How to use execution_path method in autotest

Best Python code snippet using autotest_python

configs.py

Source:configs.py

1import os2from pathlib import Path3from typing import List, Optional4class ModelConfig(object):5    def __init__(self,6                 execution_path: Optional[Path] = None,7                 session_type: Optional[str] = None,8                 model_paths: Optional[List[str]] = None,9                 input_dims: Optional[List] = None,10                 input_names: Optional[List[str]] = None,11                 ) -> None:12        super(ModelConfig, self).__init__()13        self.execution_path = execution_path14        self.session_type = session_type15        self.model_paths = model_paths16        self.input_dims = input_dims17        self.input_names = input_names18class LayoutDetectionConfig(ModelConfig):19    """20        Configuration for Layout Detection model.21    """22    def __init__(self,23                 execution_path=None,24                 session_type="layout_detection",25                 model_paths=["models/layout_detection.onnx"],26                 input_dims=[[(1, 1, 256, 532)]],27                 input_names=['input'],28                 img_shape=(532, 256), 29                 mean_color=232.39989563566854,30                 std_color=18.40033969873782431                 ) -> None:32        self.execution_path = execution_path33        self.session_type = session_type34        self.model_paths = model_paths35        self.input_dims = input_dims36        self.input_names = input_names37        self.img_shape = img_shape38        self.mean_color = mean_color39        self.std_color = std_color40        if self.execution_path is not None:41            for i in range(len(model_paths)):42                self.model_paths[i] = str(os.path.join(self.execution_path, 43                                          Path(self.model_paths[i])))44class ColumnSegmentationConfig(ModelConfig):45    """46        Configuration for Column Segmentation model.47    """48    def __init__(self,49                 execution_path=None,50                 session_type='column_segmentation',51                 model_paths=["models/column_segmentation.onnx"],52                 input_dims=[[(1, 1, 512, 512), (1, 7), (1, 7)]],53                 input_names=["input", "y_def_top", "y_def_bot"],54                 max_y=900,55                 border_size=(900, 900),56                 model_size=(512, 512),57                 fil_color=231.16418034341754,58                 mean_color=231.16418034341754,59                 std_color=28.86447999069045560                 ):61        62        self.execution_path = execution_path63        self.session_type = session_type64        self.model_paths = model_paths65        self.input_dims = input_dims66        self.input_names = input_names67        self.max_y = max_y68        self.border_size = border_size69        self.model_size = model_size70        self.fil_color = fil_color71        self.mean_color = mean_color72        self.std_color = std_color73        if self.execution_path is not None:74            for i in range(len(model_paths)):75                self.model_paths[i] = str(os.path.join(self.execution_path, 76                                          Path(self.model_paths[i])))77class SignalRetrievalConfig(ModelConfig):78    """79        Configuration for Signal Retrieval model.80    """81    def __init__(self,82                 execution_path=None,83                 session_type='signal_retrieval',84                 model_paths=["models/signal_retrieval_bs_2.onnx", "models/signal_retrieval_bs_5.onnx", "models/signal_retrieval_bs_11.onnx"],85                 input_dims=[[(2, 1, 168, 300)], [(5, 1, 168, 300)], [(11, 1, 168, 300)]],86                 input_names=['input'],87                 img_shape=(168, 300),88                 mean_color=228.9562434174826,89                 std_color=44.05940553015075,90                 window_size=300,91                 max_height=168,92                 supported_track_lengths=[450, 900, 1800]93                 ) -> None:94        95        self.execution_path = execution_path96        self.session_type = session_type97        self.model_paths = model_paths98        self.input_dims = input_dims99        self.input_names = input_names100        self.img_shape = img_shape101        self.mean_color = mean_color102        self.std_color = std_color103        self.window_size = window_size104        self.max_height = max_height105        self.supported_track_lengths = supported_track_lengths106        if self.execution_path is not None:107            for i in range(len(model_paths)):108                self.model_paths[i] = str(os.path.join(self.execution_path, 109                                          Path(self.model_paths[i])))110class SignalReconstructionConfig(ModelConfig):111    """112        Configuration for Signal Reconstruction model.113    """114    def __init__(self,115                 execution_path=None,116                 session_type='signal_reconstruction',117                 model_paths=["models/signal_reconstruction_length_1250.onnx", "models/signal_reconstruction_length_2500.onnx", "models/signal_reconstruction_length_5000.onnx"],118                 input_dims=[[(1, 1250, 168), (1, 1, 1), (1, 1250)], [(1, 2500, 168), (1, 1, 1), (1, 2500)], [(1, 5000, 168), (1, 1, 1), (1, 5000)]],119                 input_names=["input_seq", "input_scaling_factor", "input_mask"],120                 max_lengths=[1250, 2500, 5000],121                 ) -> None:122        123        self.execution_path = execution_path124        self.session_type = session_type125        self.model_paths = model_paths126        self.input_dims = input_dims127        self.input_names = input_names128        self.max_lengths = max_lengths129        if self.execution_path is not None:130            for i in range(len(model_paths)):131                self.model_paths[i] = str(os.path.join(self.execution_path, ...

fireNet.py

Source:fireNet.py

1from imageai.Detection.Custom import CustomObjectDetection, CustomVideoObjectDetection2import os3execution_path = os.getcwd()4def train_detection_model():5    from imageai.Detection.Custom import DetectionModelTrainer6    trainer = DetectionModelTrainer()7    trainer.setModelTypeAsYOLOv3()8    trainer.setDataDirectory(data_directory="fire-dataset")9    trainer.setTrainConfig(object_names_array=["fire"], batch_size=8, num_experiments=100,10                           train_from_pretrained_model="pretrained-yolov3.h5")11    # download 'pretrained-yolov3.h5' from the link below12    # https://github.com/OlafenwaMoses/ImageAI/releases/download/essential-v4/pretrained-yolov3.h513    trainer.trainModel()14def detect_from_image():15    detector = CustomObjectDetection()16    detector.setModelTypeAsYOLOv3()17    detector.setModelPath(detection_model_path=os.path.join(execution_path, "detection_model-ex-33--loss-4.97.h5"))18    detector.setJsonPath(configuration_json=os.path.join(execution_path, "detection_config.json"))19    detector.loadModel()20    detections = detector.detectObjectsFromImage(input_image=os.path.join(execution_path, "1.jpg"),21                                                 output_image_path=os.path.join(execution_path, "1-detected.jpg"),22                                                 minimum_percentage_probability=40)23    for detection in detections:24        print(detection["name"], " : ", detection["percentage_probability"], " : ", detection["box_points"])25def detect_from_video():26    detector = CustomVideoObjectDetection()27    detector.setModelTypeAsYOLOv3()28    detector.setModelPath(detection_model_path=os.path.join(execution_path, "detection_model-ex-33--loss-4.97.h5"))29    detector.setJsonPath(configuration_json=os.path.join(execution_path, "detection_config.json"))30    detector.loadModel()...

main.py

Source:main.py

1"""2This module implements the ReallySmartBrain exercise of the Section 21 of the course.3"""4import os5from imageai.Prediction import ImagePrediction6def calculate_prediction():7    # Get te execution path8    execution_path = os.getcwd()9    # Create the image prediction object and load the model10    # See https://github.com/OlafenwaMoses/ImageAI/blob/master/imageai/Prediction/CUSTOMPREDICTION.md#customprediction11    prediction = ImagePrediction()12    prediction.setModelTypeAsSqueezeNet()13    prediction.setModelPath(os.path.join(execution_path, "models/squeezenet_weights_tf_dim_ordering_tf_kernels.h5"))14    prediction.loadModel()15    # Calculate the prediction16    predictions, probabilities = prediction.predictImage(os.path.join(execution_path, "images/giraffe.jpg"),17                                                         result_count=5)18    # Print the results19    for eachPrediction, eachProbability in zip(predictions, probabilities):20        print(eachPrediction, " : ", eachProbability)21# Entry point of the script22if __name__ == '__main__':23    # Call the calculate prediction function...

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