class DuelingHead(nn.Module):
def __init__(self, max_num_leaves, in_dim, use_noisylayers):
super(DuelingHead, self).__init__()
self._max_num_leaves = max_num_leaves
if use_noisylayers:
self._V_out = NoisyLinear(in_dim, 1)
self._A_out = NoisyLinear(in_dim, 1)
else:
self._V_out = nn.Linear(in_dim, 1)
self._A_out = nn.Linear(in_dim, 1)
def forward(self, h, leaves_mask, _):
h_V, h_A = h
# [m, 1]
V = self._V_out(h_V)
# [m, max_num_leaves]
A = self._A_out(h_A).view(-1, self._max_num_leaves)
# [m, max_num_leaves]
Q = V + A - ((A * leaves_mask).sum(1, keepdim=True) /
leaves_mask.sum(1, keepdim=True))
inf_mask = leaves_mask.clone()
inf_mask[leaves_mask == 0] = float('inf')
inf_mask[leaves_mask == 1] = 0
Q -= inf_mask
return Q
def reset_noise(self):
self._V_out.reset_noise()
self._A_out.reset_noise()
class DuelingC51Head(nn.Module):
def __init__(self, max_num_leaves, in_dim, use_noisylayers, num_atoms):
super(DuelingC51Head, self).__init__()
self._max_num_leaves = max_num_leaves
self._num_atoms
if use_noisylayers:
self._V_out = NoisyLinear(in_dim, 1 * self._num_atoms)
self._A_out = NoisyLinear(in_dim, 1 * self._num_atoms)
else:
self._V_out = nn.Linear(in_dim, self._num_atoms)
self._A_out = nn.Linear(in_dim, self._num_atoms)
def forward(self, h, leaves_mask, log=False):
h_V, h_A = h
# [m, 1, num_atoms]
V = self._V_out(h_V).view(-1, 1, self._num_atoms)
# [m, max_num_leaves, num_atoms]
leaves_mask_expanded = leaves_mask.unsqueeze(2).expand(
-1, -1, self._num_atoms)
# [m, max_num_leaves, num_atoms]
A = self._A_out(h_A).view(-1, self._max_num_leaves, self._num_atoms)
# [m, max_num_leaves, num_atoms]
Y = V + A - ((A * leaves_mask_expanded).sum(1, keepdim=True) /
leaves_mask_expanded.sum(1, keepdim=True))
if log:
P = F.log_softmax(Y, dim=2)
else:
P = F.softmax(Y, dim=2)
return P, leaves_mask
def reset_noise(self):
self._V_out.reset_noise()
self._A_out.reset_noise()
def __init__(self, max_num_leaves, in_dim, use_noisylayers, num_atoms):
super(C51Head, self).__init__()
self._max_num_leaves = max_num_leaves
self._num_atoms = num_atoms
if use_noisylayers:
self._out = NoisyLinear(in_dim, self._num_atoms)
else:
self._out = nn.Linear(in_dim, self._num_atoms)
def __init__(self, max_num_leaves, in_dim, use_noisylayers):
super(DuelingHead, self).__init__()
self._max_num_leaves = max_num_leaves
if use_noisylayers:
self._V_out = NoisyLinear(in_dim, 1)
self._A_out = NoisyLinear(in_dim, 1)
else:
self._V_out = nn.Linear(in_dim, 1)
self._A_out = nn.Linear(in_dim, 1)
class C51Head(nn.Module):
def __init__(self, max_num_leaves, in_dim, use_noisylayers, num_atoms):
super(C51Head, self).__init__()
self._max_num_leaves = max_num_leaves
self._num_atoms = num_atoms
if use_noisylayers:
self._out = NoisyLinear(in_dim, self._num_atoms)
else:
self._out = nn.Linear(in_dim, self._num_atoms)
def forward(self, h, leaves_mask, log=False):
Y = self._out(h).view(-1, self._max_num_leaves, self._num_atoms)
if log:
P = F.log_softmax(Y, dim=2)
else:
P = F.softmax(Y, dim=2)
# [m, max_num_leaves, num_atoms], [m, max_num_leaves]
return P, leaves_mask
def reset_noise(self):
self._out.reset_noise()
def __init__(self, max_num_leaves, max_num_goal_tokens, dom_stream_dim,
use_noisylayers):
super(Head, self).__init__()
self._max_num_leaves = max_num_leaves
self._max_num_goal_tokens = max_num_goal_tokens
mode_stream_dim, token_stream_dim = int(dom_stream_dim / 4), int(
dom_stream_dim / 2)
if use_noisylayers:
self._out_dom = NoisyLinear(dom_stream_dim, 1)
self._out_mode = NoisyLinear(mode_stream_dim, 2)
self._out_token = NoisyLinear(token_stream_dim, 1)
else:
self._out_dom = nn.Linear(dom_stream_dim, 1)
self._out_mode = nn.Linear(mode_stream_dim, 2)
self._out_token = nn.Linear(token_stream_dim, 1)
class Head(nn.Module):
def __init__(self, max_num_leaves, max_num_goal_tokens, dom_stream_dim,
use_noisylayers):
super(Head, self).__init__()
self._max_num_leaves = max_num_leaves
self._max_num_goal_tokens = max_num_goal_tokens
mode_stream_dim, token_stream_dim = int(dom_stream_dim / 4), int(
dom_stream_dim / 2)
if use_noisylayers:
self._out_dom = NoisyLinear(dom_stream_dim, 1)
self._out_mode = NoisyLinear(mode_stream_dim, 2)
self._out_token = NoisyLinear(token_stream_dim, 1)
else:
self._out_dom = nn.Linear(dom_stream_dim, 1)
self._out_mode = nn.Linear(mode_stream_dim, 2)
self._out_token = nn.Linear(token_stream_dim, 1)
def forward(self, h_list, leaves_mask, goal_mask, _):
"""
Outputs: Q_dom for dom, Q_type for click or type(binary),
Q_token for selected goal token idx
"""
h_dom, h_mode, h_token = h_list
Q_dom = self._out_dom(h_dom)
# 1. [m, max_num_leaves] <= [m*max_num_leaves]
Q_dom = Q_dom.view(-1, self._max_num_leaves)
inf_mask = leaves_mask.clone()
inf_mask[leaves_mask == 0] = float('inf')
inf_mask[leaves_mask == 1] = 0
Q_dom -= inf_mask
# 2. [m, 2]
Q_mode = self._out_mode(h_mode)
Q_token = self._out_token(h_token)
# 3. [m, max_num_tokens] <= [m*max_num_tokens]
Q_token = Q_token.view(-1, self._max_num_goal_tokens)
inf_mask = goal_mask.clone()
inf_mask[goal_mask == 0] = float('inf')
inf_mask[goal_mask == 1] = 0
# [m, max_num_tokens]
Q_token -= inf_mask
return Q_dom, Q_mode, Q_token
def reset_noise(self):
self._out_dom.reset_noise()
self._out_mode.reset_noise()
self._out_token.reset_noise()
def __init__(self, in_dom_global_dim, max_num_leaves, max_num_goal_tokens,
in_dom_dim, in_token_dim, h_dom_dim, use_noisylayers,
use_local, use_neighbor, use_global):
super(Body, self).__init__()
assert use_local or use_neighbor
self._use_local = use_local
self._use_neighbor = use_neighbor
self._use_global = use_global
if not use_global:
if use_local and use_neighbor:
self._in_cat_dom_dim = in_dom_dim * 2
else:
self._in_cat_dom_dim = in_dom_dim
else:
if use_local and use_neighbor:
self._in_cat_dom_dim = in_dom_dim * 2 + in_dom_global_dim
else:
self._in_cat_dom_dim = in_dom_dim + in_dom_global_dim
self._in_mode_dim = in_dom_global_dim
self._in_token_dim = in_token_dim
self._max_num_leaves = max_num_leaves
self._max_num_goal_tokens = max_num_goal_tokens
h_mode_dim, h_token_dim = int(h_dom_dim / 4), int(h_dom_dim / 2)
if use_noisylayers:
self._fc1_dom = NoisyLinear(self._in_cat_dom_dim, h_dom_dim)
self._fc2_dom = NoisyLinear(h_dom_dim, h_dom_dim)
self._fc1_mode = NoisyLinear(self._in_mode_dim, h_mode_dim)
self._fc2_mode = NoisyLinear(h_mode_dim, h_mode_dim)
self._fc1_token = NoisyLinear(self._in_token_dim, h_token_dim)
self._fc2_token = NoisyLinear(h_token_dim, h_token_dim)
else:
pass
class Body(nn.Module):
def __init__(self, in_dom_global_dim, max_num_leaves, max_num_goal_tokens,
in_dom_dim, in_token_dim, h_dom_dim, use_noisylayers,
use_local, use_neighbor, use_global):
super(Body, self).__init__()
assert use_local or use_neighbor
self._use_local = use_local
self._use_neighbor = use_neighbor
self._use_global = use_global
if not use_global:
if use_local and use_neighbor:
self._in_cat_dom_dim = in_dom_dim * 2
else:
self._in_cat_dom_dim = in_dom_dim
else:
if use_local and use_neighbor:
self._in_cat_dom_dim = in_dom_dim * 2 + in_dom_global_dim
else:
self._in_cat_dom_dim = in_dom_dim + in_dom_global_dim
self._in_mode_dim = in_dom_global_dim
self._in_token_dim = in_token_dim
self._max_num_leaves = max_num_leaves
self._max_num_goal_tokens = max_num_goal_tokens
h_mode_dim, h_token_dim = int(h_dom_dim / 4), int(h_dom_dim / 2)
if use_noisylayers:
self._fc1_dom = NoisyLinear(self._in_cat_dom_dim, h_dom_dim)
self._fc2_dom = NoisyLinear(h_dom_dim, h_dom_dim)
self._fc1_mode = NoisyLinear(self._in_mode_dim, h_mode_dim)
self._fc2_mode = NoisyLinear(h_mode_dim, h_mode_dim)
self._fc1_token = NoisyLinear(self._in_token_dim, h_token_dim)
self._fc2_token = NoisyLinear(h_token_dim, h_token_dim)
else:
pass
def forward(self, e_local, e_neighbor, e_global, e_tokens):
# [m, d]
e_global_ = e_global
# [m, max_num_leaves(copied), d]<-[m, d]
e_global = e_global.unsqueeze(1).expand(-1, self._max_num_leaves, -1)
# [m * self._max_num_leaves, 3*d]
if not self._use_global:
if self._use_local and self._use_neighbor:
e_dom = torch.cat((e_local, e_neighbor), dim=2)
elif not self._use_local:
e_dom = e_neighbor
else:
e_dom = e_local
else:
if self._use_local and self._use_neighbor:
e_dom = torch.cat((e_local, e_neighbor, e_global), dim=2)
elif not self._use_local:
e_dom = torch.cat((e_neighbor, e_glboal), dim=2)
else:
e_dom = torch.cat((e_local, e_global), dim=2)
# [1] DOM stream
# [m*V_size, in_dom_dim] <- [m, V_size, in_dom_dim]
e_dom = e_dom.view(-1, self._in_cat_dom_dim)
h_dom = F.relu(self._fc1_dom(e_dom))
h_dom = F.relu(self._fc2_dom(h_dom))
# [2] Mode stream
h_mode = F.relu(self._fc1_mode(e_global_))
h_mode = F.relu(self._fc2_mode(h_mode))
# [3] Token Stream
# [m*max_num_tokens, in_token_dim] <- [m, max_num_tokens, in_token_dim]
e_tokens = e_tokens.view(-1, self._in_token_dim)
h_tokens = F.relu(self._fc1_token(e_tokens))
h_tokens = F.relu(self._fc2_token(h_tokens))
return h_dom, h_mode, h_tokens
def reset_noise(self):
self._fc1_dom.reset_noise()
self._fc2_dom.reset_noise()
self._fc1_mode.reset_noise()
self._fc2_mode.reset_noise()
self._fc1_token.reset_noise()
self._fc2_token.reset_noise()