modify format and remove loss

ppcls/arch/loss_metrics/loss.py

-# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
-#
-# 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 paddle
-import paddle.nn.functional as F
-
-__all__ = ['CELoss', 'MixCELoss', 'GoogLeNetLoss', 'JSDivLoss', 'MultiLabelLoss']
-
-
-class Loss(object):
-    """
-    Loss
-    """
-
-    def __init__(self, class_dim=1000, epsilon=None):
-        assert class_dim > 1, "class_dim=%d is not larger than 1" % (class_dim)
-        self._class_dim = class_dim
-        if epsilon is not None and epsilon >= 0.0 and epsilon <= 1.0:
-            self._epsilon = epsilon
-            self._label_smoothing = True
-        else:
-            self._epsilon = None
-            self._label_smoothing = False
-
-    def _labelsmoothing(self, target):
-        if target.shape[-1] != self._class_dim:
-            one_hot_target = F.one_hot(target, self._class_dim)
-        else:
-            one_hot_target = target
-        soft_target = F.label_smooth(one_hot_target, epsilon=self._epsilon)
-        soft_target = paddle.reshape(soft_target, shape=[-1, self._class_dim])
-        return soft_target
-    
-    def _binary_crossentropy(self, input, target):
-        if self._label_smoothing:
-            target = self._labelsmoothing(target)
-            cost = F.binary_cross_entropy_with_logits(logit=input, label=target)
-        else:
-            cost = F.binary_cross_entropy_with_logits(logit=input, label=target)
-
-        avg_cost = paddle.mean(cost)
-
-        return avg_cost
-
-    def _crossentropy(self, input, target):
-        if self._label_smoothing:
-            target = self._labelsmoothing(target)
-            input = -F.log_softmax(input, axis=-1)
-            cost = paddle.sum(target * input, axis=-1)
-        else:
-            cost = F.cross_entropy(input=input, label=target) 
-        avg_cost = paddle.mean(cost)
-        return avg_cost
-
-    def _kldiv(self, input, target, name=None):
-        eps = 1.0e-10
-        cost = target * paddle.log(
-            (target + eps) / (input + eps)) * self._class_dim
-        return cost
-
-    def _jsdiv(self, input, target):
-        input = F.softmax(input)
-        target = F.softmax(target)
-        cost = self._kldiv(input, target) + self._kldiv(target, input)
-        cost = cost / 2
-        avg_cost = paddle.mean(cost)
-        return avg_cost
-
-    def __call__(self, input, target):
-        pass
-    
-    
-class MultiLabelLoss(Loss):
-    """
-    Multilabel loss based binary cross entropy
-    """
-
-    def __init__(self, class_dim=1000, epsilon=None):
-        super(MultiLabelLoss, self).__init__(class_dim, epsilon)
-
-    def __call__(self, input, target):
-        cost = self._binary_crossentropy(input, target)
-
-        return cost
-
-
-class CELoss(Loss):
-    """
-    Cross entropy loss
-    """
-
-    def __init__(self, class_dim=1000, epsilon=None):
-        super(CELoss, self).__init__(class_dim, epsilon)
-
-    def __call__(self, input, target):
-        cost = self._crossentropy(input, target)
-        return cost
-
-
-class MixCELoss(Loss):
-    """
-    Cross entropy loss with mix(mixup, cutmix, fixmix)
-    """
-
-    def __init__(self, class_dim=1000, epsilon=None):
-        super(MixCELoss, self).__init__(class_dim, epsilon)
-
-    def __call__(self, input, target0, target1, lam):
-        cost0 = self._crossentropy(input, target0)
-        cost1 = self._crossentropy(input, target1)
-        cost = lam * cost0 + (1.0 - lam) * cost1  
-        avg_cost = paddle.mean(cost)
-        return avg_cost
-
-
-class GoogLeNetLoss(Loss):
-    """
-    Cross entropy loss used after googlenet
-    """
-
-    def __init__(self, class_dim=1000, epsilon=None):
-        super(GoogLeNetLoss, self).__init__(class_dim, epsilon)
-
-    def __call__(self, input0, input1, input2, target):
-        cost0 = self._crossentropy(input0, target)
-        cost1 = self._crossentropy(input1, target)
-        cost2 = self._crossentropy(input2, target)
-        cost = cost0 + 0.3 * cost1 + 0.3 * cost2
-        avg_cost = paddle.mean(cost)
-        return avg_cost
-
-
-class JSDivLoss(Loss):
-    """
-    JSDiv loss
-    """
-
-    def __init__(self, class_dim=1000, epsilon=None):
-        super(JSDivLoss, self).__init__(class_dim, epsilon)
-
-    def __call__(self, input, target):
-        cost = self._jsdiv(input, target)
-        return cost

ppcls/data/dataloader/imagenet_dataset.py

@@ -14,22 +14,13 @@
 
 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):
 
+class ImageNetDataset(CommonDataset):
     def _load_anno(self, seed=None):
         assert os.path.exists(self._cls_path)
         assert os.path.exists(self._img_root)
@@ -47,5 +38,3 @@ class ImageNetDataset(CommonDataset):
                 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/dataloader/multilabel_dataset.py

@@ -14,26 +14,17 @@
 
 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):
 
+class MultiLabelDataset(CommonDataset):
     def _load_anno(self):
         assert os.path.exists(self._cls_path)
         assert os.path.exists(self._img_root)