| from .batchnorm import _BatchNorm |
| from .. import functional as F |
| from ..._jit_internal import weak_module, weak_script_method |
| |
| |
| class _InstanceNorm(_BatchNorm): |
| __constants__ = ['running_mean', 'running_var', 'weight', 'bias', |
| 'track_running_stats', 'momentum', 'eps'] |
| |
| 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) |
| |
| @weak_script_method |
| 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) |
| |
| @weak_script_method |
| 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) |
| |
| |
| @weak_module |
| class InstanceNorm1d(_InstanceNorm): |
| r"""Applies Instance Normalization over a 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 |
| """ |
| |
| @weak_script_method |
| def _check_input_dim(self, input): |
| if input.dim() == 2: |
| raise ValueError( |
| 'InstanceNorm1d returns 0-filled tensor to 2D tensor.' |
| 'This is because InstanceNorm1d reshapes inputs to' |
| '(1, N * C, ...) from (N, C,...) and this makes' |
| 'variances 0.' |
| ) |
| if input.dim() != 3: |
| raise ValueError('expected 3D input (got {}D input)' |
| .format(input.dim())) |
| |
| |
| @weak_module |
| 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 |
| """ |
| |
| @weak_script_method |
| def _check_input_dim(self, input): |
| if input.dim() != 4: |
| raise ValueError('expected 4D input (got {}D input)' |
| .format(input.dim())) |
| |
| |
| @weak_module |
| 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 |
| """ |
| |
| @weak_script_method |
| def _check_input_dim(self, input): |
| if input.dim() != 5: |
| raise ValueError('expected 5D input (got {}D input)' |
| .format(input.dim())) |
