Merge pull request #395 from Programmer-RD-AI/deepsource-fix-2581d75f

Put docstring into a single line
Programmer-RD-AI · Aug 23, 2022 · 7a8d27a · 7a8d27a
2 parents 186ba00 + 3693032
commit 7a8d27a
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Showing 9 changed files with 67 additions and 201 deletions.
diff --git a/.Other/V2/Model.py b/.Other/V2/Model.py
@@ -58,9 +58,7 @@
 
 
 class Model:
-    """
-    This class helps anyone to train a detectron2 model for this project easily so anyone can train this model.
-    """
+    """This class helps anyone to train a detectron2 model for this project easily so anyone can train this model."""
 
     def __init__(
         self,
@@ -165,9 +163,7 @@ def __init__(
 
     @staticmethod
     def remove_files_in_output() -> None:
-        """
-        - remove_files_in_output - remove all of the file in ./output/
-        """
+        """- remove_files_in_output - remove all of the file in ./output/"""
         files_to_remove = os.listdir(
             "./output/")  # Get the files in the directory
         try:
@@ -318,9 +314,7 @@ def __train(self, ) -> DefaultTrainer:
         return trainer
 
     def create_predictor(self) -> DefaultPredictor:
-        """
-        - create_predictor - create the predictor to predict images
-        """
+        """- create_predictor - create the predictor to predict images"""
         torch.cuda.empty_cache()
         self.cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = (
             self.SCORE_THRESH_TEST)  # Setting SCORE_THRESH_TEST
@@ -352,9 +346,7 @@ def create_coco_eval(self,
 
     @staticmethod
     def metrics_file_to_dict() -> list:
-        """
-        - metrics_file_to_dict - in ./output/metrics.json it logs the metrics of the model
-        """
+        """- metrics_file_to_dict - in ./output/metrics.json it logs the metrics of the model"""
         new_logs = []
         try:
             logs = open("./output/metrics.json", "r").read().split("\n")
@@ -371,9 +363,7 @@ def metrics_file_to_dict() -> list:
             return new_logs
 
     def predict_test_images(self, predictor: DefaultPredictor) -> list:
-        """
-        - predict_test_images - predict test images
-        """
+        """- predict_test_images - predict test images"""
         imgs = []
         torch.cuda.empty_cache()
         for img in tqdm(
@@ -393,9 +383,7 @@ def predict_test_images(self, predictor: DefaultPredictor) -> list:
         return imgs
 
     def create_target_and_preds(self, predictor: DefaultPredictor) -> tuple:
-        """
-        - create_target_and_preds - create the target and predictions
-        """
+        """- create_target_and_preds - create the target and predictions"""
         info = self.data.iloc[self.create_target_and_preds_iter]
         img = cv2.imread("./Img/" + info["Path"])
         height, width = cv2.imread("./Img/" + info["Path"]).shape[:2]
@@ -425,9 +413,7 @@ def create_target_and_preds(self, predictor: DefaultPredictor) -> tuple:
 
     @staticmethod
     def create_rmse(preds: torch.tensor, target: torch.tensor) -> float:
-        """
-        - create_rmse - Create Root-mean-square deviation
-        """
+        """- create_rmse - Create Root-mean-square deviation"""
         lowest_rmse = 0
         r_mean_squared_error = MeanSquaredError(squared=False)
         preds_new = (preds["instances"].__dict__["_fields"]
@@ -470,9 +456,7 @@ def create_iou(preds: torch.tensor, targets: torch.tensor) -> float:
 
     @staticmethod
     def create_mse(preds: torch.tensor, target: torch.tensor) -> float:
-        """
-        - create_mse - Create Mean-square deviation
-        """
+        """- create_mse - Create Mean-square deviation"""
         lowest_mse = 0
         mean_squared_error = MeanSquaredError(squared=True)
         preds_new = (preds["instances"].__dict__["_fields"]
@@ -485,9 +469,7 @@ def create_mse(preds: torch.tensor, target: torch.tensor) -> float:
 
     @staticmethod
     def create_x_y_w_h(xmin: int, ymin: int, xmax: int, ymax: int) -> list:
-        """
-        - create_x_y_w_h - Conver xmin,ymin, xmax, ymax to x,y,w,h
-        """
+        """- create_x_y_w_h - Conver xmin,ymin, xmax, ymax to x,y,w,h"""
         x = xmin
         y = ymin
         w = xmax - xmin
@@ -496,18 +478,14 @@ def create_x_y_w_h(xmin: int, ymin: int, xmax: int, ymax: int) -> list:
 
     @staticmethod
     def crop_img(x: int, y: int, w: int, h: int, img: np.array) -> np.array:
-        """
-        - crop_img - cropping the image using x,y,w,h
-        """
+        """- crop_img - cropping the image using x,y,w,h"""
         crop = img[y:y + h, x:x + w]
         cv2.imwrite("./test.png", crop)
         return crop
 
     def create_ssim(self, preds: torch.tensor, target: torch.tensor,
                     height: int, width: int) -> float:
-        """
-        - create_ssim - create SSIM # TODO it is not done yet
-        """
+        """- create_ssim - create SSIM # TODO it is not done yet"""
         lowest_ssim = 0
         ssim = SSIM()
         preds_new = (preds["instances"].__dict__["_fields"]
@@ -529,9 +507,7 @@ def create_ssim(self, preds: torch.tensor, target: torch.tensor,
 
     @staticmethod
     def create_psnr(preds: torch.tensor, target: torch.tensor) -> float:
-        """
-        - create_psnr - Peak signal-to-noise ratio (how similar is a image)
-        """
+        """- create_psnr - Peak signal-to-noise ratio (how similar is a image)"""
         lowest_psnr = 0
         psnr = PSNR()
         preds_new = (preds["instances"].__dict__["_fields"]
@@ -544,9 +520,7 @@ def create_psnr(preds: torch.tensor, target: torch.tensor) -> float:
 
     @staticmethod
     def create_mae(preds: torch.tensor, target: torch.tensor) -> float:
-        """
-        - create_mae - Mean absolute error
-        """
+        """- create_mae - Mean absolute error"""
         lowest_mae = 0
         mae = MeanAbsoluteError()
         preds_new = (preds["instances"].__dict__["_fields"]
@@ -619,9 +593,7 @@ def evaluation(self, predictor):
         }
 
     def train(self) -> dict:
-        """
-        - train - trains the model
-        """
+        """- train - trains the model"""
         torch.cuda.empty_cache()
         wandb.init(
             project=PROJECT_NAME,
@@ -711,9 +683,7 @@ def __init__(self, ) -> None:
 
     @staticmethod
     def tune(params: dict) -> dict:
-        """
-        Tune all of the parameters
-        """
+        """Tune all of the parameters"""
         final_metrics = []
         model = Model()
         params = ParameterGrid(params)
@@ -742,9 +712,7 @@ def tune(params: dict) -> dict:
 
     @staticmethod
     def ray_tune_func(config):
-        """
-        https://docs.ray.io/en/latest/tune/index.html
-        """
+        """https://docs.ray.io/en/latest/tune/index.html"""
         base_lr = config["BASE_LR"]
         ims_per_batch = (config["IMS_PER_BATCH"], )
         batch_size_per_image = config["BATCH_SIZE_PER_IMAGE"]
@@ -761,9 +729,7 @@ def ray_tune_func(config):
         tune.report(average_precisions=ap)
 
     def ray_tune(self):
-        """
-        https://docs.ray.io/en/latest/tune/user-guide.html
-        """
+        """https://docs.ray.io/en/latest/tune/user-guide.html"""
         analysis = tune.run(self.ray_tune_func,
                             config=params,
                             resources_per_trial={

diff --git a/.Other/V2/Model/models/detectron2.py b/.Other/V2/Model/models/detectron2.py
@@ -1,7 +1,5 @@
 class Model:
-    """
-    This class helps anyone to train a detectron2 model for this project easily so anyone can train this model.
-    """
+    """This class helps anyone to train a detectron2 model for this project easily so anyone can train this model."""
 
     def __init__(
         self,
@@ -106,9 +104,7 @@ def __init__(
 
     @staticmethod
     def remove_files_in_output() -> None:
-        """
-        - remove_files_in_output - remove all of the file in ./output/
-        """
+        """- remove_files_in_output - remove all of the file in ./output/"""
         files_to_remove = os.listdir(
             "./output/")  # Get the files in the directory
         try:
@@ -259,9 +255,7 @@ def __train(self, ) -> DefaultTrainer:
         return trainer
 
     def create_predictor(self) -> DefaultPredictor:
-        """
-        - create_predictor - create the predictor to predict images
-        """
+        """- create_predictor - create the predictor to predict images"""
         torch.cuda.empty_cache()
         self.cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = (
             self.SCORE_THRESH_TEST)  # Setting SCORE_THRESH_TEST
@@ -293,9 +287,7 @@ def create_coco_eval(self,
 
     @staticmethod
     def metrics_file_to_dict() -> list:
-        """
-        - metrics_file_to_dict - in ./output/metrics.json it logs the metrics of the model
-        """
+        """- metrics_file_to_dict - in ./output/metrics.json it logs the metrics of the model"""
         new_logs = []
         try:
             logs = open("./output/metrics.json", "r").read().split("\n")
@@ -312,9 +304,7 @@ def metrics_file_to_dict() -> list:
             return new_logs
 
     def predict_test_images(self, predictor: DefaultPredictor) -> list:
-        """
-        - predict_test_images - predict test images
-        """
+        """- predict_test_images - predict test images"""
         imgs = []
         torch.cuda.empty_cache()
         for img in tqdm(
@@ -334,9 +324,7 @@ def predict_test_images(self, predictor: DefaultPredictor) -> list:
         return imgs
 
     def create_target_and_preds(self, predictor: DefaultPredictor) -> tuple:
-        """
-        - create_target_and_preds - create the target and predictions
-        """
+        """- create_target_and_preds - create the target and predictions"""
         info = self.data.iloc[self.create_target_and_preds_iter]
         img = cv2.imread("./Img/" + info["Path"])
         height, width = cv2.imread("./Img/" + info["Path"]).shape[:2]
@@ -365,9 +353,7 @@ def create_target_and_preds(self, predictor: DefaultPredictor) -> tuple:
 
     @staticmethod
     def create_x_y_w_h(xmin: int, ymin: int, xmax: int, ymax: int) -> list:
-        """
-        - create_x_y_w_h - Conver xmin,ymin, xmax, ymax to x,y,w,h
-        """
+        """- create_x_y_w_h - Conver xmin,ymin, xmax, ymax to x,y,w,h"""
         x = xmin
         y = ymin
         w = xmax - xmin
@@ -376,9 +362,7 @@ def create_x_y_w_h(xmin: int, ymin: int, xmax: int, ymax: int) -> list:
 
     @staticmethod
     def crop_img(x: int, y: int, w: int, h: int, img: np.array) -> np.array:
-        """
-        - crop_img - cropping the image using x,y,w,h
-        """
+        """- crop_img - cropping the image using x,y,w,h"""
         crop = img[y:y + h, x:x + w]
         cv2.imwrite("./test.png", crop)
         return crop
@@ -421,9 +405,7 @@ def evaluation(self, predictor):
         }
 
     def train(self) -> dict:
-        """
-        - train - trains the model
-        """
+        """- train - trains the model"""
         torch.cuda.empty_cache()
         wandb.init(
             project=PROJECT_NAME,

diff --git a/.Other/V2/Model/param_tunning.py b/.Other/V2/Model/param_tunning.py
@@ -8,9 +8,7 @@ def __init__(self, ) -> None:
 
     @staticmethod
     def tune(params: dict) -> dict:
-        """
-        Tune all of the parameters
-        """
+        """Tune all of the parameters"""
         final_metrics = []
         model = Model()
         params = ParameterGrid(params)
@@ -39,9 +37,7 @@ def tune(params: dict) -> dict:
 
     @staticmethod
     def ray_tune_func(config):
-        """
-        https://docs.ray.io/en/latest/tune/index.html
-        """
+        """https://docs.ray.io/en/latest/tune/index.html"""
         base_lr = config["BASE_LR"]
         ims_per_batch = (config["IMS_PER_BATCH"], )
         batch_size_per_image = config["BATCH_SIZE_PER_IMAGE"]
@@ -58,9 +54,7 @@ def ray_tune_func(config):
         tune.report(average_precisions=ap)
 
     def ray_tune(self):
-        """
-        https://docs.ray.io/en/latest/tune/user-guide.html
-        """
+        """https://docs.ray.io/en/latest/tune/user-guide.html"""
         analysis = tune.run(self.ray_tune_func,
                             config=params,
                             resources_per_trial={

diff --git a/.history/ML/Model/modelling/detectron2_20220429065528.py b/.history/ML/Model/modelling/detectron2_20220429065528.py
@@ -26,9 +26,7 @@
 
 
 class Detectron2:
-    """
-    This class helps anyone to train a detectron2 model for this project easily so anyone can train this model.
-    """
+    """This class helps anyone to train a detectron2 model for this project easily so anyone can train this model."""
 
     def __init__(
         self,
@@ -138,9 +136,7 @@ def __init__(
 
     @staticmethod
     def remove_files_in_output() -> None:
-        """
-        - remove_files_in_output - remove all of the file in ./output/
-        """
+        """- remove_files_in_output - remove all of the file in ./output/"""
         files_to_remove = os.listdir(
             "/media/indika/Sync/Programmer-RD-AI/Programming/Projects/Python/Rest-Api/Car-Object-Detection-REST-API/Find-Card/ML/Model/output/"
         )  # Get the files in the directory
@@ -313,9 +309,7 @@ def __train(self, ) -> DefaultTrainer:
         return trainer
 
     def create_predictor(self) -> DefaultPredictor:
-        """
-        - create_predictor - create the predictor to predict images
-        """
+        """- create_predictor - create the predictor to predict images"""
         torch.cuda.empty_cache()
         self.cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = (
             self.SCORE_THRESH_TEST)  # Setting SCORE_THRESH_TEST
@@ -375,9 +369,7 @@ def create_coco_eval_detectron2(self,
 
     @staticmethod
     def metrics_file_to_dict_detectron2() -> list:
-        """
-        - metrics_file_to_dict_detectron2 - in ./output/metrics.json it logs the metrics of the model
-        """
+        """- metrics_file_to_dict_detectron2 - in ./output/metrics.json it logs the metrics of the model"""
         new_logs = []
         try:
             logs = open("./Model/output/metrics.json", "r").read().split("\n")
@@ -395,9 +387,7 @@ def metrics_file_to_dict_detectron2() -> list:
 
     def predict_test_images_detectron2(self,
                                        predictor: DefaultPredictor) -> list:
-        """
-        - predict_test_images_detectron2 - predict test images
-        """
+        """- predict_test_images_detectron2 - predict test images"""
         imgs = []
         torch.cuda.empty_cache()
         for img in tqdm(
@@ -431,9 +421,7 @@ def predict_test_images_detectron2(self,
 
     def create_target_and_preds_detectron2(
             self, predictor: DefaultPredictor) -> tuple:
-        """
-        - create_target_and_preds_detectron2 - create the target and predictions
-        """
+        """- create_target_and_preds_detectron2 - create the target and predictions"""
         info = self.data.iloc[self.create_target_and_preds_iter]
         img = cv2.imread(
             "/media/indika/Sync/Programmer-RD-AI/Programming/Projects/Python/Rest-Api/Car-Object-Detection-REST-API/Find-Card/ML/Model/dataset/Img/"
@@ -467,9 +455,7 @@ def create_target_and_preds_detectron2(
                 width)
 
     def train(self, PROJECT_NAME, param) -> dict:
-        """
-        - train - trains the model
-        """
+        """- train - trains the model"""
         torch.cuda.empty_cache()
         wandb.init(
             project=PROJECT_NAME,