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dataset.py
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dataset.py
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from .imports import *
from .torch_imports import *
from .core import *
from .transforms import *
from .layer_optimizer import *
from .dataloader import DataLoader
def get_cv_idxs(n, cv_idx=0, val_pct=0.2, seed=42):
np.random.seed(seed)
n_val = int(val_pct*n)
idx_start = cv_idx*n_val
idxs = np.random.permutation(n)
return idxs[idx_start:idx_start+n_val]
def resize_img(fname, targ, path, new_path):
dest = os.path.join(path,new_path,str(targ),fname)
if os.path.exists(dest): return
im = Image.open(os.path.join(path, fname)).convert('RGB')
r,c = im.size
ratio = targ/min(r,c)
sz = (scale_to(r, ratio, targ), scale_to(c, ratio, targ))
os.makedirs(os.path.split(dest)[0], exist_ok=True)
im.resize(sz, Image.LINEAR).save(dest)
def resize_imgs(fnames, targ, path, new_path):
if not os.path.exists(os.path.join(path,new_path,str(targ),fnames[0])):
with ThreadPoolExecutor(8) as e:
ims = e.map(lambda x: resize_img(x, targ, path, 'tmp'), fnames)
for x in tqdm(ims, total=len(fnames), leave=False): pass
return os.path.join(path,new_path,str(targ))
def read_dir(path, folder):
# TODO: warn or error if no files found?
full_path = os.path.join(path, folder)
fnames = iglob(f"{full_path}/*.*")
return [os.path.relpath(f,path) for f in fnames]
def read_dirs(path, folder):
labels, filenames, all_labels = [], [], []
full_path = os.path.join(path, folder)
for label in sorted(os.listdir(full_path)):
all_labels.append(label)
for fname in os.listdir(os.path.join(full_path, label)):
filenames.append(os.path.join(folder, label, fname))
labels.append(label)
return filenames, labels, all_labels
def n_hot(ids, c):
res = np.zeros((c,), dtype=np.float32)
res[ids] = 1
return res
def folder_source(path, folder):
fnames, lbls, all_labels = read_dirs(path, folder)
label2idx = {v:k for k,v in enumerate(all_labels)}
idxs = [label2idx[lbl] for lbl in lbls]
c = len(all_labels)
label_arr = np.array(idxs, dtype=int)
return fnames, label_arr, all_labels
def parse_csv_labels(fn, skip_header=True):
skip = 1 if skip_header else 0
csv_lines = [o.strip().split(',') for o in open(fn)][skip:]
csv_labels = {a:b.split(' ') for a,b in csv_lines}
all_labels = sorted(list(set(p for o in csv_labels.values() for p in o)))
label2idx = {v:k for k,v in enumerate(all_labels)}
return sorted(csv_labels.keys()), csv_labels, all_labels, label2idx
def nhot_labels(label2idx, csv_labels, fnames, c):
all_idx = {k: n_hot([label2idx[o] for o in v], c)
for k,v in csv_labels.items()}
return np.stack([all_idx[o] for o in fnames])
def csv_source(folder, csv_file, skip_header=True, suffix='', continuous=False):
fnames,csv_labels,all_labels,label2idx = parse_csv_labels(csv_file, skip_header)
full_names = [os.path.join(folder,fn+suffix) for fn in fnames]
if continuous:
label_arr = np.array([csv_labels[i] for i in fnames]).astype(np.float32)
else:
label_arr = nhot_labels(label2idx, csv_labels, fnames, len(all_labels))
is_single = np.all(label_arr.sum(axis=1)==1)
if is_single: label_arr = np.argmax(label_arr, axis=1)
return full_names, label_arr, all_labels
class BaseDataset(Dataset):
def __init__(self, transform=None):
self.transform = transform
self.n = self.get_n()
self.c = self.get_c()
self.sz = self.get_sz()
def __getitem__(self, idx):
x,y = self.get_x(idx),self.get_y(idx)
return self.get(self.transform, x, y)
def __len__(self): return self.n
def get(self, tfm, x, y):
return (x,y) if tfm is None else tfm(x,y)
@abstractmethod
def get_n(self): raise NotImplementedError
@abstractmethod
def get_c(self): raise NotImplementedError
@abstractmethod
def get_sz(self): raise NotImplementedError
@abstractmethod
def get_x(self, i): raise NotImplementedError
@abstractmethod
def get_y(self, i): raise NotImplementedError
@property
def is_multi(self): return False
@property
def is_reg(self): return False
class FilesDataset(BaseDataset):
def __init__(self, fnames, transform, path):
self.path,self.fnames = path,fnames
super().__init__(transform)
def get_n(self): return len(self.y)
def get_sz(self): return self.transform.sz
def get_x(self, i):
flags = cv2.IMREAD_UNCHANGED+cv2.IMREAD_ANYDEPTH+cv2.IMREAD_ANYCOLOR
fn = os.path.join(self.path, self.fnames[i])
return cv2.cvtColor(cv2.imread(fn, flags), cv2.COLOR_BGR2RGB).astype(np.float32)/255
def resize_imgs(self, targ, new_path):
dest = resize_imgs(self.fnames, targ, self.path, new_path)
return self.__class__(self.fnames, self.y, self.transform, dest)
def denorm(self,arr):
"""Reverse the normalization done to a batch of images.
Arguments:
arr: of shape/size (N,3,sz,sz)
"""
if type(arr) is not np.ndarray: arr = to_np(arr)
if len(arr.shape)==3: arr = arr[None]
return self.transform.denorm(np.rollaxis(arr,1,4))
class FilesArrayDataset(FilesDataset):
def __init__(self, fnames, y, transform, path):
self.y=y
assert(len(fnames)==len(y))
super().__init__(fnames, transform, path)
def get_y(self, i): return self.y[i]
def get_c(self): return self.y.shape[1]
class FilesIndexArrayDataset(FilesArrayDataset):
def get_c(self): return int(self.y.max())+1
class FilesNhotArrayDataset(FilesArrayDataset):
@property
def is_multi(self): return True
class FilesIndexArrayRegressionDataset(FilesArrayDataset):
def is_reg(self): return True
class ArraysDataset(BaseDataset):
def __init__(self, x, y, transform):
self.x,self.y=x,y
assert(len(x)==len(y))
super().__init__(transform)
def get_x(self, i): return self.x[i]
def get_y(self, i): return self.y[i]
def get_n(self): return len(self.y)
def get_sz(self): return self.x.shape[1]
class ArraysIndexDataset(ArraysDataset):
def get_c(self): return int(self.y.max())+1
class ArraysNhotDataset(ArraysDataset):
def get_c(self): return self.y.shape[1]
@property
def is_multi(self): return True
class ModelData():
def __init__(self, path, trn_dl, val_dl, test_dl=None):
self.path,self.trn_dl,self.val_dl,self.test_dl = path,trn_dl,val_dl,test_dl
@classmethod
def from_dls(cls, path,trn_dl,val_dl,test_dl=None):
trn_dl,val_dl = ModelDataLoader(trn_dl),ModelDataLoader(val_dl)
if test_dl: test_dl = ModelDataLoader(test_dl)
return cls(path, trn_dl, val_dl, test_dl)
@property
def is_reg(self): return self.trn_ds.is_reg
@property
def trn_ds(self): return self.trn_dl.dataset
@property
def val_ds(self): return self.val_dl.dataset
@property
def test_ds(self): return self.test_dl.dataset
@property
def trn_y(self): return self.trn_ds.y
@property
def val_y(self): return self.val_ds.y
class ModelDataLoader():
def __init__(self, dl): self.dl=dl
@classmethod
def create_dl(cls, *args, **kwargs): return cls(DataLoader(*args, **kwargs))
def __iter__(self):
self.it,self.i = iter(self.dl),0
return self
def __len__(self): return len(self.dl)
def __next__(self):
if self.i>=len(self.dl): raise StopIteration
self.i+=1
return next(self.it)
@property
def dataset(self): return self.dl.dataset
class ImageData(ModelData):
def __init__(self, path, datasets, bs, num_workers, classes):
trn_ds,val_ds,fix_ds,aug_ds,test_ds,test_aug_ds = datasets
self.path,self.bs,self.num_workers,self.classes = path,bs,num_workers,classes
self.trn_dl,self.val_dl,self.fix_dl,self.aug_dl,self.test_dl,self.test_aug_dl = [
self.get_dl(ds,shuf) for ds,shuf in [
(trn_ds,True),(val_ds,False),(fix_ds,False),(aug_ds,False),
(test_ds,False),(test_aug_ds,False)
]
]
def get_dl(self, ds, shuffle):
if ds is None: return None
return ModelDataLoader.create_dl(ds, batch_size=self.bs, shuffle=shuffle,
num_workers=self.num_workers, pin_memory=False)
@property
def sz(self): return self.trn_ds.sz
@property
def c(self): return self.trn_ds.c
def resized(self, dl, targ, new_path):
return dl.dataset.resize_imgs(targ,new_path) if dl else None
def resize(self, targ, new_path):
new_ds = []
dls = [self.trn_dl,self.val_dl,self.fix_dl,self.aug_dl]
if self.test_dl: dls += [self.test_dl, self.test_aug_dl]
else: dls += [None,None]
t = tqdm_notebook(dls)
for dl in t: new_ds.append(self.resized(dl, targ, new_path))
t.close()
return self.__class__(new_ds[0].path, new_ds, self.bs, self.num_workers, self.classes)
class ImageClassifierData(ImageData):
@property
def is_multi(self): return self.trn_dl.dataset.is_multi
@staticmethod
def get_ds(fn, trn, val, tfms, test=None, **kwargs):
res = [
fn(trn[0], trn[1], tfms[0], **kwargs), # train
fn(val[0], val[1], tfms[1], **kwargs), # val
fn(trn[0], trn[1], tfms[1], **kwargs), # fix
fn(val[0], val[1], tfms[0], **kwargs) # aug
]
if test is not None:
test_lbls = np.zeros((len(test),1))
res += [
fn(test, test_lbls, tfms[1], **kwargs), # test
fn(test, test_lbls, tfms[0], **kwargs) # test_aug
]
else: res += [None,None]
return res
@classmethod
def from_arrays(cls, path, trn, val, bs=64, tfms=(None,None), classes=None, num_workers=4, test=None):
""" Read in images and their labels given as numpy arrays
Arguments:
path: a root path of the data (used for storing trained models, precomputed values, etc)
trn: a tuple of training data matrix and target label/classification array (e.g. `trn=(x,y)` where `x` has the
shape of `(5000, 784)` and `y` has the shape of `(5000,)`)
val: a tuple of validation data matrix and target label/classification array.
bs: batch size
tfms: transformations (for data augmentations). e.g. output of `tfms_from_model`
classes: a list of all labels/classifications
num_workers: a number of workers
test: a matrix of test data (the shape should match `trn[0]`)
Returns:
ImageClassifierData
"""
datasets = cls.get_ds(ArraysIndexDataset, trn, val, tfms, test=test)
return cls(path, datasets, bs, num_workers, classes=classes)
@classmethod
def from_paths(cls, path, bs=64, tfms=(None,None), trn_name='train', val_name='valid', test_name=None, num_workers=8):
""" Read in images and their labels given as sub-folder names
Arguments:
path: a root path of the data (used for storing trained models, precomputed values, etc)
bs: batch size
tfms: transformations (for data augmentations). e.g. output of `tfms_from_model`
trn_name: a name of the folder that contains training images.
val_name: a name of the folder that contains validation images.
test_name: a name of the folder that contains test images.
num_workers: number of workers
Returns:
ImageClassifierData
"""
trn,val = [folder_source(path, o) for o in (trn_name, val_name)]
test_fnames = read_dir(path, test_name) if test_name else None
datasets = cls.get_ds(FilesIndexArrayDataset, trn, val, tfms, path=path, test=test_fnames)
return cls(path, datasets, bs, num_workers, classes=trn[2])
@classmethod
def from_csv(cls, path, folder, csv_fname, bs=64, tfms=(None,None),
val_idxs=None, suffix='', test_name=None, continuous=False, skip_header=True, num_workers=8):
""" Read in images and their labels given as a CSV file.
This method should be used when training image labels are given in an CSV file as opposed to
sub-directories with label names.
Arguments:
path: a root path of the data (used for storing trained models, precomputed values, etc)
folder: a name of the folder in which training images are contained.
csv_fname: a name of the CSV file which contains target labels.
bs: batch size
tfms: transformations (for data augmentations). e.g. output of `tfms_from_model`
val_idxs: index of images to be used for validation. e.g. output of `get_cv_idxs`
suffix: suffix to add to image names in CSV file (sometimes CSV only contains the file name without file
extension e.g. '.jpg' - in which case, you can set suffix as '.jpg')
test_name: a name of the folder which contains test images.
continuous: TODO
skip_header: skip the first row of the CSV file.
num_workers: number of workers
Returns:
ImageClassifierData
"""
fnames,y,classes = csv_source(folder, csv_fname, skip_header, suffix, continuous=continuous)
((val_fnames,trn_fnames),(val_y,trn_y)) = split_by_idx(val_idxs, np.array(fnames), y)
test_fnames = read_dir(path, test_name) if test_name else None
if continuous:
f = FilesIndexArrayRegressionDataset
else:
f = FilesIndexArrayDataset if len(trn_y.shape)==1 else FilesNhotArrayDataset
datasets = cls.get_ds(f, (trn_fnames,trn_y), (val_fnames,val_y), tfms,
path=path, test=test_fnames)
return cls(path, datasets, bs, num_workers, classes=classes)
def split_by_idx(idxs, *a):
mask = np.zeros(len(a[0]),dtype=bool)
mask[np.array(idxs)] = True
return [(o[mask],o[~mask]) for o in a]