深度学习系列44. Siren和Deep-Daze模型

Posted 2022-06-17 IE06

1. Siren

Siren就是用sin函数代替原先的激活函数（例如ReLU）

1.1 代码实现

首先定义一个sineLayer层，输出为$sin({\omega }_{0}f(x))$，其中f(x)为全连接层。

class SineLayer(nn.Module):
    def __init__(self, in_features, out_features):
        self.linear = nn.Linear(in_features, out_features)
        
    def forward(self, input):
        return torch.sin(self.omega_0 * self.linear(input))

然后基于此定义siren层

class Siren(nn.Module):
    def __init__(self, in_features, hidden_features, hidden_layers, out_features, outermost_linear=False):
        self.net = []
        # 第一层
        self.net.append(SineLayer(in_features, hidden_features))
        # 隐藏层
        for i in range(hidden_layers):
            self.net.append(SineLayer(hidden_features, hidden_features))
        if outermost_linear:
       		# 最后一层
            final_linear = nn.Linear(hidden_features, out_features)
            self.net.append(final_linear)
        else:
        	# 中间层
            self.net.append(SineLayer(hidden_features, out_features))
        
        self.net = nn.Sequential(*self.net)
    
    def forward(self, coords):
        coords = coords.clone().detach().requires_grad_(True) # allows to take derivative w.r.t. input
        output = self.net(coords)
        return output, coords  

1.2 测试

我们使用经典的摄影师图像，首先来进行图像拟合。使用像素点坐标作为输入，尝试拟合出图像：

cameraman = ImageFitting(256)
dataloader = DataLoader(cameraman, batch_size=1, pin_memory=True, num_workers=0)
img_siren = Siren(in_features=2, out_features=1, hidden_features=256, hidden_layers=3, outermost_linear=True)
total_steps = 500
optim = torch.optim.Adam(lr=1e-4)
for step in range(total_steps):
    model_output, coords = img_siren(model_input)    
    loss = ((model_output - ground_truth)**2).mean()
    optim.zero_grad()
    loss.backward()
    optim.step()

原始图、梯度图、拉普拉斯变换图分别如下：

2. 模型使用

安装pip install deep-daze

2.1 命令行模式

调用$ imagine "a house in the forest"，效果图如下：

如果内存足够，可以加上$ imagine "shattered plates on the ground" --deeper
也可以加深层数（默认是16）：$ imagine "stranger in strange lands" --num-layers 32
也可以从一张初始图开始：$ imagine 'a clear night sky filled with stars' --start_image_path ./cloudy-night-sky.jpg

查看Deep-Daze对于图片的理解：
$ imagine --img samples/hot-dog.jpg

或者：$ imagine "A psychedelic experience." --img samples/hot-dog.jpg

2.2 python api

from deep_daze import Imagine

imagine = Imagine(
    text = 'cosmic love and attention',
    # 下面的都是可选项
    num_layers = 24,
    save_every=4, 
    save_date_time=True,
    save_progress=True
)
imagine()

如果显存足够(>16G)/中等/很少(<4G)，可以如下配置：

    num_layers=42/24/256,
    batch_size=64/16/16,
    gradient_accumulate_every=1/2/16

2.3 代码简析

model = Siren(2, 256, 16, 3).cuda()
optimizer = torch.optim.Adam()
out = model(get_mgrid(sideX))
tx = clip.tokenize("a beautiful Waluigi")
t = perceptor.encode_text(tx.cuda())
for epochs in range(10000):
	# 对out做各种裁剪和缩放(64个)，然后拼接成into
	iii = perceptor.encode_image(into)
	loss = -100*torch.cosine_similarity(t, iii, dim=-1).mean()
	optimizer.zero_grad()
	loss.backward()
	optimizer.step()