Interesting.
For 3. I augmented data at the DataSet level by rolling over the index
class AugmentedAmazonDataset(Dataset):
"""Dataset wrapping images and target labels for Kaggle - Planet Amazon from Space competition.
This dataset is augmented
Arguments:
A CSV file path
Path to image folder
Extension of images
"""
def __init__(self, csv_path, img_path, img_ext, transform=None):
tmp_df = pd.read_csv(csv_path)
assert tmp_df['image_name'].apply(lambda x: os.path.isfile(img_path + x + img_ext)).all(), \
"Some images referenced in the CSV file were not found"
self.mlb = MultiLabelBinarizer()
self.img_path = img_path
self.img_ext = img_ext
self.transform = transform
self.X_train = tmp_df['image_name']
self.y_train = self.mlb.fit_transform(tmp_df['tags'].str.split()).astype(np.float32)
self.augmentNumber = 14 # TODO, do something about this harcoded value
def __getitem__(self, index):
real_length = self.real_length()
real_index = index % real_length
img = Image.open(self.img_path + self.X_train[real_index] + self.img_ext)
img = img.convert('RGB')
## Augmentation code
if 0 <= index < real_length:
pass
### Mirroring and Rotating
elif real_length <= index < 2 * real_length:
img = img.transpose(FLIP_LEFT_RIGHT)
elif 2 * real_length <= index < 3 * real_length:
img = img.transpose(FLIP_TOP_BOTTOM)
elif 3 * real_length <= index < 4 * real_length:
img = img.transpose(ROTATE_90)
elif 4 * real_length <= index < 5 * real_length:
img = img.transpose(ROTATE_180)
elif 5 * real_length <= index < 6 * real_length:
img = img.transpose(ROTATE_270)
### Color balance
elif 6 * real_length <= index < 7 * real_length:
img = Color(img).enhance(0.95)
elif 7 * real_length <= index < 8 * real_length:
img = Color(img).enhance(1.05)
## Contrast
elif 8 * real_length <= index < 9 * real_length:
img = Contrast(img).enhance(0.95)
elif 9 * real_length <= index < 10 * real_length:
img = Contrast(img).enhance(1.05)
## Brightness
elif 10 * real_length <= index < 11 * real_length:
img = Brightness(img).enhance(0.95)
elif 11 * real_length <= index < 12 * real_length:
img = Brightness(img).enhance(1.05)
## Sharpness
elif 12 * real_length <= index < 13 * real_length:
img = Sharpness(img).enhance(0.95)
elif 13 * real_length <= index < 14 * real_length:
img = Sharpness(img).enhance(1.05)
else:
raise IndexError("Index out of bounds")
if self.transform is not None:
img = self.transform(img)
label = from_numpy(self.y_train[real_index])
return img, label
def __len__(self):
return len(self.X_train.index) * self.augmentNumber
def real_length(self):
return len(self.X_train.index)
For the point 2: train_valid_split I use the following routines:
def augmented_train_valid_split(dataset, test_size = 0.25, shuffle = False, random_seed = 0):
""" Return a list of splitted indices from a DataSet.
Indices can be used with DataLoader to build a train and validation set.
Arguments:
A Dataset
A test_size, as a float between 0 and 1 (percentage split) or as an int (fixed number split)
Shuffling True or False
Random seed
"""
length = dataset.real_length()
indices = list(range(1,length))
if shuffle == True:
random.seed(random_seed)
random.shuffle(indices)
if type(test_size) is float:
split = floor(test_size * length)
elif type(test_size) is int:
split = test_size
else:
raise ValueError('%s should be an int or a float' % str)
return indices[split:], indices[:split]
So my full loading code is:
# Loading the dataset
# ds_transform = transforms.Compose([transforms.RandomCrop(224),transforms.ToTensor()])
ds_transform = transforms.ToTensor()
X_train = AugmentedAmazonDataset('./data/train.csv','./data/train-jpg/','.jpg',
ds_transform
)
# Creating a validation split
train_idx, valid_idx = augmented_train_valid_split(X_train, 15000)
nb_augment = X_train.augmentNumber
augmented_train_idx = [i * nb_augment + idx for idx in train_idx for i in range(0,nb_augment)]
train_sampler = SubsetRandomSampler(augmented_train_idx)
valid_sampler = SubsetRandomSampler(valid_idx)
# Both dataloader loads from the same dataset but with different indices
train_loader = DataLoader(X_train,
batch_size=32,
sampler=train_sampler,
num_workers=4,
pin_memory=True)
valid_loader = DataLoader(X_train,
batch_size=64,
sampler=valid_sampler,
num_workers=1,
pin_memory=True)