cyclegan, progan

ML/Pytorch/object_detection/YOLOv3/COCO/note.txt

@@ -1,2 +0,0 @@
-Put images in images folder, text files for labels in labels folder.
-Then under COCO put train.csv, and test.csv

ML/Pytorch/object_detection/YOLOv3/PASCAL_VOC/note.txt

@@ -1,2 +0,0 @@
-Put images in images folder, text files for labels in labels folder.
-Then under PASCAL_VOC put train.csv, and test.csv

ML/Pytorch/object_detection/YOLOv3/config.py

@@ -11,11 +11,11 @@ DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 NUM_WORKERS = 4
 BATCH_SIZE = 32
 IMAGE_SIZE = 416
-NUM_CLASSES = 80
-LEARNING_RATE = 3e-5
+NUM_CLASSES = 20
+LEARNING_RATE = 1e-5
 WEIGHT_DECAY = 1e-4
 NUM_EPOCHS = 100
-CONF_THRESHOLD = 0.6
+CONF_THRESHOLD = 0.05
 MAP_IOU_THRESH = 0.5
 NMS_IOU_THRESH = 0.45
 S = [IMAGE_SIZE // 32, IMAGE_SIZE // 16, IMAGE_SIZE // 8]
@@ -47,9 +47,9 @@ train_transforms = A.Compose(
         A.OneOf(
             [
                 A.ShiftScaleRotate(
-                    rotate_limit=10, p=0.4, border_mode=cv2.BORDER_CONSTANT
+                    rotate_limit=20, p=0.5, border_mode=cv2.BORDER_CONSTANT
                 ),
-                A.IAAAffine(shear=10, p=0.4, mode="constant"),
+                A.IAAAffine(shear=15, p=0.5, mode="constant"),
             ],
             p=1.0,
         ),

ML/Pytorch/object_detection/YOLOv3/loss.py

@@ -44,7 +44,7 @@ class YoloLoss(nn.Module):
         anchors = anchors.reshape(1, 3, 1, 1, 2)
         box_preds = torch.cat([self.sigmoid(predictions[..., 1:3]), torch.exp(predictions[..., 3:5]) * anchors], dim=-1)
         ious = intersection_over_union(box_preds[obj], target[..., 1:5][obj]).detach()
-        object_loss = self.bce((predictions[..., 0:1][obj]), (ious * target[..., 0:1][obj]))
+        object_loss = self.mse(self.sigmoid(predictions[..., 0:1][obj]), ious * target[..., 0:1][obj])
 
         # ======================== #
         #   FOR BOX COORDINATES    #
@@ -64,12 +64,12 @@ class YoloLoss(nn.Module):
             (predictions[..., 5:][obj]), (target[..., 5][obj].long()),
         )
 
-        #    print("__________________________________")
-        #    print(self.lambda_box * box_loss)
-        #    print(self.lambda_obj * object_loss)
-        #    print(self.lambda_noobj * no_object_loss)
-        #    print(self.lambda_class * class_loss)
-        #    print("\n")
+        #print("__________________________________")
+        #print(self.lambda_box * box_loss)
+        #print(self.lambda_obj * object_loss)
+        #print(self.lambda_noobj * no_object_loss)
+        #print(self.lambda_class * class_loss)
+        #print("\n")
 
         return (
             self.lambda_box * box_loss

ML/Pytorch/object_detection/YOLOv3/train.py

@@ -19,6 +19,8 @@ from utils import (
     plot_couple_examples
 )
 from loss import YoloLoss
+import warnings
+warnings.filterwarnings("ignore")
 
 torch.backends.cudnn.benchmark = True
 
@@ -80,19 +82,16 @@ def main():
         #plot_couple_examples(model, test_loader, 0.6, 0.5, scaled_anchors)
         train_fn(train_loader, model, optimizer, loss_fn, scaler, scaled_anchors)
 
-        if config.SAVE_MODEL:
-            save_checkpoint(model, optimizer, filename=f"checkpoint.pth.tar")
+        #if config.SAVE_MODEL:
+        #    save_checkpoint(model, optimizer, filename=f"checkpoint.pth.tar")
 
         #print(f"Currently epoch {epoch}")
         #print("On Train Eval loader:")
-        #check_class_accuracy(model, train_eval_loader, threshold=config.CONF_THRESHOLD)
         #print("On Train loader:")
         #check_class_accuracy(model, train_loader, threshold=config.CONF_THRESHOLD)
 
-        if epoch % 10 == 0 and epoch > 0:
-            print("On Test loader:")
+        if epoch > 0 and epoch % 3 == 0:
             check_class_accuracy(model, test_loader, threshold=config.CONF_THRESHOLD)
-
             pred_boxes, true_boxes = get_evaluation_bboxes(
                 test_loader,
                 model,
@@ -108,7 +107,7 @@ def main():
                 num_classes=config.NUM_CLASSES,
             )
             print(f"MAP: {mapval.item()}")
-
+            model.train()
 
 
 if __name__ == "__main__":

ML/Pytorch/object_detection/YOLOv3/utils.py

@@ -380,8 +380,6 @@ def check_class_accuracy(model, loader, threshold):
     tot_obj, correct_obj = 0, 0
 
     for idx, (x, y) in enumerate(tqdm(loader)):
-        if idx == 100:
-            break
         x = x.to(config.DEVICE)
         with torch.no_grad():
             out = model(x)

ML/Pytorch/object_detection/YOLOv3/weights/note.txt

@@ -1 +0,0 @@
-Download and put pretrained weights here!