totensor = T.ToTensor()
normalize = T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
composition = T.Compose([totensor, normalize])
# Charge le dataset d'entraînement
train_set = ImageFolder(root='data/lego/lego/train', transform=composition)
# Crée un générateur aléatoire avec la seed
context_random = random.Random(RANDOM_SEED)
# Selectionne 10% du jeu de test aléatoirement pour alléger le calcul
test_set = ImageFolder('data/lego/lego/test', transform=composition)
idx = context_random.sample(range(len(test_set)),
k=int(0.1 * len(test_set)))
test_set.samples = [test_set.samples[i] for i in idx]
# TODO Q2C
# Créer les dataloader pytorch avec la
# classe DataLoader de pytorch
train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_set, batch_size=batch_size, shuffle=True)
# TODO Q2C
# Instancier un réseau LegoNet
# dans une variable nommée "model"
model = LegoNet(pretrained)
#Si load est vrai on charge le modèle entrainé et sauvegardé dans
#le repertoire courrant
load = True
def get_ham10000_gray_normal(dataset_path, batch_size):
####MY IMAGES FOLDER
#### CONSTANTS
transform = transforms.Compose([
transforms.Grayscale(num_output_channels=1),
transforms.ToTensor(),
transforms.Normalize(
mean=(0.5), std=(0.5)
) #transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
])
dataset = ImageFolder(root=dataset_path, transform=transform)
print("BEFORE total samples = " + str(len(dataset.samples)))
print("BEFORE total labels = " + str(len(dataset.targets)))
dataset_size = len(dataset)
classes = dataset.classes
num_classes = len(dataset.classes)
img_dict = {}
for i in range(num_classes):
img_dict[classes[i]] = 0
for i in range(dataset_size):
img, label = dataset[i]
img_dict[classes[label]] += 1
print(img_dict)
#Oversampling INIT
x_train = dataset.samples
y_train = dataset.targets
sampling_seed = 0
print("before")
print(type(x_train))
print(type(x_train[0]))
print(type(y_train))
print(type(y_train[0]))
print(x_train[0])
print(y_train[0])
from imblearn.over_sampling import RandomOverSampler
sampler = RandomOverSampler(random_state=sampling_seed)
#print(type(dataset.samples[0]))
#print(type(dataset.targets[0]))
x_train, y_train = sampler.fit_resample(x_train, y_train)
x_train = list(map(lambda x: tuple(x), x_train))
#y_train = y_train.tolist()
print("\n\nafter")
print(type(x_train))
print(type(x_train[0]))
print(type(y_train))
print(type(y_train[0]))
print(x_train[0])
print(y_train[0])
dataset.samples = x_train
dataset.targets = y_train
#Oversampling END
print("total samples = " + str(len(dataset.samples)))
print("total labels = " + str(len(dataset.targets)))
dataset_size = len(dataset)
classes = dataset.classes
num_classes = len(dataset.classes)
img_dict = {}
for i in range(num_classes):
img_dict[classes[i]] = 0
for i in range(dataset_size):
img, label = dataset[i]
img_dict[classes[int(label)]] += 1
print(img_dict)
#exit(0)
### NEW TRANSFORMS INIT
#dataset.transform = transforms.Compose([ #transforms.Resize((input_size,input_size)),
# transforms.Grayscale(num_output_channels=1),
# transforms.RandomHorizontalFlip(),
# transforms.RandomVerticalFlip(),transforms.RandomRotation(20),
# transforms.ColorJitter(brightness=0.1, contrast=0.1, hue=0.1),
# transforms.ToTensor(), transforms.Normalize(0.5, 0.5)])
#
### NEW TRANSFORMS END
data_loader = torch.utils.data.DataLoader(
dataset,
batch_size=batch_size,
num_workers=8, #num_workers=0,
pin_memory=True, # TODO -> Cuidado puede dar error
shuffle=True)
return data_loader
