Merge pull request #757 from FredHuang16/develop_reg

update dataloader

ppcls/data/__init__.py

-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+# 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.
@@ -11,18 +11,108 @@
 # 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.
-
 import copy
 import paddle
-import os
+import numpy as np
 from paddle.io import DistributedBatchSampler, BatchSampler, DataLoader
 
 from ppcls.utils import logger
 
+from . import datasets
+from . import imaug
+from . import samplers
+# dataset
+from .datasets.imagenet_dataset import ImageNetDataset
+from .dataset.multilabel_dataset import MultiLabelDataset
+from .datasets.common_dataset import create_operators
+
+# sampler
+from .samplers import DistributedRandomIdentitySampler
+
+from .preprocess import transform
+
+def build_dataloader(config, mode, device, seed=None):
+    assert mode in ['Train', 'Eval', 'Test'], "Mode should be Train, Eval or Test."
+    # build dataset
+    config_dataset = config[mode]['dataset']
+    dataset_name = config_dataset.pop('name')
+    if 'batch_transform_ops' in config_dataset:
+        batch_transform = config_dataset.pop('batch_transform_ops')
+    else:
+        batch_transform = None
+
+    dataset = eval(dataset_name)(**config_dataset)
+
+    logger.info("build dataset({}) success...".format(dataset))
+
+    # build sampler
+    config_sampler = config[mode]['sampler']
+    if "name" not in config_sampler:
+        batch_sampler = None
+        batch_size = config_sampler["batch_size"]
+        drop_last = config_sampler["drop_last"]
+        shuffle = config_sampler["shuffle"]
+    else:
+        sampler_name = config_sampler.pop("name")
+        batch_sampler = eval(sampler_name)(dataset, **config_sampler)
+
+    logger.info("build batch_sampler({}) success...".format(batch_sampler))
 
+    # build batch operator
+    def mix_collate_fn(batch):
+        batch = transform(batch, batch_ops)
+        # batch each field
+        slots = []
+        for items in batch:
+            for i, item in enumerate(items):
+                if len(slots) < len(items):
+                    slots.append([item])
+                else:
+                    slots[i].append(item)
+        return [np.stack(slot, axis=0) for slot in slots]
+
+    if isinstance(batch_transform, list):
+        batch_ops = create_operators(batch_transform)
+        batch_collate_fn = mix_collate_fn
+    else:
+        batch_collate_fn = None 
+
+    # build dataloader
+    config_loader = config[mode]['loader']
+    num_workers = config_loader["num_workers"]
+    use_shared_memory = config_loader["use_shared_memory"]
+
+    if batch_sampler is None:
+        data_loader = DataLoader(
+            dataset=dataset,
+            places=device,
+            num_workers=num_workers,
+            return_list=True,
+            use_shared_memory=use_shared_memory,
+            batch_size=batch_size,
+            shuffle=shuffle,
+            drop_last=drop_last,
+            collate_fn=batch_collate_fn)
+    else:
+        data_loader = DataLoader(
+            dataset=dataset,
+            places=device,
+            num_workers=num_workers,
+            return_list=True,
+            use_shared_memory=use_shared_memory,
+            batch_sampler=batch_sampler,
+            collate_fn=batch_collate_fn)
+
+    logger.info("build data_loader({}) success...".format(data_loader))
+    
+    return dataloader
+    
+'''
 # TODO: fix the format
 def build_dataloader(config, mode, device, seed=None):
     from . import reader
     from .reader import Reader
     dataloader = Reader(config, mode=mode, places=device)()
     return dataloader
+
+'''

ppcls/data/dataset/common_dataset.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.
+
+from __future__ import print_function
+
+import io
+import tarfile
+import numpy as np
+from PIL import Image  #all use default backend
+
+import paddle
+from paddle.io import Dataset
+import pickle
+import os
+import cv2
+import random
+
+from ppcls.data import preprocess
+from ppcls.data.preprocess import transform
+from ppcls.utils import logger
+
+
+def create_operators(params):
+    """
+    create operators based on the config
+    Args:
+        params(list): a dict list, used to create some operators
+    """
+    assert isinstance(params, list), ('operator config should be a list')
+    ops = []
+    for operator in params:
+        print(operator)
+        assert isinstance(operator,
+                          dict) and len(operator) == 1, "yaml format error"
+        op_name = list(operator)[0]
+        param = {} if operator[op_name] is None else operator[op_name]
+        op = getattr(preprocess, op_name)(**param)
+        ops.append(op)
+
+    return ops
+
+
+class CommonDataset(Dataset):
+    def __init__(
+            self,
+            image_root,
+            cls_label_path,
+            transform_ops=None, ):
+        self._img_root = image_root
+        self._cls_path = cls_label_path
+        if transform_ops:
+            self._transform_ops = create_operators(transform_ops)
+
+        self.images = []
+        self.labels = []
+        self._load_anno()
+
+    def _load_anno(self): 
+        pass
+
+    def __getitem__(self, idx):
+        try:
+            img = cv2.imread(self.images[idx])
+            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+            if self._transform_ops:
+                img = transform(img, self._transform_ops)
+            img = img.transpose((2, 0, 1))
+            return (img, self.labels[idx])
+
+        except Exception as ex:
+            logger.error("Exception occured when parse line: {} with msg: {}".
+                         format(self.images[idx], ex))
+            rnd_idx = np.random.randint(self.__len__())
+            return self.__getitem__(rnd_idx)
+
+    def __len__(self):
+        return len(self.images)
+
+    @property
+    def class_num(self):
+        return len(set(self.labels))
+

ppcls/data/dataset/imagenet_dataset.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.
+
+from __future__ import print_function
+
+import io
+import tarfile
+import numpy as np
+from PIL import Image  #all use default backend
+
+import paddle
+from paddle.io import Dataset
+import pickle
+import os
+import cv2
+import random
+
+from .common_dataset import CommonDataset
+
+class ImageNetDataset(CommonDataset):
+
+    def _load_anno(self):
+        assert os.path.exists(self._cls_path)
+        assert os.path.exists(self._img_root)
+        self.images = []
+        self.labels = []
+        with open(self._cls_path) as fd:
+            lines = fd.readlines()
+            for l in lines:
+                l = l.strip().split(" ")
+                self.images.append(os.path.join(self._img_root, l[0]))
+                self.labels.append(int(l[1]))
+                assert os.path.exists(self.images[-1])
+
+

ppcls/data/dataset/multilabel_dataset.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.
+
+from __future__ import print_function
+
+import io
+import tarfile
+import numpy as np
+from PIL import Image  #all use default backend
+
+import paddle
+from paddle.io import Dataset
+import pickle
+import os
+import cv2
+import random
+
+from ppcls.data import preprocess
+from ppcls.data.preprocess import transform
+from ppcls.utils import logger
+
+from .common_dataset import CommonDataset
+
+class MultiLabelDataset(CommonDataset):
+
+    def _load_anno(self):
+        assert os.path.exists(self._cls_path)
+        assert os.path.exists(self._img_root)
+        self.images = []
+        self.labels = []
+        with open(self._cls_path) as fd:
+            lines = fd.readlines()
+            for l in lines:
+                l = l.strip().split(" ")
+                self.images.append(os.path.join(self._img_root, l[0]))
+
+                labels = l[1].split(',')
+                labels = [int(i) for i in labels]
+
+                self.labels.append(labels)
+                assert os.path.exists(self.images[-1])
+
+    def __getitem__(self, idx):
+        try:
+            img = cv2.imread(self.images[idx])
+            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+            if self._transform_ops:
+                img = transform(img, self._transform_ops)
+            img = img.transpose((2, 0, 1))
+            label = np.array(self.labels[idx]).astype("float32")
+            return (img, label)
+        except Exception as ex:
+            logger.error("Exception occured when parse line: {} with msg: {}".
+                         format(self.images[idx], ex))
+            rnd_idx = np.random.randint(self.__len__())
+            return self.__getitem__(rnd_idx)

ppcls/data/preprocess/__init__.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.
+
+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.cutout import Cutout
+
+from ppcls.data.preprocess.ops.hide_and_seek import HideAndSeek
+from ppcls.data.preprocess.ops.random_erasing import RandomErasing
+from ppcls.data.preprocess.ops.grid import GridMask
+
+from ppcls.data.preprocess.ops.operators import DecodeImage
+from ppcls.data.preprocess.ops.operators import ResizeImage
+from ppcls.data.preprocess.ops.operators import CropImage
+from ppcls.data.preprocess.ops.operators import RandCropImage
+from ppcls.data.preprocess.ops.operators import RandFlipImage
+from ppcls.data.preprocess.ops.operators import NormalizeImage
+from ppcls.data.preprocess.ops.operators import ToCHWImage
+from ppcls.data.preprocess.ops.operators import AugMix
+
+from ppcls.data.preprocess.batch_ops.batch_operators import MixupOperator, CutmixOperator, FmixOperator
+
+import six
+import numpy as np
+from PIL import Image
+
+
+def transform(data, ops=[]):
+    """ transform """
+    for op in ops:
+        data = op(data)
+        #print(data.shape, op)
+    return data
+
+
+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)
+
+    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)
+
+        if isinstance(img, Image.Image):
+            img = np.asarray(img)
+
+        return img
+
+
+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)
+
+    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)
+
+        if isinstance(img, Image.Image):
+            img = np.asarray(img)
+
+        return img

ppcls/data/preprocess/batch_ops/__init__.py

+

ppcls/data/preprocess/batch_ops/batch_operators.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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+import numpy as np
+
+from ppcls.data.preprocess.ops.fmix import sample_mask
+
+
+class BatchOperator(object):
+    """ BatchOperator """
+    def __init__(self, *args, **kwargs):
+        pass
+
+    def _unpack(self, batch):
+        """ _unpack """
+        assert isinstance(batch, list), \
+                'batch should be a list filled with tuples (img, label)'
+        bs = len(batch)
+        assert bs > 0, 'size of the batch data should > 0'
+        #imgs, labels = list(zip(*batch))
+        imgs = []
+        labels = []
+        for item in batch:
+            imgs.append(item[0])
+            labels.append(item[1])
+        return np.array(imgs), np.array(labels), bs
+
+    def __call__(self, batch):
+        return batch
+
+
+class MixupOperator(BatchOperator):
+    """ Mixup operator """
+    def __init__(self, alpha=0.2):
+        assert alpha > 0., \
+                'parameter alpha[%f] should > 0.0' % (alpha)
+        self._alpha = alpha
+
+    def __call__(self, batch):
+        imgs, labels, bs = self._unpack(batch)
+        idx = np.random.permutation(bs)
+        lam = np.random.beta(self._alpha, self._alpha)
+        lams = np.array([lam] * bs, dtype=np.float32)
+        imgs = lam * imgs + (1 - lam) * imgs[idx]
+        return list(zip(imgs, labels, labels[idx], lams))
+
+
+class CutmixOperator(BatchOperator):
+    """ Cutmix operator """
+    def __init__(self, alpha=0.2):
+        assert alpha > 0., \
+                'parameter alpha[%f] should > 0.0' % (alpha)
+        self._alpha = alpha
+
+    def _rand_bbox(self, size, lam):
+        """ _rand_bbox """
+        w = size[2]
+        h = size[3]
+        cut_rat = np.sqrt(1. - lam)
+        cut_w = np.int(w * cut_rat)
+        cut_h = np.int(h * cut_rat)
+
+        # uniform
+        cx = np.random.randint(w)
+        cy = np.random.randint(h)
+
+        bbx1 = np.clip(cx - cut_w // 2, 0, w)
+        bby1 = np.clip(cy - cut_h // 2, 0, h)
+        bbx2 = np.clip(cx + cut_w // 2, 0, w)
+        bby2 = np.clip(cy + cut_h // 2, 0, h)
+
+        return bbx1, bby1, bbx2, bby2
+
+    def __call__(self, batch):
+        imgs, labels, bs = self._unpack(batch)
+        idx = np.random.permutation(bs)
+        lam = np.random.beta(self._alpha, self._alpha)
+
+        bbx1, bby1, bbx2, bby2 = self._rand_bbox(imgs.shape, lam)
+        imgs[:, :, bbx1:bbx2, bby1:bby2] = imgs[idx, :, bbx1:bbx2, bby1:bby2]
+        lam = 1 - (float(bbx2 - bbx1) * (bby2 - bby1) /
+                   (imgs.shape[-2] * imgs.shape[-1]))
+        lams = np.array([lam] * bs, dtype=np.float32)
+        return list(zip(imgs, labels, labels[idx], lams))
+
+
+class FmixOperator(BatchOperator):
+    """ Fmix operator """
+    def __init__(self, alpha=1, decay_power=3, max_soft=0., reformulate=False):
+        self._alpha = alpha
+        self._decay_power = decay_power
+        self._max_soft = max_soft
+        self._reformulate = reformulate
+
+    def __call__(self, batch):
+        imgs, labels, bs = self._unpack(batch)
+        idx = np.random.permutation(bs)
+        size = (imgs.shape[2], imgs.shape[3])
+        lam, mask = sample_mask(self._alpha, self._decay_power, \
+                size, self._max_soft, self._reformulate)
+        imgs = mask * imgs + (1 - mask) * imgs[idx]
+        return list(zip(imgs, labels, labels[idx], [lam] * bs))

ppcls/data/preprocess/ops/__init__.py

+

ppcls/data/preprocess/ops/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 is based on https://github.com/DeepVoltaire/AutoAugment/blob/master/autoaugment.py
+
+from PIL import Image, ImageEnhance, ImageOps
+import numpy as np
+import random
+
+
+class ImageNetPolicy(object):
+    """ Randomly choose one of the best 24 Sub-policies on ImageNet.
+
+        Example:
+        >>> policy = ImageNetPolicy()
+        >>> transformed = policy(image)
+
+        Example as a PyTorch Transform:
+        >>> transform=transforms.Compose([
+        >>>     transforms.Resize(256),
+        >>>     ImageNetPolicy(),
+        >>>     transforms.ToTensor()])
+    """
+
+    def __init__(self, fillcolor=(128, 128, 128)):
+        self.policies = [
+            SubPolicy(0.4, "posterize", 8, 0.6, "rotate", 9, fillcolor),
+            SubPolicy(0.6, "solarize", 5, 0.6, "autocontrast", 5, fillcolor),
+            SubPolicy(0.8, "equalize", 8, 0.6, "equalize", 3, fillcolor),
+            SubPolicy(0.6, "posterize", 7, 0.6, "posterize", 6, fillcolor),
+            SubPolicy(0.4, "equalize", 7, 0.2, "solarize", 4, fillcolor),
+            SubPolicy(0.4, "equalize", 4, 0.8, "rotate", 8, fillcolor),
+            SubPolicy(0.6, "solarize", 3, 0.6, "equalize", 7, fillcolor),
+            SubPolicy(0.8, "posterize", 5, 1.0, "equalize", 2, fillcolor),
+            SubPolicy(0.2, "rotate", 3, 0.6, "solarize", 8, fillcolor),
+            SubPolicy(0.6, "equalize", 8, 0.4, "posterize", 6, fillcolor),
+            SubPolicy(0.8, "rotate", 8, 0.4, "color", 0, fillcolor),
+            SubPolicy(0.4, "rotate", 9, 0.6, "equalize", 2, fillcolor),
+            SubPolicy(0.0, "equalize", 7, 0.8, "equalize", 8, fillcolor),
+            SubPolicy(0.6, "invert", 4, 1.0, "equalize", 8, fillcolor),
+            SubPolicy(0.6, "color", 4, 1.0, "contrast", 8, fillcolor),
+            SubPolicy(0.8, "rotate", 8, 1.0, "color", 2, fillcolor),
+            SubPolicy(0.8, "color", 8, 0.8, "solarize", 7, fillcolor),
+            SubPolicy(0.4, "sharpness", 7, 0.6, "invert", 8, fillcolor),
+            SubPolicy(0.6, "shearX", 5, 1.0, "equalize", 9, fillcolor),
+            SubPolicy(0.4, "color", 0, 0.6, "equalize", 3, fillcolor),
+            SubPolicy(0.4, "equalize", 7, 0.2, "solarize", 4, fillcolor),
+            SubPolicy(0.6, "solarize", 5, 0.6, "autocontrast", 5, fillcolor),
+            SubPolicy(0.6, "invert", 4, 1.0, "equalize", 8, fillcolor),
+            SubPolicy(0.6, "color", 4, 1.0, "contrast", 8, fillcolor),
+            SubPolicy(0.8, "equalize", 8, 0.6, "equalize", 3, fillcolor)
+        ]
+
+    def __call__(self, img, policy_idx=None):
+        if policy_idx is None or not isinstance(policy_idx, int):
+            policy_idx = random.randint(0, len(self.policies) - 1)
+        else:
+            policy_idx = policy_idx % len(self.policies)
+        return self.policies[policy_idx](img)
+
+    def __repr__(self):
+        return "AutoAugment ImageNet Policy"
+
+
+class CIFAR10Policy(object):
+    """ Randomly choose one of the best 25 Sub-policies on CIFAR10.
+
+        Example:
+        >>> policy = CIFAR10Policy()
+        >>> transformed = policy(image)
+
+        Example as a PyTorch Transform:
+        >>> transform=transforms.Compose([
+        >>>     transforms.Resize(256),
+        >>>     CIFAR10Policy(),
+        >>>     transforms.ToTensor()])
+    """
+
+    def __init__(self, fillcolor=(128, 128, 128)):
+        self.policies = [
+            SubPolicy(0.1, "invert", 7, 0.2, "contrast", 6, fillcolor),
+            SubPolicy(0.7, "rotate", 2, 0.3, "translateX", 9, fillcolor),
+            SubPolicy(0.8, "sharpness", 1, 0.9, "sharpness", 3, fillcolor),
+            SubPolicy(0.5, "shearY", 8, 0.7, "translateY", 9, fillcolor),
+            SubPolicy(0.5, "autocontrast", 8, 0.9, "equalize", 2, fillcolor),
+            SubPolicy(0.2, "shearY", 7, 0.3, "posterize", 7, fillcolor),
+            SubPolicy(0.4, "color", 3, 0.6, "brightness", 7, fillcolor),
+            SubPolicy(0.3, "sharpness", 9, 0.7, "brightness", 9, fillcolor),
+            SubPolicy(0.6, "equalize", 5, 0.5, "equalize", 1, fillcolor),
+            SubPolicy(0.6, "contrast", 7, 0.6, "sharpness", 5, fillcolor),
+            SubPolicy(0.7, "color", 7, 0.5, "translateX", 8, fillcolor),
+            SubPolicy(0.3, "equalize", 7, 0.4, "autocontrast", 8, fillcolor),
+            SubPolicy(0.4, "translateY", 3, 0.2, "sharpness", 6, fillcolor),
+            SubPolicy(0.9, "brightness", 6, 0.2, "color", 8, fillcolor),
+            SubPolicy(0.5, "solarize", 2, 0.0, "invert", 3, fillcolor),
+            SubPolicy(0.2, "equalize", 0, 0.6, "autocontrast", 0, fillcolor),
+            SubPolicy(0.2, "equalize", 8, 0.8, "equalize", 4, fillcolor),
+            SubPolicy(0.9, "color", 9, 0.6, "equalize", 6, fillcolor),
+            SubPolicy(0.8, "autocontrast", 4, 0.2, "solarize", 8, fillcolor),
+            SubPolicy(0.1, "brightness", 3, 0.7, "color", 0, fillcolor),
+            SubPolicy(0.4, "solarize", 5, 0.9, "autocontrast", 3, fillcolor),
+            SubPolicy(0.9, "translateY", 9, 0.7, "translateY", 9, fillcolor),
+            SubPolicy(0.9, "autocontrast", 2, 0.8, "solarize", 3, fillcolor),
+            SubPolicy(0.8, "equalize", 8, 0.1, "invert", 3, fillcolor),
+            SubPolicy(0.7, "translateY", 9, 0.9, "autocontrast", 1, fillcolor)
+        ]
+
+    def __call__(self, img, policy_idx=None):
+        if policy_idx is None or not isinstance(policy_idx, int):
+            policy_idx = random.randint(0, len(self.policies) - 1)
+        else:
+            policy_idx = policy_idx % len(self.policies)
+        return self.policies[policy_idx](img)
+
+    def __repr__(self):
+        return "AutoAugment CIFAR10 Policy"
+
+
+class SVHNPolicy(object):
+    """ Randomly choose one of the best 25 Sub-policies on SVHN.
+
+        Example:
+        >>> policy = SVHNPolicy()
+        >>> transformed = policy(image)
+
+        Example as a PyTorch Transform:
+        >>> transform=transforms.Compose([
+        >>>     transforms.Resize(256),
+        >>>     SVHNPolicy(),
+        >>>     transforms.ToTensor()])
+    """
+
+    def __init__(self, fillcolor=(128, 128, 128)):
+        self.policies = [
+            SubPolicy(0.9, "shearX", 4, 0.2, "invert", 3, fillcolor),
+            SubPolicy(0.9, "shearY", 8, 0.7, "invert", 5, fillcolor),
+            SubPolicy(0.6, "equalize", 5, 0.6, "solarize", 6, fillcolor),
+            SubPolicy(0.9, "invert", 3, 0.6, "equalize", 3, fillcolor),
+            SubPolicy(0.6, "equalize", 1, 0.9, "rotate", 3, fillcolor),
+            SubPolicy(0.9, "shearX", 4, 0.8, "autocontrast", 3, fillcolor),
+            SubPolicy(0.9, "shearY", 8, 0.4, "invert", 5, fillcolor),
+            SubPolicy(0.9, "shearY", 5, 0.2, "solarize", 6, fillcolor),
+            SubPolicy(0.9, "invert", 6, 0.8, "autocontrast", 1, fillcolor),
+            SubPolicy(0.6, "equalize", 3, 0.9, "rotate", 3, fillcolor),
+            SubPolicy(0.9, "shearX", 4, 0.3, "solarize", 3, fillcolor),
+            SubPolicy(0.8, "shearY", 8, 0.7, "invert", 4, fillcolor),
+            SubPolicy(0.9, "equalize", 5, 0.6, "translateY", 6, fillcolor),
+            SubPolicy(0.9, "invert", 4, 0.6, "equalize", 7, fillcolor),
+            SubPolicy(0.3, "contrast", 3, 0.8, "rotate", 4, fillcolor),
+            SubPolicy(0.8, "invert", 5, 0.0, "translateY", 2, fillcolor),
+            SubPolicy(0.7, "shearY", 6, 0.4, "solarize", 8, fillcolor),
+            SubPolicy(0.6, "invert", 4, 0.8, "rotate", 4, fillcolor),
+            SubPolicy(
+                0.3, "shearY", 7, 0.9, "translateX", 3, fillcolor), SubPolicy(
+                    0.1, "shearX", 6, 0.6, "invert", 5, fillcolor), SubPolicy(
+                        0.7, "solarize", 2, 0.6, "translateY", 7,
+                        fillcolor), SubPolicy(0.8, "shearY", 4, 0.8, "invert",
+                                              8, fillcolor), SubPolicy(
+                                                  0.7, "shearX", 9, 0.8,
+                                                  "translateY", 3,
+                                                  fillcolor), SubPolicy(
+                                                      0.8, "shearY", 5, 0.7,
+                                                      "autocontrast", 3,
+                                                      fillcolor),
+            SubPolicy(0.7, "shearX", 2, 0.1, "invert", 5, fillcolor)
+        ]
+
+    def __call__(self, img, policy_idx=None):
+        if policy_idx is None or not isinstance(policy_idx, int):
+            policy_idx = random.randint(0, len(self.policies) - 1)
+        else:
+            policy_idx = policy_idx % len(self.policies)
+        return self.policies[policy_idx](img)
+
+    def __repr__(self):
+        return "AutoAugment SVHN Policy"
+
+
+class SubPolicy(object):
+    def __init__(self,
+                 p1,
+                 operation1,
+                 magnitude_idx1,
+                 p2,
+                 operation2,
+                 magnitude_idx2,
+                 fillcolor=(128, 128, 128)):
+        ranges = {
+            "shearX": np.linspace(0, 0.3, 10),
+            "shearY": np.linspace(0, 0.3, 10),
+            "translateX": np.linspace(0, 150 / 331, 10),
+            "translateY": np.linspace(0, 150 / 331, 10),
+            "rotate": np.linspace(0, 30, 10),
+            "color": np.linspace(0.0, 0.9, 10),
+            "posterize": np.round(np.linspace(8, 4, 10), 0).astype(np.int),
+            "solarize": np.linspace(256, 0, 10),
+            "contrast": np.linspace(0.0, 0.9, 10),
+            "sharpness": np.linspace(0.0, 0.9, 10),
+            "brightness": np.linspace(0.0, 0.9, 10),
+            "autocontrast": [0] * 10,
+            "equalize": [0] * 10,
+            "invert": [0] * 10
+        }
+
+        # from https://stackoverflow.com/questions/5252170/specify-image-filling-color-when-rotating-in-python-with-pil-and-setting-expand
+        def rotate_with_fill(img, magnitude):
+            rot = img.convert("RGBA").rotate(magnitude)
+            return Image.composite(rot,
+                                   Image.new("RGBA", rot.size, (128, ) * 4),
+                                   rot).convert(img.mode)
+
+        func = {
+            "shearX": lambda img, magnitude: img.transform(
+                img.size, Image.AFFINE, (1, magnitude * random.choice([-1, 1]), 0, 0, 1, 0),
+                Image.BICUBIC, fillcolor=fillcolor),
+            "shearY": lambda img, magnitude: img.transform(
+                img.size, Image.AFFINE, (1, 0, 0, magnitude * random.choice([-1, 1]), 1, 0),
+                Image.BICUBIC, fillcolor=fillcolor),
+            "translateX": lambda img, magnitude: img.transform(
+                img.size, Image.AFFINE, (1, 0, magnitude * img.size[0] * random.choice([-1, 1]), 0, 1, 0),
+                fillcolor=fillcolor),
+            "translateY": lambda img, magnitude: img.transform(
+                img.size, Image.AFFINE, (1, 0, 0, 0, 1, magnitude * img.size[1] * random.choice([-1, 1])),
+                fillcolor=fillcolor),
+            "rotate": lambda img, magnitude: rotate_with_fill(img, magnitude),
+            # "rotate": lambda img, magnitude: img.rotate(magnitude * random.choice([-1, 1])),
+            "color": lambda img, magnitude: ImageEnhance.Color(img).enhance(1 + magnitude * random.choice([-1, 1])),
+            "posterize": lambda img, magnitude: ImageOps.posterize(img, magnitude),
+            "solarize": lambda img, magnitude: ImageOps.solarize(img, magnitude),
+            "contrast": lambda img, magnitude: ImageEnhance.Contrast(img).enhance(
+                1 + magnitude * random.choice([-1, 1])),
+            "sharpness": lambda img, magnitude: ImageEnhance.Sharpness(img).enhance(
+                1 + magnitude * random.choice([-1, 1])),
+            "brightness": lambda img, magnitude: ImageEnhance.Brightness(img).enhance(
+                1 + magnitude * random.choice([-1, 1])),
+            "autocontrast": lambda img, magnitude: ImageOps.autocontrast(img),
+            "equalize": lambda img, magnitude: ImageOps.equalize(img),
+            "invert": lambda img, magnitude: ImageOps.invert(img)
+        }
+
+        self.p1 = p1
+        self.operation1 = func[operation1]
+        self.magnitude1 = ranges[operation1][magnitude_idx1]
+        self.p2 = p2
+        self.operation2 = func[operation2]
+        self.magnitude2 = ranges[operation2][magnitude_idx2]
+
+    def __call__(self, img):
+        if random.random() < self.p1:
+            img = self.operation1(img, self.magnitude1)
+        if random.random() < self.p2:
+            img = self.operation2(img, self.magnitude2)
+        return img

ppcls/data/preprocess/ops/cutout.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 is based on https://github.com/uoguelph-mlrg/Cutout
+
+import numpy as np
+import random
+
+
+class Cutout(object):
+    def __init__(self, n_holes=1, length=112):
+        self.n_holes = n_holes
+        self.length = length
+
+    def __call__(self, img):
+        """ cutout_image """
+        h, w = img.shape[:2]
+        mask = np.ones((h, w), np.float32)
+
+        for n in range(self.n_holes):
+            y = np.random.randint(h)
+            x = np.random.randint(w)
+
+            y1 = np.clip(y - self.length // 2, 0, h)
+            y2 = np.clip(y + self.length // 2, 0, h)
+            x1 = np.clip(x - self.length // 2, 0, w)
+            x2 = np.clip(x + self.length // 2, 0, w)
+
+            img[y1:y2, x1:x2] = 0
+        return img

ppcls/data/preprocess/ops/fmix.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.
+
+import math
+import random
+
+import numpy as np
+from scipy.stats import beta
+
+
+def fftfreqnd(h, w=None, z=None):
+    """ Get bin values for discrete fourier transform of size (h, w, z)
+
+    :param h: Required, first dimension size
+    :param w: Optional, second dimension size
+    :param z: Optional, third dimension size
+    """
+    fz = fx = 0
+    fy = np.fft.fftfreq(h)
+
+    if w is not None:
+        fy = np.expand_dims(fy, -1)
+
+        if w % 2 == 1:
+            fx = np.fft.fftfreq(w)[:w // 2 + 2]
+        else:
+            fx = np.fft.fftfreq(w)[:w // 2 + 1]
+
+    if z is not None:
+        fy = np.expand_dims(fy, -1)
+        if z % 2 == 1:
+            fz = np.fft.fftfreq(z)[:, None]
+        else:
+            fz = np.fft.fftfreq(z)[:, None]
+
+    return np.sqrt(fx * fx + fy * fy + fz * fz)
+
+
+def get_spectrum(freqs, decay_power, ch, h, w=0, z=0):
+    """ Samples a fourier image with given size and frequencies decayed by decay power
+
+    :param freqs: Bin values for the discrete fourier transform
+    :param decay_power: Decay power for frequency decay prop 1/f**d
+    :param ch: Number of channels for the resulting mask
+    :param h: Required, first dimension size
+    :param w: Optional, second dimension size
+    :param z: Optional, third dimension size
+    """
+    scale = np.ones(1) / (np.maximum(freqs, np.array([1. / max(w, h, z)]))
+                          **decay_power)
+
+    param_size = [ch] + list(freqs.shape) + [2]
+    param = np.random.randn(*param_size)
+
+    scale = np.expand_dims(scale, -1)[None, :]
+
+    return scale * param
+
+
+def make_low_freq_image(decay, shape, ch=1):
+    """ Sample a low frequency image from fourier space
+
+    :param decay_power: Decay power for frequency decay prop 1/f**d
+    :param shape: Shape of desired mask, list up to 3 dims
+    :param ch: Number of channels for desired mask
+    """
+    freqs = fftfreqnd(*shape)
+    spectrum = get_spectrum(freqs, decay, ch,
+                            *shape)  #.reshape((1, *shape[:-1], -1))
+    spectrum = spectrum[:, 0] + 1j * spectrum[:, 1]
+    mask = np.real(np.fft.irfftn(spectrum, shape))
+
+    if len(shape) == 1:
+        mask = mask[:1, :shape[0]]
+    if len(shape) == 2:
+        mask = mask[:1, :shape[0], :shape[1]]
+    if len(shape) == 3:
+        mask = mask[:1, :shape[0], :shape[1], :shape[2]]
+
+    mask = mask
+    mask = (mask - mask.min())
+    mask = mask / mask.max()
+    return mask
+
+
+def sample_lam(alpha, reformulate=False):
+    """ Sample a lambda from symmetric beta distribution with given alpha
+
+    :param alpha: Alpha value for beta distribution
+    :param reformulate: If True, uses the reformulation of [1].
+    """
+    if reformulate:
+        lam = beta.rvs(alpha + 1, alpha)
+    else:
+        lam = beta.rvs(alpha, alpha)
+
+    return lam
+
+
+def binarise_mask(mask, lam, in_shape, max_soft=0.0):
+    """ Binarises a given low frequency image such that it has mean lambda.
+
+    :param mask: Low frequency image, usually the result of `make_low_freq_image`
+    :param lam: Mean value of final mask
+    :param in_shape: Shape of inputs
+    :param max_soft: Softening value between 0 and 0.5 which smooths hard edges in the mask.
+    :return:
+    """
+    idx = mask.reshape(-1).argsort()[::-1]
+    mask = mask.reshape(-1)
+    num = math.ceil(lam * mask.size) if random.random() > 0.5 else math.floor(
+        lam * mask.size)
+
+    eff_soft = max_soft
+    if max_soft > lam or max_soft > (1 - lam):
+        eff_soft = min(lam, 1 - lam)
+
+    soft = int(mask.size * eff_soft)
+    num_low = int(num - soft)
+    num_high = int(num + soft)
+
+    mask[idx[:num_high]] = 1
+    mask[idx[num_low:]] = 0
+    mask[idx[num_low:num_high]] = np.linspace(1, 0, (num_high - num_low))
+
+    mask = mask.reshape((1, 1, in_shape[0], in_shape[1]))
+    return mask
+
+
+def sample_mask(alpha, decay_power, shape, max_soft=0.0, reformulate=False):
+    """ Samples a mean lambda from beta distribution parametrised by alpha, creates a low frequency image and binarises
+    it based on this lambda
+
+    :param alpha: Alpha value for beta distribution from which to sample mean of mask
+    :param decay_power: Decay power for frequency decay prop 1/f**d
+    :param shape: Shape of desired mask, list up to 3 dims
+    :param max_soft: Softening value between 0 and 0.5 which smooths hard edges in the mask.
+    :param reformulate: If True, uses the reformulation of [1].
+    """
+    if isinstance(shape, int):
+        shape = (shape, )
+
+    # Choose lambda
+    lam = sample_lam(alpha, reformulate)
+
+    # Make mask, get mean / std
+    mask = make_low_freq_image(decay_power, shape)
+    mask = binarise_mask(mask, lam, shape, max_soft)
+
+    return float(lam), mask
+
+
+def sample_and_apply(x,
+                     alpha,
+                     decay_power,
+                     shape,
+                     max_soft=0.0,
+                     reformulate=False):
+    """
+
+    :param x: Image batch on which to apply fmix of shape [b, c, shape*]
+    :param alpha: Alpha value for beta distribution from which to sample mean of mask
+    :param decay_power: Decay power for frequency decay prop 1/f**d
+    :param shape: Shape of desired mask, list up to 3 dims
+    :param max_soft: Softening value between 0 and 0.5 which smooths hard edges in the mask.
+    :param reformulate: If True, uses the reformulation of [1].
+    :return: mixed input, permutation indices, lambda value of mix,
+    """
+    lam, mask = sample_mask(alpha, decay_power, shape, max_soft, reformulate)
+    index = np.random.permutation(x.shape[0])
+
+    x1, x2 = x * mask, x[index] * (1 - mask)
+    return x1 + x2, index, lam
+
+
+class FMixBase:
+    """ FMix augmentation
+
+        Args:
+            decay_power (float): Decay power for frequency decay prop 1/f**d
+            alpha (float): Alpha value for beta distribution from which to sample mean of mask
+            size ([int] | [int, int] | [int, int, int]): Shape of desired mask, list up to 3 dims
+            max_soft (float): Softening value between 0 and 0.5 which smooths hard edges in the mask.
+            reformulate (bool): If True, uses the reformulation of [1].
+    """
+
+    def __init__(self,
+                 decay_power=3,
+                 alpha=1,
+                 size=(32, 32),
+                 max_soft=0.0,
+                 reformulate=False):
+        super().__init__()
+        self.decay_power = decay_power
+        self.reformulate = reformulate
+        self.size = size
+        self.alpha = alpha
+        self.max_soft = max_soft
+        self.index = None
+        self.lam = None
+
+    def __call__(self, x):
+        raise NotImplementedError
+
+    def loss(self, *args, **kwargs):
+        raise NotImplementedError

ppcls/data/preprocess/ops/functional.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.
+
+# encoding: utf-8
+
+import numpy as np
+from PIL import Image, ImageOps, ImageEnhance
+
+
+
+def int_parameter(level, maxval):
+    """Helper function to scale `val` between 0 and maxval .
+    Args:
+      level: Level of the operation that will be between [0, `PARAMETER_MAX`].
+      maxval: Maximum value that the operation can have. This will be scaled to
+        level/PARAMETER_MAX.
+    Returns:
+      An int that results from scaling `maxval` according to `level`.
+    """
+    return int(level * maxval / 10)
+
+
+def float_parameter(level, maxval):
+    """Helper function to scale `val` between 0 and maxval.
+    Args:
+      level: Level of the operation that will be between [0, `PARAMETER_MAX`].
+      maxval: Maximum value that the operation can have. This will be scaled to
+        level/PARAMETER_MAX.
+    Returns:
+      A float that results from scaling `maxval` according to `level`.
+    """
+    return float(level) * maxval / 10.
+
+
+def sample_level(n):
+    return np.random.uniform(low=0.1, high=n)
+
+
+def autocontrast(pil_img, *args):
+    return ImageOps.autocontrast(pil_img)
+
+
+def equalize(pil_img, *args):
+    return ImageOps.equalize(pil_img)
+
+
+def posterize(pil_img, level, *args):
+    level = int_parameter(sample_level(level), 4)
+    return ImageOps.posterize(pil_img, 4 - level)
+
+
+def rotate(pil_img, level, *args):
+    degrees = int_parameter(sample_level(level), 30)
+    if np.random.uniform() > 0.5:
+        degrees = -degrees
+    return pil_img.rotate(degrees, resample=Image.BILINEAR)
+
+
+def solarize(pil_img, level, *args):
+    level = int_parameter(sample_level(level), 256)
+    return ImageOps.solarize(pil_img, 256 - level)
+
+
+def shear_x(pil_img, level):
+    level = float_parameter(sample_level(level), 0.3)
+    if np.random.uniform() > 0.5:
+        level = -level
+    return pil_img.transform(pil_img.size,
+                             Image.AFFINE, (1, level, 0, 0, 1, 0),
+                             resample=Image.BILINEAR)
+
+
+def shear_y(pil_img, level):
+    level = float_parameter(sample_level(level), 0.3)
+    if np.random.uniform() > 0.5:
+        level = -level
+    return pil_img.transform(pil_img.size,
+                             Image.AFFINE, (1, 0, 0, level, 1, 0),
+                             resample=Image.BILINEAR)
+
+
+def translate_x(pil_img, level):
+    level = int_parameter(sample_level(level), pil_img.size[0] / 3)
+    if np.random.random() > 0.5:
+        level = -level
+    return pil_img.transform(pil_img.size,
+                             Image.AFFINE, (1, 0, level, 0, 1, 0),
+                             resample=Image.BILINEAR)
+
+
+def translate_y(pil_img, level):
+    level = int_parameter(sample_level(level), pil_img.size[1] / 3)
+    if np.random.random() > 0.5:
+        level = -level
+    return pil_img.transform(pil_img.size,
+                             Image.AFFINE, (1, 0, 0, 0, 1, level),
+                             resample=Image.BILINEAR)
+
+
+# operation that overlaps with ImageNet-C's test set
+def color(pil_img, level, *args):
+    level = float_parameter(sample_level(level), 1.8) + 0.1
+    return ImageEnhance.Color(pil_img).enhance(level)
+
+
+# operation that overlaps with ImageNet-C's test set
+def contrast(pil_img, level, *args):
+    level = float_parameter(sample_level(level), 1.8) + 0.1
+    return ImageEnhance.Contrast(pil_img).enhance(level)
+
+
+# operation that overlaps with ImageNet-C's test set
+def brightness(pil_img, level, *args):
+    level = float_parameter(sample_level(level), 1.8) + 0.1
+    return ImageEnhance.Brightness(pil_img).enhance(level)
+
+
+# operation that overlaps with ImageNet-C's test set
+def sharpness(pil_img, level, *args):
+    level = float_parameter(sample_level(level), 1.8) + 0.1
+    return ImageEnhance.Sharpness(pil_img).enhance(level)
+
+
+augmentations = [
+    autocontrast, equalize, posterize, rotate, solarize, shear_x, shear_y,
+    translate_x, translate_y
+]

ppcls/data/preprocess/ops/grid.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 is based on https://github.com/akuxcw/GridMask
+
+import numpy as np
+from PIL import Image
+import pdb
+
+# curr
+CURR_EPOCH = 0
+# epoch for the prob to be the upper limit
+NUM_EPOCHS = 240
+
+
+class GridMask(object):
+    def __init__(self, d1=96, d2=224, rotate=1, ratio=0.5, mode=0, prob=1.):
+        self.d1 = d1
+        self.d2 = d2
+        self.rotate = rotate
+        self.ratio = ratio
+        self.mode = mode
+        self.st_prob = prob
+        self.prob = prob
+        self.last_prob = -1
+
+    def set_prob(self):
+        global CURR_EPOCH
+        global NUM_EPOCHS
+        self.prob = self.st_prob * min(1, 1.0 * CURR_EPOCH / NUM_EPOCHS)
+
+    def __call__(self, img):
+        self.set_prob()
+        if abs(self.last_prob - self.prob) > 1e-10:
+            global CURR_EPOCH
+            global NUM_EPOCHS
+            print(
+                "self.prob is updated, self.prob={}, CURR_EPOCH: {}, NUM_EPOCHS: {}".
+                format(self.prob, CURR_EPOCH, NUM_EPOCHS))
+            self.last_prob = self.prob
+        # print("CURR_EPOCH: {}, NUM_EPOCHS: {}, self.prob is set as: {}".format(CURR_EPOCH, NUM_EPOCHS, self.prob) )
+        if np.random.rand() > self.prob:
+            return img
+        _, h, w = img.shape
+        hh = int(1.5 * h)
+        ww = int(1.5 * w)
+        d = np.random.randint(self.d1, self.d2)
+        #d = self.d
+        self.l = int(d * self.ratio + 0.5)
+        mask = np.ones((hh, ww), np.float32)
+        st_h = np.random.randint(d)
+        st_w = np.random.randint(d)
+        for i in range(-1, hh // d + 1):
+            s = d * i + st_h
+            t = s + self.l
+            s = max(min(s, hh), 0)
+            t = max(min(t, hh), 0)
+            mask[s:t, :] *= 0
+        for i in range(-1, ww // d + 1):
+            s = d * i + st_w
+            t = s + self.l
+            s = max(min(s, ww), 0)
+            t = max(min(t, ww), 0)
+            mask[:, s:t] *= 0
+        r = np.random.randint(self.rotate)
+        mask = Image.fromarray(np.uint8(mask))
+        mask = mask.rotate(r)
+        mask = np.asarray(mask)
+        mask = mask[(hh - h) // 2:(hh - h) // 2 + h, (ww - w) // 2:(ww - w) //
+                    2 + w]
+
+        if self.mode == 1:
+            mask = 1 - mask
+
+        mask = np.expand_dims(mask, axis=0)
+        img = (img * mask).astype(img.dtype)
+
+        return img

ppcls/data/preprocess/ops/hide_and_seek.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 is based on https://github.com/kkanshul/Hide-and-Seek
+
+import numpy as np
+import random
+
+
+class HideAndSeek(object):
+    def __init__(self):
+        # possible grid size, 0 means no hiding
+        self.grid_sizes = [0, 16, 32, 44, 56]
+        # hiding probability
+        self.hide_prob = 0.5
+
+    def __call__(self, img):
+        # randomly choose one grid size
+        grid_size = np.random.choice(self.grid_sizes)
+
+        _, h, w = img.shape
+
+        # hide the patches
+        if grid_size == 0:
+            return img
+        for x in range(0, w, grid_size):
+            for y in range(0, h, grid_size):
+                x_end = min(w, x + grid_size)
+                y_end = min(h, y + grid_size)
+                if (random.random() <= self.hide_prob):
+                    img[:, x:x_end, y:y_end] = 0
+
+        return img

ppcls/data/preprocess/ops/operators.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.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import six
+import math
+import random
+import cv2
+import numpy as np
+from PIL import Image
+
+from .autoaugment import ImageNetPolicy
+from .functional import augmentations
+
+class OperatorParamError(ValueError):
+    """ OperatorParamError
+    """
+    pass
+
+class DecodeImage(object):
+    """ decode image """
+
+    def __init__(self, to_rgb=True, to_np=False, channel_first=False):
+        self.to_rgb = to_rgb
+        self.to_np = to_np  # to numpy
+        self.channel_first = channel_first  # only enabled when to_np is True
+
+    def __call__(self, img):
+        if six.PY2:
+            assert type(img) is str and len(
+                img) > 0, "invalid input 'img' in DecodeImage"
+        else:
+            assert type(img) is bytes and len(
+                img) > 0, "invalid input 'img' in DecodeImage"
+        data = np.frombuffer(img, dtype='uint8')
+        img = cv2.imdecode(data, 1)
+        if self.to_rgb:
+            assert img.shape[2] == 3, 'invalid shape of image[%s]' % (
+                img.shape)
+            img = img[:, :, ::-1]
+
+        if self.channel_first:
+            img = img.transpose((2, 0, 1))
+
+        return img
+
+
+class ResizeImage(object):
+    """ resize image """
+
+    def __init__(self, size=None, resize_short=None, interpolation=-1):
+        self.interpolation = interpolation if interpolation >= 0 else None
+        if resize_short is not None and resize_short > 0:
+            self.resize_short = resize_short
+            self.w = None
+            self.h = None
+        elif size is not None:
+            self.resize_short = None
+            self.w = size if type(size) is int else size[0]
+            self.h = size if type(size) is int else size[1]
+        else:
+            raise OperatorParamError("invalid params for ReisizeImage for '\
+                'both 'size' and 'resize_short' are None")
+
+    def __call__(self, img):
+        img_h, img_w = img.shape[:2]
+        if self.resize_short is not None:
+            percent = float(self.resize_short) / min(img_w, img_h)
+            w = int(round(img_w * percent))
+            h = int(round(img_h * percent))
+        else:
+            w = self.w
+            h = self.h
+        if self.interpolation is None:
+            return cv2.resize(img, (w, h))
+        else:
+            return cv2.resize(img, (w, h), interpolation=self.interpolation)
+
+
+class CropImage(object):
+    """ crop image """
+
+    def __init__(self, size):
+        if type(size) is int:
+            self.size = (size, size)
+        else:
+            self.size = size  # (h, w)
+
+    def __call__(self, img):
+        w, h = self.size
+        img_h, img_w = img.shape[:2]
+        w_start = (img_w - w) // 2
+        h_start = (img_h - h) // 2
+
+        w_end = w_start + w
+        h_end = h_start + h
+        return img[h_start:h_end, w_start:w_end, :]
+
+
+class RandCropImage(object):
+    """ random crop image """
+
+    def __init__(self, size, scale=None, ratio=None, interpolation=-1):
+
+        self.interpolation = interpolation if interpolation >= 0 else None
+        if type(size) is int:
+            self.size = (size, size)  # (h, w)
+        else:
+            self.size = size
+
+        self.scale = [0.08, 1.0] if scale is None else scale
+        self.ratio = [3. / 4., 4. / 3.] if ratio is None else ratio
+
+    def __call__(self, img):
+        size = self.size
+        scale = self.scale
+        ratio = self.ratio
+
+        aspect_ratio = math.sqrt(random.uniform(*ratio))
+        w = 1. * aspect_ratio
+        h = 1. / aspect_ratio
+
+        img_h, img_w = img.shape[:2]
+
+        bound = min((float(img_w) / img_h) / (w**2),
+                    (float(img_h) / img_w) / (h**2))
+        scale_max = min(scale[1], bound)
+        scale_min = min(scale[0], bound)
+
+        target_area = img_w * img_h * random.uniform(scale_min, scale_max)
+        target_size = math.sqrt(target_area)
+        w = int(target_size * w)
+        h = int(target_size * h)
+
+        i = random.randint(0, img_w - w)
+        j = random.randint(0, img_h - h)
+
+        img = img[j:j + h, i:i + w, :]
+        if self.interpolation is None:
+            return cv2.resize(img, size)
+        else:
+            return cv2.resize(img, size, interpolation=self.interpolation)
+
+
+class RandFlipImage(object):
+    """ random flip image
+        flip_code:
+            1: Flipped Horizontally
+            0: Flipped Vertically
+            -1: Flipped Horizontally & Vertically
+    """
+
+    def __init__(self, flip_code=1):
+        assert flip_code in [-1, 0, 1
+                             ], "flip_code should be a value in [-1, 0, 1]"
+        self.flip_code = flip_code
+
+    def __call__(self, img):
+        if random.randint(0, 1) == 1:
+            return cv2.flip(img, self.flip_code)
+        else:
+            return img
+
+
+class AutoAugment(object):
+    def __init__(self):
+        self.policy = ImageNetPolicy()
+
+    def __call__(self, img):
+        from PIL import Image
+        img = np.ascontiguousarray(img)
+        img = Image.fromarray(img)
+        img = self.policy(img)
+        img = np.asarray(img)
+
+
+class NormalizeImage(object):
+    """ normalize image such as substract mean, divide std
+    """
+
+    def __init__(self, scale=None, mean=None, std=None, order='chw'):
+        if isinstance(scale, str):
+            scale = eval(scale)
+        self.scale = np.float32(scale if scale is not None else 1.0 / 255.0)
+        mean = mean if mean is not None else [0.485, 0.456, 0.406]
+        std = std if std is not None else [0.229, 0.224, 0.225]
+
+        shape = (3, 1, 1) if order == 'chw' else (1, 1, 3)
+        self.mean = np.array(mean).reshape(shape).astype('float32')
+        self.std = np.array(std).reshape(shape).astype('float32')
+
+    def __call__(self, img):
+        from PIL import Image
+        if isinstance(img, Image.Image):
+            img = np.array(img)
+
+        assert isinstance(img,
+                          np.ndarray), "invalid input 'img' in NormalizeImage"
+        return (img.astype('float32') * self.scale - self.mean) / self.std
+
+
+class ToCHWImage(object):
+    """ convert hwc image to chw image
+    """
+
+    def __init__(self):
+        pass
+
+    def __call__(self, img):
+        from PIL import Image
+        if isinstance(img, Image.Image):
+            img = np.array(img)
+
+        return img.transpose((2, 0, 1))
+
+
+class AugMix(object):
+    """ Perform AugMix augmentation and compute mixture.
+    """
+
+    def __init__(self, prob=0.5, aug_prob_coeff=0.1, mixture_width=3, mixture_depth=1, aug_severity=1):
+        """
+        Args:
+            prob: Probability of taking augmix
+            aug_prob_coeff: Probability distribution coefficients.
+            mixture_width: Number of augmentation chains to mix per augmented example.
+            mixture_depth: Depth of augmentation chains. -1 denotes stochastic depth in [1, 3]'
+            aug_severity: Severity of underlying augmentation operators (between 1 to 10).
+        """
+        # fmt: off
+        self.prob = prob
+        self.aug_prob_coeff = aug_prob_coeff
+        self.mixture_width = mixture_width
+        self.mixture_depth = mixture_depth
+        self.aug_severity = aug_severity
+        self.augmentations = augmentations
+        # fmt: on
+
+    def __call__(self, image):
+        """Perform AugMix augmentations and compute mixture.
+        Returns:
+          mixed: Augmented and mixed image.
+        """
+        if random.random() > self.prob:
+            # Avoid the warning: the given NumPy array is not writeable
+            return np.asarray(image).copy()
+
+        ws = np.float32(
+            np.random.dirichlet([self.aug_prob_coeff] * self.mixture_width))
+        m = np.float32(np.random.beta(self.aug_prob_coeff, self.aug_prob_coeff))
+
+        # image = Image.fromarray(image)
+        mix = np.zeros([image.shape[1], image.shape[0], 3])
+        for i in range(self.mixture_width):
+            image_aug = image.copy()
+            image_aug = Image.fromarray(image_aug)
+            depth = self.mixture_depth if self.mixture_depth > 0 else np.random.randint(1, 4)
+            for _ in range(depth):
+                op = np.random.choice(self.augmentations)
+                image_aug = op(image_aug, self.aug_severity)
+            mix += ws[i] * np.asarray(image_aug)
+
+        mixed = (1 - m) * image + m * mix
+        return mixed.astype(np.uint8)

ppcls/data/preprocess/ops/randaugment.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 is based on https://github.com/heartInsert/randaugment
+
+from PIL import Image, ImageEnhance, ImageOps
+import numpy as np
+import random
+
+
+class RandAugment(object):
+    def __init__(self, num_layers=2, magnitude=5, fillcolor=(128, 128, 128)):
+        self.num_layers = num_layers
+        self.magnitude = magnitude
+        self.max_level = 10
+
+        abso_level = self.magnitude / self.max_level
+        self.level_map = {
+            "shearX": 0.3 * abso_level,
+            "shearY": 0.3 * abso_level,
+            "translateX": 150.0 / 331 * abso_level,
+            "translateY": 150.0 / 331 * abso_level,
+            "rotate": 30 * abso_level,
+            "color": 0.9 * abso_level,
+            "posterize": int(4.0 * abso_level),
+            "solarize": 256.0 * abso_level,
+            "contrast": 0.9 * abso_level,
+            "sharpness": 0.9 * abso_level,
+            "brightness": 0.9 * abso_level,
+            "autocontrast": 0,
+            "equalize": 0,
+            "invert": 0
+        }
+
+        # from https://stackoverflow.com/questions/5252170/
+        # specify-image-filling-color-when-rotating-in-python-with-pil-and-setting-expand
+        def rotate_with_fill(img, magnitude):
+            rot = img.convert("RGBA").rotate(magnitude)
+            return Image.composite(rot,
+                                   Image.new("RGBA", rot.size, (128, ) * 4),
+                                   rot).convert(img.mode)
+
+        rnd_ch_op = random.choice
+
+        self.func = {
+            "shearX": lambda img, magnitude: img.transform(
+                img.size,
+                Image.AFFINE,
+                (1, magnitude * rnd_ch_op([-1, 1]), 0, 0, 1, 0),
+                Image.BICUBIC,
+                fillcolor=fillcolor),
+            "shearY": lambda img, magnitude: img.transform(
+                img.size,
+                Image.AFFINE,
+                (1, 0, 0, magnitude * rnd_ch_op([-1, 1]), 1, 0),
+                Image.BICUBIC,
+                fillcolor=fillcolor),
+            "translateX": lambda img, magnitude: img.transform(
+                img.size,
+                Image.AFFINE,
+                (1, 0, magnitude * img.size[0] * rnd_ch_op([-1, 1]), 0, 1, 0),
+                fillcolor=fillcolor),
+            "translateY": lambda img, magnitude: img.transform(
+                img.size,
+                Image.AFFINE,
+                (1, 0, 0, 0, 1, magnitude * img.size[1] * rnd_ch_op([-1, 1])),
+                fillcolor=fillcolor),
+            "rotate": lambda img, magnitude: rotate_with_fill(img, magnitude),
+            "color": lambda img, magnitude: ImageEnhance.Color(img).enhance(
+                1 + magnitude * rnd_ch_op([-1, 1])),
+            "posterize": lambda img, magnitude:
+                ImageOps.posterize(img, magnitude),
+            "solarize": lambda img, magnitude:
+                ImageOps.solarize(img, magnitude),
+            "contrast": lambda img, magnitude:
+                ImageEnhance.Contrast(img).enhance(
+                    1 + magnitude * rnd_ch_op([-1, 1])),
+            "sharpness": lambda img, magnitude:
+                ImageEnhance.Sharpness(img).enhance(
+                    1 + magnitude * rnd_ch_op([-1, 1])),
+            "brightness": lambda img, magnitude:
+                ImageEnhance.Brightness(img).enhance(
+                    1 + magnitude * rnd_ch_op([-1, 1])),
+            "autocontrast": lambda img, magnitude:
+                ImageOps.autocontrast(img),
+            "equalize": lambda img, magnitude: ImageOps.equalize(img),
+            "invert": lambda img, magnitude: ImageOps.invert(img)
+        }
+
+    def __call__(self, img):
+        avaiable_op_names = list(self.level_map.keys())
+        for layer_num in range(self.num_layers):
+            op_name = np.random.choice(avaiable_op_names)
+            img = self.func[op_name](img, self.level_map[op_name])
+        return img

ppcls/data/preprocess/ops/random_erasing.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 is based on https://github.com/zhunzhong07/Random-Erasing
+
+import math
+import random
+
+import numpy as np
+
+
+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
+
+    def __call__(self, img):
+        if random.uniform(0, 1) > self.EPSILON:
+            return img
+
+        for attempt in range(100):
+            area = img.shape[1] * img.shape[2]
+
+            target_area = random.uniform(self.sl, self.sh) * area
+            aspect_ratio = random.uniform(self.r1, 1 / self.r1)
+
+            h = int(round(math.sqrt(target_area * aspect_ratio)))
+            w = int(round(math.sqrt(target_area / aspect_ratio)))
+
+            if w < img.shape[2] and h < img.shape[1]:
+                x1 = random.randint(0, img.shape[1] - h)
+                y1 = random.randint(0, img.shape[2] - w)
+                if img.shape[0] == 3:
+                    img[0, x1:x1 + h, y1:y1 + w] = self.mean[0]
+                    img[1, x1:x1 + h, y1:y1 + w] = self.mean[1]
+                    img[2, x1:x1 + h, y1:y1 + w] = self.mean[2]
+                else:
+                    img[0, x1:x1 + h, y1:y1 + w] = self.mean[1]
+                return img
+        return img

ppcls/data/samplers/DistributedRandomIdentitySampler.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.
+
+from __future__ import absolute_import
+from __future__ import division
+from collections import defaultdict
+import numpy as np
+import copy
+import random
+from paddle.io import DistributedBatchSampler, Sampler
+
+
+class DistributedRandomIdentitySampler(DistributedBatchSampler):
+    """
+    Randomly sample N identities, then for each identity,
+    randomly sample K instances, therefore batch size is N*K.
+    Args:
+    - data_source (list): list of (img_path, pid, camid).
+    - num_instances (int): number of instances per identity in a batch.
+    - batch_size (int): number of examples in a batch.
+    """
+
+    def __init__(self, dataset, batch_size, num_instances, drop_last, **args):
+        self.dataset = dataset
+        self.batch_size = batch_size
+        self.num_instances = num_instances
+        self.drop_last = drop_last
+        self.num_pids_per_batch = self.batch_size // self.num_instances
+        self.index_dic = defaultdict(list)
+        for index, pid in enumerate(self.dataset.labels):
+            self.index_dic[pid].append(index)
+        self.pids = list(self.index_dic.keys())
+        # estimate number of examples in an epoch
+        self.length = 0
+        for pid in self.pids:
+            idxs = self.index_dic[pid]
+            num = len(idxs)
+            if num < self.num_instances:
+                num = self.num_instances
+            self.length += num - num % self.num_instances
+
+    def __iter__(self):
+        batch_idxs_dict = defaultdict(list)
+        for pid in self.pids:
+            idxs = copy.deepcopy(self.index_dic[pid])
+            if len(idxs) < self.num_instances:
+                idxs = np.random.choice(
+                    idxs, size=self.num_instances, replace=True)
+            random.shuffle(idxs)
+            batch_idxs = []
+            for idx in idxs:
+                batch_idxs.append(idx)
+                if len(batch_idxs) == self.num_instances:
+                    batch_idxs_dict[pid].append(batch_idxs)
+                    batch_idxs = []
+        avai_pids = copy.deepcopy(self.pids)
+        final_idxs = []
+        while len(avai_pids) >= self.num_pids_per_batch:
+            selected_pids = random.sample(avai_pids, self.num_pids_per_batch)
+            for pid in selected_pids:
+                batch_idxs = batch_idxs_dict[pid].pop(0)
+                final_idxs.extend(batch_idxs)
+                if len(batch_idxs_dict[pid]) == 0:
+                    avai_pids.remove(pid)
+        _sample_iter = iter(final_idxs)
+        batch_indices = []
+        for idx in _sample_iter:
+            batch_indices.append(idx)
+            if len(batch_indices) == self.batch_size:
+                yield batch_indices
+                batch_indices = []
+        if not self.drop_last and len(batch_indices) > 0:
+            yield batch_indices
+
+    def __len__(self):
+        if self.drop_last:
+            return self.length // self.batch_size
+        else:
+            return (self.length + self.batch_size - 1) // self.batch_size

ppcls/data/samplers/__init__.py

+from .DistributedRandomIdentitySampler import DistributedRandomIdentitySampler