def _run_inference_on_images(images: List[np.ndarray], capsule: BaseCapsule):
"""Run inference on a list of images"""
# Make multiple parallel requests with different images and different
# input DetectionNodes to the capsule.
request_input = []
with ThreadPoolExecutor(
thread_name_prefix="InputOutputValidation ") \
as executor:
for stream_id, image in enumerate(images):
if capsule.input_type.size == NodeDescription.Size.NONE:
input_node = None
elif capsule.input_type.size == NodeDescription.Size.SINGLE:
input_node = make_detection_node(image.shape,
capsule.input_type)
elif capsule.input_type.size == NodeDescription.Size.ALL:
input_node = [make_detection_node(image.shape,
capsule.input_type)
for _ in range(random.randint(0, 5))]
else:
raise NotImplementedError(
"The capsule did not have a NodeDescription.Size that was "
"known!")
options = make_capsule_options(capsule)
future = executor.submit(
capsule.process_frame,
frame=image,
detection_node=input_node,
options=options,
state=capsule.stream_state())
request_input.append((future, input_node))
for future, input_node in request_input:
# Postprocess the results
prediction = future.result(timeout=90)
# Verify that the capsule performed correctly
if isinstance(prediction, DetectionNode):
# Validate that what was returned by the capsule was valid
assert (capsule.output_type.size
is NodeDescription.Size.SINGLE)
output_nodes = [prediction]
elif prediction is None and isinstance(input_node, DetectionNode):
# If the capsule didn't output something, then it must have
# modified the input node in-place. Validate the changes.
assert (capsule.output_type.size
is NodeDescription.Size.SINGLE)
output_nodes = [input_node]
elif prediction is None and isinstance(input_node, list):
# If this capsule accepts size ALL as input, then it must
# have modified the detections within the input list
assert capsule.input_type.size is NodeDescription.Size.ALL
assert capsule.output_type.size is NodeDescription.Size.ALL
output_nodes = input_node
elif isinstance(prediction, list):
# Validate that every detection node in the list is correct
assert capsule.output_type.size is NodeDescription.Size.ALL
output_nodes = prediction
else:
raise RuntimeError(f"Unknown prediction type: {prediction}")
# Validate every output node against the capsules output_type
for output_node in output_nodes:
assert capsule.output_type.describes(output_node), \
("Capsule failed to output a prediction that matches "
"the NodeDescription it had for it's output type. "
f"Prediction: {prediction}")
# Assert the nodes "extra_data" attribute can be JSON encoded
# without errors. Typically this can happen if there's a numpy
# array carelessly left in the extra_data
json.loads(json.dumps(output_node.extra_data))
# If this capsule can encode things, verify that the backend
# correctly implemented the "distance" function
if (capsule.capability.encoded
and prediction is not None
and len(prediction) > 0):
# Get one of the predictions
pred = prediction[0]
# Measure the distance from an encoding to itself
# (should be 0)
distances = capsule.backends[0].distances(
pred.encoding, np.array([pred.encoding]))
assert len(distances) == 1
assert distances[0] == 0, \
("This assertion can be removed in the case that "
"there is some new distance function where two "
"encodings that are equal no longer have a distance "
"of 0. Until that case exists, keep this assertion.")
def _run_inference_on_images(images: List[np.ndarray], capsule: BaseCapsule):
"""Run inference on a list of images"""
# Make multiple parallel requests with different images and different
# input DetectionNodes to the capsule.
request_input = []
with ThreadPoolExecutor(
thread_name_prefix="InputOutputValidation ") \
as executor:
for stream_id, image in enumerate(images):
if capsule.input_type.size == NodeDescription.Size.NONE:
node = None
elif capsule.input_type.size == NodeDescription.Size.SINGLE:
node = make_detection_node(image.shape,
capsule.input_type)
elif capsule.input_type.size == NodeDescription.Size.ALL:
node = [make_detection_node(image.shape,
capsule.input_type)
for _ in range(random.randint(0, 5))]
else:
raise NotImplementedError(
"The capsule did not have a NodeDescription.Size that was "
"known!")
options = make_capsule_options(capsule)
future = executor.submit(
capsule.process_frame,
frame=image,
detection_node=node,
options=options,
state=capsule.stream_state())
request_input.append((future, node))
for future, node in request_input:
# Postprocess the results
prediction = future.result(timeout=90)
# Verify that the capsule performed correctly
if node is not None and not isinstance(node, list):
assert capsule.output_type.describes(node)
elif prediction is not None:
assert all(capsule.output_type.describes(detection_node)
for detection_node in prediction), \
("Capsule failed to output a prediction that matches "
"the NodeDescription it had for it's output type. "
f"Prediction: {prediction}")
# If this capsule can encode things, verify that the backend
# correctly implemented the "distance" function
if (capsule.capability.encoded
and prediction is not None
and len(prediction) > 0):
# Get one of the predictions
pred = prediction[0]
# Measure the distance from an encoding to itself
# (should be 0)
distances = capsule.backends[0].distances(
pred.encoding, np.array([pred.encoding]))
assert len(distances) == 1
assert distances[0] == 0, \
("This assertion can be removed in the case that "
"there is some new distance function where two "
"encodings that are equal no longer have a distance "
"of 0. Until that case exists, keep this assertion.")