forked from mindspore-Ecosystem/mindspore
261 lines
8.4 KiB
Python
261 lines
8.4 KiB
Python
# Copyright 2019 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
|
|
|
|
import mindspore as ms
|
|
import mindspore.common.dtype as DT
|
|
import mindspore.nn as nn
|
|
from mindspore import Tensor
|
|
from mindspore import context
|
|
from mindspore.common.initializer import initializer
|
|
from mindspore.common.parameter import Parameter
|
|
from mindspore.nn import WithLossCell
|
|
from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
|
|
from mindspore.nn.optim.momentum import Momentum
|
|
from mindspore.ops import functional as F
|
|
from mindspore.ops import operations as P
|
|
from mindspore.train.model import Model
|
|
from mindspore.context import ParallelMode
|
|
from tests.dataset_mock import MindData
|
|
|
|
|
|
class Dataset(MindData):
|
|
def __init__(self, predict, label, length=3):
|
|
super(Dataset, self).__init__(size=length)
|
|
self.predict = predict
|
|
self.label = label
|
|
self.index = 0
|
|
self.length = length
|
|
|
|
def __iter__(self):
|
|
return self
|
|
|
|
def __next__(self):
|
|
if self.index >= self.length:
|
|
raise StopIteration
|
|
self.index += 1
|
|
return self.predict, self.label
|
|
|
|
def reset(self):
|
|
self.index = 0
|
|
|
|
|
|
class FusedBatchNorm(nn.Cell):
|
|
"""Batch Normalization base class."""
|
|
|
|
def __init__(self,
|
|
num_features,
|
|
eps=1e-5,
|
|
momentum=0.1,
|
|
affine=True,
|
|
gamma_init='ones',
|
|
beta_init='zeros',
|
|
moving_mean_init='zeros',
|
|
moving_var_init='ones'):
|
|
super(FusedBatchNorm, self).__init__()
|
|
if num_features < 1:
|
|
raise ValueError("num_features must be at least 1")
|
|
|
|
if momentum < 0 or momentum > 1:
|
|
raise ValueError("momentum should be a number in range [0, 1], but got {}".format(momentum))
|
|
|
|
self.num_features = num_features
|
|
self.eps = eps
|
|
self.momentum = Tensor(1.0 - momentum, DT.float32)
|
|
self.gamma = Parameter(initializer(
|
|
gamma_init, num_features), name="gamma", requires_grad=affine)
|
|
self.beta = Parameter(initializer(
|
|
beta_init, num_features), name="beta", requires_grad=affine)
|
|
self.moving_mean = Parameter(initializer(
|
|
moving_mean_init, num_features), name="mean", requires_grad=False)
|
|
self.moving_variance = Parameter(initializer(
|
|
moving_var_init, num_features), name="variance", requires_grad=False)
|
|
|
|
self.bn_train = P.BatchNorm(is_training=True,
|
|
epsilon=self.eps)
|
|
self.bn_infer = P.BatchNorm(is_training=False,
|
|
epsilon=self.eps)
|
|
self.sub_mean = P.Sub().set_strategy(((1), (1)))
|
|
self.sub_var = P.Sub().set_strategy(((1), (1)))
|
|
self.mul_mean = P.Mul().set_strategy(((1,), ()))
|
|
self.mul_var = P.Mul().set_strategy(((1,), ()))
|
|
self.assign_sub_mean = P.AssignSub().set_strategy(((1,), (1,)))
|
|
self.assign_sub_var = P.AssignSub().set_strategy(((1), (1)))
|
|
self.sub_mean2 = P.Sub().set_strategy(((1), (1)))
|
|
self.sub_var2 = P.Sub().set_strategy(((1), (1)))
|
|
|
|
def set_strategy(self, strategy):
|
|
self.bn_train.set_strategy(strategy)
|
|
self.bn_infer.set_strategy(strategy)
|
|
|
|
def _check_data_dim(self, x):
|
|
raise NotImplementedError
|
|
|
|
def construct(self, x):
|
|
if self.training:
|
|
y, batch_mean, batch_var, _, _ = \
|
|
self.bn_train(x,
|
|
self.gamma,
|
|
self.beta,
|
|
None,
|
|
None)
|
|
|
|
mean_sub = self.sub_mean(self.moving_mean, batch_mean)
|
|
temp_mean = self.mul_mean(mean_sub, self.momentum)
|
|
mean_sub2 = self.sub_var(self.moving_variance, batch_var)
|
|
temp_variance = self.mul_var(mean_sub2, self.momentum)
|
|
y = F.depend(y, self.assign_sub_mean(self.moving_mean, temp_mean))
|
|
y = F.depend(y, self.assign_sub_var(self.moving_variance, temp_variance))
|
|
|
|
else:
|
|
y = self.bn_infer(x,
|
|
self.gamma,
|
|
self.beta,
|
|
self.moving_mean,
|
|
self.moving_variance)[0]
|
|
return y
|
|
|
|
def extend_repr(self):
|
|
return 'num_features={}, eps={}, momentum={}, ' \
|
|
'beta={}, gamma={}, ' \
|
|
'moving_mean={}, moving_variance={} ' \
|
|
.format(self.num_features,
|
|
self.eps,
|
|
self.momentum,
|
|
self.beta,
|
|
self.gamma,
|
|
self.moving_mean,
|
|
self.moving_variance)
|
|
|
|
|
|
class PReLU(nn.Cell):
|
|
"""
|
|
PReLU activation function.
|
|
|
|
Computes prelu value of a 4-dim tensor(NCHW).
|
|
PReLU: out = max(0, A) + min(0, wA)
|
|
|
|
Args:
|
|
channel: Integer. The dimensionality of w. Default: 1.
|
|
w: Float. The initial value of w. Default: 0.25.
|
|
|
|
Returns:
|
|
Tensor, has the same type as features.
|
|
|
|
Examples:
|
|
prelu = nn.PReLU(1, [np.float32(0.25)]) # or prelu = nn.PReLU(33, Tensor(np.random.rand(33), ms.float32)])
|
|
input_data = Tensor(np.random.rand(1, 33, 4, 4), ms.float32)
|
|
output = prelu.construct(input_data)
|
|
"""
|
|
|
|
def __init__(self, channel=1, w=0.25):
|
|
super(PReLU, self).__init__()
|
|
if isinstance(w, (np.float32, float)):
|
|
tmp = np.empty((channel,), dtype=np.float32)
|
|
tmp.fill(w)
|
|
w = tmp
|
|
elif isinstance(w, (int, bool, complex, str)):
|
|
raise TypeError("w only support input type float32 and float")
|
|
|
|
if not isinstance(w, Tensor):
|
|
w = Tensor(w)
|
|
self.w = Parameter(initializer(w, [channel,]), name='a')
|
|
self.prelu = P.PReLU()
|
|
self.relu = P.ReLU().set_strategy(((1)))
|
|
|
|
def construct(self, x):
|
|
self.w = self.relu(self.w)
|
|
return self.prelu(x, self.w)
|
|
|
|
|
|
class BNNet(nn.Cell):
|
|
def __init__(self):
|
|
super(BNNet, self).__init__()
|
|
self.bn = FusedBatchNorm(512)
|
|
self.prelu = PReLU(512)
|
|
|
|
def construct(self, x):
|
|
x = self.bn(x)
|
|
x = self.prelu(x)
|
|
return x
|
|
|
|
|
|
def bn_net():
|
|
return BNNet()
|
|
|
|
|
|
def bn_common(parallel_mode, train_flag, strategy_loss=None):
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
context.set_auto_parallel_context(parallel_mode=parallel_mode, device_num=8)
|
|
learning_rate = 0.1
|
|
momentum = 0.9
|
|
epoch_size = 2
|
|
rank_size = 8
|
|
|
|
predict = Tensor(np.ones([32, 512]), dtype=ms.float32)
|
|
label = Tensor(np.ones([32]), dtype=ms.int32)
|
|
dataset = Dataset(predict, label, 2)
|
|
net = bn_net()
|
|
|
|
loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
|
|
loss.softmax_cross_entropy.set_strategy(strategy_loss)
|
|
opt = Momentum(net.trainable_params(), learning_rate, momentum, 0.0001, 1024 * rank_size)
|
|
|
|
if not train_flag:
|
|
net = WithLossCell(net, loss)
|
|
net.set_train()
|
|
|
|
if parallel_mode == ParallelMode.DATA_PARALLEL:
|
|
context.set_auto_parallel_context(parameter_broadcast=True)
|
|
model = Model(net, loss, opt)
|
|
if train_flag:
|
|
model.train(epoch_size, dataset, dataset_sink_mode=False)
|
|
else:
|
|
model._predict(predict, label)
|
|
|
|
|
|
def test_data_parallel():
|
|
parallel_mode = ParallelMode.DATA_PARALLEL
|
|
train_flag = True
|
|
bn_common(parallel_mode, train_flag)
|
|
|
|
|
|
def auto_parallel():
|
|
train_flag = True
|
|
parallel_mode = ParallelMode.AUTO_PARALLEL
|
|
bn_common(parallel_mode, train_flag)
|
|
|
|
|
|
def Xtest_data_parallel_predict():
|
|
parallel_mode = ParallelMode.DATA_PARALLEL
|
|
train_flag = False
|
|
bn_common(parallel_mode, train_flag)
|
|
|
|
|
|
def Xtest_semi_auto_parallel_predict():
|
|
train_flag = False
|
|
parallel_mode = ParallelMode.SEMI_AUTO_PARALLEL
|
|
bn_common(parallel_mode, train_flag)
|
|
|
|
|
|
def Xtest_auto_parallel_predict():
|
|
train_flag = False
|
|
parallel_mode = ParallelMode.AUTO_PARALLEL
|
|
bn_common(parallel_mode, train_flag)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
auto_parallel()
|