Upload config.py

config.py

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+import albumentations as A
+import cv2
+import torch
+
+from albumentations.pytorch import ToTensorV2
+# from utils import seed_everything
+
+DATASET = 'PASCAL_VOC'
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+# seed_everything()  # If you want deterministic behavior
+NUM_WORKERS = 8
+BATCH_SIZE = 32
+IMAGE_SIZE = 416
+NUM_CLASSES = 20
+LEARNING_RATE = 3e-5
+WEIGHT_DECAY = 3e-5
+NUM_EPOCHS = 150
+CONF_THRESHOLD = 0.5
+MAP_IOU_THRESH = 0.5
+NMS_IOU_THRESH = 0.45
+S = [IMAGE_SIZE // 32, IMAGE_SIZE // 16, IMAGE_SIZE // 8]
+PIN_MEMORY = True
+LOAD_MODEL = False
+SAVE_MODEL = True
+
+IMG_DIR = DATASET + "/images/"
+LABEL_DIR = DATASET + "/labels/"
+DISTILL = True
+DISTILL_FEATURES = True
+DISTILL_LOGITS = True
+BASE_CLASS = 19
+NEW_CLASS = 1
+BASE = False
+
+# CHECKPOINT_FILE = f'my_checkpoint_{BASE_CLASS}.pth.tar'
+CHECKPOINT_FILE = "/kaggle/input/yolov3-weight/2007_task2_19_1_mAP_19_1.pth.tar"
+
+#FINETUNE
+FINETUNE = True
+BATCH_SIZE_FINETUNE = 4
+FINETUNE_NUM_IMAGE_PER_STORE = -1
+
+#WARP 
+WARP = True
+TRAIN_WARP = False 
+TRAIN_WARP_AT_ITR_NO = 3
+# WARP_LAYERS = ('layers.15.pred.1.conv.weight', 'layers.22.pred.1.conv.weight', 'layers.29.pred.1.conv.weight')
+WARP_LAYERS = ('layers.22.pred.1.conv.weight')
+# WARP_LAYERS = ()
+# WARP_LAYERS = ('layers.15.pred.1.conv.weight', 'layers.29.pred.1.conv.weight')
+NUM_FEATURES_PER_CLASS = 3
+NUM_IMAGES_PER_CLASS = 10
+BATCH_SIZE_WARP = 8
+USE_FEATURE_STORE = False
+IMAGE_STORE_LOC = '/kaggle/input/yolov3-weight/'
+MOSAIC = True
+ADD_IMAGE_STORE = True
+
+
+BASE_CHECK_POINT = "/kaggle/input/yolov3-weight/2007_base_19_1_mAP_19_1.pth.tar"
+ANCHORS = [
+    [(0.28, 0.22), (0.38, 0.48), (0.9, 0.78)],
+    [(0.07, 0.15), (0.15, 0.11), (0.14, 0.29)],
+    [(0.02, 0.03), (0.04, 0.07), (0.08, 0.06)],
+]  # Note these have been rescaled to be between [0, 1]
+
+
+scale = 1.1
+def train_preprocess(height = IMAGE_SIZE, width = IMAGE_SIZE):
+    max_size = max(height, width)
+    return A.Compose(
+        [
+            A.LongestMaxSize(max_size=int(max_size * scale)),
+            A.PadIfNeeded(
+                min_height=int(height * scale),
+                min_width=int(width * scale),
+                border_mode=cv2.BORDER_CONSTANT,
+            ),
+            A.RandomCrop(width=width, height=height),
+            A.ColorJitter(brightness=0.6, contrast=0.6, saturation=0.6, hue=0.6, p=0.4),
+            A.OneOf(
+                [
+                    A.ShiftScaleRotate(
+                        rotate_limit=10, p=0.4, border_mode=cv2.BORDER_CONSTANT
+                    ),
+                    A.IAAAffine(shear=10, p=0.4, mode="constant"),
+                ],
+                p=1.0,
+            ),
+            A.HorizontalFlip(p=0.5),
+            A.Blur(p=0.1),
+            A.CLAHE(p=0.1),
+            A.Posterize(p=0.1),
+            A.ToGray(p=0.1),
+            A.ChannelShuffle(p=0.05),
+            # A.Normalize(mean=[0, 0, 0], std=[1, 1, 1], max_pixel_value=255,),
+            # ToTensorV2(),
+        ],
+        bbox_params=A.BboxParams(format="yolo", min_visibility=0.4, label_fields=[],),
+    )
+
+
+train_transforms = A.Compose(
+    [
+        A.Normalize(mean=[0, 0, 0], std=[1, 1, 1], max_pixel_value=255,),
+        ToTensorV2(),
+    ],
+    bbox_params=A.BboxParams(format="yolo", min_visibility=0.4, label_fields=[],),
+)
+
+def weak_preprocessing(height = IMAGE_SIZE, width = IMAGE_SIZE):
+    max_size = max(height, width)
+    return A.Compose(
+            [
+            A.LongestMaxSize(max_size=int(max_size * scale)),
+            A.PadIfNeeded(
+                min_height=int(height * scale),
+                min_width=int(width * scale),
+                border_mode=cv2.BORDER_CONSTANT,
+            ),
+            A.RandomCrop(width=width, height=height),
+            A.HorizontalFlip(p=0.5),
+            A.Blur(p=0.1),
+            ],
+        bbox_params=A.BboxParams(format="yolo", min_visibility=0.4, label_fields=[],),
+    )
+
+
+test_transforms = A.Compose(
+    [
+        A.LongestMaxSize(max_size=IMAGE_SIZE),
+        A.PadIfNeeded(
+            min_height=IMAGE_SIZE, min_width=IMAGE_SIZE, border_mode=cv2.BORDER_CONSTANT
+        ),
+        A.Normalize(mean=[0, 0, 0], std=[1, 1, 1], max_pixel_value=255,),
+        ToTensorV2(),
+    ],
+    bbox_params=A.BboxParams(format="yolo", min_visibility=0.4, label_fields=[]),
+)
+
+infer_transforms = A.Compose(
+    [
+        A.LongestMaxSize(max_size=IMAGE_SIZE),
+        A.PadIfNeeded(
+            min_height=IMAGE_SIZE, min_width=IMAGE_SIZE, border_mode=cv2.BORDER_CONSTANT
+        ),
+        A.Normalize(mean=[0, 0, 0], std=[1, 1, 1], max_pixel_value=255,),
+        ToTensorV2(),
+    ]
+)
+
+PASCAL_CLASSES = [
+    "aeroplane",
+    "bicycle",
+    "bird",
+    "boat",
+    "bottle",
+    "bus",
+    "car",
+    "cat",
+    "chair",
+    "cow",
+    "diningtable",
+    "dog",
+    "horse",
+    "motorbike",
+    "person",
+    "pottedplant",
+    "sheep",
+    "sofa",
+    "train",
+    "tvmonitor"
+]
+
+COCO_LABELS = ['person',
+ 'bicycle',
+ 'car',
+ 'motorcycle',
+ 'airplane',
+ 'bus',
+ 'train',
+ 'truck',
+ 'boat',
+ 'traffic light',
+ 'fire hydrant',
+ 'stop sign',
+ 'parking meter',
+ 'bench',
+ 'bird',
+ 'cat',
+ 'dog',
+ 'horse',
+ 'sheep',
+ 'cow',
+ 'elephant',
+ 'bear',
+ 'zebra',
+ 'giraffe',
+ 'backpack',
+ 'umbrella',
+ 'handbag',
+ 'tie',
+ 'suitcase',
+ 'frisbee',
+ 'skis',
+ 'snowboard',
+ 'sports ball',
+ 'kite',
+ 'baseball bat',
+ 'baseball glove',
+ 'skateboard',
+ 'surfboard',
+ 'tennis racket',
+ 'bottle',
+ 'wine glass',
+ 'cup',
+ 'fork',
+ 'knife',
+ 'spoon',
+ 'bowl',
+ 'banana',
+ 'apple',
+ 'sandwich',
+ 'orange',
+ 'broccoli',
+ 'carrot',
+ 'hot dog',
+ 'pizza',
+ 'donut',
+ 'cake',
+ 'chair',
+ 'couch',
+ 'potted plant',
+ 'bed',
+ 'dining table',
+ 'toilet',
+ 'tv',
+ 'laptop',
+ 'mouse',
+ 'remote',
+ 'keyboard',
+ 'cell phone',
+ 'microwave',
+ 'oven',
+ 'toaster',
+ 'sink',
+ 'refrigerator',
+ 'book',
+ 'clock',
+ 'vase',
+ 'scissors',
+ 'teddy bear',
+ 'hair drier',
+ 'toothbrush'
+]