Merge pull request #40 from MortenTabaka/fix/Adding-path-must-be-insi…

…de-prediction-script #patch

Fix/adding project root to the path must be inside prediction script

README.md

@@ -14,11 +14,6 @@ All credits for the dataset go to the original author and contributors.**
 
 After installing the necessary dependencies, execute the following scripts.
 
-Add project root to the Python path:
-```commandline
-python3 models/scripts/add_project_root_to_the_python_path.py
-```
-
 Run prediction on images in `models/custom_data/input`:
 ```commandline
 python3 models/scripts/run_prediction_on_folder.py

docs/conf.py

@@ -41,7 +41,7 @@
 master_doc = "index"
 
 # General information about the project.
-project = u"Improving_IoU_for_roads_semantic_segmentation_in_LandCover.ai_dataset"
+project = "Improving_IoU_for_roads_semantic_segmentation_in_LandCover.ai_dataset"
 
 # The version info for the project you're documenting, acts as replacement for
 # |version| and |release|, also used in various other places throughout the
@@ -184,8 +184,8 @@
     (
         "index",
         "roads_semantic_segmentation_in_LandCover.ai_dataset.tex",
-        u"Improving_IoU_for_roads_semantic_segmentation_in_LandCover.ai_dataset Documentation",
-        u"Marcin Tabaka",
+        "Improving_IoU_for_roads_semantic_segmentation_in_LandCover.ai_dataset Documentation",
+        "Marcin Tabaka",
         "manual",
     ),
 ]
@@ -219,8 +219,8 @@
     (
         "index",
         "roads_semantic_segmentation_in_LandCover.ai_dataset",
-        u"Improving_IoU_for_roads_semantic_segmentation_in_LandCover.ai_dataset Documentation",
-        [u"Marcin Tabaka"],
+        "Improving_IoU_for_roads_semantic_segmentation_in_LandCover.ai_dataset Documentation",
+        ["Marcin Tabaka"],
         1,
     )
 ]
@@ -238,8 +238,8 @@
     (
         "index",
         "roads_semantic_segmentation_in_LandCover.ai_dataset",
-        u"Improving_IoU_for_roads_semantic_segmentation_in_LandCover.ai_dataset Documentation",
-        u"Marcin Tabaka",
+        "Improving_IoU_for_roads_semantic_segmentation_in_LandCover.ai_dataset Documentation",
+        "Marcin Tabaka",
         "Improving_IoU_for_roads_semantic_segmentation_in_LandCover.ai_dataset",
         "Project will focus on tryingsemantic segmentation algorithms which may improve IoU for roads specifically. Dataset authors obtaied IoU=68.74% for roads, which was lowest score among all classes. ",
         "Miscellaneous",

models/scripts/run_prediction_on_folder.py

@@ -1,7 +1,16 @@
 from enum import Enum
 from pathlib import Path
 
+import os
 import typer
+import sys
+
+
+project_root = os.path.abspath(os.curdir)
+while not os.path.isfile(os.path.join(project_root, "README.md")):
+    current_dir = os.path.abspath(os.path.join(project_root, os.pardir))
+sys.path.insert(0, project_root)
+
 
 from src.features.utils import get_absolute_path_to_project_location
 from src.pipelines.prediction_processor import PredictionPipeline

src/data/image_postprocessing.py

@@ -195,7 +195,6 @@ def __get_all_base_names_from_list_of_tiles(
     def __get_tiles_according_its_base_name(
         self, base_names: List[str], tiles: List[str]
     ) -> List[List[str]]:
-
         split_tiles_according_to_base_name = []
 
         for base_name in base_names:

src/data/image_preprocessing.py

@@ -111,7 +111,6 @@ def split_custom_images_before_prediction(
         target_size: int,
         path_to_output_folder: Union[Path, str],
     ):
-
         img_paths = glob.glob(
             os.path.join(self.path_to_folder_with_input_images, "*.tiff")
         )

src/data/move_data_according_to_subsets.py

@@ -19,7 +19,6 @@ def move_data(raw_data_path, processed_data_path):
     dataSplit = ["train", "val", "test"]
 
     for whichSplit in dataSplit:
-
         tilesFolder = raw_data_path + "/tiles"
         moveToSubset = processed_data_path + "/" + whichSplit
 

src/data/scripts/create_directories_for_data.py

@@ -10,7 +10,6 @@
 
 
 def create_dir(path_list):
-
     for path in path_list:
         if not os.path.exists(path):
             os.makedirs(path)

src/features/evaluation_utils.py

@@ -201,7 +201,6 @@ def get_dataframe(self) -> pd.DataFrame:
         )
 
         for single_batch in self.dataset:
-
             y_true, y_pred = self.get_predictions_and_labels(single_batch)
 
             matrix = tf.math.confusion_matrix(

src/features/loss_functions.py

@@ -95,7 +95,7 @@ def weighted_cross_entropy_loss(self, y_true, y_pred):
 
     def focal_loss_with_logits(self, logits, targets, alpha, gamma, y_pred):
         weight_a = alpha * (1 - y_pred) ** gamma * targets
-        weight_b = (1 - alpha) * y_pred ** gamma * (1 - targets)
+        weight_b = (1 - alpha) * y_pred**gamma * (1 - targets)
 
         return (tf.math.log1p(tf.exp(-tf.abs(logits))) + tf.nn.relu(-logits)) * (
             weight_a + weight_b

src/features/metrics.py

@@ -5,6 +5,7 @@ class CustomMeanIoU(tf.keras.metrics.MeanIoU):
     """
     Source: https://github.com/tensorflow/tensorflow/issues/32875#issuecomment-707316950
     """
+
     def __init__(self, num_classes=None, name=None, dtype=None):
         super(CustomMeanIoU, self).__init__(
             num_classes=num_classes, name=name, dtype=dtype

src/pipelines/prediction_processor.py

@@ -288,7 +288,6 @@ def __get_image_for_prediction(self, filepath: str):
     def __postprocess_tiles_borders_in_concatenated_prediction(
         self, raw_image, raw_mask, base_name: str
     ):
-
         height = tf.shape(raw_mask)[1]
         width = tf.shape(raw_mask)[2]