modified resnet50 on imagenet2012 to improve the performance and accuracy

model_zoo/official/cv/resnet/eval.py

@@ -22,7 +22,6 @@ from mindspore import dataset as de
 from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
 from mindspore.train.model import Model
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
-from src.crossentropy import CrossEntropy
 
 parser = argparse.ArgumentParser(description='Image classification')
 parser.add_argument('--net', type=str, default=None, help='Resnet Model, either resnet50 or resnet101')
@@ -78,7 +77,8 @@ if __name__ == '__main__':
     if args_opt.dataset == "imagenet2012":
         if not config.use_label_smooth:
             config.label_smooth_factor = 0.0
-        loss = CrossEntropy(smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
+        loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean",
+                                             smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
     else:
         loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
 

model_zoo/official/cv/resnet/src/crossentropy.py

-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# 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.
-# ============================================================================
-"""define loss function for network"""
-from mindspore.nn.loss.loss import _Loss
-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):
-    """the redefined loss function with SoftmaxCrossEntropyWithLogits"""
-
-    def __init__(self, smooth_factor=0., num_classes=1001):
-        super(CrossEntropy, self).__init__()
-        self.onehot = P.OneHot()
-        self.on_value = Tensor(1.0 - smooth_factor, mstype.float32)
-        self.off_value = Tensor(1.0 * smooth_factor / (num_classes - 1), mstype.float32)
-        self.ce = nn.SoftmaxCrossEntropyWithLogits()
-        self.mean = P.ReduceMean(False)
-
-    def construct(self, logit, label):
-        one_hot_label = self.onehot(label, F.shape(logit)[1], self.on_value, self.off_value)
-        loss = self.ce(logit, one_hot_label)
-        loss = self.mean(loss, 0)
-        return loss

model_zoo/official/cv/resnet/src/dataset.py

@@ -121,7 +121,7 @@ def create_dataset2(dataset_path, do_train, repeat_num=1, batch_size=32, target=
     else:
         trans = [
             C.Decode(),
-            C.Resize((256, 256)),
+            C.Resize(256),
             C.CenterCrop(image_size),
             C.Normalize(mean=mean, std=std),
             C.HWC2CHW()

model_zoo/official/cv/resnet/train.py

@@ -31,7 +31,6 @@ from mindspore.communication.management import init, get_rank, get_group_size
 import mindspore.nn as nn
 import mindspore.common.initializer as weight_init
 from src.lr_generator import get_lr, warmup_cosine_annealing_lr
-from src.crossentropy import CrossEntropy
 
 parser = argparse.ArgumentParser(description='Image classification')
 parser.add_argument('--net', type=str, default=None, help='Resnet Model, either resnet50 or resnet101')
@@ -75,7 +74,7 @@ if __name__ == '__main__':
             context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
                                               mirror_mean=True)
             if args_opt.net == "resnet50":
-                auto_parallel_context().set_all_reduce_fusion_split_indices([107, 160])
+                auto_parallel_context().set_all_reduce_fusion_split_indices([85, 160])
             else:
                 auto_parallel_context().set_all_reduce_fusion_split_indices([180, 313])
             init()
@@ -128,15 +127,19 @@ if __name__ == '__main__':
     lr = Tensor(lr)
 
     # define opt
-    opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config.momentum,
-                   config.weight_decay, config.loss_scale)
-
+    decayed_params = list(filter(lambda x: 'beta' not in x.name and 'gamma' not in x.name and 'bias' not in x.name, net.trainalbe_params()))
+    no_decayed_params = [param for param in net.trainalbe_params() if param not in decayed_params]
+    group_params = [{'params': decayed_params, 'weight_decay': config.weight_decay},
+                    {'params': no_decayed_params},
+                    {'order_params': net.trainalbe_params()}]
+    opt = Momentum(group_params, lr, config.momentum, loss_scale=config.loss_scale)
     # define loss, model
     if target == "Ascend":
         if args_opt.dataset == "imagenet2012":
             if not config.use_label_smooth:
                 config.label_smooth_factor = 0.0
-            loss = CrossEntropy(smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
+            loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean",
+                                                 smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
         else:
             loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
         loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)