def __init__(self, model_bytes, labels_unparsed):
# Parse everything
self.label_map = json.loads(labels_unparsed)
self.graph, self.session = loading.parse_tf_model(model_bytes)
self.input_node = self.graph.get_tensor_by_name("Cast:0")
self.output_node = self.graph.get_tensor_by_name("prediction:0")
def __init__(self, model_bytes, labels, confidence_thresh=0.5):
self.confidence_thresh = confidence_thresh
self.label_map = labels
graph, self.session = loading.parse_tf_model(model_bytes)
# Get relevant nodes from the graph
self.input_tensor = graph.get_tensor_by_name('image_tensor:0')
self.boxes_tensor = graph.get_tensor_by_name('detection_boxes:0')
self.scores_tensor = graph.get_tensor_by_name('detection_scores:0')
self.classes_tensor = graph.get_tensor_by_name('detection_classes:0')
def __init__(self, model_bytes, labels, model_name):
assert model_name in CLASSIFIERS, \
"{model_name} is not a supported model! Supported models:" + \
str(CLASSIFIERS.keys())
self.model_params: ClassifierParams = CLASSIFIERS[model_name]
self.label_map = labels
self.graph, self.session = loading.parse_tf_model(model_bytes)
self.input_tensor = self.graph.get_tensor_by_name(
self.model_params.input_node)
self.output_tensor = self.graph.get_tensor_by_name(
self.model_params.output_node)
def __init__(self, model_bytes):
self.graph, self.session = loading.parse_tf_model(model_bytes)
self.cat_input = self.graph.get_tensor_by_name('category_input:0')
self.img_input = self.graph.get_tensor_by_name(
'FunctionBufferingResourceGetNext:0')
self.output_tensor = self.graph.get_tensor_by_name('generator/Tanh:0')
def __init__(self, model_bytes):
# Parse everything
self.graph, self.session = loading.parse_tf_model(model_bytes)
self.input_node = self.graph.get_tensor_by_name("split:0")
self.output_node = self.graph.get_tensor_by_name("model/disparities/ExpandDims:0")
def __init__(self, model_bytes):
self.graph, self.session = model_loading.parse_tf_model(model_bytes)
self.input_node = self.graph.get_tensor_by_name("Placeholder:0")
self.output_node = self.graph.get_tensor_by_name("ConvPred/ConvPred:0")