def testBatchSizeLimit(self):
temp_export_dir = self._getExportDir()
_, export_dir = batch_size_limited_classifier.simple_batch_size_limited_classifier(
None, temp_export_dir)
eval_shared_model = self.createTestEvalSharedModel(
eval_saved_model_path=export_dir, tags=[tf.saved_model.SERVING])
eval_config = config.EvalConfig(model_specs=[config.ModelSpec()])
schema = text_format.Parse(
"""
feature {
name: "classes"
type: BYTES
}
feature {
name: "scores"
type: FLOAT
}
feature {
name: "labels"
type: BYTES
}
""", schema_pb2.Schema())
tfx_io = test_util.InMemoryTFExampleRecord(
schema=schema, raw_record_column_name=constants.BATCHED_INPUT_KEY)
tensor_adapter_config = tensor_adapter.TensorAdapterConfig(
arrow_schema=tfx_io.ArrowSchema(),
tensor_representations=tfx_io.TensorRepresentations())
input_extractor = batched_input_extractor.BatchedInputExtractor(
eval_config)
predict_extractor = batched_predict_extractor_v2.BatchedPredictExtractor(
eval_config=eval_config,
eval_shared_model=eval_shared_model,
tensor_adapter_config=tensor_adapter_config)
examples = []
for _ in range(4):
examples.append(
self._makeExample(classes='first', scores=0.0, labels='third'))
with beam.Pipeline() as pipeline:
predict_extracts = (
pipeline
| 'Create' >> beam.Create(
[e.SerializeToString() for e in examples], reshuffle=False)
| 'BatchExamples' >> tfx_io.BeamSource(batch_size=1)
|
'InputsToExtracts' >> model_eval_lib.BatchedInputsToExtracts()
| input_extractor.stage_name >> input_extractor.ptransform
| predict_extractor.stage_name >> predict_extractor.ptransform)
def check_result(got):
try:
self.assertLen(got, 4)
# We can't verify the actual predictions, but we can verify the keys.
for item in got:
self.assertIn(constants.BATCHED_PREDICTIONS_KEY, item)
except AssertionError as err:
raise util.BeamAssertException(err)
util.assert_that(predict_extracts, check_result, label='result')
def testPredictExtractorWithRegressionModel(self):
temp_export_dir = self._getExportDir()
export_dir, _ = (fixed_prediction_estimator_extra_fields.
simple_fixed_prediction_estimator_extra_fields(
temp_export_dir, None))
eval_config = config.EvalConfig(model_specs=[config.ModelSpec()])
eval_shared_model = self.createTestEvalSharedModel(
eval_saved_model_path=export_dir, tags=[tf.saved_model.SERVING])
schema = text_format.Parse(
"""
feature {
name: "prediction"
type: FLOAT
}
feature {
name: "label"
type: FLOAT
}
feature {
name: "fixed_int"
type: INT
}
feature {
name: "fixed_float"
type: FLOAT
}
feature {
name: "fixed_string"
type: BYTES
}
""", schema_pb2.Schema())
tfx_io = test_util.InMemoryTFExampleRecord(
schema=schema, raw_record_column_name=constants.BATCHED_INPUT_KEY)
tensor_adapter_config = tensor_adapter.TensorAdapterConfig(
arrow_schema=tfx_io.ArrowSchema(),
tensor_representations=tfx_io.TensorRepresentations())
input_extractor = batched_input_extractor.BatchedInputExtractor(
eval_config)
predict_extractor = batched_predict_extractor_v2.BatchedPredictExtractor(
eval_config=eval_config,
eval_shared_model=eval_shared_model,
tensor_adapter_config=tensor_adapter_config)
examples = [
self._makeExample(prediction=0.2,
label=1.0,
fixed_int=1,
fixed_float=1.0,
fixed_string='fixed_string1'),
self._makeExample(prediction=0.8,
label=0.0,
fixed_int=1,
fixed_float=1.0,
fixed_string='fixed_string2'),
self._makeExample(prediction=0.5,
label=0.0,
fixed_int=2,
fixed_float=1.0,
fixed_string='fixed_string3')
]
with beam.Pipeline() as pipeline:
# pylint: disable=no-value-for-parameter
result = (
pipeline
| 'Create' >> beam.Create(
[e.SerializeToString() for e in examples], reshuffle=False)
| 'BatchExamples' >> tfx_io.BeamSource(batch_size=3)
|
'InputsToExtracts' >> model_eval_lib.BatchedInputsToExtracts()
| input_extractor.stage_name >> input_extractor.ptransform
| predict_extractor.stage_name >> predict_extractor.ptransform)
# pylint: enable=no-value-for-parameter
def check_result(got):
try:
self.assertLen(got, 1)
self.assertIn(constants.BATCHED_PREDICTIONS_KEY, got[0])
expected_preds = [0.2, 0.8, 0.5]
self.assertAlmostEqual(
got[0][constants.BATCHED_PREDICTIONS_KEY],
expected_preds)
except AssertionError as err:
raise util.BeamAssertException(err)
util.assert_that(result, check_result, label='result')
def testPredictExtractorWithSequentialKerasModel(self):
# Note that the input will be called 'test_input'
model = tf.keras.models.Sequential([
tf.keras.layers.Dense(1,
activation=tf.nn.sigmoid,
input_shape=(2, ),
name='test')
])
model.compile(optimizer=tf.keras.optimizers.Adam(lr=.001),
loss=tf.keras.losses.binary_crossentropy,
metrics=['accuracy'])
train_features = {'test_input': [[0.0, 0.0], [1.0, 1.0]]}
labels = [[1], [0]]
example_weights = [1.0, 0.5]
dataset = tf.data.Dataset.from_tensor_slices(
(train_features, labels, example_weights))
dataset = dataset.shuffle(buffer_size=1).repeat().batch(2)
model.fit(dataset, steps_per_epoch=1)
export_dir = self._getExportDir()
model.save(export_dir, save_format='tf')
eval_config = config.EvalConfig(model_specs=[config.ModelSpec()])
eval_shared_model = self.createTestEvalSharedModel(
eval_saved_model_path=export_dir, tags=[tf.saved_model.SERVING])
schema = text_format.Parse(
"""
tensor_representation_group {
key: ""
value {
tensor_representation {
key: "test"
value {
dense_tensor {
column_name: "test"
shape { dim { size: 2 } }
}
}
}
}
}
feature {
name: "test"
type: FLOAT
}
feature {
name: "non_model_feature"
type: INT
}
""", schema_pb2.Schema())
tfx_io = test_util.InMemoryTFExampleRecord(
schema=schema, raw_record_column_name=constants.BATCHED_INPUT_KEY)
tensor_adapter_config = tensor_adapter.TensorAdapterConfig(
arrow_schema=tfx_io.ArrowSchema(),
tensor_representations=tfx_io.TensorRepresentations())
input_extractor = batched_input_extractor.BatchedInputExtractor(
eval_config)
predict_extractor = batched_predict_extractor_v2.BatchedPredictExtractor(
eval_config=eval_config,
eval_shared_model=eval_shared_model,
tensor_adapter_config=tensor_adapter_config)
# Notice that the features are 'test' but the model expects 'test_input'.
# This tests that the PredictExtractor properly handles this case.
examples = [
self._makeExample(
test=[0.0,
0.0], non_model_feature=0), # should be ignored by model
self._makeExample(
test=[1.0,
1.0], non_model_feature=1), # should be ignored by model
]
with beam.Pipeline() as pipeline:
# pylint: disable=no-value-for-parameter
result = (
pipeline
| 'Create' >> beam.Create(
[e.SerializeToString() for e in examples], reshuffle=False)
| 'BatchExamples' >> tfx_io.BeamSource(batch_size=2)
|
'InputsToExtracts' >> model_eval_lib.BatchedInputsToExtracts()
| input_extractor.stage_name >> input_extractor.ptransform
| predict_extractor.stage_name >> predict_extractor.ptransform)
# pylint: enable=no-value-for-parameter
def check_result(got):
try:
self.assertLen(got, 1)
# We can't verify the actual predictions, but we can verify the keys.
for item in got:
self.assertIn(constants.BATCHED_PREDICTIONS_KEY, item)
except AssertionError as err:
raise util.BeamAssertException(err)
util.assert_that(result, check_result, label='result')
def testWriteValidationResults(self):
model_dir, baseline_dir = self._getExportDir(), self._getBaselineDir()
eval_shared_model = self._build_keras_model(model_dir, mul=0)
baseline_eval_shared_model = self._build_keras_model(baseline_dir,
mul=1)
validations_file = os.path.join(self._getTempDir(),
constants.VALIDATIONS_KEY)
schema = text_format.Parse(
"""
tensor_representation_group {
key: ""
value {
tensor_representation {
key: "input"
value {
dense_tensor {
column_name: "input"
shape { dim { size: 1 } }
}
}
}
}
}
feature {
name: "input"
type: FLOAT
}
feature {
name: "label"
type: FLOAT
}
feature {
name: "example_weight"
type: FLOAT
}
feature {
name: "extra_feature"
type: BYTES
}
""", schema_pb2.Schema())
tfx_io = test_util.InMemoryTFExampleRecord(
schema=schema, raw_record_column_name=constants.ARROW_INPUT_COLUMN)
tensor_adapter_config = tensor_adapter.TensorAdapterConfig(
arrow_schema=tfx_io.ArrowSchema(),
tensor_representations=tfx_io.TensorRepresentations())
examples = [
self._makeExample(input=0.0,
label=1.0,
example_weight=1.0,
extra_feature='non_model_feature'),
self._makeExample(input=1.0,
label=0.0,
example_weight=0.5,
extra_feature='non_model_feature'),
]
eval_config = config.EvalConfig(
model_specs=[
config.ModelSpec(name='candidate',
label_key='label',
example_weight_key='example_weight'),
config.ModelSpec(name='baseline',
label_key='label',
example_weight_key='example_weight',
is_baseline=True)
],
slicing_specs=[config.SlicingSpec()],
metrics_specs=[
config.MetricsSpec(
metrics=[
config.MetricConfig(
class_name='WeightedExampleCount',
# 1.5 < 1, NOT OK.
threshold=config.MetricThreshold(
value_threshold=config.GenericValueThreshold(
upper_bound={'value': 1}))),
config.MetricConfig(
class_name='ExampleCount',
# 2 > 10, NOT OK.
threshold=config.MetricThreshold(
value_threshold=config.GenericValueThreshold(
lower_bound={'value': 10}))),
config.MetricConfig(
class_name='MeanLabel',
# 0 > 0 and 0 > 0%?: NOT OK.
threshold=config.MetricThreshold(
change_threshold=config.GenericChangeThreshold(
direction=config.MetricDirection.
HIGHER_IS_BETTER,
relative={'value': 0},
absolute={'value': 0}))),
config.MetricConfig(
# MeanPrediction = (0+0)/(1+0.5) = 0
class_name='MeanPrediction',
# -.01 < 0 < .01, OK.
# Diff% = -.333/.333 = -100% < -99%, OK.
# Diff = 0 - .333 = -.333 < 0, OK.
threshold=config.MetricThreshold(
value_threshold=config.GenericValueThreshold(
upper_bound={'value': .01},
lower_bound={'value': -.01}),
change_threshold=config.GenericChangeThreshold(
direction=config.MetricDirection.
LOWER_IS_BETTER,
relative={'value': -.99},
absolute={'value': 0})))
],
model_names=['candidate', 'baseline']),
],
options=config.Options(
disabled_outputs={'values': ['eval_config.json']}),
)
slice_spec = [
slicer.SingleSliceSpec(spec=s) for s in eval_config.slicing_specs
]
eval_shared_models = {
'candidate': eval_shared_model,
'baseline': baseline_eval_shared_model
}
extractors = [
batched_input_extractor.BatchedInputExtractor(eval_config),
batched_predict_extractor_v2.BatchedPredictExtractor(
eval_shared_model=eval_shared_models,
eval_config=eval_config,
tensor_adapter_config=tensor_adapter_config),
unbatch_extractor.UnbatchExtractor(),
slice_key_extractor.SliceKeyExtractor(slice_spec=slice_spec)
]
evaluators = [
metrics_and_plots_evaluator_v2.MetricsAndPlotsEvaluator(
eval_config=eval_config, eval_shared_model=eval_shared_models)
]
output_paths = {
constants.VALIDATIONS_KEY: validations_file,
}
writers = [
metrics_plots_and_validations_writer.
MetricsPlotsAndValidationsWriter(output_paths,
add_metrics_callbacks=[])
]
with beam.Pipeline() as pipeline:
# pylint: disable=no-value-for-parameter
_ = (
pipeline
| 'Create' >> beam.Create(
[e.SerializeToString() for e in examples])
| 'BatchExamples' >> tfx_io.BeamSource()
|
'InputsToExtracts' >> model_eval_lib.BatchedInputsToExtracts()
| 'ExtractEvaluate' >> model_eval_lib.ExtractAndEvaluate(
extractors=extractors, evaluators=evaluators)
|
'WriteResults' >> model_eval_lib.WriteResults(writers=writers))
# pylint: enable=no-value-for-parameter
validation_result = model_eval_lib.load_validation_result(
os.path.dirname(validations_file))
expected_validations = [
text_format.Parse(
"""
metric_key {
name: "weighted_example_count"
model_name: "candidate"
}
metric_threshold {
value_threshold {
upper_bound {
value: 1.0
}
}
}
metric_value {
double_value {
value: 1.5
}
}
""", validation_result_pb2.ValidationFailure()),
text_format.Parse(
"""
metric_key {
name: "example_count"
}
metric_threshold {
value_threshold {
lower_bound {
value: 10.0
}
}
}
metric_value {
double_value {
value: 2.0
}
}
""", validation_result_pb2.ValidationFailure()),
text_format.Parse(
"""
metric_key {
name: "mean_label"
model_name: "candidate"
is_diff: true
}
metric_threshold {
change_threshold {
absolute {
value: 0.0
}
relative {
value: 0.0
}
direction: HIGHER_IS_BETTER
}
}
metric_value {
double_value {
value: 0.0
}
}
""", validation_result_pb2.ValidationFailure()),
]
self.assertFalse(validation_result.validation_ok)
self.assertLen(validation_result.metric_validations_per_slice, 1)
self.assertCountEqual(
expected_validations,
validation_result.metric_validations_per_slice[0].failures)
def testPredictExtractorWithMultiModels(self):
temp_export_dir = self._getExportDir()
export_dir1, _ = multi_head.simple_multi_head(temp_export_dir, None)
export_dir2, _ = multi_head.simple_multi_head(temp_export_dir, None)
eval_config = config.EvalConfig(model_specs=[
config.ModelSpec(name='model1'),
config.ModelSpec(name='model2')
])
eval_shared_model1 = self.createTestEvalSharedModel(
eval_saved_model_path=export_dir1, tags=[tf.saved_model.SERVING])
eval_shared_model2 = self.createTestEvalSharedModel(
eval_saved_model_path=export_dir2, tags=[tf.saved_model.SERVING])
schema = text_format.Parse(
"""
feature {
name: "age"
type: FLOAT
}
feature {
name: "langauge"
type: BYTES
}
feature {
name: "english_label"
type: FLOAT
}
feature {
name: "chinese_label"
type: FLOAT
}
feature {
name: "other_label"
type: FLOAT
}
""", schema_pb2.Schema())
tfx_io = test_util.InMemoryTFExampleRecord(
schema=schema, raw_record_column_name=constants.BATCHED_INPUT_KEY)
tensor_adapter_config = tensor_adapter.TensorAdapterConfig(
arrow_schema=tfx_io.ArrowSchema(),
tensor_representations=tfx_io.TensorRepresentations())
input_extractor = batched_input_extractor.BatchedInputExtractor(
eval_config)
predict_extractor = batched_predict_extractor_v2.BatchedPredictExtractor(
eval_config=eval_config,
eval_shared_model={
'model1': eval_shared_model1,
'model2': eval_shared_model2
},
tensor_adapter_config=tensor_adapter_config)
examples = [
self._makeExample(age=1.0,
language='english',
english_label=1.0,
chinese_label=0.0,
other_label=0.0),
self._makeExample(age=1.0,
language='chinese',
english_label=0.0,
chinese_label=1.0,
other_label=0.0),
self._makeExample(age=2.0,
language='english',
english_label=1.0,
chinese_label=0.0,
other_label=0.0),
self._makeExample(age=2.0,
language='other',
english_label=0.0,
chinese_label=1.0,
other_label=1.0)
]
with beam.Pipeline() as pipeline:
# pylint: disable=no-value-for-parameter
result = (
pipeline
| 'Create' >> beam.Create(
[e.SerializeToString() for e in examples], reshuffle=False)
| 'BatchExamples' >> tfx_io.BeamSource(batch_size=4)
|
'InputsToExtracts' >> model_eval_lib.BatchedInputsToExtracts()
| input_extractor.stage_name >> input_extractor.ptransform
| predict_extractor.stage_name >> predict_extractor.ptransform)
# pylint: enable=no-value-for-parameter
def check_result(got):
try:
self.assertLen(got, 1)
for item in got:
# We can't verify the actual predictions, but we can verify the keys
self.assertIn(constants.BATCHED_PREDICTIONS_KEY, item)
for pred in item[constants.BATCHED_PREDICTIONS_KEY]:
for model_name in ('model1', 'model2'):
self.assertIn(model_name, pred)
for output_name in ('chinese_head',
'english_head',
'other_head'):
for pred_key in ('logistic',
'probabilities',
'all_classes'):
self.assertIn(
output_name + '/' + pred_key,
pred[model_name])
except AssertionError as err:
raise util.BeamAssertException(err)
util.assert_that(result, check_result, label='result')
def testBatchedInputExtractor(self):
model_spec = config.ModelSpec(
label_key='label', example_weight_key='example_weight')
eval_config = config.EvalConfig(model_specs=[model_spec])
input_extractor = batched_input_extractor.BatchedInputExtractor(eval_config)
schema = text_format.Parse(
"""
feature {
name: "label"
type: FLOAT
}
feature {
name: "example_weight"
type: FLOAT
}
feature {
name: "fixed_int"
type: INT
}
feature {
name: "fixed_float"
type: FLOAT
}
feature {
name: "fixed_string"
type: BYTES
}
""", schema_pb2.Schema())
tfx_io = test_util.InMemoryTFExampleRecord(
schema=schema, raw_record_column_name=constants.ARROW_INPUT_COLUMN)
examples = [
self._makeExample(
label=1.0,
example_weight=0.5,
fixed_int=1,
fixed_float=1.0,
fixed_string='fixed_string1'),
self._makeExample(
label=0.0,
example_weight=0.0,
fixed_int=1,
fixed_float=1.0,
fixed_string='fixed_string2'),
self._makeExample(
label=0.0,
example_weight=1.0,
fixed_int=2,
fixed_float=0.0,
fixed_string='fixed_string3')
]
with beam.Pipeline() as pipeline:
# pylint: disable=no-value-for-parameter
result = (
pipeline
| 'Create' >> beam.Create([e.SerializeToString() for e in examples],
reshuffle=False)
| 'BatchExamples' >> tfx_io.BeamSource(batch_size=3)
| 'InputsToExtracts' >> model_eval_lib.BatchedInputsToExtracts()
| input_extractor.stage_name >> input_extractor.ptransform)
# pylint: enable=no-value-for-parameter
def check_result(got):
try:
self.assertLen(got, 1)
self.assertDictElementsAlmostEqual(got[0][constants.FEATURES_KEY][0],
{
'fixed_int': np.array([1]),
'fixed_float': np.array([1.0]),
})
self.assertEqual(got[0][constants.FEATURES_KEY][0]['fixed_string'],
np.array([b'fixed_string1']))
self.assertAlmostEqual(got[0][constants.LABELS_KEY][0],
np.array([1.0]))
self.assertAlmostEqual(got[0][constants.EXAMPLE_WEIGHTS_KEY][0],
np.array([0.5]))
self.assertDictElementsAlmostEqual(got[0][constants.FEATURES_KEY][1],
{
'fixed_int': np.array([1]),
'fixed_float': np.array([1.0]),
})
self.assertEqual(got[0][constants.FEATURES_KEY][1]['fixed_string'],
np.array([b'fixed_string2']))
self.assertAlmostEqual(got[0][constants.LABELS_KEY][1],
np.array([0.0]))
self.assertAlmostEqual(got[0][constants.EXAMPLE_WEIGHTS_KEY][1],
np.array([0.0]))
self.assertDictElementsAlmostEqual(got[0][constants.FEATURES_KEY][2],
{
'fixed_int': np.array([2]),
'fixed_float': np.array([0.0]),
})
self.assertEqual(got[0][constants.FEATURES_KEY][2]['fixed_string'],
np.array([b'fixed_string3']))
self.assertAlmostEqual(got[0][constants.LABELS_KEY][2],
np.array([0.0]))
self.assertAlmostEqual(got[0][constants.EXAMPLE_WEIGHTS_KEY][2],
np.array([1.0]))
except AssertionError as err:
raise util.BeamAssertException(err)
util.assert_that(result, check_result, label='result')
def testBatchedInputExtractorMultiModel(self):
model_spec1 = config.ModelSpec(name='model1',
label_key='label',
example_weight_key='example_weight',
prediction_key='fixed_float')
model_spec2 = config.ModelSpec(name='model2',
label_keys={
'output1': 'label1',
'output2': 'label2'
},
example_weight_keys={
'output1': 'example_weight1',
'output2': 'example_weight2'
},
prediction_keys={
'output1': 'fixed_float',
'output2': 'fixed_float'
})
eval_config = config.EvalConfig(model_specs=[model_spec1, model_spec2])
input_extractor = batched_input_extractor.BatchedInputExtractor(
eval_config)
schema = text_format.Parse(
"""
feature {
name: "label"
type: FLOAT
}
feature {
name: "label1"
type: FLOAT
}
feature {
name: "label2"
type: FLOAT
}
feature {
name: "example_weight"
type: FLOAT
}
feature {
name: "example_weight1"
type: FLOAT
}
feature {
name: "example_weight2"
type: FLOAT
}
feature {
name: "fixed_int"
type: INT
}
feature {
name: "fixed_float"
type: FLOAT
}
feature {
name: "fixed_string"
type: BYTES
}
""", schema_pb2.Schema())
tfx_io = test_util.InMemoryTFExampleRecord(
schema=schema, raw_record_column_name=constants.BATCHED_INPUT_KEY)
examples = [
self._makeExample(label=1.0,
label1=1.0,
label2=0.0,
example_weight=0.5,
example_weight1=0.5,
example_weight2=0.5,
fixed_int=1,
fixed_float=1.0,
fixed_string='fixed_string1'),
self._makeExample(label=1.0,
label1=1.0,
label2=1.0,
example_weight=0.0,
example_weight1=0.0,
example_weight2=1.0,
fixed_int=1,
fixed_float=2.0,
fixed_string='fixed_string2'),
]
with beam.Pipeline() as pipeline:
# pylint: disable=no-value-for-parameter
result = (
pipeline
| 'Create' >> beam.Create(
[e.SerializeToString() for e in examples], reshuffle=False)
| 'BatchExamples' >> tfx_io.BeamSource(batch_size=2)
|
'InputsToExtracts' >> model_eval_lib.BatchedInputsToExtracts()
| input_extractor.stage_name >> input_extractor.ptransform)
# pylint: enable=no-value-for-parameter
def check_result(got):
try:
self.assertLen(got, 1)
self.assertDictElementsAlmostEqual(
got[0][constants.BATCHED_FEATURES_KEY][0], {
'fixed_int': np.array([1]),
})
self.assertEqual(
got[0][constants.BATCHED_FEATURES_KEY][0]
['fixed_string'], np.array([b'fixed_string1']))
for model_name in ('model1', 'model2'):
self.assertIn(model_name,
got[0][constants.BATCHED_LABELS_KEY][0])
self.assertIn(
model_name,
got[0][constants.BATCHED_EXAMPLE_WEIGHTS_KEY][0])
self.assertIn(
model_name,
got[0][constants.BATCHED_PREDICTIONS_KEY][0])
self.assertAlmostEqual(
got[0][constants.BATCHED_LABELS_KEY][0]['model1'],
np.array([1.0]))
self.assertDictElementsAlmostEqual(
got[0][constants.BATCHED_LABELS_KEY][0]['model2'], {
'output1': np.array([1.0]),
'output2': np.array([0.0])
})
self.assertAlmostEqual(
got[0][constants.BATCHED_EXAMPLE_WEIGHTS_KEY][0]
['model1'], np.array([0.5]))
self.assertDictElementsAlmostEqual(
got[0][constants.BATCHED_EXAMPLE_WEIGHTS_KEY][0]
['model2'], {
'output1': np.array([0.5]),
'output2': np.array([0.5])
})
self.assertAlmostEqual(
got[0][constants.BATCHED_PREDICTIONS_KEY][0]['model1'],
np.array([1.0]))
self.assertDictElementsAlmostEqual(
got[0][constants.BATCHED_PREDICTIONS_KEY][0]['model2'],
{
'output1': np.array([1.0]),
'output2': np.array([1.0])
})
self.assertDictElementsAlmostEqual(
got[0][constants.BATCHED_FEATURES_KEY][1], {
'fixed_int': np.array([1]),
})
self.assertEqual(
got[0][constants.BATCHED_FEATURES_KEY][1]
['fixed_string'], np.array([b'fixed_string2']))
for model_name in ('model1', 'model2'):
self.assertIn(model_name,
got[0][constants.BATCHED_LABELS_KEY][1])
self.assertIn(
model_name,
got[0][constants.BATCHED_EXAMPLE_WEIGHTS_KEY][1])
self.assertIn(
model_name,
got[0][constants.BATCHED_PREDICTIONS_KEY][1])
self.assertAlmostEqual(
got[0][constants.BATCHED_LABELS_KEY][1]['model1'],
np.array([1.0]))
self.assertDictElementsAlmostEqual(
got[0][constants.BATCHED_LABELS_KEY][1]['model2'], {
'output1': np.array([1.0]),
'output2': np.array([1.0])
})
self.assertAlmostEqual(
got[0][constants.BATCHED_EXAMPLE_WEIGHTS_KEY][1]
['model1'], np.array([0.0]))
self.assertDictElementsAlmostEqual(
got[0][constants.BATCHED_EXAMPLE_WEIGHTS_KEY][1]
['model2'], {
'output1': np.array([0.0]),
'output2': np.array([1.0])
})
self.assertAlmostEqual(
got[0][constants.BATCHED_PREDICTIONS_KEY][1]['model1'],
np.array([2.0]))
self.assertDictElementsAlmostEqual(
got[0][constants.BATCHED_PREDICTIONS_KEY][1]['model2'],
{
'output1': np.array([2.0]),
'output2': np.array([2.0])
})
except AssertionError as err:
raise util.BeamAssertException(err)
util.assert_that(result, check_result, label='result')
def testBatchedInputExtractor(self, label):
model_spec = config.ModelSpec(
label_key=label, example_weight_key='example_weight')
eval_config = config.EvalConfig(model_specs=[model_spec])
input_extractor = batched_input_extractor.BatchedInputExtractor(eval_config)
label_feature = ''
if label is not None:
label_feature = """
feature {
name: "%s"
type: FLOAT
}
""" % label
schema = text_format.Parse(
label_feature + """
feature {
name: "example_weight"
type: FLOAT
}
feature {
name: "fixed_int"
type: INT
}
feature {
name: "fixed_float"
type: FLOAT
}
feature {
name: "fixed_string"
type: BYTES
}
""", schema_pb2.Schema())
tfx_io = test_util.InMemoryTFExampleRecord(
schema=schema, raw_record_column_name=constants.ARROW_INPUT_COLUMN)
def maybe_add_key(d, key, value):
if key is not None:
d[key] = value
return d
example_kwargs = [
maybe_add_key(
{
'example_weight': 0.5,
'fixed_int': 1,
'fixed_float': 1.0,
'fixed_string': 'fixed_string1'
}, label, 1.0),
maybe_add_key(
{
'example_weight': 0.0,
'fixed_int': 1,
'fixed_float': 1.0,
'fixed_string': 'fixed_string2'
}, label, 0.0),
maybe_add_key(
{
'example_weight': 1.0,
'fixed_int': 2,
'fixed_float': 0.0,
'fixed_string': 'fixed_string3'
}, label, 0.0),
]
with beam.Pipeline() as pipeline:
# pylint: disable=no-value-for-parameter
result = (
pipeline
| 'Create' >> beam.Create([
self._makeExample(**kwargs).SerializeToString()
for kwargs in example_kwargs
],
reshuffle=False)
| 'BatchExamples' >> tfx_io.BeamSource(batch_size=3)
| 'InputsToExtracts' >> model_eval_lib.BatchedInputsToExtracts()
| input_extractor.stage_name >> input_extractor.ptransform)
# pylint: enable=no-value-for-parameter
def check_result(got):
try:
self.assertLen(got, 1)
self.assertDictElementsAlmostEqual(
got[0][constants.FEATURES_KEY][0],
maybe_add_key(
{
'fixed_int': np.array([1]),
'fixed_float': np.array([1.0]),
'example_weight': np.array([0.5]),
}, label, np.array([1.0])))
self.assertEqual(got[0][constants.FEATURES_KEY][0]['fixed_string'],
np.array([b'fixed_string1']))
self.assertAlmostEqual(got[0][constants.LABELS_KEY][0],
np.array([1.0]) if label is not None else None)
self.assertAlmostEqual(got[0][constants.EXAMPLE_WEIGHTS_KEY][0],
np.array([0.5]))
self.assertDictElementsAlmostEqual(
got[0][constants.FEATURES_KEY][1],
maybe_add_key(
{
'fixed_int': np.array([1]),
'fixed_float': np.array([1.0]),
'example_weight': np.array([0.0]),
}, label, np.array([0.0])))
self.assertEqual(got[0][constants.FEATURES_KEY][1]['fixed_string'],
np.array([b'fixed_string2']))
self.assertAlmostEqual(got[0][constants.LABELS_KEY][1],
np.array([0.0]) if label is not None else None)
self.assertAlmostEqual(got[0][constants.EXAMPLE_WEIGHTS_KEY][1],
np.array([0.0]))
self.assertDictElementsAlmostEqual(
got[0][constants.FEATURES_KEY][2],
maybe_add_key(
{
'fixed_int': np.array([2]),
'fixed_float': np.array([0.0]),
'example_weight': np.array([1.0]),
}, label, np.array([0.0])))
self.assertEqual(got[0][constants.FEATURES_KEY][2]['fixed_string'],
np.array([b'fixed_string3']))
self.assertAlmostEqual(got[0][constants.LABELS_KEY][2],
np.array([0.0]) if label is not None else None)
self.assertAlmostEqual(got[0][constants.EXAMPLE_WEIGHTS_KEY][2],
np.array([1.0]))
except AssertionError as err:
raise util.BeamAssertException(err)
util.assert_that(result, check_result, label='result')
def testBatchSizeLimitWithKerasModel(self):
input1 = tf.keras.layers.Input(shape=(1, ),
batch_size=1,
name='input1')
input2 = tf.keras.layers.Input(shape=(1, ),
batch_size=1,
name='input2')
inputs = [input1, input2]
input_layer = tf.keras.layers.concatenate(inputs)
def add_1(tensor):
return tf.add_n([tensor, tf.constant(1.0, shape=(1, 2))])
assert_layer = tf.keras.layers.Lambda(add_1)(input_layer)
model = tf.keras.models.Model(inputs, assert_layer)
model.compile(optimizer=tf.keras.optimizers.Adam(lr=.001),
loss=tf.keras.losses.binary_crossentropy,
metrics=['accuracy'])
export_dir = self._getExportDir()
model.save(export_dir, save_format='tf')
eval_config = config.EvalConfig(model_specs=[config.ModelSpec()])
eval_shared_model = self.createTestEvalSharedModel(
eval_saved_model_path=export_dir, tags=[tf.saved_model.SERVING])
schema = text_format.Parse(
"""
tensor_representation_group {
key: ""
value {
tensor_representation {
key: "input1"
value {
dense_tensor {
column_name: "input1"
shape { dim { size: 1 } }
}
}
}
tensor_representation {
key: "input2"
value {
dense_tensor {
column_name: "input2"
shape { dim { size: 1 } }
}
}
}
}
}
feature {
name: "input1"
type: FLOAT
}
feature {
name: "input2"
type: FLOAT
}
""", schema_pb2.Schema())
tfx_io = test_util.InMemoryTFExampleRecord(
schema=schema, raw_record_column_name=constants.BATCHED_INPUT_KEY)
tensor_adapter_config = tensor_adapter.TensorAdapterConfig(
arrow_schema=tfx_io.ArrowSchema(),
tensor_representations=tfx_io.TensorRepresentations())
input_extractor = batched_input_extractor.BatchedInputExtractor(
eval_config)
predict_extractor = batched_predict_extractor_v2.BatchedPredictExtractor(
eval_config=eval_config,
eval_shared_model=eval_shared_model,
tensor_adapter_config=tensor_adapter_config)
examples = []
for _ in range(4):
examples.append(self._makeExample(input1=0.0, input2=1.0))
with beam.Pipeline() as pipeline:
predict_extracts = (
pipeline
| 'Create' >> beam.Create(
[e.SerializeToString() for e in examples], reshuffle=False)
| 'BatchExamples' >> tfx_io.BeamSource(batch_size=1)
|
'InputsToExtracts' >> model_eval_lib.BatchedInputsToExtracts()
| input_extractor.stage_name >> input_extractor.ptransform
| predict_extractor.stage_name >> predict_extractor.ptransform)
# pylint: enable=no-value-for-parameter
def check_result(got):
try:
self.assertLen(got, 4)
# We can't verify the actual predictions, but we can verify the keys.
for item in got:
self.assertIn(constants.BATCHED_PREDICTIONS_KEY, item)
except AssertionError as err:
raise util.BeamAssertException(err)
util.assert_that(predict_extracts, check_result, label='result')
def testPredictExtractorWithMultiClassModel(self):
temp_export_dir = self._getExportDir()
export_dir, _ = dnn_classifier.simple_dnn_classifier(temp_export_dir,
None,
n_classes=3)
eval_config = config.EvalConfig(model_specs=[config.ModelSpec()])
eval_shared_model = self.createTestEvalSharedModel(
eval_saved_model_path=export_dir, tags=[tf.saved_model.SERVING])
schema = text_format.Parse(
"""
feature {
name: "age"
type: FLOAT
}
feature {
name: "langauge"
type: BYTES
}
feature {
name: "label"
type: INT
}
""", schema_pb2.Schema())
tfx_io = test_util.InMemoryTFExampleRecord(
schema=schema, raw_record_column_name=constants.ARROW_INPUT_COLUMN)
tensor_adapter_config = tensor_adapter.TensorAdapterConfig(
arrow_schema=tfx_io.ArrowSchema(),
tensor_representations=tfx_io.TensorRepresentations())
input_extractor = batched_input_extractor.BatchedInputExtractor(
eval_config)
predict_extractor = batched_predict_extractor_v2.BatchedPredictExtractor(
eval_config=eval_config,
eval_shared_model=eval_shared_model,
tensor_adapter_config=tensor_adapter_config)
examples = [
self._makeExample(age=1.0, language='english', label=0),
self._makeExample(age=2.0, language='chinese', label=1),
self._makeExample(age=3.0, language='english', label=2),
self._makeExample(age=4.0, language='chinese', label=1),
]
with beam.Pipeline() as pipeline:
# pylint: disable=no-value-for-parameter
result = (
pipeline
| 'Create' >> beam.Create(
[e.SerializeToString() for e in examples], reshuffle=False)
| 'BatchExamples' >> tfx_io.BeamSource(batch_size=4)
|
'InputsToExtracts' >> model_eval_lib.BatchedInputsToExtracts()
| input_extractor.stage_name >> input_extractor.ptransform
| predict_extractor.stage_name >> predict_extractor.ptransform)
# pylint: enable=no-value-for-parameter
def check_result(got):
try:
self.assertLen(got, 1)
# We can't verify the actual predictions, but we can verify the keys.
for item in got:
self.assertIn(constants.PREDICTIONS_KEY, item)
for pred in item[constants.PREDICTIONS_KEY]:
for pred_key in ('probabilities', 'all_classes'):
self.assertIn(pred_key, pred)
except AssertionError as err:
raise util.BeamAssertException(err)
util.assert_that(result, check_result, label='result')
