def getNextBatch(batch_num, batch_size, host, port, datapath, keyname):
global sock
if sock is None:
initialize(host, port)
sock.send_json({'batch_num': batch_num,
'batch_size': batch_size,
'path': datapath,
'keys': [(keyname, 'images0'),
(keyname, 'images1'),
(keyname, 'actions'),
(keyname, 'timediff')]})
images = recv_array(sock)
futures = recv_array(sock)
futurediffs = normalize(images.astype('float') - futures.astype('float'))
images = norml(images)
futurediffs = norml(futurediffs)
actions = recv_array(sock)
timediff = recv_array(sock)
batch = {'current': images,
'future': futurediffs,
'actions': actions,
'timediff': timediff[:, np.newaxis]
}
return batch
def getNextBatch(batch_num, batch_size, host, port, datapath):
global sock
if sock is None:
initialize(host, port)
sock.send_json(
{
"batch_num": batch_num,
"batch_size": batch_size,
"path": datapath,
"keys": [
("randompermpairs2", "images0"),
("randompermpairs2", "images1"),
("randompermpairs2", "actions"),
("randompermpairs2", "timediff"),
],
}
)
images = norml(recv_array(sock))
futures = norml(recv_array(sock))
actions = recv_array(sock)
timediff = recv_array(sock)
batch = {"current": images, "future": futures, "actions": actions, "timediff": timediff[:, np.newaxis]}
return batch
def getNextBatch(N, rng, batch_size):
sock.send_json({'batch_num': N,
'batch_size': 128})
info = sock.recv_json()
ims = norml(recv_array(sock))
norms = norml(recv_array(sock))
objs = recv_array(sock)
obss = []
time_diffs = []
actionss = []
future_inds = []
while (len(obss) < batch_size):
i = rng.randint(len(ims))
if i < OBSERVATION_LENGTH - 1:
continue
_b = [info[j].get('teleport_random', False) for j in range(i-OBSERVATION_LENGTH+1, i+1)]
if any(_b):
continue
for j in range(i, min(i+1+MAX_NUM_ACTIONS, len(info))):
if 'teleport_random' in info[j]s and info[j]['teleport_random']:
break
if j == i:
continue
t = rng.randint(i+1, j+1)
if (t, t-i) in zip(future_inds, time_diffs):
continue
future_inds.append(t)
time_diffs.append(t - i)
newshape = (IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS * OBSERVATION_LENGTH)
obs = ims[i - OBSERVATION_LENGTH: i].transpose((1, 2, 3, 0)).reshape(newshape)
obss.append(obs)
action_seq = []
for _j in range(i, i + MAX_NUM_ACTIONS):
if _j < len(info):
action_seq.extend(process_action(info[_j]))
else:
action_seq.extend([0] * ATOMIC_ACTION_LENGTH)
actionss.append(action_seq)
def getNextBatch(batch_num, batch_size, host, port, datapath):
global sock
if sock is None:
initialize(host, port)
sock.send_json(
{
"batch_num": batch_num,
"batch_size": batch_size,
"path": datapath,
"keys": [("randomperm", "images"), ("randomperm", "normals")],
}
)
images = norml(recv_array(sock))
normals = norml(recv_array(sock))
batch = {"images": images, "normals": normals} # images
return batch
def getNextBatch(batch_num, batch_size, host, port, datapath):
global sock
if sock is None:
initialize(host, port)
sock.send_json({'batch_num': batch_num,
'batch_size': batch_size,
'path': datapath,
'keys': [('randomperm', 'images'), ('randomperm', 'objectcounts')]})
images = norml(recv_array(sock))
counts = recv_array(sock)
objidvec = np.zeros((batch_size, counts.shape[1] + 1)).astype(np.float)
objidvec[:, 1:] = counts
noobj = objidvec.sum(1) == 0
objidvec[noobj, 0] = 1
objidvec = objidvec / objidvec.sum(1)[:, np.newaxis]
batch = {'images': images, #images
'object_count_distributions': objidvec #object-id-present vector
}
return batch
