def __init__(self, is_site):
# load classifier
if (is_site):
self.model = SiteModel(
"light_classification/models/frozen_inference_graph.pb")
else:
self.model = SimModel()
def run():
from sim_model import SimModel
from sim_array import SimArray
pstudy = SimArray(sim_model=SimModel())
pstudy_view = SimArrayView(model=pstudy)
pstudy_view.configure_traits()
def __init__(self, scenario):
if scenario == "sim":
self.model = SimModel()
else:
# self.model = RealModel("light_classification/models/tl_model")
self.model = RealModel(
"light_classification/models/frozen_inference_graph.pb")
def __init__(self, scenario):
if scenario == "sim":
self.model = SimModel()
else:
# self.model = RealModel("light_classification/models/tl_model")
self.model = RealModel(
"light_classification/frozen_models/faster_rcnn_resnet101_coco_2018_01_28/frozen_inference_graph.pb"
)
def run():
from sim_model import SimModel
sim_array = SimArray(sim_model=SimModel())
print sim_array.levels2run[:, 0, 0, 0]
print sim_array[:, 0, 0, 0]
print sim_array.run_table[0]
print 'array_content', sim_array.output_array[0, 0, 0, 0, :]
sim_array.clear_cache()
print 'array_content', sim_array.output_array[0, 0, 0, 0, :]
sim_array.configure_traits()
def __init__(self, is_sim):
self.is_sim = is_sim
if is_sim:
self.model = SimModel()
else:
self.threshold = THRESHOLD
self.graph = self.load_graph(GRAPH_PATH_SIM)
self.image_tensor = self.graph.get_tensor_by_name('image_tensor:0')
self.detect_boxes = self.graph.get_tensor_by_name(
'detection_boxes:0')
self.detect_scores = self.graph.get_tensor_by_name(
'detection_scores:0')
self.detect_classes = self.graph.get_tensor_by_name(
'detection_classes:0')
self.num_detections = self.graph.get_tensor_by_name(
'num_detections:0')
self.sess = tf.Session(graph=self.graph)
# A trait is regarded as Factor if it specifies
# a set of levels in form ps_levels = True.
# - get_sim_outputs() as instances of SimOut class
# defining the name and order of outputs
# - peval() method returning a vector of results
# the order of outputs is prescribed by the
# specification given in the get_outputs() method
#
# Here we just import predefined Foo model with three four inputs
#
# [ index_1, material_model, param_1, param_2 ]
#
# The inputs have the types [ Int, Callable, Float, Float
#
from sim_model import SimModel
sim_model = SimModel()
# The model response is obtained by issuing
#
print 'default evaluation', sim_model.peval()
# returning an array with two values.
# In this call, the default values of the factors
# [input_1, material_model, param_1, param_2 ]
# were taken. The factor levels were ignored.
# DEFINING A STUDY
# --------------
# In order to study the response of the model in a broader range
# of specified levels we now construct the parametric study
#
def run():
from sim_model import SimModel
pstudy_app = SimPStudy(sim_model=SimModel())
pstudy_app.configure_traits(kind='live')
def _sim_model_default(self):
from sim_model import SimModel
return SimModel()