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import albumentations as A
import cv2
import torch
import os
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
IN_CHANNELS = 3
NUM_WORKERS = os.cpu_count() - 2
BATCH_SIZE = 32
IMAGE_SIZE = 416
NUM_CLASSES = 20
LEARNING_RATE = 1e-5
MAX_LEARNING_RATE = 5e-4
WEIGHT_DECAY = 1e-4
NUM_EPOCHS = 100
CONF_THRESHOLD = 0.05
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
CHECKPOINT_FILE = "checkpoint.pth.tar"
IMG_DIR = DATASET + "/images/"
LABEL_DIR = DATASET + "/labels/"
MOSAIC_PROB_TRAIN = 0.75
MOSAIC_PROB_TEST = 0.
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]
means = [0.485, 0.456, 0.406]
scale = 1.1
train_transforms = A.Compose(
[
A.LongestMaxSize(max_size=int(IMAGE_SIZE * scale)),
A.PadIfNeeded(
min_height=int(IMAGE_SIZE * scale),
min_width=int(IMAGE_SIZE * scale),
border_mode=cv2.BORDER_CONSTANT,
),
A.Rotate(limit = 10, interpolation=1, border_mode=4),
A.RandomCrop(width=IMAGE_SIZE, height=IMAGE_SIZE),
A.ColorJitter(brightness=0.6, contrast=0.6, saturation=0.6, hue=0.6, p=0.4),
A.OneOf(
[
A.ShiftScaleRotate(
rotate_limit=20, p=0.5, border_mode=cv2.BORDER_CONSTANT
),
# A.Affine(shear=15, p=0.5, 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=[],),
)
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(),
])
infer_transforms_visualization = A.Compose(
[
A.LongestMaxSize(max_size=IMAGE_SIZE),
A.PadIfNeeded(
min_height=IMAGE_SIZE, min_width=IMAGE_SIZE, border_mode=cv2.BORDER_CONSTANT
),
ToTensorV2(),
])
SCALED_ANCHORS = (
torch.tensor(ANCHORS) * torch.tensor(S).unsqueeze(1).unsqueeze(1).repeat(1, 3, 2)
)
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'
] |