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import torchvision.transforms as transforms
from torch.utils.data import DataLoader, Dataset
from sklearn.preprocessing import LabelEncoder
from tqdm import tqdm
from PIL import Image
import torch
import imagehash
ImageCache = None
class AnimalDataset(Dataset):
def __init__(self, df, transform=None):
self.paths = df["path"].values
self.targets = df["target"].values
self.encoded_target = df['encoded_target'].values
self.transform = transform
self.images = []
for path in tqdm(self.paths):
self.images.append(Image.open(path).convert("RGB").resize((224, 224)))
def __len__(self):
return len(self.paths)
def __getitem__(self, idx):
img = self.images[idx]
if self.transform:
img = self.transform(img)
target = self.targets[idx]
encoded_target = torch.tensor(self.encoded_target[idx]).type(torch.LongTensor)
return img, encoded_target, target
train_transform = transforms.Compose([
transforms.Resize((224,224)),
transforms.RandomHorizontalFlip(),
transforms.RandomRotation(10),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
# Define the transformation pipeline
transform = transforms.Compose([
transforms.Resize((224,224)),
transforms.ToTensor(), # Convert the images to PyTorch tensors
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
class CustomImageCache:
def __init__(self, cache_size=50, debug=False):
self.cache = dict()
self.cache_size = 50
self.debug = debug
self.cache_hits = 0
self.cache_misses = 0
def __getitem__(self, image):
if isinstance(image, dict):
# Its the image and a mask as pillow both -> Combine them to one image
image = Image.blend(image["image"], image["mask"], alpha=0.5)
key = imagehash.average_hash(image)
if key in self.cache:
if self.debug: print("Cache hit!")
self.cache_hits += 1
return self.cache[key]
else:
if self.debug: print("Cache miss!")
self.cache_misses += 1
if len(self.cache.keys()) >= self.cache_size:
if self.debug: print("Cache full, popping item!")
self.cache.popitem()
self.cache[key] = image
return self.cache[key]
def __len__(self):
return len(self.cache.keys())
def print_info(self):
print(f"Cache size: {len(self)}")
print(f"Cache hits: {self.cache_hits}")
print(f"Cache misses: {self.cache_misses}")
def imageCacheWrapper(fn):
def wrapper(image):
return fn(ImageCache[image])
return wrapper |