Source code for mindspore.nn.layer.normalization

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# Licensed under the Apache License, Version 2.0 (the "License");
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# ============================================================================
"""normalization"""
import itertools
import numbers

from mindspore.ops import operations as P
from mindspore.ops import functional as F
from mindspore.ops.operations import _inner_ops as inner
from mindspore.common.parameter import Parameter
from mindspore.common.initializer import initializer, Initializer
from mindspore.common.tensor import Tensor
from mindspore.common._decorator import deprecated
from mindspore.ops.primitive import constexpr
import mindspore.context as context
from mindspore._checkparam import Rel
from mindspore._checkparam import Validator as validator
from mindspore._extends import cell_attr_register
from mindspore.communication.management import get_group_size, get_rank
from mindspore.communication import management
from mindspore.common import dtype as mstype
from mindspore.parallel._utils import _is_in_auto_parallel_mode
from ..cell import Cell

__all__ = ['BatchNorm1d', 'BatchNorm2d', 'BatchNorm3d', 'LayerNorm', 'GroupNorm',
           'GlobalBatchNorm', 'SyncBatchNorm', 'InstanceNorm2d']

SYNC_BN_GROUP_NAME = ""


class _BatchNorm(Cell):
    """Batch Normalization base class."""

    @cell_attr_register
    def __init__(self,
                 num_features,
                 eps=1e-5,
                 momentum=0.9,
                 affine=True,
                 gamma_init='ones',
                 beta_init='zeros',
                 moving_mean_init='zeros',
                 moving_var_init='ones',
                 use_batch_statistics=None,
                 device_num_each_group=1,
                 process_groups=0,
                 input_dims='2d',
                 data_format='NCHW'):
        """Initialize _BatchNorm."""
        super(_BatchNorm, self).__init__()
        validator.check_value_type('num_features', num_features, [int], self.cls_name)
        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.input_dims = input_dims
        self.format = validator.check_string(data_format, ['NCHW', 'NHWC'], 'format', self.cls_name)
        if context.get_context("device_target") != "GPU" and self.format == "NHWC":
            raise ValueError("NHWC format only support in GPU target.")
        self.use_batch_statistics = use_batch_statistics
        if self.use_batch_statistics is not None and not isinstance(self.use_batch_statistics, bool):
            raise ValueError("use_batch_statistics should be a boolean value or None.")
        self.num_features = num_features
        self.eps = eps
        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.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.group_device_num = validator.check_positive_int(device_num_each_group)
        self.process_groups = process_groups
        self.is_global = False
        self.parallel_mode = context.get_auto_parallel_context("parallel_mode")
        global SYNC_BN_GROUP_NAME
        # for GlobalBatchNorm
        if self.group_device_num != 1:
            self.rank_id = get_rank()
            self.rank_size = get_group_size()
            self.device_list = [i for i in range(0, self.rank_size)]
            self.rank_list = self.list_group(self.device_list, self.group_device_num)
            self.rank_list_idx = len(self.rank_list)
            self._create_global_groups()
        # for SyncBatchNorm
        if self.process_groups != 0:
            self.rank_id = get_rank()
            self.rank_size = get_group_size()
            if self.process_groups is not None:
                validator.check_isinstance("process_groups", self.process_groups, list)
                self._check_rank_ids(self.process_groups, self.rank_size)
                self._create_sync_groups()
            elif self.rank_size > 1:
                self.is_global = True
                self.group_device_num = self.rank_size
                self.device_list = [i for i in range(0, self.rank_size)]
                if SYNC_BN_GROUP_NAME == "":
                    SYNC_BN_GROUP_NAME = "sync_bn_group0"
                    management.create_group(SYNC_BN_GROUP_NAME, self.device_list)

        self.shape = P.Shape()
        self.reduce_mean = P.ReduceMean(keep_dims=True)
        self.square = P.Square()
        self.sqrt = P.Sqrt()
        self.cast = P.Cast()
        self.dtype = P.DType()
        self.reshape = P.Reshape()
        self._target = context.get_context("device_target")
        self.is_graph_mode = context.get_context("mode") == context.GRAPH_MODE
        self.momentum = 1.0 - momentum
        if context.get_context("enable_ge"):
            self.is_ge_backend = True
        else:
            self.is_ge_backend = False

        self.bn_train = P.BatchNorm(is_training=True,
                                    epsilon=self.eps,
                                    momentum=self.momentum,
                                    data_format=self.format)
        if self.is_global:
            self.bn_train = inner.SyncBatchNorm(epsilon=self.eps,
                                                momentum=self.momentum,
                                                group=SYNC_BN_GROUP_NAME,
                                                device_num=self.group_device_num)

        self.bn_infer = P.BatchNorm(is_training=False, epsilon=self.eps, data_format=self.format)
        if _is_in_auto_parallel_mode():
            data_parallel_strategy = ((1,), (1,))
            data_parallel_strategy_one = ((1,), ())
        else:
            data_parallel_strategy = None
            data_parallel_strategy_one = None
        self.sub_mean = P.Sub().shard(data_parallel_strategy)
        self.sub_var = P.Sub().shard(data_parallel_strategy)
        self.mul_mean = P.Mul().shard(data_parallel_strategy_one)
        self.mul_var = P.Mul().shard(data_parallel_strategy_one)
        self.assign_sub_mean = P.AssignSub().shard(data_parallel_strategy)
        self.assign_sub_var = P.AssignSub().shard(data_parallel_strategy)

    def _check_data_dim(self, x):
        raise NotImplementedError

    def list_group(self, world_rank, group_size):
        if group_size > get_group_size():
            raise ValueError("group size can not be greater than local rank size, group size is {}, "
                             "local_rank_size is {}".format(group_size, get_group_size()))
        if len(world_rank) % group_size != 0:
            raise ValueError("please make your group size correct.")
        world_rank_list = zip(*(iter(world_rank),) * group_size)
        group_list = [list(i) for i in world_rank_list]
        return group_list

    def _check_rank_ids(self, process_groups, rank_size):
        seen = set()
        for rid in itertools.chain(*process_groups):
            validator.check_int_range(rid, 0, rank_size, Rel.INC_LEFT, "rank id in process_groups")
            if rid in seen:
                raise ValueError("rank id in process_groups should not be duplicated.")
            seen.add(rid)

    def _create_global_groups(self):
        for i in range(self.rank_list_idx):
            if self.rank_id in self.rank_list[i]:
                self.is_global = True
                global SYNC_BN_GROUP_NAME
                if SYNC_BN_GROUP_NAME == "":
                    SYNC_BN_GROUP_NAME = "sync_bn_group" + str(i)
                    management.create_group(SYNC_BN_GROUP_NAME, self.rank_list[i])

    def _create_sync_groups(self):
        for i in range(len(self.process_groups)):
            validator.check_isinstance("process_groups[" + str(i) + "]", self.process_groups[i], list)
            self.group_device_num = len(self.process_groups[i])
            if self.rank_id in self.process_groups[i] and self.group_device_num > 1:
                self.is_global = True
                global SYNC_BN_GROUP_NAME
                if SYNC_BN_GROUP_NAME == "":
                    SYNC_BN_GROUP_NAME = "sync_bn_group" + str(i)
                    management.create_group(SYNC_BN_GROUP_NAME, self.process_groups[i])

    def construct(self, x):
        _shape_check_bn(self.shape(x), self.input_dims)
        if self.use_batch_statistics is None:
            if self.training:
                return self.bn_train(x,
                                     self.gamma,
                                     self.beta,
                                     self.moving_mean,
                                     self.moving_variance)[0]
            if not self.training:
                return self.bn_infer(x,
                                     self.gamma,
                                     self.beta,
                                     self.moving_mean,
                                     self.moving_variance)[0]

        if self.use_batch_statistics is True:
            return self.bn_train(x,
                                 self.gamma,
                                 self.beta,
                                 self.moving_mean,
                                 self.moving_variance)[0]

        return self.bn_infer(x,
                             self.gamma,
                             self.beta,
                             self.moving_mean,
                             self.moving_variance)[0]

    def extend_repr(self):
        return 'num_features={}, eps={}, momentum={}, gamma={}, beta={}, moving_mean={}, moving_variance={}'.format(
            self.num_features, self.eps, self.momentum, self.gamma, self.beta, self.moving_mean, self.moving_variance)


@constexpr
def _channel_check(channel, num_channel):
    if channel != num_channel:
        raise ValueError("the input channel is not equal with num_channel")


@constexpr
def _shape_check(in_shape):
    if len(in_shape) != 4:
        raise ValueError("The input must has 4 dims.")


@constexpr
def _shape_check_bn(in_shape, in_dims):
    """check input dims of batch norm."""
    dim = len(in_shape)
    if in_dims == '1d' and dim != 2:
        raise ValueError("The input must has 2 dims.")
    if in_dims == '2d' and dim != 4:
        raise ValueError("The input must has 4 dims.")
    if in_dims == '3d' and dim != 5:
        raise ValueError("The input must has 5 dims.")
    if in_dims == 'both' and dim != 2 and dim != 4:
        raise ValueError("The input must has 2 dims or 4 dims.")


@constexpr
def _shape_infer(x_shape, num_feature):
    """global Batch Normalization shape and axes infer"""
    if len(x_shape) == 4:
        axes = (0, 2, 3)
        re_shape = (1, num_feature, 1, 1)
    else:
        axes = (0,)
        re_shape = (1, num_feature)
    return axes, re_shape


[docs]class BatchNorm1d(_BatchNorm): r""" Batch Normalization layer over a 2D input. Batch Normalization is widely used in convolutional networks. This layer applies Batch Normalization over a 2D input (a mini-batch of 1D inputs) to reduce internal covariate shift as described in the paper `Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift <https://arxiv.org/abs/1502.03167>`_. It rescales and recenters the feature using a mini-batch of data and the learned parameters which can be described in the following formula. .. math:: y = \frac{x - \mathrm{E}[x]}{\sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta Note: The implementation of BatchNorm is different in graph mode and pynative mode, therefore the mode is not recommended to be changed after net was initialized. Args: num_features (int): `C` from an expected input of size (N, C). eps (float): A value added to the denominator for numerical stability. Default: 1e-5. momentum (float): A floating hyperparameter of the momentum for the running_mean and running_var computation. Default: 0.9. affine (bool): A bool value. When set to True, gamma and beta can be learned. Default: True. gamma_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the gamma weight. The values of str refer to the function `initializer` including 'zeros', 'ones', etc. Default: 'ones'. beta_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the beta weight. The values of str refer to the function `initializer` including 'zeros', 'ones', etc. Default: 'zeros'. moving_mean_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the moving mean. The values of str refer to the function `initializer` including 'zeros', 'ones', etc. Default: 'zeros'. moving_var_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the moving variance. The values of str refer to the function `initializer` including 'zeros', 'ones', etc. Default: 'ones'. use_batch_statistics (bool): If true, use the mean value and variance value of current batch data. If false, use the mean value and variance value of specified value. If None, the training process will use the mean and variance of current batch data and track the running mean and variance, the evaluation process will use the running mean and variance. Default: None. Inputs: - **x** (Tensor) - Tensor of shape :math:`(N, C_{in})`. Outputs: Tensor, the normalized, scaled, offset tensor, of shape :math:`(N, C_{out})`. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Raises: TypeError: If `num_features` is not an int. TypeError: If `eps` is not a float. ValueError: If `num_features` is less than 1. ValueError: If `momentum` is not in range [0, 1]. Examples: >>> import numpy as np >>> import mindspore.nn as nn >>> from mindspore import Tensor >>> net = nn.BatchNorm1d(num_features=4) >>> x = Tensor(np.array([[0.7, 0.5, 0.5, 0.6], ... [0.5, 0.4, 0.6, 0.9]]).astype(np.float32)) >>> output = net(x) >>> print(output) [[ 0.6999965 0.4999975 0.4999975 0.59999704 ] [ 0.4999975 0.399998 0.59999704 0.89999545 ]] """ def __init__(self, num_features, eps=1e-5, momentum=0.9, affine=True, gamma_init='ones', beta_init='zeros', moving_mean_init='zeros', moving_var_init='ones', use_batch_statistics=None): """Initialize BatchNorm1d.""" super(BatchNorm1d, self).__init__(num_features, eps, momentum, affine, gamma_init, beta_init, moving_mean_init, moving_var_init, use_batch_statistics, input_dims='1d') def _check_data_dim(self, x): if x.ndim != 2: pass
[docs]class BatchNorm2d(_BatchNorm): r""" Batch Normalization layer over a 4D input. Batch Normalization is widely used in convolutional networks. This layer applies Batch Normalization over a 4D input (a mini-batch of 2D inputs with additional channel dimension) to avoid internal covariate shift as described in the paper `Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift <https://arxiv.org/abs/1502.03167>`_. It rescales and recenters the feature using a mini-batch of data and the learned parameters which can be described in the following formula. .. math:: y = \frac{x - \mathrm{E}[x]}{\sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta Note: The implementation of BatchNorm is different in graph mode and pynative mode, therefore that mode can not be changed after net was initialized. Note that the formula for updating the running_mean and running_var is :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times x_t + \text{momentum} \times \hat{x}`, where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the new observed value. Args: num_features (int): `C` from an expected input of size (N, C, H, W). eps (float): A value added to the denominator for numerical stability. Default: 1e-5. momentum (float): A floating hyperparameter of the momentum for the running_mean and running_var computation. Default: 0.9. affine (bool): A bool value. When set to True, gamma and beta can be learned. Default: True. gamma_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the gamma weight. The values of str refer to the function `initializer` including 'zeros', 'ones', etc. Default: 'ones'. beta_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the beta weight. The values of str refer to the function `initializer` including 'zeros', 'ones', etc. Default: 'zeros'. moving_mean_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the moving mean. The values of str refer to the function `initializer` including 'zeros', 'ones', etc. Default: 'zeros'. moving_var_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the moving variance. The values of str refer to the function `initializer` including 'zeros', 'ones', etc. Default: 'ones'. use_batch_statistics (bool): - If true, use the mean value and variance value of current batch data and track running mean and running varance. - If false, use the mean value and variance value of specified value, and not track statistical value. - If None, The use_batch_statistics is automatically assigned process according to the training and eval mode. During training, batchnorm2d process will be the same with use_batch_statistics=True. Contrarily, in eval, batchnorm2d process will be the same with use_batch_statistics=False. Default: None. data_format (str): The optional value for data format, is 'NHWC' or 'NCHW'. Default: 'NCHW'. Inputs: - **x** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`. Outputs: Tensor, the normalized, scaled, offset tensor, of shape :math:`(N, C_{out}, H_{out}, W_{out})`. Raises: TypeError: If `num_features` is not an int. TypeError: If `eps` is not a float. ValueError: If `num_features` is less than 1. ValueError: If `momentum` is not in range [0, 1]. ValueError: If `data_format` is neither 'NHWC' not 'NCHW'. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: >>> import numpy as np >>> import mindspore.nn as nn >>> from mindspore import Tensor >>> net = nn.BatchNorm2d(num_features=3) >>> x = Tensor(np.ones([1, 3, 2, 2]).astype(np.float32)) >>> output = net(x) >>> print(output) [[[[ 0.999995 0.999995 ] [ 0.999995 0.999995 ]] [[ 0.999995 0.999995 ] [ 0.999995 0.999995 ]] [[ 0.999995 0.999995 ] [ 0.999995 0.999995 ]]]] """ def __init__(self, num_features, eps=1e-5, momentum=0.9, affine=True, gamma_init='ones', beta_init='zeros', moving_mean_init='zeros', moving_var_init='ones', use_batch_statistics=None, data_format='NCHW'): """Initialize BatchNorm2d.""" super(BatchNorm2d, self).__init__(num_features, eps, momentum, affine, gamma_init, beta_init, moving_mean_init, moving_var_init, use_batch_statistics, input_dims='2d', data_format=data_format) def _check_data_dim(self, x): if x.ndim != 4: pass
@constexpr def _check_3d_shape(input_shape): if len(input_shape) != 5: raise ValueError("For BatchNorm3d, input data must be 5-dimensional.")
[docs]class BatchNorm3d(Cell): r""" Batch Normalization layer over a 5D input. Batch Normalization is widely used in convolutional networks. This layer applies Batch Normalization over a 5D input (a mini-batch of 3D inputs with additional channel dimension) to avoid internal covariate shift. .. math:: y = \frac{x - \mathrm{E}[x]}{\sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta Note: The implementation of BatchNorm is different in graph mode and pynative mode, therefore that mode can not be changed after net was initialized. Note that the formula for updating the running_mean and running_var is :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times x_t + \text{momentum} \times \hat{x}`, where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the new observed value. Args: num_features (int): `C` from an expected input of size (N, C, D, H, W). eps (float): A value added to the denominator for numerical stability. Default: 1e-5. momentum (float): A floating hyperparameter of the momentum for the running_mean and running_var computation. Default: 0.9. affine (bool): A bool value. When set to True, gamma and beta can be learned. Default: True. gamma_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the gamma weight. The values of str refer to the function `initializer` including 'zeros', 'ones', etc. Default: 'ones'. beta_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the beta weight. The values of str refer to the function `initializer` including 'zeros', 'ones', etc. Default: 'zeros'. moving_mean_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the moving mean. The values of str refer to the function `initializer` including 'zeros', 'ones', etc. Default: 'zeros'. moving_var_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the moving variance. The values of str refer to the function `initializer` including 'zeros', 'ones', etc. Default: 'ones'. use_batch_statistics (bool): If true, use the mean value and variance value of current batch data. If false, use the mean value and variance value of specified value. If None, the training process will use the mean and variance of current batch data and track the running mean and variance, the evaluation process will use the running mean and variance. Default: None. data_format (str): The optional value for data format is 'NCDHW'. Default: 'NCDHW'. Inputs: - **x** (Tensor) - Tensor of shape :math:`(N, C_{in}, D_{in}, H_{in}, W_{in})`. Outputs: Tensor, the normalized, scaled, offset tensor, of shape :math:`(N, C_{out}, D_{out},H_{out}, W_{out})`. Raises: TypeError: If `num_features` is not an int. TypeError: If `eps` is not a float. ValueError: If `num_features` is less than 1. ValueError: If `momentum` is not in range [0, 1]. ValueError: If `data_format` is not 'NCDHW'. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: >>> import numpy as np >>> import mindspore.nn as nn >>> from mindspore import Tensor >>> net = nn.BatchNorm3d(num_features=3) >>> x = Tensor(np.ones([16, 3, 10, 32, 32]).astype(np.float32)) >>> output = net(x) >>> print(output.shape) (16, 3, 10, 32, 32) """ def __init__(self, num_features, eps=1e-5, momentum=0.9, affine=True, gamma_init='ones', beta_init='zeros', moving_mean_init='zeros', moving_var_init='ones', use_batch_statistics=None, data_format='NCDHW'): """Initialize BatchNorm3d.""" super(BatchNorm3d, self).__init__() self.format = validator.check_string(data_format, ['NCDHW'], 'format', self.cls_name) self.reshape = P.Reshape() self.bn2d = BatchNorm2d(num_features=num_features, eps=eps, momentum=momentum, affine=affine, gamma_init=gamma_init, beta_init=beta_init, moving_mean_init=moving_mean_init, moving_var_init=moving_var_init, use_batch_statistics=use_batch_statistics, data_format="NCHW") def construct(self, input_x): x_shape = F.shape(input_x) _check_3d_shape(x_shape) input_x = self.reshape(input_x, (x_shape[0], x_shape[1], x_shape[2] * x_shape[3], x_shape[4])) bn2d_out = self.bn2d(input_x) bn3d_out = self.reshape(bn2d_out, x_shape) return bn3d_out
[docs]class GlobalBatchNorm(_BatchNorm): r""" Global Batch Normalization layer over a N-dimension input. Global Batch Normalization is cross device synchronized Batch Normalization. The implementation of Batch Normalization only normalizes the data within each device. Global Normalization will normalize the input within the group.It has been described in the paper `Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift <https://arxiv.org/abs/1502.03167>`_. It rescales and recenters the feature using a mini-batch of data and the learned parameters which can be described in the following formula. .. math:: y = \frac{x - \mathrm{E}[x]}{\sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta Note: Currently, GlobalBatchNorm only supports 2D and 4D inputs. Args: num_features (int): `C` from an expected input of size (N, C, H, W). device_num_each_group (int): The number of devices in each group. Default: 2. eps (float): A value added to the denominator for numerical stability. Default: 1e-5. momentum (float): A floating hyperparameter of the momentum for the running_mean and running_var computation. Default: 0.9. affine (bool): A bool value. When set to True, gamma and beta can be learned. Default: True. gamma_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the gamma weight. The values of str refer to the function `initializer` including 'zeros', 'ones', 'xavier_uniform', 'he_uniform', etc. Default: 'ones'. beta_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the beta weight. The values of str refer to the function `initializer` including 'zeros', 'ones', 'xavier_uniform', 'he_uniform', etc. Default: 'zeros'. moving_mean_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the moving mean. The values of str refer to the function `initializer` including 'zeros', 'ones', 'xavier_uniform', 'he_uniform', etc. Default: 'zeros'. moving_var_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the moving variance. The values of str refer to the function `initializer` including 'zeros', 'ones', 'xavier_uniform', 'he_uniform', etc. Default: 'ones'. use_batch_statistics (bool): If true, use the mean value and variance value of current batch data. If false, use the mean value and variance value of specified value. If None, training process will use the mean and variance of current batch data and track the running mean and variance, eval process will use the running mean and variance. Default: None. Inputs: - **x** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`. Outputs: Tensor, the normalized, scaled, offset tensor, of shape :math:`(N, C_{out}, H_{out}, W_{out})`. Raises: TypeError: If `num_features` or `device_num_each_group` is not an int. TypeError: If `eps` is not a float. ValueError: If `num_features` is less than 1. ValueError: If `momentum` is not in range [0, 1]. ValueError: If `device_num_each_group` is less than 2. Supported Platforms: ``Ascend`` Examples: >>> # This example should be run with multiple processes. >>> # Please refer to the tutorial > Distributed Training on mindspore.cn. >>> import numpy as np >>> from mindspore.communication import init >>> from mindspore import context >>> from mindspore.context import ParallelMode >>> from mindspore import nn >>> from mindspore import Tensor >>> from mindspore.common import dtype as mstype >>> >>> context.set_context(mode=context.GRAPH_MODE) >>> init() >>> context.reset_auto_parallel_context() >>> context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL) >>> global_bn_op = nn.GlobalBatchNorm(num_features=3, device_num_each_group=2) >>> x = Tensor(np.ones([1, 3, 2, 2]).astype(np.float32)) >>> output = global_bn_op(x) >>> print(output) [[[[ 0.999995 0.999995 ] [ 0.999995 0.999995 ]] [[ 0.999995 0.999995 ] [ 0.999995 0.999995 ]] [[ 0.999995 0.999995 ] [ 0.999995 0.999995 ]]]] """ @deprecated("1.2", "SyncBatchNorm", True) def __init__(self, num_features, eps=1e-5, momentum=0.9, affine=True, gamma_init='ones', beta_init='zeros', moving_mean_init='zeros', moving_var_init='ones', use_batch_statistics=None, device_num_each_group=2): """Initialize GlobalBatchNorm.""" super(GlobalBatchNorm, self).__init__(num_features, eps, momentum, affine, gamma_init, beta_init, moving_mean_init, moving_var_init, use_batch_statistics, device_num_each_group, input_dims='both') self.group_device_num = validator.check_positive_int(device_num_each_group) if self.group_device_num <= 1: raise ValueError("the number of group must be greater than 1.") def _check_data_dim(self, x): if x.dim == 0: pass
[docs]class SyncBatchNorm(_BatchNorm): r""" Sync Batch Normalization layer over a N-dimension input. Sync Batch Normalization is cross device synchronized Batch Normalization. The implementation of Batch Normalization only normalizes the data within each device. Sync Batch Normalization will normalize the input within the group. It has been described in the paper `Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift <https://arxiv.org/abs/1502.03167>`_. It rescales and recenters the feature using a mini-batch of data and the learned parameters which can be described in the following formula. .. math:: y = \frac{x - \mathrm{E}[x]}{\sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta Note: Currently, SyncBatchNorm only supports 2D and 4D inputs. Args: num_features (int): `C` from an expected input of size (N, C, H, W). eps (float): A value added to the denominator for numerical stability. Default: 1e-5. momentum (float): A floating hyperparameter of the momentum for the running_mean and running_var computation. Default: 0.9. affine (bool): A bool value. When set to True, gamma and beta can be learned. Default: True. gamma_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the gamma weight. The values of str refer to the function `initializer` including 'zeros', 'ones', 'xavier_uniform', 'he_uniform', etc. Default: 'ones'. beta_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the beta weight. The values of str refer to the function `initializer` including 'zeros', 'ones', 'xavier_uniform', 'he_uniform', etc. Default: 'zeros'. moving_mean_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the moving mean. The values of str refer to the function `initializer` including 'zeros', 'ones', 'xavier_uniform', 'he_uniform', etc. Default: 'zeros'. moving_var_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the moving variance. The values of str refer to the function `initializer` including 'zeros', 'ones', 'xavier_uniform', 'he_uniform', etc. Default: 'ones'. use_batch_statistics (bool): If true, use the mean value and variance value of current batch data. If false, use the mean value and variance value of specified value. If None, training process will use the mean and variance of current batch data and track the running mean and variance, eval process will use the running mean and variance. Default: None. process_groups (list): A list to divide devices into different sync groups, containing N subtraction lists. Each subtraction list contains int numbers identifying rank ids which need to be synchronized in the same group. All int values must be in [0, rank_size) and different from each other. Default: None, indicating synchronization across all devices. Inputs: - **x** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`. Outputs: Tensor, the normalized, scaled, offset tensor, of shape :math:`(N, C_{out}, H_{out}, W_{out})`. Raises: TypeError: If `num_features` is not an int. TypeError: If `eps` is not a float. TypeError: If `process_groups` is not a list. ValueError: If `num_features` is less than 1. ValueError: If `momentum` is not in range [0, 1]. ValueError: If rank_id in `process_groups` is not in range [0, rank_size). Supported Platforms: ``Ascend`` Examples: >>> # This example should be run with multiple processes. >>> # Please refer to the tutorial > Distributed Training on mindspore.cn. >>> import numpy as np >>> from mindspore.communication import init >>> from mindspore import context >>> from mindspore.context import ParallelMode >>> from mindspore import Tensor >>> from mindspore import nn >>> from mindspore.common import dtype as mstype >>> >>> context.set_context(mode=context.GRAPH_MODE) >>> init() >>> context.reset_auto_parallel_context() >>> context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL) >>> sync_bn_op = nn.SyncBatchNorm(num_features=3, process_groups=[[0, 1], [2, 3]]) >>> x = Tensor(np.ones([1, 3, 2, 2]), mstype.float32) >>> output = sync_bn_op(x) >>> print(output) [[[[ 0.999995 0.999995 ] [ 0.999995 0.999995 ]] [[ 0.999995 0.999995 ] [ 0.999995 0.999995 ]] [[ 0.999995 0.999995 ] [ 0.999995 0.999995 ]]]] """ def __init__(self, num_features, eps=1e-5, momentum=0.9, affine=True, gamma_init='ones', beta_init='zeros', moving_mean_init='zeros', moving_var_init='ones', use_batch_statistics=None, process_groups=None): """Initialize SyncBatchNorm.""" super(SyncBatchNorm, self).__init__(num_features, eps, momentum, affine, gamma_init, beta_init, moving_mean_init, moving_var_init, use_batch_statistics, process_groups=process_groups, input_dims='both') def _check_data_dim(self, x): if x.dim == 0: pass
[docs]class LayerNorm(Cell): r""" Applies Layer Normalization over a mini-batch of inputs. Layer Normalization is widely used in recurrent neural networks. It applies normalization on a mini-batch of inputs for each single training case as described in the paper `Layer Normalization <https://arxiv.org/pdf/1607.06450.pdf>`_. Unlike Batch Normalization, Layer Normalization performs exactly the same computation at training and testing time. It can be described using the following formula. It is applied across all channels and pixel but only one batch size. .. math:: y = \frac{x - \mathrm{E}[x]}{\sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta Args: normalized_shape (Union(tuple[int], list[int]): The normalization is performed over axis `begin_norm_axis ... R - 1`. begin_norm_axis (int): The first normalization dimension: normalization will be performed along dimensions `begin_norm_axis: rank(inputs)`, the value should be in [-1, rank(input)). Default: -1. begin_params_axis (int): The first parameter(beta, gamma)dimension: scale and centering parameters will have dimensions `begin_params_axis: rank(inputs)` and will be broadcast with the normalized inputs accordingly, the value should be in [-1, rank(input)). Default: -1. gamma_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the gamma weight. The values of str refer to the function `initializer` including 'zeros', 'ones', 'xavier_uniform', 'he_uniform', etc. Default: 'ones'. beta_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the beta weight. The values of str refer to the function `initializer` including 'zeros', 'ones', 'xavier_uniform', 'he_uniform', etc. Default: 'zeros'. epsilon (float): A value added to the denominator for numerical stability. Default: 1e-7. Inputs: - **x** (Tensor) - The shape of 'x' is :math:`(x_1, x_2, ..., x_R)`, and `input_shape[begin_norm_axis:]` is equal to `normalized_shape`. Outputs: Tensor, the normalized and scaled offset tensor, has the same shape and data type as the `x`. Raises: TypeError: If `normalized_shape` is neither a list nor tuple. TypeError: If `begin_norm_axis` or `begin_params_axis` is not an int. TypeError: If `epsilon` is not a float. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: >>> x = Tensor(np.ones([20, 5, 10, 10]), mindspore.float32) >>> shape1 = x.shape[1:] >>> m = nn.LayerNorm(shape1, begin_norm_axis=1, begin_params_axis=1) >>> output = m(x).shape >>> print(output) (20, 5, 10, 10) """ def __init__(self, normalized_shape, begin_norm_axis=-1, begin_params_axis=-1, gamma_init='ones', beta_init='zeros', epsilon=1e-7 ): """Initialize LayerNorm.""" super(LayerNorm, self).__init__() if not isinstance(normalized_shape, (tuple, list)): raise TypeError("The type of 'normalized_shape' should be tuple[int] or list[int], but '{}' type is {}." .format(normalized_shape, type(normalized_shape))) self.normalized_shape = normalized_shape self.begin_norm_axis = begin_norm_axis self.begin_params_axis = begin_params_axis self.epsilon = epsilon self.gamma = Parameter(initializer( gamma_init, normalized_shape), name="gamma") self.beta = Parameter(initializer( beta_init, normalized_shape), name="beta") self.layer_norm = P.LayerNorm(begin_norm_axis=self.begin_norm_axis, begin_params_axis=self.begin_params_axis, epsilon=self.epsilon) def construct(self, input_x): y, _, _ = self.layer_norm(input_x, self.gamma, self.beta) return y
[docs] def extend_repr(self): """Display instance object as string.""" return 'normalized_shape={}, begin_norm_axis={}, begin_params_axis={}, gamma{}, beta={}'.format( self.normalized_shape, self.begin_norm_axis, self.begin_params_axis, self.gamma, self.beta)
[docs]class InstanceNorm2d(Cell): r""" Instance Normalization layer over a 4D input. This layer applies Instance Normalization over a 4D input (a mini-batch of 2D inputs with additional channel dimension) as described in the paper `Instance Normalization: The Missing Ingredient for Fast Stylization <https://arxiv.org/abs/1607.08022>`_. It rescales and recenters the feature using a mini-batch of data and the learned parameters which can be described in the following formula. .. math:: y = \frac{x - \mathrm{E}[x]}{\sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta \gamma and \beta are learnable parameter vectors of size num_features if affine is True. The standard-deviation is calculated via the biased estimator. This layer uses instance statistics computed from input data in both training and evaluation modes. InstanceNorm2d and BatchNorm2d are very similar, but have some differences. InstanceNorm2d is applied on each channel of channeled data like RGB images, but BatchNorm2d is usually applied on each batch of batched data. Note: Note that the formula for updating the running_mean and running_var is :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times x_t + \text{momentum} \times \hat{x}`, where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the new observed value. Args: num_features (int): `C` from an expected input of size (N, C, H, W). eps (float): A value added to the denominator for numerical stability. Default: 1e-5. momentum (float): A floating hyperparameter of the momentum for the running_mean and running_var computation. Default: 0.1. affine (bool): A bool value. When set to True, gamma and beta can be learned. Default: True. gamma_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the gamma weight. The values of str refer to the function `initializer` including 'zeros', 'ones', etc. Default: 'ones'. beta_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the beta weight. The values of str refer to the function `initializer` including 'zeros', 'ones', etc. Default: 'zeros'. Inputs: - **x** (Tensor) - Tensor of shape :math:`(N, C, H, W)`. Data type: float16 or float32. Outputs: Tensor, the normalized, scaled, offset tensor, of shape :math:`(N, C, H, W)`. Same type and shape as the `x`. Supported Platforms: ``GPU`` Raises: TypeError: If `num_features` is not an int. TypeError: If `eps` is not a float. TypeError: If `momentum` is not a float. TypeError: If `affine` is not a bool. TypeError: If the type of `gamma_init`/`beta_init` is not same, or if the initialized element type is not float32. ValueError: If `num_features` is less than 1. ValueError: If `momentum` is not in range [0, 1]. KeyError: If any of `gamma_init`/`beta_init` is str and the homonymous class inheriting from `Initializer` not exists. Examples: >>> import mindspore >>> import numpy as np >>> import mindspore.nn as nn >>> from mindspore import Tensor >>> net = nn.InstanceNorm2d(3) >>> x = Tensor(np.ones([2, 3, 2, 2]), mindspore.float32) >>> output = net(x) >>> print(output.shape) (2, 3, 2, 2) """ @cell_attr_register def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True, gamma_init='ones', beta_init='zeros'): """Initialize InstanceNorm2d.""" super(InstanceNorm2d, self).__init__() validator.check_value_type('num_features', num_features, [int], self.cls_name) validator.check_value_type('eps', eps, [float], self.cls_name) validator.check_value_type('momentum', momentum, [float], self.cls_name) validator.check_value_type('affine', affine, [bool], self.cls_name) args_input = {"gamma_init": gamma_init, "beta_init": beta_init} self.check_types_valid(args_input, 'InstanceNorm2d') 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.input_dims = '2d' self.moving_mean = Parameter(initializer('zeros', num_features), name="mean", requires_grad=False) self.moving_variance = Parameter(initializer('ones', num_features), name="variance", requires_grad=False) 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.shape = P.Shape() self.momentum = momentum self.instance_bn = P.InstanceNorm(epsilon=self.eps, momentum=self.momentum) def _check_data_dim(self, x): raise NotImplementedError def construct(self, x): _shape_check_bn(self.shape(x), self.input_dims) return self.instance_bn(x, self.gamma, self.beta, self.moving_mean, self.moving_variance)[0] def extend_repr(self): return 'num_features={}, eps={}, momentum={}, gamma={}, beta={}, moving_mean={}, moving_variance={}'.format( self.num_features, self.eps, self.momentum, self.gamma, self.beta, self.moving_mean, self.moving_variance) def check_types_valid(self, args_dict, name): for key, _ in args_dict.items(): val = args_dict[key] if not isinstance(val, (Tensor, numbers.Number, str, Initializer)): raise TypeError(f"[{name}]Supported type for arg {key} is [Tensor, numbers.Number, str, Initializer]," f"but got {type(val)}") if isinstance(val, Tensor) and val.dtype != mstype.float32: raise TypeError(f"[{name}]The type of arg {key} should be float32, but got {val.dtype}")
[docs]class GroupNorm(Cell): r""" Group Normalization over a mini-batch of inputs. Group Normalization is widely used in recurrent neural networks. It applies normalization on a mini-batch of inputs for each single training case as described in the paper `Group Normalization <https://arxiv.org/pdf/1803.08494.pdf>`_. Group Normalization divides the channels into groups and computes within each group the mean and variance for normalization, and it performs very stable over a wide range of batch size. It can be described using the following formula. .. math:: y = \frac{x - \mathrm{E}[x]}{\sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta Args: num_groups (int): The number of groups to be divided along the channel dimension. num_channels (int): The number of channels per group. eps (float): A value added to the denominator for numerical stability. Default: 1e-5. affine (bool): A bool value, this layer will have learnable affine parameters when set to true. Default: True. gamma_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the gamma weight. The values of str refer to the function `initializer` including 'zeros', 'ones', 'xavier_uniform', 'he_uniform', etc. Default: 'ones'. If gamma_init is a Tensor, the shape must be [num_channels]. beta_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the beta weight. The values of str refer to the function `initializer` including 'zeros', 'ones', 'xavier_uniform', 'he_uniform', etc. Default: 'zeros'. If beta_init is a Tensor, the shape must be [num_channels]. Inputs: - **x** (Tensor) - The input feature with shape [N, C, H, W]. Outputs: Tensor, the normalized and scaled offset tensor, has the same shape and data type as the `x`. Raises: TypeError: If `num_groups` or `num_channels` is not an int. TypeError: If `eps` is not a float. TypeError: If `affine` is not a bool. ValueError: If `num_groups` or `num_channels` is less than 1. ValueError: If `num_channels` is not divided by `num_groups`. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: >>> goup_norm_op = nn.GroupNorm(2, 2) >>> x = Tensor(np.ones([1, 2, 4, 4], np.float32)) >>> output = goup_norm_op(x) >>> print(output) [[[[0. 0. 0. 0.] [0. 0. 0. 0.] [0. 0. 0. 0.] [0. 0. 0. 0.]] [[0. 0. 0. 0.] [0. 0. 0. 0.] [0. 0. 0. 0.] [0. 0. 0. 0.]]]] """ def __init__(self, num_groups, num_channels, eps=1e-05, affine=True, gamma_init='ones', beta_init='zeros'): """Initialize GroupNorm.""" super(GroupNorm, self).__init__() self.num_groups = validator.check_positive_int(num_groups) self.num_channels = validator.check_positive_int(num_channels) if num_channels % num_groups != 0: raise ValueError("num_channels should be divided by num_groups") self.eps = validator.check_value_type('eps', eps, (float,), type(self).__name__) self.affine = validator.check_bool(affine) gamma = initializer(gamma_init, num_channels) beta = initializer(beta_init, num_channels) if self.affine: self.gamma = Parameter(gamma, name='gamma') self.beta = Parameter(beta, name='beta') else: self.gamma = gamma self.beta = beta self.shape = F.shape self.reshape = F.reshape self.reduce_mean = P.ReduceMean(keep_dims=True) self.square = F.square self.reduce_sum = P.ReduceSum(keep_dims=True) self.sqrt = P.Sqrt() def _cal_output(self, x): """calculate groupnorm output""" batch, channel, height, width = self.shape(x) _channel_check(channel, self.num_channels) x = self.reshape(x, (batch, self.num_groups, -1)) mean = self.reduce_mean(x, 2) var = self.reduce_sum(self.square(x - mean), 2) / (channel * height * width / self.num_groups) std = self.sqrt(var + self.eps) x = (x - mean) / std x = self.reshape(x, (batch, channel, height, width)) output = x * self.reshape(self.gamma, (-1, 1, 1)) + self.reshape(self.beta, (-1, 1, 1)) return output def construct(self, x): _shape_check(self.shape(x)) output = self._cal_output(x) return output
[docs] def extend_repr(self): """Display instance object as string.""" return 'num_groups={}, num_channels={}'.format(self.num_groups, self.num_channels)