589 lines
22 KiB
Python
589 lines
22 KiB
Python
import torch
|
|
from pprint import pprint
|
|
from metrics.evaluation_metrics import jsd_between_point_cloud_sets as JSD
|
|
from metrics.evaluation_metrics import compute_all_metrics, EMD_CD
|
|
|
|
import torch.nn as nn
|
|
import torch.optim as optim
|
|
import torch.utils.data
|
|
|
|
import argparse
|
|
from model.unet import get_model
|
|
from torch.distributions import Normal
|
|
|
|
from utils.file_utils import *
|
|
from utils.visualize import *
|
|
from utils.mitsuba_renderer import write_to_xml_batch
|
|
from model.pvcnn_generation import PVCNN2Base
|
|
|
|
from tqdm import tqdm
|
|
|
|
from datasets.shapenet_data_pc import ShapeNet15kPointClouds
|
|
|
|
'''
|
|
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 - .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. - 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. - betas
|
|
alphas_cumprod = torch.from_numpy(np.cumprod(alphas, axis=0)).float()
|
|
alphas_cumprod_prev = torch.from_numpy(np.append(1., 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. - alphas_cumprod).float()
|
|
self.log_one_minus_alphas_cumprod = torch.log(1. - alphas_cumprod).float()
|
|
self.sqrt_recip_alphas_cumprod = torch.sqrt(1. / alphas_cumprod).float()
|
|
self.sqrt_recipm1_alphas_cumprod = torch.sqrt(1. / 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. - alphas_cumprod_prev) / (1. - 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. - alphas_cumprod)
|
|
self.posterior_mean_coef2 = (1. - alphas_cumprod_prev) * torch.sqrt(alphas) / (1. - 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. - 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, -.5, .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)
|
|
|
|
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.,
|
|
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.,
|
|
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
|
|
)
|
|
return tr_dataset, te_dataset
|
|
|
|
|
|
|
|
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['test_points']
|
|
m, s = data['mean'].float(), data['std'].float()
|
|
|
|
ref.append(x*s + m)
|
|
|
|
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['test_points'].transpose(1,2)
|
|
m, s = data['mean'].float(), data['std'].float()
|
|
|
|
gen = model.gen_samples(x.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)
|
|
|
|
visualize_pointcloud_batch(os.path.join(str(Path(opt.eval_path).parent), 'x.png'), gen[:64], None,
|
|
None, None)
|
|
|
|
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='/viscam/u/alexzhou907/01DATA/shapenet/ShapeNetCore.v2.PC15k', help='input batch size')
|
|
parser.add_argument('--category', default='car')
|
|
|
|
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)
|
|
|
|
# results in /viscam/u/alexzhou907/research/diffusion/shapenet/output/test_chair |