norm[i] = _get_bboxsize(gt)
if dataset == 'LS3D-W' or dataset == '300VW-3D':
for i in range(3):
if dataset == 'LS3D-W':
category = {'0': 'Easy', '1': 'Media', '2': 'Hard'}[str(i)]
l, f = 2400 * i, 2400 * (i + 1)
else:
category = {
'0': 'Category A',
'1': 'Category B',
'2': 'Category C'
}[str(i)]
l, f = {
0: [0, 62643],
1: [62643, 62642 + 32872],
2: [95515, -1]
}[i]
# For LS3D-W dataset which landmark indexed on `0`
dist = calc_dists(preds[l:f] - 1., gts[l:f], norm[l:f])
auc = calc_metrics(dist, save_dir, category)
print("FINAL: Mean Error: {}. AUC: {} of {} subset".format(
round(torch.mean(dist) * 100., 2), auc, category))
else:
dists = calc_dists(preds, gts, norm)
#print(dists.shape)
auc = calc_metrics(dists, save_dir)
print(
"FINAL: Mean Error: {}. AUC: {}".format(
torch.mean(dists) * 100., 2), auc)
if __name__ == "__main__":
import opts
args = opts.argparser()
dataset = args.data.split('/')[-1]
save_dir = osp.join(args.checkpoint, dataset)
print("save dictory: " + save_dir)
preds = torch.from_numpy(loadpreds_if_exists(osp.join(save_dir, 'preds_valid.mat')))
gts, _ = loadgts(args.data, args.pointType)
norm = np.ones(preds.size(0))
for i, gt in enumerate(gts):
norm[i] = _get_bboxsize(gt)
if dataset == 'LS3D-W' or dataset == '300VW-3D':
for i in range(3):
if dataset == 'LS3D-W':
category = {'0': 'Easy', '1': 'Media', '2': 'Hard'}[str(i)]
l, f = 2400*i, 2400*(i+1)
else:
category = {'0': 'Category A', '1': 'Category B', '2': 'Category C'}[str(i)]
l, f = {0: [0, 62643], 1: [62643, 62642+32872], 2: [95515,-1]}[i]
# For LS3D-W dataset which landmark indexed on `0`
dist = calc_dists(preds[l:f] - 1., gts[l:f], norm[l:f])
auc = calc_metrics(dist, save_dir, category)
print("FINAL: Mean Error: {}. AUC: {} of {} subset".format(round(torch.mean(dist) * 100., 2), auc, category))
else:
dists = calc_dists(preds, gts, norm)
auc = calc_metrics(dists, save_dir)
print("FINAL: Mean Error: {}. AUC: {}".format(round(torch.mean(dists) * 100., 2), auc))
def validate(loader, model, criterion, netType, debug, flip):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
acces = AverageMeter()
end = time.time()
# predictions
predictions = torch.Tensor(loader.dataset.__len__(), 68, 2)
model.eval()
gt_win, pred_win = None, None
bar = Bar('Validating', max=len(loader))
all_dists = torch.zeros((68, loader.dataset.__len__()))
for i, (inputs, target, meta) in enumerate(loader):
data_time.update(time.time() - end)
input_var = torch.autograd.Variable(inputs.cuda())
target_var = torch.autograd.Variable(target.cuda(async=True))
output = model(input_var)
score_map = output[-1].data.cpu()
if flip:
flip_input_var = torch.autograd.Variable(
torch.from_numpy(shufflelr(
inputs.clone().numpy())).float().cuda())
flip_output_var = model(flip_input_var)
flip_output = flip_back(flip_output_var[-1].data.cpu())
score_map += flip_output
# intermediate supervision
loss = 0
for o in output:
loss += criterion(o, target_var)
acc, batch_dists = accuracy(score_map, target.cpu(), idx, thr=0.07)
all_dists[:, i * args.val_batch:(i + 1) * args.val_batch] = batch_dists
preds = final_preds(score_map, meta['center'], meta['scale'], [64, 64])
for n in range(score_map.size(0)):
predictions[meta['index'][n], :, :] = preds[n, :, :]
if debug:
gt_batch_img = batch_with_heatmap(inputs, target)
pred_batch_img = batch_with_heatmap(inputs, score_map)
if not gt_win or not pred_win:
plt.subplot(121)
gt_win = plt.imshow(gt_batch_img)
plt.subplot(122)
pred_win = plt.imshow(pred_batch_img)
else:
gt_win.set_data(gt_batch_img)
pred_win.set_data(pred_batch_img)
plt.pause(.05)
plt.draw()
losses.update(loss.data[0], inputs.size(0))
acces.update(acc[0], inputs.size(0))
batch_time.update(time.time() - end)
end = time.time()
bar.suffix = '({batch}/{size}) Data: {data:.6f}s | Batch: {bt:.3f}s | Total: {total:} | ETA: {eta:} | Loss: {loss:.4f} | Acc: {acc: .4f}'.format(
batch=i + 1,
size=len(loader),
data=data_time.val,
bt=batch_time.val,
total=bar.elapsed_td,
eta=bar.eta_td,
loss=losses.avg,
acc=acces.avg)
bar.next()
bar.finish()
mean_error = torch.mean(all_dists)
auc = calc_metrics(all_dists) # this is auc of predicted maps and target.
print("=> Mean Error: {:.2f}, [email protected]: {} based on maps".format(
mean_error * 100., auc))
return losses.avg, acces.avg, predictions, auc
def validate(loader, model, criterion, netType, debug, flip):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
acces = AverageMeter()
end = time.time()
# predictions
predictions = torch.Tensor(loader.dataset.__len__(), 68, 2)
model.eval()
gt_win, pred_win = None, None
bar = Bar('Validating', max=len(loader))
all_dists = torch.zeros((68, loader.dataset.__len__()))
for i, (inputs, target, meta) in enumerate(loader):
data_time.update(time.time() - end)
input_var = torch.autograd.Variable(inputs.cuda())
target_var = torch.autograd.Variable(target.cuda(async=True))
output = model(input_var)
score_map = output[-1].data.cpu()
if flip:
flip_input_var = torch.autograd.Variable(
torch.from_numpy(shufflelr(inputs.clone().numpy())).float().cuda())
flip_output_var = model(flip_input_var)
flip_output = flip_back(flip_output_var[-1].data.cpu())
score_map += flip_output
# intermediate supervision
loss = 0
for o in output:
loss += criterion(o, target_var)
acc, batch_dists = accuracy(score_map, target.cpu(), idx, thr=0.07)
all_dists[:, i * args.val_batch:(i + 1) * args.val_batch] = batch_dists
preds = final_preds(score_map, meta['center'], meta['scale'], [64, 64])
for n in range(score_map.size(0)):
predictions[meta['index'][n], :, :] = preds[n, :, :]
if debug:
gt_batch_img = batch_with_heatmap(inputs, target)
pred_batch_img = batch_with_heatmap(inputs, score_map)
if not gt_win or not pred_win:
plt.subplot(121)
gt_win = plt.imshow(gt_batch_img)
plt.subplot(122)
pred_win = plt.imshow(pred_batch_img)
else:
gt_win.set_data(gt_batch_img)
pred_win.set_data(pred_batch_img)
plt.pause(.05)
plt.draw()
losses.update(loss.data[0], inputs.size(0))
acces.update(acc[0], inputs.size(0))
batch_time.update(time.time() - end)
end = time.time()
bar.suffix = '({batch}/{size}) Data: {data:.6f}s | Batch: {bt:.3f}s | Total: {total:} | ETA: {eta:} | Loss: {loss:.4f} | Acc: {acc: .4f}'.format(
batch=i + 1,
size=len(loader),
data=data_time.val,
bt=batch_time.val,
total=bar.elapsed_td,
eta=bar.eta_td,
loss=losses.avg,
acc=acces.avg)
bar.next()
bar.finish()
mean_error = torch.mean(all_dists)
auc = calc_metrics(all_dists) # this is auc of predicted maps and target.
print("=> Mean Error: {:.2f}, [email protected]: {} based on maps".format(mean_error*100., auc))
return losses.avg, acces.avg, predictions, auc