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| # original implementation: https://github.com/odegeasslbc/FastGAN-pytorch/blob/main/models.py | |
| # | |
| # modified by Axel Sauer for "Projected GANs Converge Faster" | |
| # | |
| import torch.nn as nn | |
| from blocks import (InitLayer, UpBlockBig, UpBlockBigCond, UpBlockSmall, UpBlockSmallCond, SEBlock, conv2d) | |
| from huggingface_hub import PyTorchModelHubMixin | |
| def normalize_second_moment(x, dim=1, eps=1e-8): | |
| return x * (x.square().mean(dim=dim, keepdim=True) + eps).rsqrt() | |
| class DummyMapping(nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| def forward(self, z, c, **kwargs): | |
| return z.unsqueeze(1) # to fit the StyleGAN API | |
| class FastganSynthesis(nn.Module): | |
| def __init__(self, ngf=128, z_dim=256, nc=3, img_resolution=256, lite=False): | |
| super().__init__() | |
| self.img_resolution = img_resolution | |
| self.z_dim = z_dim | |
| # channel multiplier | |
| nfc_multi = {2: 16, 4:16, 8:8, 16:4, 32:2, 64:2, 128:1, 256:0.5, | |
| 512:0.25, 1024:0.125} | |
| nfc = {} | |
| for k, v in nfc_multi.items(): | |
| nfc[k] = int(v*ngf) | |
| # layers | |
| self.init = InitLayer(z_dim, channel=nfc[2], sz=4) | |
| UpBlock = UpBlockSmall if lite else UpBlockBig | |
| self.feat_8 = UpBlock(nfc[4], nfc[8]) | |
| self.feat_16 = UpBlock(nfc[8], nfc[16]) | |
| self.feat_32 = UpBlock(nfc[16], nfc[32]) | |
| self.feat_64 = UpBlock(nfc[32], nfc[64]) | |
| self.feat_128 = UpBlock(nfc[64], nfc[128]) | |
| self.feat_256 = UpBlock(nfc[128], nfc[256]) | |
| self.se_64 = SEBlock(nfc[4], nfc[64]) | |
| self.se_128 = SEBlock(nfc[8], nfc[128]) | |
| self.se_256 = SEBlock(nfc[16], nfc[256]) | |
| self.to_big = conv2d(nfc[img_resolution], nc, 3, 1, 1, bias=True) | |
| if img_resolution > 256: | |
| self.feat_512 = UpBlock(nfc[256], nfc[512]) | |
| self.se_512 = SEBlock(nfc[32], nfc[512]) | |
| if img_resolution > 512: | |
| self.feat_1024 = UpBlock(nfc[512], nfc[1024]) | |
| def forward(self, input, c, **kwargs): | |
| # map noise to hypersphere as in "Progressive Growing of GANS" | |
| input = normalize_second_moment(input[:, 0]) | |
| feat_4 = self.init(input) | |
| feat_8 = self.feat_8(feat_4) | |
| feat_16 = self.feat_16(feat_8) | |
| feat_32 = self.feat_32(feat_16) | |
| feat_64 = self.se_64(feat_4, self.feat_64(feat_32)) | |
| feat_128 = self.se_128(feat_8, self.feat_128(feat_64)) | |
| if self.img_resolution >= 128: | |
| feat_last = feat_128 | |
| if self.img_resolution >= 256: | |
| feat_last = self.se_256(feat_16, self.feat_256(feat_last)) | |
| if self.img_resolution >= 512: | |
| feat_last = self.se_512(feat_32, self.feat_512(feat_last)) | |
| if self.img_resolution >= 1024: | |
| feat_last = self.feat_1024(feat_last) | |
| return self.to_big(feat_last) | |
| class FastganSynthesisCond(nn.Module): | |
| def __init__(self, ngf=64, z_dim=256, nc=3, img_resolution=256, num_classes=1000, lite=False): | |
| super().__init__() | |
| self.z_dim = z_dim | |
| nfc_multi = {2: 16, 4:16, 8:8, 16:4, 32:2, 64:2, 128:1, 256:0.5, | |
| 512:0.25, 1024:0.125, 2048:0.125} | |
| nfc = {} | |
| for k, v in nfc_multi.items(): | |
| nfc[k] = int(v*ngf) | |
| self.img_resolution = img_resolution | |
| self.init = InitLayer(z_dim, channel=nfc[2], sz=4) | |
| UpBlock = UpBlockSmallCond if lite else UpBlockBigCond | |
| self.feat_8 = UpBlock(nfc[4], nfc[8], z_dim) | |
| self.feat_16 = UpBlock(nfc[8], nfc[16], z_dim) | |
| self.feat_32 = UpBlock(nfc[16], nfc[32], z_dim) | |
| self.feat_64 = UpBlock(nfc[32], nfc[64], z_dim) | |
| self.feat_128 = UpBlock(nfc[64], nfc[128], z_dim) | |
| self.feat_256 = UpBlock(nfc[128], nfc[256], z_dim) | |
| self.se_64 = SEBlock(nfc[4], nfc[64]) | |
| self.se_128 = SEBlock(nfc[8], nfc[128]) | |
| self.se_256 = SEBlock(nfc[16], nfc[256]) | |
| self.to_big = conv2d(nfc[img_resolution], nc, 3, 1, 1, bias=True) | |
| if img_resolution > 256: | |
| self.feat_512 = UpBlock(nfc[256], nfc[512]) | |
| self.se_512 = SEBlock(nfc[32], nfc[512]) | |
| if img_resolution > 512: | |
| self.feat_1024 = UpBlock(nfc[512], nfc[1024]) | |
| self.embed = nn.Embedding(num_classes, z_dim) | |
| def forward(self, input, c, update_emas=False): | |
| c = self.embed(c.argmax(1)) | |
| # map noise to hypersphere as in "Progressive Growing of GANS" | |
| input = normalize_second_moment(input[:, 0]) | |
| feat_4 = self.init(input) | |
| feat_8 = self.feat_8(feat_4, c) | |
| feat_16 = self.feat_16(feat_8, c) | |
| feat_32 = self.feat_32(feat_16, c) | |
| feat_64 = self.se_64(feat_4, self.feat_64(feat_32, c)) | |
| feat_128 = self.se_128(feat_8, self.feat_128(feat_64, c)) | |
| if self.img_resolution >= 128: | |
| feat_last = feat_128 | |
| if self.img_resolution >= 256: | |
| feat_last = self.se_256(feat_16, self.feat_256(feat_last, c)) | |
| if self.img_resolution >= 512: | |
| feat_last = self.se_512(feat_32, self.feat_512(feat_last, c)) | |
| if self.img_resolution >= 1024: | |
| feat_last = self.feat_1024(feat_last, c) | |
| return self.to_big(feat_last) | |
| class MyGenerator(nn.Module, PyTorchModelHubMixin): | |
| def __init__( | |
| self, | |
| z_dim=256, | |
| c_dim=0, | |
| w_dim=0, | |
| img_resolution=256, | |
| img_channels=3, | |
| ngf=128, | |
| cond=0, | |
| mapping_kwargs={}, | |
| synthesis_kwargs={} | |
| ): | |
| super().__init__() | |
| #self.config = kwargs.pop("config", None) | |
| self.z_dim = z_dim | |
| self.c_dim = c_dim | |
| self.w_dim = w_dim | |
| self.img_resolution = img_resolution | |
| self.img_channels = img_channels | |
| # Mapping and Synthesis Networks | |
| self.mapping = DummyMapping() # to fit the StyleGAN API | |
| Synthesis = FastganSynthesisCond if cond else FastganSynthesis | |
| self.synthesis = Synthesis(ngf=ngf, z_dim=z_dim, nc=img_channels, img_resolution=img_resolution, **synthesis_kwargs) | |
| def forward(self, z, c, **kwargs): | |
| w = self.mapping(z, c) | |
| img = self.synthesis(w, c) | |
| return img | |