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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
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