def sub_adapter_job(self, tag: str, sub_adapter: ModuleServiceAdapterABC, job: JobDescriptor):
new_service_descriptor = ServiceDescriptor(sub_adapter.service)
new_job = JobDescriptor(new_service_descriptor)
item_count = 0
for job_item in job:
# Just pass the inputs on directly to the subtasks.
new_job_item = {}
new_job_item['input_type'] = job_item['input_type']
new_job_item['input_url'] = job_item['input_url']
output_type = job_item['output_type']
new_job_item['output_type'] = output_type
# Transform the output URL so we can get separate output files for each sub-adapter
# That way we can assemble the various parts at the end using these separate URLs.
if output_type == 'file_url_put':
output_url = job_item['output_url']
sub_adapter_output_url = self.transform_output_url(tag, item_count, output_url)
else:
raise RuntimeError("Bad output type %s for job %s" % (output_type, self))
new_job_item['output_url'] = sub_adapter_output_url
new_job.append_job_item(new_job_item)
item_count += 1
return new_job
def test_jobs():
print()
setup_logging()
init_test_jobs()
test_jobs = JobDescriptor.get_test_jobs(ontology.DOCUMENT_SUMMARIZER_ID)
for job in test_jobs:
service_id = 0
if str(job) != "NO_JOB":
service_id = ontology.DOCUMENT_SUMMARIZER_ID
job_parameters = {
'input_type': 'file',
'input_url': 'http://test.com/inputs/test_input.txt',
'output_type': 'file_url_put',
'output_url': 'test_output.txt'
}
job_parameters_2 = {
'input_type': 'file',
'input_url': 'http://test.com/inputs/test_input.txt',
'output_type': 'file_url_put',
'output_url': 'test_output.txt'
}
service_id = ontology.DOCUMENT_SUMMARIZER_ID
# Create a new job descriptor with four sets of parameters.
job_list = [
job_parameters, job_parameters, job_parameters, job_parameters
]
new_job = JobDescriptor(ServiceDescriptor(service_id), job_list)
file_count = 0
for job_item in new_job:
if job_item['input_type'] == 'file':
file_count += 1
else:
file_count = 0
assert (file_count == 4)
# Cover and test iteration and list item retrieval and length.
new_job[0] = job_parameters_2
assert (new_job[0] == job_parameters_2)
job_count = len(new_job)
del new_job[1]
assert (len(new_job) == job_count - 1)
# Test equality and string conversion functions.
last_job = new_job
assert (last_job == new_job)
assert (str(last_job) == str(new_job))
test_jobs.append(new_job)
total_jobs = len(test_jobs)
test_jobs[0] = new_job
del test_jobs[total_jobs - 1]
# Check the string conversion with no ServiceDescriptor...
new_job = JobDescriptor(None, [job_parameters])
assert (str(new_job) != "")
async def perform(service_node_id=None, job_params=None, context=None):
service_descriptor = ServiceDescriptor(service_node_id)
job = JobDescriptor(service_descriptor, job_params)
app = context
result = await perform_job(app, job)
logging.debug('Result of perform was %s', result)
return result
def test_tensorflow_mnist_adapter(app):
setup_logging()
log.debug("Testing Tensorflow NNIST Adapter")
# Setup a test job for classifying a test mnist image. The test is a 28 x 28 image of a 7 which
# has been flattened into a single float 784 element vector format as required by the tensorflow
# example (see mnist_seven_image definition above).
job_parameters = {
'input_type': 'attached',
'input_data': {
'images': [mnist_seven_image],
},
'output_type': 'attached',
}
# Get the service for an MNIST classifier. A service identifies a unique service provided by
# SingularityNET and is part of the ontology.
ontology = app['ontology']
mnist_service = ontology.get_service(MNIST_CLASSIFIER_ID)
# Create the Tensorflow MNIST service adapter.
mnist_service_adapter = TensorflowMNIST(app, mnist_service)
# Create a service descriptor. These are post-contract negotiated descriptors that may include
# other parameters like quality of service, input and output formats, etc.
mnist_service_descriptor = ServiceDescriptor(MNIST_CLASSIFIER_ID)
# Create a new job descriptor with a single set of parameters for the test image of a 7 in the
# format defined above for the python variable: mnist_seven_image.
job_list = [job_parameters]
job = JobDescriptor(mnist_service_descriptor, job_list)
# Setup the service manager. NOTE: This will add services that are (optionally) passed in
# so you can manually create services in addition to those that are loaded from the config
# file. After all the services are added, it will call post_load_initialize on all the
# services.
setup_service_manager(app, [mnist_service_adapter])
# Test perform for the mnist service adapter.
try:
exception_caught = False
results = mnist_service_adapter.perform(job)
except RuntimeError as exception:
exception_caught = True
log.error(" Exception caught %s", exception)
log.debug(" Error performing %s %s", job, mnist_service_adapter)
assert not exception_caught
# Check our results for format and content.
assert len(results) == 1
assert results[0]['predictions'] == [7]
assert results[0]['confidences'][0] > 0.9900
if results[0]['predictions'] == [7]:
log.debug(
"Tensorflow NNIST Adapter - CORRECT evaluation of image as 7")
def init_test_jobs():
test_jobs[tests.DOCUMENT_SUMMARIZER_ID] = []
test_jobs[tests.WORD_SENSE_DISAMBIGUATER_ID] = []
test_jobs[tests.FACE_RECOGNIZER_ID] = []
test_jobs[tests.TEXT_SUMMARIZER_ID] = []
test_jobs[tests.VIDEO_SUMMARIZER_ID] = []
test_jobs[tests.ENTITY_EXTRACTER_ID] = []
job_parameters = [
{
'input_type': 'file',
'input_url': 'http://test.com/inputs/test_input.txt',
'output_type': 'file_url_put',
'output_url': 'test_output.txt'
}
]
job_parameters_2 = [
{
'input_type': 'file',
'input_url': 'http://test.com/inputs/test_input_2.txt',
'output_type': 'file_url_put',
'output_url': 'test_output_2.txt'
}
]
# Create test jobs for the document summarizer.
service_id = tests.DOCUMENT_SUMMARIZER_ID
job = JobDescriptor(ServiceDescriptor(service_id), job_parameters)
test_jobs[service_id].append(job)
job = JobDescriptor(ServiceDescriptor(service_id), job_parameters_2)
test_jobs[service_id].append(job)
# Create test jobs for the word-sense disambiguator.
service_id = tests.WORD_SENSE_DISAMBIGUATER_ID
job = JobDescriptor(ServiceDescriptor(service_id), job_parameters)
test_jobs[service_id].append(job)
job = JobDescriptor(ServiceDescriptor(service_id), job_parameters_2)
test_jobs[service_id].append(job)
# Create test jobs for the face recognizer.
service_id = tests.FACE_RECOGNIZER_ID
job = JobDescriptor(ServiceDescriptor(service_id), job_parameters)
test_jobs[service_id].append(job)
# Create test jobs for the text summarizer.
service_id = tests.TEXT_SUMMARIZER_ID
job = JobDescriptor(ServiceDescriptor(service_id), job_parameters)
test_jobs[service_id].append(job)
# Create test jobs for the entity extractor.
service_id = tests.ENTITY_EXTRACTER_ID
job = JobDescriptor(ServiceDescriptor(service_id), job_parameters)
test_jobs[service_id].append(job)
def init_test_jobs():
test_jobs[ontology.DOCUMENT_SUMMARIZER_ID] = []
test_jobs[ontology.WORD_SENSE_DISAMBIGUATER_ID] = []
test_jobs[ontology.FACE_RECOGNIZER_ID] = []
test_jobs[ontology.TEXT_SUMMARIZER_ID] = []
test_jobs[ontology.VIDEO_SUMMARIZER_ID] = []
test_jobs[ontology.ENTITY_EXTRACTER_ID] = []
job_parameters = {
'input_type': 'file',
'input_url': 'http://test.com/inputs/test_input.txt',
'output_type': 'file_url_put',
'output_url': 'test_output.txt'
}
job_parameters_2 = {
'input_type': 'file',
'input_url': 'http://test.com/inputs/test_input_2.txt',
'output_type': 'file_url_put',
'output_url': 'test_output_2.txt'
}
service_id = ontology.DOCUMENT_SUMMARIZER_ID
job = JobDescriptor(ServiceDescriptor(service_id), job_parameters)
test_jobs[service_id].append(job)
job = JobDescriptor(ServiceDescriptor(service_id), job_parameters_2)
test_jobs[service_id].append(job)
service_id = ontology.WORD_SENSE_DISAMBIGUATER_ID
job = JobDescriptor(ServiceDescriptor(service_id), job_parameters)
test_jobs[service_id].append(job)
job = JobDescriptor(ServiceDescriptor(service_id), job_parameters_2)
test_jobs[service_id].append(job)
service_id = ontology.FACE_RECOGNIZER_ID
job = JobDescriptor(ServiceDescriptor(service_id), job_parameters)
test_jobs[service_id].append(job)
service_id = ontology.TEXT_SUMMARIZER_ID
job = JobDescriptor(ServiceDescriptor(service_id), job_parameters)
test_jobs[service_id].append(job)
service_id = ontology.ENTITY_EXTRACTER_ID
job = JobDescriptor(ServiceDescriptor(service_id), job_parameters)
test_jobs[service_id].append(job)
def test_tensorflow_imagenet_adapter(app):
setup_logging()
log.debug("Testing Tensorflow ImageNet Adapter")
# images to be tested
images = ["bucket.jpg", "cup.jpg", "bowtie.png"]
encoded_images = []
image_types = []
for image in images:
# Load each image and encode it base 64.
image_path = os.path.join(TEST_DIRECTORY, "data", "imagenet", image)
image_file = open(image_path, 'rb')
image_bytes = image_file.read()
encoded_images.append(base64.b64encode(image_bytes))
image_types.append(image.split('.')[1])
# Setup a test job for classifying the test images.
job_parameters = {
'input_type': 'attached',
'input_data': {
'images': encoded_images,
'image_types': image_types
},
'output_type': 'attached',
}
# Get the service for an ImageNet classifier. A service identifies a unique service provided by
# SingularityNET and is part of the ontology.
ontology = app['ontology']
imagenet_service = ontology.get_service(IMAGENET_CLASSIFIER_ID)
# Create the Tensorflow ImageNet service adapter.
imagenet_service_adapter = TensorflowImageNet(app, imagenet_service)
# Create a service descriptor. These are post-contract negotiated descriptors that may include
# other parameters like quality of service, input and output formats, etc.
imagenet_service_descriptor = ServiceDescriptor(IMAGENET_CLASSIFIER_ID)
# Create a new job descriptor with a single set of parameters for the test image of a 7 in the
# format defined above for the python variable: mnist_seven_image.
job_list = [job_parameters]
job = JobDescriptor(imagenet_service_descriptor, job_list)
# Setup the service manager. NOTE: This will add services that are (optionally) passed in
# so you can manually create services in addition to those that are loaded from the config
# file. After all the services are added, it will call post_load_initialize on all the
# services.
setup_service_manager(app, [imagenet_service_adapter])
# Test perform for the ImageNet service adapter.
try:
exception_caught = False
results = imagenet_service_adapter.perform(job)
except RuntimeError as exception:
exception_caught = True
log.error(" Exception caught %s", exception)
log.debug(" Error performing %s %s", job, imagenet_service_adapter)
assert not exception_caught
print(results)
# Check our results for format and content.
assert len(results) == 1
assert results[0]['predictions'] == [['bucket, pail'],
['cup', 'coffee mug'],
['bow tie, bow-tie, bowtie']]
assert results[0]['confidences'][0][0] > 0.9600 and results[0][
'confidences'][2][0] < 1.0
assert results[0]['confidences'][1][0] > 0.4000 and results[0][
'confidences'][1][1] < 0.4200
assert results[0]['confidences'][1][1] > 0.4000 and results[0][
'confidences'][1][1] < 0.4100
assert results[0]['confidences'][2][0] > 0.9990 and results[0][
'confidences'][2][0] < 1.0
async def can_perform(service_node_id=None, context=None):
# figure out what we are being asked to perform and answer
service = ServiceDescriptor(service_node_id)
app = context
return await can_perform_service(app, service)
async def internal_can_perform(app, service_node_id):
# figure out what we are being asked to perform and answer
service = ServiceDescriptor(service_node_id)
return await can_perform_service(app, service)
async def internal_perform_job(app, service_node_id, job_params):
service_descriptor = ServiceDescriptor(service_node_id)
job = JobDescriptor(service_descriptor, job_params)
result = await perform_job(app, job)
return result
async def internal_offer(app, service_node_id, price):
service_descriptor = ServiceDescriptor(service_node_id)
result = app['accounting'].incoming_offer(service_descriptor, price)
return result