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import os.path as osp
import numpy as np
import numpy.random as npr
import PIL
import torch
import torchvision
import xml.etree.ElementTree as ET
import json
import copy
import math
def singleton(class_):
instances = {}
def getinstance(*args, **kwargs):
if class_ not in instances:
instances[class_] = class_(*args, **kwargs)
return instances[class_]
return getinstance
@singleton
class get_transform(object):
def __init__(self):
self.transform = {}
def register(self, transf):
self.transform[transf.__name__] = transf
def __call__(self, cfg):
if cfg is None:
return None
if isinstance(cfg, list):
loader = []
for ci in cfg:
t = ci.type
loader.append(self.transform[t](**ci.args))
return compose(loader)
t = cfg.type
return self.transform[t](**cfg.args)
def register():
def wrapper(class_):
get_transform().register(class_)
return class_
return wrapper
def have(must=[], may=[]):
"""
The nextgen decorator that have two list of
input tells what category the transform
will operate on.
Args:
must: [] of str,
the names of the items that must be included
inside the element.
If element[name] exist: do the transform
If element[name] is None: raise Exception.
If element[name] not exist: raise Exception.
may: [] of str,
the names of the items that may be contained
inside the element for transform.
If element[name] exist: do the transform
If element[name] is None: ignore it.
If element[name] not exist: ignore it.
"""
def route(self, item, e, d):
"""
Route the element to a proper function
for calculation.
Args:
self: object,
the transform functor.
item: str,
the item name of the data.
e: {},
the element
d: nparray, tensor or PIL.Image,
the data to transform.
"""
if isinstance(d, np.ndarray):
dtype = 'nparray'
elif isinstance(d, torch.Tensor):
dtype = 'tensor'
elif isinstance(d, PIL.Image.Image):
dtype = 'pilimage'
else:
raise ValueError
# find function by order
f = None
for attrname in [
'exec_{}_{}'.format(item, dtype),
'exec_{}'.format(item),
'exec_{}'.format(dtype),
'exec']:
f = getattr(self, attrname, None)
if f is not None:
break
d, e = f(d, e)
e[item] = d
return e
def wrapper(func):
def inner(self, e):
e['imsize_previous'] = e['imsize_current']
imsize_tag_cnt = 0
imsize_tag = 'imsize_before_' + self.__class__.__name__
while True:
if imsize_tag_cnt != 0:
tag = imsize_tag + str(imsize_tag_cnt)
else:
tag = imsize_tag
if not tag in e:
e[tag] = e['imsize_current']
break
imsize_tag_cnt += 1
e = func(self, e)
# must transform list
for item in must:
try:
d = e[item]
except:
raise ValueError
if d is None:
raise ValueError
e = route(self, item, e, d)
# may transform list
for item in may:
try:
d = e[item]
except:
d = None
if d is not None:
e = route(self, item, e, d)
return e
return inner
return wrapper
class compose(object):
def __init__(self, transforms):
self.transforms = transforms
def __call__(self, element):
for t in self.transforms:
element = t(element)
return element
class TBase(object):
def __init__(self):
pass
def exec(self, data, element):
raise ValueError
def rand(self,
uid,
tag,
rand_f,
*args,
**kwargs):
"""
Args:
uid: string element['unique_id']
tag: string tells the tag uses when tracking the random number.
Or the tag to restore the tracked random number.
rand_f: the random function use to generate random number.
**kwargs: the argument for the given random function.
"""
# if rnduh().hdata is not None:
# return rnduh().get_history(uid, self.__class__.__name__, tag)
# if rnduh().record_path is None:
# return rand_f(*args, **kwargs)
# the special mode to create the random file.
d = rand_f(*args, **kwargs)
# rnduh().record(uid, self.__class__.__name__, tag, d)
return d
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