def test_datetime_iso_with_zulu_offset(self):
timestamp_non_zulu = "2019-12-11T12:22:32+00:00"
timestamp_zulu = "2019-12-11T12:22:32Z"
expected = datetime(2019, 12, 11, 12, 22, 32, tzinfo=timezone.utc)
actual1 = fields.DateTime().deserialize(timestamp_non_zulu)
actual2 = fields.DateTime().deserialize(timestamp_zulu)
assert expected == actual1
assert expected == actual2
class Model(rdf.schema.RDF):
raw_nodes = raw_nodes
class Meta:
unknown = RAISE
bioimageio_description = f"""# BioImage.IO Model Resource Description File Specification {get_args(raw_nodes.FormatVersion)[-1]}
This specification defines the fields used in a BioImage.IO-compliant resource description file (`RDF`) for describing AI models with pretrained weights.
These fields are typically stored in YAML files which we called Model Resource Description Files or `model RDF`.
The model RDFs can be downloaded or uploaded to the bioimage.io website, produced or consumed by BioImage.IO-compatible consumers(e.g. image analysis software or other website).
The model RDF YAML file contains mandatory and optional fields. In the following description, optional fields are indicated by _optional_.
_optional*_ with an asterisk indicates the field is optional depending on the value in another field.
"""
# todo: unify authors with RDF (optional or required?)
authors = fields.List(
fields.Nested(Author()), required=True, bioimageio_description=rdf.schema.RDF.authors_bioimageio_description
)
badges = missing_
cite = fields.List(
fields.Nested(CiteEntry()),
required=True, # todo: unify authors with RDF (optional or required?)
bioimageio_description=rdf.schema.RDF.cite_bioimageio_description,
)
documentation = fields.Union(
[
fields.URL(),
fields.RelativeLocalPath(
validate=field_validators.Attribute(
"suffix",
field_validators.Equal(
".md", error="{!r} is invalid; expected markdown file with '.md' extension."
),
)
),
],
required=True,
bioimageio_description="Relative path to file with additional documentation in markdown. This means: 1) only "
"relative file path is allowed 2) the file must be in markdown format with `.md` file name extension 3) URL is "
"not allowed. It is recommended to use `README.md` as the documentation name.",
)
download_url = missing_
dependencies = fields.Dependencies( # todo: add validation (0.4.0?)
bioimageio_description="Dependency manager and dependency file, specified as `<dependency manager>:<relative "
"path to file>`. For example: 'conda:./environment.yaml', 'maven:./pom.xml', or 'pip:./requirements.txt'"
)
format_version = fields.String(
validate=field_validators.OneOf(get_args_flat(raw_nodes.FormatVersion)),
required=True,
bioimageio_description_order=0,
bioimageio_description=f"""Version of the BioImage.IO Model Resource Description File Specification used.
This is mandatory, and important for the consumer software to verify before parsing the fields.
The recommended behavior for the implementation is to keep backward compatibility and throw an error if the model yaml
is in an unsupported format version. The current format version described here is
{get_args(raw_nodes.FormatVersion)[-1]}""",
)
framework = fields.String(
validate=field_validators.OneOf(get_args(raw_nodes.Framework)),
bioimageio_description=f"The deep learning framework of the source code. One of: "
f"{', '.join(get_args(raw_nodes.Framework))}. This field is only required if the field `source` is present.",
)
git_repo = fields.String(
validate=field_validators.URL(schemes=["http", "https"]),
bioimageio_description=rdf.schema.RDF.git_repo_bioimageio_description
+ "If the model is contained in a subfolder of a git repository, then a url to the exact folder"
+ "(which contains the configuration yaml file) should be used.",
)
icon = missing_
kwargs = fields.Kwargs(
bioimageio_description="Keyword arguments for the implementation specified by `source`. "
"This field is only required if the field `source` is present."
)
language = fields.String(
validate=field_validators.OneOf(get_args(raw_nodes.Language)),
bioimageio_maybe_required=True,
bioimageio_description=f"Programming language of the source code. One of: "
f"{', '.join(get_args(raw_nodes.Language))}. This field is only required if the field `source` is present.",
)
license = fields.String(
required=True, # todo: unify license with RDF (optional or required?)
bioimageio_description=rdf.schema.RDF.license_bioimageio_description,
)
name = fields.String(
# validate=field_validators.Length(max=36), # todo: enforce in future version (0.4.0?)
required=True,
bioimageio_description="Name of this model. It should be human-readable and only contain letters, numbers, "
"underscore '_', minus '-' or spaces and not be longer than 36 characters.",
)
packaged_by = fields.List(
fields.Nested(Author()),
bioimageio_description=f"The persons that have packaged and uploaded this model. Only needs to be specified if "
f"different from `authors` in root or any entry in `weights`.",
)
parent = fields.Nested(
ModelParent(),
bioimageio_description="Parent model from which the trained weights of this model have been derived, e.g. by "
"finetuning the weights of this model on a different dataset. For format changes of the same trained model "
"checkpoint, see `weights`.",
)
run_mode = fields.Nested(
RunMode(),
bioimageio_description="Custom run mode for this model: for more complex prediction procedures like test time "
"data augmentation that currently cannot be expressed in the specification. "
"No standard run modes are defined yet.",
)
sha256 = fields.String(
validate=field_validators.Length(equal=64),
bioimageio_description="SHA256 checksum of the model source code file."
+ _common_sha256_hint
+ " This field is only required if the field source is present.",
)
source = fields.ImportableSource(
bioimageio_maybe_required=True,
bioimageio_description="Language and framework specific implementation. As some weights contain the model "
"architecture, the source is optional depending on the present weight formats. `source` can either point to a "
"local implementation: `<relative path to file>:<identifier of implementation within the source file>` or the "
"implementation in an available dependency: `<root-dependency>.<sub-dependency>.<identifier>`.\nFor example: "
"`my_function.py:MyImplementation` or `core_library.some_module.some_function`.",
)
timestamp = fields.DateTime(
required=True,
bioimageio_description="Timestamp of the initial creation of this model in [ISO 8601]"
"(#https://en.wikipedia.org/wiki/ISO_8601) format.",
)
weights = fields.Dict(
fields.String(
validate=field_validators.OneOf(get_args(raw_nodes.WeightsFormat)),
required=True,
bioimageio_description=f"Format of this set of weights. Weight formats can define additional (optional or "
f"required) fields. See [weight_formats_spec_0_3.md]"
f"(https://github.com/bioimage-io/spec-bioimage-io/blob/gh-pages/weight_formats_spec_0_3.md). "
f"One of: {', '.join(get_args(raw_nodes.WeightsFormat))}",
),
fields.Union([fields.Nested(we()) for we in get_args(WeightsEntry)]),
required=True,
bioimageio_description="The weights for this model. Weights can be given for different formats, but should "
"otherwise be equivalent. The available weight formats determine which consumers can use this model.",
)
@pre_load
def add_weights_format_key_to_weights_entry_value(self, data: dict, many=False, partial=False, **kwargs):
data = deepcopy(data) # Schema.validate() calls pre_load methods, thus we should not modify the input data
if many or partial:
raise NotImplementedError
for weights_format, weights_entry in data.get("weights", {}).items():
if "weights_format" in weights_entry:
raise ValidationError(f"Got unexpected key 'weights_format' in weights entry {weights_format}")
weights_entry["weights_format"] = weights_format
return data
inputs = fields.List(
fields.Nested(InputTensor()), bioimageio_description="Describes the input tensors expected by this model."
)
outputs = fields.List(
fields.Nested(OutputTensor()), bioimageio_description="Describes the output tensors from this model."
)
test_inputs = fields.List(
fields.Union([fields.URI(), fields.RelativeLocalPath()]),
required=True,
bioimageio_description="List of URIs or local relative paths to test inputs as described in inputs for "
"**a single test case**. "
"This means if your model has more than one input, you should provide one URI for each input. "
"Each test input should be a file with a ndarray in "
"[numpy.lib file format](https://numpy.org/doc/stable/reference/generated/numpy.lib.format.html#module-numpy.lib.format)."
"The extension must be '.npy'.",
)
test_outputs = fields.List(
fields.Union([fields.URI(), fields.RelativeLocalPath()]),
required=True,
bioimageio_description="Analog to to test_inputs.",
)
sample_inputs = fields.List(
fields.Union([fields.URI(), fields.RelativeLocalPath()]),
bioimageio_description="List of URIs/local relative paths to sample inputs to illustrate possible inputs for "
"the model, for example stored as png or tif images. "
"The model is not tested with these sample files that serve to inform a human user about an example use case.",
)
sample_outputs = fields.List(
fields.Union([fields.URI(), fields.RelativeLocalPath()]),
bioimageio_description="List of URIs/local relative paths to sample outputs corresponding to the "
"`sample_inputs`.",
)
config = fields.YamlDict(
bioimageio_description=rdf.schema.RDF.config_bioimageio_description
+ """
For example:
```yaml
config:
# custom config for DeepImageJ, see https://github.com/bioimage-io/configuration/issues/23
deepimagej:
model_keys:
# In principle the tag "SERVING" is used in almost every tf model
model_tag: tf.saved_model.tag_constants.SERVING
# Signature definition to call the model. Again "SERVING" is the most general
signature_definition: tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
test_information:
input_size: [2048x2048] # Size of the input images
output_size: [1264x1264 ]# Size of all the outputs
device: cpu # Device used. In principle either cpu or GPU
memory_peak: 257.7 Mb # Maximum memory consumed by the model in the device
runtime: 78.8s # Time it took to run the model
pixel_size: [9.658E-4µmx9.658E-4µm] # Size of the pixels of the input
```
"""
)
@validates_schema
def language_and_framework_match(self, data, **kwargs):
field_names = ("language", "framework")
valid_combinations = [
("python", "scikit-learn"), # todo: remove
("python", "pytorch"),
("python", "tensorflow"),
("java", "tensorflow"),
]
if "source" not in data:
valid_combinations.append((missing_, missing_))
valid_combinations.append(("python", missing_))
valid_combinations.append(("java", missing_))
combination = tuple(data.get(name, missing_) for name in field_names)
if combination not in valid_combinations:
raise ValidationError(f"invalid combination of {dict(zip(field_names, combination))}")
@validates_schema
def source_specified_if_required(self, data, **kwargs):
if "source" in data:
return
weights_format_requires_source = {
"pytorch_state_dict": True,
"pytorch_script": False,
"keras_hdf5": False,
"tensorflow_js": False,
"tensorflow_saved_model_bundle": False,
"onnx": False,
}
require_source = {wf for wf in data["weights"] if weights_format_requires_source[wf]}
if require_source:
raise ValidationError(
f"These specified weight formats require source code to be specified: {require_source}"
)
@validates_schema
def validate_reference_tensor_names(self, data, **kwargs):
valid_input_tensor_references = [ipt.name for ipt in data["inputs"]]
for out in data["outputs"]:
if out.postprocessing is missing_:
continue
for postpr in out.postprocessing:
if postpr.kwargs is missing_:
continue
ref_tensor = postpr.kwargs.get("reference_tensor", missing_)
if ref_tensor is not missing_ and ref_tensor not in valid_input_tensor_references:
raise ValidationError(f"{ref_tensor} not found in inputs")
@validates_schema
def weights_entries_match_weights_formats(self, data, **kwargs):
weights: typing.Dict[str, _WeightsEntryBase] = data["weights"]
for weights_format, weights_entry in weights.items():
if weights_format in ["keras_hdf5", "tensorflow_js", "tensorflow_saved_model_bundle"]:
assert isinstance(
weights_entry,
(
raw_nodes.KerasHdf5WeightsEntry,
raw_nodes.TensorflowJsWeightsEntry,
raw_nodes.TensorflowSavedModelBundleWeightsEntry,
),
)
if weights_entry.tensorflow_version is missing_:
# todo: raise ValidationError (allow -> require)?
warnings.warn(f"missing 'tensorflow_version' entry for weights format {weights_format}")
if weights_format == "onnx":
assert isinstance(weights_entry, raw_nodes.OnnxWeightsEntry)
if weights_entry.opset_version is missing_:
# todo: raise ValidationError?
warnings.warn(f"missing 'opset_version' entry for weights format {weights_format}")
def test_datetime_from_str(self):
timestamp = "2019-12-11T12:22:32+00:00"
expected = datetime.fromisoformat(timestamp)
actual = fields.DateTime().deserialize(timestamp)
assert expected == actual
def test_datetime_from_datetime(self):
expected = datetime.now()
assert expected == fields.DateTime().deserialize(expected)
class Model(rdf.schema.RDF):
raw_nodes = raw_nodes
class Meta:
unknown = RAISE
exclude = ("source",
) # while RDF does have a source field, Model does not
bioimageio_description = f"""# BioImage.IO Model Resource Description File Specification {get_args(raw_nodes.FormatVersion)[-1]}
This specification defines the fields used in a BioImage.IO-compliant resource description file (`RDF`) for describing AI models with pretrained weights.
These fields are typically stored in YAML files which we call Model Resource Description Files or `model RDF`.
The model RDFs can be downloaded or uploaded to the bioimage.io website, produced or consumed by BioImage.IO-compatible consumers(e.g. image analysis software or other website).
The model RDF YAML file contains mandatory and optional fields. In the following description, optional fields are indicated by _optional_.
_optional*_ with an asterisk indicates the field is optional depending on the value in another field.
"""
# todo: sync authors with RDF
authors = fields.List(
fields.Nested(rdf.schema.Author()),
validate=field_validators.Length(min=1),
required=True,
bioimageio_description=rdf.schema.RDF.authors_bioimageio_description,
)
badges = missing # todo: allow badges for Model (RDF has it)
cite = fields.List(
fields.Nested(rdf.schema.CiteEntry()),
required=True, # todo: unify authors with RDF (optional or required?)
validate=field_validators.Length(min=1),
bioimageio_description=rdf.schema.RDF.cite_bioimageio_description,
)
config = fields.YamlDict(
bioimageio_description=rdf.schema.RDF.config_bioimageio_description +
"""
For example:
```yaml
config:
# custom config for DeepImageJ, see https://github.com/bioimage-io/configuration/issues/23
deepimagej:
model_keys:
# In principle the tag "SERVING" is used in almost every tf model
model_tag: tf.saved_model.tag_constants.SERVING
# Signature definition to call the model. Again "SERVING" is the most general
signature_definition: tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
test_information:
input_size: [2048x2048] # Size of the input images
output_size: [1264x1264 ]# Size of all the outputs
device: cpu # Device used. In principle either cpu or GPU
memory_peak: 257.7 Mb # Maximum memory consumed by the model in the device
runtime: 78.8s # Time it took to run the model
pixel_size: [9.658E-4µmx9.658E-4µm] # Size of the pixels of the input
```
""")
documentation = fields.Union(
[
fields.URL(),
fields.RelativeLocalPath(validate=field_validators.Attribute(
"suffix",
field_validators.Equal(
".md",
error=
"{!r} is invalid; expected markdown file with '.md' extension."
),
)),
],
required=True,
bioimageio_description=
"Relative path or URL to file with additional documentation in markdown. "
"The file must be in markdown format with `.md` file name extension"
"It is recommended to use `README.md` as the documentation name.",
)
format_version = fields.String(
validate=field_validators.OneOf(get_args_flat(
raw_nodes.FormatVersion)),
required=True,
bioimageio_description_order=0,
bioimageio_description=
f"""Version of the BioImage.IO Model Resource Description File Specification used.
This is mandatory, and important for the consumer software to verify before parsing the fields.
The recommended behavior for the implementation is to keep backward compatibility and throw an error if the model yaml
is in an unsupported format version. The current format version described here is
{get_args(raw_nodes.FormatVersion)[-1]}""",
)
git_repo = fields.URL(
bioimageio_description=rdf.schema.RDF.git_repo_bioimageio_description +
"If the model is contained in a subfolder of a git repository, then a url to the exact folder"
+ "(which contains the configuration yaml file) should be used.")
inputs = fields.List(
fields.Nested(InputTensor()),
validate=field_validators.Length(min=1),
required=True,
bioimageio_description=
"Describes the input tensors expected by this model.",
)
@validates("inputs")
def no_duplicate_input_tensor_names(
self, value: typing.List[raw_nodes.InputTensor]):
if not isinstance(value, list) or not all(
isinstance(v, raw_nodes.InputTensor) for v in value):
raise ValidationError(
"Could not check for duplicate input tensor names due to another validation error."
)
names = [t.name for t in value]
if len(names) > len(set(names)):
raise ValidationError(
"Duplicate input tensor names are not allowed.")
license = fields.String(
validate=field_validators.OneOf(LICENSES),
required=True,
bioimageio_description=rdf.schema.RDF.license_bioimageio_description,
)
name = fields.String(
required=True,
bioimageio_description=
"Name of this model. It should be human-readable and only contain letters, numbers, "
"underscore '_', minus '-' or spaces and not be longer than 64 characters.",
)
outputs = fields.List(
fields.Nested(OutputTensor()),
validate=field_validators.Length(min=1),
bioimageio_description="Describes the output tensors from this model.",
)
@validates("outputs")
def no_duplicate_output_tensor_names(
self, value: typing.List[raw_nodes.OutputTensor]):
if not isinstance(value, list) or not all(
isinstance(v, raw_nodes.OutputTensor) for v in value):
raise ValidationError(
"Could not check for duplicate output tensor names due to another validation error."
)
names = [t["name"] if isinstance(t, dict) else t.name for t in value]
if len(names) > len(set(names)):
raise ValidationError(
"Duplicate output tensor names are not allowed.")
@validates_schema
def inputs_and_outputs(self, data, **kwargs):
ipts: typing.List[raw_nodes.InputTensor] = data.get("inputs")
outs: typing.List[raw_nodes.OutputTensor] = data.get("outputs")
if any([
not isinstance(ipts, list),
not isinstance(outs, list),
not all(isinstance(v, raw_nodes.InputTensor) for v in ipts),
not all(isinstance(v, raw_nodes.OutputTensor) for v in outs),
]):
raise ValidationError(
"Could not check for duplicate tensor names due to another validation error."
)
# no duplicate tensor names
names = [t.name for t in ipts + outs] # type: ignore
if len(names) > len(set(names)):
raise ValidationError("Duplicate tensor names are not allowed.")
tensors_by_name: typing.Dict[str,
typing.Union[raw_nodes.InputTensor,
raw_nodes.OutputTensor]] = {
t.name: t
for t in ipts +
outs # type: ignore
}
# minimum shape leads to valid output:
# output with subtracted halo has to result in meaningful output even for the minimal input
# see https://github.com/bioimage-io/spec-bioimage-io/issues/392
def get_min_shape(t) -> numpy.ndarray:
if isinstance(t.shape, raw_nodes.ParametrizedInputShape):
shape = numpy.array(t.shape.min)
elif isinstance(t.shape, raw_nodes.ImplicitOutputShape):
shape = get_min_shape(tensors_by_name[t.shape.reference_tensor]
) * t.shape.scale + 2 * numpy.array(
t.shape.offset)
else:
shape = numpy.array(t.shape)
return shape
for out in outs:
if isinstance(
out.shape,
raw_nodes.ImplicitOutputShape) and len(out.shape) != len(
tensors_by_name[out.shape.reference_tensor].shape):
raise ValidationError(
f"Referenced tensor {out.shape.reference_tensor} "
f"with {len(tensors_by_name[out.shape.reference_tensor].shape)} dimensions does not match "
f"output tensor {out.name} with {len(out.shape)} dimensions."
)
min_out_shape = get_min_shape(out)
if out.halo:
halo = out.halo
halo_msg = f" for halo {out.halo}"
else:
halo = [0] * len(min_out_shape)
halo_msg = ""
if any([s - 2 * h < 1 for s, h in zip(min_out_shape, halo)]):
raise ValidationError(
f"Minimal shape {min_out_shape} of output {out.name} is too small{halo_msg}."
)
packaged_by = fields.List(
fields.Nested(rdf.schema.Author()),
bioimageio_description=
f"The persons that have packaged and uploaded this model. Only needs to be specified if "
f"different from `authors` in root or any entry in `weights`.",
)
parent = fields.Nested(
ModelParent(),
bioimageio_description=
"The model from which this model is derived, e.g. by fine-tuning the weights.",
)
run_mode = fields.Nested(
RunMode(),
bioimageio_description=
"Custom run mode for this model: for more complex prediction procedures like test time "
"data augmentation that currently cannot be expressed in the specification. "
"No standard run modes are defined yet.",
)
sample_inputs = fields.List(
fields.Union([fields.URI(), fields.RelativeLocalPath()]),
validate=field_validators.Length(min=1),
bioimageio_description=
"List of URIs/local relative paths to sample inputs to illustrate possible inputs for "
"the model, for example stored as png or tif images. "
"The model is not tested with these sample files that serve to inform a human user about an example use case.",
)
sample_outputs = fields.List(
fields.Union([fields.URI(), fields.RelativeLocalPath()]),
validate=field_validators.Length(min=1),
bioimageio_description=
"List of URIs/local relative paths to sample outputs corresponding to the "
"`sample_inputs`.",
)
test_inputs = fields.List(
fields.Union([fields.URI(), fields.RelativeLocalPath()]),
validate=field_validators.Length(min=1),
required=True,
bioimageio_description=
"List of URIs or local relative paths to test inputs as described in inputs for "
"**a single test case**. "
"This means if your model has more than one input, you should provide one URI for each input."
"Each test input should be a file with a ndarray in "
"[numpy.lib file format](https://numpy.org/doc/stable/reference/generated/numpy.lib.format.html#module-numpy.lib.format)."
"The extension must be '.npy'.",
)
test_outputs = fields.List(
fields.Union([fields.URI(), fields.RelativeLocalPath()]),
validate=field_validators.Length(min=1),
required=True,
bioimageio_description="Analog to test_inputs.",
)
timestamp = fields.DateTime(
required=True,
bioimageio_description=
"Timestamp of the initial creation of this model in [ISO 8601]"
"(#https://en.wikipedia.org/wiki/ISO_8601) format.",
)
training_data = fields.Union(
[fields.Nested(Dataset()),
fields.Nested(LinkedDataset())])
weights = fields.Dict(
fields.String(
validate=field_validators.OneOf(get_args(raw_nodes.WeightsFormat)),
required=True,
bioimageio_description="Format of this set of weights. "
f"One of: {', '.join(get_args(raw_nodes.WeightsFormat))}",
),
fields.Union(
[fields.Nested(we()) for we in get_args(WeightsEntry)],
short_bioimageio_description=
("The weights for this model. Weights can be given for different formats, but should "
"otherwise be equivalent. "
"See [weight_formats_spec_0_4.md]"
"(https://github.com/bioimage-io/spec-bioimage-io/blob/gh-pages/weight_formats_spec_0_4.md) "
"for the required and optional fields per weight format. "
"The available weight formats determine which consumers can use this model."
),
),
required=True,
)
@pre_load
def add_weights_format_key_to_weights_entry_value(self,
data: dict,
many=False,
partial=False,
**kwargs):
data = deepcopy(
data
) # Schema.validate() calls pre_load methods, thus we should not modify the input data
if many or partial:
raise NotImplementedError
for weights_format, weights_entry in data.get("weights", {}).items():
if "weights_format" in weights_entry:
raise ValidationError(
f"Got unexpected key 'weights_format' in weights entry {weights_format}"
)
weights_entry["weights_format"] = weights_format
return data
@validates_schema
def validate_reference_tensor_names(self, data, **kwargs):
def get_tnames(tname: str):
return [
t.get("name") if isinstance(t, dict) else t.name
for t in data.get(tname, [])
]
valid_input_tensor_references = get_tnames("inputs")
ins = data.get("inputs", [])
outs = data.get("outputs", [])
if not isinstance(ins, list) or not isinstance(outs, list):
raise ValidationError(
f"Failed to validate reference tensor names due to other validation errors in inputs/outputs."
)
for t in outs:
if not isinstance(t, raw_nodes.OutputTensor):
raise ValidationError(
"Failed to validate reference tensor names due to validation errors in outputs"
)
if t.postprocessing is missing:
continue
for postpr in t.postprocessing:
if postpr.kwargs is missing:
continue
ref_tensor = postpr.kwargs.get("reference_tensor", missing)
if ref_tensor is not missing and ref_tensor not in valid_input_tensor_references:
raise ValidationError(f"{ref_tensor} not found in inputs")
for t in ins:
if not isinstance(t, raw_nodes.InputTensor):
raise ValidationError(
"Failed to validate reference tensor names due to validation errors in inputs"
)
if t.preprocessing is missing:
continue
for prep in t.preprocessing:
if prep.kwargs is missing:
continue
ref_tensor = prep.kwargs.get("reference_tensor", missing)
if ref_tensor is not missing and ref_tensor not in valid_input_tensor_references:
raise ValidationError(f"{ref_tensor} not found in inputs")
if ref_tensor == t.name:
raise ValidationError(
f"invalid self reference for preprocessing of tensor {t.name}"
)
@validates_schema
def weights_entries_match_weights_formats(self, data, **kwargs):
weights: typing.Dict[str, WeightsEntry] = data.get("weights", {})
for weights_format, weights_entry in weights.items():
if not isinstance(weights_entry, get_args(raw_nodes.WeightsEntry)):
raise ValidationError(
"Cannot validate keys in weights field due to other validation errors."
)
if weights_format in ["pytorch_state_dict", "torchscript"]:
if weights_format == "pytorch_state_dict":
assert isinstance(weights_entry,
raw_nodes.PytorchStateDictWeightsEntry)
elif weights_format == "torchscript":
assert isinstance(weights_entry,
raw_nodes.TorchscriptWeightsEntry)
else:
raise NotImplementedError
if weights_entry.dependencies is missing and weights_entry.pytorch_version is missing:
self.warn(f"weights:{weights_format}",
"missing 'pytorch_version'")
if weights_format in [
"keras_hdf5", "tensorflow_js",
"tensorflow_saved_model_bundle"
]:
if weights_format == "keras_hdf5":
assert isinstance(weights_entry,
raw_nodes.KerasHdf5WeightsEntry)
elif weights_format == "tensorflow_js":
assert isinstance(weights_entry,
raw_nodes.TensorflowJsWeightsEntry)
elif weights_format == "tensorflow_saved_model_bundle":
assert isinstance(
weights_entry,
raw_nodes.TensorflowSavedModelBundleWeightsEntry)
else:
raise NotImplementedError
if weights_entry.dependencies is missing and weights_entry.tensorflow_version is missing:
self.warn(f"weights:{weights_format}",
"missing 'tensorflow_version'")
if weights_format == "onnx":
assert isinstance(weights_entry, raw_nodes.OnnxWeightsEntry)
if weights_entry.dependencies is missing and weights_entry.opset_version is missing:
self.warn(f"weights:{weights_format}",
"missing 'opset_version'")