def get_game(opt):
feat_size = 4096
out_hidden_size = 20
emb_size = 10
sender = InformedSender(opt.game_size,
feat_size,
opt.embedding_size,
opt.hidden_size,
out_hidden_size,
temp=opt.tau_s)
receiver = Receiver(opt.game_size,
feat_size,
opt.embedding_size,
out_hidden_size,
reinforce=(opts.mode == 'rf'))
if opts.mode == 'rf':
sender = core.RnnSenderReinforce(sender,
opt.vocab_size,
emb_size,
out_hidden_size,
cell="gru",
max_len=2)
receiver = core.RnnReceiverReinforce(receiver,
opt.vocab_size,
emb_size,
out_hidden_size,
cell="gru")
game = core.SenderReceiverRnnReinforce(sender,
receiver,
loss,
sender_entropy_coeff=0.01,
receiver_entropy_coeff=0.01)
elif opts.mode == 'gs':
sender = core.GumbelSoftmaxWrapper(sender, temperature=opt.gs_tau)
game = core.SymbolGameGS(sender, receiver, loss_nll)
else:
raise RuntimeError(f"Unknown training mode: {opts.mode}")
return game
num_workers=1)
assert train_loader or dump_loader, 'Either training or dump data must be specified'
sender, receiver, loss = build_model(opts, train_loader, dump_loader)
if opts.train_mode.lower() == 'rf':
sender = core.RnnSenderReinforce(sender,
opts.vocab_size,
opts.sender_embedding,
opts.sender_hidden,
cell=opts.sender_cell,
max_len=opts.max_len,
num_layers=opts.sender_layers)
receiver = core.RnnReceiverReinforce(receiver,
opts.vocab_size,
opts.receiver_embedding,
opts.receiver_hidden,
cell=opts.receiver_cell,
num_layers=opts.receiver_layers)
game = core.SenderReceiverRnnReinforce(
sender,
receiver,
non_differentiable_loss,
sender_entropy_coeff=opts.sender_entropy_coeff,
receiver_entropy_coeff=opts.receiver_entropy_coeff)
elif opts.train_mode.lower() == 'gs':
sender = core.RnnSenderGS(sender,
opts.vocab_size,
opts.sender_embedding,
opts.sender_hidden,
cell=opts.sender_cell,
def get_my_game(opt):
feat_size = 4096
out_hidden_size = 20
emb_size = 10
pop = opts.pop_size
sender_list = []
receiver_list = []
for i in range(pop):
if not opts.multi_head:
sender = InformedSender(opt.game_size,
feat_size,
opt.embedding_size,
opt.hidden_size,
out_hidden_size,
temp=opt.tau_s)
else:
sender = InformedSenderMultiHead(opt.game_size,
feat_size,
opt.embedding_size,
opt.hidden_size,
out_hidden_size,
temp=opt.tau_s)
receiver = MyReceiver(opt.game_size,
feat_size,
opt.embedding_size,
out_hidden_size,
reinforce=(opts.mode == 'rf'))
if opts.mode == 'rf':
sender = core.MyRnnSenderReinforce(sender,
opt.vocab_size,
emb_size,
out_hidden_size,
multi_head=opt.multi_head,
cell="gru",
max_len=opt.max_len)
receiver = core.RnnReceiverReinforce(receiver,
opt.vocab_size,
emb_size,
out_hidden_size,
cell="gru")
elif opts.mode == 'gs':
sender = core.GumbelSoftmaxWrapper(sender, temperature=opt.gs_tau)
else:
raise RuntimeError(f"Unknown training mode: {opts.mode}")
sender_list.append(sender)
receiver_list.append(receiver)
if opts.mode == 'rf':
if opts.pop_mode == 0:
game = core.PopSenderReceiverRnnReinforce(
sender_list,
receiver_list,
pop,
loss,
sender_entropy_coeff=0.01,
receiver_entropy_coeff=0.01)
elif opts.pop_mode == 1:
game = core.PopUncSenderReceiverRnnReinforce(
sender_list,
receiver_list,
pop,
loss,
use_critic_baseline=False,
sender_entropy_coeff=0.01,
receiver_entropy_coeff=0.01)
else:
game = core.PopUncSenderReceiverRnnReinforce(
sender_list,
receiver_list,
pop,
loss,
use_critic_baseline=True,
sender_entropy_coeff=0.01,
receiver_entropy_coeff=0.01)
elif opts.mode == 'gs':
game = core.PopSymbolGameGS(sender_list, receiver_list, pop, loss_nll)
else:
raise RuntimeError(f"Unknown training mode: {opts.mode}")
return game