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Source code for torch.nn.modules.instancenorm

from .batchnorm import _BatchNorm
from .. import functional as F


class _InstanceNorm(_BatchNorm):
    def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=False,
                 track_running_stats=False):
        super(_InstanceNorm, self).__init__(
            num_features, eps, momentum, affine, track_running_stats)

    def _check_input_dim(self, input):
        raise NotImplementedError

    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
                              missing_keys, unexpected_keys, error_msgs):
        version = local_metadata.get('version', None)
        # at version 1: removed running_mean and running_var when
        # track_running_stats=False (default)
        if version is None and not self.track_running_stats:
            running_stats_keys = []
            for name in ('running_mean', 'running_var'):
                key = prefix + name
                if key in state_dict:
                    running_stats_keys.append(key)
            if len(running_stats_keys) > 0:
                error_msgs.append(
                    'Unexpected running stats buffer(s) {names} for {klass} '
                    'with track_running_stats=False. If state_dict is a '
                    'checkpoint saved before 0.4.0, this may be expected '
                    'because {klass} does not track running stats by default '
                    'since 0.4.0. Please remove these keys from state_dict. If '
                    'the running stats are actually needed, instead set '
                    'track_running_stats=True in {klass} to enable them. See '
                    'the documentation of {klass} for details.'
                    .format(names=" and ".join('"{}"'.format(k) for k in running_stats_keys),
                            klass=self.__class__.__name__))
                for key in running_stats_keys:
                    state_dict.pop(key)

        super(_InstanceNorm, self)._load_from_state_dict(
            state_dict, prefix, local_metadata, strict,
            missing_keys, unexpected_keys, error_msgs)

    def forward(self, input):
        self._check_input_dim(input)

        return F.instance_norm(
            input, self.running_mean, self.running_var, self.weight, self.bias,
            self.training or not self.track_running_stats, self.momentum, self.eps)


[docs]class InstanceNorm1d(_InstanceNorm): r"""Applies Instance Normalization over a 2D or 3D input (a mini-batch of 1D inputs with optional additional channel dimension) as described in the paper `Instance Normalization: The Missing Ingredient for Fast Stylization`_ . .. math:: y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta The mean and standard-deviation are calculated per-dimension separately for each object in a mini-batch. :math:`\gamma` and :math:`\beta` are learnable parameter vectors of size `C` (where `C` is the input size) if :attr:`affine` is ``True``. By default, this layer uses instance statistics computed from input data in both training and evaluation modes. If :attr:`track_running_stats` is set to ``True``, during training this layer keeps running estimates of its computed mean and variance, which are then used for normalization during evaluation. The running estimates are kept with a default :attr:`momentum` of 0.1. .. note:: This :attr:`momentum` argument is different from one used in optimizer classes and the conventional notion of momentum. Mathematically, the update rule for running statistics here is :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momemtum} \times x_t`, where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the new observed value. .. note:: :class:`InstanceNorm1d` and :class:`LayerNorm` are very similar, but have some subtle differences. :class:`InstanceNorm1d` is applied on each channel of channeled data like multidimensional time series, but :class:`LayerNorm` is usually applied on entire sample and often in NLP tasks. Additionaly, :class:`LayerNorm` applies elementwise affine transform, while :class:`InstanceNorm1d` usually don't apply affine transform. Args: num_features: :math:`C` from an expected input of size :math:`(N, C, L)` or :math:`L` from input of size :math:`(N, L)` eps: a value added to the denominator for numerical stability. Default: 1e-5 momentum: the value used for the running_mean and running_var computation. Default: 0.1 affine: a boolean value that when set to ``True``, this module has learnable affine parameters, initialized the same way as done for batch normalization. Default: ``False``. track_running_stats: a boolean value that when set to ``True``, this module tracks the running mean and variance, and when set to ``False``, this module does not track such statistics and always uses batch statistics in both training and eval modes. Default: ``False`` Shape: - Input: :math:`(N, C, L)` - Output: :math:`(N, C, L)` (same shape as input) Examples:: >>> # Without Learnable Parameters >>> m = nn.InstanceNorm1d(100) >>> # With Learnable Parameters >>> m = nn.InstanceNorm1d(100, affine=True) >>> input = torch.randn(20, 100, 40) >>> output = m(input) .. _`Instance Normalization: The Missing Ingredient for Fast Stylization`: https://arxiv.org/abs/1607.08022 """ def _check_input_dim(self, input): if input.dim() != 2 and input.dim() != 3: raise ValueError('expected 2D or 3D input (got {}D input)' .format(input.dim()))
[docs]class InstanceNorm2d(_InstanceNorm): r"""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`_ . .. math:: y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta The mean and standard-deviation are calculated per-dimension separately for each object in a mini-batch. :math:`\gamma` and :math:`\beta` are learnable parameter vectors of size `C` (where `C` is the input size) if :attr:`affine` is ``True``. By default, this layer uses instance statistics computed from input data in both training and evaluation modes. If :attr:`track_running_stats` is set to ``True``, during training this layer keeps running estimates of its computed mean and variance, which are then used for normalization during evaluation. The running estimates are kept with a default :attr:`momentum` of 0.1. .. note:: This :attr:`momentum` argument is different from one used in optimizer classes and the conventional notion of momentum. Mathematically, the update rule for running statistics here is :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momemtum} \times x_t`, where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the new observed value. .. note:: :class:`InstanceNorm2d` and :class:`LayerNorm` are very similar, but have some subtle differences. :class:`InstanceNorm2d` is applied on each channel of channeled data like RGB images, but :class:`LayerNorm` is usually applied on entire sample and often in NLP tasks. Additionaly, :class:`LayerNorm` applies elementwise affine transform, while :class:`InstanceNorm2d` usually don't apply affine transform. Args: num_features: :math:`C` from an expected input of size :math:`(N, C, H, W)` eps: a value added to the denominator for numerical stability. Default: 1e-5 momentum: the value used for the running_mean and running_var computation. Default: 0.1 affine: a boolean value that when set to ``True``, this module has learnable affine parameters, initialized the same way as done for batch normalization. Default: ``False``. track_running_stats: a boolean value that when set to ``True``, this module tracks the running mean and variance, and when set to ``False``, this module does not track such statistics and always uses batch statistics in both training and eval modes. Default: ``False`` Shape: - Input: :math:`(N, C, H, W)` - Output: :math:`(N, C, H, W)` (same shape as input) Examples:: >>> # Without Learnable Parameters >>> m = nn.InstanceNorm2d(100) >>> # With Learnable Parameters >>> m = nn.InstanceNorm2d(100, affine=True) >>> input = torch.randn(20, 100, 35, 45) >>> output = m(input) .. _`Instance Normalization: The Missing Ingredient for Fast Stylization`: https://arxiv.org/abs/1607.08022 """ def _check_input_dim(self, input): if input.dim() != 4: raise ValueError('expected 4D input (got {}D input)' .format(input.dim()))
[docs]class InstanceNorm3d(_InstanceNorm): r"""Applies Instance Normalization over a 5D input (a mini-batch of 3D inputs with additional channel dimension) as described in the paper `Instance Normalization: The Missing Ingredient for Fast Stylization`_ . .. math:: y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta The mean and standard-deviation are calculated per-dimension separately for each object in a mini-batch. :math:`\gamma` and :math:`\beta` are learnable parameter vectors of size C (where C is the input size) if :attr:`affine` is ``True``. By default, this layer uses instance statistics computed from input data in both training and evaluation modes. If :attr:`track_running_stats` is set to ``True``, during training this layer keeps running estimates of its computed mean and variance, which are then used for normalization during evaluation. The running estimates are kept with a default :attr:`momentum` of 0.1. .. note:: This :attr:`momentum` argument is different from one used in optimizer classes and the conventional notion of momentum. Mathematically, the update rule for running statistics here is :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momemtum} \times x_t`, where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the new observed value. .. note:: :class:`InstanceNorm3d` and :class:`LayerNorm` are very similar, but have some subtle differences. :class:`InstanceNorm3d` is applied on each channel of channeled data like 3D models with RGB color, but :class:`LayerNorm` is usually applied on entire sample and often in NLP tasks. Additionaly, :class:`LayerNorm` applies elementwise affine transform, while :class:`InstanceNorm3d` usually don't apply affine transform. Args: num_features: :math:`C` from an expected input of size :math:`(N, C, D, H, W)` eps: a value added to the denominator for numerical stability. Default: 1e-5 momentum: the value used for the running_mean and running_var computation. Default: 0.1 affine: a boolean value that when set to ``True``, this module has learnable affine parameters, initialized the same way as done for batch normalization. Default: ``False``. track_running_stats: a boolean value that when set to ``True``, this module tracks the running mean and variance, and when set to ``False``, this module does not track such statistics and always uses batch statistics in both training and eval modes. Default: ``False`` Shape: - Input: :math:`(N, C, D, H, W)` - Output: :math:`(N, C, D, H, W)` (same shape as input) Examples:: >>> # Without Learnable Parameters >>> m = nn.InstanceNorm3d(100) >>> # With Learnable Parameters >>> m = nn.InstanceNorm3d(100, affine=True) >>> input = torch.randn(20, 100, 35, 45, 10) >>> output = m(input) .. _`Instance Normalization: The Missing Ingredient for Fast Stylization`: https://arxiv.org/abs/1607.08022 """ def _check_input_dim(self, input): if input.dim() != 5: raise ValueError('expected 5D input (got {}D input)' .format(input.dim()))

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