forked from mindspore-Ecosystem/mindspore
261 lines
8.5 KiB
Python
261 lines
8.5 KiB
Python
# 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 as ms
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import mindspore.common.dtype as DT
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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.common.initializer import initializer
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from mindspore.common.parameter import Parameter
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from mindspore.nn import WithLossCell
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from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
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from mindspore.nn.optim.momentum import Momentum
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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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from mindspore.train.model import Model
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from mindspore.train.parallel_utils import ParallelMode
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from tests.dataset_mock import MindData
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class Dataset(MindData):
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def __init__(self, predict, label, length=3):
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super(Dataset, self).__init__(size=length)
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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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class FusedBatchNorm(nn.Cell):
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"""Batch Normalization base class."""
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def __init__(self,
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num_features,
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eps=1e-5,
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momentum=0.1,
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affine=True,
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gamma_init='ones',
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beta_init='zeros',
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moving_mean_init='zeros',
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moving_var_init='ones'):
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super(FusedBatchNorm, self).__init__()
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if num_features < 1:
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raise ValueError("num_features must be at least 1")
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if momentum < 0 or momentum > 1:
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raise ValueError("momentum should be a number in range [0, 1], but got {}".format(momentum))
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self.num_features = num_features
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self.eps = eps
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self.momentum = Tensor(1.0 - momentum, DT.float32)
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self.gamma = Parameter(initializer(
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gamma_init, num_features), name="gamma", requires_grad=affine)
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self.beta = Parameter(initializer(
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beta_init, num_features), name="beta", requires_grad=affine)
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self.moving_mean = Parameter(initializer(
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moving_mean_init, num_features), name="mean", requires_grad=False)
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self.moving_variance = Parameter(initializer(
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moving_var_init, num_features), name="variance", requires_grad=False)
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self.bn_train = P.BatchNorm(is_training=True,
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epsilon=self.eps)
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self.bn_infer = P.BatchNorm(is_training=False,
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epsilon=self.eps)
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self.sub_mean = P.Sub().set_strategy(((1), (1)))
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self.sub_var = P.Sub().set_strategy(((1), (1)))
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self.mul_mean = P.Mul().set_strategy(((1,), ()))
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self.mul_var = P.Mul().set_strategy(((1,), ()))
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self.assign_sub_mean = P.AssignSub().set_strategy(((1,), (1,)))
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self.assign_sub_var = P.AssignSub().set_strategy(((1), (1)))
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self.sub_mean2 = P.Sub().set_strategy(((1), (1)))
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self.sub_var2 = P.Sub().set_strategy(((1), (1)))
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def set_strategy(self, strategy):
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self.bn_train.set_strategy(strategy)
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self.bn_infer.set_strategy(strategy)
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def _check_data_dim(self, x):
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raise NotImplementedError
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def construct(self, x):
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if self.training:
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y, batch_mean, batch_var, _, _ = \
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self.bn_train(x,
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self.gamma,
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self.beta,
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None,
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None)
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mean_sub = self.sub_mean(self.moving_mean, batch_mean)
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temp_mean = self.mul_mean(mean_sub, self.momentum)
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mean_sub2 = self.sub_var(self.moving_variance, batch_var)
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temp_variance = self.mul_var(mean_sub2, self.momentum)
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y = F.depend(y, self.assign_sub_mean(self.moving_mean, temp_mean))
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y = F.depend(y, self.assign_sub_var(self.moving_variance, temp_variance))
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else:
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y = self.bn_infer(x,
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self.gamma,
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self.beta,
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self.moving_mean,
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self.moving_variance)[0]
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return y
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def extend_repr(self):
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return 'num_features={}, eps={}, momentum={}, ' \
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'beta={}, gamma={}, ' \
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'moving_mean={}, moving_variance={} ' \
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.format(self.num_features,
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self.eps,
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self.momentum,
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self.beta,
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self.gamma,
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self.moving_mean,
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self.moving_variance)
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class PReLU(nn.Cell):
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"""
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PReLU activation function.
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Computes prelu value of a 4-dim tensor(NCHW).
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PReLU: out = max(0, A) + min(0, wA)
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Args:
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channel: Integer. The dimensionality of w. Default: 1.
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w: Float. The initial value of w. Default: 0.25.
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Returns:
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Tensor, has the same type as features.
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Examples:
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prelu = nn.PReLU(1, [np.float32(0.25)]) # or prelu = nn.PReLU(33, Tensor(np.random.rand(33), ms.float32)])
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input_data = Tensor(np.random.rand(1, 33, 4, 4), ms.float32)
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output = prelu.construct(input_data)
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"""
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def __init__(self, channel=1, w=0.25):
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super(PReLU, self).__init__()
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if isinstance(w, (np.float32, float)):
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tmp = np.empty((channel,), dtype=np.float32)
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tmp.fill(w)
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w = tmp
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elif isinstance(w, (int, bool, complex, str)):
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raise TypeError("w only support input type float32 and float")
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if not isinstance(w, Tensor):
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w = Tensor(w)
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self.w = Parameter(initializer(w, [channel,]), name='a')
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self.prelu = P.PReLU()
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self.relu = P.ReLU().set_strategy(((1)))
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def construct(self, x):
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self.w = self.relu(self.w)
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return self.prelu(x, self.w)
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class BNNet(nn.Cell):
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def __init__(self):
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super(BNNet, self).__init__()
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self.bn = FusedBatchNorm(512)
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self.prelu = PReLU(512)
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def construct(self, x):
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x = self.bn(x)
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x = self.prelu(x)
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return x
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def bn_net():
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return BNNet()
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def bn_common(parallel_mode, train_flag, strategy_loss=None):
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context.set_context(mode=context.GRAPH_MODE)
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learning_rate = 0.1
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momentum = 0.9
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epoch_size = 2
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rank_size = 8
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predict = Tensor(np.ones([32, 512]), dtype=ms.float32)
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label = Tensor(np.ones([32]), dtype=ms.int32)
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dataset = Dataset(predict, label, 2)
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net = bn_net()
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loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
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loss.softmax_cross_entropy.set_strategy(strategy_loss)
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opt = Momentum(net.trainable_params(), learning_rate, momentum, 0.0001, 1024 * rank_size)
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if not train_flag:
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net = WithLossCell(net, loss)
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net.set_train()
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if parallel_mode == ParallelMode.DATA_PARALLEL:
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context.set_auto_parallel_context(parameter_broadcast=True)
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context.set_auto_parallel_context(parallel_mode=parallel_mode, device_num=8)
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model = Model(net, loss, opt)
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if train_flag:
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model.train(epoch_size, dataset, dataset_sink_mode=False)
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else:
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model._predict(predict, label)
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def test_data_parallel():
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parallel_mode = ParallelMode.DATA_PARALLEL
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train_flag = True
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bn_common(parallel_mode, train_flag)
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def auto_parallel():
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train_flag = True
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parallel_mode = ParallelMode.AUTO_PARALLEL
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bn_common(parallel_mode, train_flag)
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def Xtest_data_parallel_predict():
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parallel_mode = ParallelMode.DATA_PARALLEL
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train_flag = False
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bn_common(parallel_mode, train_flag)
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def Xtest_semi_auto_parallel_predict():
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train_flag = False
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parallel_mode = ParallelMode.SEMI_AUTO_PARALLEL
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bn_common(parallel_mode, train_flag)
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def Xtest_auto_parallel_predict():
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train_flag = False
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parallel_mode = ParallelMode.AUTO_PARALLEL
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bn_common(parallel_mode, train_flag)
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if __name__ == '__main__':
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auto_parallel()
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