def create_somenet_from_feature(self,
hidden,
netname,
elu,
lstm,
initialized_as_zero=False,
nolu_at_final=False,
batch_normalization=None):
# featvec = self.cur_featvec
'''
Note flattened multi-view feature vector [B, 1, F*V] should be used
'''
featvec = self.cur_mvfeatvec
if lstm is True:
featvec = self.get_lstm_featvec('LSTM', featvec)
net = vision.ConvApplier(None,
hidden,
netname,
elu,
initialized_as_zero=initialized_as_zero,
nolu_at_final=nolu_at_final,
batch_normalization=batch_normalization)
_, out = net.infer(featvec)
'''
TODO: Check if this returns LSTM as well (probably not)
'''
params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope=netname)
print('{} Params {}'.format(netname, params))
print('LSTM Params {}'.format(self.acquire_lstm_params()))
return out, params, [net]
def get_forward_model(self, jointfw=False, output_fn=-1):
if self.forward_output_tensor is not None:
return self.forward_model_params, self.forward_output_tensor
'''
our pipeline use [None, V, N] feature vector
3D tensor unifies per-view tensors and combined-view tensors.
'''
if jointfw:
'''
Joint Prediction of V views
Expecting better accuracy with V^2 size of network.
'''
V = int(self.cur_featvec.shape[1])
N = int(self.cur_featvec.shape[2])
input_featvec = tf.reshape(self.cur_featvec, [-1, 1, V * N])
atensor = self.action_tensor
name = 'JointForwardModelNet'
else:
'''
Without --jointfw, get_local_action would generate action in local
coord. sys. and then get_forward_model() predicts each view individually
'''
atensor = self.get_local_action(self.action_tensor)
input_featvec = self.cur_featvec
name = 'ForwardModelNet'
featnums = self.fwhidden_params
if output_fn <= 0:
featnums += [int(input_featvec.shape[-1])]
else:
featnums += [output_fn]
featvec_plus_action = tf.concat([atensor, input_featvec], 2)
self.forward_fc_applier = vision.ConvApplier(None,
featnums,
name,
self.elu,
nolu_at_final=True)
# FIXME: ConvApplier.infer returns tuples, which is unsuitable for Optimizer
params, out = self.forward_fc_applier.infer(featvec_plus_action)
if jointfw:
if output_fn <= 0:
out = tf.reshape(out, [-1, V, N])
else:
pass # Return [-1, 1, output_fn] as requested
self.forward_model_params = params
self.forward_output_tensor = out
print('FWD Params {}'.format(params))
params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=name)
print('FWD Collected Params {}'.format(params))
return params, out
def __init__(self, learning_rate, args, batch_normalization=None):
self._debug_trans_only = 'tunnel_finder_trans_only' in args.debug_flags
self.view_num, self.views = rlutil.get_view_cfg(args)
w = h = args.res
self.args = args
self.batch_normalization = batch_normalization
batch_size = None if args.EXPLICIT_BATCH_SIZE < 0 else args.EXPLICIT_BATCH_SIZE
self.batch_size = batch_size
# tf.reshape does not accept None as dimension
self.reshape_batch_size = -1 if batch_size is None else batch_size
common_shape = [batch_size, self.view_num, w, h]
self.action_space_dimension = 6 # Magic number, 3D + Axis Angle
self.pred_action_size = self.action_space_dimension * self.get_number_of_predictions(
)
action_shape = self._get_action_placeholder_shape(batch_size)
self.action_tensor = tf.placeholder(tf.float32,
shape=action_shape,
name='CActionPh')
self.rgb_tensor = tf.placeholder(tf.float32,
shape=common_shape + [3],
name='RgbPh')
self.dep_tensor = tf.placeholder(tf.float32,
shape=common_shape + [1],
name='DepPh')
assert self.view_num == 1 or args.sharedmultiview, "must be shared multiview, or single view"
assert args.ferev in [
11, 13
], 'Assumes --ferev 11 or 13 (implied by --visionformula )'
# Let's try ResNet 18 True
self.feature_extractor = vision.FeatureExtractorResNet(
config.SV_RESNET18_TRUE,
args.fehidden + [args.featnum],
'VisionNetRev13',
args.elu,
batch_normalization=batch_normalization)
self._vision_params, self.featvec = self.feature_extractor.infer(
self.rgb_tensor, self.dep_tensor)
B, V, N = self.featvec.shape
self.joint_featvec = tf.reshape(self.featvec, [-1, 1, int(V) * int(N)])
naming = 'TunnelFinderNet'
self._finder_net = vision.ConvApplier(
None, args.polhidden + [self.pred_action_size], naming, args.elu)
self._finder_params, self.finder_pred = self._finder_net.infer(
self.joint_featvec)
def get_inverse_model(self):
if self.inverse_output_tensor is not None:
return self.inverse_model_params, self.inverse_output_tensor
input_featvec = tf.concat([self.cur_featvec, self.next_featvec], 2)
print('inverse_model input {}'.format(input_featvec))
# featnums = [config.INVERSE_MODEL_HIDDEN_LAYER, int(self.action_tensor.shape[-1])]
# featnums = config.INVERSE_MODEL_HIDDEN_LAYER + [int(self.action_tensor.shape[-1])]
featnums = self.imhidden_params + [self.action_space_dimension]
print('inverse_model featnums {}'.format(featnums))
self.inverse_fc_applier = vision.ConvApplier(None, featnums,
'InverseModelNet',
self.elu)
params, out = self.inverse_fc_applier.infer(input_featvec)
self.inverse_model_params = params
self.inverse_output_tensor = self.vote(out)
return params, out
def create_somenet_from_feature(self, hidden, netname):
outs = []
nets = []
paramss = []
for i in range(self.view_num):
icm = self.icms[i]
with tf.variable_scope(netname):
vsn = 'View_{}'.format(i)
featvec = icm.cur_featvec
nets.append(vision.ConvApplier(None, hidden, vsn, elu))
_, out = nets[-1].infer(featvec)
outs.append(tf.nn.softmax(out))
paramss.append()
out = tf.add_n(outs)
params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope=netname)
return out, params, nets
def __init__(self, learning_rate, args, batch_normalization=None):
super(TunnelFinderTwin1,
self).__init__(learning_rate=learning_rate,
args=args,
batch_normalization=batch_normalization)
self.coarse_action = self.action_tensor
self.coarse_rgb = self.rgb_tensor
self.coarse_dep = self.dep_tensor
self.fine_action = self.action_tensor
self.fine_rgb = self.rgb_tensor
self.fine_dep = self.dep_tensor
self._coarse_net = self._finder_net
self.coarse_pred = self.finder_pred
naming = 'TunnelFinderNet_Fine'
self._fine_net = vision.ConvApplier(
None, args.polhidden + [self.action_space_dimension], naming,
args.elu)
self._fine_params, self.fine_pred = self._fine_net.infer(
self.joint_featvec)
adists[i, 0, actions[i]] = 1.0
return adists
# GT Input
action_tensor = tf.placeholder(tf.float32,
shape=[BSize, 1, action_space_dimension],
name='ActionPh')
V_tensor = tf.placeholder(tf.float32, shape=[BSize], name='VPh')
# Feature Vector
featvec = tf.placeholder(tf.float32, shape=[BSize, 1, FEAT], name='ActionPh')
# pi and value net
pi_net = vision.ConvApplier(None, [HIDDEN, HIDDEN, action_space_dimension],
'PiNet',
elu=True,
initialized_as_zero=False,
nolu_at_final=True,
batch_normalization=None)
_, raw_pi = pi_net.infer(featvec)
print("raw_pi {}".format(raw_pi.shape))
value_net = vision.ConvApplier(None, [HIDDEN, HIDDEN, 1],
'ValueNet',
elu=True,
initialized_as_zero=False,
nolu_at_final=True,
batch_normalization=None)
_, raw_value = value_net.infer(featvec)
print("raw_value {}".format(raw_value.shape))
if len(actionset) != action_space_dimension:
# Selective softmax
def __init__(self, learning_rate, args, master=None, shadowid=-1):
super(TabularRL, self).__init__()
self.h = self.w = args.res * 2 + 1 # [-res, -res] to [res, res]
self.mani = args.mani
self.action_tensor = tf.placeholder(tf.float32,
shape=[None, 1, 4],
name='ActionPh')
self.rgb_1_tensor = tf.placeholder(tf.int32,
shape=[None, 2],
name='Rgb1Ph')
self.dep_1_tensor = tf.placeholder(tf.int32,
shape=[None],
name='Dep1Ph')
self.rgb_2_tensor = tf.placeholder(tf.int32,
shape=[None, 2],
name='Rgb2Ph')
self.dep_2_tensor = tf.placeholder(tf.int32,
shape=[None],
name='Dep2Ph')
if shadowid >= 0:
vprefix = 'Shadow{}_'.format(shadowid)
else:
vprefix = ''
frgb_1 = tf.cast(self.rgb_1_tensor, tf.float32)
frgb_2 = tf.cast(self.rgb_2_tensor, tf.float32)
if not self.mani:
self.polparams = tf.get_variable(
vprefix + 'polgrid',
shape=[self.w, self.h, 4],
dtype=tf.float32,
initializer=tf.zeros_initializer())
self.valparams = tf.get_variable(
vprefix + 'valgrid',
shape=[self.w, self.h],
dtype=tf.float32,
initializer=tf.zeros_initializer())
self.smpolparams = tf.nn.softmax(logits=self.polparams)
self.indices = tf.reshape(self.rgb_1_tensor, [-1, 2]) + args.res
self.polout = tf.gather_nd(self.smpolparams, indices=self.indices)
print('polout shape {}'.format(self.polout.shape))
self.valout = tf.gather_nd(self.valparams, indices=self.indices)
self.smpol = tf.reshape(self.policy, [-1, 1, 4])
else:
febase = [64, 64]
featnums = febase + [4]
self.pol_applier = vision.ConvApplier(None,
featnums,
vprefix + 'PolMani',
elu=True,
nolu_at_final=True)
self.polparams, self.polout = self.pol_applier.infer(frgb_1)
featnums = febase + [1]
self.val_applier = vision.ConvApplier(None,
featnums,
vprefix + 'ValMani',
elu=True,
nolu_at_final=True)
self.valparams, self.valout = self.val_applier.infer(frgb_1)
self.smpol = tf.nn.softmax(tf.reshape(self.policy, [-1, 1, 4]))
rgb_list = []
for x in range(-args.res, args.res + 1):
for y in range(-args.res, args.res + 1):
rgb_list.append([x, y])
self.pvgrid = np.array(rgb_list, dtype=np.int32)
atensor = tf.reshape(self.action_tensor, [-1, 4])
fwd_input = tf.concat([atensor, frgb_1], 1)
featnums = [64, 2]
self.forward_applier = vision.ConvApplier(None,
featnums,
vprefix + 'ForwardModelNet',
elu=True,
nolu_at_final=True)
self.forward_params, self.forward = self.forward_applier.infer(
fwd_input)
# Per action curiosity
print('FWD {}'.format(self.forward.shape))
print('FRGB_2 {}'.format(frgb_2.shape))
self.sqdiff = tf.squared_difference(self.forward, frgb_2)
print('SQDIFF {}'.format(self.sqdiff.shape))
self.curiosity = tf.reduce_mean(self.sqdiff, axis=[1], keepdims=False)
print('> Polout {} Valout {} Curiosity {}'.format(
self.polout.shape, self.valout.shape, self.curiosity.shape))
self.loss = None
self.debug = args.debug
self.cr = args.cr
self.master = master
ws = self.get_weights()
if master is not None:
mws = self.master.get_weights()
self.download_op = [
tf.assign(target, source) for target, source in zip(ws, mws)
]
else:
self.grads_in1 = [
tf.placeholder(tensor.dtype, shape=tensor.shape)
for tensor in ws
]
self.grads_in2 = [
tf.placeholder(tensor.dtype, shape=tensor.shape)
for tensor in ws
]
self.grads_op = [
tf.assign_add(var, in2 - in1)
for var, in1, in2 in zip(ws, self.grads_in1, self.grads_in2)
]
h = r.pbufferHeight
# Action taken
self.a3c_batch_a_tensor = tf.placeholder(tf.float32, shape=[None, self.action_size])
self.rgb1_tensor = tf.placeholder(tf.float32, shape=[None, view_num, w, h, 3])
self.rgb2_tensor = tf.placeholder(tf.float32, shape=[None, view_num, w, h, 3])
self.dep1_tensor = tf.placeholder(tf.float32, shape=[None, view_num, w, h, 1])
self.dep2_tensor = tf.placeholder(tf.float32, shape=[None, view_num, w, h, 1])
self.icm = icm.IntrinsicCuriosityModule(
self.a3c_batch_a_tensor,
self.rgb1_tensor, self.dep1_tensor,
self.rgb2_tensor, self.dep2_tensor,
svconfdict, mvconfdict,
mv_featnum)
self.policy_applier = vision.ConvApplier(None, config.POLICY_HIDDEN_LAYERS + [output_number])
params,out = self.policy_applier.infer(self.icm.cur_featvec)
self.policy_fc_params = params
self.policy_params = self.cur_nn_params + params
self.policy_out = out
print('RLDRIVER POLICY PARAMS {}'.format(self.policy_out))
print('RLDRIVER POLICY OUTPUT {}'.format(self.policy_out))
self.value_applier = vision.ConvApplier(None, config.VALUE_HIDDEN_LAYERS + [1])
params,out = self.value_applier.infer(self.icm.cur_featvec)
self.value_fc_params = params
self.value_params = self.cur_nn_params + params
self.value_out = out
print('RLDRIVER VALUE PARAMS {}'.format(self.value_out))
print('RLDRIVER VALUE OUTPUT {}'.format(self.value_out))
