def __init__(self, attr):
Attr.__init__(self, attr)
self._className = attr['Type'].rsplit(None, 1)[-1]
self._backRefAttr = None # init'ed in awakeFromRead()
if self.get('Min') is not None:
self['Min'] = int(self['Min'])
if self.get('Max') is not None:
self['Max'] = int(self['Max'])
def __init__(self, dict):
Attr.__init__(self, dict)
self._className = dict['Type'].split()[-1]
self._backRefAttr = None # init'ed in awakeFromRead()
if self.get('Min') is not None:
self['Min'] = int(self['Min'])
if self.get('Max') is not None:
self['Max'] = int(self['Max'])
def __init__(self, dict):
Attr.__init__(self, dict)
# We expect than an 'Enums' key holds the enumeration values
enums = self['Enums']
enums = enums.split(',')
enums = [enum.strip() for enum in enums]
self._enums = enums
set = {}
for enum in self._enums:
set[enum] = 1
self._enumSet = set
def build(self):
self.attr_net = Attr()
self.speed_lstm = SpeedLSTM()
self.road_lstm = RoadLSTM()
self.bi_lstm = nn.LSTM(
input_size = self.attr_net.out_size() + 64, \
hidden_size = 64, \
num_layers = 2, \
batch_first = True, \
bidirectional = True, \
dropout = 0.25
)
self.lnhiddens = nn.LayerNorm(self.attr_net.out_size() + 64,
elementwise_affine=True)
class PredictionBiLSTM(nn.Module):
def __init__(self):
super(PredictionBiLSTM, self).__init__()
self.build()
def build(self):
self.attr_net = Attr()
self.speed_lstm = SpeedLSTM()
self.road_lstm = RoadLSTM()
self.bi_lstm = nn.LSTM(
input_size = self.attr_net.out_size() + 64, \
hidden_size = 64, \
num_layers = 2, \
batch_first = True, \
bidirectional = True, \
dropout = 0.25
)
self.lnhiddens = nn.LayerNorm(self.attr_net.out_size() + 64,
elementwise_affine=True)
nn.init.uniform_(self.bi_lstm.state_dict()['weight_hh_l0'],
a=-0.05,
b=0.05)
def forward(self, attr, traj):
speeds_t = self.speed_lstm(attr, traj)
roads_t = self.road_lstm(attr, traj)
attr_t = self.attr_net(attr)
attr_t = torch.unsqueeze(attr_t, dim=1)
expand_attr_t = attr_t.expand(roads_t.size()[:2] +
(attr_t.size()[-1], ))
hiddens = torch.cat([expand_attr_t, speeds_t, roads_t], dim=2)
hiddens = self.lnhiddens(hiddens)
lens = copy.deepcopy(traj['lens'])
lens = list(map(lambda x: x, lens))
packed_inputs = nn.utils.rnn.pack_padded_sequence(hiddens,
lens,
batch_first=True)
packed_hiddens, (h_n, c_n) = self.bi_lstm(packed_inputs)
hiddens, lens = nn.utils.rnn.pad_packed_sequence(packed_hiddens,
batch_first=True)
return hiddens
def build(self):
self.attr_net = Attr()
self.speed_lstm = SpeedLSTM()
self.road_lstm = RoadLSTM()
self.bi_lstm = nn.LSTM(
input_size = self.attr_net.out_size() + 64, \
hidden_size = 64, \
num_layers = 2, \
batch_first = True, \
bidirectional = True, \
dropout = 0.25
)
self.lnhiddens = nn.LayerNorm(self.attr_net.out_size() + 64,
elementwise_affine=True)
nn.init.uniform_(self.bi_lstm.state_dict()['weight_hh_l0'],
a=-0.05,
b=0.05)
def ParseValue(self, v, t):
if t == 'int':
return int(v)
elif t == 'str':
return str(v)
elif t == 'attr':
return Attr(str(v))
elif t == 'reward':
return Reward(str(v))
elif t == 'listint':
return ListInt(str(v))
elif t == 'starrate':
return StarRate(str(v))
return 0
def getAttributeNode(self, name):
return Attr(self.doc, self, name) if self.tag.has_attr(name) else None
def __init__(self, dict): Attr.__init__(self, dict)
def __init__(self, dict): Attr.__init__(self, dict) self._className = dict['Type']
def __init__(self, attr):
Attr.__init__(self, attr)
self._className = attr['Type']
def __init__(self, attr):
Attr.__init__(self, attr)
def __init__(self, attr):
Attr.__init__(self, attr)
def __init__(self, attr):
Attr.__init__(self, attr)
# We expect that an 'Enums' key holds the enumeration values
self._enums = [enum.strip() for enum in self['Enums'].split(',')]
self._enumSet = dict((enum, i) for i, enum in enumerate(self._enums))
def __init__(self, dict):
Attr.__init__(self, dict)
self._className = dict['Type'].split()[-1]
def __init__(self, dict): Attr.__init__(self, dict)
def createAttribute(self, name):
return Attr(self, None, name)
def __init__(self, attr):
Attr.__init__(self, attr)
self._className = attr['Type']
