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import torch.nn.functional as F
from torch.nn import Module, Parameter, ModuleList
import numpy as np
import torch.nn as nn
from .common import *
import pdb
class VarianceSchedule(Module):
def __init__(self, num_steps, mode='linear',beta_1=1e-4, beta_T=5e-2,cosine_s=8e-3):
super().__init__()
assert mode in ('linear', 'cosine')
self.num_steps = num_steps
self.beta_1 = beta_1
self.beta_T = beta_T
self.mode = mode
if mode == 'linear':
betas = torch.linspace(beta_1, beta_T, steps=num_steps)
elif mode == 'cosine':
timesteps = (
torch.arange(num_steps + 1) / num_steps + cosine_s
)
alphas = timesteps / (1 + cosine_s) * math.pi / 2
alphas = torch.cos(alphas).pow(2)
alphas = alphas / alphas[0]
betas = 1 - alphas[1:] / alphas[:-1]
betas = betas.clamp(max=0.999)
betas = torch.cat([torch.zeros([1]), betas], dim=0) # Padding
alphas = 1 - betas
log_alphas = torch.log(alphas)
for i in range(1, log_alphas.size(0)): # 1 to T
log_alphas[i] += log_alphas[i - 1]
alpha_bars = log_alphas.exp()
sigmas_flex = torch.sqrt(betas)
sigmas_inflex = torch.zeros_like(sigmas_flex)
for i in range(1, sigmas_flex.size(0)):
sigmas_inflex[i] = ((1 - alpha_bars[i-1]) / (1 - alpha_bars[i])) * betas[i]
sigmas_inflex = torch.sqrt(sigmas_inflex)
self.register_buffer('betas', betas)
self.register_buffer('alphas', alphas)
self.register_buffer('alpha_bars', alpha_bars)
self.register_buffer('sigmas_flex', sigmas_flex)
self.register_buffer('sigmas_inflex', sigmas_inflex)
def uniform_sample_t(self, batch_size):
ts = np.random.choice(np.arange(1, self.num_steps+1), batch_size)
return ts.tolist()
def get_sigmas(self, t, flexibility):
assert 0 <= flexibility and flexibility <= 1
sigmas = self.sigmas_flex[t] * flexibility + self.sigmas_inflex[t] * (1 - flexibility)
return sigmas
class DiffusionTraj(Module):
def __init__(self, net, var_sched:VarianceSchedule):
super().__init__()
self.net = net
self.var_sched = var_sched
def get_loss(self, x_0, context, t=None):
batch_size, _, point_dim = x_0.size()
if t == None:
t = self.var_sched.uniform_sample_t(batch_size)
alpha_bar = self.var_sched.alpha_bars[t]
beta = self.var_sched.betas[t].cuda()
c0 = torch.sqrt(alpha_bar).view(-1, 1, 1).cuda() # (B, 1, 1)
c1 = torch.sqrt(1 - alpha_bar).view(-1, 1, 1).cuda() # (B, 1, 1)
e_rand = torch.randn_like(x_0).cuda() # (B, N, d)
e_theta = self.net(c0 * x_0 + c1 * e_rand, beta=beta, context=context)
loss = F.mse_loss(e_theta.view(-1, point_dim), e_rand.view(-1, point_dim), reduction='mean')
return loss
def sample(self, num_points, context, sample, bestof, point_dim=2, flexibility=0.0, ret_traj=False, sampling="ddpm", step=100):
traj_list = []
for i in range(sample):
batch_size = context.size(0)
if bestof:
x_T = torch.randn([batch_size, num_points, point_dim]).to(context.device)
else:
x_T = torch.zeros([batch_size, num_points, point_dim]).to(context.device)
traj = {self.var_sched.num_steps: x_T}
# stride = step
stride = int(100/step)
for t in range(self.var_sched.num_steps, 0, -stride):
z = torch.randn_like(x_T) if t > 1 else torch.zeros_like(x_T)
alpha = self.var_sched.alphas[t]
alpha_bar = self.var_sched.alpha_bars[t]
alpha_bar_next = self.var_sched.alpha_bars[t-stride]
#pdb.set_trace()
sigma = self.var_sched.get_sigmas(t, flexibility)
c0 = 1.0 / torch.sqrt(alpha)
c1 = (1 - alpha) / torch.sqrt(1 - alpha_bar)
x_t = traj[t]
beta = self.var_sched.betas[[t]*batch_size]
e_theta = self.net(x_t, beta=beta, context=context)
if sampling == "ddpm":
x_next = c0 * (x_t - c1 * e_theta) + sigma * z
elif sampling == "ddim":
x0_t = (x_t - e_theta * (1 - alpha_bar).sqrt()) / alpha_bar.sqrt()
x_next = alpha_bar_next.sqrt() * x0_t + (1 - alpha_bar_next).sqrt() * e_theta
else:
pdb.set_trace()
traj[t-stride] = x_next.detach() # Stop gradient and save trajectory.
traj[t] = traj[t].cpu() # Move previous output to CPU memory.
if not ret_traj:
del traj[t]
if ret_traj:
traj_list.append(traj)
else:
traj_list.append(traj[0])
return torch.stack(traj_list)
class TrajNet(Module):
def __init__(self, point_dim, context_dim, residual):
super().__init__()
self.act = F.leaky_relu
self.residual = residual
self.layers = ModuleList([
ConcatSquashLinear(2, 128, context_dim+3),
ConcatSquashLinear(128, 256, context_dim+3),
ConcatSquashLinear(256, 512, context_dim+3),
ConcatSquashLinear(512, 256, context_dim+3),
ConcatSquashLinear(256, 128, context_dim+3),
ConcatSquashLinear(128, 2, context_dim+3),
])
def forward(self, x, beta, context):
"""
Args:
x: Point clouds at some timestep t, (B, N, d).
beta: Time. (B, ).
context: Shape latents. (B, F).
"""
batch_size = x.size(0)
beta = beta.view(batch_size, 1, 1) # (B, 1, 1)
context = context.view(batch_size, 1, -1) # (B, 1, F)
time_emb = torch.cat([beta, torch.sin(beta), torch.cos(beta)], dim=-1) # (B, 1, 3)
ctx_emb = torch.cat([time_emb, context], dim=-1) # (B, 1, F+3)
out = x
#pdb.set_trace()
for i, layer in enumerate(self.layers):
out = layer(ctx=ctx_emb, x=out)
if i < len(self.layers) - 1:
out = self.act(out)
if self.residual:
return x + out
else:
return out
class TransformerConcatLinear(Module):
def __init__(self, point_dim, context_dim, tf_layer, residual):
super().__init__()
self.residual = residual
self.pos_emb = PositionalEncoding(d_model=2*context_dim, dropout=0.1, max_len=24)
self.concat1 = ConcatSquashLinear(2,2*context_dim,context_dim+3)
self.layer = nn.TransformerEncoderLayer(d_model=2*context_dim, nhead=4, dim_feedforward=4*context_dim)
self.transformer_encoder = nn.TransformerEncoder(self.layer, num_layers=tf_layer)
self.concat3 = ConcatSquashLinear(2*context_dim,context_dim,context_dim+3)
self.concat4 = ConcatSquashLinear(context_dim,context_dim//2,context_dim+3)
self.linear = ConcatSquashLinear(context_dim//2, 2, context_dim+3)
#self.linear = nn.Linear(128,2)
def forward(self, x, beta, context):
batch_size = x.size(0)
beta = beta.view(batch_size, 1, 1) # (B, 1, 1)
context = context.view(batch_size, 1, -1) # (B, 1, F)
time_emb = torch.cat([beta, torch.sin(beta), torch.cos(beta)], dim=-1) # (B, 1, 3)
ctx_emb = torch.cat([time_emb, context], dim=-1) # (B, 1, F+3)
x = self.concat1(ctx_emb,x)
final_emb = x.permute(1,0,2)
final_emb = self.pos_emb(final_emb)
trans = self.transformer_encoder(final_emb).permute(1,0,2)
trans = self.concat3(ctx_emb, trans)
trans = self.concat4(ctx_emb, trans)
return self.linear(ctx_emb, trans)
class TransformerLinear(Module):
def __init__(self, point_dim, context_dim, residual):
super().__init__()
self.residual = residual
self.pos_emb = PositionalEncoding(d_model=128, dropout=0.1, max_len=24)
self.y_up = nn.Linear(2, 128)
self.ctx_up = nn.Linear(context_dim+3, 128)
self.layer = nn.TransformerEncoderLayer(d_model=128, nhead=2, dim_feedforward=512)
self.transformer_encoder = nn.TransformerEncoder(self.layer, num_layers=3)
self.linear = nn.Linear(128, point_dim)
def forward(self, x, beta, context):
batch_size = x.size(0)
beta = beta.view(batch_size, 1, 1) # (B, 1, 1)
context = context.view(batch_size, 1, -1) # (B, 1, F)
time_emb = torch.cat([beta, torch.sin(beta), torch.cos(beta)], dim=-1) # (B, 1, 3)
ctx_emb = torch.cat([time_emb, context], dim=-1) # (B, 1, F+3)
ctx_emb = self.ctx_up(ctx_emb)
emb = self.y_up(x)
final_emb = torch.cat([ctx_emb, emb], dim=1).permute(1,0,2)
#pdb.set_trace()
final_emb = self.pos_emb(final_emb)
trans = self.transformer_encoder(final_emb) # 13 * b * 128
trans = trans[1:].permute(1,0,2) # B * 12 * 128, drop the first one which is the z
return self.linear(trans)
class LinearDecoder(Module):
def __init__(self):
super().__init__()
self.act = F.leaky_relu
self.layers = ModuleList([
#nn.Linear(2, 64),
nn.Linear(32, 64),
nn.Linear(64, 128),
nn.Linear(128, 256),
nn.Linear(256, 512),
nn.Linear(512, 256),
nn.Linear(256, 128),
nn.Linear(128, 12)
#nn.Linear(2, 64),
#nn.Linear(2, 64),
])
def forward(self, code):
out = code
for i, layer in enumerate(self.layers):
out = layer(out)
if i < len(self.layers) - 1:
out = self.act(out)
return out
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