network_first, network_second, network = create_network(
batchsize=T.shape[0], window=T.shape[2], hidden=T.shape[1])
network_func = theano.function([input],
network(input),
allow_input_downcast=True)
start = time.clock()
X = network_func(T)
X = (X * preprocess['Xstd']) + preprocess['Xmean']
Xtail = (T * preprocess['Xstd'][:, -7:]) + preprocess['Xmean'][:, -7:]
X = constrain(X,
network_second[0],
network_second[1],
preprocess,
multiconstraint(foot_sliding(Xtail[:, -4:]), joint_lengths(),
trajectory(Xtail[:, :3])),
alpha=0.01,
iterations=10)
X[:, -7:] = Xtail
#############
animation_plot([X[0:1, :, :200], X[10:11, :, :200], X[20:21, :, :200]],
interval=15.15)
X = np.swapaxes(X, 1, 2)
joints = X[:, :, :-7].reshape((X.shape[0], X.shape[1], -1, 3))
joints = -Quaternions(
data[scene + '_rot'][:, cstart:cend])[:, :, np.newaxis] * joints
dtype=theano.config.floatX) * preprocess['Xstd']) + preprocess['Xmean']
def style_transfer(H, V):
s, c = style_amount, 1.0
s, c = s / (s + c), c / (s + c)
return s * T.mean((gram_matrix(H) - G)**2) + c * T.mean((H - network_C[0](C))**2)
Xstyl = (S * preprocess['Xstd']) + preprocess['Xmean']
Xcntn = (C * preprocess['Xstd']) + preprocess['Xmean']
Xtrsf = N
Xtrsf = constrain(Xtrsf, network_C[0], network_C[1], preprocess, style_transfer, iterations=250, alpha=0.01)
Xtrsfvel = np.mean(np.sqrt(Xtrsf[:,-7:-6]**2 + Xtrsf[:,-6:-5]**2), axis=2)[:,:,np.newaxis]
Xcntnvel = np.mean(np.sqrt(Xcntn[:,-7:-6]**2 + Xcntn[:,-6:-5]**2), axis=2)[:,:,np.newaxis]
Xtail = Xtrsfvel * (Xcntn[:,-7:] / Xcntnvel)
Xtail[:,-5:] = Xcntn[:,-5:]
Xtrsf = constrain(Xtrsf, network_C[0], network_C[1], preprocess, multiconstraint(
foot_sliding(Xtail[:,-4:]),
joint_lengths(),
trajectory(Xtail[:,:3])), alpha=0.01, iterations=100)
Xtrsf[:,-7:] = Xtail
Xstyl = np.concatenate([Xstyl, Xstyl], axis=2)
from AnimationPlot import animation_plot
animation_plot([Xstyl, Xcntn, Xtrsf], interval=15.15)
feet = np.array([9,10,11,12,13,14,21,22,23,24,25,26])
Y = X[:,feet]
batchsize = 1
window = X.shape[2]
network_first = create_regressor(batchsize=batchsize, window=window, input=Y.shape[1], dropout=0.0)
network_second = create_core(batchsize=batchsize, window=window, dropout=0.0, depooler=lambda x,**kw:x/2)
network_second.load(np.load('network_core.npz'))
network = Network(network_first, network_second[1], params=network_first.params)
network.load(np.load('network_regression_kick.npz'))
from AnimationPlot import animation_plot
for i in range(len(X)):
network_func = theano.function([], network(Y[i:i+1]))
Xorig = np.array(X[i:i+1])
start = time.clock()
Xrecn = network_func()
Xorig = (Xorig * preprocess['Xstd']) + preprocess['Xmean']
Xrecn = (Xrecn * preprocess['Xstd']) + preprocess['Xmean']
Xrecn = constrain(Xrecn, network_second[0], network_second[1], preprocess, multiconstraint(
foot_sliding(Xrecn[:,-4:].copy()),
joint_lengths()), alpha=0.01, iterations=50)
animation_plot([Xorig, Xrecn], interval=15.15)
