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- # 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.
- # ============================================================================
- """
- Resnet50_distributed_training
- """
- import os
- import mindspore.nn as nn
- from mindspore import dtype as mstype
- import mindspore.ops as ops
- import mindspore.dataset as ds
- import mindspore.dataset.vision.c_transforms as vision
- import mindspore.dataset.transforms.c_transforms as C
- from mindspore.communication import init, get_rank, get_group_size
- from mindspore import Tensor, Model, context
- from mindspore.nn import Momentum
- from mindspore.context import ParallelMode
- from mindspore.train.callback import LossMonitor
- from resnet import resnet50
-
- device_id = int(os.getenv('DEVICE_ID'))
- context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
- context.set_context(device_id=device_id) # set device_id
- init()
-
- def create_dataset(data_path, repeat_num=1, batch_size=32, rank_id=0, rank_size=1): # pylint: disable=missing-docstring
- resize_height = 224
- resize_width = 224
- rescale = 1.0 / 255.0
- shift = 0.0
-
- # get rank_id and rank_size
- rank_id = get_rank()
- rank_size = get_group_size()
- data_set = ds.Cifar10Dataset(data_path, num_shards=rank_size, shard_id=rank_id)
-
- # define map operations
- random_crop_op = vision.RandomCrop((32, 32), (4, 4, 4, 4))
- random_horizontal_op = vision.RandomHorizontalFlip()
- resize_op = vision.Resize((resize_height, resize_width))
- rescale_op = vision.Rescale(rescale, shift)
- normalize_op = vision.Normalize((0.4465, 0.4822, 0.4914), (0.2010, 0.1994, 0.2023))
- changeswap_op = vision.HWC2CHW()
- type_cast_op = C.TypeCast(mstype.int32)
-
- c_trans = [random_crop_op, random_horizontal_op]
- c_trans += [resize_op, rescale_op, normalize_op, changeswap_op]
-
- # apply map operations on images
- data_set = data_set.map(operations=type_cast_op, input_columns="label")
- data_set = data_set.map(operations=c_trans, input_columns="image")
-
- # apply shuffle operations
- data_set = data_set.shuffle(buffer_size=10)
-
- # apply batch operations
- data_set = data_set.batch(batch_size=batch_size, drop_remainder=True)
-
- # apply repeat operations
- data_set = data_set.repeat(repeat_num)
-
- return data_set
-
-
- class SoftmaxCrossEntropyExpand(nn.Cell): # pylint: disable=missing-docstring
- def __init__(self, sparse=False):
- super(SoftmaxCrossEntropyExpand, self).__init__()
- self.exp = ops.Exp()
- self.sum = ops.ReduceSum(keep_dims=True)
- self.onehot = ops.OneHot()
- self.on_value = Tensor(1.0, mstype.float32)
- self.off_value = Tensor(0.0, mstype.float32)
- self.div = ops.RealDiv()
- self.log = ops.Log()
- self.sum_cross_entropy = ops.ReduceSum(keep_dims=False)
- self.mul = ops.Mul()
- self.mul2 = ops.Mul()
- self.mean = ops.ReduceMean(keep_dims=False)
- self.sparse = sparse
- self.max = ops.ReduceMax(keep_dims=True)
- self.sub = ops.Sub()
- self.eps = Tensor(1e-24, mstype.float32)
-
- def construct(self, logit, label): # pylint: disable=missing-docstring
- logit_max = self.max(logit, -1)
- exp = self.exp(self.sub(logit, logit_max))
- exp_sum = self.sum(exp, -1)
- softmax_result = self.div(exp, exp_sum)
- if self.sparse:
- label = self.onehot(label, ops.shape(logit)[1], self.on_value, self.off_value)
-
- softmax_result_log = self.log(softmax_result + self.eps)
- loss = self.sum_cross_entropy((self.mul(softmax_result_log, label)), -1)
- loss = self.mul2(ops.scalar_to_array(-1.0), loss)
- loss = self.mean(loss, -1)
-
- return loss
-
-
- def test_train_cifar(epoch_size=10): # pylint: disable=missing-docstring
- context.set_auto_parallel_context(parallel_mode=ParallelMode.AUTO_PARALLEL, gradients_mean=True)
- loss_cb = LossMonitor()
- data_path = os.getenv('DATA_PATH')
- dataset = create_dataset(data_path)
- batch_size = 32
- num_classes = 10
- net = resnet50(batch_size, num_classes)
- loss = SoftmaxCrossEntropyExpand(sparse=True)
- opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, 0.9)
- model = Model(net, loss_fn=loss, optimizer=opt)
- model.train(epoch_size, dataset, callbacks=[loss_cb], dataset_sink_mode=True)
-
-
- # DataI: add an entrypoint for python run
- def main():
- test_train_cifar()
-
- if __name__ == '__main__':
- main()
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