spatial distributions - Githubissues

Hi!

To visualize spatial distributions similar to those in Figure 7 of our paper, you can refer to the following example. This example provides a step-by-step guide on how to generate and visualize data, as well as how to train a simple diffusion model.

First, we generate a moon-like figure using the make_moons function from sklearn and visualize it using matplotlib:

import matplotlib.pyplot as plt
import numpy as np
from sklearn.datasets import make_moons
import torch
import torch.nn as nn
import io
from PIL import Image

moons_curve,_ = make_moons(10**4,noise=0.05)
print("shape of moons:",np.shape(moons_curve))

data = moons_curve.T

fig,ax = plt.subplots()
ax.scatter(*data,color='blue',edgecolor='white');
ax.axis('off')
dataset = torch.Tensor(moons_curve).float()

Next, we visualize the forward process:

num_steps = 100             # 100 steps
betas = torch.linspace(-6, 6, num_steps)
betas = torch.sigmoid(betas)*(0.5e-2 - 1e-5)+1e-5  

#calculate alpha、alpha_prod、alpha_prod_previous、alpha_bar_sqrt
alphas = 1-betas            
alphas_prod = torch.cumprod(alphas, 0)
alphas_prod_p = torch.cat([torch.tensor([1]).float(), alphas_prod[:-1]], 0) 
alphas_bar_sqrt = torch.sqrt(alphas_prod)
one_minus_alphas_bar_log = torch.log(1 - alphas_prod)
one_minus_alphas_bar_sqrt = torch.sqrt(1 - alphas_prod)

assert alphas.shape == alphas_prod.shape == alphas_prod_p.shape == alphas_bar_sqrt.shape == one_minus_alphas_bar_log.shape == one_minus_alphas_bar_sqrt.shape
print("all the same shape", betas.shape)    

def q_x(x_0,t):
    noise = torch.randn_like(x_0)
    alphas_t = alphas_bar_sqrt[t]
    alphas_1_m_t = one_minus_alphas_bar_sqrt[t]
    return (alphas_t * x_0 + alphas_1_m_t * noise) 

num_shows = 20
fig, axs = plt.subplots(2, 10, figsize=(28, 3))
plt.rc('text', color='black')

for i in range(num_shows):
    j = i // 10
    k = i % 10
    q_i = q_x(dataset, torch.tensor([i*num_steps//num_shows]))  # 生成t时刻的采样数据
    axs[j, k].scatter(q_i[:, 0], q_i[:, 1], color='red', edgecolor='white')
    axs[j, k].set_axis_off()
    axs[j, k].set_title('$q(\mathbf{x}_{'+str(i*num_steps//num_shows)+'})$')
fig.show()

To visualize the denoising process, we need to train a toy diffusion model:

class MLPDiffusion(nn.Module):                  # define a simple MLP model
    def __init__(self, n_steps, num_units=128):
        super(MLPDiffusion, self).__init__()

        self.linears = nn.ModuleList(
            [
                nn.Linear(2, num_units),
                nn.ReLU(),
                nn.Linear(num_units, num_units),
                nn.ReLU(),
                nn.Linear(num_units, num_units),
                nn.ReLU(),
                nn.Linear(num_units, 2),
            ]
        )
        self.step_embeddings = nn.ModuleList(
            [
                nn.Embedding(n_steps, num_units),
                nn.Embedding(n_steps, num_units),
                nn.Embedding(n_steps, num_units),
            ]
        )

    def forward(self, x, t):
        # x = x_0
        for idx, embedding_layer in enumerate(self.step_embeddings):
            t_embedding = embedding_layer(t)
            x = self.linears[2 * idx](x)
            x += t_embedding
            x = self.linears[2 * idx + 1](x)

        x = self.linears[-1](x)

        return x

def diffusion_loss_fn(model, x_0, alphas_bar_sqrt, one_minus_alphas_bar_sqrt, n_steps):
    batch_size = x_0.shape[0]

    t = torch.randint(0, n_steps, size=(batch_size // 2,))  
    t = torch.cat([t, n_steps - 1 - t], dim=0)                   
    t = t.unsqueeze(-1)

    a = alphas_bar_sqrt[t]

    aml = one_minus_alphas_bar_sqrt[t]

    e = torch.randn_like(x_0)

    x = x_0 * a + e * aml

    output = model(x, t.squeeze(-1))

    return (e - output).square().mean()

def p_sample_loop(model, shape, n_steps, betas, one_minus_alphas_bar_sqrt):
    cur_x = torch.randn(shape)
    x_seq = [cur_x]
    for i in reversed(range(n_steps)):
        cur_x = p_sample(model, cur_x, i, betas, one_minus_alphas_bar_sqrt)
        x_seq.append(cur_x)
    return x_seq

def p_sample(model, x, t, betas, one_minus_alphas_bar_sqrt):
    t = torch.tensor([t])

    coeff = betas[t] / one_minus_alphas_bar_sqrt[t]

    eps_theta = model(x, t)

    mean = (1 / (1 - betas[t]).sqrt()) * (x - (coeff * eps_theta))

    z = torch.randn_like(x)
    sigma_t = betas[t].sqrt()

    sample = mean + sigma_t * z

    return (sample)

seed = 1234
print('Training model...')
batch_size = 128
dataloader = torch.utils.data.DataLoader(dataset,batch_size=batch_size,shuffle=True)
num_epoch = 4001
plt.rc('text',color='blue')

model = MLPDiffusion(num_steps)
optimizer = torch.optim.Adam(model.parameters(),lr=1e-3)

for t in range(num_epoch):
    for idx,batch_x in enumerate(dataloader):
        loss = diffusion_loss_fn(model, batch_x, alphas_bar_sqrt, one_minus_alphas_bar_sqrt, num_steps)
        optimizer.zero_grad()
        loss.backward()
        torch.nn.utils.clip_grad_norm_(model.parameters(), 1.)
        optimizer.step()

    if(t%100==0):           
        print(loss)
        x_seq = p_sample_loop(model, dataset.shape, num_steps, betas, one_minus_alphas_bar_sqrt)

        fig,axs = plt.subplots(1,10,figsize=(28,3))
        for i in range(1,11):
            cur_x = x_seq[i*10].detach()
            axs[i-1].scatter(cur_x[:,0], cur_x[:,1], color='red', edgecolor='white');
            axs[i-1].set_axis_off();
            axs[i-1].set_title('$q(\mathbf{x}_{'+str(i*10)+'})$')
        fig.show()

The above code is adapted from toy Diffusion.

tsinghua-fib-lab / Spatio-temporal-Diffusion-Point-Processes

spatial distributions #6