99 lines
3.9 KiB
Python
99 lines
3.9 KiB
Python
from kfp.components import InputPath, OutputPath
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def StatisticsGen(
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examples_path: InputPath('Examples'),
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#examples_path: 'ExamplesUri',
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statistics_path: OutputPath('ExampleStatistics'),
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#statistics_path: 'ExampleStatisticsUri',
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stats_options: {'JsonObject': {'data_type': 'proto:tensorflow_data_validation.StatsOptions'}} = None,
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schema_path: InputPath('Schema') = None,
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#schema_path: 'SchemaUri' = None,
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):
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#) -> NamedTuple('Outputs', [
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# ('statistics', 'ExampleStatisticsUri'),
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#]):
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"""Construct a StatisticsGen component.
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Args:
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examples: A Channel of `ExamplesPath` type, likely generated by the
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[ExampleGen component](https://www.tensorflow.org/tfx/guide/examplegen).
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This needs to contain two splits labeled `train` and `eval`. _required_
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schema: A `Schema` channel to use for automatically configuring the value
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of stats options passed to TFDV.
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Returns:
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statistics: `ExampleStatistics` channel for statistics of each split
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provided in the input examples.
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"""
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from tfx.components.statistics_gen.component import StatisticsGen as component_class
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#Generated code
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import json
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import os
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import tensorflow
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from google.protobuf import json_format, message
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from tfx.types import Artifact, channel_utils, artifact_utils
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arguments = locals().copy()
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component_class_args = {}
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for name, execution_parameter in component_class.SPEC_CLASS.PARAMETERS.items():
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argument_value_obj = argument_value = arguments.get(name, None)
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if argument_value is None:
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continue
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parameter_type = execution_parameter.type
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if isinstance(parameter_type, type) and issubclass(parameter_type, message.Message): # Maybe FIX: execution_parameter.type can also be a tuple
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argument_value_obj = parameter_type()
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json_format.Parse(argument_value, argument_value_obj)
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component_class_args[name] = argument_value_obj
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for name, channel_parameter in component_class.SPEC_CLASS.INPUTS.items():
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artifact_path = arguments[name + '_path']
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if artifact_path:
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artifact = channel_parameter.type()
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artifact.uri = artifact_path + '/' # ?
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if channel_parameter.type.PROPERTIES and 'split_names' in channel_parameter.type.PROPERTIES:
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# Recovering splits
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subdirs = tensorflow.io.gfile.listdir(artifact_path)
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artifact.split_names = artifact_utils.encode_split_names(sorted(subdirs))
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component_class_args[name] = channel_utils.as_channel([artifact])
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component_class_instance = component_class(**component_class_args)
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input_dict = {name: channel.get() for name, channel in component_class_instance.inputs.get_all().items()}
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output_dict = {name: channel.get() for name, channel in component_class_instance.outputs.get_all().items()}
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exec_properties = component_class_instance.exec_properties
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# Generating paths for output artifacts
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for name, artifacts in output_dict.items():
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base_artifact_path = arguments[name + '_path']
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# Are there still cases where output channel has multiple artifacts?
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for idx, artifact in enumerate(artifacts):
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subdir = str(idx + 1) if idx > 0 else ''
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artifact.uri = os.path.join(base_artifact_path, subdir) # Ends with '/'
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print('component instance: ' + str(component_class_instance))
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#executor = component_class.EXECUTOR_SPEC.executor_class() # Same
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executor = component_class_instance.executor_spec.executor_class()
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executor.Do(
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input_dict=input_dict,
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output_dict=output_dict,
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exec_properties=exec_properties,
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)
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#return (statistics_path,)
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if __name__ == '__main__':
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import kfp
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kfp.components.func_to_container_op(
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StatisticsGen,
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base_image='tensorflow/tfx:0.21.4',
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output_component_file='component.yaml'
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)
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