使用可变学习率

loss/loss.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.
+# ============================================================================
+
+from mindspore import Tensor
+import mindspore.common.dtype as mstype
+import mindspore.nn as nn
+from mindspore.ops import operations as P
+
+
+class SoftmaxCrossEntropyLoss(nn.Cell):
+    def __init__(self, num_cls=21, ignore_label=255):
+        super(SoftmaxCrossEntropyLoss, self).__init__()
+        self.one_hot = P.OneHot(axis=-1)
+        self.on_value = Tensor(1.0, mstype.float32)
+        self.off_value = Tensor(0.0, mstype.float32)
+        self.cast = P.Cast()
+        self.ce = nn.SoftmaxCrossEntropyWithLogits()
+        self.not_equal = P.NotEqual()
+        self.num_cls = num_cls
+        self.ignore_label = ignore_label
+        self.mul = P.Mul()
+        self.sum = P.ReduceSum(False)
+        self.div = P.RealDiv()
+        self.transpose = P.Transpose()
+        self.reshape = P.Reshape()
+
+    def construct(self, logits, labels):
+        labels_int = self.cast(labels, mstype.int32)
+        labels_int = self.reshape(labels_int, (-1,))
+        logits_ = self.transpose(logits, (0, 2, 3, 1))
+        logits_ = self.reshape(logits_, (-1, self.num_cls))
+        weights = self.not_equal(labels_int, self.ignore_label)
+        weights = self.cast(weights, mstype.float32)
+        one_hot_labels = self.one_hot(labels_int, self.num_cls, self.on_value, self.off_value)
+        loss = self.ce(logits_, one_hot_labels)
+        loss = self.mul(weights, loss)
+        loss = self.div(self.sum(loss), self.sum(weights))
+        return loss

main.py

@@ -14,6 +14,7 @@ from unet_medical.unet_model import UNetMedical
 from nets.deeplab_v3 import deeplab_v3
 from dataset import GetDatasetGenerator
 from loss import SoftmaxCrossEntropyLoss
+from utils.learning_rates import exponential_lr
 
 context.set_context(mode=context.PYNATIVE_MODE, save_graphs=False,
                     device_target='Ascend', device_id=7)
@@ -24,6 +25,8 @@ train_dataset_generator = GetDatasetGenerator('./datasets', 'train')
 train_dataset = ds.GeneratorDataset(train_dataset_generator, ["data", "label"], shuffle=True)
 train_dataset = train_dataset.batch(4, drop_remainder=True)
 
+lr_iter = exponential_lr(3e-5, 20, 0.98, 500, staircase=True)
+
 net_loss = SoftmaxCrossEntropyLoss(6, 255)
 net_opt = nn.Adam(net.trainable_params(), learning_rate=3e-5)
 

utils/learning_rates.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.
+# ============================================================================
+
+import numpy as np
+
+
+def cosine_lr(base_lr, decay_steps, total_steps):
+    for i in range(total_steps):
+        step_ = min(i, decay_steps)
+        yield base_lr * 0.5 * (1 + np.cos(np.pi * step_ / decay_steps))
+
+
+def poly_lr(base_lr, decay_steps, total_steps, end_lr=0.0001, power=0.9):
+    for i in range(total_steps):
+        step_ = min(i, decay_steps)
+        yield (base_lr - end_lr) * ((1.0 - step_ / decay_steps) ** power) + end_lr
+
+
+def exponential_lr(base_lr, decay_steps, decay_rate, total_steps, staircase=False):
+    for i in range(total_steps):
+        if staircase:
+            power_ = i // decay_steps
+        else:
+            power_ = float(i) / decay_steps
+        yield base_lr * (decay_rate ** power_)