def one_step(self, inputs):
gt_verts, smooth_verts, thetas, _, _, _ = inputs
thetas = ops.mask_thetas(thetas, self.garment_class)
gt_verts = gt_verts.to(device)
smooth_verts = smooth_verts.to(device)
thetas = thetas.to(device)
# predicts residual over smooth groundtruth.
pred_verts = self.model(thetas).view(gt_verts.shape) + smooth_verts
# L1 loss
data_loss = (pred_verts - gt_verts).abs().sum(-1).mean()
return pred_verts, data_loss
def one_step(self, inputs):
gt_verts, thetas, betas, gammas, _ = inputs
thetas = ops.mask_thetas(thetas, self.garment_class)
gt_verts = gt_verts.to(device)
thetas = thetas.to(device)
betas = betas.to(device)
gammas = gammas.to(device)
ss2g_verts = self.ss2g_runner.forward(betas=betas, gammas=gammas).view(
gt_verts.shape)
pred_verts = ss2g_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 one_step(self, inputs):
"""One forward pass.
Takes `inputs` tuple. Returns output(s) and loss.
"""
gt_verts, thetas, betas, gammas, _ = inputs
thetas = ops.mask_thetas(thetas, 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 evaluate():
"""Evaluate TailorNet (or any model for that matter) on test set."""
from dataset.static_pose_shape_final import MultiStyleShape
from torch.utils.data import DataLoader
from utils.eval import AverageMeter
from models import ops
gender = 'female'
garment_class = 'old-t-shirt'
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()
runner = get_best_runner(garment_class, gender)
# from trainer.base_trainer import get_best_runner as baseline_runner
# runner = baseline_runner("/BS/cpatel/work/data/learn_anim/tn_baseline/{}_{}".format(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 = ops.mask_thetas(thetas, 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 forward(self, thetas, betas=None, gammas=None):
thetas = ops.mask_thetas(thetas=thetas,
garment_class=self.garment_class)
pred_verts = self.model(thetas)
return pred_verts
def forward(self, thetas, betas, gammas):
thetas = ops.mask_thetas(thetas=thetas,
garment_class=self.garment_class)
pred_verts = self.model(torch.cat((thetas, betas, gammas), dim=1))
return pred_verts