ade2732c82
This is a concept from "Lifelong Learning GAN", although I'm skeptical of it's novelty - basically you scale and shift the weights for the generator and discriminator of a pretrained GAN to "shift" into new modalities, e.g. faces->birds or whatever. There are some interesting applications of this that I would like to try out.
136 lines
5.0 KiB
Python
136 lines
5.0 KiB
Python
import math
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch import Tensor
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from torch.nn import Parameter, init
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from torch.nn.modules.conv import _ConvNd
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from torch.nn.modules.utils import _ntuple
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_pair = _ntuple(2)
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class TransferConv2d(_ConvNd):
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def __init__(
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self,
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in_channels: int,
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out_channels: int,
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kernel_size,
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stride = 1,
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padding = 0,
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dilation = 1,
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groups: int = 1,
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bias: bool = True,
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padding_mode: str = 'zeros',
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transfer_mode: bool = False
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):
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kernel_size = _pair(kernel_size)
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stride = _pair(stride)
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padding = _pair(padding)
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dilation = _pair(dilation)
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super().__init__(
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in_channels, out_channels, kernel_size, stride, padding, dilation,
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False, _pair(0), groups, bias, padding_mode)
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self.transfer_mode = transfer_mode
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if transfer_mode:
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self.transfer_scale = nn.Parameter(torch.ones(out_channels, in_channels, 1, 1))
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self.transfer_scale.FOR_TRANSFER_LEARNING = True
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self.transfer_shift = nn.Parameter(torch.zeros(out_channels, in_channels, 1, 1))
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self.transfer_shift.FOR_TRANSFER_LEARNING = True
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def _conv_forward(self, input, weight):
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if self.transfer_mode:
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weight = weight * self.transfer_scale + self.transfer_shift
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else:
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weight = weight
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if self.padding_mode != 'zeros':
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return F.conv2d(F.pad(input, self._reversed_padding_repeated_twice, mode=self.padding_mode),
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weight, self.bias, self.stride,
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_pair(0), self.dilation, self.groups)
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return F.conv2d(input, weight, self.bias, self.stride,
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self.padding, self.dilation, self.groups)
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def forward(self, input: Tensor) -> Tensor:
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return self._conv_forward(input, self.weight)
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class TransferLinear(nn.Module):
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__constants__ = ['in_features', 'out_features']
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in_features: int
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out_features: int
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weight: Tensor
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def __init__(self, in_features: int, out_features: int, bias: bool = True, transfer_mode: bool = False) -> None:
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super().__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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self.transfer_mode = transfer_mode
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if transfer_mode:
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self.transfer_scale = nn.Parameter(torch.ones(out_features, in_features))
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self.transfer_scale.FOR_TRANSFER_LEARNING = True
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self.transfer_shift = nn.Parameter(torch.zeros(out_features, in_features))
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self.transfer_shift.FOR_TRANSFER_LEARNING = True
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def reset_parameters(self) -> None:
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init.kaiming_uniform_(self.weight, a=math.sqrt(5))
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if self.bias is not None:
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fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight)
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bound = 1 / math.sqrt(fan_in)
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init.uniform_(self.bias, -bound, bound)
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def forward(self, input: Tensor) -> Tensor:
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if self.transfer_mode:
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weight = self.weight * self.transfer_scale + self.transfer_shift
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else:
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weight = self.weight
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return F.linear(input, weight, self.bias)
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def extra_repr(self) -> str:
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return 'in_features={}, out_features={}, bias={}'.format(
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self.in_features, self.out_features, self.bias is not None
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)
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class TransferConvGnLelu(nn.Module):
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def __init__(self, filters_in, filters_out, kernel_size=3, stride=1, activation=True, norm=True, bias=True, num_groups=8, weight_init_factor=1, transfer_mode=False):
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super().__init__()
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padding_map = {1: 0, 3: 1, 5: 2, 7: 3}
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assert kernel_size in padding_map.keys()
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self.conv = TransferConv2d(filters_in, filters_out, kernel_size, stride, padding_map[kernel_size], bias=bias, transfer_mode=transfer_mode)
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if norm:
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self.gn = nn.GroupNorm(num_groups, filters_out)
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else:
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self.gn = None
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if activation:
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self.lelu = nn.LeakyReLU(negative_slope=.2)
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else:
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self.lelu = None
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# Init params.
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for m in self.modules():
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if isinstance(m, TransferConv2d):
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nn.init.kaiming_normal_(m.weight, a=.1, mode='fan_out',
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nonlinearity='leaky_relu' if self.lelu else 'linear')
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m.weight.data *= weight_init_factor
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if m.bias is not None:
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m.bias.data.zero_()
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elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
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nn.init.constant_(m.weight, 1)
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nn.init.constant_(m.bias, 0)
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def forward(self, x):
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x = self.conv(x)
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if self.gn:
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x = self.gn(x)
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if self.lelu:
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return self.lelu(x)
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else:
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return x |