def plotting(predict_joints, predict_groundtruth_seq, X_1):
for x in range(batch):
#out_seq=np.array(predict_joints[x].data.tolist()).reshape(-1,66)
out_seq = np.array(predict_joints[x].data.tolist()).reshape(-1, 66)
out_gt_seq = np.array(
predict_groundtruth_seq[x].data.tolist()).reshape(-1, 66)
print('out_seq size:', out_seq.shape)
print('out_seq:', out_seq)
output_format = np.zeros((1, 73))
output_format[:out_seq.shape[0], :out_seq.shape[1]] = out_seq
print('output_format:', output_format.shape)
output = output_format[:, :, np.newaxis]
print('output:', output.shape)
output_gt_format = np.zeros((1, 73))
output_gt_format[:out_gt_seq.shape[0], :out_gt_seq.shape[1]] = out_gt_seq
print('output_format:', output_gt_format.shape)
output_gt = output_gt_format[:, :, np.newaxis]
print('output:', output_gt.shape)
preprocess_data = np.load('D:/motionsynth_data/synth/preprocess_core.npz')
output = (output * preprocess_data['Xstd']) + preprocess_data['Xmean']
output_gt = (output_gt *
preprocess_data['Xstd']) + preprocess_data['Xmean']
X_1 = (X_1 * preprocess_data['Xstd']) + preprocess_data['Xmean']
from AnimationPlot import animation_plot
animation_plot([output, output_gt, X_1], interval=15.15)
def run(*args):
for i in range(1):
start = time.time()
Xbasis0 = np.zeros((1, 256, window // 2), dtype=theano.config.floatX)
Xbasis1 = np.zeros((1, 256, window // 2), dtype=theano.config.floatX)
Xbasis2 = np.zeros((1, 256, window // 2), dtype=theano.config.floatX)
Xbasis0[:, i * 3 +
0] = 1 + 2 * np.sin(np.linspace(0.0, np.pi * 8, window // 2))
Xbasis1[:, i * 3 +
1] = 1 + 2 * np.sin(np.linspace(0.0, np.pi * 8, window // 2))
Xbasis2[:, i * 3 +
2] = 1 + 2 * np.sin(np.linspace(0.0, np.pi * 8, window // 2))
Xbasis0 = np.array(network[1](theano.shared(Xbasis0,
borrow=True)).eval())
a = (theano.shared(Xbasis0, borrow=True)).eval()
print a, a.shape
Xbasis1 = np.array(network[1](theano.shared(Xbasis1,
borrow=True)).eval())
b = (theano.shared(Xbasis1, borrow=True)).eval()
print b, b.shape
Xbasis2 = np.array(network[1](theano.shared(Xbasis2,
borrow=True)).eval())
Xbasis0 = (Xbasis0 * preprocess['Xstd']) + preprocess['Xmean']
Xbasis1 = (Xbasis1 * preprocess['Xstd']) + preprocess['Xmean']
Xbasis2 = (Xbasis2 * preprocess['Xstd']) + preprocess['Xmean']
print start - time.time()
animation_plot([Xbasis0, Xbasis1, Xbasis2], interval=15.15)
def plotting(predict_joints, predict_groundtruth_seq, X_1):
reshape = feet = np.array([
15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 12, 13, 14,
9, 10, 11, 6, 7, 8, 3, 4, 5, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42,
43, 44, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 63, 64, 65, 66,
67, 68, 69, 70, 71, 72, 73, 74
])
predict_joints = predict_joints[:, reshape]
predict_groundtruth_seq = predict_groundtruth_seq[:, reshape]
for x in range(batch):
#out_seq=np.array(predict_joints[x].data.tolist()).reshape(-1,66)
out_seq = np.array(predict_joints[x].data.tolist()).reshape(-1, 66)
out_gt_seq = np.array(
predict_groundtruth_seq[x].data.tolist()).reshape(-1, 66)
print('out_seq size:', out_seq.shape)
print('out_seq:', out_seq)
output_format = np.zeros((1, 73))
output_format[:out_seq.shape[0], :out_seq.shape[1]] = out_seq
print('output_format:', output_format.shape)
output = output_format[:, :, np.newaxis]
print('output:', output.shape)
output_gt_format = np.zeros((1, 73))
output_gt_format[:out_gt_seq.shape[0], :out_gt_seq.shape[1]] = out_gt_seq
print('output_format:', output_gt_format.shape)
output_gt = output_gt_format[:, :, np.newaxis]
print('output:', output_gt.shape)
preprocess_data = np.load('D:/motionsynth_data/synth/preprocess_core.npz')
output = (output * preprocess_data['Xstd']) + preprocess_data['Xmean']
output_gt = (output_gt *
preprocess_data['Xstd']) + preprocess_data['Xmean']
X_1 = (X_1 * preprocess_data['Xstd']) + preprocess_data['Xmean']
from AnimationPlot import animation_plot
animation_plot([output, output_gt, X_1], interval=15.15)
for i in range(10):
Xbasis0 = np.zeros((1, 256, window // 2))
Xbasis1 = np.zeros((1, 256, window // 2))
Xbasis2 = np.zeros((1, 256, window // 2))
Xbasis0[:, i * 3 +
0] = 1 + 2 * np.sin(np.linspace(0.0, np.pi * 8, window // 2))
Xbasis1[:, i * 3 +
1] = 1 + 2 * np.sin(np.linspace(0.0, np.pi * 8, window // 2))
Xbasis2[:, i * 3 +
2] = 1 + 2 * np.sin(np.linspace(0.0, np.pi * 8, window // 2))
Xbasis0 = decoder(network,
torch.from_numpy(Xbasis0).cuda().to(dtype=torch.float))
Xbasis1 = decoder(network,
torch.from_numpy(Xbasis1).cuda().to(dtype=torch.float))
Xbasis2 = decoder(network,
torch.from_numpy(Xbasis2).cuda().to(dtype=torch.float))
Xbasis0 = Xbasis0.cpu().numpy()
Xbasis1 = Xbasis1.cpu().numpy()
Xbasis2 = Xbasis2.cpu().numpy()
Xbasis0 = (Xbasis0 * preprocess['Xstd']) + preprocess['Xmean']
Xbasis1 = (Xbasis1 * preprocess['Xstd']) + preprocess['Xmean']
Xbasis2 = (Xbasis2 * preprocess['Xstd']) + preprocess['Xmean']
animation_plot([Xbasis0, Xbasis1, Xbasis2], interval=15.15)
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)
batchsize = 1
window = X.shape[2]
X = theano.shared(X, borrow=True)
network = create_core(batchsize=batchsize, window=window, dropout=0.0, depooler=lambda x,**kw: x/2)
network.load(np.load('network_core.npz'))
from AnimationPlot import animation_plot
for _ in range(10):
index = rng.randint(X.shape[0].eval())
Xorgi = np.array(X[index:index+1].eval())
Xnois = Xorgi.copy()
Xnois[:,16*3-1:17*3] = 0.0
Xrecn = np.array(network(Xnois).eval())
Xorgi = (Xorgi * preprocess['Xstd']) + preprocess['Xmean']
Xnois = (Xnois * preprocess['Xstd']) + preprocess['Xmean']
Xrecn = (Xrecn * preprocess['Xstd']) + preprocess['Xmean']
Xrecn = constrain(Xrecn, network[0], network[1], preprocess, multiconstraint(
foot_sliding(Xorgi[:,-4:].copy()),
joint_lengths(),
trajectory(Xorgi[:,-7:-4])), alpha=0.01, iterations=50)
Xrecn[:,-7:-4] = Xorgi[:,-7:-4]
animation_plot([Xnois, Xrecn, Xorgi], interval=15.15)
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
joints[:, :, :,
0] += data[scene + '_pos'][:, cstart:cend][:, :,
np.newaxis][:, :, :, 0]
joints[:, :, :,
2] += data[scene + '_pos'][:, cstart:cend][:, :,
np.newaxis][:, :, :, 2]
#np.savez_compressed('./videos/crowd/'+scene+'.npz', X=joints)
#Y shape:(1,12,240)
network_func = theano.function([], network(Y[i:i + 1]))
Y_pad_ori = np.array(Y_pad[i:i + 1])
Xorig = np.array(X[i:i + 1])
print('X shape: ', X.shape)
#X shape: 121,73,240
print('Xorig shape: ', Xorig.shape)
#Xorig shape : 1,73,240
start = time.clock()
print('before network_func')
Xrecn = network_func()
# meaning that Y is inserted into network function then output will be Xrecn
print('Xrecn shape: ', Xrecn.shape)
#Xrecn shape: (1,73,240)
Xorig = (Xorig * preprocess['Xstd']) + preprocess['Xmean']
Xrecn = (Xrecn * preprocess['Xstd']) + preprocess['Xmean']
Y_pad_ori = (Y_pad_ori * preprocess['Xstd']) + preprocess['Xmean']
print
print('before constrain')
#here Xrecn already same as Xorig, but next is add constraint
Xrecn = constrain(Xrecn,
network_second[0],
network_second[1],
preprocess,
multiconstraint(foot_sliding(Xrecn[:, -4:].copy()),
joint_lengths()),
alpha=0.01,
iterations=50)
#print(data_kicking_cmu[i])
animation_plot([Xorig], interval=15.15)
print('Footsteps: %0.4f' % (time.clock() - start))
#############
network_first, network_second, network = create_network(
Torig.shape[2], Torig.shape[1])
network_func = theano.function([input],
network(input),
allow_input_downcast=True)
start = time.clock()
Xrecn = network_func(Torig)
Xrecn = (Xrecn * preprocess['Xstd']) + preprocess['Xmean']
Xtraj = ((Torig * preprocess['Xstd'][:, -7:]) +
preprocess['Xmean'][:, -7:]).copy()
print('Synthesis: %0.4f' % (time.clock() - start))
Xnonc = Xrecn.copy()
Xrecn = constrain(Xrecn,
network_second[0],
network_second[1],
preprocess,
multiconstraint(foot_sliding(Xtraj[:, -4:]),
trajectory(Xtraj[:, :3]),
joint_lengths()),
alpha=0.01,
iterations=250)
Xrecn[:, -7:] = Xtraj
animation_plot([Xnonc, Xrecn], interval=15.15)
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)