def initLatent(loader, model, Y, nViews, S, AVG = False):
model.eval()
nIters = len(loader)
N = loader.dataset.nImages
M = np.zeros((N, ref.J, 3))
bar = Bar('==>', max=nIters)
sum_sigma2 = 0
cnt_sigma2 = 1
for i, (input, target, meta) in enumerate(loader):
output = (model(torch.autograd.Variable(input)).data).cpu().numpy()
G = output.shape[0] / nViews
output = output.reshape(G, nViews, ref.J, 3)
if AVG:
for g in range(G):
id = int(meta[g * nViews, 1])
for j in range(nViews):
RR, tt = horn87(output[g, j].transpose(), output[g, 0].transpose())
MM = (np.dot(RR, output[g, j].transpose())).transpose().copy()
M[id] += MM.copy() / nViews
else:
for g in range(G):
#assert meta[g * nViews, 0] > 1 + ref.eps
p = np.zeros(nViews)
sigma2 = 0.1
for j in range(nViews):
for kk in range(Y.shape[0] / S):
k = kk * S
d = Dis(Y[k], output[g, j])
sum_sigma2 += d
cnt_sigma2 += 1
p[j] += np.exp(- d / 2 / sigma2)
id = int(meta[g * nViews, 1])
M[id] = output[g, p.argmax()]
if DEBUG and g == 0:
print 'M[id]', id, M[id], p.argmax()
debugger = Debugger()
for j in range(nViews):
RR, tt = horn87(output[g, j].transpose(), output[g, p.argmax()].transpose())
MM = (np.dot(RR, output[g, j].transpose())).transpose().copy()
debugger.addPoint3D(MM, 'b')
debugger.addImg(input[g * nViews + j].numpy().transpose(1, 2, 0), j)
debugger.showAllImg()
debugger.addPoint3D(M[id], 'r')
debugger.show3D()
Bar.suffix = 'Init : [{0:3}/{1:3}] | Total: {total:} | ETA: {eta:} | Dis: {dis:.6f}'.format(i, nIters, total=bar.elapsed_td, eta=bar.eta_td, dis = sum_sigma2 / cnt_sigma2)
bar.next()
bar.finish()
#print 'mean sigma2', sum_sigma2 / cnt_sigma2
return M
def check_logic():
preproc = nn.ModuleList([model.conv1_, model.bn1, model.relu, model.r1, model.maxpool, model.r4, model.r5 ])
hg = model.hourglass[0]
lower_hg = getEncoder(hg)
data_loader = torch.utils.data.DataLoader(
H36M(opts, 'val'),
batch_size = 1,
shuffle = False,
num_workers = int(ref.nThreads)
)
for k, (input, target) in enumerate(data_loader):
if(k>nSamples):
break
input_var = torch.autograd.Variable(input).float().cuda()
for mod in preproc:
input_var = mod(input_var)
for mod in lower_hg:
input_var = mod(input_var)
#decode
ups = input_var
upper_hg = nn.ModuleList(getDecoder(hg))
for mod in upper_hg:
ups = mod(ups)
Residual = model.Residual
for j in range(nModules):
ups = Residual[j](ups)
lin_ = model.lin_
ups = lin_[0](ups)
tmpOut = model.tmpOut
ups = tmpOut[0](ups)
pred = eval.getPreds(ups.data.cpu().numpy())*4
gt = eval.getPreds(target.cpu().numpy()) * 4
# init = getPreds(input.numpy()[:, 3:])
debugger = Debugger()
img = (input[0].numpy()[:3].transpose(1, 2, 0)*256).astype(np.uint8).copy()
print(img.shape)
debugger.addImg(img)
debugger.addPoint2D(pred[0], (255, 0, 0))
debugger.addPoint2D(gt[0], (0, 0, 255))
# debugger.addPoint2D(init[0], (0, 255, 0))
debugger.showAllImg(pause = True)
def step(split, epoch, opt, dataLoader, model, criterion, optimizer=None):
if split == 'train':
model.train()
else:
model.eval()
Loss, Acc = AverageMeter(), AverageMeter()
preds = []
nIters = len(dataLoader)
bar = Bar('{}'.format(opt.expID), max=nIters)
for i, (input, target, target2, meta) in enumerate(dataLoader):
input_var = torch.autograd.Variable(input).float().cuda()
target_var = torch.autograd.Variable(target).float().cuda()
target_var2 = torch.autograd.Variable(target2).float().cuda()
#print( input_var)
output = model(input_var)
#print(output[-1].size())
if opt.DEBUG >= 2:
gt = getPreds(target.cpu().numpy()) * 4
pred = getPreds((output[opt.nStack - 1].data).cpu().numpy()) * 4
debugger = Debugger()
img = (input[0].numpy().transpose(1, 2, 0) * 256).astype(
np.uint8).copy()
debugger.addImg(img)
debugger.addPoint2D(pred[0], (255, 0, 0))
debugger.addPoint2D(gt[0], (0, 0, 255))
debugger.showAllImg(pause=True)
loss = criterion(output[0], target_var)
for k in range(1, opt.nStack):
loss += criterion(output[k], target_var)
Loss.update(loss.data[0], input.size(0))
Acc.update(
Accuracy((output[opt.nStack - 1].data).cpu().numpy(),
(target_var.data).cpu().numpy()))
if split == 'train':
optimizer.zero_grad()
loss.backward()
optimizer.step()
else:
input_ = input.cpu().numpy()
input_[0] = Flip(input_[0]).copy()
inputFlip_var = torch.autograd.Variable(
torch.from_numpy(input_).view(1, input_.shape[1], ref.inputRes,
ref.inputRes)).float().cuda(
opt.GPU)
outputFlip = model(inputFlip_var)
outputFlip = ShuffleLR(
Flip((outputFlip[opt.nStack -
1].data).cpu().numpy()[0])).reshape(
1, ref.nJoints, 64, 64)
output_ = (
(output[opt.nStack - 1].data).cpu().numpy() + outputFlip) / 2
preds.append(
finalPreds(output_, meta['center'], meta['scale'],
meta['rotate'])[0])
Bar.suffix = '{split} Epoch: [{0}][{1}/{2}]| Total: {total:} | ETA: {eta:} | Loss {loss.avg:.6f} | Acc {Acc.avg:.6f} ({Acc.val:.6f})'.format(
epoch,
i,
nIters,
total=bar.elapsed_td,
eta=bar.eta_td,
loss=Loss,
Acc=Acc,
split=split)
bar.next()
bar.finish()
return {'Loss': Loss.avg, 'Acc': Acc.avg}, preds
def main():
opt = opts().parse()
model = torch.load(opt.loadModel)
img = cv2.imread(opt.demo)
s = max(img.shape[0], img.shape[1]) * 1.0
c = np.array([img.shape[1] / 2., img.shape[0] / 2.])
img = Crop(img, c, s, 0, ref.inputRes) / 256.
input = torch.from_numpy(img.copy()).float()
input = input.view(1, input.size(0), input.size(1), input.size(2))
input_var = torch.autograd.Variable(input).float()
if opt.GPU > -1:
model = model.cuda(opt.GPU)
input_var = input_var.cuda(opt.GPU)
output = model(input_var)
hm = output[-1].data.cpu().numpy()
debugger = Debugger()
img = (input[0].numpy().transpose(1, 2, 0) * 256).astype(np.uint8).copy()
inp = img.copy()
star = (cv2.resize(hm[0, 0], (ref.inputRes, ref.inputRes)) * 255)
star[star > 255] = 255
star[star < 0] = 0
star = np.tile(star, (3, 1, 1)).transpose(1, 2, 0)
trans = 0.8
star = (trans * star + (1. - trans) * img).astype(np.uint8)
ps = parseHeatmap(hm[0], thresh=0.1)
canonical, pred, color, score = [], [], [], []
for k in range(len(ps[0])):
x, y, z = ((hm[0, 1:4, ps[0][k], ps[1][k]] + 0.5) *
ref.outputRes).astype(np.int32)
dep = ((hm[0, 4, ps[0][k], ps[1][k]] + 0.5) * ref.outputRes).astype(
np.int32)
canonical.append([x, y, z])
pred.append([ps[1][k], ref.outputRes - dep, ref.outputRes - ps[0][k]])
score.append(hm[0, 0, ps[0][k], ps[1][k]])
color.append((1.0 * x / ref.outputRes, 1.0 * y / ref.outputRes,
1.0 * z / ref.outputRes))
cv2.circle(img, (ps[1][k] * 4, ps[0][k] * 4), 4, (255, 255, 255), -1)
cv2.circle(img, (ps[1][k] * 4, ps[0][k] * 4), 2,
(int(z * 4), int(y * 4), int(x * 4)), -1)
pred = np.array(pred).astype(np.float32)
canonical = np.array(canonical).astype(np.float32)
pointS = canonical * 1.0 / ref.outputRes
pointT = pred * 1.0 / ref.outputRes
R, t, s = horn87(pointS.transpose(), pointT.transpose(), score)
rotated_pred = s * np.dot(
R, canonical.transpose()).transpose() + t * ref.outputRes
debugger.addImg(inp, 'inp')
debugger.addImg(star, 'star')
debugger.addImg(img, 'nms')
debugger.addPoint3D(canonical / ref.outputRes - 0.5, c=color, marker='^')
debugger.addPoint3D(pred / ref.outputRes - 0.5, c=color, marker='x')
debugger.addPoint3D(rotated_pred / ref.outputRes - 0.5,
c=color,
marker='*')
debugger.showAllImg(pause=True)
debugger.show3D()
dep = ((hm[0, 4, ps[0][k], ps[1][k]] + 0.5) *
ref.outputRes).astype(np.int32)
color.append((1.0 * x / ref.outputRes, 1.0 * y / ref.outputRes,
1.0 * z / ref.outputRes))
cv2.circle(img, (ps[1][k] * 4, ps[0][k] * 4), 6,
(int(x * 4), int(y * 4), int(z * 4)), -1)
debugger.addImg(img)
debugger.addImg(star, 'star')
debugger.addPoint3D(np.array((pred)) / 64. - 0.5, c=color, marker='x')
rotated = Rotate(canonical, gt_view)
rotated[:,
2], rotated[:,
1] = -rotated[:, 1].copy(), -rotated[:, 2].copy()
debugger.addPoint3D(np.array(rotated) / 64. - 0.5, c=color, marker='^')
debugger.showAllImg(pause=False)
debugger.show3D()
bar.finish()
accAll10 = 0.
accAll30 = 0.
numAll = 0.
mid = {}
err_all = []
for k, v in ref.ObjectNet3DClassName.items():
accAll10 += acc10[v]
accAll30 += acc30[v]
numAll += num[v]
if num[v] > 0:
acc10[v] = 1.0 * acc10[v] / num[v]
acc30[v] = 1.0 * acc30[v] / num[v]
mid[v] = np.sort(np.array(err[v]))[len(err[v]) // 2]
def step(split, epoch, opt, dataLoader, model, criterion, optimizer=None):
if split == 'train':
model.train()
else:
model.eval()
Loss, Acc = AverageMeter(), AverageMeter()
nIters = len(dataLoader)
# bar = Bar('==>', max=nIters)
start_time = time.time()
for i, (input, target) in enumerate(dataLoader):
input_var = torch.autograd.Variable(input).float().cuda()
target_var = torch.autograd.Variable(
target.cuda(async=True)).float().cuda()
if (model.hgType == 'vae'):
output, latentspace = model(input_var)
else:
output = model(input_var)
if opt.DEBUG >= 2:
gt = getPreds(target.cpu().numpy()) * 4
pred = getPreds((output[opt.nStack - 1].data).cpu().numpy()) * 4
# init = getPreds(input.numpy()[:, 3:])
debugger = Debugger()
img = (input[0].numpy()[:3].transpose(1, 2, 0) * 256).astype(
np.uint8).copy()
debugger.addImg(img)
debugger.addPoint2D(pred[0], (255, 0, 0))
debugger.addPoint2D(gt[0], (0, 0, 255))
# debugger.addPoint2D(init[0], (0, 255, 0))
debugger.showAllImg(pause=True)
#debugger.saveImg('debug/{}.png'.format(i))
loss = criterion(output[0], target_var)
for k in range(1, opt.nStack):
loss += criterion(output[k], target_var)
if (model.hgType == 'vae'):
for k in range(0, opt.nStack):
loss += ref.vaeloss_wt * _compute_kl(latentspace[k])
Loss.update(loss.data[0], input.size(0))
Acc.update(
Accuracy((output[opt.nStack - 1].data).cpu().numpy(),
(target_var.data).cpu().numpy()))
if split == 'train':
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Bar.suffix = '{split} Epoch: [{0}][{1}/{2}]| Total: {total:} | ETA: {eta:} | Loss {loss.avg:.6f} | Acc {Acc.avg:.6f} ({Acc.val:.6f})'.format(epoch, i, nIters, total=bar.elapsed_td, eta=bar.eta_td, loss=Loss, Acc=Acc, split = split)
# bar.next()
curr_time = time.time()
print(
'{split} Epoch: [{0}][{1}/{2}]| Total: {total:f} | ETA: {eta:f} | Loss {loss.avg:.6f} | Acc {Acc.avg:.6f} ({Acc.val:.6f})'
.format(epoch,
i,
nIters,
total=curr_time - start_time,
eta=(curr_time - start_time) * (nIters - i + 1) / (i + 1),
loss=Loss,
Acc=Acc,
split=split))
# bar.finish()
return Loss.avg, Acc.avg
def step(split, epoch, opt, dataLoader, model, criterion, optimizer=None):
if split == 'train':
model.train()
else:
model.eval()
Loss, Acc = AverageMeter(), AverageMeter()
preds = []
nIters = len(dataLoader)
bar = Bar('{}'.format(opt.expID), max=nIters)
for i, (input, targets, action, meta) in enumerate(dataLoader):
input_var = torch.autograd.Variable(input).float().cuda(opt.GPU)
target_var = []
for t in range(len(targets)):
target_var.append(
torch.autograd.Variable(targets[t]).float().cuda(opt.GPU))
z = []
for k in range(opt.numNoise):
noise = torch.autograd.Variable(
torch.randn((input_var.shape[0], 1, 64, 64))).cuda(opt.GPU)
z.append(noise)
output, samples = model(input_var, z, action)
pred_sample = maximumExpectedUtility(samples, criterion)
target = maximumExpectedUtility(target_var, criterion)
if opt.DEBUG >= 2:
gt = getPreds(target.cpu().numpy()) * 4
pred = getPreds((pred_sample.data).cpu().numpy()) * 4
debugger = Debugger()
img = (input[0].numpy().transpose(1, 2, 0) * 256).astype(
np.uint8).copy()
debugger.addImg(img)
debugger.addPoint2D(pred[0], (255, 0, 0))
debugger.addPoint2D(gt[0], (0, 0, 255))
debugger.showAllImg(pause=True)
loss = DiscoLoss(output, samples, target_var, criterion)
Loss.update(loss.item(), input.size(0))
Acc.update(
Accuracy((pred_sample.data).cpu().numpy(),
(target.data).cpu().numpy()))
if split == 'train':
optimizer.zero_grad()
loss.backward()
optimizer.step()
else:
input_ = input.cpu().numpy()
input_[0] = Flip(input_[0]).copy()
inputFlip_var = torch.autograd.Variable(
torch.from_numpy(input_).view(1, input_.shape[1], ref.inputRes,
ref.inputRes)).float().cuda(
opt.GPU)
_, samplesFlip = model(inputFlip_var, z, action)
pred_sample_flip = maximumExpectedUtility(samplesFlip, criterion)
outputFlip = ShuffleLR(
Flip((pred_sample_flip.data).cpu().numpy()[0])).reshape(
1, ref.nJoints, ref.outputRes, ref.outputRes)
output_ = old_div(((pred_sample.data).cpu().numpy() + outputFlip),
2)
preds.append(
finalPreds(output_, meta['center'], meta['scale'],
meta['rotate'])[0])
Bar.suffix = '{split} Epoch: [{0}][{1}/{2}]| Total: {total:} | ETA: {eta:} | Loss {loss.avg:.6f} | Acc {Acc.avg:.6f} ({Acc.val:.6f})'.format(
epoch,
i,
nIters,
total=bar.elapsed_td,
eta=bar.eta_td,
loss=Loss,
Acc=Acc,
split=split)
bar.next()
bar.finish()
return {'Loss': Loss.avg, 'Acc': Acc.avg}, preds
def step(split, epoch, opt, dataLoader, model, criterion, optimizer=None):
if split == 'train':
model.train()
else:
model.eval()
preds = []
Loss, LossStar = AverageMeter(), AverageMeter()
nIters = len(dataLoader)
bar = Bar('{}'.format(opt.expID), max=nIters)
for i, (input, target, mask) in enumerate(dataLoader):
if mask.size(1) > 1:
mask[:, 1:, :, :] *= ref.outputRes * (opt.regWeight**0.5)
if opt.GPU > -1:
input_var = torch.autograd.Variable(input.cuda(
opt.GPU, async=True)).float().cuda(opt.GPU)
target_var = torch.autograd.Variable(
target.cuda(opt.GPU, async=True)).float().cuda(opt.GPU)
mask_var = torch.autograd.Variable(mask.cuda(
opt.GPU, async=True)).float().cuda(opt.GPU)
else:
input_var = torch.autograd.Variable(input).float()
target_var = torch.autograd.Variable(target).float()
mask_var = torch.autograd.Variable(mask).float()
output = model(input_var)
output_pred = output[opt.nStack - 1].data.cpu().numpy().copy()
for k in range(opt.nStack):
output[k] = mask_var * output[k]
target_var = mask_var * target_var
loss = 0
for k in range(opt.nStack):
loss += criterion(output[k], target_var)
LossStar.update((
(target.float()[:, 0, :, :] -
output[opt.nStack - 1].cpu().data.float()[:, 0, :, :])**2).mean())
Loss.update(loss.data[0], input.size(0))
if split == 'train':
optimizer.zero_grad()
loss.backward()
optimizer.step()
else:
if opt.test:
out = {}
input_ = input.cpu().numpy()
input_[0] = Flip(input_[0]).copy()
inputFlip_var = torch.autograd.Variable(
torch.from_numpy(input_).view(
1, input_.shape[1], ref.inputRes,
ref.inputRes)).float().cuda(opt.GPU)
outputFlip = model(inputFlip_var)
output_flip = outputFlip[opt.nStack - 1].data.cpu().numpy()
output_flip[0] = Flip(output_flip[0])
if not (opt.task == 'star'):
output_flip[0, 1, :, :] = -output_flip[0, 1, :, :]
output_pred = (output_pred + output_flip) / 2.0
out['map'] = output_pred
preds.append(out)
Bar.suffix = '{split:5} Epoch: [{0}][{1}/{2}]| Total: {total:} | ETA: {eta:} | Loss {loss.avg:.6f} | LossStar {lossStar.avg:.6f}'.format(
epoch,
i,
nIters,
total=bar.elapsed_td,
eta=bar.eta_td,
loss=Loss,
lossStar=LossStar,
split=split)
bar.next()
if opt.DEBUG > 1 or (opt.DEBUG == 1 and i % (nIters / 200) == 0):
for j in range(input.size(0)):
debugger = Debugger()
img = (input[j].numpy()[:3].transpose(1, 2, 0) * 256).astype(
np.uint8).copy()
img2 = img.copy().astype(np.float32)
img3 = img.copy().astype(np.float32)
imgMNS = img.copy()
out = (cv2.resize(
((output[opt.nStack - 1][j, 0].data).cpu().numpy()).copy(),
(ref.inputRes, ref.inputRes)) * 256)
gtmap = (cv2.resize((target[j, 0].cpu().numpy()).copy(),
(ref.inputRes, ref.inputRes)) * 256)
out[out < 0] = 0
out[out > 255] = 255
img2[:, :, 0] = (img2[:, :, 0] + out)
img2[img2 > 255] = 255
img3[:, :, 2] = (img3[:, :, 2] + gtmap)
img3[img3 > 255] = 255
gtmap[gtmap > 255] = 255
idx = i * input.size(0) + j if opt.DEBUG == 1 else 0
img2, out, gtmap, img3 = img2.astype(np.uint8), out.astype(
np.uint8), gtmap.astype(np.uint8), img3.astype(np.uint8)
if 'emb' in opt.task:
gt, pred = [], []
ps = parseHeatmap(target[j].numpy())
print('ps', ps)
for k in range(len(ps[0])):
print('target', k, target[j, 1:4, ps[0][k],
ps[1][k]].numpy())
x, y, z = (
(target[j, 1:4, ps[0][k], ps[1][k]].numpy() + 0.5)
* 255).astype(np.int32)
gt.append(target[j, 1:4, ps[0][k], ps[1][k]].numpy())
cv2.circle(imgMNS, (ps[1][k] * 4, ps[0][k] * 4), 6,
(int(x), int(y), int(z)), -1)
ps = parseHeatmap(output_pred[j])
for k in range(len(ps[0])):
print('pred', k, output_pred[j, 1:4, ps[0][k],
ps[1][k]])
x, y, z = (
(output_pred[j, 1:4, ps[0][k], ps[1][k]] + 0.5) *
255).astype(np.int32)
pred.append(output_pred[j, 1:4, ps[0][k], ps[1][k]])
cv2.circle(imgMNS, (ps[1][k] * 4, ps[0][k] * 4), 4,
(255, 255, 255), -1)
cv2.circle(imgMNS, (ps[1][k] * 4, ps[0][k] * 4), 2,
(int(x), int(y), int(z)), -1)
debugger.addPoint3D(np.array(gt), c='auto', marker='o')
#debugger.addPoint3D(np.array(pred), c = 'auto', marker = 'x')
debugger.addImg(imgMNS, '{}_mns'.format(idx))
debugger.addImg(out, '{}_out'.format(idx))
debugger.addImg(gtmap, '{}_gt'.format(idx))
debugger.addImg(img, '{}_img'.format(idx))
debugger.addImg(img2, '{}_img2'.format(idx))
debugger.addImg(img3, '{}_img3'.format(idx))
if opt.DEBUG == 1:
debugger.saveAllImg(path=opt.debugPath)
else:
debugger.showAllImg(pause=not ('emb' in opt.task))
if 'emb' in opt.task:
debugger.show3D()
bar.finish()
return {'Loss': Loss.avg, 'LossStar': LossStar.avg}, preds
