Esempi in Python per cifar10_policies

Esempio n. 1

File: augment.py Progetto: peternara/fixmatch-aug-image-classification

import functools
import itertools
import multiprocessing
import random

import numpy as np
import tensorflow as tf
from absl import flags

from libml import utils, ctaugment
from libml.utils import EasyDict
from third_party.auto_augment import augmentations, policies

FLAGS = flags.FLAGS
POOL = None
POLICIES = EasyDict(cifar10=policies.cifar10_policies(),
                    cifar100=policies.cifar10_policies(),
                    svhn=policies.svhn_policies(),
                    svhn_noextra=policies.svhn_policies())

RANDOM_POLICY_OPS = (
    'Identity', 'AutoContrast', 'Equalize', 'Rotate',
    'Solarize', 'Color', 'Contrast', 'Brightness',
    'Sharpness', 'ShearX', 'TranslateX', 'TranslateY',
    'Posterize', 'ShearY'
)
AUGMENT_ENUM = 'd x m aa aac ra rac'.split() + ['r%d_%d_%d' % (nops, mag, cutout) for nops, mag, cutout in
                                                itertools.product(range(1, 5), range(1, 16), range(0, 100, 25))] + [
                   'rac%d' % (mag) for mag in range(1, 10)]

flags.DEFINE_integer('K', 1, 'Number of strong augmentation for unlabeled data.')

Esempio n. 2

import functools
import itertools
import multiprocessing
import random

import numpy as np
import tensorflow as tf
from absl import flags

from libml import utils, ctaugment
from libml.utils import EasyDict
from third_party.auto_augment import augmentations, policies

FLAGS = flags.FLAGS
POOL = None
POLICIES = EasyDict(fixmatch_train=policies.cifar10_policies())

RANDOM_POLICY_OPS = (
    'Identity', 'AutoContrast', 'Equalize', 'Rotate',
    'Solarize', 'Color', 'Contrast', 'Brightness',
    'Sharpness', 'ShearX', 'TranslateX', 'TranslateY',
    'Posterize', 'ShearY'
)
AUGMENT_ENUM = 'd x m aa aac ra rac'.split() + ['r%d_%d_%d' % (nops, mag, cutout) for nops, mag, cutout in
                                                itertools.product(range(1, 5), range(1, 16), range(0, 100, 25))] + [
                   'rac%d' % (mag) for mag in range(1, 10)]

flags.DEFINE_integer('K', 1, 'Number of strong augmentation for unlabeled data.')
flags.DEFINE_enum('augment', 'd.d',
                  [x + '.' + y for x, y in itertools.product(AUGMENT_ENUM, AUGMENT_ENUM)] +
                  [x + '.' + y + '.' + z for x, y, z in itertools.product(AUGMENT_ENUM, AUGMENT_ENUM, AUGMENT_ENUM)] + [

Esempio n. 3

import itertools
import multiprocessing
import random

import numpy as np
import tensorflow as tf
from absl import flags

from libml import utils, ctaugment
from libml.utils import EasyDict
from third_party.auto_augment import augmentations, policies

FLAGS = flags.FLAGS
POOL = None
POLICIES = EasyDict(
    cifar10=policies.cifar10_policies(),
    cifar10p=policies.cifar10_policies(),
    #                    color=policies.color_policies(),
    cifar10imb=policies.cifar10_policies(),
    cifar100=policies.cifar10_policies(),
    svhn=policies.svhn_policies(),
    svhnp=policies.svhn_policies(),
    svhnp_noextra=policies.svhn_policies(),
    svhn_noextra=policies.svhn_policies())

RANDOM_POLICY_OPS = ('Identity', 'AutoContrast', 'Equalize', 'Rotate',
                     'Solarize', 'Color', 'Contrast', 'Brightness',
                     'Sharpness', 'ShearX', 'TranslateX', 'TranslateY',
                     'Posterize', 'ShearY')
AUGMENT_ENUM = 'd x m aa aac aacc ra rac'.split() + [
    'r%d_%d_%d' % (nops, mag, cutout)