def main(unused_argv):
# create output dirs
output_dir = Path(FLAGS.output_dir)
Path.mkdir(output_dir, exist_ok=True)
decode_dbl = parse_emotion_dbl(FLAGS.eval_file_path)
if FLAGS.cpc_path is not None:
cpc = load_model(FLAGS.cpc_path).eval().to(device)
else:
cpc = NoCPC().eval().to(device)
model = load_model(FLAGS.model_path).eval().to(device)
set_seeds()
# Need the enumeration to ensure unique files
for i, dbl_entry in enumerate(decode_dbl):
filename = Path(dbl_entry.audio_path)
preds = decode_emotions_from_file(filename.as_posix(), cpc, model,
FLAGS.window_size)
with open(str(output_dir / filename.name) + "_" + str(i),
"w") as out_f:
for pred in preds:
out_f.write("{:.3f} {:.3f} {}\n".format(
pred.start, pred.end, pred.label))
with open(output_dir / "score.dbl", "a") as dbl_fh:
dbl_fh.write(str(output_dir / filename.name) + "_" + str(i) + "\n")
def setup(self):
set_seeds(self.config.seed, "cuda" in self.config.device)
self.setup_data()
self.setup_model()
self.config.epoch_str_template = "{:0" + str(
len(str(self.config.n_epochs))) + "d}"
self.clog.show_text(self.model.__repr__(), "Model")
def main(unused_argv):
# create output dirs
output_dir = Path(FLAGS.output_dir)
Path.mkdir(output_dir, exist_ok=True)
if FLAGS.cpc_path is not None:
cpc = load_model(FLAGS.cpc_path).eval().to(device)
else:
cpc = NoCPC().eval().to(device)
model = load_model(FLAGS.model_path).eval().to(device)
dataset = AudioDataset(FLAGS.eval_file_path, train=False)
dataloader = AudioDataLoader(
dataset,
window_size=None,
batch_size=1,
feature_transform=cpc.data_class,
num_workers=8,
shuffle=False,
)
set_seeds()
# Need the enumeration to ensure unique files
for i, batch in enumerate(dataloader):
data = batch["data"].to(device)
cpc.reset_state()
preds = []
prev_end_s = 0.0
windows = torch.split(data, FLAGS.window_size, dim=1)
for window in windows:
with torch.no_grad():
features = cpc(window)
pred = model(features).argmax(dim=2).squeeze(dim=0)
outputs, prev_end_s = preds_to_output(
pred,
window.shape[1],
dataloader.sampling_rate,
prev_end_s,
)
preds.extend(outputs)
filename = Path(batch["files"][0])
with open(str(output_dir / filename.name) + "_" + str(i),
"w") as out_f:
for pred in preds:
out_f.write("{:.3f} {:.3f} {}\n".format(
pred.start, pred.end, pred.label))
with open(output_dir / "score.dbl", "a") as dbl_fh:
dbl_fh.write(str(output_dir / filename.name) + "_" + str(i) + "\n")
def train(unused_argv):
set_seeds(FLAGS.seed)
write_current_pid(FLAGS.expdir)
# setup logging
tb_logger = prepare_tb_logging()
prepare_standard_logging("training")
loss_dir = Path(f"{FLAGS.expdir}/losses")
loss_dir.mkdir(exist_ok=True)
train_losses_fh = open(loss_dir / "train.txt", "a", buffering=1)
valid_losses_fh = open(loss_dir / "valid.txt", "a", buffering=1)
if FLAGS.dry_run is True:
setup_dry_run(FLAGS)
if not FLAGS.model_out:
FLAGS.model_out = FLAGS.expdir + "/model.pt"
if not FLAGS.checkpoint_out:
FLAGS.checkpoint_out = FLAGS.expdir + "/checkpoint.pt"
if FLAGS.checkpoint_autoload is True and not FLAGS.checkpoint:
FLAGS.checkpoint = get_checkpoint_to_start_from(FLAGS.checkpoint_out)
logging.info(f"autosetting checkpoint: {FLAGS.checkpoint}")
if FLAGS.cpc_path is not None:
cpc = load_model(FLAGS.cpc_path).to(device)
cpc.reset_state()
else:
cpc = NoCPC()
cpc.eval()
# write information about cpc into metadata
with open(f"{FLAGS.expdir}/metadata.txt", "a") as fh:
fh.write(f"sampling_rate_hz {cpc.data_class.SAMPLING_RATE_HZ}\n")
fh.write(f"feat_dim {cpc.feat_dim}\n")
# define training data
parsed_train_dbl = parse_emotion_dbl(FLAGS.train_data)
train_streams = [
DblStream(
DblSampler(parsed_train_dbl),
EmotionIDSingleFileStream,
FLAGS.window_size,
emotion_set_path=FLAGS.emotion_set_path,
audiostream_class=cpc.data_class,
)
for _ in range(FLAGS.batch_size)
]
train_datastream = MultiStreamDataLoader(train_streams, device=device)
# define validation data
parsed_valid_dbl = parse_emotion_dbl(FLAGS.val_data)
parsed_test_dbl = parse_emotion_dbl(FLAGS.test_data)
val_streams = [
DblStream(
DblSampler(parsed_valid_dbl),
EmotionIDSingleFileStream,
FLAGS.window_size,
emotion_set_path=FLAGS.emotion_set_path,
audiostream_class=cpc.data_class, # TODO ensure un-augmented stream
)
for _ in range(FLAGS.batch_size)
]
valid_datastream = MultiStreamDataLoader(val_streams, device=device)
if not FLAGS.val_every:
FLAGS.val_every = max(100, FLAGS.steps // 50)
if not FLAGS.save_every:
FLAGS.save_every = FLAGS.val_every
if not FLAGS.valid_steps:
FLAGS.valid_steps = max(20, FLAGS.val_every // 100)
valid_frames = FLAGS.batch_size * FLAGS.window_size * FLAGS.valid_steps
feat_dim = cpc.feat_dim
num_emotions = len(get_emotion_to_id_mapping(FLAGS.emotion_set_path))
if FLAGS.model == "linear":
model = LinearEmotionIDModel(feat_dim, num_emotions).to(device)
elif FLAGS.model == "baseline":
model = BaselineEmotionIDModel(feat_dim, num_emotions).to(device)
elif FLAGS.model == "mlp2":
model = MLPEmotionIDModel(
feat_dim,
num_emotions,
no_layers=2,
hidden_size=FLAGS.hidden_size,
dropout_prob=FLAGS.dropout_prob,
batch_norm_on=FLAGS.batch_norm,
).to(device)
elif FLAGS.model == "mlp4":
model = MLPEmotionIDModel(
feat_dim,
num_emotions,
no_layers=4,
hidden_size=FLAGS.hidden_size,
dropout_prob=FLAGS.dropout_prob,
batch_norm_on=FLAGS.batch_norm,
).to(device)
elif FLAGS.model == "conv":
model = ConvEmotionIDModel(
feat_dim,
num_emotions,
no_layers=4,
hidden_size=FLAGS.hidden_size,
dropout_prob=FLAGS.dropout_prob,
).to(device)
elif FLAGS.model == "rnn":
model = RecurrentEmotionIDModel(
feat_dim=feat_dim,
num_emotions=num_emotions,
bidirectional=False,
hidden_size=FLAGS.hidden_size,
dropout_prob=FLAGS.dropout_prob,
).to(device)
elif FLAGS.model == "rnn_bi":
model = RecurrentEmotionIDModel(
feat_dim=feat_dim,
num_emotions=num_emotions,
bidirectional=True,
hidden_size=FLAGS.hidden_size,
dropout_prob=FLAGS.dropout_prob,
).to(device)
elif FLAGS.model == "wavenet":
model = WaveNetEmotionIDModel(feat_dim, num_emotions).to(device)
padding_percentage = 100 * model.max_padding / FLAGS.window_size
logging.info(f"max padding {model.max_padding}, percentage {padding_percentage}%")
logging.info(f"receptve field {model.receptive_field}")
elif FLAGS.model == "wavenet_unmasked":
model = WaveNetEmotionIDModel(feat_dim, num_emotions, masked=False).to(device)
padding_percentage = 100 * model.max_padding / FLAGS.window_size
logging.info(f"max padding {model.max_padding}, percentage {padding_percentage}%")
logging.info(f"receptve field {model.receptive_field}")
else:
raise NameError("Model name not found")
logging.info(f"number of classes {num_emotions}")
logging.info(f"model param count {sum(x.numel() for x in model.parameters()):,}")
optimizer = RAdam(model.parameters(), eps=1e-05, lr=FLAGS.lr)
if FLAGS.lr_schedule:
scheduler = FlatCA(optimizer, steps=FLAGS.steps, eta_min=0)
else:
scheduler = EmptyScheduler(optimizer)
step = 0
optimizer.best_val_loss = inf
if FLAGS.checkpoint:
# loading state_dicts in-place
load_checkpoint(FLAGS.checkpoint, model, optimizer, scheduler=scheduler)
step = optimizer.restored_step
dump_checkpoint_on_kill(model, optimizer, scheduler, FLAGS.checkpoint_out)
set_seeds(FLAGS.seed + step)
model.train()
for batch in train_datastream:
data, labels = batch["data"].to(device), batch["labels"]
features = cpc(data)
pred = model(features)
labels = resample_1d(labels, pred.shape[1]).reshape(-1).to(device)
# get cross entropy loss against emotion labels and take step
optimizer.zero_grad()
output = model(features).reshape(-1, num_emotions)
loss = F.cross_entropy(output, labels)
loss.backward()
clip_grad_norm_(model.parameters(), FLAGS.clip_thresh)
optimizer.step()
scheduler.step()
# log training losses
logging.info(f"{step} train steps, loss={loss.item():.5}")
tb_logger.add_scalar("train/loss", loss, step)
train_losses_fh.write(f"{step}, {loss.item()}\n")
tb_logger.add_scalar("train/lr", scheduler.get_lr()[0], step)
# validate periodically
if step % FLAGS.val_every == 0 and step != 0:
valid_loss = validate(valid_datastream, cpc, model, num_emotions)
# log validation losses
logging.info(
f"{step} validation, loss={valid_loss.item():.5}, "
f"{valid_frames:,} items validated"
)
tb_logger.add_scalar("valid/loss", valid_loss, step)
valid_losses_fh.write(f"{step}, {valid_loss}\n")
val_results = validate_filewise(parsed_valid_dbl, cpc, model, num_emotions)
test_results = validate_filewise(parsed_test_dbl, cpc, model, num_emotions)
for results, dataset in zip([val_results, test_results], ["valid", "test"]):
tb_logger.add_scalar(f"{dataset}/full_loss", results["average_loss"], step)
for name in ["framewise", "filewise"]:
cm = fig2tensor(results[name]["confusion_matrix"])
tb_logger.add_scalar(f"{dataset}/accuracy", results[name]["accuracy"], step)
tb_logger.add_scalar(f"{dataset}/f1_score", results[name]["average_f1"], step)
tb_logger.add_image(f"{dataset}/confusion_matrix", cm, step)
for emotion, f1 in val_results["framewise"]["class_f1"].items():
tb_logger.add_scalar(f"f1/{emotion}", f1, step)
if valid_loss.item() < optimizer.best_val_loss:
logging.info("Saving new best validation")
save(model, FLAGS.model_out + ".bestval")
optimizer.best_val_loss = valid_loss.item()
# save out model periodically
if step % FLAGS.save_every == 0 and step != 0:
save(model, FLAGS.model_out + ".step" + str(step))
if step >= FLAGS.steps:
break
step += 1
save(model, FLAGS.model_out)
# close loss logging file handles
train_losses_fh.close()
valid_losses_fh.close()
def train_net(self
# train_set_,
# val_set_,
# writer,
# seed_,
# save_path
):
writer = SummaryWriter(self.log_path)
best_validation_error = 1000.0
set_seeds(self.seed)
# dataset loaders
train_dataset, train_loader = init_dataset(self.train_set, train=True, batch_size=self.batch_size, workers=self.workers)
test_dataset, test_loader = init_dataset(self.validation_set, train=False, batch_size=self.batch_size, workers=self.workers)
# load model
self.model = self.model.train()
if self.on_GPU:
self.model = self.model.cuda()
# define optimizer
optimizer = torch.optim.SGD(self.model.get_config_optim(self.lr, self.lrp),
lr=self.lr,
momentum=self.momentum,
weight_decay=self.weight_decay)
criterion = nn.MSELoss()
j = 0
for epoch in range(self.num_epochs): # loop over the dataset multiple times
train_loader = tqdm(train_loader, desc='Training')
self.model.train()
for i, data in enumerate(train_loader):
# get the inputs
inputs_datas, labels = data
inputs, img_names = inputs_datas
# wrap them in Variable
if self.on_GPU:
inputs, labels = Variable(inputs.cuda()), Variable(labels.cuda())
else:
inputs, labels = Variable(inputs), Variable(labels)
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = self.model.forward(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
n_iter = j
writer.add_scalar('Train/Loss', loss.data, n_iter)
j += 1
# save a new model for each epoch
with torch.no_grad():
validation_error = self.validate_current_model(val_loader=test_loader)
print("validation error: ", validation_error)
if validation_error < best_validation_error:
best_validation_error = validation_error
# save a new model for each epoch
print('saving model: %s_epoch_%d' % (self.save_path, epoch))
torch.save(self.model.state_dict(), ('%s/seed_%d_best_checkpoint.pth' % (self.save_path, self.seed)))
print('Finished Training')
return self.model, best_validation_error
def train(unused_argv):
set_seeds(FLAGS.seed)
# setup logging
tb_logger = SummaryWriter(FLAGS.expdir, flush_secs=10)
loss_dir = Path(f"{FLAGS.expdir}/losses")
loss_dir.mkdir(exist_ok=True)
train_losses_fh = open(loss_dir / "train.txt", "a", buffering=1)
valid_losses_fh = open(loss_dir / "valid.txt", "a", buffering=1)
if not FLAGS.model_out:
FLAGS.model_out = FLAGS.expdir + "/model.pt"
if FLAGS.cpc_path is not None:
cpc = load_model(FLAGS.cpc_path).to(device)
cpc.reset_state()
else:
cpc = NoCPC()
cpc.eval()
# write information about cpc into metadata
with open(f"{FLAGS.expdir}/metadata.txt", "a") as fh:
fh.write(f"data_class {cpc.data_class}\n")
fh.write(f"feat_dim {cpc.feat_dim}\n")
# define training data
train_dataset = EmotionDataset(FLAGS.train_data, FLAGS.emotion_set_path)
train_dataloader = AudioDataLoader(
train_dataset,
window_size=FLAGS.window_size,
batch_size=FLAGS.batch_size,
feature_transform=cpc.data_class,
num_workers=FLAGS.num_workers,
shuffle=True,
drop_last=True,
)
# define validation data
val_dataset = EmotionDataset(FLAGS.val_data, FLAGS.emotion_set_path)
val_dataloader = AudioDataLoader(
val_dataset,
window_size=FLAGS.window_size,
batch_size=FLAGS.batch_size,
feature_transform=cpc.data_class,
num_workers=FLAGS.num_workers,
shuffle=False,
drop_last=True,
)
# filewise validation (like decode time)
decode_dataset = EmotionDataset(FLAGS.val_data,
FLAGS.emotion_set_path,
train=False)
decode_dataloader = AudioDataLoader(
decode_dataset,
window_size=None,
batch_size=1,
feature_transform=cpc.data_class,
num_workers=FLAGS.num_workers,
shuffle=False,
)
if not FLAGS.val_every:
FLAGS.val_every = max(100, FLAGS.steps // 50)
if not FLAGS.save_every:
FLAGS.save_every = FLAGS.val_every
if not FLAGS.valid_steps:
FLAGS.valid_steps = max(20, FLAGS.val_every // 100)
valid_frames = FLAGS.batch_size * FLAGS.window_size * FLAGS.valid_steps
feat_dim = cpc.feat_dim
num_emotions = len(
train_dataset.get_emotion_to_id_mapping(FLAGS.emotion_set_path))
if FLAGS.model == "linear":
model = LinearEmotionIDModel(feat_dim, num_emotions).to(device)
elif FLAGS.model == "baseline":
model = BaselineEmotionIDModel(feat_dim, num_emotions).to(device)
elif FLAGS.model == "mlp2":
model = MLPEmotionIDModel(
feat_dim,
num_emotions,
no_layers=2,
hidden_size=FLAGS.hidden_size,
dropout_prob=FLAGS.dropout_prob,
batch_norm_on=FLAGS.batch_norm,
).to(device)
elif FLAGS.model == "mlp4":
model = MLPEmotionIDModel(
feat_dim,
num_emotions,
no_layers=4,
hidden_size=FLAGS.hidden_size,
dropout_prob=FLAGS.dropout_prob,
batch_norm_on=FLAGS.batch_norm,
).to(device)
elif FLAGS.model == "conv":
model = ConvEmotionIDModel(
feat_dim,
num_emotions,
no_layers=4,
hidden_size=FLAGS.hidden_size,
dropout_prob=FLAGS.dropout_prob,
).to(device)
elif FLAGS.model == "rnn":
model = RecurrentEmotionIDModel(
feat_dim=feat_dim,
num_emotions=num_emotions,
bidirectional=False,
hidden_size=FLAGS.hidden_size,
dropout_prob=FLAGS.dropout_prob,
).to(device)
elif FLAGS.model == "rnn_bi":
model = RecurrentEmotionIDModel(
feat_dim=feat_dim,
num_emotions=num_emotions,
bidirectional=True,
hidden_size=FLAGS.hidden_size,
dropout_prob=FLAGS.dropout_prob,
).to(device)
elif FLAGS.model == "wavenet":
model = WaveNetEmotionIDModel(feat_dim, num_emotions).to(device)
padding_percentage = 100 * model.max_padding / FLAGS.window_size
logging.info(
f"max padding {model.max_padding}, percentage {padding_percentage}%"
)
logging.info(f"receptve field {model.receptive_field}")
elif FLAGS.model == "wavenet_unmasked":
model = WaveNetEmotionIDModel(feat_dim, num_emotions,
masked=False).to(device)
padding_percentage = 100 * model.max_padding / FLAGS.window_size
logging.info(
f"max padding {model.max_padding}, percentage {padding_percentage}%"
)
logging.info(f"receptve field {model.receptive_field}")
else:
raise NameError("Model name not found")
logging.info(f"number of classes {num_emotions}")
logging.info(
f"model param count {sum(x.numel() for x in model.parameters()):,}")
optimizer = RAdam(model.parameters(), eps=1e-05, lr=FLAGS.lr)
scheduler = CosineAnnealingLR(optimizer, FLAGS.steps, eta_min=1e-6)
step = 0
best_val_loss = inf
model.train()
for batch in train_dataloader:
data, labels = batch["data"].to(device), batch["labels"].to(device)
features = cpc(data)
pred = model(features)
# get cross entropy loss against emotion labels and take step
optimizer.zero_grad()
pred = pred.reshape(-1, num_emotions)
labels = labels.reshape(-1)
loss = F.cross_entropy(pred, labels)
loss.backward()
clip_grad_norm_(model.parameters(), FLAGS.clip_thresh)
optimizer.step()
scheduler.step()
# log training losses
logging.info(f"{step} train steps, loss={loss.item():.5}")
tb_logger.add_scalar("01_train/loss", loss, step)
train_losses_fh.write(f"{step}, {loss.item()}\n")
tb_logger.add_scalar("01_train/lr", scheduler.get_lr()[0], step)
# validate periodically
if step % FLAGS.val_every == 0 and step != 0:
valid_loss = validate(val_dataloader, cpc, model, num_emotions)
# log validation losses
logging.info(f"{step} validation, loss={valid_loss.item():.5}, "
f"{valid_frames:,} items validated")
tb_logger.add_scalar("02_valid/loss", valid_loss, step)
valid_losses_fh.write(f"{step}, {valid_loss}\n")
val_results = validate_filewise(decode_dataloader, cpc, model,
num_emotions)
tb_logger.add_scalar("02_valid/full_loss",
val_results["average_loss"], step)
for name in ["framewise", "filewise"]:
cm = fig2tensor(val_results[name]["confusion_matrix"])
tb_logger.add_scalar(f"02_valid/accuracy_{name}",
val_results[name]["accuracy"], step)
tb_logger.add_scalar(f"02_valid/f1_score_{name}",
val_results[name]["average_f1"], step)
tb_logger.add_image(f"02_valid/confusion_matrix_{name}", cm,
step)
for emotion, f1 in val_results["framewise"]["class_f1"].items():
tb_logger.add_scalar(f"03_f1/{emotion}", f1, step)
if valid_loss.item() < best_val_loss:
logging.info("Saving new best validation")
save(model, FLAGS.model_out + ".bestval")
best_val_loss = valid_loss.item()
# save out model periodically
if step % FLAGS.save_every == 0 and step != 0:
save(model, FLAGS.model_out + ".step" + str(step))
if step >= FLAGS.steps:
break
step += 1
save(model, FLAGS.model_out)
# close loss logging file handles
train_losses_fh.close()
valid_losses_fh.close()
def readCommand(argv):
"""
Processes the command used to run pacman from the command line.
"""
from optparse import OptionParser
usageStr = """
USAGE: python pacman.py <options>
"""
parser = OptionParser(usageStr)
parser.add_option('-T',
'--teamName',
dest='teamName',
help="Enter your team's name",
default=None)
parser.add_option(
'-p',
'--agentName',
dest='agentName',
help="Enter your agents's name (for testing purposes only)",
default=None)
parser.add_option('-n',
'--numGames',
dest='numGames',
type='int',
help=default('the number of GAMES to play'),
default=1)
parser.add_option('-d',
'--dataCollectionMode',
action='store_true',
dest='dataCollectionMode',
help='Enter data-collection mode',
default=False)
parser.add_option(
'-a',
'--agentArgs',
dest='agentArgs',
help=
'Comma separated values sent to agent. e.g. "opt1=val1,opt2,opt3=val3"'
)
parser.add_option('-q',
'--quietTextGraphics',
action='store_true',
dest='quietGraphics',
help='Generate minimal output and no graphics',
default=False)
parser.add_option('-m',
'--maxMoves',
dest='maxMoves',
type='int',
help=default('the maximum number of moves in a game'),
default=-1)
parser.add_option('-z',
'--zoom',
type='float',
dest='zoom',
help=default('Zoom the size of the graphics window'),
default=1.0)
parser.add_option(
'-t',
'--frameTime',
dest='frameTime',
type='float',
help=default('Time to delay between frames; Must be > 0'),
default=0.1)
parser.add_option('-s',
'--seed',
dest='seed',
type='int',
help=default('random seed'),
default=3)
options, otherjunk = parser.parse_args()
if len(otherjunk) != 0:
raise Exception('Command line input not understood: ' + otherjunk)
if options.frameTime <= 0:
raise Exception("the frameTime option must be greater than 0")
# set the seed before we do anything else
util.set_seeds(options.seed)
# add team directory to python path
#sys.path.append(os.path.join(os.path.dirname(os.path.abspath("pacman.py")), options.teamDirectory))
sys.path.append(os.path.dirname(os.path.abspath("pacman.py")))
args = dict()
# generate a layout
import layout
args['layout'] = layout.RandomLayout()
# make sure we have a team name, not a directory
if options.teamName is not None and options.teamName.endswith("/"):
options.teamName = options.teamName[:-1]
# Choose a Pacman agent
pacmanType = loadAgent(options.teamName, options.agentName)
agentOpts = parseAgentArgs(options.agentArgs)
pacman = pacmanType(**agentOpts) # Instantiate Pacman with agentArgs
args['pacman'] = pacman
import graphicsDisplay
if options.quietGraphics:
args['display'] = graphicsDisplay.QuietGraphics()
else:
args['display'] = graphicsDisplay.FirstPersonPacmanGraphics(options.zoom, \
True, \
frameTime = options.frameTime)
args['numGames'] = options.numGames
args['maxMoves'] = options.maxMoves
args['dataCollectionMode'] = options.dataCollectionMode
return args
def run_cpc(unused_argv):
# setup logging
set_seeds(FLAGS.seed)
# initialise unset flags
if not FLAGS.model_out:
FLAGS.model_out = FLAGS.expdir + "/model.pt"
if not FLAGS.val_every:
FLAGS.val_every = max(100, FLAGS.steps // 50)
if not FLAGS.val_steps:
FLAGS.val_steps = max(20, FLAGS.steps // FLAGS.batch_size // 50)
if not FLAGS.save_every:
FLAGS.save_every = FLAGS.val_every
logging.info(f"model_out {FLAGS.model_out}")
logging.info(f"steps {FLAGS.steps}")
logging.info(f"val_steps {FLAGS.val_steps}")
logging.info(f"log_every {FLAGS.log_every}")
logging.info(f"log_tb_every {FLAGS.log_tb_every}")
logging.info(f"val_every {FLAGS.val_every}")
logging.info(f"save_every {FLAGS.save_every}")
# model and optimization
model = CPCModel(
FLAGS.features_in,
FLAGS.timestep,
FLAGS.batch_size,
FLAGS.window_size,
FLAGS.hidden_size,
FLAGS.out_size,
FLAGS.no_gru_layers,
).to(device)
logging.info(
f"param count {sum(p.numel() for p in model.parameters() if p.requires_grad)}"
)
optimizer = RAdam(model.parameters(), lr=4e-4)
scheduler = CosineAnnealingLR(optimizer, FLAGS.steps, eta_min=1e-6)
# dataloaders
train_dataset = AudioDataset(FLAGS.train_data)
train_dataloader = AudioDataLoader(
train_dataset,
window_size=FLAGS.window_size,
batch_size=FLAGS.batch_size,
feature_transform=FLAGS.features_in,
num_workers=FLAGS.num_workers,
shuffle=True,
drop_last=True,
)
val_dataset = AudioDataset(FLAGS.val_data)
val_dataloader = AudioDataLoader(
val_dataset,
window_size=FLAGS.window_size,
batch_size=FLAGS.batch_size,
feature_transform=FLAGS.features_in,
num_workers=FLAGS.num_workers,
shuffle=False,
drop_last=True,
)
# start training
train(model, optimizer, scheduler, train_dataloader, val_dataloader, FLAGS)
def readCommand( argv ):
"""
Processes the command used to run pacman from the command line.
"""
from optparse import OptionParser
usageStr = """
USAGE: python pacman.py <options>
"""
parser = OptionParser(usageStr)
parser.add_option('-T', '--teamName', dest='teamName',
help="Enter your team's name", default=None)
parser.add_option('-p', '--agentName', dest='agentName',
help="Enter your agents's name (for testing purposes only)",
default=None)
parser.add_option('-n', '--numGames', dest='numGames', type='int',
help=default('the number of GAMES to play'), default=1)
parser.add_option('-d', '--dataCollectionMode', action='store_true',
dest='dataCollectionMode', help='Enter data-collection mode', default=False)
parser.add_option('-a','--agentArgs',dest='agentArgs',
help='Comma separated values sent to agent. e.g. "opt1=val1,opt2,opt3=val3"')
parser.add_option('-q', '--quietTextGraphics', action='store_true', dest='quietGraphics',
help='Generate minimal output and no graphics', default=False)
parser.add_option('-m', '--maxMoves', dest='maxMoves', type='int',
help=default('the maximum number of moves in a game'), default=1000)
parser.add_option('-z', '--zoom', type='float', dest='zoom',
help=default('Zoom the size of the graphics window'), default=1.0)
parser.add_option('-t', '--frameTime', dest='frameTime', type='float',
help=default('Time to delay between frames; Must be > 0'), default=0.1)
parser.add_option('-s', '--seed', dest='seed', type='int',
help=default('random seed'), default=3)
options, otherjunk = parser.parse_args()
if len(otherjunk) != 0:
raise Exception('Command line input not understood: ' + otherjunk)
if options.frameTime <= 0:
raise Exception("the frameTime option must be greater than 0")
# set the seed before we do anything else
util.set_seeds(options.seed)
# add team directory to python path
#sys.path.append(os.path.join(os.path.dirname(os.path.abspath("pacman.py")), options.teamDirectory))
sys.path.append(os.path.dirname(os.path.abspath("pacman.py")))
args = dict()
# generate a layout
import layout
args['layout'] = layout.RandomLayout()
# make sure we have a team name, not a directory
if options.teamName is not None and options.teamName.endswith("/"):
options.teamName = options.teamName[:-1]
# Choose a Pacman agent
pacmanType = loadAgent(options.teamName, options.agentName)
agentOpts = parseAgentArgs(options.agentArgs)
pacman = pacmanType(**agentOpts) # Instantiate Pacman with agentArgs
args['pacman'] = pacman
import graphicsDisplay
if options.quietGraphics:
args['display'] = graphicsDisplay.QuietGraphics()
else:
args['display'] = graphicsDisplay.FirstPersonPacmanGraphics(options.zoom, \
True, \
frameTime = options.frameTime)
args['numGames'] = options.numGames
args['maxMoves'] = options.maxMoves
args['dataCollectionMode'] = options.dataCollectionMode
return args
def run_cpc(unused_argv):
# setup logging
set_seeds(FLAGS.seed)
prepare_standard_logging("training")
# dry run
if FLAGS.dry_run is True:
setup_dry_run(FLAGS)
# initialise unset flags
if not FLAGS.model_out:
FLAGS.model_out = FLAGS.expdir + "/model.pt"
if not FLAGS.val_every:
FLAGS.val_every = max(100, FLAGS.steps // 50)
if not FLAGS.val_steps:
FLAGS.val_steps = max(20, FLAGS.steps // FLAGS.batch_size // 50)
if not FLAGS.save_every:
FLAGS.save_every = FLAGS.val_every
logging.info(f"model_out {FLAGS.model_out}")
logging.info(f"steps {FLAGS.steps}")
logging.info(f"val_steps {FLAGS.val_steps}")
logging.info(f"log_every {FLAGS.log_every}")
logging.info(f"log_tb_every {FLAGS.log_tb_every}")
logging.info(f"val_every {FLAGS.val_every}")
logging.info(f"save_every {FLAGS.save_every}")
# model and optimization
model = CPCModel(
FLAGS.features_in,
FLAGS.timestep,
FLAGS.batch_size,
FLAGS.window_size,
FLAGS.hidden_size,
FLAGS.out_size,
FLAGS.no_gru_layers,
).to(device)
logging.info(
f"param count {sum(p.numel() for p in model.parameters() if p.requires_grad)}"
)
optimizer = RAdam(model.parameters(), lr=4e-4)
scheduler = FlatCA(optimizer,
steps=FLAGS.steps,
eta_min=1e-6,
decay_proportion=1.0 / 3)
# dataloaders
if FLAGS.features_in == "raw":
stream_class = RawStream
elif FLAGS.features_in == "fbank":
stream_class = FbankStream
else:
raise (f"Feature input {FLAGS.features_in} has not been implemented")
train_dbl = parse_audio_dbl(FLAGS.train_data)
train_streams = [
DblStream(DblSampler(train_dbl), stream_class, FLAGS.window_size)
for _ in range(FLAGS.batch_size)
]
train_datastream = MultiStreamDataLoader(train_streams, device=device)
val_dbl = parse_audio_dbl(FLAGS.val_data)
val_streams = [
DblStream(DblSampler(val_dbl), stream_class, FLAGS.window_size)
for _ in range(FLAGS.batch_size)
]
val_datastream = MultiStreamDataLoader(val_streams, device=device)
# start training
train(model, optimizer, scheduler, train_datastream, val_datastream, FLAGS)