def __init__(self, name, param): core.CWorkflowTask.__init__(self, name) # Add input/output of the process here self.addOutput(datasetio.IkDatasetIO("yolo")) self.addOutput(dataprocess.CNumericIO()) # Create parameters class if param is None: self.setParam(DatasetYoloParam()) else: self.setParam(copy.deepcopy(param))
def __init__(self, name, param): core.CWorkflowTask.__init__(self, name) # Add input/output of the process here # Example : self.addInput(PyDataProcess.CImageProcessIO()) # self.addOutput(PyDataProcess.CImageProcessIO()) self.addOutput(datasetio.IkDatasetIO("other")) self.addOutput(dataprocess.CNumericIO()) # Create parameters class if param is None: self.setParam(DatasetCwfidParam()) else: self.setParam(copy.deepcopy(param))
def __init__(self, name, param): dataprocess.C2dImageTask.__init__(self, name) self.model = None self.names = None self.colors = None self.update = False # Detect if we have a GPU available self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") # Add graphics output self.addOutput(dataprocess.CGraphicsOutput()) # Add numeric output self.addOutput(dataprocess.CNumericIO()) # Create parameters class if param is None: self.setParam(InferYoloV5Param()) else: self.setParam(copy.deepcopy(param))
def __init__(self, name, param): dataprocess.C2dImageTask.__init__(self, name) self.model = None self.class_names = [] # Detect if we have a GPU available self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") # Remove graphics input self.removeInput(1) # Add graphics output self.addOutput(dataprocess.CGraphicsOutput()) # Add numeric output self.addOutput(dataprocess.CNumericIO()) # Create parameters class if param is None: self.setParam(MnasnetParam()) else: self.setParam(copy.deepcopy(param))
def __init__(self, name, param): dataprocess.C2dImageTask.__init__(self, name) # Add graphics output self.addOutput(dataprocess.CGraphicsOutput()) # Add numeric output self.addOutput(dataprocess.CNumericIO()) # Network members self.net = None self.class_names = [] # Create parameters class if param is None: self.setParam(EmotionFerPlusParam()) else: self.setParam(copy.deepcopy(param)) # Load class names model_folder = os.path.dirname(os.path.realpath(__file__)) + "/models" with open(model_folder + "/class_names") as f: for row in f: self.class_names.append(row[:-1])
def __init__(self, name, param): dataprocess.C2dImageTask.__init__(self, name) self.model = None self.class_names = [] # Detect if we have a GPU available self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") # Remove graphics input self.removeInput(1) # Segmentation mask output self.setOutputDataType(core.IODataType.IMAGE_LABEL, 0) # Result image self.addOutput(dataprocess.CImageIO(core.IODataType.IMAGE)) # Add graphics output self.addOutput(dataprocess.CGraphicsOutput()) # Add numeric output self.addOutput(dataprocess.CNumericIO()) # Create parameters class if param is None: self.setParam(MaskRcnnParam()) else: self.setParam(copy.deepcopy(param))
def __init__(self, name, param): dataprocess.C2dImageTask.__init__(self, name) # Add graphics output self.addOutput(dataprocess.CGraphicsOutput()) # Add numeric output self.addOutput(dataprocess.CNumericIO()) # Create parameters class if param is None: self.setParam(CovidNetParam()) else: self.setParam(copy.deepcopy(param)) param = self.getParam() self.covid_model = CovidNet(model_path=param.model_path) # Load class names self.class_names = [] class_names_path = os.path.dirname(os.path.realpath(__file__)) + "/models/class_names" with open(class_names_path) as f: for row in f: self.class_names.append(row[:-1])