import json
import os
import torchvision.transforms as transforms
import torch.nn.functional as F
from torch.utils.data import Dataset
import numpy as np
from PIL import Image
from typing import Tuple
from datasets.utils import set_default_from_args
from utils import smart_joint
from utils.conf import base_path
from datasets.utils.continual_dataset import ContinualDataset, fix_class_names_order, store_masked_loaders
from datasets.transforms.denormalization import DeNormalize
from torchvision.transforms.functional import InterpolationMode
from utils.prompt_templates import templates
[docs]
class CropDisease(Dataset):
LABELS = [
"Apple___Apple_scab",
"Apple___Black_rot",
"Apple___healthy",
"Blueberry___healthy",
"Cherry___Powdery_mildew",
"Cherry___healthy",
"Corn___Cercospora_leaf_spot Gray_leaf_spot",
"Corn___Common_rust",
"Corn___Northern_Leaf_Blight",
"Corn___healthy",
"Grape___Black_rot",
"Grape___Esca_(Black_Measles)",
"Grape___Leaf_blight_(Isariopsis_Leaf_Spot)",
"Grape___healthy",
"Orange___Haunglongbing_(Citrus_greening)",
"Peach___Bacterial_spot",
"Pepper,_bell___Bacterial_spot",
"Pepper,_bell___healthy",
"Potato___Early_blight",
"Potato___Late_blight",
"Raspberry___healthy",
"Soybean___healthy",
"Squash___Powdery_mildew",
"Strawberry___Leaf_scorch",
"Strawberry___healthy",
"Tomato___Bacterial_spot",
"Tomato___Early_blight",
"Tomato___Late_blight",
"Tomato___Leaf_Mold",
"Tomato___Septoria_leaf_spot",
"Tomato___Spider_mites Two-spotted_spider_mite",
"Tomato___Target_Spot",
"Tomato___Tomato_Yellow_Leaf_Curl_Virus",
"Tomato___Tomato_mosaic_virus",
"Tomato___healthy",
]
def __init__(self, root, train=True, transform=None,
target_transform=None, download=False) -> None:
self.root = root
self.train = train
self.transform = transform
self.target_transform = target_transform
self.not_aug_transform = transforms.Compose([
transforms.Resize((224, 224), interpolation=InterpolationMode.BICUBIC),
transforms.ToTensor()]
)
if download:
if os.path.isdir(root) and len(os.listdir(root)) > 0:
print('Download not needed, files already on disk.')
else:
from onedrivedownloader import download
ln = "https://unimore365-my.sharepoint.com/:u:/g/personal/215580_unimore_it/EZUaXKQUAVBPrhjHTUdflDEBNu0YiPWrdpAdDhnEU4nD2A?e=GPrCYF"
print('Downloading dataset')
parent_dir = os.path.dirname(root)
download(ln, filename=os.path.join(root, 'cropdisease.tar.gz'), unzip=True, unzip_path=parent_dir, clean=True)
filename = smart_joint(root, ('train' if train else 'test') + '.json')
with open(filename) as f:
data_config = json.load(f)
self.data = np.array([smart_joint(root, 'images', d) for d in data_config['data']])
self.targets = np.array(data_config['labels']).astype(np.int16)
def __len__(self):
return len(self.targets)
def __getitem__(self, index: int) -> Tuple[Image.Image, int, Image.Image]:
"""
Gets the requested element from the dataset.
:param index: index of the element to be returned
:returns: tuple: (image, target) where target is index of the target class.
"""
img, target = self.data[index], self.targets[index]
img = Image.open(img).convert('RGB')
original_img = img.copy()
not_aug_img = self.not_aug_transform(original_img)
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
target = self.target_transform(target)
if not self.train:
return img, target
if hasattr(self, 'logits'):
return img, target, not_aug_img, self.logits[index]
return img, target, not_aug_img
[docs]
class SequentialCropDisease(ContinualDataset):
NAME = 'seq-cropdisease'
SETTING = 'class-il'
N_TASKS = 7
N_CLASSES = 35
N_CLASSES_PER_TASK = N_CLASSES // N_TASKS
SIZE = (224, 224)
MEAN, STD = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
TRANSFORM = transforms.Compose([
transforms.RandomResizedCrop(SIZE, interpolation=InterpolationMode.BICUBIC),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=MEAN, std=STD),
])
TEST_TRANSFORM = transforms.Compose([
transforms.Resize(size=SIZE, interpolation=InterpolationMode.BICUBIC),
transforms.CenterCrop(SIZE),
transforms.ToTensor(),
transforms.Normalize(mean=MEAN, std=STD),
])
[docs]
def get_data_loaders(self):
train_dataset = CropDisease(base_path() + 'cropdisease', train=True,
download=True, transform=self.TRANSFORM)
test_dataset = CropDisease(base_path() + 'cropdisease', train=False,
download=True, transform=self.TEST_TRANSFORM)
train, test = store_masked_loaders(train_dataset, test_dataset, self)
return train, test
[docs]
def get_class_names(self):
if self.class_names is not None:
return self.class_names
classes = [x.replace('_', ' ') for x in CropDisease.LABELS] # .split('___')[-1]
classes = fix_class_names_order(classes, self.args)
self.class_names = classes
return self.class_names
[docs]
@staticmethod
def get_prompt_templates():
return templates['cifar100']
[docs]
@set_default_from_args("backbone")
def get_backbone():
return "vit"
[docs]
@staticmethod
def get_loss():
return F.cross_entropy
[docs]
@set_default_from_args('n_epochs')
def get_epochs(self):
return 5
[docs]
@set_default_from_args('batch_size')
def get_batch_size(self):
return 128