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 update_metrics(self, metrics, inputs, outputs):
"""Update metrics from inputs and predicted outputs."""
gt_verts = inputs[0]
pred_verts = outputs
dist = ops.verts_dist(gt_verts, pred_verts.cpu()) * 1000.
metrics['val_dist'].update(dist.item(), gt_verts.shape[0])