def _infer(self, request):
"""Returns JSON for the `vz-line-chart`s for a feature.
Args:
request: A request that should contain 'inference_address', 'model_name',
'model_type, 'model_version', 'model_signature' and 'label_vocab_path'.
Returns:
A list of JSON objects, one for each chart.
"""
vocab_path = request.args.get('label_vocab_path')
if vocab_path:
try:
with tf.gfile.GFile(vocab_path, 'r') as f:
label_vocab = [line.rstrip('\n') for line in f]
except tf.errors.NotFoundError as err:
tf.logging.error('error reading vocab file: %s', err)
label_vocab = []
else:
label_vocab = []
try:
if request.method != 'GET':
tf.logging.error('%s requests are forbidden.', request.method)
return http_util.Respond(request, {'error': 'invalid non-GET request'},
'application/json', code=405)
serving_bundle = inference_utils.ServingBundle(
request.args.get('inference_address'),
request.args.get('model_name'), request.args.get('model_type'),
request.args.get('model_version'),
request.args.get('model_signature'),
request.args.get('use_predict') == 'true',
request.args.get('predict_input_tensor'),
request.args.get('predict_output_tensor'))
indices_to_infer = sorted(self.updated_example_indices)
examples_to_infer = [self.examples[index] for index in indices_to_infer]
# Get inference results proto and combine with indices of inferred
# examples and respond with this data as json.
inference_result_proto = platform_utils.call_servo(
examples_to_infer, serving_bundle)
new_inferences = inference_utils.wrap_inference_results(
inference_result_proto)
infer_json = json_format.MessageToJson(
new_inferences, including_default_value_fields=True)
infer_obj = json.loads(infer_json)
resp = {'indices': indices_to_infer, 'results': infer_obj}
self.updated_example_indices = set()
return http_util.Respond(request, {'inferences': json.dumps(resp),
'vocab': json.dumps(label_vocab)},
'application/json')
except common_utils.InvalidUserInputError as e:
return http_util.Respond(request, {'error': e.message},
'application/json', code=400)
except AbortionError as e:
return http_util.Respond(request, {'error': e.details},
'application/json', code=400)
def test_wrap_inference_results_regression(self):
"""Test wrapping a regression result."""
inference_result_proto = regression_pb2.RegressionResponse()
regression = inference_result_proto.result.regressions.add()
regression.value = 0.45
regression = inference_result_proto.result.regressions.add()
regression.value = 0.55
wrapped = inference_utils.wrap_inference_results(inference_result_proto)
self.assertEqual(2, len(wrapped.regression_result.regressions))
def _infer(self, request):
"""Returns JSON for the `vz-line-chart`s for a feature.
Args:
request: A request that should contain 'inference_address', 'model_name',
'model_type, 'model_version', 'model_signature' and 'label_vocab_path'.
Returns:
A list of JSON objects, one for each chart.
"""
vocab_path = request.args.get('label_vocab_path')
if vocab_path:
try:
with tf.gfile.GFile(vocab_path, 'r') as f:
label_vocab = [line.rstrip('\n') for line in f]
except tf.errors.NotFoundError as err:
tf.logging.error('error reading vocab file: %s', err)
label_vocab = []
else:
label_vocab = []
try:
if request.method != 'GET':
tf.logging.error('%s requests are forbidden.', request.method)
return http_util.Respond(request, {'error': 'invalid non-GET request'},
'application/json', code=405)
serving_bundle = inference_utils.ServingBundle(
request.args.get('inference_address'),
request.args.get('model_name'), request.args.get('model_type'),
request.args.get('model_version'),
request.args.get('model_signature'))
indices_to_infer = sorted(self.updated_example_indices)
examples_to_infer = [self.examples[index] for index in indices_to_infer]
# Get inference results proto and combine with indices of inferred
# examples and respond with this data as json.
inference_result_proto = platform_utils.call_servo(
examples_to_infer, serving_bundle)
new_inferences = inference_utils.wrap_inference_results(
inference_result_proto)
infer_json = json_format.MessageToJson(
new_inferences, including_default_value_fields=True)
infer_obj = json.loads(infer_json)
resp = {'indices': indices_to_infer, 'results': infer_obj}
self.updated_example_indices = set()
return http_util.Respond(request, {'inferences': json.dumps(resp),
'vocab': json.dumps(label_vocab)},
'application/json')
except common_utils.InvalidUserInputError as e:
return http_util.Respond(request, {'error': e.message},
'application/json', code=400)
except AbortionError as e:
return http_util.Respond(request, {'error': e.details},
'application/json', code=400)
def test_wrap_inference_results_classification(self):
"""Test wrapping a classification result."""
inference_result_proto = classification_pb2.ClassificationResponse()
classification = inference_result_proto.result.classifications.add()
inference_class = classification.classes.add()
inference_class.label = 'class_b'
inference_class.score = 0.3
inference_class = classification.classes.add()
inference_class.label = 'class_a'
inference_class.score = 0.7
wrapped = inference_utils.wrap_inference_results(inference_result_proto)
self.assertEqual(1, len(wrapped.classification_result.classifications))
self.assertEqual(
2, len(wrapped.classification_result.classifications[0].classes))
