Merge pull request #1169 from TingquanGao/dev/fix_augmentation

fix: fix augmentation

ppcls/data/preprocess/__init__.py

@@ -14,6 +14,7 @@
 
 from ppcls.data.preprocess.ops.autoaugment import ImageNetPolicy as RawImageNetPolicy
 from ppcls.data.preprocess.ops.randaugment import RandAugment as RawRandAugment
+from ppcls.data.preprocess.ops.timm_autoaugment import RawTimmAutoAugment
 from ppcls.data.preprocess.ops.cutout import Cutout
 
 from ppcls.data.preprocess.ops.hide_and_seek import HideAndSeek
@@ -31,7 +32,6 @@ from ppcls.data.preprocess.ops.operators import AugMix
 
 from ppcls.data.preprocess.batch_ops.batch_operators import MixupOperator, CutmixOperator, OpSampler, FmixOperator
 
-import six
 import numpy as np
 from PIL import Image
 
@@ -47,20 +47,14 @@ class AutoAugment(RawImageNetPolicy):
     """ ImageNetPolicy wrapper to auto fit different img types """
 
     def __init__(self, *args, **kwargs):
-        if six.PY2:
-            super(AutoAugment, self).__init__(*args, **kwargs)
-        else:
-            super().__init__(*args, **kwargs)
+        super().__init__(*args, **kwargs)
 
     def __call__(self, img):
         if not isinstance(img, Image.Image):
             img = np.ascontiguousarray(img)
             img = Image.fromarray(img)
 
-        if six.PY2:
-            img = super(AutoAugment, self).__call__(img)
-        else:
-            img = super().__call__(img)
+        img = super().__call__(img)
 
         if isinstance(img, Image.Image):
             img = np.asarray(img)
@@ -72,20 +66,33 @@ class RandAugment(RawRandAugment):
     """ RandAugment wrapper to auto fit different img types """
 
     def __init__(self, *args, **kwargs):
-        if six.PY2:
-            super(RandAugment, self).__init__(*args, **kwargs)
-        else:
-            super().__init__(*args, **kwargs)
+        super().__init__(*args, **kwargs)
 
     def __call__(self, img):
         if not isinstance(img, Image.Image):
             img = np.ascontiguousarray(img)
             img = Image.fromarray(img)
 
-        if six.PY2:
-            img = super(RandAugment, self).__call__(img)
-        else:
-            img = super().__call__(img)
+        img = super().__call__(img)
+
+        if isinstance(img, Image.Image):
+            img = np.asarray(img)
+
+        return img
+
+
+class TimmAutoAugment(RawTimmAutoAugment):
+    """ TimmAutoAugment wrapper to auto fit different img tyeps. """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def __call__(self, img):
+        if not isinstance(img, Image.Image):
+            img = np.ascontiguousarray(img)
+            img = Image.fromarray(img)
+
+        img = super().__call__(img)
 
         if isinstance(img, Image.Image):
             img = np.asarray(img)

ppcls/data/preprocess/ops/operators.py

@@ -26,6 +26,7 @@ import random
 import cv2
 import numpy as np
 from PIL import Image
+from paddle.vision.transforms import ColorJitter as RawColorJitter
 
 from .autoaugment import ImageNetPolicy
 from .functional import augmentations
@@ -363,3 +364,20 @@ class AugMix(object):
 
         mixed = (1 - m) * image + m * mix
         return mixed.astype(np.uint8)
+
+
+class ColorJitter(RawColorJitter):
+    """ColorJitter.
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def __call__(self, img):
+        if not isinstance(img, Image.Image):
+            img = np.ascontiguousarray(img)
+            img = Image.fromarray(img)
+        img = super()._apply_image(img)
+        if isinstance(img, Image.Image):
+            img = np.asarray(img)
+        return img

ppcls/data/preprocess/ops/random_erasing.py

@@ -12,7 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-#This code is based on https://github.com/zhunzhong07/Random-Erasing
+#This code is adapted from https://github.com/zhunzhong07/Random-Erasing, and refer to Timm.
+
+from functools import partial
 
 import math
 import random
@@ -20,36 +22,69 @@ import random
 import numpy as np
 
 
+class Pixels(object):
+    def __init__(self, mode="const", mean=[0., 0., 0.]):
+        self._mode = mode
+        self._mean = mean
+
+    def __call__(self, h=224, w=224, c=3):
+        if self._mode == "rand":
+            return np.random.normal(size=(1, 1, 3))
+        elif self._mode == "pixel":
+            return np.random.normal(size=(h, w, c))
+        elif self._mode == "const":
+            return self._mean
+        else:
+            raise Exception(
+                "Invalid mode in RandomErasing, only support \"const\", \"rand\", \"pixel\""
+            )
+
+
 class RandomErasing(object):
-    def __init__(self, EPSILON=0.5, sl=0.02, sh=0.4, r1=0.3,
-                 mean=[0., 0., 0.]):
-        self.EPSILON = EPSILON
-        self.mean = mean
-        self.sl = sl
-        self.sh = sh
-        self.r1 = r1
+    """RandomErasing.
+    """
+
+    def __init__(self,
+                 EPSILON=0.5,
+                 sl=0.02,
+                 sh=0.4,
+                 r1=0.3,
+                 mean=[0., 0., 0.],
+                 attempt=100,
+                 use_log_aspect=False,
+                 mode='const'):
+        self.EPSILON = eval(EPSILON) if isinstance(EPSILON, str) else EPSILON
+        self.sl = eval(sl) if isinstance(sl, str) else sl
+        self.sh = eval(sh) if isinstance(sh, str) else sh
+        r1 = eval(r1) if isinstance(r1, str) else r1
+        self.r1 = (math.log(r1), math.log(1 / r1)) if use_log_aspect else (
+            r1, 1 / r1)
+        self.use_log_aspect = use_log_aspect
+        self.attempt = attempt
+        self.get_pixels = Pixels(mode, mean)
 
     def __call__(self, img):
-        if random.uniform(0, 1) > self.EPSILON:
+        if random.random() > self.EPSILON:
             return img
 
-        for _ in range(100):
+        for _ in range(self.attempt):
             area = img.shape[0] * img.shape[1]
 
             target_area = random.uniform(self.sl, self.sh) * area
-            aspect_ratio = random.uniform(self.r1, 1 / self.r1)
+            aspect_ratio = random.uniform(*self.r1)
+            if self.use_log_aspect:
+                aspect_ratio = math.exp(aspect_ratio)
 
             h = int(round(math.sqrt(target_area * aspect_ratio)))
             w = int(round(math.sqrt(target_area / aspect_ratio)))
 
             if w < img.shape[1] and h < img.shape[0]:
+                pixels = self.get_pixels(h, w, img.shape[2])
                 x1 = random.randint(0, img.shape[0] - h)
                 y1 = random.randint(0, img.shape[1] - w)
-                if img.shape[0] == 3:
-                    img[x1:x1 + h, y1:y1 + w, 0] = self.mean[0]
-                    img[x1:x1 + h, y1:y1 + w, 1] = self.mean[1]
-                    img[x1:x1 + h, y1:y1 + w, 2] = self.mean[2]
+                if img.shape[2] == 3:
+                    img[x1:x1 + h, y1:y1 + w, :] = pixels
                 else:
-                    img[0, x1:x1 + h, y1:y1 + w] = self.mean[1]
+                    img[x1:x1 + h, y1:y1 + w, 0] = pixels[0]
                 return img
         return img

ppcls/data/preprocess/ops/timm_autoaugment.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+This code implements is borrowed from Timm: https://github.com/rwightman/pytorch-image-models.
+hacked together by / Copyright 2020 Ross Wightman
+"""
+
+import random
+import math
+import re
+from PIL import Image, ImageOps, ImageEnhance, ImageChops
+import PIL
+import numpy as np
+
+IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
+
+_PIL_VER = tuple([int(x) for x in PIL.__version__.split('.')[:2]])
+
+_FILL = (128, 128, 128)
+
+# This signifies the max integer that the controller RNN could predict for the
+# augmentation scheme.
+_MAX_LEVEL = 10.
+
+_HPARAMS_DEFAULT = dict(
+    translate_const=250,
+    img_mean=_FILL, )
+
+_RANDOM_INTERPOLATION = (Image.BILINEAR, Image.BICUBIC)
+
+
+def _pil_interp(method):
+    if method == 'bicubic':
+        return Image.BICUBIC
+    elif method == 'lanczos':
+        return Image.LANCZOS
+    elif method == 'hamming':
+        return Image.HAMMING
+    else:
+        # default bilinear, do we want to allow nearest?
+        return Image.BILINEAR
+
+
+def _interpolation(kwargs):
+    interpolation = kwargs.pop('resample', Image.BILINEAR)
+    if isinstance(interpolation, (list, tuple)):
+        return random.choice(interpolation)
+    else:
+        return interpolation
+
+
+def _check_args_tf(kwargs):
+    if 'fillcolor' in kwargs and _PIL_VER < (5, 0):
+        kwargs.pop('fillcolor')
+    kwargs['resample'] = _interpolation(kwargs)
+
+
+def shear_x(img, factor, **kwargs):
+    _check_args_tf(kwargs)
+    return img.transform(img.size, Image.AFFINE, (1, factor, 0, 0, 1, 0),
+                         **kwargs)
+
+
+def shear_y(img, factor, **kwargs):
+    _check_args_tf(kwargs)
+    return img.transform(img.size, Image.AFFINE, (1, 0, 0, factor, 1, 0),
+                         **kwargs)
+
+
+def translate_x_rel(img, pct, **kwargs):
+    pixels = pct * img.size[0]
+    _check_args_tf(kwargs)
+    return img.transform(img.size, Image.AFFINE, (1, 0, pixels, 0, 1, 0),
+                         **kwargs)
+
+
+def translate_y_rel(img, pct, **kwargs):
+    pixels = pct * img.size[1]
+    _check_args_tf(kwargs)
+    return img.transform(img.size, Image.AFFINE, (1, 0, 0, 0, 1, pixels),
+                         **kwargs)
+
+
+def translate_x_abs(img, pixels, **kwargs):
+    _check_args_tf(kwargs)
+    return img.transform(img.size, Image.AFFINE, (1, 0, pixels, 0, 1, 0),
+                         **kwargs)
+
+
+def translate_y_abs(img, pixels, **kwargs):
+    _check_args_tf(kwargs)
+    return img.transform(img.size, Image.AFFINE, (1, 0, 0, 0, 1, pixels),
+                         **kwargs)
+
+
+def rotate(img, degrees, **kwargs):
+    _check_args_tf(kwargs)
+    if _PIL_VER >= (5, 2):
+        return img.rotate(degrees, **kwargs)
+    elif _PIL_VER >= (5, 0):
+        w, h = img.size
+        post_trans = (0, 0)
+        rotn_center = (w / 2.0, h / 2.0)
+        angle = -math.radians(degrees)
+        matrix = [
+            round(math.cos(angle), 15),
+            round(math.sin(angle), 15),
+            0.0,
+            round(-math.sin(angle), 15),
+            round(math.cos(angle), 15),
+            0.0,
+        ]
+
+        def transform(x, y, matrix):
+            (a, b, c, d, e, f) = matrix
+            return a * x + b * y + c, d * x + e * y + f
+
+        matrix[2], matrix[5] = transform(-rotn_center[0] - post_trans[0],
+                                         -rotn_center[1] - post_trans[1],
+                                         matrix)
+        matrix[2] += rotn_center[0]
+        matrix[5] += rotn_center[1]
+        return img.transform(img.size, Image.AFFINE, matrix, **kwargs)
+    else:
+        return img.rotate(degrees, resample=kwargs['resample'])
+
+
+def auto_contrast(img, **__):
+    return ImageOps.autocontrast(img)
+
+
+def invert(img, **__):
+    return ImageOps.invert(img)
+
+
+def equalize(img, **__):
+    return ImageOps.equalize(img)
+
+
+def solarize(img, thresh, **__):
+    return ImageOps.solarize(img, thresh)
+
+
+def solarize_add(img, add, thresh=128, **__):
+    lut = []
+    for i in range(256):
+        if i < thresh:
+            lut.append(min(255, i + add))
+        else:
+            lut.append(i)
+    if img.mode in ("L", "RGB"):
+        if img.mode == "RGB" and len(lut) == 256:
+            lut = lut + lut + lut
+        return img.point(lut)
+    else:
+        return img
+
+
+def posterize(img, bits_to_keep, **__):
+    if bits_to_keep >= 8:
+        return img
+    return ImageOps.posterize(img, bits_to_keep)
+
+
+def contrast(img, factor, **__):
+    return ImageEnhance.Contrast(img).enhance(factor)
+
+
+def color(img, factor, **__):
+    return ImageEnhance.Color(img).enhance(factor)
+
+
+def brightness(img, factor, **__):
+    return ImageEnhance.Brightness(img).enhance(factor)
+
+
+def sharpness(img, factor, **__):
+    return ImageEnhance.Sharpness(img).enhance(factor)
+
+
+def _randomly_negate(v):
+    """With 50% prob, negate the value"""
+    return -v if random.random() > 0.5 else v
+
+
+def _rotate_level_to_arg(level, _hparams):
+    # range [-30, 30]
+    level = (level / _MAX_LEVEL) * 30.
+    level = _randomly_negate(level)
+    return level,
+
+
+def _enhance_level_to_arg(level, _hparams):
+    # range [0.1, 1.9]
+    return (level / _MAX_LEVEL) * 1.8 + 0.1,
+
+
+def _enhance_increasing_level_to_arg(level, _hparams):
+    # the 'no change' level is 1.0, moving away from that towards 0. or 2.0 increases the enhancement blend
+    # range [0.1, 1.9]
+    level = (level / _MAX_LEVEL) * .9
+    level = 1.0 + _randomly_negate(level)
+    return level,
+
+
+def _shear_level_to_arg(level, _hparams):
+    # range [-0.3, 0.3]
+    level = (level / _MAX_LEVEL) * 0.3
+    level = _randomly_negate(level)
+    return level,
+
+
+def _translate_abs_level_to_arg(level, hparams):
+    translate_const = hparams['translate_const']
+    level = (level / _MAX_LEVEL) * float(translate_const)
+    level = _randomly_negate(level)
+    return level,
+
+
+def _translate_rel_level_to_arg(level, hparams):
+    # default range [-0.45, 0.45]
+    translate_pct = hparams.get('translate_pct', 0.45)
+    level = (level / _MAX_LEVEL) * translate_pct
+    level = _randomly_negate(level)
+    return level,
+
+
+def _posterize_level_to_arg(level, _hparams):
+    # As per Tensorflow TPU EfficientNet impl
+    # range [0, 4], 'keep 0 up to 4 MSB of original image'
+    # intensity/severity of augmentation decreases with level
+    return int((level / _MAX_LEVEL) * 4),
+
+
+def _posterize_increasing_level_to_arg(level, hparams):
+    # As per Tensorflow models research and UDA impl
+    # range [4, 0], 'keep 4 down to 0 MSB of original image',
+    # intensity/severity of augmentation increases with level
+    return 4 - _posterize_level_to_arg(level, hparams)[0],
+
+
+def _posterize_original_level_to_arg(level, _hparams):
+    # As per original AutoAugment paper description
+    # range [4, 8], 'keep 4 up to 8 MSB of image'
+    # intensity/severity of augmentation decreases with level
+    return int((level / _MAX_LEVEL) * 4) + 4,
+
+
+def _solarize_level_to_arg(level, _hparams):
+    # range [0, 256]
+    # intensity/severity of augmentation decreases with level
+    return int((level / _MAX_LEVEL) * 256),
+
+
+def _solarize_increasing_level_to_arg(level, _hparams):
+    # range [0, 256]
+    # intensity/severity of augmentation increases with level
+    return 256 - _solarize_level_to_arg(level, _hparams)[0],
+
+
+def _solarize_add_level_to_arg(level, _hparams):
+    # range [0, 110]
+    return int((level / _MAX_LEVEL) * 110),
+
+
+LEVEL_TO_ARG = {
+    'AutoContrast': None,
+    'Equalize': None,
+    'Invert': None,
+    'Rotate': _rotate_level_to_arg,
+    # There are several variations of the posterize level scaling in various Tensorflow/Google repositories/papers
+    'Posterize': _posterize_level_to_arg,
+    'PosterizeIncreasing': _posterize_increasing_level_to_arg,
+    'PosterizeOriginal': _posterize_original_level_to_arg,
+    'Solarize': _solarize_level_to_arg,
+    'SolarizeIncreasing': _solarize_increasing_level_to_arg,
+    'SolarizeAdd': _solarize_add_level_to_arg,
+    'Color': _enhance_level_to_arg,
+    'ColorIncreasing': _enhance_increasing_level_to_arg,
+    'Contrast': _enhance_level_to_arg,
+    'ContrastIncreasing': _enhance_increasing_level_to_arg,
+    'Brightness': _enhance_level_to_arg,
+    'BrightnessIncreasing': _enhance_increasing_level_to_arg,
+    'Sharpness': _enhance_level_to_arg,
+    'SharpnessIncreasing': _enhance_increasing_level_to_arg,
+    'ShearX': _shear_level_to_arg,
+    'ShearY': _shear_level_to_arg,
+    'TranslateX': _translate_abs_level_to_arg,
+    'TranslateY': _translate_abs_level_to_arg,
+    'TranslateXRel': _translate_rel_level_to_arg,
+    'TranslateYRel': _translate_rel_level_to_arg,
+}
+
+NAME_TO_OP = {
+    'AutoContrast': auto_contrast,
+    'Equalize': equalize,
+    'Invert': invert,
+    'Rotate': rotate,
+    'Posterize': posterize,
+    'PosterizeIncreasing': posterize,
+    'PosterizeOriginal': posterize,
+    'Solarize': solarize,
+    'SolarizeIncreasing': solarize,
+    'SolarizeAdd': solarize_add,
+    'Color': color,
+    'ColorIncreasing': color,
+    'Contrast': contrast,
+    'ContrastIncreasing': contrast,
+    'Brightness': brightness,
+    'BrightnessIncreasing': brightness,
+    'Sharpness': sharpness,
+    'SharpnessIncreasing': sharpness,
+    'ShearX': shear_x,
+    'ShearY': shear_y,
+    'TranslateX': translate_x_abs,
+    'TranslateY': translate_y_abs,
+    'TranslateXRel': translate_x_rel,
+    'TranslateYRel': translate_y_rel,
+}
+
+
+class AugmentOp(object):
+    def __init__(self, name, prob=0.5, magnitude=10, hparams=None):
+        hparams = hparams or _HPARAMS_DEFAULT
+        self.aug_fn = NAME_TO_OP[name]
+        self.level_fn = LEVEL_TO_ARG[name]
+        self.prob = prob
+        self.magnitude = magnitude
+        self.hparams = hparams.copy()
+        self.kwargs = dict(
+            fillcolor=hparams['img_mean'] if 'img_mean' in hparams else _FILL,
+            resample=hparams['interpolation']
+            if 'interpolation' in hparams else _RANDOM_INTERPOLATION, )
+
+        # If magnitude_std is > 0, we introduce some randomness
+        # in the usually fixed policy and sample magnitude from a normal distribution
+        # with mean `magnitude` and std-dev of `magnitude_std`.
+        # NOTE This is my own hack, being tested, not in papers or reference impls.
+        self.magnitude_std = self.hparams.get('magnitude_std', 0)
+
+    def __call__(self, img):
+        if self.prob < 1.0 and random.random() > self.prob:
+            return img
+        magnitude = self.magnitude
+        if self.magnitude_std and self.magnitude_std > 0:
+            magnitude = random.gauss(magnitude, self.magnitude_std)
+        magnitude = min(_MAX_LEVEL, max(0, magnitude))  # clip to valid range
+        level_args = self.level_fn(
+            magnitude, self.hparams) if self.level_fn is not None else tuple()
+        return self.aug_fn(img, *level_args, **self.kwargs)
+
+
+def auto_augment_policy_v0(hparams):
+    # ImageNet v0 policy from TPU EfficientNet impl, cannot find a paper reference.
+    policy = [
+        [('Equalize', 0.8, 1), ('ShearY', 0.8, 4)],
+        [('Color', 0.4, 9), ('Equalize', 0.6, 3)],
+        [('Color', 0.4, 1), ('Rotate', 0.6, 8)],
+        [('Solarize', 0.8, 3), ('Equalize', 0.4, 7)],
+        [('Solarize', 0.4, 2), ('Solarize', 0.6, 2)],
+        [('Color', 0.2, 0), ('Equalize', 0.8, 8)],
+        [('Equalize', 0.4, 8), ('SolarizeAdd', 0.8, 3)],
+        [('ShearX', 0.2, 9), ('Rotate', 0.6, 8)],
+        [('Color', 0.6, 1), ('Equalize', 1.0, 2)],
+        [('Invert', 0.4, 9), ('Rotate', 0.6, 0)],
+        [('Equalize', 1.0, 9), ('ShearY', 0.6, 3)],
+        [('Color', 0.4, 7), ('Equalize', 0.6, 0)],
+        [('Posterize', 0.4, 6), ('AutoContrast', 0.4, 7)],
+        [('Solarize', 0.6, 8), ('Color', 0.6, 9)],
+        [('Solarize', 0.2, 4), ('Rotate', 0.8, 9)],
+        [('Rotate', 1.0, 7), ('TranslateYRel', 0.8, 9)],
+        [('ShearX', 0.0, 0), ('Solarize', 0.8, 4)],
+        [('ShearY', 0.8, 0), ('Color', 0.6, 4)],
+        [('Color', 1.0, 0), ('Rotate', 0.6, 2)],
+        [('Equalize', 0.8, 4), ('Equalize', 0.0, 8)],
+        [('Equalize', 1.0, 4), ('AutoContrast', 0.6, 2)],
+        [('ShearY', 0.4, 7), ('SolarizeAdd', 0.6, 7)],
+        [('Posterize', 0.8, 2), ('Solarize', 0.6, 10)
+         ],  # This results in black image with Tpu posterize
+        [('Solarize', 0.6, 8), ('Equalize', 0.6, 1)],
+        [('Color', 0.8, 6), ('Rotate', 0.4, 5)],
+    ]
+    pc = [[AugmentOp(*a, hparams=hparams) for a in sp] for sp in policy]
+    return pc
+
+
+def auto_augment_policy_v0r(hparams):
+    # ImageNet v0 policy from TPU EfficientNet impl, with variation of Posterize used
+    # in Google research implementation (number of bits discarded increases with magnitude)
+    policy = [
+        [('Equalize', 0.8, 1), ('ShearY', 0.8, 4)],
+        [('Color', 0.4, 9), ('Equalize', 0.6, 3)],
+        [('Color', 0.4, 1), ('Rotate', 0.6, 8)],
+        [('Solarize', 0.8, 3), ('Equalize', 0.4, 7)],
+        [('Solarize', 0.4, 2), ('Solarize', 0.6, 2)],
+        [('Color', 0.2, 0), ('Equalize', 0.8, 8)],
+        [('Equalize', 0.4, 8), ('SolarizeAdd', 0.8, 3)],
+        [('ShearX', 0.2, 9), ('Rotate', 0.6, 8)],
+        [('Color', 0.6, 1), ('Equalize', 1.0, 2)],
+        [('Invert', 0.4, 9), ('Rotate', 0.6, 0)],
+        [('Equalize', 1.0, 9), ('ShearY', 0.6, 3)],
+        [('Color', 0.4, 7), ('Equalize', 0.6, 0)],
+        [('PosterizeIncreasing', 0.4, 6), ('AutoContrast', 0.4, 7)],
+        [('Solarize', 0.6, 8), ('Color', 0.6, 9)],
+        [('Solarize', 0.2, 4), ('Rotate', 0.8, 9)],
+        [('Rotate', 1.0, 7), ('TranslateYRel', 0.8, 9)],
+        [('ShearX', 0.0, 0), ('Solarize', 0.8, 4)],
+        [('ShearY', 0.8, 0), ('Color', 0.6, 4)],
+        [('Color', 1.0, 0), ('Rotate', 0.6, 2)],
+        [('Equalize', 0.8, 4), ('Equalize', 0.0, 8)],
+        [('Equalize', 1.0, 4), ('AutoContrast', 0.6, 2)],
+        [('ShearY', 0.4, 7), ('SolarizeAdd', 0.6, 7)],
+        [('PosterizeIncreasing', 0.8, 2), ('Solarize', 0.6, 10)],
+        [('Solarize', 0.6, 8), ('Equalize', 0.6, 1)],
+        [('Color', 0.8, 6), ('Rotate', 0.4, 5)],
+    ]
+    pc = [[AugmentOp(*a, hparams=hparams) for a in sp] for sp in policy]
+    return pc
+
+
+def auto_augment_policy_original(hparams):
+    # ImageNet policy from https://arxiv.org/abs/1805.09501
+    policy = [
+        [('PosterizeOriginal', 0.4, 8), ('Rotate', 0.6, 9)],
+        [('Solarize', 0.6, 5), ('AutoContrast', 0.6, 5)],
+        [('Equalize', 0.8, 8), ('Equalize', 0.6, 3)],
+        [('PosterizeOriginal', 0.6, 7), ('PosterizeOriginal', 0.6, 6)],
+        [('Equalize', 0.4, 7), ('Solarize', 0.2, 4)],
+        [('Equalize', 0.4, 4), ('Rotate', 0.8, 8)],
+        [('Solarize', 0.6, 3), ('Equalize', 0.6, 7)],
+        [('PosterizeOriginal', 0.8, 5), ('Equalize', 1.0, 2)],
+        [('Rotate', 0.2, 3), ('Solarize', 0.6, 8)],
+        [('Equalize', 0.6, 8), ('PosterizeOriginal', 0.4, 6)],
+        [('Rotate', 0.8, 8), ('Color', 0.4, 0)],
+        [('Rotate', 0.4, 9), ('Equalize', 0.6, 2)],
+        [('Equalize', 0.0, 7), ('Equalize', 0.8, 8)],
+        [('Invert', 0.6, 4), ('Equalize', 1.0, 8)],
+        [('Color', 0.6, 4), ('Contrast', 1.0, 8)],
+        [('Rotate', 0.8, 8), ('Color', 1.0, 2)],
+        [('Color', 0.8, 8), ('Solarize', 0.8, 7)],
+        [('Sharpness', 0.4, 7), ('Invert', 0.6, 8)],
+        [('ShearX', 0.6, 5), ('Equalize', 1.0, 9)],
+        [('Color', 0.4, 0), ('Equalize', 0.6, 3)],
+        [('Equalize', 0.4, 7), ('Solarize', 0.2, 4)],
+        [('Solarize', 0.6, 5), ('AutoContrast', 0.6, 5)],
+        [('Invert', 0.6, 4), ('Equalize', 1.0, 8)],
+        [('Color', 0.6, 4), ('Contrast', 1.0, 8)],
+        [('Equalize', 0.8, 8), ('Equalize', 0.6, 3)],
+    ]
+    pc = [[AugmentOp(*a, hparams=hparams) for a in sp] for sp in policy]
+    return pc
+
+
+def auto_augment_policy_originalr(hparams):
+    # ImageNet policy from https://arxiv.org/abs/1805.09501 with research posterize variation
+    policy = [
+        [('PosterizeIncreasing', 0.4, 8), ('Rotate', 0.6, 9)],
+        [('Solarize', 0.6, 5), ('AutoContrast', 0.6, 5)],
+        [('Equalize', 0.8, 8), ('Equalize', 0.6, 3)],
+        [('PosterizeIncreasing', 0.6, 7), ('PosterizeIncreasing', 0.6, 6)],
+        [('Equalize', 0.4, 7), ('Solarize', 0.2, 4)],
+        [('Equalize', 0.4, 4), ('Rotate', 0.8, 8)],
+        [('Solarize', 0.6, 3), ('Equalize', 0.6, 7)],
+        [('PosterizeIncreasing', 0.8, 5), ('Equalize', 1.0, 2)],
+        [('Rotate', 0.2, 3), ('Solarize', 0.6, 8)],
+        [('Equalize', 0.6, 8), ('PosterizeIncreasing', 0.4, 6)],
+        [('Rotate', 0.8, 8), ('Color', 0.4, 0)],
+        [('Rotate', 0.4, 9), ('Equalize', 0.6, 2)],
+        [('Equalize', 0.0, 7), ('Equalize', 0.8, 8)],
+        [('Invert', 0.6, 4), ('Equalize', 1.0, 8)],
+        [('Color', 0.6, 4), ('Contrast', 1.0, 8)],
+        [('Rotate', 0.8, 8), ('Color', 1.0, 2)],
+        [('Color', 0.8, 8), ('Solarize', 0.8, 7)],
+        [('Sharpness', 0.4, 7), ('Invert', 0.6, 8)],
+        [('ShearX', 0.6, 5), ('Equalize', 1.0, 9)],
+        [('Color', 0.4, 0), ('Equalize', 0.6, 3)],
+        [('Equalize', 0.4, 7), ('Solarize', 0.2, 4)],
+        [('Solarize', 0.6, 5), ('AutoContrast', 0.6, 5)],
+        [('Invert', 0.6, 4), ('Equalize', 1.0, 8)],
+        [('Color', 0.6, 4), ('Contrast', 1.0, 8)],
+        [('Equalize', 0.8, 8), ('Equalize', 0.6, 3)],
+    ]
+    pc = [[AugmentOp(*a, hparams=hparams) for a in sp] for sp in policy]
+    return pc
+
+
+def auto_augment_policy(name='v0', hparams=None):
+    hparams = hparams or _HPARAMS_DEFAULT
+    if name == 'original':
+        return auto_augment_policy_original(hparams)
+    elif name == 'originalr':
+        return auto_augment_policy_originalr(hparams)
+    elif name == 'v0':
+        return auto_augment_policy_v0(hparams)
+    elif name == 'v0r':
+        return auto_augment_policy_v0r(hparams)
+    else:
+        assert False, 'Unknown AA policy (%s)' % name
+
+
+class AutoAugment(object):
+    def __init__(self, policy):
+        self.policy = policy
+
+    def __call__(self, img):
+        sub_policy = random.choice(self.policy)
+        for op in sub_policy:
+            img = op(img)
+        return img
+
+
+def auto_augment_transform(config_str, hparams):
+    """
+    Create a AutoAugment transform
+
+    :param config_str: String defining configuration of auto augmentation. Consists of multiple sections separated by
+    dashes ('-'). The first section defines the AutoAugment policy (one of 'v0', 'v0r', 'original', 'originalr').
+    The remaining sections, not order sepecific determine
+        'mstd' -  float std deviation of magnitude noise applied
+    Ex 'original-mstd0.5' results in AutoAugment with original policy, magnitude_std 0.5
+
+    :param hparams: Other hparams (kwargs) for the AutoAugmentation scheme
+
+    :return: A callable Transform Op
+    """
+    config = config_str.split('-')
+    policy_name = config[0]
+    config = config[1:]
+    for c in config:
+        cs = re.split(r'(\d.*)', c)
+        if len(cs) < 2:
+            continue
+        key, val = cs[:2]
+        if key == 'mstd':
+            # noise param injected via hparams for now
+            hparams.setdefault('magnitude_std', float(val))
+        else:
+            assert False, 'Unknown AutoAugment config section'
+    aa_policy = auto_augment_policy(policy_name, hparams=hparams)
+    return AutoAugment(aa_policy)
+
+
+_RAND_TRANSFORMS = [
+    'AutoContrast',
+    'Equalize',
+    'Invert',
+    'Rotate',
+    'Posterize',
+    'Solarize',
+    'SolarizeAdd',
+    'Color',
+    'Contrast',
+    'Brightness',
+    'Sharpness',
+    'ShearX',
+    'ShearY',
+    'TranslateXRel',
+    'TranslateYRel',
+    #'Cutout'  # NOTE I've implement this as random erasing separately
+]
+
+_RAND_INCREASING_TRANSFORMS = [
+    'AutoContrast',
+    'Equalize',
+    'Invert',
+    'Rotate',
+    'PosterizeIncreasing',
+    'SolarizeIncreasing',
+    'SolarizeAdd',
+    'ColorIncreasing',
+    'ContrastIncreasing',
+    'BrightnessIncreasing',
+    'SharpnessIncreasing',
+    'ShearX',
+    'ShearY',
+    'TranslateXRel',
+    'TranslateYRel',
+    #'Cutout'  # NOTE I've implement this as random erasing separately
+]
+
+# These experimental weights are based loosely on the relative improvements mentioned in paper.
+# They may not result in increased performance, but could likely be tuned to so.
+_RAND_CHOICE_WEIGHTS_0 = {
+    'Rotate': 0.3,
+    'ShearX': 0.2,
+    'ShearY': 0.2,
+    'TranslateXRel': 0.1,
+    'TranslateYRel': 0.1,
+    'Color': .025,
+    'Sharpness': 0.025,
+    'AutoContrast': 0.025,
+    'Solarize': .005,
+    'SolarizeAdd': .005,
+    'Contrast': .005,
+    'Brightness': .005,
+    'Equalize': .005,
+    'Posterize': 0,
+    'Invert': 0,
+}
+
+
+def _select_rand_weights(weight_idx=0, transforms=None):
+    transforms = transforms or _RAND_TRANSFORMS
+    assert weight_idx == 0  # only one set of weights currently
+    rand_weights = _RAND_CHOICE_WEIGHTS_0
+    probs = [rand_weights[k] for k in transforms]
+    probs /= np.sum(probs)
+    return probs
+
+
+def rand_augment_ops(magnitude=10, hparams=None, transforms=None):
+    hparams = hparams or _HPARAMS_DEFAULT
+    transforms = transforms or _RAND_TRANSFORMS
+    return [
+        AugmentOp(
+            name, prob=0.5, magnitude=magnitude, hparams=hparams)
+        for name in transforms
+    ]
+
+
+class RandAugment(object):
+    def __init__(self, ops, num_layers=2, choice_weights=None):
+        self.ops = ops
+        self.num_layers = num_layers
+        self.choice_weights = choice_weights
+
+    def __call__(self, img):
+        # no replacement when using weighted choice
+        ops = np.random.choice(
+            self.ops,
+            self.num_layers,
+            replace=self.choice_weights is None,
+            p=self.choice_weights)
+        for op in ops:
+            img = op(img)
+        return img
+
+
+def rand_augment_transform(config_str, hparams):
+    """
+    Create a RandAugment transform
+
+    :param config_str: String defining configuration of random augmentation. Consists of multiple sections separated by
+    dashes ('-'). The first section defines the specific variant of rand augment (currently only 'rand'). The remaining
+    sections, not order sepecific determine
+        'm' - integer magnitude of rand augment
+        'n' - integer num layers (number of transform ops selected per image)
+        'w' - integer probabiliy weight index (index of a set of weights to influence choice of op)
+        'mstd' -  float std deviation of magnitude noise applied
+        'inc' - integer (bool), use augmentations that increase in severity with magnitude (default: 0)
+    Ex 'rand-m9-n3-mstd0.5' results in RandAugment with magnitude 9, num_layers 3, magnitude_std 0.5
+    'rand-mstd1-w0' results in magnitude_std 1.0, weights 0, default magnitude of 10 and num_layers 2
+
+    :param hparams: Other hparams (kwargs) for the RandAugmentation scheme
+
+    :return: A callable Transform Op
+    """
+    magnitude = _MAX_LEVEL  # default to _MAX_LEVEL for magnitude (currently 10)
+    num_layers = 2  # default to 2 ops per image
+    weight_idx = None  # default to no probability weights for op choice
+    transforms = _RAND_TRANSFORMS
+    config = config_str.split('-')
+    assert config[0] == 'rand'
+    config = config[1:]
+    for c in config:
+        cs = re.split(r'(\d.*)', c)
+        if len(cs) < 2:
+            continue
+        key, val = cs[:2]
+        if key == 'mstd':
+            # noise param injected via hparams for now
+            hparams.setdefault('magnitude_std', float(val))
+        elif key == 'inc':
+            if bool(val):
+                transforms = _RAND_INCREASING_TRANSFORMS
+        elif key == 'm':
+            magnitude = int(val)
+        elif key == 'n':
+            num_layers = int(val)
+        elif key == 'w':
+            weight_idx = int(val)
+        else:
+            assert False, 'Unknown RandAugment config section'
+    ra_ops = rand_augment_ops(
+        magnitude=magnitude, hparams=hparams, transforms=transforms)
+    choice_weights = None if weight_idx is None else _select_rand_weights(
+        weight_idx)
+    return RandAugment(ra_ops, num_layers, choice_weights=choice_weights)
+
+
+_AUGMIX_TRANSFORMS = [
+    'AutoContrast',
+    'ColorIncreasing',  # not in paper
+    'ContrastIncreasing',  # not in paper
+    'BrightnessIncreasing',  # not in paper
+    'SharpnessIncreasing',  # not in paper
+    'Equalize',
+    'Rotate',
+    'PosterizeIncreasing',
+    'SolarizeIncreasing',
+    'ShearX',
+    'ShearY',
+    'TranslateXRel',
+    'TranslateYRel',
+]
+
+
+def augmix_ops(magnitude=10, hparams=None, transforms=None):
+    hparams = hparams or _HPARAMS_DEFAULT
+    transforms = transforms or _AUGMIX_TRANSFORMS
+    return [
+        AugmentOp(
+            name, prob=1.0, magnitude=magnitude, hparams=hparams)
+        for name in transforms
+    ]
+
+
+class AugMixAugment(object):
+    """ AugMix Transform
+    Adapted and improved from impl here: https://github.com/google-research/augmix/blob/master/imagenet.py
+    From paper: 'AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty -
+    https://arxiv.org/abs/1912.02781
+    """
+
+    def __init__(self, ops, alpha=1., width=3, depth=-1, blended=False):
+        self.ops = ops
+        self.alpha = alpha
+        self.width = width
+        self.depth = depth
+        self.blended = blended  # blended mode is faster but not well tested
+
+    def _calc_blended_weights(self, ws, m):
+        ws = ws * m
+        cump = 1.
+        rws = []
+        for w in ws[::-1]:
+            alpha = w / cump
+            cump *= (1 - alpha)
+            rws.append(alpha)
+        return np.array(rws[::-1], dtype=np.float32)
+
+    def _apply_blended(self, img, mixing_weights, m):
+        # This is my first crack and implementing a slightly faster mixed augmentation. Instead
+        # of accumulating the mix for each chain in a Numpy array and then blending with original,
+        # it recomputes the blending coefficients and applies one PIL image blend per chain.
+        # TODO the results appear in the right ballpark but they differ by more than rounding.
+        img_orig = img.copy()
+        ws = self._calc_blended_weights(mixing_weights, m)
+        for w in ws:
+            depth = self.depth if self.depth > 0 else np.random.randint(1, 4)
+            ops = np.random.choice(self.ops, depth, replace=True)
+            img_aug = img_orig  # no ops are in-place, deep copy not necessary
+            for op in ops:
+                img_aug = op(img_aug)
+            img = Image.blend(img, img_aug, w)
+        return img
+
+    def _apply_basic(self, img, mixing_weights, m):
+        # This is a literal adaptation of the paper/official implementation without normalizations and
+        # PIL <-> Numpy conversions between every op. It is still quite CPU compute heavy compared to the
+        # typical augmentation transforms, could use a GPU / Kornia implementation.
+        img_shape = img.size[0], img.size[1], len(img.getbands())
+        mixed = np.zeros(img_shape, dtype=np.float32)
+        for mw in mixing_weights:
+            depth = self.depth if self.depth > 0 else np.random.randint(1, 4)
+            ops = np.random.choice(self.ops, depth, replace=True)
+            img_aug = img  # no ops are in-place, deep copy not necessary
+            for op in ops:
+                img_aug = op(img_aug)
+            mixed += mw * np.asarray(img_aug, dtype=np.float32)
+        np.clip(mixed, 0, 255., out=mixed)
+        mixed = Image.fromarray(mixed.astype(np.uint8))
+        return Image.blend(img, mixed, m)
+
+    def __call__(self, img):
+        mixing_weights = np.float32(
+            np.random.dirichlet([self.alpha] * self.width))
+        m = np.float32(np.random.beta(self.alpha, self.alpha))
+        if self.blended:
+            mixed = self._apply_blended(img, mixing_weights, m)
+        else:
+            mixed = self._apply_basic(img, mixing_weights, m)
+        return mixed
+
+
+def augment_and_mix_transform(config_str, hparams):
+    """ Create AugMix transform
+
+    :param config_str: String defining configuration of random augmentation. Consists of multiple sections separated by
+    dashes ('-'). The first section defines the specific variant of rand augment (currently only 'rand'). The remaining
+    sections, not order sepecific determine
+        'm' - integer magnitude (severity) of augmentation mix (default: 3)
+        'w' - integer width of augmentation chain (default: 3)
+        'd' - integer depth of augmentation chain (-1 is random [1, 3], default: -1)
+        'b' - integer (bool), blend each branch of chain into end result without a final blend, less CPU (default: 0)
+        'mstd' -  float std deviation of magnitude noise applied (default: 0)
+    Ex 'augmix-m5-w4-d2' results in AugMix with severity 5, chain width 4, chain depth 2
+
+    :param hparams: Other hparams (kwargs) for the Augmentation transforms
+
+    :return: A callable Transform Op
+    """
+    magnitude = 3
+    width = 3
+    depth = -1
+    alpha = 1.
+    blended = False
+    config = config_str.split('-')
+    assert config[0] == 'augmix'
+    config = config[1:]
+    for c in config:
+        cs = re.split(r'(\d.*)', c)
+        if len(cs) < 2:
+            continue
+        key, val = cs[:2]
+        if key == 'mstd':
+            # noise param injected via hparams for now
+            hparams.setdefault('magnitude_std', float(val))
+        elif key == 'm':
+            magnitude = int(val)
+        elif key == 'w':
+            width = int(val)
+        elif key == 'd':
+            depth = int(val)
+        elif key == 'a':
+            alpha = float(val)
+        elif key == 'b':
+            blended = bool(val)
+        else:
+            assert False, 'Unknown AugMix config section'
+    ops = augmix_ops(magnitude=magnitude, hparams=hparams)
+    return AugMixAugment(
+        ops, alpha=alpha, width=width, depth=depth, blended=blended)
+
+
+class RawTimmAutoAugment(object):
+    """TimmAutoAugment API for PaddleClas."""
+
+    def __init__(self,
+                 config_str="rand-m9-mstd0.5-inc1",
+                 interpolation="bicubic",
+                 img_size=224,
+                 mean=IMAGENET_DEFAULT_MEAN):
+        if isinstance(img_size, (tuple, list)):
+            img_size_min = min(img_size)
+        else:
+            img_size_min = img_size
+
+        aa_params = dict(
+            translate_const=int(img_size_min * 0.45),
+            img_mean=tuple([min(255, round(255 * x)) for x in mean]), )
+        if interpolation and interpolation != 'random':
+            aa_params['interpolation'] = _pil_interp(interpolation)
+        if config_str.startswith('rand'):
+            self.augment_func = rand_augment_transform(config_str, aa_params)
+        elif config_str.startswith('augmix'):
+            aa_params['translate_pct'] = 0.3
+            self.augment_func = augment_and_mix_transform(config_str,
+                                                          aa_params)
+        elif config_str.startswith('auto'):
+            self.augment_func = auto_augment_transform(config_str, aa_params)
+        else:
+            raise Exception(
+                "ConfigError: The TimmAutoAugment Op only support RandAugment, AutoAugment, AugMix, and the config_str only starts with \"rand\", \"augmix\", \"auto\"."
+            )
+
+    def __call__(self, img):
+        return self.augment_func(img)