/
recon_game.py
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/
recon_game.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import egg.core as core
from utils import View, kaiming_init, TopographicSimilarityLatents, ConsoleFileLogger
from modules import BaseGame, DspritesSenderCNN, SymbolicSenderMLP
from data_loader import get_dsprites_dataloader, get_symbolic_dataloader
class DSpritesReceiverCNN(nn.Module):
"""The specific CNN receiver for dSprite images."""
def __init__(self, hidden_size=256) -> None:
super().__init__()
self.hidden_size = hidden_size
self.decoder = nn.Sequential(
nn.Linear(hidden_size, 256), # B, 256
nn.ReLU(True),
nn.Linear(256, 256), # B, 256
nn.ReLU(True),
nn.Linear(256, 32*4*4), # B, 512
nn.ReLU(True),
View((-1, 32, 4, 4)), # B, 32, 4, 4
nn.ConvTranspose2d(32, 32, 4, 2, 1), # B, 32, 8, 8
nn.ReLU(True),
nn.ConvTranspose2d(32, 32, 4, 2, 1), # B, 32, 16, 16
nn.ReLU(True),
nn.ConvTranspose2d(32, 32, 4, 2, 1), # B, 32, 32, 32
nn.ReLU(True),
nn.ConvTranspose2d(32, 1, 4, 2, 1), # B, nc, 64, 64
# nn.Sigmoid(),
View((-1, 64*64))
)
self.weight_init()
def forward(self, x, input=None):
return torch.squeeze(self.decoder(x), 1)
def weight_init(self):
for block in self._modules:
for m in self._modules[block]:
kaiming_init(m)
class DspritesReconGame(BaseGame):
def __init__(self, data_path:str=None) -> None:
super().__init__()
self.train_loader, self.test_loader = \
get_dsprites_dataloader(batch_size=self.batch_size, path_to_data=data_path)
self.sender = core.RnnSenderGS(
DspritesSenderCNN(self.hidden_size),
self.vocab_size,
self.emb_size,
self.hidden_size,
max_len=self.max_len,
cell="lstm",
temperature=1.0
)
self.receiver = core.RnnReceiverGS(
DSpritesReceiverCNN(self.hidden_size),
self.vocab_size,
self.emb_size,
self.hidden_size,
cell="lstm"
)
self.game = core.SenderReceiverRnnGS(self.sender, self.receiver, self.loss)
self.optimiser = core.build_optimizer(self.game.parameters())
self.callbacks = []
self.callbacks.append(core.ConsoleLogger(as_json=True,print_train_loss=True))
self.callbacks.append(core.TemperatureUpdater(agent=self.sender, decay=0.9, minimum=0.1))
self.callbacks.append(TopographicSimilarityLatents('euclidean', 'edit'))
self.trainer = core.Trainer(
game=self.game, optimizer=self.optimiser, train_data=self.train_loader, validation_data=self.test_loader,
callbacks=self.callbacks
)
@staticmethod
def loss(sender_input, _message, _receiver_input, receiver_output, _labels):
# loss = F.binary_cross_entropy(receiver_output, sender_input.view(-1, 4096), reduction='none').sum(dim=1)
loss = F.mse_loss(receiver_output, sender_input.view(-1, 4096), reduction = 'none').sum(dim=1)
return loss, {}
class SymbolicReceiverMLP(nn.Module):
"""The specific MLP receiver for symbolic dataset."""
def __init__(self, output_dim=18, hidden_size=256) -> None:
super().__init__()
self.output_dim = output_dim
self.hidden_size = hidden_size
self.decoder = nn.Sequential(
nn.Linear(hidden_size, hidden_size), # B, 256
nn.ReLU(True),
nn.Linear(hidden_size, hidden_size), # B, 256
nn.ReLU(True),
nn.Linear(hidden_size, output_dim), # B, out_dim
nn.Sigmoid()
)
self.weight_init()
def forward(self, x, input=None):
return torch.squeeze(self.decoder(x), 1)
def weight_init(self):
for block in self._modules:
for m in self._modules[block]:
kaiming_init(m)
class SymbolicReconGame(BaseGame):
def __init__(self, training_log=None) -> None:
super().__init__()
self.training_log = training_log if training_log is not None else core.get_opts().training_log_path
self.train_loader, self.test_loader = \
get_symbolic_dataloader(
n_attributes=self.n_attributes,
n_values=self.n_values,
batch_size=self.batch_size
)
self.sender = core.RnnSenderGS(
SymbolicSenderMLP(input_dim=self.n_attributes*self.n_values, hidden_dim=self.hidden_size),
self.vocab_size,
self.emb_size,
self.hidden_size,
max_len=self.max_len,
cell="lstm",
temperature=1.0
)
self.receiver = core.RnnReceiverGS(
SymbolicReceiverMLP(self.n_attributes * self.n_values, self.hidden_size),
self.vocab_size,
self.emb_size,
self.hidden_size,
cell="lstm"
)
self.game = core.SenderReceiverRnnGS(self.sender, self.receiver, self.loss)
self.optimiser = core.build_optimizer(self.game.parameters())
self.callbacks = []
self.callbacks.append(ConsoleFileLogger(as_json=True,print_train_loss=True,logfile_path=self.training_log))
self.callbacks.append(core.TemperatureUpdater(agent=self.sender, decay=0.9, minimum=0.1))
self.callbacks.append(TopographicSimilarityLatents('hamming', 'edit', log_path=core.get_opts().topo_path))
self.trainer = core.Trainer(
game=self.game, optimizer=self.optimiser, train_data=self.train_loader, validation_data=self.test_loader,
callbacks=self.callbacks
)
@staticmethod
def loss(sender_input, _message, _receiver_input, receiver_output, _labels):
# loss_f = nn.BCELoss()
# loss = loss_f(receiver_output, sender_input)
loss = F.mse_loss(receiver_output, sender_input)
return loss, {}