,
"test",
inputs=inputs,
outputs=[],
initializer=initializer,
value_info=[],
)
return helper.make_model(graph)
def test_infer_with_initializer_without_input_above_ir4(self) -> None:
# This is for testing IR>=4: some tensors can only exist in initializer and not in input
# So shape_inference should make use of initializer shapes
initializer_shape = (8, 7)
original_model = self.prepare_input_initializer_tensors(initializer_shape, None)
inferred_model = onnx.shape_inference.infer_shapes(
original_model, strict_mode=True
)
# If shape inference fails, it will throw IndexError
z_tenor = inferred_model.graph.value_info.pop()
z_shape = (
z_tenor.type.tensor_type.shape.dim[0].dim_value,
z_tenor.type.tensor_type.shape.dim[1].dim_value,
)
assert z_shape == initializer_shape
def test_infer_with_initializer_without_input_below_ir4(self) -> None:
# This is for testing IR<4: tensors must exist both in initializer and input
# So shape_inference should not make use of initializer shapes
# Use (None, None) as empty input
initializer_shape = (8, 7)
input_shape = (None, None)
original_model = self.prepare_input_initializer_tensors(
initializer_shape, input_shape
)
original_model.ir_version = 3 # test ir_version < 4
inferred_model = onnx.shape_inference.infer_shapes(
original_model, strict_mode=True
)
z_tenor = inferred_model.graph.value_info.pop()
z_shape = (
z_tenor.type.tensor_type.shape.dim[0].dim_value,
z_tenor.type.tensor_type.shape.dim[1].dim_value,
)
# If the input is not updated by the initializer, the output shape will keep empty (0, 0)
assert z_shape == (0, 0)
def test_infer_initializer_input_mismatch(self) -> None:
# Catch error if initializer and input mismatch
initializer_shape = (8, 7)
input_shape = (4, 3)
original_model = self.prepare_input_initializer_tensors(
initializer_shape, input_shape
)
# Inferred shape and existing shape differ in dimension 0
self.assertRaises(
onnx.shape_inference.InferenceError,
onnx.shape_inference.infer_shapes,
original_model,
strict_mode=True,
)
def test_infer_initializer_input_consistency_all_none(self) -> None:
initializer_shape = (8, 7)
input_shape = (None, None) # accepatble
original_model = self.prepare_input_initializer_tensors(
initializer_shape, input_shape
)
onnx.shape_inference.infer_shapes(original_model, strict_mode=True)
def test_infer_initializer_input_consistency_single_none(self) -> None:
initializer_shape = (8, 7)
input_shape = (None, 7) # accepatble
original_model = self.prepare_input_initializer_tensors(
initializer_shape, input_shape
)
onnx.shape_inference.infer_shapes(original_model, strict_mode=True)
def test_infer_initializer_input_consistency_differnt_rank(self) -> None:
initializer_shape = (8, 7, 9)
input_shape = (None, 7) # accepatble
original_model = self.prepare_input_initializer_tensors(
initializer_shape, input_shape
)
# Inferred shape and existing shape differ in rank: (3) vs (2)
self.assertRaises(
onnx.shape_inference.InferenceError,
onnx.shape_inference.infer_shapes,
original_model,
strict_mode=True,
)
def test_infer_initializer_input_consistency_all_none_serialized(self) -> None:
# Reuse test_infer_initializer_input_consistency_all_none test case and check with
# Serialized model
initializer_shape = (8, 7)
input_shape = (None, None) # accepatble
original_model = self.prepare_input_initializer_tensors(
initializer_shape, input_shape
)
onnx.shape_inference.infer_shapes(
original_model.SerializeToString(), strict_mode=True
)
def test_trilu_upper(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (3, 4, 5)), ("k", TensorProto.INT64, ())],
[make_node("Trilu", ["x", "k"], ["y"])],
[],
initializer=[make_tensor("k", TensorProto.INT64, (), (2,))],
)
self._assert_inferred(graph, [make_tensor_value_info("y", TensorProto.FLOAT, (3, 4, 5))]) # type: ignore
def test_trilu_lower(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (3, 4, 5)), ("k", TensorProto.INT64, ())],
[make_node("Trilu", ["x", "k"], ["y"], upper=0)],
[],
initializer=[make_tensor("k", TensorProto.INT64, (), (10,))],
)
self._assert_inferred(graph, [make_tensor_value_info("y", TensorProto.FLOAT, (3, 4, 5))]) # type: ignore
def test_trilu_upper_zero(self) -> None:
graph = self._make_graph(
[("x", TensorProto.INT64, (0, 5)), ("k", TensorProto.INT64, ())],
[make_node("Trilu", ["x", "k"], ["y"], upper=1)],
[],
initializer=[make_tensor("k", TensorProto.INT64, (), (5,))],
)
self._assert_inferred(graph, [make_tensor_value_info("y", TensorProto.INT64, (0, 5))]) # type: ignore
def test_trilu_lower_one(self) -> None:
graph = self._make_graph(
[("x", TensorProto.INT32, (3, 1, 5))],
[make_node("Trilu", ["x"], ["y"], upper=0)],
[],
)
self._assert_inferred(graph, [make_tensor_value_info("y", TensorProto.INT32, (3, 1, 5))]) # type: ignore
def test_batch_norm_train(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (3, 4, 5, 6, 7)),
("scale", TensorProto.FLOAT, (4,)),
("b", TensorProto.FLOAT, (4,)),
("input_mean", TensorProto.FLOAT, (4,)),
("input_var", TensorProto.FLOAT, (4,)),
],
[
make_node(
"BatchNormalization",
["x", "scale", "b", "input_mean", "input_var"],
["out", "output_mean", "output_var"],
training_mode=1,
)
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("out", TensorProto.FLOAT, (3, 4, 5, 6, 7)), # type: ignore
make_tensor_value_info("output_mean", TensorProto.FLOAT, (4,)), # type: ignore
make_tensor_value_info("output_var", TensorProto.FLOAT, (4,)), # type: ignore
],
)
def test_batch_norm_train_dim_param(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (3, "C", 5, 6, 7)),
("scale", TensorProto.FLOAT, ("C",)),
("b", TensorProto.FLOAT, ("C",)),
("input_mean", TensorProto.FLOAT, ("C",)),
("input_var", TensorProto.FLOAT, ("C",)),
],
[
make_node(
"BatchNormalization",
["x", "scale", "b", "input_mean", "input_var"],
["out", "output_mean", "output_var"],
training_mode=1,
)
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("out", TensorProto.FLOAT, (3, "C", 5, 6, 7)), # type: ignore
make_tensor_value_info("output_mean", TensorProto.FLOAT, ("C",)), # type: ignore
make_tensor_value_info("output_var", TensorProto.FLOAT, ("C",)), # type: ignore
],
)
def test_batch_norm_train_with_diff_type(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT16, (3, 4, 5, 6, 7)),
("scale", TensorProto.FLOAT16, (4,)),
("b", TensorProto.FLOAT16, (4,)),
("input_mean", TensorProto.FLOAT, (4,)),
("input_var", TensorProto.FLOAT, (4,)),
],
[
make_node(
"BatchNormalization",
["x", "scale", "b", "input_mean", "input_var"],
["out", "output_mean", "output_var"],
training_mode=1,
)
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("out", TensorProto.FLOAT16, (3, 4, 5, 6, 7)), # type: ignore
make_tensor_value_info("output_mean", TensorProto.FLOAT, (4,)), # type: ignore
make_tensor_value_info("output_var", TensorProto.FLOAT, (4,)), # type: ignore
],
)
def test_batch_norm_test(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (3, 4, 5, 6, 7)),
("scale", TensorProto.FLOAT, (4,)),
("b", TensorProto.FLOAT, (4,)),
("input_mean", TensorProto.FLOAT, (4,)),
("input_var", TensorProto.FLOAT, (4,)),
],
[
make_node(
"BatchNormalization",
["x", "scale", "b", "input_mean", "input_var"],
["out"],
training_mode=0,
)
],
[],
)
self._assert_inferred(graph, [make_tensor_value_info("out", TensorProto.FLOAT, (3, 4, 5, 6, 7))]) # type: ignore
def test_batch_norm_test_no_dim(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (3, 4, None, None, None)),
("scale", TensorProto.FLOAT, (4,)),
("b", TensorProto.FLOAT, (4,)),
("input_mean", TensorProto.FLOAT, (None,)),
("input_var", TensorProto.FLOAT, (4,)),
],
[
make_node(
"BatchNormalization",
["x", "scale", "b", "input_mean", "input_var"],
["out"],
training_mode=0,
)
],
[],
)
self._assert_inferred(graph, [make_tensor_value_info("out", TensorProto.FLOAT, (3, 4, None, None, None))]) # type: ignore
def test_batch_norm_train_no_shape(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, None),
("scale", TensorProto.FLOAT, None),
("b", TensorProto.FLOAT, None),
("input_mean", TensorProto.FLOAT, ("C",)),
("input_var", TensorProto.FLOAT, ("C",)),
],
[
make_node(
"BatchNormalization",
["x", "scale", "b", "input_mean", "input_var"],
["out", "running_mean", "running_var"],
training_mode=1,
)
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("out", TensorProto.FLOAT, None), # type: ignore
make_tensor_value_info("running_mean", TensorProto.FLOAT, ("C",)), # type: ignore
make_tensor_value_info("running_var", TensorProto.FLOAT, ("C",)), # type: ignore
],
)
def test_nonzero(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (None,))],
[make_node("NonZero", ["x"], ["out"])],
[],
)
self._assert_inferred(graph, [make_tensor_value_info("out", TensorProto.INT64, (1, None))]) # type: ignore
def test_nonzero_no_shape(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, None)], [make_node("NonZero", ["x"], ["out"])], []
)
self._assert_inferred(graph, [make_tensor_value_info("out", TensorProto.INT64, (None, None))]) # type: ignore
def test_nonzero_existing_dim_param(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, (3,))],
[make_node("NonZero", ["x"], ["y"])],
[make_tensor_value_info("y", TensorProto.INT64, (None, "NZ"))],
)
self._assert_inferred(graph, [make_tensor_value_info("y", TensorProto.INT64, (1, "NZ"))]) # type: ignore
def test_nonzero_scalar(self) -> None:
graph = self._make_graph(
[("x", TensorProto.FLOAT, ())], [make_node("NonZero", ["x"], ["out"])], []
)
self._assert_inferred(graph, [make_tensor_value_info("out", TensorProto.INT64, (0, None))]) # type: ignore
def test_optional_construct_empty_tensor(self) -> None:
tensor_type_proto = helper.make_tensor_type_proto(
elem_type=TensorProto.FLOAT, shape=[1, 2, 3]
)
optional_type_proto = helper.make_optional_type_proto(tensor_type_proto)
optional_val_info = helper.make_value_info(
name="output", type_proto=optional_type_proto
)
graph = self._make_graph(
[], [make_node("Optional", [], ["output"], type=tensor_type_proto)], []
)
self._assert_inferred(graph, [optional_val_info]) # type: ignore
def test_optional_construct_empty_sequence(self) -> None:
tensor_type_proto = helper.make_tensor_type_proto(
elem_type=TensorProto.INT32, shape=[1, 2, 3]
)
sequence_type_proto = helper.make_sequence_type_proto(tensor_type_proto)
optional_type_proto = helper.make_optional_type_proto(sequence_type_proto)
optional_val_info = helper.make_value_info(
name="output_sequence", type_proto=optional_type_proto
)
graph = self._make_graph(
[],
[make_node("Optional", [], ["output_sequence"], type=sequence_type_proto)],
[],
)
self._assert_inferred(graph, [optional_val_info]) # type: ignore
def test_optional_construct_tensor(self) -> None:
tensor_type_proto = helper.make_tensor_type_proto(
elem_type=TensorProto.FLOAT, shape=[2, 3, 4]
)
optional_type_proto = helper.make_optional_type_proto(tensor_type_proto)
optional_val_info = helper.make_value_info(
name="output", type_proto=optional_type_proto
)
graph = self._make_graph(
[("input1", TensorProto.FLOAT, (2, 3, 4))],
[make_node("Optional", ["input1"], ["output"])],
[],
)
self._assert_inferred(graph, [optional_val_info]) # type: ignore
def test_optional_construct_sequence(self) -> None:
tensor_type_proto = helper.make_tensor_type_proto(
elem_type=TensorProto.INT64, shape=[2, 3, 0]
)
sequence_type_proto = helper.make_sequence_type_proto(tensor_type_proto)
sequence_val_info = helper.make_value_info(
name="input_sequence", type_proto=sequence_type_proto
)
optional_type_proto = helper.make_optional_type_proto(sequence_type_proto)
optional_val_info = helper.make_value_info(
name="output_sequence", type_proto=optional_type_proto
)
graph = self._make_graph(
[("input1", TensorProto.INT64, (2, 3, 0))],
[
make_node("SequenceConstruct", ["input1"], ["input_sequence"]),
make_node("Optional", ["input_sequence"], ["output_sequence"]),
],
[],
)
self._assert_inferred(graph, [sequence_val_info, optional_val_info]) # type: ignore
def test_optional_tensor_has_element(self) -> None:
tensor_type_proto = helper.make_tensor_type_proto(
elem_type=TensorProto.FLOAT, shape=[2, 3, 4]
)
optional_type_proto = helper.make_optional_type_proto(tensor_type_proto)
optional_val_info = helper.make_value_info(
name="sequence", type_proto=optional_type_proto
)
graph = self._make_graph(
[("input1", TensorProto.FLOAT, (2, 3, 4))],
[
make_node("Optional", ["input1"], ["sequence"]),
make_node("OptionalHasElement", ["sequence"], ["output"]),
],
[],
)
self._assert_inferred(
graph,
[optional_val_info, make_tensor_value_info("output", TensorProto.BOOL, ())],
) # type: ignore
def test_optional_sequence_has_element(self) -> None:
tensor_type_proto = helper.make_tensor_type_proto(
elem_type=TensorProto.FLOAT, shape=[0, 3, 4]
)
sequence_type_proto = helper.make_sequence_type_proto(tensor_type_proto)
sequence_val_info = helper.make_value_info(
name="sequence", type_proto=sequence_type_proto
)
optional_type_proto = helper.make_optional_type_proto(sequence_type_proto)
optional_val_info = helper.make_value_info(
name="optional", type_proto=optional_type_proto
)
graph = self._make_graph(
[("input1", TensorProto.FLOAT, (0, 3, 4))],
[
make_node("SequenceConstruct", ["input1"], ["sequence"]),
make_node("Optional", ["sequence"], ["optional"]),
make_node("OptionalHasElement", ["optional"], ["output"]),
],
[],
)
self._assert_inferred(
graph,
[
sequence_val_info,
optional_val_info,
make_tensor_value_info("output", TensorProto.BOOL, ()),
],
) # type: ignore
def test_tensor_get_element(self) -> None:
tensor_type_proto = helper.make_tensor_type_proto(
elem_type=TensorProto.DOUBLE, shape=[2, 1, 4]
)
output_tensor_val_info = helper.make_value_info(
name="output", type_proto=tensor_type_proto
)
graph = self._make_graph(
[("input", TensorProto.DOUBLE, (2, 1, 4))],
[
make_node("OptionalGetElement", ["input"], ["output"]),
],
[],
)
self._assert_inferred(graph, [output_tensor_val_info]) # type: ignore
@parameterized.expand(all_versions_for("StringSplit"))
def test_string_split_basic(self, _, version) -> None:
substrings = make_tensor_value_info(
"substrings",
TensorProto.STRING,
(2, None),
)
length = make_tensor_value_info("length", TensorProto.INT64, (2,))
graph = self._make_graph(
[
("x", TensorProto.STRING, (2,)),
],
[make_node("StringSplit", ["x"], ["substrings", "length"])],
[substrings, length],
)
self._assert_inferred(
graph,
[substrings, length],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("StringSplit"))
def test_string_split_symbolic(self, _, version) -> None:
substrings = make_tensor_value_info(
"substrings",
TensorProto.STRING,
("A", None),
)
length = make_tensor_value_info("length", TensorProto.INT64, ("A",))
graph = self._make_graph(
[
("x", TensorProto.STRING, ("A",)),
],
[make_node("StringSplit", ["x"], ["substrings", "length"])],
[substrings, length],
)
self._assert_inferred(
graph,
[substrings, length],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("StringSplit"))
def test_string_split_nested(self, _, version) -> None:
substrings = make_tensor_value_info(
"substrings", TensorProto.STRING, (2, 4, 3, None)
)
length = make_tensor_value_info("length", TensorProto.INT64, (2, 4, 3))
graph = self._make_graph(
[
("x", TensorProto.STRING, (2, 4, 3)),
],
[make_node("StringSplit", ["x"], ["substrings", "length"], maxsplit=2)],
[substrings, length],
)
self._assert_inferred(
graph,
[substrings, length],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("StringSplit"))
def test_string_split_zero_dimensional_input(self, _, version) -> None:
substrings = make_tensor_value_info("substrings", TensorProto.STRING, (None,))
length = make_tensor_value_info("length", TensorProto.INT64, ())
graph = self._make_graph(
[
("x", TensorProto.STRING, ()),
],
[make_node("StringSplit", ["x"], ["substrings", "length"], maxsplit=2)],
[substrings, length],
)
self._assert_inferred(
graph,
[substrings, length],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
@parameterized.expand(all_versions_for("StringSplit"))
def test_string_split_empty_input(self, _, version) -> None:
substrings = make_tensor_value_info(
"substrings", TensorProto.STRING, ("M", 3, 0, None)
)
length = make_tensor_value_info("length", TensorProto.INT64, ("M", 3, 0))
graph = self._make_graph(
[
("x", TensorProto.STRING, ("M", 3, 0)),
],
[make_node("StringSplit", ["x"], ["substrings", "length"], maxsplit=2)],
[substrings, length],
)
self._assert_inferred(
graph,
[substrings, length],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, version)],
)
def test_optional_tensor_get_element(self) -> None:
tensor_type_proto = helper.make_tensor_type_proto(
elem_type=TensorProto.DOUBLE, shape=[2, 1, 4]
)
tensor_val_into = helper.make_value_info(
name="output", type_proto=tensor_type_proto
)
optional_type_proto = helper.make_optional_type_proto(tensor_type_proto)
optional_val_info = helper.make_value_info(
name="optional", type_proto=optional_type_proto
)
graph = self._make_graph(
[("input1", TensorProto.DOUBLE, (2, 1, 4))],
[
make_node("Optional", ["input1"], ["optional"]),
make_node("OptionalGetElement", ["optional"], ["output"]),
],
[],
)
self._assert_inferred(graph, [optional_val_info, tensor_val_into]) # type: ignore
def test_optional_sequence_get_element(self) -> None:
tensor_type_proto = helper.make_tensor_type_proto(
elem_type=TensorProto.INT32, shape=[2, 0, 4]
)
sequence_type_proto = helper.make_sequence_type_proto(tensor_type_proto)
sequence_val_into = helper.make_value_info(
name="sequence", type_proto=sequence_type_proto
)
optional_type_proto = helper.make_optional_type_proto(sequence_type_proto)
optional_val_info = helper.make_value_info(
name="optional", type_proto=optional_type_proto
)
output_val_into = helper.make_value_info(
name="output", type_proto=sequence_type_proto
)
graph = self._make_graph(
[("input1", TensorProto.INT32, (2, 0, 4))],
[
make_node("SequenceConstruct", ["input1"], ["sequence"]),
make_node("Optional", ["sequence"], ["optional"]),
make_node("OptionalGetElement", ["optional"], ["output"]),
],
[],
)
self._assert_inferred(graph, [optional_val_info, sequence_val_into, output_val_into]) # type: ignore
def test_where_bfloat(self) -> None:
graph = self._make_graph(
[
("cond", TensorProto.BOOL, (10,)),
("x", TensorProto.BFLOAT16, (10,)),
("y", TensorProto.BFLOAT16, (10,)),
],
[make_node("Where", ["cond", "x", "y"], ["out"])],
[],
)
self._assert_inferred(graph, [make_tensor_value_info("out", TensorProto.BFLOAT16, (10,))]) # type: ignore
def test_parse_data_with_unsupported_tensor_type(self) -> None:
model = helper.make_model(
graph=helper.make_graph(
name="graph_with_unsupported_type",
inputs=[],
outputs=[
helper.make_tensor_value_info("y", TensorProto.FLOAT, shape=None)
],
nodes=[make_node("ConstantOfShape", ["x"], ["y"])],
# ConstantOfShape only accepts np.int64 instead of np.int32
initializer=[
numpy_helper.from_array(np.array([4, 3], dtype=np.int32), name="x")
],
)
)
# Strict shape inference should catch this invalid type error (int32 is not supported)
self.assertRaises(
onnx.shape_inference.InferenceError,
onnx.shape_inference.infer_shapes,
model,
strict_mode=True,
)
# Even nornmal shape inference should not produce any invalid shape due to wrong type for ParseData
inferred_model = onnx.shape_inference.infer_shapes(model)
self.assertFalse(
inferred_model.graph.output[0].type.tensor_type.HasField("shape")
)
def test_parse_data_with_undefined_tensor_type(self) -> None:
model = helper.make_model(
graph=helper.make_graph(
name="graph_with_undefined_type",
inputs=[],
outputs=[
helper.make_tensor_value_info("y", TensorProto.FLOAT, shape=None)
],
nodes=[make_node("ConstantOfShape", ["x"], ["y"])],
initializer=[
numpy_helper.from_array(np.array([4, 3], dtype=np.int64), name="x")
],
)
)
# Hardcode the tensor type as UNDEFINED to test catching undefined type error
model.graph.initializer[0].data_type = TensorProto.UNDEFINED
# Strict shape inference should catch this undefined type error
self.assertRaises(
onnx.shape_inference.InferenceError,
onnx.shape_inference.infer_shapes,
model,
strict_mode=True,
)
# Even nornmal shape inference should not produce any invalid shape due to undefined type for ParseData
inferred_model = onnx.shape_inference.infer_shapes(model)
self.assertFalse(
inferred_model.graph.output[0].type.tensor_type.HasField("shape")
)
graph = self._make_graph(
[("x", TensorProto.UINT8, (1, 0, 0)), ("shape", TensorProto.INT64, (3,))],
[make_node("Reshape", ["x", "shape"], ["y"], allowzero=1)],
[],
initializer=[make_tensor("shape", TensorProto.INT64, (3,), (0, 1, 1))],
)
self._assert_inferred(
graph, [make_tensor_value_info("y", TensorProto.UINT8, (0, 1, 1))]
)
def test_affinegrid_2d(self) -> None:
N, C, H, W = 2, 3, 4, 5
graph = self._make_graph(
[
("theta", TensorProto.FLOAT, (N, 2, 3)),
("size", TensorProto.INT64, (4,)),
],
[
make_node(
"AffineGrid",
["theta", "size"],
["grid"],
align_corners=1,
)
],
[],
initializer=[make_tensor("size", TensorProto.INT64, (4,), (N, C, H, W))],
)
self._assert_inferred(
graph, [make_tensor_value_info("grid", TensorProto.FLOAT, (N, H, W, 2))]
) # type: ignore
def test_affinegrid_3d(self) -> None:
N, C, D, H, W = 2, 3, 4, 5, 6
graph = self._make_graph(
[
("theta", TensorProto.FLOAT, (N, 3, 4)),
("size", TensorProto.INT64, (5,)),
],
[
make_node(
"AffineGrid",
["theta", "size"],
["grid"],
)
],
[],
initializer=[make_tensor("size", TensorProto.INT64, (5,), (N, C, D, H, W))],
)
self._assert_inferred(
graph, [make_tensor_value_info("grid", TensorProto.FLOAT, (N, D, H, W, 3))]
) # type: ignore
def test_gridsample_2d(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (1, 1, 3, 3)),
("grid", TensorProto.INT64, (1, 3, 3, 2)),
],
[
make_node(
"GridSample",
["x", "grid"],
["y"],
mode="nearest",
padding_mode="border",
align_corners=1,
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("y", TensorProto.FLOAT, (1, 1, 3, 3))]
) # type: ignore
def test_gridsample_3d(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, (1, 1, 3, 3, 3)),
("grid", TensorProto.INT64, (1, 3, 2, 3, 3)),
],
[
make_node(
"GridSample",
["x", "grid"],
["y"],
mode="nearest",
padding_mode="border",
align_corners=1,
)
],
[],
)
self._assert_inferred(
graph, [make_tensor_value_info("y", TensorProto.FLOAT, (1, 1, 3, 2, 3))]
) # type: ignore
def test_gridsample_2d_defaults(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, ("N", "C", "H", "W")),
("grid", TensorProto.FLOAT, ("N", "H_out", "W_out", 2)),
],
[make_node("GridSample", ["x", "grid"], ["y"])],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info(
"y", TensorProto.FLOAT, ("N", "C", "H_out", "W_out")
)
],
) # type: ignore
def test_gridsample_3d_defaults(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, ("N", "C", "D", "H", "W")),
("grid", TensorProto.FLOAT, ("N", "D_out", "H_out", "W_out", 3)),
],
[make_node("GridSample", ["x", "grid"], ["y"])],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info(
"y", TensorProto.FLOAT, ("N", "C", "D_out", "H_out", "W_out")
)
],
) # type: ignore
def test_gridsample_2d_no_dim(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, ("N", "C", None, None)),
("grid", TensorProto.FLOAT, ("N", None, None, 2)),
],
[
make_node(
"GridSample",
["x", "grid"],
["y"],
mode="linear",
padding_mode="border",
)
],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.FLOAT, ("N", "C", None, None))],
) # type: ignore
def test_gridsample_3d_no_dim(self) -> None:
graph = self._make_graph(
[
("x", TensorProto.FLOAT, ("N", "C", None, None, None)),
("grid", TensorProto.FLOAT, ("N", None, None, None, 3)),
],
[
make_node(
"GridSample",
["x", "grid"],
["y"],
mode="linear",
padding_mode="border",
)
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info(
"y", TensorProto.FLOAT, ("N", "C", None, None, None)
)
],
) # type: ignore
def test_sequence_map_identity_known_dims(self): # type: () -> None
input_value_infos = [
make_tensor_value_info("input", TensorProto.FLOAT, (220, 220, 3))
]
output_value_infos = [
make_tensor_value_info("output", TensorProto.FLOAT, (220, 220, 3))
]
body_graph = helper.make_graph(
[make_node("Identity", ["input"], ["output"])],
"body_graph",
input_value_infos,
output_value_infos,
)
graph = self._make_graph(
[
("input1", TensorProto.FLOAT, (220, 220, 3)),
("input2", TensorProto.FLOAT, (220, 220, 3)),
("input3", TensorProto.FLOAT, (220, 220, 3)),
],
[
make_node(
"SequenceConstruct", ["input1", "input2", "input3"], ["in_sequence"]
),
make_node(
"SequenceMap", ["in_sequence"], ["out_sequence"], body=body_graph
),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_sequence_value_info(
"in_sequence", TensorProto.FLOAT, (220, 220, 3)
),
make_tensor_sequence_value_info(
"out_sequence", TensorProto.FLOAT, (220, 220, 3)
),
],
) # type: ignore
def test_sequence_map_identity_unknown_dims(self): # type: () -> None
input_value_infos = [
make_tensor_value_info("input", TensorProto.FLOAT, ("H", "W", 3))
]
output_value_infos = [
make_tensor_value_info("output", TensorProto.FLOAT, ("H", "W", 3))
]
body_graph = helper.make_graph(
[make_node("Identity", ["input"], ["output"])],
"body_graph",
input_value_infos,
output_value_infos,
)
graph = self._make_graph(
[
("input1", TensorProto.FLOAT, (200, 300, 3)),
("input2", TensorProto.FLOAT, (100, 200, 3)),
("input3", TensorProto.FLOAT, (5, 1, 3)),
],
[
make_node(
"SequenceConstruct", ["input1", "input2", "input3"], ["in_sequence"]
),
make_node(
"SequenceMap", ["in_sequence"], ["out_sequence"], body=body_graph
),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_sequence_value_info(
"in_sequence", TensorProto.FLOAT, (None, None, 3)
),
make_tensor_sequence_value_info(
"out_sequence", TensorProto.FLOAT, (None, None, 3)
),
],
) # type: ignore
def test_sequence_map_slice_outs_known_dims(self): # type: () -> None
body_graph = helper.make_graph(
nodes=[
make_node("Slice", ["x", "starts1", "ends1", "axes", ""], ["y1"]),
make_node("Slice", ["x", "starts2", "ends2", "axes", ""], ["y2"]),
],
name="body_graph",
inputs=[
onnx.helper.make_tensor_value_info(
"x", onnx.TensorProto.FLOAT, ("H", "W", 3)
)
],
outputs=[
onnx.helper.make_tensor_value_info(
"y1", onnx.TensorProto.FLOAT, (10, 20, 3)
),
onnx.helper.make_tensor_value_info(
"y2", onnx.TensorProto.FLOAT, (30, 40, 3)
),
],
initializer=[
make_tensor("axes", TensorProto.INT64, (2,), (0, 1)),
make_tensor("starts1", TensorProto.INT64, (2,), (0, 0)),
make_tensor("ends1", TensorProto.INT64, (2,), (10, 20)),
make_tensor("starts2", TensorProto.INT64, (2,), (0, 0)),
make_tensor("ends2", TensorProto.INT64, (2,), (30, 40)),
],
) # type: ignore
graph = self._make_graph(
[
("input1", TensorProto.FLOAT, (220, 310, 3)),
("input2", TensorProto.FLOAT, (110, 210, 3)),
("input3", TensorProto.FLOAT, (90, 110, 3)),
],
[
make_node(
"SequenceConstruct", ["input1", "input2", "input3"], ["in_sequence"]
),
make_node(
"SequenceMap",
["in_sequence"],
["out_sequence1", "out_sequence2"],
body=body_graph,
),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_sequence_value_info(
"in_sequence", TensorProto.FLOAT, (None, None, 3)
),
make_tensor_sequence_value_info(
"out_sequence1", TensorProto.FLOAT, (10, 20, 3)
),
make_tensor_sequence_value_info(
"out_sequence2", TensorProto.FLOAT, (30, 40, 3)
),
],
) # type: ignore
def test_sequence_map_slice_outs_unknown_dims(self): # type: () -> None
body_graph = helper.make_graph(
nodes=[
make_node("Slice", ["x", "starts1", "ends1", "axes", ""], ["y1"]),
make_node("Slice", ["x", "starts2", "ends2", "axes", ""], ["y2"]),
],
name="body_graph",
inputs=[
onnx.helper.make_tensor_value_info(
"x", onnx.TensorProto.FLOAT, ("H", "W", 3)
)
],
outputs=[
onnx.helper.make_tensor_value_info(
"y1", onnx.TensorProto.FLOAT, ("H1", "W1", 3)
),
onnx.helper.make_tensor_value_info(
"y2", onnx.TensorProto.FLOAT, ("H2", "W2", 3)
),
],
initializer=[
make_tensor("axes", TensorProto.INT64, (2,), (0, 1)),
make_tensor("starts1", TensorProto.INT64, (2,), (0, 0)),
make_tensor("ends1", TensorProto.INT64, (2,), (10, 20)),
make_tensor("starts2", TensorProto.INT64, (2,), (0, 0)),
make_tensor("ends2", TensorProto.INT64, (2,), (30, 40)),
],
) # type: ignore
graph = self._make_graph(
[
("input1", TensorProto.FLOAT, (220, 310, 3)),
("input2", TensorProto.FLOAT, (110, 210, 3)),
("input3", TensorProto.FLOAT, (90, 110, 3)),
],
[
make_node(
"SequenceConstruct", ["input1", "input2", "input3"], ["in_sequence"]
),
make_node(
"SequenceMap",
["in_sequence"],
["out_sequence1", "out_sequence2"],
body=body_graph,
),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_sequence_value_info(
"in_sequence", TensorProto.FLOAT, (None, None, 3)
),
make_tensor_sequence_value_info(
"out_sequence1", TensorProto.FLOAT, (None, None, 3)
),
make_tensor_sequence_value_info(
"out_sequence2", TensorProto.FLOAT, (None, None, 3)
),
],
) # type: ignore
def test_sequence_map_different_tensor_type(self): # type: () -> None
body_graph = helper.make_graph(
nodes=[make_node("Shape", ["x"], ["shape"])],
name="body_graph",
inputs=[
onnx.helper.make_tensor_value_info(
"x", onnx.TensorProto.FLOAT, ("H", "W", "C")
)
],
outputs=[
onnx.helper.make_tensor_value_info(
"shape", onnx.TensorProto.INT64, (3,)
)
],
) # type: ignore
graph = self._make_graph(
[
("input1", TensorProto.FLOAT, (220, 310, 3)),
("input2", TensorProto.FLOAT, (110, 210, 3)),
("input3", TensorProto.FLOAT, (90, 110, 3)),
],
[
make_node(
"SequenceConstruct", ["input1", "input2", "input3"], ["in_sequence"]
),
make_node("SequenceMap", ["in_sequence"], ["shapes"], body=body_graph),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_sequence_value_info(
"in_sequence", TensorProto.FLOAT, (None, None, 3)
),
make_tensor_sequence_value_info("shapes", TensorProto.INT64, (3,)),
],
) # type: ignore
def test_hammingwindow(self): # type: () -> None
graph = self._make_graph(
[],
[
make_node(
"Constant",
[],
["shape"],
value=make_tensor("shape", TensorProto.INT64, (), (10,)),
),
make_node("HammingWindow", ["shape"], ["y"]),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("shape", TensorProto.INT64, ()),
make_tensor_value_info("y", TensorProto.FLOAT, (10,)),
],
) # type: ignore
graph = self._make_graph(
[],
[
make_node(
"Constant",
[],
["shape"],
value=make_tensor("shape", TensorProto.INT64, (), (10,)),
),
make_node("HammingWindow", ["shape"], ["y"], periodic=0),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("shape", TensorProto.INT64, ()),
make_tensor_value_info("y", TensorProto.FLOAT, (10,)),
],
) # type: ignore
def test_hannwindow(self): # type: () -> None
graph = self._make_graph(
[],
[
make_node(
"Constant",
[],
["shape"],
value=make_tensor("shape", TensorProto.INT64, (), (10,)),
),
make_node("HannWindow", ["shape"], ["y"]),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("shape", TensorProto.INT64, ()),
make_tensor_value_info("y", TensorProto.FLOAT, (10,)),
],
) # type: ignore
graph = self._make_graph(
[],
[
make_node(
"Constant",
[],
["shape"],
value=make_tensor("shape", TensorProto.INT64, (), (10,)),
),
make_node("HannWindow", ["shape"], ["y"], periodic=0),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("shape", TensorProto.INT64, ()),
make_tensor_value_info("y", TensorProto.FLOAT, (10,)),
],
) # type: ignore
def test_blackmanwindow(self): # type: () -> None
graph = self._make_graph(
[],
[
make_node(
"Constant",
[],
["shape"],
value=make_tensor("shape", TensorProto.INT64, (), (10,)),
),
make_node("BlackmanWindow", ["shape"], ["y"]),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("shape", TensorProto.INT64, ()),
make_tensor_value_info("y", TensorProto.FLOAT, (10,)),
],
) # type: ignore
graph = self._make_graph(
[],
[
make_node(
"Constant",
[],
["shape"],
value=make_tensor("shape", TensorProto.INT64, (), (10,)),
),
make_node("BlackmanWindow", ["shape"], ["y"], periodic=0),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("shape", TensorProto.INT64, ()),
make_tensor_value_info("y", TensorProto.FLOAT, (10,)),
],
) # type: ignore
def test_dft_reals(self): # type: () -> None
graph = self._make_graph(
[],
[
make_node(
"Constant",
[],
["input"],
value=make_tensor(
"input",
TensorProto.FLOAT,
(2, 5, 1),
(
0,
0,
0,
0,
0,
1,
1,
1,
1,
1,
),
),
),
make_node("DFT", ["input", ""], ["output"]),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("shape", TensorProto.FLOAT, (2, 5, 1)),
make_tensor_value_info("y", TensorProto.FLOAT, (2, 5, 2)),
],
) # type: ignore
def test_dft_reals2(self): # type: () -> None
graph = self._make_graph(
[],
[
make_node(
"Constant",
[],
["input"],
value=make_tensor(
"input",
TensorProto.FLOAT,
(
1,
5,
10,
1,
),
(
0,
0,
0,
0,
0,
1,
1,
1,
1,
1,
2,
2,
2,
2,
2,
3,
3,
3,
3,
3,
4,
4,
4,
4,
4,
0,
0,
0,
0,
0,
1,
1,
1,
1,
1,
2,
2,
2,
2,
2,
3,
3,
3,
3,
3,
4,
4,
4,
4,
4,
),
),
),
make_node("DFT", ["input", ""], ["output"], axis=1, onesided=1),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("shape", TensorProto.FLOAT, (1, 5, 10, 1)),
make_tensor_value_info("y", TensorProto.FLOAT, (1, 3, 10, 2)),
],
) # type: ignore
graph = self._make_graph(
[],
[
make_node(
"Constant",
[],
["input"],
value=make_tensor(
"input",
TensorProto.FLOAT,
(
1,
5,
10,
1,
),
(
0,
0,
0,
0,
0,
1,
1,
1,
1,
1,
2,
2,
2,
2,
2,
3,
3,
3,
3,
3,
4,
4,
4,
4,
4,
0,
0,
0,
0,
0,
1,
1,
1,
1,
1,
2,
2,
2,
2,
2,
3,
3,
3,
3,
3,
4,
4,
4,
4,
4,
),
),
),
make_node("DFT", ["input", ""], ["output"], axis=2, onesided=1),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("shape", TensorProto.FLOAT, (1, 5, 10, 1)),
make_tensor_value_info("y", TensorProto.FLOAT, (1, 5, 6, 2)),
],
) # type: ignore
graph = self._make_graph(
[],
[
make_node(
"Constant",
[],
["input"],
value=make_tensor(
"input",
TensorProto.FLOAT,
(
1,
5,
10,
1,
),
(
0,
0,
0,
0,
0,
1,
1,
1,
1,
1,
2,
2,
2,
2,
2,
3,
3,
3,
3,
3,
4,
4,
4,
4,
4,
0,
0,
0,
0,
0,
1,
1,
1,
1,
1,
2,
2,
2,
2,
2,
3,
3,
3,
3,
3,
4,
4,
4,
4,
4,
),
),
),
make_node("DFT", ["input", ""], ["output"], axis=1, onesided=0),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("shape", TensorProto.FLOAT, (1, 5, 10, 1)),
make_tensor_value_info("y", TensorProto.FLOAT, (1, 5, 10, 2)),
],
) # type: ignore
graph = self._make_graph(
[],
[
make_node(
"Constant",
[],
["input"],
value=make_tensor(
"input",
TensorProto.FLOAT,
(
1,
5,
10,
1,
),
(
0,
0,
0,
0,
0,
1,
1,
1,
1,
1,
2,
2,
2,
2,
2,
3,
3,
3,
3,
3,
4,
4,
4,
4,
4,
0,
0,
0,
0,
0,
1,
1,
1,
1,
1,
2,
2,
2,
2,
2,
3,
3,
3,
3,
3,
4,
4,
4,
4,
4,
),
),
),
make_node("DFT", ["input", ""], ["output"], axis=2, onesided=0),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("shape", TensorProto.FLOAT, (1, 5, 10, 1)),
make_tensor_value_info("y", TensorProto.FLOAT, (1, 5, 10, 2)),
],
) # type: ignore
def test_dft_complex(self): # type: () -> None
graph = self._make_graph(
[],
[
make_node(
"Constant",
[],
["input"],
value=make_tensor(
"input",
TensorProto.FLOAT,
(2, 5, 2),
(
0,
0,
0,
0,
0,
1,
1,
1,
1,
1,
0,
0,
0,
0,
0,
1,
1,
1,
1,
1,
),
),
),
make_node("DFT", ["input", ""], ["output"]),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("shape", TensorProto.FLOAT, (2, 5, 2)),
make_tensor_value_info("y", TensorProto.FLOAT, (2, 5, 2)),
],
) # type: ignore
def test_dft_reals_onesided(self): # type: () -> None
graph = self._make_graph(
[],
[
make_node(
"Constant",
[],
["input"],
value=make_tensor(
"input",
TensorProto.FLOAT,
(2, 5, 1),
(
0,
0,
0,
0,
0,
1,
1,
1,
1,
1,
),
),
),
make_node("DFT", ["input", ""], ["output"], onesided=1),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("shape", TensorProto.FLOAT, (2, 5, 1)),
make_tensor_value_info("y", TensorProto.FLOAT, (2, 3, 2)),
],
) # type: ignore
def test_dft_complex_onesided(self): # type: () -> None
graph = self._make_graph(
[],
[
make_node(
"Constant",
[],
["input"],
value=make_tensor(
"input",
TensorProto.FLOAT,
(2, 5, 2),
(
0,
0,
0,
0,
0,
1,
1,
1,
1,
1,
0,
0,
0,
0,
0,
1,
1,
1,
1,
1,
),
),
),
make_node("DFT", ["input", ""], ["output"], onesided=1),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("shape", TensorProto.FLOAT, (2, 5, 2)),
make_tensor_value_info("y", TensorProto.FLOAT, (2, 3, 2)),
],
) # type: ignore
def test_dft_reals_inverse(self): # type: () -> None
graph = self._make_graph(
[],
[
make_node(
"Constant",
[],
["input"],
value=make_tensor(
"input",
TensorProto.FLOAT,
(2, 5, 1),
(
0,
0,
0,
0,
0,
1,
1,
1,
1,
1,
),
),
),
make_node("DFT", ["input", ""], ["output"], inverse=1),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("shape", TensorProto.FLOAT, (2, 5, 1)),
make_tensor_value_info("y", TensorProto.FLOAT, (2, 5, 2)),
],
) # type: ignore
def test_dft_complex_inverse(self): # type: () -> None
graph = self._make_graph(
[],
[
make_node(
"Constant",
[],
["input"],
value=make_tensor(
"input",
TensorProto.FLOAT,
(2, 5, 2),
(
0,
0,
0,
0,
0,
1,
1,
1,
1,
1,
0,
0,
0,
0,
0,
1,
1,
1,
1,
1,
),
),
),
make_node("DFT", ["input", ""], ["output"], inverse=1),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("shape", TensorProto.FLOAT, (2, 5, 2)),
make_tensor_value_info("y", TensorProto.FLOAT, (2, 5, 2)),
],
) # type: ignore
def test_stft_reals(self): # type: () -> None
graph = self._make_graph(
[],
[
make_node(
"Constant",
[],
["signal"],
value=make_tensor(
"signal",
TensorProto.FLOAT,
(2, 10, 1),
(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3),
),
),
make_node(
"Constant",
[],
["frame_step"],
value=make_tensor("frame_step", TensorProto.INT64, (), (2,)),
),
make_node(
"Constant",
[],
["window"],
value=make_tensor(
"window", TensorProto.INT64, (5,), (1, 2, 3, 4, 5)
),
),
make_node("STFT", ["signal", "frame_step", "window"], ["output"]),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("signal", TensorProto.FLOAT, (2, 10, 1)),
make_tensor_value_info("frame_step", TensorProto.INT64, ()),
make_tensor_value_info("window", TensorProto.INT64, (5,)),
make_tensor_value_info("output", TensorProto.FLOAT, (2, 3, 5, 2)),
],
) # type: ignore
graph = self._make_graph(
[],
[
make_node(
"Constant",
[],
["signal"],
value=make_tensor(
"signal",
TensorProto.FLOAT,
(2, 10, 1),
(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3),
),
),
make_node(
"Constant",
[],
["frame_step"],
value=make_tensor("frame_step", TensorProto.INT64, (), (2,)),
),
make_node(
"Constant",
[],
["window"],
value=make_tensor(
"window", TensorProto.INT64, (5,), (1, 2, 3, 4, 5)
),
),
make_node(
"Constant",
[],
["frame_length"],
value=make_tensor("frame_length", TensorProto.INT64, (), (5,)),
),
make_node("STFT", ["signal", "frame_step", "window"], ["output"]),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("signal", TensorProto.FLOAT, (2, 10, 1)),
make_tensor_value_info("frame_step", TensorProto.INT64, ()),
make_tensor_value_info("window", TensorProto.INT64, (5,)),
make_tensor_value_info("frame_length", TensorProto.INT64, ()),
make_tensor_value_info("output", TensorProto.FLOAT, (2, 3, 5, 2)),
],
) # type: ignore
graph = self._make_graph(
[],
[
make_node(
"Constant",
[],
["signal"],
value=make_tensor(
"signal",
TensorProto.FLOAT,
(2, 10, 1),
(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3),
),
),
make_node(
"Constant",
[],
["frame_step"],
value=make_tensor("frame_step", TensorProto.INT64, (), (2,)),
),
make_node(
"Constant",
[],
["frame_length"],
value=make_tensor("frame_length", TensorProto.INT64, (), (5,)),
),
make_node(
"STFT", ["signal", "frame_step", "", "frame_length"], ["output"]
),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("signal", TensorProto.FLOAT, (2, 10, 1)),
make_tensor_value_info("frame_step", TensorProto.INT64, ()),
make_tensor_value_info("frame_length", TensorProto.INT64, ()),
make_tensor_value_info("output", TensorProto.FLOAT, (2, 3, 5, 2)),
],
) # type: ignore
def test_melweightmatrix(self): # type: () -> None
graph = self._make_graph(
[],
[
make_node(
"Constant",
[],
["num_mel_bins"],
value=make_tensor("num_mel_bins", TensorProto.INT64, (), (10,)),
),
make_node(
"Constant",
[],
["dft_length"],
value=make_tensor("dft_length", TensorProto.INT64, (), (128,)),
),
make_node(
"Constant",
[],
["sample_rate"],
value=make_tensor("sample_rate", TensorProto.INT64, (), (10,)),
),
make_node(
"Constant",
[],
["lower_edge_hertz"],
value=make_tensor(
"lower_edge_hertz", TensorProto.FLOAT, (), (10.0,)
),
),
make_node(
"Constant",
[],
["upper_edge_hertz"],
value=make_tensor(
"upper_edge_hertz", TensorProto.FLOAT, (), (100.0,)
),
),
make_node(
"MelWeightMatrix",
[
"num_mel_bins",
"dft_length",
"sample_rate",
"lower_edge_hertz",
"upper_edge_hertz",
],
["output"],
),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("num_mel_bins", TensorProto.INT64, ()),
make_tensor_value_info("dft_length", TensorProto.INT64, ()),
make_tensor_value_info("sample_rate", TensorProto.INT64, ()),
make_tensor_value_info("lower_edge_hertz", TensorProto.FLOAT, ()),
make_tensor_value_info("upper_edge_hertz", TensorProto.FLOAT, ()),
make_tensor_value_info("output", TensorProto.FLOAT, (65, 10)),
],
) # type: ignore
def test_melweightmatrix_with_output_datatype(self): # type: () -> None
graph = self._make_graph(
[],
[
make_node(
"Constant",
[],
["num_mel_bins"],
value=make_tensor("num_mel_bins", TensorProto.INT64, (), (10,)),
),
make_node(
"Constant",
[],
["dft_length"],
value=make_tensor("dft_length", TensorProto.INT64, (), (128,)),
),
make_node(
"Constant",
[],
["sample_rate"],
value=make_tensor("sample_rate", TensorProto.INT64, (), (10,)),
),
make_node(
"Constant",
[],
["lower_edge_hertz"],
value=make_tensor(
"lower_edge_hertz", TensorProto.FLOAT, (), (10.0,)
),
),
make_node(
"Constant",
[],
["upper_edge_hertz"],
value=make_tensor(
"upper_edge_hertz", TensorProto.FLOAT, (), (100.0,)
),
),
make_node(
"MelWeightMatrix",
[
"num_mel_bins",
"dft_length",
"sample_rate",
"lower_edge_hertz",
"upper_edge_hertz",
],
["output"],
output_datatype=TensorProto.DOUBLE,
),
],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("num_mel_bins", TensorProto.INT64, ()),
make_tensor_value_info("dft_length", TensorProto.INT64, ()),
make_tensor_value_info("sample_rate", TensorProto.INT64, ()),
make_tensor_value_info("lower_edge_hertz", TensorProto.FLOAT, ()),
make_tensor_value_info("upper_edge_hertz", TensorProto.FLOAT, ()),
make_tensor_value_info("output", TensorProto.DOUBLE, (65, 10)),
],
) # type: ignore
def test_center_crop_pad_hwc_crop(self): # type: () -> None
graph = self._make_graph(
[
("input_data", TensorProto.FLOAT, (20, 10, 3)),
("shape", TensorProto.INT64, (2,)),
],
[make_node("CenterCropPad", ["input_data", "shape"], ["y"], axes=[0, 1])],
[],
initializer=[make_tensor("shape", TensorProto.INT64, (2,), (10, 8))],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.FLOAT, (10, 8, 3))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 18)],
)
def test_center_crop_pad_chw_crop(self): # type: () -> None
graph = self._make_graph(
[
("input_data", TensorProto.FLOAT, (3, 20, 10)),
("shape", TensorProto.INT64, (2,)),
],
[make_node("CenterCropPad", ["input_data", "shape"], ["y"], axes=[1, 2])],
[],
initializer=[make_tensor("shape", TensorProto.INT64, (2,), (10, 8))],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.FLOAT, (3, 10, 8))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 18)],
)
def test_center_crop_pad_hwc_croppad(self): # type: () -> None
graph = self._make_graph(
[
("input_data", TensorProto.FLOAT, (10, 10, 3)),
("shape", TensorProto.INT64, (2,)),
],
[make_node("CenterCropPad", ["input_data", "shape"], ["y"], axes=[0, 1])],
[],
initializer=[make_tensor("shape", TensorProto.INT64, (2,), (20, 8))],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.FLOAT, (20, 8, 3))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 18)],
)
def test_center_crop_pad_chw_croppad(self): # type: () -> None
graph = self._make_graph(
[
("input_data", TensorProto.FLOAT, (3, 10, 10)),
("shape", TensorProto.INT64, (2,)),
],
[make_node("CenterCropPad", ["input_data", "shape"], ["y"], axes=[1, 2])],
[],
initializer=[make_tensor("shape", TensorProto.INT64, (2,), (20, 8))],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.FLOAT, (3, 20, 8))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 18)],
)
def test_center_crop_pad_without_input_shape(self): # type: () -> None
graph = self._make_graph(
[
("input_data", TensorProto.FLOAT, (3, 2)),
("shape", TensorProto.INT64, (2,)),
],
[make_node("CenterCropPad", ["input_data", "shape"], ["y"])],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.FLOAT, None)],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 18)],
)
def test_center_crop_pad_with_input_shape_containing_dim_params(
self,
): # type: () -> None
graph = self._make_graph(
[
("input_data", TensorProto.FLOAT, (20, "W", 3)),
("shape", TensorProto.INT64, (2,)),
],
[make_node("CenterCropPad", ["input_data", "shape"], ["y"], axes=[0, 1])],
[],
initializer=[make_tensor("shape", TensorProto.INT64, (2,), (10, 8))],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.FLOAT, (10, 8, 3))],
opset_imports=[helper.make_opsetid(ONNX_DOMAIN, 18)],
)
@unittest.skipUnless(ONNX_ML, "ONNX_ML required to test ai.onnx.ml operators")
def test_category_mapper(self) -> None:
cat = make_node(
"CategoryMapper",
["x"],
["y"],
domain=ONNX_ML_DOMAIN,
)
graph_int = self._make_graph(
[("x", TensorProto.INT64, (30, 4, 5))],
[cat],
[],
)
self._assert_inferred(
graph_int,
[make_tensor_value_info("y", TensorProto.STRING, (30, 4, 5))],
opset_imports=[
make_opsetid(ONNX_ML_DOMAIN, 1),
make_opsetid(ONNX_DOMAIN, 11),
],
)
graph_str = self._make_graph(
[("x", TensorProto.STRING, (30, 5, 4))],
[cat],
[],
)
self._assert_inferred(
graph_str,
[make_tensor_value_info("y", TensorProto.INT64, (30, 5, 4))],
opset_imports=[
make_opsetid(ONNX_ML_DOMAIN, 1),
make_opsetid(ONNX_DOMAIN, 11),
],
)
@unittest.skipUnless(ONNX_ML, "ONNX_ML required to test ai.onnx.ml operators")
def test_tree_ensemble_regressor(self) -> None:
tree = make_node(
"TreeEnsembleRegressor",
["x"],
["y"],
domain=ONNX_ML_DOMAIN,
n_targets=5,
)
graph = self._make_graph(
[("x", TensorProto.DOUBLE, (30, 3))],
[tree],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.FLOAT, (30, 5))],
opset_imports=[
make_opsetid(ONNX_ML_DOMAIN, 3),
make_opsetid(ONNX_DOMAIN, 11),
],
)
@unittest.skipUnless(ONNX_ML, "ONNX_ML required to test ai.onnx.ml operators")
def test_tree_ensemble_classifier(self) -> None:
tree = make_node(
"TreeEnsembleClassifier",
["x"],
["y", "z"],
classlabels_int64s=[0, 1, 2, 3, 4],
domain=ONNX_ML_DOMAIN,
)
graph = self._make_graph(
[("x", TensorProto.DOUBLE, (30, 3))],
[tree],
[],
)
self._assert_inferred(
graph,
[
make_tensor_value_info("y", TensorProto.INT64, (30,)),
make_tensor_value_info("z", TensorProto.FLOAT, (30, 5)),
],
opset_imports=[
make_opsetid(ONNX_ML_DOMAIN, 3),
make_opsetid(ONNX_DOMAIN, 11),
],
)
@unittest.skipUnless(ONNX_ML, "ONNX_ML required to test ai.onnx.ml operators")
def test_array_feature_extractor(self) -> None:
node = make_node(
"ArrayFeatureExtractor",
["x", "y"],
["z"],
domain=ONNX_ML_DOMAIN,
)
for axes_shape, expected in [
((2,), 2),
((), "unk__0"),
(("N",), "N"),
]:
graph = self._make_graph(
[
("x", TensorProto.INT64, (3, 4, 5)),
("y", TensorProto.INT64, axes_shape),
],
[node],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("z", TensorProto.INT64, (3, 4, expected))], # type: ignore
opset_imports=[
make_opsetid(ONNX_ML_DOMAIN, 3),
make_opsetid(ONNX_DOMAIN, 18),
],
)
@unittest.skipUnless(ONNX_ML, "ONNX_ML required to test ai.onnx.ml operators")
def test_binarizer(self) -> None:
node = make_node(
"Binarizer",
["x"],
["y"],
domain=ONNX_ML_DOMAIN,
)
graph = self._make_graph(
[
("x", TensorProto.INT64, (3, 4, 5)),
],
[node],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("y", TensorProto.INT64, (3, 4, 5))], # type: ignore
opset_imports=[
make_opsetid(ONNX_ML_DOMAIN, 3),
make_opsetid(ONNX_DOMAIN, 18),
],
)
@unittest.skipUnless(ONNX_ML, "ONNX_ML required to test ai.onnx.ml operators")
def test_one_hot_encoder(self) -> None:
graph = self._make_graph(
[("input", TensorProto.INT64, (2, "N", 3))],
[
make_node(
"OneHotEncoder",
["input"],
["output"],
cats_int64s=[1, 2, 3, 4],
domain="ai.onnx.ml",
)
],
[],
)
self._assert_inferred(
graph,
[make_tensor_value_info("output", TensorProto.FLOAT, (2, "N", 3, 4))],
opset_imports=[
make_opsetid(ONNX_ML_DOMAIN, 1),
make_opsetid(ONNX_DOMAIN, 18),
],
)
@unittest.skipUnless(ONNX_ML, "ONNX_ML required to test ai.onnx.ml operators")
def test_zip_map(self) -> None:
params = (
({"classlabels_int64s": [1, 2, 3]}, onnx.TensorProto.INT64),
({"classlabels_strings": ["a", "b", "c"]}, onnx.TensorProto.STRING),
)
for attrs, input_type in params:
with self.subTest(attrs=attrs, input_type=input_type):
self.zip_map_test_case(attrs, input_type)
def zip_map_test_case(self, attrs, input_type) -> None:
graph = self._make_graph(
[("input", TensorProto.FLOAT, ("N", 3))],
[
make_node(
"ZipMap",
["input"],
["output"],
**attrs,
domain="ai.onnx.ml",
)
],
[],
)
typ = onnx.helper.make_map_type_proto(
input_type, onnx.helper.make_tensor_type_proto(TensorProto.FLOAT, ())
)
self._assert_inferred(
graph,
[
onnx.helper.make_value_info(
"output", onnx.helper.make_sequence_type_proto(typ)
)
],
opset_imports=[
make_opsetid(ONNX_ML_DOMAIN, 1),
make_opsetid(ONNX_DOMAIN, 18),
],
)
def test_compress_without_axis(self) -> None:
graph = self._make_graph(
[
("input", TensorProto.INT64, (2, "N", 3, 4)),
("condition", TensorProto.BOOL, (None,)),
],
[make_node("Compress", ["input", "condition"], ["output"])],
[],
)
self._assert_inferred(graph, [make_tensor_value_info("output", TensorProto.INT64, (None,))]) # type: ignore
def test_compress_with_axis(self) -> None:
graph = self._make_graph(
[
("input", TensorProto.INT64, (2, "N", 3, 4)),
("condition", TensorProto.BOOL, (None,)),
],
[make_node("Compress", ["input", "condition"], ["output"], axis=-1)],
[],
)
self._assert_inferred(graph, [make_tensor_value_info("output", TensorProto.INT64, (2, "N", 3, None))]) # type: ignore
if __name__ == "__main__":
unittest.main()
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59,114 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/mish.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class Mish(Base):
@staticmethod
def export() -> None:
node = onnx.helper.make_node("Mish", inputs=["X"], outputs=["Y"])
input_data = np.linspace(-10, 10, 10000, dtype=np.float32)
# Calculate expected output data
expected_output = input_data * np.tanh(np.log1p(np.exp(input_data)))
expect(node, inputs=[input_data], outputs=[expected_output], name="test_mish")
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59,115 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_expand.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
import numpy as np
from onnx.reference.op_run import OpRun
def common_reference_implementation(data: np.ndarray, shape: np.ndarray) -> np.ndarray:
ones = np.ones(shape, dtype=data.dtype)
return data * ones # type: ignore
class Expand(OpRun):
def _run(self, data, shape): # type: ignore
return (common_reference_implementation(data, shape),)
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59,116 | onnx/onnx | refs/heads/main | /onnx/reference/ops/aionnxml/op_label_encoder.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0913,R0914,W0221
import numpy as np
from onnx.reference.ops.aionnxml._op_run_aionnxml import OpRunAiOnnxMl
class LabelEncoder(OpRunAiOnnxMl):
def _run( # type: ignore
self,
x,
default_float=None,
default_int64=None,
default_string=None,
keys_floats=None,
keys_int64s=None,
keys_strings=None,
values_floats=None,
values_int64s=None,
values_strings=None,
):
keys = keys_floats or keys_int64s or keys_strings
values = values_floats or values_int64s or values_strings
classes = dict(zip(keys, values))
if id(keys) == id(keys_floats):
cast = float
elif id(keys) == id(keys_int64s):
cast = int # type: ignore
else:
cast = str # type: ignore
if id(values) == id(values_floats):
defval = default_float
dtype = np.float32
elif id(values) == id(values_int64s):
defval = default_int64
dtype = np.int64 # type: ignore
else:
defval = default_string
if not isinstance(defval, str):
defval = ""
dtype = np.str_ # type: ignore
shape = x.shape
if len(x.shape) > 1:
x = x.flatten()
res = []
for i in range(0, x.shape[0]):
v = classes.get(cast(x[i]), defval)
res.append(v)
return (np.array(res, dtype=dtype).reshape(shape),)
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59,117 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/meanvariancenormalization.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class MeanVarianceNormalization(Base):
@staticmethod
def export() -> None:
node = onnx.helper.make_node(
"MeanVarianceNormalization", inputs=["X"], outputs=["Y"]
)
input_data = np.array(
[
[
[[0.8439683], [0.5665144], [0.05836735]],
[[0.02916367], [0.12964272], [0.5060197]],
[[0.79538304], [0.9411346], [0.9546573]],
],
[
[[0.17730942], [0.46192095], [0.26480448]],
[[0.6746842], [0.01665257], [0.62473077]],
[[0.9240844], [0.9722341], [0.11965699]],
],
[
[[0.41356155], [0.9129373], [0.59330076]],
[[0.81929934], [0.7862604], [0.11799799]],
[[0.69248444], [0.54119414], [0.07513223]],
],
],
dtype=np.float32,
)
# Calculate expected output data
data_mean = np.mean(input_data, axis=(0, 2, 3), keepdims=1)
data_mean_squared = np.power(data_mean, 2)
data_squared = np.power(input_data, 2)
data_squared_mean = np.mean(data_squared, axis=(0, 2, 3), keepdims=1)
std = np.sqrt(data_squared_mean - data_mean_squared)
expected_output = (input_data - data_mean) / (std + 1e-9)
expect(node, inputs=[input_data], outputs=[expected_output], name="test_mvn")
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"/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_string_concat.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_batch_normalization.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cum_sum.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_prelu.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_unsqueeze.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/globalaveragepool.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/__init__.py": ["/onnx/backend/test/runner/__init__.py"], "/onnx/test/parser_test.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_regex_full_match.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/xor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/shape.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/dequantizelinear.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py"], "/onnx/reference/ops/op_isnan.py": ["/onnx/reference/ops/_op.py"], "/onnx/backend/test/case/node/mul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stringnormalizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducemin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/tile.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_flatten.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_scatternd.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_optional.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/rnn.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/image_decoder.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_tree_ensemble_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_tree_ensemble_helper.py"], "/workflow_scripts/test_model_zoo.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/gelu.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/printer.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/nonzero.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/identity.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/sin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gemm.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_layer_normalization.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_bernoulli.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/backend/test/case/node/deformconv.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_upsample.py": ["/onnx/reference/op_run.py"], "/onnx/test/test_with_ort.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_thresholded_relu.py": ["/onnx/reference/ops/_op.py"], "/onnx/backend/test/case/node/concat.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/stat_coverage.py": ["/onnx/__init__.py", "/onnx/backend/test/case/__init__.py", "/onnx/backend/test/loader/__init__.py", "/onnx/backend/test/runner/__init__.py"], "/onnx/reference/ops/op_constant.py": ["/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/upsample.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_squeeze.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_einsum.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/div.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_one_hot_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_random_uniform.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/test/reference_evaluator_test.py": ["/onnx/__init__.py", "/onnx/checker.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/__init__.py", "/onnx/reference/ops/_op_common_indices.py", "/onnx/reference/ops/_op_list.py", "/onnx/reference/ops/op_celu.py", "/onnx/reference/ops/op_col2im.py", "/onnx/reference/ops/op_conv.py", "/onnx/reference/ops_optimized/__init__.py", "/onnx/reference/ops_optimized/op_conv_optimized.py"], "/onnx/backend/test/cmd_tools.py": ["/onnx/backend/test/case/model/__init__.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/__init__.py"], "/onnx/reference/ops/op_slice.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/aionnxml/op_binarizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/optionalgetelement.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/loop.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/conf.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_sequence_construct.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatterelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel2.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/bernoulli.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/constant.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/resize.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/reference/ops/op_resize.py"], "/onnx/reference/ops/aionnxml/op_svm_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/op_sequence_map.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/scatternd.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/numpy_helper_test.py": ["/onnx/__init__.py"], "/onnx/reference/ops/op_tfidf_vectorizer.py": ["/onnx/reference/op_run.py"], "/onnx/test/checker_test.py": ["/onnx/defs/__init__.py", "/onnx/__init__.py"], "/onnx/reference/ops/_op_common_random.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/backend/base.py": ["/onnx/checker.py", "/onnx/__init__.py"], "/onnx/backend/test/case/node/reduce_log_sum.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_linear_regressor.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/reference/ops/op_softplus.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_sub.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_quantize_linear.py": ["/onnx/__init__.py", "/onnx/helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_gathernd.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/qlinearmatmul.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/shape_inference_test.py": ["/onnx/shape_inference.py", "/onnx/__init__.py", "/onnx/defs/__init__.py", "/onnx/helper.py", "/onnx/parser.py"], "/onnx/backend/test/case/node/mish.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_expand.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/aionnxml/op_label_encoder.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/meanvariancenormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/docs/docsgen/source/onnx_sphinx.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/defs/__init__.py"], "/onnx/reference/ops/op_cast_like.py": ["/onnx/helper.py", "/onnx/reference/op_run.py", "/onnx/reference/ops/op_cast.py"], "/onnx/backend/test/case/node/matmulinteger.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/splittosequence.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/serialization.py": ["/onnx/__init__.py"], "/onnx/reference/ops/aionnxml/op_svm_classifier.py": ["/onnx/reference/ops/aionnxml/_common_classifier.py", "/onnx/reference/ops/aionnxml/_op_run_aionnxml.py", "/onnx/reference/ops/aionnxml/op_svm_helper.py"], "/onnx/reference/ops/_helpers.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/tfidfvectorizer.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_average_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/runner/item.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/gatherelements.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/slice.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/stft.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_matmul.py": ["/onnx/reference/ops/_op.py"], "/onnx/reference/ops/op_mel_weight_matrix.py": ["/onnx/helper.py", "/onnx/reference/op_run.py"], "/onnx/reference/ops/op_cast.py": ["/onnx/helper.py", "/onnx/numpy_helper.py", "/onnx/reference/custom_element_types.py", "/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/asin.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/aionnxml/op_normalizer.py": ["/onnx/reference/ops/aionnxml/_op_run_aionnxml.py"], "/onnx/backend/test/case/node/unique.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gather_elements.py": ["/onnx/reference/op_run.py"], "/onnx/helper.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/layernormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/groupnormalization.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/__init__.py": ["/onnx/reference/ops/_op_list.py"], "/onnx/reference/ops/op_random_normal.py": ["/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_hann_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/reference/ops/op_softmax_cross_entropy_loss.py": ["/onnx/reference/op_run.py"], "/onnx/reference/ops/op_string_split.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/max.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/backend/test/case/utils.py"], "/onnx/backend/test/case/model/expand.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/model/__init__.py"], "/onnx/backend/test/case/node/erf.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/backend/test/case/node/reducel1.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops_optimized/op_conv_optimized.py": ["/onnx/reference/op_run.py"], "/onnx/backend/test/case/node/floor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_blackman_window.py": ["/onnx/reference/ops/_op_common_window.py"], "/onnx/backend/test/case/node/bitwisexor.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/round.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_random_normal_like.py": ["/onnx/helper.py", "/onnx/reference/ops/_op_common_random.py"], "/onnx/reference/ops/op_conv_integer.py": ["/onnx/reference/op_run.py", "/onnx/reference/ops/op_conv.py"], "/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
59,118 | onnx/onnx | refs/heads/main | /docs/docsgen/source/onnx_sphinx.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
"""Automates the generation of ONNX operators."""
import difflib
import importlib
import inspect
import keyword
import os
import pathlib
import re
import shutil
import sys
import textwrap
from typing import Any
import jinja2
import numpy as np
from sphinx.util import logging
import onnx
from onnx.backend.test.case.base import _Exporter
from onnx.defs import OpSchema
REPO_DOCS_EXCLUDE = {
"Changelog-ml.md",
"Changelog.md",
"CIPipelines.md",
"CONTRIBUTING.md",
"Operators-ml.md",
"Operators.md",
"Relicensing.md",
"TestCoverage-ml.md",
"TestCoverage.md",
}
def _get_diff_template():
return jinja2.Template(
textwrap.dedent(
"""
<div id="{{ div_name }}"></div>
<link rel="stylesheet" type="text/css" href="../_static/diff2html.min.css" />
<script type="text/javascript" src="../_static/diff2html-ui-slim.min.js"></script>
<script>
const diffString = `
--- a/{{ op_name }}{{ version1 }}
+++ b/{{ op_name }}{{ version2 }}
@@ -1 +1 @@
{{ diff_content }}
`;
document.addEventListener('DOMContentLoaded', function () {
var targetElement = document.getElementById('{{ div_name }}');
var configuration = {
drawFileList: true,
fileListToggle: false,
fileListStartVisible: false,
fileContentToggle: false,
matching: 'lines',
outputFormat: 'line-by-line',
synchronisedScroll: true,
highlight: true,
renderNothingWhenEmpty: false,
};
var diff2htmlUi = new Diff2HtmlUI(targetElement, diffString, configuration);
diff2htmlUi.draw();
diff2htmlUi.highlightCode();
});
</script>
"""
),
autoescape=True,
)
def _get_ops_template():
return jinja2.Template(
textwrap.dedent(
"""
{% for sch in schemas %}
.. tag-diff-insert.
(l-onnx-op{{sch.domain.lower().replace(".", "-")}}-{{sch.name.lower()}}-{{str(sch.since_version)}})=
## {{format_name_with_domain(sch)}}
### Version
- **name**: [{{sch.name}} (GitHub)]({{build_doc_url(sch)}}{{sch.name}})
- **domain**: `{% if sch.domain == '' %}main{% else %}{{sch.domain}}{% endif %}`
- **since_version**: `{{sch.since_version}}`
- **function**: `{{sch.has_function or sch.has_context_dependent_function}}`
- **support_level**: `{{sch.support_level}}`
- **shape inference**: `{{sch.has_type_and_shape_inference_function}}`
{% if sch.support_level == OpSchema.SupportType.EXPERIMENTAL %}
No versioning maintained for experimental ops.
{% else %}
This version of the operator has been {% if
sch.deprecated %}deprecated{% else %}available{% endif %}
**since version {{sch.since_version}}{% if
sch.domain %} of domain {{sch.domain}}{% endif %}**.
{% if len(sch.versions) > 1 %}
Other versions of this operator:
{% for v in sch.version[:-1] %} {{v}} {% endfor %}
{% endif %}
{% endif %}
### Summary
{{process_documentation(sch.doc)}}
{% if sch.attributes %}
### Attributes
{% for _, attr in sorted(sch.attributes.items())
%}* **{{attr.name}} - {{str(attr.type).split('.')[-1]}}**{%
if attr.required %} (required){% endif %} {%
if attr.default_value %}{{clean_default_value(attr)}}{%
endif %}: {{text_wrap(attr.description, 2)}}
{% endfor %}
{% endif %}
{% if sch.inputs %}
### Inputs
{% if sch.min_input != sch.max_input %}Between {{sch.min_input
}} and {{sch.max_input}} inputs.
{% endif %}
{% for ii, inp in enumerate(sch.inputs) %}
- **{{getname(inp, ii)}}**{{format_option(inp)}} - **{{inp.type_str}}**:
{{text_wrap(inp.description, 2)}}{% endfor %}
{% endif %}
{% if sch.outputs %}
### Outputs
{% if sch.min_output != sch.max_output %}Between {{sch.min_output
}} and {{sch.max_output}} outputs.
{% endif %}
{% for ii, out in enumerate(sch.outputs) %}
- **{{getname(out, ii)}}**{{format_option(out)}} - **{{out.type_str}}**:
{{text_wrap(out.description, 2)}}{% endfor %}
{% endif %}
{% if sch.type_constraints %}
### Type Constraints
{% for ii, type_constraint in enumerate(sch.type_constraints)
%}* {{get_constraint(type_constraint, ii)}}:
{{text_wrap(type_constraint.description, 2)}}
{% endfor %}
{% endif %}
{% if examples and is_last_schema(sch): %}
### Examples
{% for example, code in examples.items(): %}
#### {{ example }}
```python
{{ format_example(code) }}
```
{% endfor %}
{% endif %}
{% endfor %}
"""
),
autoescape=False,
)
def _get_main_template():
return jinja2.Template(
textwrap.dedent(
"""
.. _l-onnx-operators:
{{ title }}
{{ "=" * len(title) }}
Lists out all the ONNX operators. For each operator, lists out the usage guide,
parameters, examples, and line-by-line version history.
This section also includes tables detailing each operator
with its versions, as done in `Operators.md
<https://github.com/onnx/onnx/blob/main/docs/Operators.md>`_.
All examples end by calling function `expect`.
which checks a runtime produces the expected output for this example.
One implementation based on `onnxruntime <https://onnxruntime.ai/>`_
can be found at :ref:`l-function-expect`.
.. toctree::
:hidden:
../expect_onnxruntime
{% for p in pages %}{{ os.path.split(p)[-1] }}
{% endfor %}
.. tabs::
{% for t in tabs %}.. tab:: {{ t.domain_name }}
{{ t.render(indent=" ") }}
{% endfor %}
"""
),
autoescape=True,
)
def _clean_unicode(text):
text = text.replace(""", '"')
text = text.replace("—", "-")
text = text.replace(" ", " ")
text = text.replace("'", "'")
text = text.replace(">", ">")
text = text.replace("<", "<")
return text
_template_diff = _get_diff_template()
_template_operator = _get_ops_template()
_template_main = _get_main_template()
_all_schemas_with_history = None
_attribute_conversion_functions = {
onnx.AttributeProto.FLOAT: lambda att: np.float32(att.f),
onnx.AttributeProto.FLOATS: lambda att: [np.float32(f) for f in att.floats],
# AttributeProto.GRAPH(5)
# AttributeProto.GRAPHS(10)
onnx.AttributeProto.INT: lambda att: int(att.i),
onnx.AttributeProto.INTS: lambda att: [int(i) for i in att.ints],
# AttributeProto.SPARSE_TENSOR(11)
# AttributeProto.SPARSE_TENSORS(12)
onnx.AttributeProto.STRING: lambda att: att.s.decode("utf-8"),
onnx.AttributeProto.STRINGS: lambda att: [s.decode("utf-8") for s in att.strings],
onnx.AttributeProto.TENSOR: lambda att: onnx.numpy_helper.to_array(att.t),
# AttributeProto.TENSORS(9)
# onnx.AttributeProto.TYPE_PROTO: lambda att: OnnxType(att.tp),
# AttributeProto.TYPE_PROTOS(14)
}
def _populate_all_schemas_with_history():
res: dict[str, Any] = {}
for schema in onnx.defs.get_all_schemas_with_history():
domain = schema.domain
version = schema.since_version
name = schema.name
if domain not in res:
res[domain] = {}
if name not in res[domain]:
res[domain][name] = {}
res[domain][name][version] = schema
return res
def _get_all_schemas_with_history():
global _all_schemas_with_history # pylint: disable=global-statement
if _all_schemas_with_history is None:
_all_schemas_with_history = _populate_all_schemas_with_history()
return _all_schemas_with_history
def get_operator_schemas(op_name, version=None, domain=None):
"""
Returns all schemas mapped to an operator name.
:param op_name: name of the operator
:param version: version
:param domain: domain
:return: list of schemas
"""
if version == "last" and op_name is not None:
if domain is not None:
return [onnx.defs.get_schema(op_name, domain=domain)]
all_schemas = _get_all_schemas_with_history()
if domain is None:
domains = []
for dom, ops in all_schemas.items():
if op_name is None or op_name in ops:
domains.append(dom)
else:
domains = [domain]
# schemas
sch = []
for dom in domains:
ops = all_schemas[dom]
if op_name is None:
for op, v in ops.items():
if version is None:
sch.extend(v.values())
elif version == "last" and (dom == "" or "onnx" in dom):
try:
sch.append(onnx.defs.get_schema(op, domain=dom))
except onnx.defs.SchemaError:
sch.append(v[max(v)])
elif version == "last":
sch.append(v[max(v)])
else:
sch.append(v[version])
elif op_name in ops:
if version is None:
sch.extend(ops[op_name].values())
elif version in ops[op_name]:
sch.append(ops[op_name][version])
# sort
vals = [(s.domain, s.name, -s.since_version, s) for s in sch]
vals.sort()
return [v[-1] for v in vals]
def get_markdown_doc(
folder,
op_name=None,
domain=None,
version="last",
clean=True,
diff=False,
example=False,
):
"""
Returns a documentation in Markdown format
for all :class:`OnnxOperator`.
:param op_name: operator name of None for all
:param domain: domain
:param version: version, None for all, `'last'` for the most recent one
:param clean: clean empty lines
:param diff: highlights differences between two versions
:param example: add example to the documentation
:return: string
"""
schemas = get_operator_schemas(op_name, domain=domain, version=version)
def format_name_with_domain(sch):
if version == "last":
if sch.domain:
return f"{sch.name} ({sch.domain})"
return sch.name
if sch.domain:
return f"{sch.name} - {sch.since_version} ({sch.domain})"
return f"{sch.name} - {sch.since_version}"
def format_option(obj):
opts = []
if OpSchema.FormalParameterOption.Optional == obj.option:
opts.append("optional")
elif OpSchema.FormalParameterOption.Variadic == obj.option:
opts.append("variadic")
if getattr(obj, "is_homogeneous", False):
opts.append("heterogeneous")
if opts:
return f" ({', '.join(opts)})"
return ""
def format_example(code):
return code
def get_constraint(const, ii):
if const.type_param_str:
name = const.type_param_str
else:
name = str(ii)
name = f"**{name}** in ("
if const.allowed_type_strs:
types = [f"`{type_str}`" for type_str in sorted(const.allowed_type_strs)]
text = ", ".join(types)
name += " " + text + " )"
return name
def getname(obj, i):
name = obj.name
if len(name) == 0:
return str(i)
return name
def process_documentation(doc):
if doc is None:
doc = ""
if not isinstance(doc, str):
raise TypeError(f"doc must be a string not {type(doc)!r} - {doc + 42!r}.")
main_docs_url = "https://github.com/onnx/onnx/blob/main/"
rep = {
"[the doc](IR.md)": f"[ONNX IR]({main_docs_url}docs/IR.md)",
"[the doc](Broadcasting.md)": f"[Broadcasting in ONNX]({main_docs_url}docs/Broadcasting.md)",
}
for key, value in rep.items():
doc = doc.replace(key, value)
return textwrap.dedent(doc)
def build_doc_url(sch):
doc_url = "https://github.com/onnx/onnx/blob/main/docs/Operators"
if "ml" in sch.domain:
doc_url += "-ml"
doc_url += ".md"
doc_url += "#"
if sch.domain not in (None, "", "ai.onnx"):
doc_url += sch.domain + "."
return doc_url
def format_default_value(value):
if isinstance(value, float):
formatted = str(np.round(value, 5))
# use default formatting, unless too long.
if len(formatted) > 10:
formatted = f"({value:e})"
return formatted
if isinstance(value, (bytes, bytearray)):
return value.decode("utf-8")
return str(value)
def clean_default_value(attr):
if not attr.default_value.name:
return ""
default_value = onnx.helper.get_attribute_value(attr.default_value)
if isinstance(default_value, onnx.AttributeProto) and hasattr(
default_value, "default_value"
):
if attr.type in _attribute_conversion_functions:
sval = _attribute_conversion_functions[attr.type](default_value)
return f"(default is `{sval!r}`)"
if isinstance(default_value, list):
sval = [format_default_value(val) for val in default_value]
else:
sval = format_default_value(default_value)
return f"(default is `{sval!r}`)"
def text_wrap(text, indent):
s = " " * indent
lines = textwrap.wrap(text, initial_indent=s, subsequent_indent=s)
return "\n".join(lines)
examples = get_onnx_example(op_name, domain) if example else {}
docs = _template_operator.render(
schemas=schemas,
OpSchema=OpSchema,
len=len,
getattr=getattr,
sorted=sorted,
format_option=format_option,
get_constraint=get_constraint,
getname=getname,
enumerate=enumerate,
format_name_with_domain=format_name_with_domain,
process_documentation=process_documentation,
build_doc_url=build_doc_url,
text_wrap=text_wrap,
str=str,
clean_default_value=clean_default_value,
examples=examples,
format_example=format_example,
is_last_schema=is_last_schema,
)
d_links = {}
for schema in schemas:
sdom = schema.domain.replace(".", "-")
d_links[
schema.since_version
] = f"l-onnx-op{sdom}-{schema.name.lower()}-{schema.since_version}"
if diff:
lines = docs.split("\n")
new_lines = [""]
for line in lines:
line = line.rstrip("\r\t ")
if len(line) == 0 and len(new_lines[-1]) == 0:
continue
new_lines.append(line)
docs = "\n".join(new_lines)
docs, d_links_diff = _insert_diff(
folder,
docs,
".. tag-diff-insert.",
op_name=op_name,
version=version,
domain=domain,
)
d_links.update(d_links_diff)
if clean:
lines = docs.split("\n")
new_lines = [""]
for line in lines:
line = line.rstrip("\r\t ")
if len(line) == 0 and len(new_lines[-1]) == 0:
continue
new_lines.append(line)
docs = "\n".join(new_lines)
return docs, d_links, len(examples)
def _insert_diff(
folder, docs, split=".. tag-diff-insert.", op_name=None, version=None, domain=None
):
"""
Splits a using `split`, insert HTML differences between pieces.
The function relies on package `pyquickhelper`.
"""
doc_parts = docs.split(split)
if len(doc_parts) <= 1:
return docs
reg = re.compile("([A-Z][A-Za-z0-9_]*) - ([0-9]+)")
d_links = {}
pieces = [doc_parts[0]]
mds = []
for i in range(1, len(doc_parts)):
spl1 = doc_parts[i - 1].strip("\n ")
spl2 = doc_parts[i].strip("\n ")
vers1 = reg.findall(spl1)
vers2 = reg.findall(spl2)
spl1 = spl1.split("### Examples")[0].replace("`", "")
spl2 = spl2.split("### Examples")[0].replace("`", "")
spl1 = spl1.split("### Summary")[-1].strip("\n ")
spl2 = spl2.split("### Summary")[-1].strip("\n ")
if len(spl1) < 5 or len(spl2) < 5:
pieces.append(doc_parts[i])
continue
if not vers1:
raise ValueError(f"Unable to find version {version!r} in\n{spl1}")
if not vers2:
raise ValueError(f"Unable to find version {version!r} in\n{spl2}")
v2 = vers2[0][1]
v1 = vers1[0][1]
if not mds:
mds.append(
(v1, textwrap.dedent(spl1.strip(" \n\r\t")).splitlines(keepends=True))
)
mds.append(
(v2, textwrap.dedent(spl2.strip(" \n\r\t")).splitlines(keepends=True))
)
if len(mds) > 1:
show_diff_toc = True
else:
show_diff_toc = False
if show_diff_toc:
pieces.append("```{toctree}")
for di in range(len(mds) - 1):
dj = len(mds) - 1
v1, s1 = mds[di]
v2, s2 = mds[dj]
differ = difflib.Differ()
result = list(differ.compare(s2, s1))
raw = "".join(result)
diff = _template_diff.render(
op_name=op_name,
version1=v2,
version2=v1,
div_name=f"div_{op_name}_{i}",
diff_content=raw,
)
diff = _clean_unicode(diff)
title = f"{op_name} - {v2} vs {v1}"
name = f"text_diff_{op_name}_{v2}_{v1}"
domain_str = domain.replace(".", "-")
link = f"l-onnx-op{domain_str}-{op_name.lower()}-d{v2}-{v1}"
d_links[int(v2), int(v1)] = link
content = "\n".join(
[
"",
f".. _{link}:",
"",
title,
"=" * len(title),
"",
"Next section compares an older to a newer version of the same operator ",
"after both definition are converted into markdown text.",
"Green means an addition to the newer version, red means a deletion.",
"Anything else is unchanged.",
"",
".. raw:: html",
"",
textwrap.indent(diff, " "),
]
)
filename = os.path.join(folder, name + ".rst")
pathlib.Path(filename).write_text(content, encoding="utf-8")
# Add diff page to the toctree using myst syntax
pieces.append(name)
if show_diff_toc:
# End the toctree
pieces.append("```")
pieces.extend(["", doc_parts[i]])
return "\n".join(pieces), d_links
def pascal_to_snake_case(name: str) -> str:
"""
Switches from *AaBb* into *aa_bb*.
:param name: name to convert
:return: converted name
"""
s1 = re.sub("(.)([A-Z][a-z]+)", r"\1_\2", name)
s2 = re.sub("([a-z0-9])([A-Z])", r"\1_\2", s1).lower()
return s2 if not keyword.iskeyword(s2) else s2 + "_"
def _process_example(code: str) -> str:
"""
Add necessary imports to make the example work.
"""
code = code.replace("", "")
missing_imports = ["import numpy as np", "import onnx"]
elements = [*missing_imports, "", "", code.strip("\n")]
return "\n".join(elements)
def get_onnx_example(op_name, domain):
"""
Retrieves examples associated to one operator
stored in onnx packages.
:param op_name: operator name
:param domain: operator domain
:param fmt: rendering format
:return: dictionary
"""
if domain in (None, "ai.onnx"):
modules = [
f"onnx.backend.test.case.node.{op_name.lower()}",
f"onnx.backend.test.case.node.{pascal_to_snake_case(op_name)}",
]
else:
domain_ = domain.replace(".", "_")
modules = [
f"onnx.backend.test.case.node.{domain_}.{op_name.lower()}",
f"onnx.backend.test.case.node.{domain_}.{pascal_to_snake_case(op_name)}",
]
module = None
for m in modules:
try:
mod = importlib.import_module(m)
module = m
except ImportError:
continue
if module is None:
# Unable to find an example for 'op_name'.
return {}
results: dict[str, Any] = {}
for v in mod.__dict__.values():
if not isinstance(v, _Exporter):
continue
code_cls = inspect.getsource(v)
codes = code_cls.split("@staticmethod")
for me in v.__dict__:
if not me.startswith("export"):
continue
sub = f" {me}()"
found = None
for code in codes:
if sub in code:
found = code
if found is None:
raise RuntimeError(f"Unable to find {sub!r} in\n{code_cls}")
found = textwrap.dedent(found)
lines = found.split("\n")
first = 0
for i in range(len(lines)): # pylint: disable=C0200
if lines[i].startswith("def "):
first = i + 1
found = textwrap.dedent("\n".join(lines[first:]))
key = me[len("export") :]
if key == "":
key = "default"
if key in results:
key = f"example {len(results) + 1}"
results[key] = _process_example(found)
return results
def is_last_schema(sch: OpSchema) -> bool:
"""
Tells if this is the most recent schema for this operator.
:param sch: schema
:return: True
"""
try:
last = onnx.defs.get_schema(sch.name, domain=sch.domain)
except onnx.defs.SchemaError:
return True
return last.since_version == sch.since_version
def onnx_documentation_folder(
folder, title="ONNX Operators", flog=None, max_opsets=None
):
"""
Creates documentation in a folder for all known
ONNX operators or a subset.
:param folder: folder where to write the documentation
:param title: index title
:param flog: logging function
:param max_opsets: included operator definition up to this opsets
:return: list of creates files
"""
class _Table:
def __init__(self, ops, domain, title=None):
self.title = title or domain
self.domain = domain
self.ops = ops
@property
def domain_name(self):
title = self.domain
if title == "":
title = "ai.onnx"
return title
def render(self, indent=""):
table_dom = [""]
table_dom.extend(
[
".. list-table::",
" :widths: 10 10 10",
" :header-rows: 1",
"",
" * - operator",
" - versions",
" - differences",
]
)
for op in self.ops:
name = op["name"]
dom = self.domain.replace(".", "-")
table_dom.append(f" * - :ref:`{name} <l-onnx-doc{dom}-{name}>`")
versions = sorted(
[(k, v) for k, v in op["links"].items() if isinstance(k, int)],
reverse=True,
)
col1 = ", ".join(f":ref:`{k} <{v}>`" for k, v in versions)
diffs = sorted(
[(k, v) for k, v in op["links"].items() if isinstance(k, tuple)],
reverse=True,
)
col2 = ", ".join(f":ref:`{k[1]}/{k[0]} <{v}>`" for k, v in diffs)
table_dom.append(f" - {col1}")
table_dom.append(f" - {col2}")
table_dom.append("")
if indent != "":
for i in range(len(table_dom)): # pylint: disable=C0200
table_dom[i] = indent + table_dom[i]
res = "\n".join(table_dom)
return res
all_schemas_available = _get_all_schemas_with_history()
if len(all_schemas_available) < 3:
raise RuntimeError(
f"At least three domains are expected, found {list(all_schemas_available)}."
)
# filter out operator under development
all_schemas = {}
for domain, opset in all_schemas_available.items():
max_version = None if max_opsets is None else max_opsets.get(domain, None)
d = {}
for op, schemas in opset.items():
vers = {}
for version, schema in schemas.items():
if max_version is not None and version > max_version:
continue
vers[version] = schema
d[op] = vers
all_schemas[domain] = d
if len(all_schemas) < 3:
raise RuntimeError(
f"At leat three domains are expected, found {list(all_schemas)} in all_schemas."
)
if not os.path.exists(folder):
os.makedirs(folder)
pages = []
tables = []
# loop on domains
for dom in sorted(all_schemas):
sdom = "ai.onnx" if dom == "" else dom
dom_pages = []
do = all_schemas[dom]
if len(do) == 0:
raise RuntimeError(f"No operator for domain={dom!r}.")
# loop on operators
for op in sorted(do):
if flog is not None:
flog(f"generate page for onnx {dom!r} - {op!r}")
page_name = f"onnx_{dom.replace('.', '')}_{op}"
doc, d_links, n_examples = get_markdown_doc(
folder, op, domain=dom, version=None, example=True, diff=True
)
if flog is not None and n_examples == 0:
flog(f"{' '* 14}no_example for {op} from domain {domain}")
if dom == "":
main = op
else:
main = f"{dom} - {op}"
sdom = dom.replace(".", "-")
# Target in MyST https://myst-parser.readthedocs.io/en/v0.15.1/syntax/syntax.html?highlight=role#extra-markdown-syntax
ref_link = f"(l-onnx-doc{sdom}-{op})="
rows = [
"",
ref_link,
"",
f"# {main}",
"",
doc,
]
full = os.path.join(folder, page_name + ".md")
content = "\n".join(rows)
pathlib.Path(full).write_text(content, encoding="utf-8")
pages.append(full)
dom_pages.append({"name": op, "links": d_links})
tables.append(_Table(dom_pages, dom, sdom))
# final
if len(tables) < 3:
raise RuntimeError(f"At least three domain are expected not {len(tables)}.")
index = _template_main.render(pages=pages, tabs=tables, os=os, len=len, title=title)
index = _clean_unicode(index)
page_name = os.path.join(folder, "index.rst")
pathlib.Path(page_name).write_text(index, encoding="utf-8")
pages.append(page_name)
return pages
def _generate_op_doc(app):
logger = logging.getLogger(__name__)
folder = app.config.onnx_doc_folder
max_opsets = app.config.max_opsets
onnx_documentation_folder(folder, flog=logger.info, max_opsets=max_opsets)
def _copy_repo_docs(app):
logger = logging.getLogger(__name__)
dest_name = app.config.onnx_md_folder
docs_dir = pathlib.Path(__file__).parent.parent.parent # docs
dest_folder = docs_dir / "docsgen" / "source" / dest_name
dest_folder.mkdir(parents=True, exist_ok=True)
# Copy all the markdown files from the folder except for the blocklisted ones
logger.info("Copying Markdown files from '%s' to '%s'", docs_dir, dest_folder)
for file in docs_dir.glob("*.md"):
if file.name in REPO_DOCS_EXCLUDE:
continue
shutil.copy(file, dest_folder)
logger.info("Copying '%s'", file.name)
def setup(app):
"""
Sphinx extension `onnx_sphinx` displays documentation
on ONN Operators.
"""
import sphinx
app.add_config_value("onnx_doc_folder", "operators", "env")
# Folder for storing the Markdown documentation from the repository
app.add_config_value("onnx_md_folder", "repo-docs", "env")
app.add_config_value("max_opsets", {}, "env")
app.connect("builder-inited", _generate_op_doc)
app.connect("builder-inited", _copy_repo_docs)
return {"version": sphinx.__display_version__, "parallel_read_safe": True}
if "debug" in sys.argv:
print("DEBUG")
onnx_documentation_folder("_debug", flog=print)
print("END")
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59,119 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_cast_like.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
from onnx.helper import np_dtype_to_tensor_dtype
from onnx.onnx_pb import TensorProto
from onnx.reference.op_run import OpRun
from onnx.reference.ops.op_cast import (
bfloat16,
cast_to,
float8e4m3fn,
float8e4m3fnuz,
float8e5m2,
float8e5m2fnuz,
)
def _cast_like(x, y, saturate):
if y.dtype == bfloat16 and y.dtype.descr[0][0] == "bfloat16":
# np.uint16 == np.uint16 is True as well as np.uint16 == bfloat16
to = TensorProto.BFLOAT16
elif y.dtype == float8e4m3fn and y.dtype.descr[0][0] == "e4m3fn":
to = TensorProto.FLOAT8E4M3FN
elif y.dtype == float8e4m3fnuz and y.dtype.descr[0][0] == "e4m3fnuz":
to = TensorProto.FLOAT8E4M3FNUZ
elif y.dtype == float8e5m2 and y.dtype.descr[0][0] == "e5m2":
to = TensorProto.FLOAT8E5M2
elif y.dtype == float8e5m2fnuz and y.dtype.descr[0][0] == "e5m2fnuz":
to = TensorProto.FLOAT8E5M2FNUZ
else:
to = np_dtype_to_tensor_dtype(y.dtype) # type: ignore
return (cast_to(x, to, saturate),)
class CastLike_15(OpRun):
def _run(self, x, y): # type: ignore
return _cast_like(x, y, True)
class CastLike_19(OpRun):
def _run(self, x, y, saturate=None): # type: ignore
return _cast_like(x, y, saturate)
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"/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
59,120 | onnx/onnx | refs/heads/main | /onnx/backend/test/case/node/matmulinteger.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class MatMulInteger(Base):
@staticmethod
def export() -> None:
node = onnx.helper.make_node(
"MatMulInteger",
inputs=["A", "B", "a_zero_point", "b_zero_point"],
outputs=["Y"],
)
A = np.array(
[
[11, 7, 3],
[10, 6, 2],
[9, 5, 1],
[8, 4, 0],
],
dtype=np.uint8,
)
a_zero_point = np.array([12], dtype=np.uint8)
B = np.array(
[
[1, 4],
[2, 5],
[3, 6],
],
dtype=np.uint8,
)
b_zero_point = np.array([0], dtype=np.uint8)
output = np.array(
[
[-38, -83],
[-44, -98],
[-50, -113],
[-56, -128],
],
dtype=np.int32,
)
expect(
node,
inputs=[A, B, a_zero_point, b_zero_point],
outputs=[output],
name="test_matmulinteger",
)
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"/onnx/backend/test/case/node/cast.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py", "/onnx/helper.py", "/onnx/numpy_helper.py"], "/onnx/backend/test/case/node/hammingwindow.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_lp_pool.py": ["/onnx/reference/ops/op_pool_common.py"], "/onnx/backend/test/case/node/split.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/test/hub_test.py": ["/onnx/__init__.py"], "/onnx/backend/test/case/node/shrink.py": ["/onnx/__init__.py", "/onnx/backend/test/case/base.py", "/onnx/backend/test/case/node/__init__.py"], "/onnx/reference/ops/op_gru.py": ["/onnx/reference/op_run.py"]} |
59,121 | onnx/onnx | refs/heads/main | /onnx/reference/ops/op_gather.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
import numpy as np
from onnx.reference.op_run import OpRun
class Gather(OpRun):
def _run(self, x, indices, axis=None): # type: ignore
if not x.flags["C_CONTIGUOUS"]:
x = np.ascontiguousarray(x)
if not indices.flags["C_CONTIGUOUS"]:
indices = indices.ascontiguousarray()
if indices.size == 0:
return (np.empty((0,), dtype=x.dtype),)
try:
return (np.take(x, indices, axis=axis),)
except TypeError:
# distribution x86 requires int32.
return (np.take(x, indices.astype(int), axis=axis),)
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