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Merge pull request #20 from Schrodinger-Hat/feat/ai-conversion
feat: add AI conversion with a new method + minore fixies
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| Original file line number | Diff line number | Diff line change |
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| @@ -1,4 +1,4 @@ | ||
| # gonord version | ||
| __version__ = "0.1.7" | ||
| __version__ = "1.0.0" | ||
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| from ImageGoNord.GoNord import * |
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| @@ -0,0 +1,141 @@ | ||
| from functools import partial | ||
| import torch | ||
| import torch.nn as nn | ||
| import torch.nn.functional as F | ||
| from collections import OrderedDict | ||
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| device = "cuda" if torch.cuda.is_available() else "cpu" | ||
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| class Conv2dAuto(nn.Conv2d): | ||
| def __init__(self, *args, **kwargs): | ||
| super().__init__(*args, **kwargs) | ||
| self.padding = (self.kernel_size[0] // 2, self.kernel_size[1] // 2) #dynamic add padding based on the kernel_size | ||
| conv3x3 = partial(Conv2dAuto, kernel_size=3, bias=False) | ||
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| def activation_func(activation): #Activation function as mentioned in the paper - Leaky Relu | ||
| return nn.ModuleDict([ | ||
| ['relu', nn.ReLU(inplace=True)], | ||
| ['leaky_relu', nn.LeakyReLU(negative_slope=0.01, inplace=True)], | ||
| ['none', nn.Identity()] | ||
| ])[activation] | ||
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| class ResidualBlock(nn.Module): | ||
| def __init__(self, in_channels, out_channels, activation='relu'): | ||
| super().__init__() | ||
| self.in_channels, self.out_channels,self.activation = in_channels, out_channels, activation | ||
| self.blocks = nn.Identity() | ||
| self.shortcut = nn.Identity() | ||
| self.activate = activation_func(activation) | ||
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| def forward(self, x): | ||
| residual = x | ||
| if self.should_apply_shortcut: residual = self.shortcut(x) | ||
| x = self.blocks(x) | ||
| x += residual | ||
| x = self.activate(x) | ||
| return x | ||
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| @property | ||
| def should_apply_shortcut(self): | ||
| return self.in_channels != self.out_channels | ||
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| class ResNetResidualBlock(ResidualBlock): | ||
| def __init__(self, in_channels, out_channels, expansion=1, downsampling=2, conv=conv3x3, *args, **kwargs): | ||
| super().__init__(in_channels, out_channels) | ||
| self.expansion, self.downsampling, self.conv = expansion, downsampling, conv | ||
| self.shortcut = nn.Sequential(OrderedDict( | ||
| { | ||
| 'conv' : nn.Conv2d(self.in_channels, self.expanded_channels, kernel_size=1, | ||
| stride=self.downsampling, bias=False, padding=0), | ||
| 'bn' : nn.InstanceNorm2d(self.expanded_channels) | ||
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| })) if self.should_apply_shortcut else None | ||
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| @property | ||
| def expanded_channels(self): | ||
| return self.out_channels * self.expansion | ||
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| @property | ||
| def should_apply_shortcut(self): | ||
| return self.in_channels != self.expanded_channels | ||
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| def conv_bn(in_channels, out_channels, conv, *args, **kwargs): | ||
| return nn.Sequential(OrderedDict({'conv': conv(in_channels, out_channels, *args, **kwargs), | ||
| 'bn': nn.InstanceNorm2d(out_channels) })) | ||
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| class ResNetBasicBlock(ResNetResidualBlock): | ||
| expansion = 1 | ||
| def __init__(self, in_channels, out_channels, activation=nn.LeakyReLU, *args, **kwargs): | ||
| super().__init__(in_channels, out_channels, *args, **kwargs) | ||
| self.blocks = nn.Sequential( | ||
| conv_bn(self.in_channels, self.out_channels,conv=self.conv, bias=False, stride=self.downsampling), | ||
| activation(negative_slope=0.02), | ||
| conv_bn(self.out_channels, self.expanded_channels,conv=self.conv, bias=False), | ||
| ) | ||
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| class FeatureEncoder(nn.Module): | ||
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| def __init__(self,*args,**kwargs): | ||
| super(FeatureEncoder,self).__init__() | ||
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| self.conv=nn.Conv2d(in_channels=3,out_channels=64,kernel_size=3,stride=1,padding=1) #3xHxW | ||
| self.norm=nn.InstanceNorm2d(64) | ||
| self.pool=nn.MaxPool2d(kernel_size=2, stride=2, padding=0) | ||
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| self.res1 = ResNetBasicBlock(64, 128) | ||
| self.res2 = ResNetBasicBlock(128, 256) | ||
| self.res3 = ResNetBasicBlock(256, 512) | ||
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| def forward(self, x): | ||
| x = F.relu(self.norm(self.conv(x))) | ||
| c4 = self.pool(x) | ||
| c3 = self.res1(c4) | ||
| c2 = self.res2(c3) | ||
| c1 = self.res3(c2) | ||
| return c1,c2,c3,c4 | ||
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| def de_conv(in_channels, out_channels,kernel_size=3): #deconvolution | ||
| return nn.Sequential( | ||
| nn.ConvTranspose2d(in_channels, out_channels,kernel_size=3,stride=2,output_padding=1, padding=1,bias=True), | ||
| nn.InstanceNorm2d(out_channels), | ||
| nn.LeakyReLU(negative_slope=0.02,inplace=True) | ||
| ) | ||
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| class RecoloringDecoder(nn.Module): | ||
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| def __init__(self): | ||
| super().__init__() | ||
| self.dconv_up_4 = de_conv(18 + 512, 256) #pt,c1 | ||
| self.dconv_up_3 = de_conv(256 + 256, 128) #c2,d1 | ||
| self.dconv_up_2 = de_conv(18 + 128 + 128, 64) #pt,c3,d2 | ||
| self.dconv_up_1 = de_conv(18 + 64 + 64, 64) #pt,c4,d3 | ||
| self.conv_last = nn.Conv2d(1 + 64, 2, kernel_size=3,padding=1) #Illu,d4 | ||
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| def forward(self, c1, c2, c3, c4, target_palettes_1d, illu): | ||
| bz, h, w = c1.shape[0], c1.shape[2], c1.shape[3] #1,24,16 | ||
| tp_reshpaed = target_palettes_1d.reshape(bz,18,1,1) | ||
| tp_c1 = tp_reshpaed.repeat(1,1,h,w) | ||
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| x = torch.cat((c1,tp_c1), 1) | ||
| x = self.dconv_up_4(x) | ||
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| x = torch.cat([c2, x], dim=1) #c2,d1(x) | ||
| x = self.dconv_up_3(x) | ||
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| bz, h, w = x.shape[0], x.shape[2], x.shape[3] | ||
| tp_c3 = tp_reshpaed.repeat(1,1,h,w) | ||
| x = torch.cat([tp_c3,c3,x], dim=1) #Pt,c3,x | ||
| x = self.dconv_up_2(x) | ||
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| bz, h, w = x.shape[0], x.shape[2], x.shape[3] | ||
| tp_c4 = tp_reshpaed.repeat(1,1,h,w) | ||
| x = torch.cat([tp_c4,c4,x], dim=1) #Pt,c4,x | ||
| x = self.dconv_up_1(x) | ||
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| illu = illu.view(illu.size(0), 1, illu.size(2), illu.size(3)) | ||
| x = torch.cat((x, illu), dim = 1) | ||
| #illu,x | ||
| x = self.conv_last(x) | ||
| x = torch.tanh(x) | ||
| return x |
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