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94 lines
2.9 KiB
94 lines
2.9 KiB
# Copyright 2019 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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import numpy as np
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore import context
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from mindspore.ops import operations as P
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from mindspore.train.model import Model
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class CrossEntropyLoss(nn.Cell):
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def __init__(self, reduction='mean'):
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super(CrossEntropyLoss, self).__init__()
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self.reduce_mean = P.ReduceMean()
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self.cross_entropy = nn.SoftmaxCrossEntropyWithLogits()
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self.reduction = reduction
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def construct(self, logits, label):
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loss = self.cross_entropy(logits, label)
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if self.reduction == 'mean':
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loss = self.reduce_mean(loss, (-1,))
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return loss
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class DatasetLenet():
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def __init__(self, predict, label, length=3):
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self.predict = predict
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self.label = label
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self.index = 0
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self.length = length
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def __iter__(self):
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return self
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def __next__(self):
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if self.index >= self.length:
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raise StopIteration
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self.index += 1
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return self.predict, self.label
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def reset(self):
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self.index = 0
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def get_dataset_size(self):
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return 32
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def get_repeat_count(self):
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return 1
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def create_tuple_iterator(self, num_epochs=-1, do_copy=True):
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return self
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class Net(nn.Cell):
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def __init__(self):
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super().__init__()
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self.conv = nn.Conv2d(in_channels=3, out_channels=64, kernel_size=1, stride=1, pad_mode='valid',
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has_bias=True, weight_init='ones', bias_init='ones')
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self.reduce_mean = P.ReduceMean(keep_dims=False).shard(((1, 1, 1, 8),))
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self.flat = nn.Flatten()
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def construct(self, inputs):
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x = self.conv(inputs)
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x = self.reduce_mean(x, -1)
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x = self.flat(x)
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return x
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def test_bias_add():
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context.set_context(mode=context.GRAPH_MODE)
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context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8)
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input_np = np.ones([16, 3, 32, 32]).astype(np.float32)
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label_np = np.zeros([16, 2048]).astype(np.float32)
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dataset = DatasetLenet(Tensor(input_np), Tensor(label_np), 1)
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net = Net()
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loss = CrossEntropyLoss()
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opt = nn.Momentum(learning_rate=0.01, momentum=0.9, params=net.get_parameters())
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model = Model(network=net, loss_fn=loss, optimizer=opt)
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model.train(epoch=1, train_dataset=dataset, dataset_sink_mode=False)
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