我正在使用 TensorFlow 中的自定义训练循环训练 Keras 模型，其中权重是使用梯度磁带而不是model.fit()方法更新的。因此，模型不是在训练之前编译的。
导出 saved_model 后，我可以成功加载它进行推理：
model = tf.saved_model.load("path/to/saved_model")
pred_fn = model.signatures["serving_default"]
results = pred_fn(tf.constant(examples))
但是，当我尝试使用 TFMA 加载它时run_model_analysis：
eval_shared_model = tfma.default_eval_shared_model("path/to/saved_model", eval_config=eval_config)
eval_results = tfma.run_model_analysis(
eval_shared_model=eval_shared_model,
data_location=test_tfrecords_path,
file_format="tfrecords"
)
我收到以下错误：
WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.
-----------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-107-19f51f42014a> in <module>
2 eval_shared_model=eval_shared_model,
3 data_location=test_tfrecords_path,
----> 4 file_format="tfrecords"
5 )
~/.pyenv/versions/miniconda3-4.3.30/envs/tensorflow/lib/python3.7/site-packages/tensorflow_model_analysis/api/model_eval_lib.py in run_model_analysis(eval_shared_model, eval_config, data_location, file_format, output_path, extractors, evaluators, writers, pipeline_options, slice_spec, write_config, compute_confidence_intervals, min_slice_size, random_seed_for_testing, schema)
1200 random_seed_for_testing=random_seed_for_testing,
1201 tensor_adapter_config=tensor_adapter_config,
-> 1202 schema=schema))
1203 # pylint: enable=no-value-for-parameter
1204
~/.pyenv/versions/miniconda3-4.3.30/envs/tensorflow/lib/python3.7/site-packages/apache_beam/pvalue.py in __or__(self, ptransform)
138
139 def __or__(self, ptransform):
--> 140 return self.pipeline.apply(ptransform, self)
141
142
~/.pyenv/versions/miniconda3-4.3.30/envs/tensorflow/lib/python3.7/site-packages/apache_beam/pipeline.py in apply(self, transform, pvalueish, label)
575 if isinstance(transform, ptransform._NamedPTransform):
576 return self.apply(
--> 577 transform.transform, pvalueish, label or transform.label)
578
579 if not isinstance(transform, ptransform.PTransform):
~/.pyenv/versions/miniconda3-4.3.30/envs/tensorflow/lib/python3.7/site-packages/apache_beam/pipeline.py in apply(self, transform, pvalueish, label)
585 try:
586 old_label, transform.label = transform.label, label
--> 587 return self.apply(transform, pvalueish)
588 finally:
589 transform.label = old_label
~/.pyenv/versions/miniconda3-4.3.30/envs/tensorflow/lib/python3.7/site-packages/apache_beam/pipeline.py in apply(self, transform, pvalueish, label)
628 transform.type_check_inputs(pvalueish)
629
--> 630 pvalueish_result = self.runner.apply(transform, pvalueish, self._options)
631
632 if type_options is not None and type_options.pipeline_type_check:
~/.pyenv/versions/miniconda3-4.3.30/envs/tensorflow/lib/python3.7/site-packages/apache_beam/runners/runner.py in apply(self, transform, input, options)
196 m = getattr(self, 'apply_%s' % cls.__name__, None)
197 if m:
--> 198 return m(transform, input, options)
199 raise NotImplementedError(
200 'Execution of [%s] not implemented in runner %s.' % (transform, self))
~/.pyenv/versions/miniconda3-4.3.30/envs/tensorflow/lib/python3.7/site-packages/apache_beam/runners/runner.py in apply_PTransform(self, transform, input, options)
226 def apply_PTransform(self, transform, input, options):
227 # The base case of apply is to call the transform's expand.
--> 228 return transform.expand(input)
229
230 def run_transform(self,
~/.pyenv/versions/miniconda3-4.3.30/envs/tensorflow/lib/python3.7/site-packages/apache_beam/transforms/ptransform.py in expand(self, pcoll)
921 # Might not be a function.
922 pass
--> 923 return self._fn(pcoll, *args, **kwargs)
924
925 def default_label(self):
~/.pyenv/versions/miniconda3-4.3.30/envs/tensorflow/lib/python3.7/site-packages/tensorflow_model_analysis/api/model_eval_lib.py in ExtractEvaluateAndWriteResults(examples, eval_shared_model, eval_config, extractors, evaluators, writers, output_path, display_only_data_location, display_only_file_format, slice_spec, write_config, compute_confidence_intervals, min_slice_size, random_seed_for_testing, tensor_adapter_config, schema)
1079 | 'ExtractAndEvaluate' >> ExtractAndEvaluate(
1080 extractors=extractors, evaluators=evaluators)
-> 1081 | 'WriteResults' >> WriteResults(writers=writers))
1082
1083 return beam.pvalue.PDone(examples.pipeline)
~/.pyenv/versions/miniconda3-4.3.30/envs/tensorflow/lib/python3.7/site-packages/apache_beam/pvalue.py in __or__(self, ptransform)
138
139 def __or__(self, ptransform):
--> 140 return self.pipeline.apply(ptransform, self)
141
142
~/.pyenv/versions/miniconda3-4.3.30/envs/tensorflow/lib/python3.7/site-packages/apache_beam/pipeline.py in apply(self, transform, pvalueish, label)
575 if isinstance(transform, ptransform._NamedPTransform):
576 return self.apply(
--> 577 transform.transform, pvalueish, label or transform.label)
578
579 if not isinstance(transform, ptransform.PTransform):
~/.pyenv/versions/miniconda3-4.3.30/envs/tensorflow/lib/python3.7/site-packages/apache_beam/pipeline.py in apply(self, transform, pvalueish, label)
585 try:
586 old_label, transform.label = transform.label, label
--> 587 return self.apply(transform, pvalueish)
588 finally:
589 transform.label = old_label
~/.pyenv/versions/miniconda3-4.3.30/envs/tensorflow/lib/python3.7/site-packages/apache_beam/pipeline.py in apply(self, transform, pvalueish, label)
628 transform.type_check_inputs(pvalueish)
629
--> 630 pvalueish_result = self.runner.apply(transform, pvalueish, self._options)
631
632 if type_options is not None and type_options.pipeline_type_check:
~/.pyenv/versions/miniconda3-4.3.30/envs/tensorflow/lib/python3.7/site-packages/apache_beam/runners/runner.py in apply(self, transform, input, options)
196 m = getattr(self, 'apply_%s' % cls.__name__, None)
197 if m:
--> 198 return m(transform, input, options)
199 raise NotImplementedError(
200 'Execution of [%s] not implemented in runner %s.' % (transform, self))
~/.pyenv/versions/miniconda3-4.3.30/envs/tensorflow/lib/python3.7/site-packages/apache_beam/runners/runner.py in apply_PTransform(self, transform, input, options)
226 def apply_PTransform(self, transform, input, options):
227 # The base case of apply is to call the transform's expand.
--> 228 return transform.expand(input)
229
230 def run_transform(self,
~/.pyenv/versions/miniconda3-4.3.30/envs/tensorflow/lib/python3.7/site-packages/apache_beam/transforms/ptransform.py in expand(self, pcoll)
921 # Might not be a function.
922 pass
--> 923 return self._fn(pcoll, *args, **kwargs)
924
925 def default_label(self):
~/.pyenv/versions/miniconda3-4.3.30/envs/tensorflow/lib/python3.7/site-packages/tensorflow_model_analysis/api/model_eval_lib.py in ExtractAndEvaluate(extracts, extractors, evaluators)
818 for v in evaluators:
819 if v.run_after == x.stage_name:
--> 820 update(evaluation, extracts | v.stage_name >> v.ptransform)
821 for v in evaluators:
822 if v.run_after == extractor.LAST_EXTRACTOR_STAGE_NAME:
~/.pyenv/versions/miniconda3-4.3.30/envs/tensorflow/lib/python3.7/site-packages/apache_beam/pvalue.py in __or__(self, ptransform)
138
139 def __or__(self, ptransform):
--> 140 return self.pipeline.apply(ptransform, self)
141
142
~/.pyenv/versions/miniconda3-4.3.30/envs/tensorflow/lib/python3.7/site-packages/apache_beam/pipeline.py in apply(self, transform, pvalueish, label)
575 if isinstance(transform, ptransform._NamedPTransform):
576 return self.apply(
--> 577 transform.transform, pvalueish, label or transform.label)
578
579 if not isinstance(transform, ptransform.PTransform):
~/.pyenv/versions/miniconda3-4.3.30/envs/tensorflow/lib/python3.7/site-packages/apache_beam/pipeline.py in apply(self, transform, pvalueish, label)
585 try:
586 old_label, transform.label = transform.label, label
--> 587 return self.apply(transform, pvalueish)
588 finally:
589 transform.label = old_label
~/.pyenv/versions/miniconda3-4.3.30/envs/tensorflow/lib/python3.7/site-packages/apache_beam/pipeline.py in apply(self, transform, pvalueish, label)
628 transform.type_check_inputs(pvalueish)
629
--> 630 pvalueish_result = self.runner.apply(transform, pvalueish, self._options)
631
632 if type_options is not None and type_options.pipeline_type_check:
~/.pyenv/versions/miniconda3-4.3.30/envs/tensorflow/lib/python3.7/site-packages/apache_beam/runners/runner.py in apply(self, transform, input, options)
196 m = getattr(self, 'apply_%s' % cls.__name__, None)
197 if m:
--> 198 return m(transform, input, options)
199 raise NotImplementedError(
200 'Execution of [%s] not implemented in runner %s.' % (transform, self))
~/.pyenv/versions/miniconda3-4.3.30/envs/tensorflow/lib/python3.7/site-packages/apache_beam/runners/runner.py in apply_PTransform(self, transform, input, options)
226 def apply_PTransform(self, transform, input, options):
227 # The base case of apply is to call the transform's expand.
--> 228 return transform.expand(input)
229
230 def run_transform(self,
~/.pyenv/versions/miniconda3-4.3.30/envs/tensorflow/lib/python3.7/site-packages/apache_beam/transforms/ptransform.py in expand(self, pcoll)
921 # Might not be a function.
922 pass
--> 923 return self._fn(pcoll, *args, **kwargs)
924
925 def default_label(self):
~/.pyenv/versions/miniconda3-4.3.30/envs/tensorflow/lib/python3.7/site-packages/tensorflow_model_analysis/evaluators/metrics_and_plots_evaluator_v2.py in _EvaluateMetricsAndPlots(extracts, eval_config, eval_shared_models, metrics_key, plots_key, validations_key, schema, random_seed_for_testing)
757 plots_key=plots_key,
758 schema=schema,
--> 759 random_seed_for_testing=random_seed_for_testing))
760
761 for k, v in evaluation.items():
~/.pyenv/versions/miniconda3-4.3.30/envs/tensorflow/lib/python3.7/site-packages/apache_beam/pvalue.py in __or__(self, ptransform)
138
139 def __or__(self, ptransform):
--> 140 return self.pipeline.apply(ptransform, self)
141
142
~/.pyenv/versions/miniconda3-4.3.30/envs/tensorflow/lib/python3.7/site-packages/apache_beam/pipeline.py in apply(self, transform, pvalueish, label)
575 if isinstance(transform, ptransform._NamedPTransform):
576 return self.apply(
--> 577 transform.transform, pvalueish, label or transform.label)
578
579 if not isinstance(transform, ptransform.PTransform):
~/.pyenv/versions/miniconda3-4.3.30/envs/tensorflow/lib/python3.7/site-packages/apache_beam/pipeline.py in apply(self, transform, pvalueish, label)
585 try:
586 old_label, transform.label = transform.label, label
--> 587 return self.apply(transform, pvalueish)
588 finally:
589 transform.label = old_label
~/.pyenv/versions/miniconda3-4.3.30/envs/tensorflow/lib/python3.7/site-packages/apache_beam/pipeline.py in apply(self, transform, pvalueish, label)
628 transform.type_check_inputs(pvalueish)
629
--> 630 pvalueish_result = self.runner.apply(transform, pvalueish, self._options)
631
632 if type_options is not None and type_options.pipeline_type_check:
~/.pyenv/versions/miniconda3-4.3.30/envs/tensorflow/lib/python3.7/site-packages/apache_beam/runners/runner.py in apply(self, transform, input, options)
196 m = getattr(self, 'apply_%s' % cls.__name__, None)
197 if m:
--> 198 return m(transform, input, options)
199 raise NotImplementedError(
200 'Execution of [%s] not implemented in runner %s.' % (transform, self))
~/.pyenv/versions/miniconda3-4.3.30/envs/tensorflow/lib/python3.7/site-packages/apache_beam/runners/runner.py in apply_PTransform(self, transform, input, options)
226 def apply_PTransform(self, transform, input, options):
227 # The base case of apply is to call the transform's expand.
--> 228 return transform.expand(input)
229
230 def run_transform(self,
~/.pyenv/versions/miniconda3-4.3.30/envs/tensorflow/lib/python3.7/site-packages/apache_beam/transforms/ptransform.py in expand(self, pcoll)
921 # Might not be a function.
922 pass
--> 923 return self._fn(pcoll, *args, **kwargs)
924
925 def default_label(self):
~/.pyenv/versions/miniconda3-4.3.30/envs/tensorflow/lib/python3.7/site-packages/tensorflow_model_analysis/evaluators/metrics_and_plots_evaluator_v2.py in _ComputeMetricsAndPlots(extracts, eval_config, metrics_specs, eval_shared_models, metrics_key, plots_key, schema, random_seed_for_testing)
582 if eval_shared_model.model_type == constants.TF_KERAS:
583 keras_specs = keras_util.metrics_specs_from_keras(
--> 584 model_name, eval_shared_model.model_loader)
585 metrics_specs = keras_specs + metrics_specs[:]
586 # TODO(mdreves): Add support for calling keras.evaluate().
~/.pyenv/versions/miniconda3-4.3.30/envs/tensorflow/lib/python3.7/site-packages/tensorflow_model_analysis/evaluators/keras_util.py in metrics_specs_from_keras(model_name, model_loader)
60 # y_true, y_pred as inputs so it can't be calculated via standard inputs so
61 # we remove it.
---> 62 metrics.extend(model.compiled_loss.metrics[1:])
63 metrics.extend(model.compiled_metrics.metrics)
64 metric_names = [m.name for m in metrics]
AttributeError: 'NoneType' object has no attribute 'metrics'
我怀疑这可能是因为我在导出之前没有编译 Keras 模型。TFMA 是否只支持编译模型？
我正在使用tensorflow==2.3.0和tensorflow-model-analysis==0.22.1