def one_step(self, inputs):
gt_verts, _, betas, gammas, _ = inputs
_, betas, gammas = ops.mask_inputs(None, betas, gammas,
self.garment_class)
gt_verts = gt_verts.to(device)
betas = betas.to(device)
gammas = gammas.to(device)
pred_verts = self.model(torch.cat((betas, gammas),
dim=1)).view(gt_verts.shape)
# L1 loss
data_loss = (pred_verts - gt_verts).abs().sum(-1).mean()
return pred_verts, data_loss
def evaluate():
"""Evaluate TailorNet (or any model for that matter) on test set."""
from dataset.static_pose_shape_final import MultiStyleShape
import torch
from torch.utils.data import DataLoader
from utils.eval import AverageMeter
from models import ops
gender = 'female'
garment_class = 'skirt'
dataset = MultiStyleShape(garment_class=garment_class,
gender=gender,
split='test')
dataloader = DataLoader(dataset,
batch_size=32,
num_workers=0,
shuffle=False,
drop_last=False)
print(len(dataset))
val_dist = AverageMeter()
from models.tailornet_model import get_best_runner as tn_runner
runner = tn_runner(garment_class, gender)
# from trainer.base_trainer import get_best_runner as baseline_runner
# runner = baseline_runner("/BS/cpatel/work/data/learn_anim/{}_{}_weights/tn_orig_baseline/{}_{}".format(garment_class, gender, garment_class, gender))
device = torch.device('cuda:0')
with torch.no_grad():
for i, inputs in enumerate(dataloader):
gt_verts, thetas, betas, gammas, _ = inputs
thetas, betas, gammas = ops.mask_inputs(thetas, betas, gammas,
garment_class)
gt_verts = gt_verts.to(device)
thetas = thetas.to(device)
betas = betas.to(device)
gammas = gammas.to(device)
pred_verts = runner.forward(thetas=thetas,
betas=betas,
gammas=gammas).view(gt_verts.shape)
dist = ops.verts_dist(gt_verts, pred_verts) * 1000.
val_dist.update(dist.item(), gt_verts.shape[0])
print(i, len(dataloader))
print(val_dist.avg)
def one_step(self, inputs):
"""One forward pass.
Takes `inputs` tuple. Returns output(s) and loss.
"""
gt_verts, thetas, betas, gammas, _ = inputs
thetas, betas, gammas = ops.mask_inputs(thetas, betas, gammas, self.garment_class)
gt_verts = gt_verts.to(device)
thetas = thetas.to(device)
betas = betas.to(device)
gammas = gammas.to(device)
pred_verts = self.model(
torch.cat((thetas, betas, gammas), dim=1)).view(gt_verts.shape)
# L1 loss
data_loss = (pred_verts - gt_verts).abs().sum(-1).mean()
return pred_verts, data_loss
def forward(self, thetas, betas, gammas):
thetas, betas, gammas = ops.mask_inputs(
thetas, betas, gammas, garment_class=self.garment_class)
pred_verts = self.model(torch.cat((thetas, betas, gammas), dim=1))
return pred_verts
def evaluate_save():
"""Evaluate TailorNet (or any model for that matter) on test set."""
from dataset.static_pose_shape_final import MultiStyleShape
import torch
from torch.utils.data import DataLoader
from utils.eval import AverageMeter
from models import ops
from models.smpl4garment import SMPL4Garment
import os
gender = 'female'
garment_class = 'skirt'
smpl = SMPL4Garment(gender)
vis_freq = 512
log_dir = "/BS/cpatel/work/code_test2/try"
dataset = MultiStyleShape(garment_class=garment_class,
gender=gender,
split='test')
dataloader = DataLoader(dataset,
batch_size=32,
num_workers=0,
shuffle=False,
drop_last=False)
print(len(dataset))
val_dist = AverageMeter()
from models.tailornet_model import get_best_runner as tn_runner
runner = tn_runner(garment_class, gender)
# from trainer.base_trainer import get_best_runner as baseline_runner
# runner = baseline_runner("/BS/cpatel/work/data/learn_anim/{}_{}_weights/tn_orig_baseline/{}_{}".format(garment_class, gender, garment_class, gender))
device = torch.device('cuda:0')
with torch.no_grad():
for i, inputs in enumerate(dataloader):
gt_verts, thetas, betas, gammas, idxs = inputs
thetas, betas, gammas = ops.mask_inputs(thetas, betas, gammas,
garment_class)
gt_verts = gt_verts.to(device)
thetas = thetas.to(device)
betas = betas.to(device)
gammas = gammas.to(device)
pred_verts = runner.forward(thetas=thetas,
betas=betas,
gammas=gammas).view(gt_verts.shape)
for lidx, idx in enumerate(idxs):
if idx % vis_freq != 0:
continue
theta = thetas[lidx].cpu().numpy()
beta = betas[lidx].cpu().numpy()
pred_vert = pred_verts[lidx].cpu().numpy()
gt_vert = gt_verts[lidx].cpu().numpy()
body_m, pred_m = smpl.run(theta=theta,
garment_d=pred_vert,
beta=beta,
garment_class=garment_class)
_, gt_m = smpl.run(theta=theta,
garment_d=gt_vert,
beta=beta,
garment_class=garment_class)
save_dir = log_dir
pred_m.write_ply(
os.path.join(save_dir, "pred_{}.ply".format(idx)))
gt_m.write_ply(os.path.join(save_dir, "gt_{}.ply".format(idx)))
body_m.write_ply(
os.path.join(save_dir, "body_{}.ply".format(idx)))
print(val_dist.avg)
