Safran/PVD
1
0
Fork 0
Code Issues Pull requests Packages Projects Releases Wiki Activity
PVD/test_generation.py
Laurent FAINSIN 4beb963aff ✨ print number of parameters of network
2023-05-22 09:33:10 +02:00

646 lines
23 KiB
Python
Raw Permalink Blame History

import argparse
from pprint import pprint
import datasets
import torch
import torch.nn as nn
import torch.utils.data
from torch.distributions import Normal
from tqdm import tqdm
from dataset.rotor37_data import MEAN, STD
# from dataset.shapenet_data_pc import ShapeNet15kPointClouds
from metrics.evaluation_metrics import compute_all_metrics
from metrics.evaluation_metrics import jsd_between_point_cloud_sets as JSD
from model.pvcnn_generation import PVCNN2Base
from utils.file_utils import *
from utils.visualize import *
"""
models
"""
def normal_kl(mean1, logvar1, mean2, logvar2):
"""
KL divergence between normal distributions parameterized by mean and log-variance.
"""
return 0.5 * (-1.0 + logvar2 - logvar1 + torch.exp(logvar1 - logvar2) + (mean1 - mean2) ** 2 * torch.exp(-logvar2))
def discretized_gaussian_log_likelihood(x, *, means, log_scales):
# Assumes data is integers [0, 1]
assert x.shape == means.shape == log_scales.shape
px0 = Normal(torch.zeros_like(means), torch.ones_like(log_scales))
centered_x = x - means
inv_stdv = torch.exp(-log_scales)
plus_in = inv_stdv * (centered_x + 0.5)
cdf_plus = px0.cdf(plus_in)
min_in = inv_stdv * (centered_x - 0.5)
cdf_min = px0.cdf(min_in)
log_cdf_plus = torch.log(torch.max(cdf_plus, torch.ones_like(cdf_plus) * 1e-12))
log_one_minus_cdf_min = torch.log(torch.max(1.0 - cdf_min, torch.ones_like(cdf_min) * 1e-12))
cdf_delta = cdf_plus - cdf_min
log_probs = torch.where(
x < 0.001,
log_cdf_plus,
torch.where(
x > 0.999, log_one_minus_cdf_min, torch.log(torch.max(cdf_delta, torch.ones_like(cdf_delta) * 1e-12))
),
)
assert log_probs.shape == x.shape
return log_probs
class GaussianDiffusion:
def __init__(self, betas, loss_type, model_mean_type, model_var_type):
self.loss_type = loss_type
self.model_mean_type = model_mean_type
self.model_var_type = model_var_type
assert isinstance(betas, np.ndarray)
self.np_betas = betas = betas.astype(np.float64) # computations here in float64 for accuracy
assert (betas > 0).all() and (betas <= 1).all()
(timesteps,) = betas.shape
self.num_timesteps = int(timesteps)
# initialize twice the actual length so we can keep running for eval
# betas = np.concatenate([betas, np.full_like(betas[:int(0.2*len(betas))], betas[-1])])
alphas = 1.0 - betas
alphas_cumprod = torch.from_numpy(np.cumprod(alphas, axis=0)).float()
alphas_cumprod_prev = torch.from_numpy(np.append(1.0, alphas_cumprod[:-1])).float()
self.betas = torch.from_numpy(betas).float()
self.alphas_cumprod = alphas_cumprod.float()
self.alphas_cumprod_prev = alphas_cumprod_prev.float()
# calculations for diffusion q(x_t | x_{t-1}) and others
self.sqrt_alphas_cumprod = torch.sqrt(alphas_cumprod).float()
self.sqrt_one_minus_alphas_cumprod = torch.sqrt(1.0 - alphas_cumprod).float()
self.log_one_minus_alphas_cumprod = torch.log(1.0 - alphas_cumprod).float()
self.sqrt_recip_alphas_cumprod = torch.sqrt(1.0 / alphas_cumprod).float()
self.sqrt_recipm1_alphas_cumprod = torch.sqrt(1.0 / alphas_cumprod - 1).float()
betas = torch.from_numpy(betas).float()
alphas = torch.from_numpy(alphas).float()
# calculations for posterior q(x_{t-1} | x_t, x_0)
posterior_variance = betas * (1.0 - alphas_cumprod_prev) / (1.0 - alphas_cumprod)
# above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
self.posterior_variance = posterior_variance
# below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
self.posterior_log_variance_clipped = torch.log(
torch.max(posterior_variance, 1e-20 * torch.ones_like(posterior_variance))
)
self.posterior_mean_coef1 = betas * torch.sqrt(alphas_cumprod_prev) / (1.0 - alphas_cumprod)
self.posterior_mean_coef2 = (1.0 - alphas_cumprod_prev) * torch.sqrt(alphas) / (1.0 - alphas_cumprod)
@staticmethod
def _extract(a, t, x_shape):
"""
Extract some coefficients at specified timesteps,
then reshape to [batch_size, 1, 1, 1, 1, ...] for broadcasting purposes.
"""
(bs,) = t.shape
assert x_shape[0] == bs
out = torch.gather(a, 0, t)
assert out.shape == torch.Size([bs])
return torch.reshape(out, [bs] + ((len(x_shape) - 1) * [1]))
def q_mean_variance(self, x_start, t):
mean = self._extract(self.sqrt_alphas_cumprod.to(x_start.device), t, x_start.shape) * x_start
variance = self._extract(1.0 - self.alphas_cumprod.to(x_start.device), t, x_start.shape)
log_variance = self._extract(self.log_one_minus_alphas_cumprod.to(x_start.device), t, x_start.shape)
return mean, variance, log_variance
def q_sample(self, x_start, t, noise=None):
"""
Diffuse the data (t == 0 means diffused for 1 step)
"""
if noise is None:
noise = torch.randn(x_start.shape, device=x_start.device)
assert noise.shape == x_start.shape
return (
self._extract(self.sqrt_alphas_cumprod.to(x_start.device), t, x_start.shape) * x_start
+ self._extract(self.sqrt_one_minus_alphas_cumprod.to(x_start.device), t, x_start.shape) * noise
)
def q_posterior_mean_variance(self, x_start, x_t, t):
"""
Compute the mean and variance of the diffusion posterior q(x_{t-1} | x_t, x_0)
"""
assert x_start.shape == x_t.shape
posterior_mean = (
self._extract(self.posterior_mean_coef1.to(x_start.device), t, x_t.shape) * x_start
+ self._extract(self.posterior_mean_coef2.to(x_start.device), t, x_t.shape) * x_t
)
posterior_variance = self._extract(self.posterior_variance.to(x_start.device), t, x_t.shape)
posterior_log_variance_clipped = self._extract(
self.posterior_log_variance_clipped.to(x_start.device), t, x_t.shape
)
assert (
posterior_mean.shape[0]
== posterior_variance.shape[0]
== posterior_log_variance_clipped.shape[0]
== x_start.shape[0]
)
return posterior_mean, posterior_variance, posterior_log_variance_clipped
def p_mean_variance(self, denoise_fn, data, t, clip_denoised: bool, return_pred_xstart: bool):
model_output = denoise_fn(data, t)
if self.model_var_type in ["fixedsmall", "fixedlarge"]:
# below: only log_variance is used in the KL computations
model_variance, model_log_variance = {
# for fixedlarge, we set the initial (log-)variance like so to get a better decoder log likelihood
"fixedlarge": (
self.betas.to(data.device),
torch.log(torch.cat([self.posterior_variance[1:2], self.betas[1:]])).to(data.device),
),
"fixedsmall": (
self.posterior_variance.to(data.device),
self.posterior_log_variance_clipped.to(data.device),
),
}[self.model_var_type]
model_variance = self._extract(model_variance, t, data.shape) * torch.ones_like(data)
model_log_variance = self._extract(model_log_variance, t, data.shape) * torch.ones_like(data)
else:
raise NotImplementedError(self.model_var_type)
if self.model_mean_type == "eps":
x_recon = self._predict_xstart_from_eps(data, t=t, eps=model_output)
if clip_denoised:
x_recon = torch.clamp(x_recon, -0.5, 0.5)
model_mean, _, _ = self.q_posterior_mean_variance(x_start=x_recon, x_t=data, t=t)
else:
raise NotImplementedError(self.loss_type)
assert model_mean.shape == x_recon.shape == data.shape
assert model_variance.shape == model_log_variance.shape == data.shape
if return_pred_xstart:
return model_mean, model_variance, model_log_variance, x_recon
else:
return model_mean, model_variance, model_log_variance
def _predict_xstart_from_eps(self, x_t, t, eps):
assert x_t.shape == eps.shape
return (
self._extract(self.sqrt_recip_alphas_cumprod.to(x_t.device), t, x_t.shape) * x_t
- self._extract(self.sqrt_recipm1_alphas_cumprod.to(x_t.device), t, x_t.shape) * eps
)
""" samples """
def p_sample(self, denoise_fn, data, t, noise_fn, clip_denoised=False, return_pred_xstart=False, use_var=True):
"""
Sample from the model
"""
model_mean, _, model_log_variance, pred_xstart = self.p_mean_variance(
denoise_fn, data=data, t=t, clip_denoised=clip_denoised, return_pred_xstart=True
)
noise = noise_fn(size=data.shape, dtype=data.dtype, device=data.device)
assert noise.shape == data.shape
# no noise when t == 0
nonzero_mask = torch.reshape(1 - (t == 0).float(), [data.shape[0]] + [1] * (len(data.shape) - 1))
sample = model_mean
if use_var:
sample = sample + nonzero_mask * torch.exp(0.5 * model_log_variance) * noise
assert sample.shape == pred_xstart.shape
return (sample, pred_xstart) if return_pred_xstart else sample
def p_sample_loop(
self,
denoise_fn,
shape,
device,
noise_fn=torch.randn,
constrain_fn=lambda x, t: x,
clip_denoised=True,
max_timestep=None,
keep_running=False,
):
"""
Generate samples
keep_running: True if we run 2 x num_timesteps, False if we just run num_timesteps
"""
if max_timestep is None:
final_time = self.num_timesteps
else:
final_time = max_timestep
assert isinstance(shape, (tuple, list))
img_t = noise_fn(size=shape, dtype=torch.float, device=device)
for t in reversed(range(0, final_time if not keep_running else len(self.betas))):
img_t = constrain_fn(img_t, t)
t_ = torch.empty(shape[0], dtype=torch.int64, device=device).fill_(t)
img_t = self.p_sample(
denoise_fn=denoise_fn,
data=img_t,
t=t_,
noise_fn=noise_fn,
clip_denoised=clip_denoised,
return_pred_xstart=False,
).detach()
assert img_t.shape == shape
return img_t
def reconstruct(self, x0, t, denoise_fn, noise_fn=torch.randn, constrain_fn=lambda x, t: x):
assert t >= 1
t_vec = torch.empty(x0.shape[0], dtype=torch.int64, device=x0.device).fill_(t - 1)
encoding = self.q_sample(x0, t_vec)
img_t = encoding
for k in reversed(range(0, t)):
img_t = constrain_fn(img_t, k)
t_ = torch.empty(x0.shape[0], dtype=torch.int64, device=x0.device).fill_(k)
img_t = self.p_sample(
denoise_fn=denoise_fn,
data=img_t,
t=t_,
noise_fn=noise_fn,
clip_denoised=False,
return_pred_xstart=False,
use_var=True,
).detach()
return img_t
class PVCNN2(PVCNN2Base):
sa_blocks = [
((32, 2, 32), (1024, 0.1, 32, (32, 64))),
((64, 3, 16), (256, 0.2, 32, (64, 128))),
((128, 3, 8), (64, 0.4, 32, (128, 256))),
(None, (16, 0.8, 32, (256, 256, 512))),
]
fp_blocks = [
((256, 256), (256, 3, 8)),
((256, 256), (256, 3, 8)),
((256, 128), (128, 2, 16)),
((128, 128, 64), (64, 2, 32)),
]
def __init__(
self,
num_classes,
embed_dim,
use_att,
dropout,
extra_feature_channels=3,
width_multiplier=1,
voxel_resolution_multiplier=1,
):
super().__init__(
num_classes=num_classes,
embed_dim=embed_dim,
use_att=use_att,
dropout=dropout,
extra_feature_channels=extra_feature_channels,
width_multiplier=width_multiplier,
voxel_resolution_multiplier=voxel_resolution_multiplier,
)
class Model(nn.Module):
def __init__(self, args, betas, loss_type: str, model_mean_type: str, model_var_type: str):
super(Model, self).__init__()
self.diffusion = GaussianDiffusion(betas, loss_type, model_mean_type, model_var_type)
self.model = PVCNN2(
num_classes=args.nc,
embed_dim=args.embed_dim,
use_att=args.attention,
dropout=args.dropout,
extra_feature_channels=0,
)
pytorch_total_params = sum(p.numel() for p in self.model.parameters() if p.requires_grad)
print("Total params: {}".format(pytorch_total_params))
def prior_kl(self, x0):
return self.diffusion._prior_bpd(x0)
def all_kl(self, x0, clip_denoised=True):
total_bpd_b, vals_bt, prior_bpd_b, mse_bt = self.diffusion.calc_bpd_loop(self._denoise, x0, clip_denoised)
return {"total_bpd_b": total_bpd_b, "terms_bpd": vals_bt, "prior_bpd_b": prior_bpd_b, "mse_bt": mse_bt}
def _denoise(self, data, t):
B, D, N = data.shape
assert data.dtype == torch.float
assert t.shape == torch.Size([B]) and t.dtype == torch.int64
out = self.model(data, t)
assert out.shape == torch.Size([B, D, N])
return out
def get_loss_iter(self, data, noises=None):
B, D, N = data.shape
t = torch.randint(0, self.diffusion.num_timesteps, size=(B,), device=data.device)
if noises is not None:
noises[t != 0] = torch.randn((t != 0).sum(), *noises.shape[1:]).to(noises)
losses = self.diffusion.p_losses(denoise_fn=self._denoise, data_start=data, t=t, noise=noises)
assert losses.shape == t.shape == torch.Size([B])
return losses
def gen_samples(
self,
shape,
device,
noise_fn=torch.randn,
constrain_fn=lambda x, t: x,
clip_denoised=False,
max_timestep=None,
keep_running=False,
):
return self.diffusion.p_sample_loop(
self._denoise,
shape=shape,
device=device,
noise_fn=noise_fn,
constrain_fn=constrain_fn,
clip_denoised=clip_denoised,
max_timestep=max_timestep,
keep_running=keep_running,
)
def reconstruct(self, x0, t, constrain_fn=lambda x, t: x):
return self.diffusion.reconstruct(x0, t, self._denoise, constrain_fn=constrain_fn)
def train(self):
self.model.train()
def eval(self):
self.model.eval()
def multi_gpu_wrapper(self, f):
self.model = f(self.model)
def get_betas(schedule_type, b_start, b_end, time_num):
if schedule_type == "linear":
betas = np.linspace(b_start, b_end, time_num)
elif schedule_type == "warm0.1":
betas = b_end * np.ones(time_num, dtype=np.float64)
warmup_time = int(time_num * 0.1)
betas[:warmup_time] = np.linspace(b_start, b_end, warmup_time, dtype=np.float64)
elif schedule_type == "warm0.2":
betas = b_end * np.ones(time_num, dtype=np.float64)
warmup_time = int(time_num * 0.2)
betas[:warmup_time] = np.linspace(b_start, b_end, warmup_time, dtype=np.float64)
elif schedule_type == "warm0.5":
betas = b_end * np.ones(time_num, dtype=np.float64)
warmup_time = int(time_num * 0.5)
betas[:warmup_time] = np.linspace(b_start, b_end, warmup_time, dtype=np.float64)
else:
raise NotImplementedError(schedule_type)
return betas
def get_constrain_function(ground_truth, mask, eps, num_steps=1):
"""
:param target_shape_constraint: target voxels
:return: constrained x
"""
# eps_all = list(reversed(np.linspace(0,np.float_power(eps, 1/2), 500)**2))
eps_all = list(reversed(np.linspace(0, np.sqrt(eps), 1000) ** 2))
def constrain_fn(x, t):
eps_ = eps_all[t] if (t < 1000) else 0
for _ in range(num_steps):
x = x - eps_ * ((x - ground_truth) * mask)
return x
return constrain_fn
#############################################################################
def get_dataset(dataroot, npoints, category, use_mask=False):
# tr_dataset = ShapeNet15kPointClouds(
# root_dir=dataroot,
# categories=[category],
# split="train",
# tr_sample_size=npoints,
# te_sample_size=npoints,
# scale=1.0,
# normalize_per_shape=False,
# normalize_std_per_axis=False,
# random_subsample=True,
# use_mask=use_mask,
# )
# te_dataset = ShapeNet15kPointClouds(
# root_dir=dataroot,
# categories=[category],
# split="val",
# tr_sample_size=npoints,
# te_sample_size=npoints,
# scale=1.0,
# normalize_per_shape=False,
# normalize_std_per_axis=False,
# all_points_mean=tr_dataset.all_points_mean,
# all_points_std=tr_dataset.all_points_std,
# use_mask=use_mask,
# )
train_ds = datasets.load_dataset("dataset/rotor37_data.py", split="train")
train_ds = train_ds.with_format("torch")
test_ds = datasets.load_dataset("dataset/rotor37_data.py", split="test")
test_ds = test_ds.with_format("torch")
return train_ds, test_ds
def evaluate_gen(opt, ref_pcs, logger):
if ref_pcs is None:
_, test_dataset = get_dataset(opt.dataroot, opt.npoints, opt.category, use_mask=False)
test_dataloader = torch.utils.data.DataLoader(
test_dataset, batch_size=opt.batch_size, shuffle=False, num_workers=int(opt.workers), drop_last=False
)
ref = []
for data in tqdm(test_dataloader, total=len(test_dataloader), desc="Generating Samples"):
x = data["positions"]
# m, s = data["mean"].float(), data["std"].float()
# ref.append(x * s + m)
ref.append(x)
ref_pcs = torch.cat(ref, dim=0).contiguous()
logger.info("Loading sample path: %s" % (opt.eval_path))
sample_pcs = torch.load(opt.eval_path).contiguous()
logger.info("Generation sample size:%s reference size: %s" % (sample_pcs.size(), ref_pcs.size()))
# Compute metrics
results = compute_all_metrics(sample_pcs, ref_pcs, opt.batch_size)
results = {k: (v.cpu().detach().item() if not isinstance(v, float) else v) for k, v in results.items()}
pprint(results)
logger.info(results)
jsd = JSD(sample_pcs.numpy(), ref_pcs.numpy())
pprint("JSD: {}".format(jsd))
logger.info("JSD: {}".format(jsd))
def generate(model, opt):
_, test_dataset = get_dataset(opt.dataroot, opt.npoints, opt.category)
test_dataloader = torch.utils.data.DataLoader(
test_dataset, batch_size=opt.batch_size, shuffle=False, num_workers=int(opt.workers), drop_last=False
)
with torch.no_grad():
samples = []
ref = []
for i, data in tqdm(enumerate(test_dataloader), total=len(test_dataloader), desc="Generating Samples"):
x = data["positions"].transpose(1, 2)
# m, s = data["mean"].float(), data["std"].float()
shape = torch.Size((*x.shape[:-1], 75000))
gen = model.gen_samples(shape, "cuda", clip_denoised=False).detach().cpu()
gen = gen.transpose(1, 2).contiguous()
x = x.transpose(1, 2).contiguous()
# gen = gen * s + m
# x = x * s + m
samples.append(gen)
ref.append(x)
# save pointcloud to txt for paraview viz
for idx, blade in enumerate(gen):
pc = blade
# unnormalize
pc = pc * STD + MEAN
print(f"Saving point cloud {idx}...")
np.savetxt(f"output/gen_{i}_{idx}.txt", pc)
if idx >= 10:
break
samples = torch.cat(samples, dim=0)
ref = torch.cat(ref, dim=0)
torch.save(samples, opt.eval_path)
return ref
def main(opt):
if opt.category == "airplane":
opt.beta_start = 1e-5
opt.beta_end = 0.008
opt.schedule_type = "warm0.1"
exp_id = os.path.splitext(os.path.basename(__file__))[0]
dir_id = os.path.dirname(__file__)
output_dir = get_output_dir(dir_id, exp_id)
copy_source(__file__, output_dir)
logger = setup_logging(output_dir)
(outf_syn,) = setup_output_subdirs(output_dir, "syn")
betas = get_betas(opt.schedule_type, opt.beta_start, opt.beta_end, opt.time_num)
model = Model(opt, betas, opt.loss_type, opt.model_mean_type, opt.model_var_type)
if opt.cuda:
model.cuda()
def _transform_(m):
return nn.parallel.DataParallel(m)
model = model.cuda()
model.multi_gpu_wrapper(_transform_)
model.eval()
with torch.no_grad():
logger.info("Resume Path:%s" % opt.model)
resumed_param = torch.load(opt.model)
model.load_state_dict(resumed_param["model_state"])
ref = None
if opt.generate:
opt.eval_path = os.path.join(outf_syn, "samples.pth")
Path(opt.eval_path).parent.mkdir(parents=True, exist_ok=True)
ref = generate(model, opt)
if opt.eval_gen:
# Evaluate generation
evaluate_gen(opt, ref, logger)
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--dataroot", default="ShapeNetCore.v2.PC15k/")
parser.add_argument("--category", default="chair")
parser.add_argument("--batch_size", type=int, default=50, help="input batch size")
parser.add_argument("--workers", type=int, default=16, help="workers")
parser.add_argument("--niter", type=int, default=10000, help="number of epochs to train for")
parser.add_argument("--generate", default=True)
parser.add_argument("--eval_gen", default=True)
parser.add_argument("--nc", default=3)
parser.add_argument("--npoints", default=2048)
"""model"""
parser.add_argument("--beta_start", default=0.0001)
parser.add_argument("--beta_end", default=0.02)
parser.add_argument("--schedule_type", default="linear")
parser.add_argument("--time_num", default=1000)
# params
parser.add_argument("--attention", default=True)
parser.add_argument("--dropout", default=0.1)
parser.add_argument("--embed_dim", type=int, default=64)
parser.add_argument("--loss_type", default="mse")
parser.add_argument("--model_mean_type", default="eps")
parser.add_argument("--model_var_type", default="fixedsmall")
parser.add_argument("--model", default="", required=True, help="path to model (to continue training)")
"""eval"""
parser.add_argument("--eval_path", default="")
parser.add_argument("--manualSeed", default=42, type=int, help="random seed")
parser.add_argument("--gpu", type=int, default=0, metavar="S", help="gpu id (default: 0)")
opt = parser.parse_args()
if torch.cuda.is_available():
opt.cuda = True
else:
opt.cuda = False
return opt
if __name__ == "__main__":
opt = parse_args()
set_seed(opt)
main(opt)
Reference in a new issue View git blame Copy permalink