panfengfeng
4 years ago
parent
6138b75518
commit
fc310bc5ae
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import mindspore.nn as nn
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import mindspore.ops.operations as P
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class ShuffleV2Block(nn.Cell):
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def __init__(self, inp, oup, mid_channels, *, ksize, stride):
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super(ShuffleV2Block, self).__init__()
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self.stride = stride
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##assert stride in [1, 2]
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self.mid_channels = mid_channels
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self.ksize = ksize
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pad = ksize // 2
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self.pad = pad
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self.inp = inp
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outputs = oup - inp
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branch_main = [
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# pw
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nn.Conv2d(in_channels=inp, out_channels=mid_channels, kernel_size=1, stride=1,
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pad_mode='pad', padding=0, has_bias=False),
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nn.BatchNorm2d(num_features=mid_channels, momentum=0.9),
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nn.ReLU(),
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# dw
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nn.Conv2d(in_channels=mid_channels, out_channels=mid_channels, kernel_size=ksize, stride=stride,
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pad_mode='pad', padding=pad, group=mid_channels, has_bias=False),
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nn.BatchNorm2d(num_features=mid_channels, momentum=0.9),
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# pw-linear
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nn.Conv2d(in_channels=mid_channels, out_channels=outputs, kernel_size=1, stride=1,
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pad_mode='pad', padding=0, has_bias=False),
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nn.BatchNorm2d(num_features=outputs, momentum=0.9),
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nn.ReLU(),
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]
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self.branch_main = nn.SequentialCell(branch_main)
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if stride == 2:
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branch_proj = [
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# dw
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nn.Conv2d(in_channels=inp, out_channels=inp, kernel_size=ksize, stride=stride,
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pad_mode='pad', padding=pad, group=inp, has_bias=False),
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nn.BatchNorm2d(num_features=inp, momentum=0.9),
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# pw-linear
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nn.Conv2d(in_channels=inp, out_channels=inp, kernel_size=1, stride=1,
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pad_mode='pad', padding=0, has_bias=False),
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nn.BatchNorm2d(num_features=inp, momentum=0.9),
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nn.ReLU(),
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]
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self.branch_proj = nn.SequentialCell(branch_proj)
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else:
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self.branch_proj = None
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def construct(self, old_x):
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if self.stride == 1:
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x_proj, x = self.channel_shuffle(old_x)
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return P.Concat(1)((x_proj, self.branch_main(x)))
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if self.stride == 2:
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x_proj = old_x
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x = old_x
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return P.Concat(1)((self.branch_proj(x_proj), self.branch_main(x)))
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return None
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def channel_shuffle(self, x):
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batchsize, num_channels, height, width = P.Shape()(x)
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##assert (num_channels % 4 == 0)
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x = P.Reshape()(x, (batchsize * num_channels // 2, 2, height * width,))
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x = P.Transpose()(x, (1, 0, 2,))
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x = P.Reshape()(x, (2, -1, num_channels // 2, height, width,))
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return x[0], x[1]
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@ -0,0 +1,38 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""define loss function for network"""
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.common import dtype as mstype
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from mindspore.nn.loss.loss import _Loss
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from mindspore.ops import functional as F
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from mindspore.ops import operations as P
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class CrossEntropySmooth(_Loss):
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"""CrossEntropy"""
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def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=1000):
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super(CrossEntropySmooth, self).__init__()
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self.onehot = P.OneHot()
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self.sparse = sparse
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self.on_value = Tensor(1.0 - smooth_factor, mstype.float32)
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self.off_value = Tensor(1.0 * smooth_factor / (num_classes - 1), mstype.float32)
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self.ce = nn.SoftmaxCrossEntropyWithLogits(reduction=reduction)
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def construct(self, logit, label):
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if self.sparse:
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label = self.onehot(label, F.shape(logit)[1], self.on_value, self.off_value)
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loss = self.ce(logit, label)
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return loss
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@ -1,60 +0,0 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""define loss function for network."""
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from mindspore.common import dtype as mstype
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from mindspore.nn.loss.loss import _Loss
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from mindspore.ops import operations as P
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from mindspore.ops import functional as F
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from mindspore import Tensor
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import mindspore.nn as nn
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class CrossEntropy(_Loss):
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"""the redefined loss function with SoftmaxCrossEntropyWithLogits"""
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def __init__(self, smooth_factor=0, num_classes=1000, factor=0.4):
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super(CrossEntropy, self).__init__()
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self.factor = factor
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self.onehot = P.OneHot()
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self.on_value = Tensor(1.0 - smooth_factor, mstype.float32)
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self.off_value = Tensor(1.0 * smooth_factor / (num_classes - 1), mstype.float32)
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self.ce = nn.SoftmaxCrossEntropyWithLogits()
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self.mean = P.ReduceMean(False)
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def construct(self, logits, label):
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logit, aux = logits
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one_hot_label = self.onehot(label, F.shape(logit)[1], self.on_value, self.off_value)
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loss_logit = self.ce(logit, one_hot_label)
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loss_logit = self.mean(loss_logit, 0)
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one_hot_label_aux = self.onehot(label, F.shape(aux)[1], self.on_value, self.off_value)
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loss_aux = self.ce(aux, one_hot_label_aux)
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loss_aux = self.mean(loss_aux, 0)
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return loss_logit + self.factor*loss_aux
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class CrossEntropy_Val(_Loss):
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"""the redefined loss function with SoftmaxCrossEntropyWithLogits, will be used in inference process"""
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def __init__(self, smooth_factor=0, num_classes=1000):
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super(CrossEntropy_Val, self).__init__()
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self.onehot = P.OneHot()
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self.on_value = Tensor(1.0 - smooth_factor, mstype.float32)
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self.off_value = Tensor(1.0 * smooth_factor / (num_classes - 1), mstype.float32)
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self.ce = nn.SoftmaxCrossEntropyWithLogits()
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self.mean = P.ReduceMean(False)
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def construct(self, logits, label):
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one_hot_label = self.onehot(label, F.shape(logits)[1], self.on_value, self.off_value)
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loss_logit = self.ce(logits, one_hot_label)
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loss_logit = self.mean(loss_logit, 0)
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return loss_logit
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