def convert(self, sequence):
"""convert NYU sequence"""
config = sequence.config
name = '{}/{}_{}.h5'.format(self.cacheDir, self.datasetName,
sequence.name)
if os.path.isfile(name):
print '{} exist, please check if h5 is right!'.format(name)
return
Dataset = NYUDataset([sequence])
dpt, gt3D = Dataset.imgStackDepthOnly(sequence.name)
depth = []
gtorig = []
gtcrop = []
T = []
gt3Dorig = []
gt3Dcrop = []
com = []
fileName = []
for i in xrange(len(sequence.data)):
data = sequence.data[i]
depth.append(data.dpt)
gtorig.append(data.gtorig)
gtcrop.append(data.gtcrop)
T.append(data.T)
gt3Dorig.append(data.gt3Dorig)
gt3Dcrop.append(data.gt3Dcrop)
com.append(data.com)
fileName.append(int(data.fileName[-11:-4]))
dataset = h5py.File(name, 'w')
dataset['com'] = np.asarray(com)
dataset['inds'] = np.asarray(fileName)
dataset['config'] = config['cube']
dataset['depth'] = np.asarray(dpt)
dataset['joint'] = np.asarray(gt3D)
dataset['gt3Dorig'] = np.asarray(gt3Dorig)
dataset.close()
def convert(self, sequence, size_before=None):
"""convert sequence data"""
config = sequence.config
if self.name == 'NYU':
Dataset = NYUDataset([sequence])
elif self.name == 'ICVL':
Dataset = ICVLDataset([sequence])
dpt, gt3D = Dataset.imgStackDepthOnly(sequence.name)
dataset = {}
com = []
fileName = []
dpt3D = []
size = len(sequence.data)
print('size of {} {} dataset is {}'.format(self.name, sequence.name,
size))
for i in xrange(len(sequence.data)):
data = sequence.data[i]
com.append(data.com)
dpt3D.append(data.dpt3D)
if self.name == 'NYU':
fileName.append(int(data.fileName[-11:-4]))
elif self.name == 'ICVL' and size_before is not None:
fileName.append(int(data.fileName[(data.fileName.find('image_') + 6): \
(data.fileName.find('.png'))]) + size_before)
else:
fileName.append(int(data.fileName[(data.fileName.find('image_') + 6): \
(data.fileName.find('.png'))]))
dataset['depth'] = np.asarray(dpt)
dataset['dpt3D'] = np.asarray(dpt3D)
dataset['com'] = np.asarray(com)
dataset['inds'] = np.asarray(fileName)
dataset['config'] = np.asarray(config['cube']).reshape(
1, self.dim).repeat(size, axis=0)
dataset['joint'] = np.asarray(gt3D)
return dataset
max_error = max_euclidian_joints(vector_max)
ff_error = number_frames_within_dist(vector_ff)
return euclidian_distance, max_error, ff_error
if __name__ == '__main__':
global gpu_id
dataset = 'test'
di = NYUImporter('../../DeepPrior/data/NYU')
#datadir = '../../DeepPrior/src/data'
#di = NYUImporter(datadir)
Seq = di.loadSequence(dataset)
testSeqs = [Seq]
testDataset = NYUDataset(testSeqs)
use_gpu = True
gpu_id = 0
if use_gpu:
xp = cuda.cupy
else:
xp = np
J = 14
X_test, Y_test = testDataset.imgStackDepthOnly(dataset)
Y_test = xp.reshape(Y_test,
(Y_test.shape[0], Y_test.shape[1] * Y_test.shape[2]))
np.random.seed(0)
beta = 1.0
seed = 0
alpha = 0 #0.5
C = 1e-3
print("create data")
aug_modes = ['com', 'rot', 'none'] # 'sc',
comref = None # "./eval/NYU_COM_AUGMENT/net_NYU_COM_AUGMENT.pkl"
docom = False
di = NYUImporter('../data/NYU/', refineNet=comref)
Seq1 = di.loadSequence('train', shuffle=True, rng=rng, docom=docom)
trainSeqs = [Seq1]
Seq2_1 = di.loadSequence('test_1', docom=docom)
Seq2_2 = di.loadSequence('test_2', docom=docom)
testSeqs = [Seq2_1, Seq2_2]
# create training data
trainDataSet = NYUDataset(trainSeqs)
train_data, train_gt3D = trainDataSet.imgStackDepthOnly('train')
train_data_cube = numpy.asarray([Seq1.config['cube']] *
train_data.shape[0],
dtype='float32')
train_data_com = numpy.asarray([d.com for d in Seq1.data], dtype='float32')
train_gt3Dcrop = numpy.asarray([d.gt3Dcrop for d in Seq1.data],
dtype='float32')
mb = (train_data.nbytes) / (1024 * 1024)
print("data size: {}Mb".format(mb))
valDataSet = NYUDataset(testSeqs)
val_data, val_gt3D = valDataSet.imgStackDepthOnly('test_1')
testDataSet = NYUDataset(testSeqs)
if __name__ == '__main__':
print "start"
generator = Generator()
discriminator = Discriminator()
print "cuda"
discriminator.cuda()
generator.cuda()
print "opt"
criterion = nn.BCELoss()
d_optimizer = torch.optim.Adam(discriminator.parameters(), lr=0.0005)
g_optimizer = torch.optim.Adam(generator.parameters(), lr=0.0005)
#loading data
di = NYUImporter('../../DeepPrior/data/NYU')
Seq = di.loadSequence('test')
trainDataset = NYUDataset([Seq])
X_train, Y_train = trainDataset.imgStackDepthOnly('test')
Y_train = np.reshape(
Y_train, (Y_train.shape[0], Y_train.shape[1] * Y_train.shape[2]))
x_train, x_val, y_train, y_val = train_test_split(X_train,
Y_train,
test_size=0.2)
N = y_train.shape[0]
for epoch in range(Nepoch):
sum_dis_loss = np.float32(0)
sum_gen_loss = np.float32(0)
#xp.random.shuffle(train_data)
for i in range(0, N, batchsize):
input_images = torch.FloatTensor(x_train[i:i + batchsize])
images = Variable(input_images.cuda())
real_poses = Variable(torch.FloatTensor(