diff --git a/official/cv/vit/src/cross_entropy.py b/official/cv/vit/src/cross_entropy.py index 7cbedb5f0ec0d72afb67e9ab2c10e59ba20ddebb..1e7865b91f05a735ff77b23beb7d67d48d422fce 100644 --- a/official/cv/vit/src/cross_entropy.py +++ b/official/cv/vit/src/cross_entropy.py @@ -17,13 +17,7 @@ from mindspore import nn from mindspore import Tensor from mindspore.common import dtype as mstype -try: - from mindspore.nn.loss.loss import Loss -except ImportError: - try: - from mindspore.nn.loss.loss import LossBase as Loss - except ImportError: - from mindspore.nn.loss.loss import _Loss as Loss +from mindspore.nn.loss.loss import LossBase as Loss from mindspore.ops import functional as F from mindspore.ops import operations as P diff --git a/research/cv/HRNetW48_cls/src/loss.py b/research/cv/HRNetW48_cls/src/loss.py index 33d8415259f630d6d7ac9689ea19c18e84ac2fba..ce56f8a4187a7843a0995f92ac46eabe905fd355 100644 --- a/research/cv/HRNetW48_cls/src/loss.py +++ b/research/cv/HRNetW48_cls/src/loss.py @@ -16,12 +16,12 @@ import mindspore.nn as nn from mindspore import Tensor from mindspore.common import dtype as mstype -from mindspore.nn.loss.loss import _Loss +from mindspore.nn.loss.loss import LossBase from mindspore.ops import functional as F from mindspore.ops import operations as P -class CrossEntropySmooth(_Loss): +class CrossEntropySmooth(LossBase): """CrossEntropy""" def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=1000): super(CrossEntropySmooth, self).__init__() diff --git a/research/cv/HRNetW48_seg/src/loss.py b/research/cv/HRNetW48_seg/src/loss.py index 9cde41d4cedc070db8aa26801671a77f06228851..9f88cbde67d0ea99149942e603198e51c104568c 100644 --- a/research/cv/HRNetW48_seg/src/loss.py +++ b/research/cv/HRNetW48_seg/src/loss.py @@ -18,10 +18,10 @@ import mindspore.ops.operations as P import mindspore.ops as F from mindspore.common.tensor import Tensor from mindspore import dtype as mstype -from mindspore.nn.loss.loss import _Loss +from mindspore.nn.loss.loss import LossBase -class CrossEntropyWithLogits(_Loss): +class CrossEntropyWithLogits(LossBase): """ Cross-entropy loss function for semantic segmentation, and different classes have the same weight. @@ -61,7 +61,7 @@ class CrossEntropyWithLogits(_Loss): return loss -class CrossEntropyWithWeights(_Loss): +class CrossEntropyWithWeights(LossBase): """ Cross-entropy loss function for semantic segmentation, and different classes have different weights. diff --git a/research/cv/ICNet/Res50V1_PRE/src/CrossEntropySmooth.py b/research/cv/ICNet/Res50V1_PRE/src/CrossEntropySmooth.py index 24bb6995ed8e3d87db55e21821ef430a14408e7f..149f334546cb432462c91315515804f9fc05e37c 100644 --- a/research/cv/ICNet/Res50V1_PRE/src/CrossEntropySmooth.py +++ b/research/cv/ICNet/Res50V1_PRE/src/CrossEntropySmooth.py @@ -16,12 +16,12 @@ import mindspore.nn as nn from mindspore import Tensor from mindspore.common import dtype as mstype -from mindspore.nn.loss.loss import _Loss +from mindspore.nn.loss.loss import LossBase from mindspore.ops import functional as F from mindspore.ops import operations as P -class CrossEntropySmooth(_Loss): +class CrossEntropySmooth(LossBase): """CrossEntropy""" def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=1000): super(CrossEntropySmooth, self).__init__() diff --git a/research/cv/NFNet/src/tools/criterion.py b/research/cv/NFNet/src/tools/criterion.py index bf8a490e9076f3cfb408fc0d6bd3aafc20c8df13..adef5e682b8cd1548e9ede96f5d9e23cf7fd900e 100644 --- a/research/cv/NFNet/src/tools/criterion.py +++ b/research/cv/NFNet/src/tools/criterion.py @@ -17,12 +17,12 @@ import mindspore.nn as nn from mindspore import Tensor from mindspore import ops from mindspore.common import dtype as mstype -from mindspore.nn.loss.loss import _Loss +from mindspore.nn.loss.loss import LossBase from mindspore.ops import functional as F from mindspore.ops import operations as P -class SoftTargetCrossEntropy(_Loss): +class SoftTargetCrossEntropy(LossBase): """SoftTargetCrossEntropy for MixUp Augment""" def __init__(self): @@ -38,7 +38,7 @@ class SoftTargetCrossEntropy(_Loss): return self.mean_ops(loss) -class CrossEntropySmooth(_Loss): +class CrossEntropySmooth(LossBase): """CrossEntropy""" def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=1000): diff --git a/research/cv/OCRNet/src/loss.py b/research/cv/OCRNet/src/loss.py index 99af216d15412e2e3dc5e3fe70d8c026fa33a410..5842464a4ba390391c12071b5253b3a85794bac6 100644 --- a/research/cv/OCRNet/src/loss.py +++ b/research/cv/OCRNet/src/loss.py @@ -19,7 +19,7 @@ import mindspore.ops.operations as P import mindspore.ops as F from mindspore.common.tensor import Tensor from mindspore import dtype as mstype -from mindspore.nn.loss.loss import _Loss +from mindspore.nn.loss.loss import LossBase from src.config import config_hrnetv2_w48 as config @@ -30,7 +30,7 @@ weights_list = [0.8373, 0.918, 0.866, 1.0345, 1.0865, 1.1529, 1.0507] -class CrossEntropyWithLogits(_Loss): +class CrossEntropyWithLogits(LossBase): """ Cross-entropy loss function for semantic segmentation, and different classes have the same weight. @@ -71,7 +71,7 @@ class CrossEntropyWithLogits(_Loss): return loss -class CrossEntropyWithLogitsAndWeights(_Loss): +class CrossEntropyWithLogitsAndWeights(LossBase): """ Cross-entropy loss function for semantic segmentation, and different classes have different weights. diff --git a/research/cv/Pix2Pix/src/models/loss.py b/research/cv/Pix2Pix/src/models/loss.py index eff9c47768422a292277363fd12b9df0b7a648aa..c9cca0cb97213843e6381c2b8bc440cdae375335 100644 --- a/research/cv/Pix2Pix/src/models/loss.py +++ b/research/cv/Pix2Pix/src/models/loss.py @@ -24,12 +24,12 @@ import mindspore.ops.operations as P from mindspore.parallel._utils import (_get_device_num, _get_gradients_mean, _get_parallel_mode) from mindspore.context import ParallelMode from mindspore.nn.wrap.grad_reducer import DistributedGradReducer -from mindspore.nn.loss.loss import _Loss +from mindspore.nn.loss.loss import LossBase from src.utils.config import get_args args = get_args() -class SigmoidCrossEntropyWithLogits(_Loss): +class SigmoidCrossEntropyWithLogits(LossBase): def __init__(self): super(SigmoidCrossEntropyWithLogits, self).__init__() self.cross_entropy = P.SigmoidCrossEntropyWithLogits() @@ -38,7 +38,7 @@ class SigmoidCrossEntropyWithLogits(_Loss): x = self.cross_entropy(data, label) return self.get_loss(x) -class D_Loss(_Loss): +class D_Loss(LossBase): """ Define Dloss. """ @@ -70,7 +70,7 @@ class D_WithLossCell(nn.Cell): pred0 = self.netD(realA, fakeB) return self._loss_fn(pred1, pred0) -class G_Loss(_Loss): +class G_Loss(LossBase): """ Define Gloss. """ diff --git a/research/cv/ProtoNet/src/PrototypicalLoss.py b/research/cv/ProtoNet/src/PrototypicalLoss.py index 8faa95927f5552619e3bc93fb4b2b083b88ab628..0a3a784f9222c1953c6ffe462a1938b9fdc247c2 100644 --- a/research/cv/ProtoNet/src/PrototypicalLoss.py +++ b/research/cv/ProtoNet/src/PrototypicalLoss.py @@ -18,11 +18,11 @@ loss function script. import mindspore.ops as ops import mindspore.nn as nn from mindspore import Tensor -from mindspore.nn.loss.loss import _Loss +from mindspore.nn.loss.loss import LossBase import mindspore as ms import numpy as np -class PrototypicalLoss(_Loss): +class PrototypicalLoss(LossBase): ''' Loss class deriving from Module for the prototypical loss function defined below ''' diff --git a/research/cv/ResNeSt50/src/crossentropy.py b/research/cv/ResNeSt50/src/crossentropy.py index a5abe60219632e15c9de81e7c8cbbba5d697d1d5..01039e39ea3e4fe4a7fa353a738a6d96ada2809b 100644 --- a/research/cv/ResNeSt50/src/crossentropy.py +++ b/research/cv/ResNeSt50/src/crossentropy.py @@ -15,14 +15,14 @@ """ define loss function for network. """ -from mindspore.nn.loss.loss import _Loss +from mindspore.nn.loss.loss import LossBase from mindspore.ops import operations as P from mindspore.ops import functional as F from mindspore import Tensor from mindspore.common import dtype as mstype import mindspore.nn as nn -class CrossEntropy(_Loss): +class CrossEntropy(LossBase): """ the redefined loss function with SoftmaxCrossEntropyWithLogits. """ diff --git a/research/cv/SE_ResNeXt50/eval.py b/research/cv/SE_ResNeXt50/eval.py index cb109902cf254828bac98bef26e0764fec7a6691..a12f096dc9d2043e16dcb33e980996e6084ebfdf 100644 --- a/research/cv/SE_ResNeXt50/eval.py +++ b/research/cv/SE_ResNeXt50/eval.py @@ -24,7 +24,7 @@ from mindspore.train.serialization import load_checkpoint, load_param_into_net from mindspore.common import set_seed from mindspore import Tensor from mindspore.common import dtype as mstype -from mindspore.nn.loss.loss import _Loss +from mindspore.nn.loss.loss import LossBase from mindspore.ops import functional as F from mindspore.ops import operations as P @@ -41,7 +41,7 @@ parser.add_argument('--checkpoint_path', type=str, default='./ckpt_0', help='Che args_opt = parser.parse_args() -class CrossEntropySmooth(_Loss): +class CrossEntropySmooth(LossBase): """CrossEntropy""" def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=1000): super(CrossEntropySmooth, self).__init__() diff --git a/research/cv/SE_ResNeXt50/train.py b/research/cv/SE_ResNeXt50/train.py index b99da2f4657d60e32a6e81ebe0d28d6139432f34..972626ae0e57cf3a1655e527cd9d5ab0c4906a6b 100644 --- a/research/cv/SE_ResNeXt50/train.py +++ b/research/cv/SE_ResNeXt50/train.py @@ -34,7 +34,7 @@ from mindspore.context import ParallelMode from mindspore.train.serialization import load_checkpoint, load_param_into_net from mindspore.common import set_seed from mindspore.common import dtype as mstype -from mindspore.nn.loss.loss import _Loss +from mindspore.nn.loss.loss import LossBase from mindspore.ops import functional as F from mindspore.ops import operations as P @@ -152,7 +152,7 @@ def warmup_cosine_annealing_lr(lr5, steps_per_epoch, warmup_epochs, max_epoch, T return np.array(lr_each_step).astype(np.float32) -class CrossEntropySmooth(_Loss): +class CrossEntropySmooth(LossBase): """CrossEntropy""" def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=1000): super(CrossEntropySmooth, self).__init__() diff --git a/research/cv/TNT/src/tools/criterion.py b/research/cv/TNT/src/tools/criterion.py index 1afdd719f3a77188b32e4f22b756b8151adf572c..ee963c1fee2724003b422532b2c1cb36ba0e9391 100644 --- a/research/cv/TNT/src/tools/criterion.py +++ b/research/cv/TNT/src/tools/criterion.py @@ -17,12 +17,12 @@ import mindspore.nn as nn from mindspore import Tensor from mindspore import ops from mindspore.common import dtype as mstype -from mindspore.nn.loss.loss import _Loss +from mindspore.nn.loss.loss import LossBase from mindspore.ops import functional as F from mindspore.ops import operations as P -class SoftTargetCrossEntropy(_Loss): +class SoftTargetCrossEntropy(LossBase): """SoftTargetCrossEntropy for MixUp Augment""" def __init__(self): @@ -38,7 +38,7 @@ class SoftTargetCrossEntropy(_Loss): return self.mean_ops(loss) -class CrossEntropySmooth(_Loss): +class CrossEntropySmooth(LossBase): """CrossEntropy""" def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=1000): diff --git a/research/cv/VehicleNet/src/loss.py b/research/cv/VehicleNet/src/loss.py index 6aca541533d9de1a1c55e7396ce83f31394d9555..673a8ab1cad51be711aa204e4270faad1e4d9068 100644 --- a/research/cv/VehicleNet/src/loss.py +++ b/research/cv/VehicleNet/src/loss.py @@ -13,14 +13,14 @@ # limitations under the License. # ============================================================================ """loss function""" -from mindspore.nn.loss.loss import _Loss +from mindspore.nn.loss.loss import LossBase from mindspore.ops import operations as P from mindspore.ops import functional as F from mindspore import Tensor from mindspore.common import dtype as mstype import mindspore.nn as nn -class CrossEntropy(_Loss): +class CrossEntropy(LossBase): """CrossEntropy""" def __init__(self, smooth_factor=0., num_classes=1000): super(CrossEntropy, self).__init__() diff --git a/research/cv/aecrnet/src/contras_loss.py b/research/cv/aecrnet/src/contras_loss.py index 93b3d61bdfa22d8b1d627382486808de8d5942cb..fb8ea8430b9ebe9d566a74f6f18b5ec641117f2e 100644 --- a/research/cv/aecrnet/src/contras_loss.py +++ b/research/cv/aecrnet/src/contras_loss.py @@ -16,7 +16,7 @@ import os import mindspore.ops as ops -from mindspore.nn.loss.loss import _Loss +from mindspore.nn.loss.loss import LossBase from mindspore import nn from mindspore import load_checkpoint, load_param_into_net from mindspore.ops.functional import stop_gradient @@ -82,7 +82,7 @@ class Vgg19(nn.Cell): return out -class ContrastLoss(_Loss): +class ContrastLoss(LossBase): """[ContrastLoss] Args: diff --git a/research/cv/cct/src/tools/criterion.py b/research/cv/cct/src/tools/criterion.py index 4ed0f0534393cc82a9a286542b583be52c31a4b3..257aec2dac97d193787982db2e0bdce19c088a78 100644 --- a/research/cv/cct/src/tools/criterion.py +++ b/research/cv/cct/src/tools/criterion.py @@ -17,12 +17,12 @@ import mindspore.nn as nn from mindspore import Tensor from mindspore import ops from mindspore.common import dtype as mstype -from mindspore.nn.loss.loss import _Loss +from mindspore.nn.loss.loss import LossBase from mindspore.ops import functional as F from mindspore.ops import operations as P -class SoftTargetCrossEntropy(_Loss): +class SoftTargetCrossEntropy(LossBase): """SoftTargetCrossEntropy for MixUp Augment""" def __init__(self): @@ -38,7 +38,7 @@ class SoftTargetCrossEntropy(_Loss): return self.mean_ops(loss) -class CrossEntropySmooth(_Loss): +class CrossEntropySmooth(LossBase): """CrossEntropy""" def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=1000): diff --git a/research/cv/csd/src/contras_loss.py b/research/cv/csd/src/contras_loss.py index 8326af2601dfc659b9194a61486e96038c181714..ac9bfa655f436ae3fa873145517e1457b602aaeb 100644 --- a/research/cv/csd/src/contras_loss.py +++ b/research/cv/csd/src/contras_loss.py @@ -16,7 +16,7 @@ import os import mindspore.ops as ops -from mindspore.nn.loss.loss import _Loss +from mindspore.nn.loss.loss import LossBase from mindspore import nn from mindspore import load_checkpoint, load_param_into_net from mindspore.ops.functional import stop_gradient @@ -108,7 +108,7 @@ class Vgg19(nn.Cell): return out -class ContrastLoss(_Loss): +class ContrastLoss(LossBase): """[ContrastLoss] Args: diff --git a/research/cv/efficientnet-b0/src/loss.py b/research/cv/efficientnet-b0/src/loss.py index 6d63b66694652da2298ec4cea541537be22d843c..8400b5413bf85c37dd9793ae0664f50e2cf16396 100644 --- a/research/cv/efficientnet-b0/src/loss.py +++ b/research/cv/efficientnet-b0/src/loss.py @@ -16,12 +16,12 @@ import mindspore.nn as nn from mindspore import Tensor from mindspore.common import dtype as mstype -from mindspore.nn.loss.loss import _Loss +from mindspore.nn.loss.loss import LossBase from mindspore.ops import functional as F from mindspore.ops import operations as P -class CrossEntropySmooth(_Loss): +class CrossEntropySmooth(LossBase): """CrossEntropy""" def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=1000): super(CrossEntropySmooth, self).__init__() diff --git a/research/cv/efficientnet-b1/src/loss.py b/research/cv/efficientnet-b1/src/loss.py index 06cb51616065dddcc1e79cc46cec0f031ce3a3a4..14ebd144f507c47b3fc6b986f24e8f33d7abd12c 100644 --- a/research/cv/efficientnet-b1/src/loss.py +++ b/research/cv/efficientnet-b1/src/loss.py @@ -13,7 +13,7 @@ # limitations under the License. # ============================================================================ """define loss function for network.""" -from mindspore.nn.loss.loss import _Loss +from mindspore.nn.loss.loss import LossBase from mindspore.ops import operations as P from mindspore.ops import functional as F from mindspore.common import dtype as mstype @@ -21,7 +21,7 @@ from mindspore import Tensor import mindspore.nn as nn -class LabelSmoothingCrossEntropy(_Loss): +class LabelSmoothingCrossEntropy(LossBase): """LabelSmoothingCrossEntropy""" def __init__(self, smooth_factor=0.1, num_classes=1000): super(LabelSmoothingCrossEntropy, self).__init__() @@ -38,7 +38,7 @@ class LabelSmoothingCrossEntropy(_Loss): return loss_logit -class CrossEntropySmooth(_Loss): +class CrossEntropySmooth(LossBase): """CrossEntropy""" def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=1000): super(CrossEntropySmooth, self).__init__() diff --git a/research/cv/efficientnet-b3/src/loss.py b/research/cv/efficientnet-b3/src/loss.py index 6d63b66694652da2298ec4cea541537be22d843c..8400b5413bf85c37dd9793ae0664f50e2cf16396 100644 --- a/research/cv/efficientnet-b3/src/loss.py +++ b/research/cv/efficientnet-b3/src/loss.py @@ -16,12 +16,12 @@ import mindspore.nn as nn from mindspore import Tensor from mindspore.common import dtype as mstype -from mindspore.nn.loss.loss import _Loss +from mindspore.nn.loss.loss import LossBase from mindspore.ops import functional as F from mindspore.ops import operations as P -class CrossEntropySmooth(_Loss): +class CrossEntropySmooth(LossBase): """CrossEntropy""" def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=1000): super(CrossEntropySmooth, self).__init__() diff --git a/research/cv/fishnet99/eval.py b/research/cv/fishnet99/eval.py index 4cebaf5c122ea1b10b4d498161675b17934b719d..84cabbeaaed7590093301029421483425e77f2e8 100644 --- a/research/cv/fishnet99/eval.py +++ b/research/cv/fishnet99/eval.py @@ -24,7 +24,7 @@ from mindspore.train.serialization import load_checkpoint, load_param_into_net from mindspore.common import set_seed from mindspore import Tensor from mindspore.common import dtype as mstype -from mindspore.nn.loss.loss import _Loss +from mindspore.nn.loss.loss import LossBase from mindspore.ops import functional as F from mindspore.ops import operations as P @@ -43,7 +43,7 @@ parser.add_argument('--checkpoint_path', type=str, default='./ckpt_0', help='Che args_opt = parser.parse_args() -class CrossEntropySmooth(_Loss): +class CrossEntropySmooth(LossBase): """CrossEntropy""" def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=1000): super(CrossEntropySmooth, self).__init__() diff --git a/research/cv/fishnet99/train.py b/research/cv/fishnet99/train.py index a5401d1587fa0ff3ad1c735deee889f8894a6c72..3901c2b2a58ec0d637af3efc36db520e65947467 100644 --- a/research/cv/fishnet99/train.py +++ b/research/cv/fishnet99/train.py @@ -31,7 +31,7 @@ from mindspore.context import ParallelMode from mindspore.train.serialization import load_checkpoint, load_param_into_net from mindspore.common import set_seed from mindspore.common import dtype as mstype -from mindspore.nn.loss.loss import _Loss +from mindspore.nn.loss.loss import LossBase from mindspore.ops import functional as F from mindspore.ops import operations as P @@ -81,7 +81,7 @@ def warmup_cosine_annealing_lr(lr5, steps_per_epoch, warmup_epochs, max_epoch, T return np.array(lr_each_step).astype(np.float32) -class CrossEntropySmooth(_Loss): +class CrossEntropySmooth(LossBase): """CrossEntropy""" def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=1000): super(CrossEntropySmooth, self).__init__() diff --git a/research/cv/ghostnet/src/CrossEntropySmooth.py b/research/cv/ghostnet/src/CrossEntropySmooth.py index 6d63b66694652da2298ec4cea541537be22d843c..8400b5413bf85c37dd9793ae0664f50e2cf16396 100644 --- a/research/cv/ghostnet/src/CrossEntropySmooth.py +++ b/research/cv/ghostnet/src/CrossEntropySmooth.py @@ -16,12 +16,12 @@ import mindspore.nn as nn from mindspore import Tensor from mindspore.common import dtype as mstype -from mindspore.nn.loss.loss import _Loss +from mindspore.nn.loss.loss import LossBase from mindspore.ops import functional as F from mindspore.ops import operations as P -class CrossEntropySmooth(_Loss): +class CrossEntropySmooth(LossBase): """CrossEntropy""" def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=1000): super(CrossEntropySmooth, self).__init__() diff --git a/research/cv/inception_resnet_v2/modelarts/train_start.py b/research/cv/inception_resnet_v2/modelarts/train_start.py index 37ab60bfba772628977b681b5d2c87465ba1b20a..83162b746c98ace4e935b1e1d0d25266f7645330 100644 --- a/research/cv/inception_resnet_v2/modelarts/train_start.py +++ b/research/cv/inception_resnet_v2/modelarts/train_start.py @@ -33,7 +33,7 @@ from mindspore.common.initializer import XavierUniform, initializer from mindspore.communication import init from mindspore.context import ParallelMode from mindspore.nn import RMSProp, Momentum -from mindspore.nn.loss.loss import _Loss +from mindspore.nn.loss.loss import LossBase from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, TimeMonitor, LossMonitor from mindspore.train.loss_scale_manager import FixedLossScaleManager from mindspore.train.serialization import load_checkpoint, load_param_into_net @@ -65,7 +65,7 @@ args = arg_parser.parse_args() set_seed(1) -class CrossEntropySmooth(_Loss): +class CrossEntropySmooth(LossBase): """CrossEntropy""" def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=1000): diff --git a/research/cv/inception_resnet_v2/train.py b/research/cv/inception_resnet_v2/train.py index 34f42bd1aa1776962dd454655bd407a2849607a4..e5de258f7c0ae75724a90dedbd0bef7aacb17b5f 100644 --- a/research/cv/inception_resnet_v2/train.py +++ b/research/cv/inception_resnet_v2/train.py @@ -29,7 +29,7 @@ from mindspore.common.initializer import XavierUniform, initializer from mindspore.communication import init, get_rank from mindspore.context import ParallelMode from mindspore.nn import RMSProp, Momentum -from mindspore.nn.loss.loss import _Loss +from mindspore.nn.loss.loss import LossBase from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, TimeMonitor, LossMonitor from mindspore.train.loss_scale_manager import FixedLossScaleManager from mindspore.train.serialization import load_checkpoint, load_param_into_net @@ -42,7 +42,7 @@ os.environ['PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION'] = 'python' set_seed(1) -class CrossEntropySmooth(_Loss): +class CrossEntropySmooth(LossBase): """CrossEntropy""" def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=1000): diff --git a/research/cv/mnasnet/src/loss.py b/research/cv/mnasnet/src/loss.py index 6d63b66694652da2298ec4cea541537be22d843c..8400b5413bf85c37dd9793ae0664f50e2cf16396 100644 --- a/research/cv/mnasnet/src/loss.py +++ b/research/cv/mnasnet/src/loss.py @@ -16,12 +16,12 @@ import mindspore.nn as nn from mindspore import Tensor from mindspore.common import dtype as mstype -from mindspore.nn.loss.loss import _Loss +from mindspore.nn.loss.loss import LossBase from mindspore.ops import functional as F from mindspore.ops import operations as P -class CrossEntropySmooth(_Loss): +class CrossEntropySmooth(LossBase): """CrossEntropy""" def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=1000): super(CrossEntropySmooth, self).__init__() diff --git a/research/cv/mobilenetV3_small_x1_0/src/loss.py b/research/cv/mobilenetV3_small_x1_0/src/loss.py index 827f15ccbe161cecd1208d5058b2b468abb71943..4b1af5aa17eb77300ee4b84be90b22463da7477e 100644 --- a/research/cv/mobilenetV3_small_x1_0/src/loss.py +++ b/research/cv/mobilenetV3_small_x1_0/src/loss.py @@ -16,12 +16,12 @@ import mindspore.nn as nn from mindspore import Tensor from mindspore.common import dtype as mstype -from mindspore.nn.loss.loss import _Loss +from mindspore.nn.loss.loss import LossBase from mindspore.ops import functional as F from mindspore.ops import operations as P -class CrossEntropyWithLabelSmooth(_Loss): +class CrossEntropyWithLabelSmooth(LossBase): """CrossEntropyWithLabelSmooth""" def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=1000): super(CrossEntropyWithLabelSmooth, self).__init__() diff --git a/research/cv/mobilenetv3_large/train.py b/research/cv/mobilenetv3_large/train.py index 75fbd94374d79316bd0b03df2c6bdd1130eec8b4..8daf1d726601794c90095c84b0c87867c4ec2de9 100644 --- a/research/cv/mobilenetv3_large/train.py +++ b/research/cv/mobilenetv3_large/train.py @@ -25,7 +25,7 @@ from mindspore import Tensor from mindspore import nn from mindspore.nn.optim.momentum import Momentum from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits -from mindspore.nn.loss.loss import _Loss +from mindspore.nn.loss.loss import LossBase from mindspore.ops import operations as P from mindspore.ops import functional as F from mindspore.common import dtype as mstype @@ -90,7 +90,7 @@ class SaveCallback(Callback): print("Save the maximum accuracy checkpoint,the accuracy is", self.acc) -class CrossEntropyWithLabelSmooth(_Loss): +class CrossEntropyWithLabelSmooth(LossBase): """ CrossEntropyWith LabelSmooth. diff --git a/research/cv/pointnet2/src/pointnet2.py b/research/cv/pointnet2/src/pointnet2.py index 57ec6408fc157866d0a81c58f4feac352152e619..9e12ce7df2224b993f122bf588f33080133dac72 100644 --- a/research/cv/pointnet2/src/pointnet2.py +++ b/research/cv/pointnet2/src/pointnet2.py @@ -16,7 +16,7 @@ import mindspore.nn as nn import mindspore.ops as P -from mindspore.nn.loss.loss import _Loss +from mindspore.nn.loss.loss import LossBase from mindspore.ops import functional as F from src.layers import Dense @@ -74,7 +74,7 @@ class PointNet2(nn.Cell): return x -class NLLLoss(_Loss): +class NLLLoss(LossBase): """NLL loss""" def __init__(self, reduction='mean'): diff --git a/research/cv/resnet3d/src/loss.py b/research/cv/resnet3d/src/loss.py index b91818183df92a39f931398482c80b00f1112e2a..4f66d40aec830e8ae4bb0dc0283143a77fc5089a 100644 --- a/research/cv/resnet3d/src/loss.py +++ b/research/cv/resnet3d/src/loss.py @@ -18,7 +18,7 @@ define loss for resnet3d import mindspore.nn as nn from mindspore import Tensor from mindspore.common import dtype as mstype -from mindspore.nn.loss.loss import _Loss +from mindspore.nn.loss.loss import LossBase from mindspore.ops import functional as F from mindspore.ops import operations as P import mindspore.ops as ops @@ -61,7 +61,7 @@ class SoftmaxCrossEntropyExpand(nn.Cell): # pylint: disable=missing-docstring return loss -class CrossEntropySmooth(_Loss): +class CrossEntropySmooth(LossBase): """CrossEntropy""" def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=101): diff --git a/research/cv/resnet50_bam/eval.py b/research/cv/resnet50_bam/eval.py index 7244e79ab3635a306a02b68b1408918ce06a6426..4f2240963fbd1f52e99d4821cdd784e15d599635 100644 --- a/research/cv/resnet50_bam/eval.py +++ b/research/cv/resnet50_bam/eval.py @@ -24,7 +24,7 @@ from mindspore.train.serialization import load_checkpoint, load_param_into_net from mindspore.common import set_seed from mindspore import Tensor from mindspore.common import dtype as mstype -from mindspore.nn.loss.loss import _Loss +from mindspore.nn.loss.loss import LossBase from mindspore.ops import functional as F from mindspore.ops import operations as P @@ -45,7 +45,7 @@ parser.add_argument('--device_id', type=str, default=0, help='Device id.') args_opt = parser.parse_args() -class CrossEntropySmooth(_Loss): +class CrossEntropySmooth(LossBase): """CrossEntropy""" def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=1000): super(CrossEntropySmooth, self).__init__() diff --git a/research/cv/resnet50_bam/train.py b/research/cv/resnet50_bam/train.py index 0427dcc2a4813b1d49bba174453e969eb95230d9..59da4f65d86f000d1a3d42bba63b1d57134c5697 100644 --- a/research/cv/resnet50_bam/train.py +++ b/research/cv/resnet50_bam/train.py @@ -29,7 +29,7 @@ from mindspore.communication.management import get_group_size from mindspore.communication.management import get_rank from mindspore.communication.management import init from mindspore.context import ParallelMode -from mindspore.nn.loss.loss import _Loss +from mindspore.nn.loss.loss import LossBase from mindspore.nn.optim.momentum import Momentum from mindspore.ops import functional as F from mindspore.ops import operations as P @@ -90,7 +90,7 @@ def warmup_cosine_annealing_lr(lr5, steps_per_epoch, warmup_epochs, max_epoch, T return np.array(lr_each_step).astype(np.float32) -class CrossEntropySmooth(_Loss): +class CrossEntropySmooth(LossBase): """CrossEntropy""" def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=1000): super(CrossEntropySmooth, self).__init__() diff --git a/research/cv/resnext152_64x4d/src/crossentropy.py b/research/cv/resnext152_64x4d/src/crossentropy.py index a5abe60219632e15c9de81e7c8cbbba5d697d1d5..01039e39ea3e4fe4a7fa353a738a6d96ada2809b 100644 --- a/research/cv/resnext152_64x4d/src/crossentropy.py +++ b/research/cv/resnext152_64x4d/src/crossentropy.py @@ -15,14 +15,14 @@ """ define loss function for network. """ -from mindspore.nn.loss.loss import _Loss +from mindspore.nn.loss.loss import LossBase from mindspore.ops import operations as P from mindspore.ops import functional as F from mindspore import Tensor from mindspore.common import dtype as mstype import mindspore.nn as nn -class CrossEntropy(_Loss): +class CrossEntropy(LossBase): """ the redefined loss function with SoftmaxCrossEntropyWithLogits. """ diff --git a/research/cv/simple_baselines/src/network_with_loss.py b/research/cv/simple_baselines/src/network_with_loss.py index 011b8be011eddc1ac9e248bbd629c18cd3f23ab0..d671e68ea30f67251d062ba3795200a7aba6c27b 100644 --- a/research/cv/simple_baselines/src/network_with_loss.py +++ b/research/cv/simple_baselines/src/network_with_loss.py @@ -20,10 +20,10 @@ from __future__ import division import mindspore.nn as nn from mindspore.ops import operations as P from mindspore.ops import functional as F -from mindspore.nn.loss.loss import _Loss +from mindspore.nn.loss.loss import LossBase from mindspore.common import dtype as mstype -class JointsMSELoss(_Loss): +class JointsMSELoss(LossBase): ''' JointsMSELoss ''' diff --git a/research/cv/single_path_nas/eval.py b/research/cv/single_path_nas/eval.py index cbba5a025c30569a7e5a4635550b8378be370a24..ddd8a3e4b6c0f3a0e3783fdb56d481bcce561e38 100644 --- a/research/cv/single_path_nas/eval.py +++ b/research/cv/single_path_nas/eval.py @@ -23,7 +23,7 @@ from mindspore import Tensor from mindspore import context from mindspore.common import dtype as mstype from mindspore.common import set_seed -from mindspore.nn.loss.loss import _Loss +from mindspore.nn.loss.loss import LossBase from mindspore.ops import functional as F from mindspore.ops import operations as P from mindspore.train.model import Model @@ -48,7 +48,7 @@ parser.add_argument('--device_id', type=int, default=None, help='device id of As args_opt = parser.parse_args() -class CrossEntropySmooth(_Loss): +class CrossEntropySmooth(LossBase): """CrossEntropy""" def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=1000): diff --git a/research/cv/single_path_nas/src/CrossEntropySmooth.py b/research/cv/single_path_nas/src/CrossEntropySmooth.py index 6d63b66694652da2298ec4cea541537be22d843c..8400b5413bf85c37dd9793ae0664f50e2cf16396 100644 --- a/research/cv/single_path_nas/src/CrossEntropySmooth.py +++ b/research/cv/single_path_nas/src/CrossEntropySmooth.py @@ -16,12 +16,12 @@ import mindspore.nn as nn from mindspore import Tensor from mindspore.common import dtype as mstype -from mindspore.nn.loss.loss import _Loss +from mindspore.nn.loss.loss import LossBase from mindspore.ops import functional as F from mindspore.ops import operations as P -class CrossEntropySmooth(_Loss): +class CrossEntropySmooth(LossBase): """CrossEntropy""" def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=1000): super(CrossEntropySmooth, self).__init__() diff --git a/research/cv/sknet/src/CrossEntropySmooth.py b/research/cv/sknet/src/CrossEntropySmooth.py index 6d63b66694652da2298ec4cea541537be22d843c..8400b5413bf85c37dd9793ae0664f50e2cf16396 100644 --- a/research/cv/sknet/src/CrossEntropySmooth.py +++ b/research/cv/sknet/src/CrossEntropySmooth.py @@ -16,12 +16,12 @@ import mindspore.nn as nn from mindspore import Tensor from mindspore.common import dtype as mstype -from mindspore.nn.loss.loss import _Loss +from mindspore.nn.loss.loss import LossBase from mindspore.ops import functional as F from mindspore.ops import operations as P -class CrossEntropySmooth(_Loss): +class CrossEntropySmooth(LossBase): """CrossEntropy""" def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=1000): super(CrossEntropySmooth, self).__init__() diff --git a/research/cv/squeezenet1_1/src/CrossEntropySmooth.py b/research/cv/squeezenet1_1/src/CrossEntropySmooth.py index 6d63b66694652da2298ec4cea541537be22d843c..8400b5413bf85c37dd9793ae0664f50e2cf16396 100644 --- a/research/cv/squeezenet1_1/src/CrossEntropySmooth.py +++ b/research/cv/squeezenet1_1/src/CrossEntropySmooth.py @@ -16,12 +16,12 @@ import mindspore.nn as nn from mindspore import Tensor from mindspore.common import dtype as mstype -from mindspore.nn.loss.loss import _Loss +from mindspore.nn.loss.loss import LossBase from mindspore.ops import functional as F from mindspore.ops import operations as P -class CrossEntropySmooth(_Loss): +class CrossEntropySmooth(LossBase): """CrossEntropy""" def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=1000): super(CrossEntropySmooth, self).__init__() diff --git a/research/cv/swin_transformer/src/tools/criterion.py b/research/cv/swin_transformer/src/tools/criterion.py index 1afdd719f3a77188b32e4f22b756b8151adf572c..ee963c1fee2724003b422532b2c1cb36ba0e9391 100644 --- a/research/cv/swin_transformer/src/tools/criterion.py +++ b/research/cv/swin_transformer/src/tools/criterion.py @@ -17,12 +17,12 @@ import mindspore.nn as nn from mindspore import Tensor from mindspore import ops from mindspore.common import dtype as mstype -from mindspore.nn.loss.loss import _Loss +from mindspore.nn.loss.loss import LossBase from mindspore.ops import functional as F from mindspore.ops import operations as P -class SoftTargetCrossEntropy(_Loss): +class SoftTargetCrossEntropy(LossBase): """SoftTargetCrossEntropy for MixUp Augment""" def __init__(self): @@ -38,7 +38,7 @@ class SoftTargetCrossEntropy(_Loss): return self.mean_ops(loss) -class CrossEntropySmooth(_Loss): +class CrossEntropySmooth(LossBase): """CrossEntropy""" def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=1000): diff --git a/research/cv/tsm/src/model/cross_entropy_smooth.py b/research/cv/tsm/src/model/cross_entropy_smooth.py index 5b0b977e7852db6d2b27338f503be912cdf4d13e..596a7c86750657156eaaf1d90e1245780cf09349 100644 --- a/research/cv/tsm/src/model/cross_entropy_smooth.py +++ b/research/cv/tsm/src/model/cross_entropy_smooth.py @@ -16,12 +16,12 @@ import mindspore.nn as nn from mindspore import Tensor from mindspore.common import dtype as mstype -from mindspore.nn.loss.loss import _Loss +from mindspore.nn.loss.loss import LossBase from mindspore.ops import functional as F from mindspore.ops import operations as P -class CrossEntropySmooth(_Loss): +class CrossEntropySmooth(LossBase): """CrossEntropy""" def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=174): super(CrossEntropySmooth, self).__init__() diff --git a/research/cv/wideresnet/src/cross_entropy_smooth.py b/research/cv/wideresnet/src/cross_entropy_smooth.py index a12c43afffa3277475a182926a6eee6d2bf74015..b6d43e1efa528d57561cb16af5ee9b26215e4880 100644 --- a/research/cv/wideresnet/src/cross_entropy_smooth.py +++ b/research/cv/wideresnet/src/cross_entropy_smooth.py @@ -17,12 +17,12 @@ import mindspore.nn as nn from mindspore import Tensor from mindspore.common import dtype as mstype -from mindspore.nn.loss.loss import _Loss +from mindspore.nn.loss.loss import LossBase from mindspore.ops import functional as F from mindspore.ops import operations as P -class CrossEntropySmooth(_Loss): +class CrossEntropySmooth(LossBase): """CrossEntropy""" def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=1000): super(CrossEntropySmooth, self).__init__()