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32b3841553
* Improvize documentation 1. Add formula for erf, erfinv 2. Make exp, expm1 similar to log, log1p 3. Symbol change in ge, le, ne, isnan * Fix minor nit in the docstring * More doc improvements 1. Added some formulae 2. Complete scanning till "Other Operations" in Tensor docs * Add more changes 1. Modify all torch.Tensor wherever required * Fix Conv docs 1. Fix minor nits in the references for LAPACK routines * Improve Pooling docs 1. Fix lint error * Improve docs for RNN, Normalization and Padding 1. Fix flake8 error for pooling * Final fixes for torch.nn.* docs. 1. Improve Loss Function documentation 2. Improve Vision Layers documentation * Fix lint error * Improve docstrings in torch.nn.init * Fix lint error * Fix minor error in torch.nn.init.sparse * Fix Activation and Utils Docs 1. Fix Math Errors 2. Add explicit clean to Makefile in docs to prevent running graph generation script while cleaning 3. Fix utils docs * Make PYCMD a Makefile argument, clear up prints in the build_activation_images.py * Fix batch norm doc error
123 lines
4.1 KiB
Python
123 lines
4.1 KiB
Python
import math
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import torch
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from torch.nn.parameter import Parameter
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from .. import functional as F
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from .module import Module
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class Linear(Module):
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r"""Applies a linear transformation to the incoming data: :math:`y = Ax + b`
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Args:
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in_features: size of each input sample
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out_features: size of each output sample
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bias: If set to False, the layer will not learn an additive bias.
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Default: ``True``
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Shape:
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- Input: :math:`(N, *, in\_features)` where :math:`*` means any number of
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additional dimensions
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- Output: :math:`(N, *, out\_features)` where all but the last dimension
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are the same shape as the input.
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Attributes:
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weight: the learnable weights of the module of shape
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`(out_features x in_features)`
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bias: the learnable bias of the module of shape `(out_features)`
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Examples::
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>>> m = nn.Linear(20, 30)
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>>> input = torch.randn(128, 20)
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>>> output = m(input)
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>>> print(output.size())
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"""
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def __init__(self, in_features, out_features, bias=True):
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super(Linear, self).__init__()
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self.in_features = in_features
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self.out_features = out_features
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self.weight = Parameter(torch.Tensor(out_features, in_features))
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if bias:
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self.bias = Parameter(torch.Tensor(out_features))
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else:
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self.register_parameter('bias', None)
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self.reset_parameters()
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def reset_parameters(self):
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stdv = 1. / math.sqrt(self.weight.size(1))
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self.weight.data.uniform_(-stdv, stdv)
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if self.bias is not None:
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self.bias.data.uniform_(-stdv, stdv)
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def forward(self, input):
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return F.linear(input, self.weight, self.bias)
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def __repr__(self):
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return self.__class__.__name__ + '(' \
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+ 'in_features=' + str(self.in_features) \
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+ ', out_features=' + str(self.out_features) \
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+ ', bias=' + str(self.bias is not None) + ')'
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class Bilinear(Module):
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r"""Applies a bilinear transformation to the incoming data:
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:math:`y = x_1 A x_2 + b`
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Args:
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in1_features: size of each first input sample
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in2_features: size of each second input sample
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out_features: size of each output sample
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bias: If set to False, the layer will not learn an additive bias.
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Default: ``True``
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Shape:
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- Input: :math:`(N, in1\_features)`, :math:`(N, in2\_features)`
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- Output: :math:`(N, out\_features)`
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Attributes:
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weight: the learnable weights of the module of shape
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`(out_features x in1_features x in2_features)`
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bias: the learnable bias of the module of shape `(out_features)`
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Examples::
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>>> m = nn.Bilinear(20, 30, 40)
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>>> input1 = torch.randn(128, 20)
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>>> input2 = torch.randn(128, 30)
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>>> output = m(input1, input2)
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>>> print(output.size())
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"""
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def __init__(self, in1_features, in2_features, out_features, bias=True):
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super(Bilinear, self).__init__()
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self.in1_features = in1_features
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self.in2_features = in2_features
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self.out_features = out_features
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self.weight = Parameter(torch.Tensor(out_features, in1_features, in2_features))
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if bias:
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self.bias = Parameter(torch.Tensor(out_features))
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else:
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self.register_parameter('bias', None)
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self.reset_parameters()
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def reset_parameters(self):
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stdv = 1. / math.sqrt(self.weight.size(1))
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self.weight.data.uniform_(-stdv, stdv)
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if self.bias is not None:
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self.bias.data.uniform_(-stdv, stdv)
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def forward(self, input1, input2):
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return F.bilinear(input1, input2, self.weight, self.bias)
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def __repr__(self):
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return self.__class__.__name__ + '(' \
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+ 'in1_features=' + str(self.in1_features) \
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+ ', in2_features=' + str(self.in2_features) \
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+ ', out_features=' + str(self.out_features) \
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+ ', bias=' + str(self.bias is not None) + ')'
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# TODO: PartialLinear - maybe in sparse?
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