Add yolo weights and run case for yolo

.github/workflows/build.yml

@@ -58,3 +58,4 @@ jobs:
           pip install numpy
           pip install GDAL==$(gdal-config --version) --global-option=build_ext --global-option="-I/usr/include/gdal"
           python test_app.py
+          python test_yolo.py

test_yolo.py

@@ -1,13 +1,17 @@
+# Standard library imports
 import os
 import time
 import warnings
+
+# Third party imports
 import tensorflow as tf
 
-from hot_fair_utilities import preprocess, predict, polygonize
+# Reader imports
+from hot_fair_utilities import polygonize, predict, preprocess
 from hot_fair_utilities.preprocessing.yolo_format import yolo_format
 from train_yolo import train as train_yolo
 
-warnings.simplefilter(action='ignore', category=FutureWarning)
+warnings.simplefilter(action="ignore", category=FutureWarning)
 
 
 class print_time:
@@ -41,7 +45,7 @@ def __exit__(self, type, value, traceback):
         rasterize=True,
         rasterize_options=["binary"],
         georeference_images=True,
-        multimasks=True  # new arg
+        multimasks=True,  # new arg
     )
 
 yolo_data_dir = f"{base_path}/yolo"
@@ -50,19 +54,20 @@ def __exit__(self, type, value, traceback):
         preprocessed_dirs=preprocess_output,
         yolo_dir=yolo_data_dir,
         multimask=True,
-        p_val=0.05
+        p_val=0.05,
     )
 
-train_yolo(data=f"{base_path}",
-               weights=f"{os.getcwd()}/checkpoints/yolov8n-seg_ramp-training_ep500_bs16_deg30_pc2.0/weights/best.pt",
-               gpu="cpu",
-               epochs=2,
-               batch_size=16,
-               pc=2.0
-               )
+output_path = train_yolo(
+    data=f"{base_path}",
+    weights=f"{os.getcwd()}/weights/yolov8alb/best.pt",
+    gpu="cpu",
+    epochs=2,
+    batch_size=16,
+    pc=2.0,
+)
 
 prediction_output = f"{base_path}/prediction/output"
-model_path = f"{os.getcwd()}/checkpoints/yolov8n-seg_sample_2_ep2_bs16_pc2.0/weights/best.pt"
+model_path = f"{output_path}/weights/best.pt"
 with print_time("inference"):
     predict(
         checkpoint_path=model_path,

train_yolo.py

@@ -1,11 +1,14 @@
+# Standard library imports
 import argparse
-import torch
 import os
-import ultralytics
 from pathlib import Path
 
-from hot_fair_utilities.model.yolo import YOLOSegWithPosWeight
+# Third party imports
+import torch
+import ultralytics
 
+# Reader imports
+from hot_fair_utilities.model.yolo import YOLOSegWithPosWeight
 
 ROOT = Path(__file__).parent.absolute()
 DATA_ROOT = str(ROOT / "ramp-training")
@@ -33,36 +36,62 @@
 
 def parse_opt():
     parser = argparse.ArgumentParser()
-    parser.add_argument('--gpu', type=str, default="0", help='GPU id')
-    parser.add_argument('--data', type=str, default=os.path.join(DATA_ROOT),
-                        help='Directory containing diractory \'yolo\' with dataset.yaml.')
-    parser.add_argument('--weights', type=str, default="yolov8n-seg.yaml",
-                        help='See https://docs.ultralytics.com/tasks/detect/#train')
-    parser.add_argument('--epochs', type=int, default=100,
-                        help='Num of training epochs. Default is 100.')
-    parser.add_argument('--batch-size', type=int, default=16,
-                        help='Number of images in a single batch.')
-    parser.add_argument('--pc', type=float, default=1.0,
-                        help='Positive weight in BCE loss. pc > 1 (pc < 1) encourages higher recall (precision)')
+    parser.add_argument("--gpu", type=str, default="0", help="GPU id")
+    parser.add_argument(
+        "--data",
+        type=str,
+        default=os.path.join(DATA_ROOT),
+        help="Directory containing diractory 'yolo' with dataset.yaml.",
+    )
+    parser.add_argument(
+        "--weights",
+        type=str,
+        default="yolov8n-seg.yaml",
+        help="See https://docs.ultralytics.com/tasks/detect/#train",
+    )
+    parser.add_argument(
+        "--epochs",
+        type=int,
+        default=100,
+        help="Num of training epochs. Default is 100.",
+    )
+    parser.add_argument(
+        "--batch-size", type=int, default=16, help="Number of images in a single batch."
+    )
+    parser.add_argument(
+        "--pc",
+        type=float,
+        default=1.0,
+        help="Positive weight in BCE loss. pc > 1 (pc < 1) encourages higher recall (precision)",
+    )
     opt = parser.parse_args()
     return opt
 
 
 def main():
     opt = parse_opt()
     os.environ["CUDA_VISIBLE_DEVICES"] = str(opt.gpu)
-    print(f"GPU available: {torch.cuda.is_available()}, GPU count: {torch.cuda.device_count()}")
+    print(
+        f"GPU available: {torch.cuda.is_available()}, GPU count: {torch.cuda.device_count()}"
+    )
     train(**vars(opt))
 
 
-def train(data, weights, gpu, epochs, batch_size, pc):
-    back = "n" if "yolov8n" in weights else "s" if "yolov8s" in weights else "m" if "yolov8m" in weights else "?"
+def train(data, weights, gpu, epochs, batch_size, pc, output_path):
+    back = (
+        "n"
+        if "yolov8n" in weights
+        else "s" if "yolov8s" in weights else "m" if "yolov8m" in weights else "?"
+    )
     data_scn = str(Path(data) / "yolo" / "dataset.yaml")
     dataset = data_scn.split("/")[-3]
     kwargs = HYPERPARAM_CHANGES
 
     print(f"Backbone: {back}, Dataset: {dataset}, Epochs: {epochs}")
     name = f"yolov8{back}-seg_{dataset}_ep{epochs}_bs{batch_size}"
+
+    if output_path:
+        name = output_path
     if float(pc) != 0.0:
         name += f"_pc{pc}"
         kwargs = {**kwargs, "pc": pc}
@@ -80,8 +109,9 @@ def train(data, weights, gpu, epochs, batch_size, pc):
         resume=resume,
         deterministic=False,
         device=[int(i) for i in gpu.split(",")] if "," in gpu else gpu,
-        **kwargs
+        **kwargs,
     )
+    return name
 
 
 def check4checkpoint(name, weights):