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e0c75d6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 | import torch
import torch.nn as nn
from torchvision import transforms
from timm import create_model
from model.config import load_config
from .model_embedder import HybridEmbed
config = load_config()
class Encoder(nn.Module):
def __init__(self, latent_dims=4):
super(Encoder, self).__init__()
self.features = nn.Sequential(
nn.Conv2d(3, 16, kernel_size=3, stride=2, padding=1),
nn.BatchNorm2d(num_features=16),
nn.LeakyReLU(),
nn.Conv2d(16, 32, kernel_size=3, stride=2, padding=1),
nn.BatchNorm2d(num_features=32),
nn.LeakyReLU(),
nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1),
nn.BatchNorm2d(num_features=64),
nn.LeakyReLU(),
nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1),
nn.BatchNorm2d(num_features=128),
nn.LeakyReLU()
)
self.latent_dims = latent_dims
self.fc1 = nn.Linear(128*14*14, 256)
self.fc2 = nn.Linear(256, 128)
self.mu = nn.Linear(128*14*14, self.latent_dims)
self.var = nn.Linear(128*14*14, self.latent_dims)
self.kl = 0
self.kl_weight = 0.5#0.00025
self.relu = nn.LeakyReLU()
def reparameterize(self, x):
# https://github.com/AntixK/PyTorch-VAE/blob/a6896b944c918dd7030e7d795a8c13e5c6345ec7/models/vanilla_vae.py
std = torch.exp(0.5*self.mu(x))
eps = torch.randn_like(std)
z = eps * std + self.mu(x)
return z, std
def forward(self, x):
x = self.features(x)
x = torch.flatten(x, start_dim=1)
mu = self.mu(x)
var = self.var(x)
z,_ = self.reparameterize(x)
self.kl = self.kl_weight*torch.mean(-0.5*torch.sum(1+var - mu**2 - var.exp(), dim=1), dim=0)
return z
class Decoder(nn.Module):
def __init__(self, latent_dims=4):
super(Decoder, self).__init__()
self.features = nn.Sequential(
nn.ConvTranspose2d(256, 64, kernel_size=2, stride=2),
nn.LeakyReLU(),
nn.ConvTranspose2d(64, 32, kernel_size=2, stride=2),
nn.LeakyReLU(),
nn.ConvTranspose2d(32, 16, kernel_size=2, stride=2),
nn.LeakyReLU(),
nn.ConvTranspose2d(16, 3, kernel_size=2, stride=2),
nn.LeakyReLU()
)
self.latent_dims = latent_dims
self.unflatten = nn.Unflatten(dim=1, unflattened_size=(256, 7, 7))
def forward(self, x):
x = self.unflatten(x)
x = self.features(x)
return x
class GenConViTVAE(nn.Module):
def __init__(self, config, pretrained=True):
super(GenConViTVAE, self).__init__()
self.latent_dims = config['model']['latent_dims']
self.encoder = Encoder(self.latent_dims)
self.decoder = Decoder(self.latent_dims)
self.embedder = create_model(config['model']['embedder'], pretrained=True)
self.convnext_backbone = create_model(config['model']['backbone'], pretrained=True, num_classes=1000, drop_path_rate=0, head_init_scale=1.0)
self.convnext_backbone.patch_embed = HybridEmbed(self.embedder, img_size=config['img_size'], embed_dim=768)
self.num_feature = self.convnext_backbone.head.fc.out_features * 2
self.fc = nn.Linear(self.num_feature, self.num_feature//4)
self.fc3 = nn.Linear(self.num_feature//2, self.num_feature//4)
self.fc2 = nn.Linear(self.num_feature//4, config['num_classes'])
self.relu = nn.ReLU()
self.resize = transforms.Resize((224,224), antialias=True)
def forward(self, x):
z = self.encoder(x)
x_hat = self.decoder(z)
x1 = self.convnext_backbone(x)
x2 = self.convnext_backbone(x_hat)
x = torch.cat((x1,x2), dim=1)
x = self.fc2(self.relu(self.fc(self.relu(x))))
return x, self.resize(x_hat) |