def main():
torch.backends.cudnn.benchmark = True
parser = common_utils.Parser()
parser.add_parser('main', get_main_parser())
parser.add_parser('coach', ConvRnnCoach.get_arg_parser())
args = parser.parse()
parser.log()
options = args['main']
if not os.path.exists(options.model_folder):
os.makedirs(options.model_folder)
logger_path = os.path.join(options.model_folder, 'train.log')
if not options.dev:
sys.stdout = common_utils.Logger(logger_path)
if options.dev:
options.train_dataset = options.train_dataset.replace('train.', 'dev.')
options.val_dataset = options.val_dataset.replace('val.', 'dev.')
print('Args:\n%s\n' % pprint.pformat(vars(options)))
if options.gpu < 0:
device = torch.device('cpu')
else:
device = torch.device('cuda:%d' % options.gpu)
common_utils.set_all_seeds(options.seed)
model_args = args['coach']
if options.coach_type == 'onehot':
model = ConvOneHotCoach(model_args, 0, options.max_instruction_span,
options.num_resource_bin).to(device)
elif options.coach_type in ['rnn', 'bow']:
model = ConvRnnCoach(model_args, 0, options.max_instruction_span,
options.coach_type,
options.num_resource_bin).to(device)
elif options.coach_type == 'rnn_gen':
model = RnnGenerator(model_args, 0, options.max_instruction_span,
options.num_resource_bin).to(device)
print(model)
train_dataset = CoachDataset(
options.train_dataset,
options.moving_avg_decay,
options.num_resource_bin,
options.resource_bin_size,
options.max_num_prev_cmds,
model.inst_dict,
options.max_instruction_span,
)
val_dataset = CoachDataset(
options.val_dataset,
options.moving_avg_decay,
options.num_resource_bin,
options.resource_bin_size,
options.max_num_prev_cmds,
model.inst_dict,
options.max_instruction_span,
)
eval_dataset = CoachDataset(options.val_dataset,
options.moving_avg_decay,
options.num_resource_bin,
options.resource_bin_size,
options.max_num_prev_cmds,
model.inst_dict,
options.max_instruction_span,
num_instructions=model.args.num_pos_inst)
if not options.dev:
compute_cache(train_dataset)
compute_cache(val_dataset)
compute_cache(eval_dataset)
if options.optim == 'adamax':
optimizer = torch.optim.Adamax(model.parameters(),
lr=options.lr,
betas=(options.beta1, options.beta2))
elif options.optim == 'adam':
optimizer = torch.optim.Adam(model.parameters(),
lr=options.lr,
betas=(options.beta1, options.beta2))
else:
assert False, 'not supported'
train_loader = DataLoader(
train_dataset,
options.batch_size,
shuffle=True,
num_workers=1, # if options.dev else 10,
pin_memory=(options.gpu >= 0))
val_loader = DataLoader(
val_dataset,
options.batch_size,
shuffle=False,
num_workers=1, # if options.dev else 10,
pin_memory=(options.gpu >= 0))
eval_loader = DataLoader(
eval_dataset,
options.batch_size,
shuffle=False,
num_workers=1, #0 if options.dev else 10,
pin_memory=(options.gpu >= 0))
best_val_nll = float('inf')
overfit_count = 0
for epoch in range(1, options.epochs + 1):
print('==========')
train(model, device, optimizer, options.grad_clip, train_loader, epoch)
with torch.no_grad(), common_utils.EvalMode(model):
val_nll = evaluate(model, device, val_loader, epoch, 'val', False)
eval_nll = evaluate(model, device, eval_loader, epoch, 'eval',
True)
model_file = os.path.join(options.model_folder,
'checkpoint%d.pt' % epoch)
print('saving model to', model_file)
model.save(model_file)
if val_nll < best_val_nll:
print('!!!New Best Model')
overfit_count = 0
best_val_nll = val_nll
best_model_file = os.path.join(options.model_folder,
'best_checkpoint.pt')
print('saving best model to', best_model_file)
model.save(best_model_file)
else:
overfit_count += 1
if overfit_count == 2:
break
print('train DONE')
def main():
torch.backends.cudnn.benchmark = True
parser = common_utils.Parser()
parser.add_parser('main', get_main_parser())
parser.add_parser('executor', Executor.get_arg_parser())
args = parser.parse()
parser.log()
options = args['main']
# print('Args:\n%s\n' % pprint.pformat(vars(options)))
# option_map = parse_args()
# options = option_map.getOptions()
if not os.path.exists(options.model_folder):
os.makedirs(options.model_folder)
logger_path = os.path.join(options.model_folder, 'train.log')
if not options.dev:
sys.stdout = common_utils.Logger(logger_path)
if options.dev:
options.train_dataset = options.train_dataset.replace('train.', 'dev.')
options.val_dataset = options.val_dataset.replace('val.', 'dev.')
print('Args:\n%s\n' % pprint.pformat(vars(options)))
if options.gpu < 0:
device = torch.device('cpu')
else:
device = torch.device('cuda:%d' % options.gpu)
common_utils.set_all_seeds(options.seed)
model = Executor(args['executor'], options.num_resource_bin).to(device)
inst_dict = model.inst_dict
print(model)
# model = nn.DataParallel(model, [0, 1])
train_dataset = BehaviorCloneDataset(
options.train_dataset,
options.num_resource_bin,
options.resource_bin_size,
options.max_num_prev_cmds,
inst_dict=inst_dict,
word_based=is_word_based(args['executor'].inst_encoder_type))
val_dataset = BehaviorCloneDataset(options.val_dataset,
options.num_resource_bin,
options.resource_bin_size,
options.max_num_prev_cmds,
inst_dict=inst_dict,
word_based=is_word_based(
args['executor'].inst_encoder_type))
if options.optim == 'adamax':
optimizer = torch.optim.Adamax(model.parameters(),
lr=options.lr,
betas=(options.beta1, options.beta2))
elif options.optim == 'adam':
optimizer = torch.optim.Adam(model.parameters(),
lr=options.lr,
betas=(options.beta1, options.beta2))
else:
assert False, 'not supported'
train_loader = DataLoader(
train_dataset,
options.batch_size,
shuffle=True,
num_workers=20, # if options.dev else 20,
pin_memory=(options.gpu >= 0))
val_loader = DataLoader(
val_dataset,
options.batch_size,
shuffle=False,
num_workers=20, # if options.dev else 20,
pin_memory=(options.gpu >= 0))
best_eval_nll = float('inf')
overfit_count = 0
train_stat = common_utils.MultiCounter(
os.path.join(options.model_folder, 'train'))
eval_stat = common_utils.MultiCounter(
os.path.join(options.model_folder, 'eval'))
for epoch in range(1, options.epochs + 1):
train_stat.start_timer()
train(model, device, optimizer, options.grad_clip, train_loader, epoch,
train_stat)
train_stat.summary(epoch)
train_stat.reset()
with torch.no_grad(), common_utils.EvalMode(model):
eval_stat.start_timer()
eval_nll = evaluate(model, device, val_loader, epoch, eval_stat)
eval_stat.summary(epoch)
eval_stat.reset()
model_file = os.path.join(options.model_folder,
'checkpoint%d.pt' % epoch)
print('saving model to', model_file)
if isinstance(model, nn.DataParallel):
model.module.save(model_file)
else:
model.save(model_file)
if eval_nll < best_eval_nll:
print('!!!New Best Model')
overfit_count = 0
best_eval_nll = eval_nll
best_model_file = os.path.join(options.model_folder,
'best_checkpoint.pt')
print('saving best model to', best_model_file)
if isinstance(model, nn.DataParallel):
model.module.save(best_model_file)
else:
model.save(best_model_file)
else:
overfit_count += 1
if overfit_count == 2:
break
print('train DONE')
assert args.method in ["vdn", "iql"]
return args
if __name__ == "__main__":
torch.backends.cudnn.benchmark = True
args = parse_args()
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
logger_path = os.path.join(args.save_dir, "train.log")
sys.stdout = common_utils.Logger(logger_path)
saver = common_utils.TopkSaver(args.save_dir, 5)
common_utils.set_all_seeds(args.seed)
pprint.pprint(vars(args))
if args.method == "vdn":
args.batchsize = int(np.round(args.batchsize / args.num_player))
args.replay_buffer_size //= args.num_player
args.burn_in_frames //= args.num_player
explore_eps = utils.generate_explore_eps(args.act_base_eps,
args.act_eps_alpha, args.num_eps)
expected_eps = np.mean(explore_eps)
print("explore eps:", explore_eps)
print("avg explore eps:", np.mean(explore_eps))
games = create_envs(
args.num_thread * args.num_game_per_thread,
def main(args):
# torch.backends.cudnn.benchmark = True
log.info(f"Working dir: {os.getcwd()}")
log.info("\n" + common_utils.get_git_hash())
log.info("\n" + common_utils.get_git_diffs())
common_utils.set_all_seeds(args.seed)
log.info(args.pretty())
# Setup latent variables.
root = Latent("s", args)
iterator = LatentIterator(root)
dataset = SimpleDataset(iterator, args.N)
bs = args.batchsize
loader = torch.utils.data.DataLoader(dataset,
batch_size=bs,
shuffle=True,
num_workers=4)
load_saved = False
need_train = True
if load_saved:
model = torch.load(args.save_file)
else:
model = Model(iterator, args.hid, args.eps)
model.cuda()
if need_train:
loss_func = nn.MSELoss().cuda()
optimizer = optim.SGD(model.parameters(), lr=args.lr)
stats = common_utils.MultiCounter("./")
stats_corrs = {
parent.name: common_utils.StatsCorr()
for parent in iterator.top_down()
}
for i in range(args.num_epoch):
for _, v in stats_corrs.items():
v.reset()
connections = dict()
n_samples = [0, 0]
with torch.no_grad():
for batch in tqdm.tqdm(loader, total=int(len(loader))):
d = batch2dict(batch)
label = d["x_label"]
f, d_hs, d_inputs = model(d["x"])
Js = model.computeJ(d_hs)
# Correlation.
# batch_size * K
d_gt = dataset.split_generated(d["x_all"])
for v in iterator.top_down():
name = v.name
stats_corrs[name].add(d_gt[name].unsqueeze(1).float(),
d_hs[name])
J = Js[name]
inputs = d_inputs[name].detach()
conn = torch.einsum("ia,ibc->iabc", inputs, J)
conn = conn.view(conn.size(0),
conn.size(1) * conn.size(2),
conn.size(3))
# group by labels.
conn0 = conn[label == 0, :, :].sum(dim=0)
conn1 = conn[label == 1, :, :].sum(dim=0)
conns = torch.stack([conn0, conn1])
# Accumulate connection.
if name in connections:
connections[name] += conns
else:
connections[name] = conns
for j in range(2):
n_samples[j] += (label == j).sum().item()
json_result = dict(epoch=i)
for name in connections.keys():
conns = connections[name]
n_total_sample = n_samples[0] + n_samples[1]
avg_conn = conns.sum(dim=0) / n_total_sample
cov_op = torch.zeros(avg_conn.size(0),
avg_conn.size(0)).to(avg_conn.device)
for j in range(2):
conns[j, :, :] /= n_samples[j]
diff = conns[j, :, :] - avg_conn
cov_op += diff @ diff.t() * n_samples[j] / n_total_sample
dd = cov_op.size(0)
json_result["conn_" + name] = dict(size=dd,
norm=cov_op.norm().item())
json_result["weight_norm_" +
name] = model.nets[name].weight.norm().item()
layer_avgs = [[0, 0] for j in range(args.depth + 1)]
for p in iterator.top_down():
corr = stats_corrs[p.name].get()["corr"]
# Note that we need to take absolute value (since -1/+1 are both good)
res = common_utils.corr_summary(corr.abs())
best = res["best_corr"].item()
json_result["best_corr_" + p.name] = best
layer_avgs[p.depth][0] += best
layer_avgs[p.depth][1] += 1
# Check average correlation for each layer
# log.info("CovOp norm at every location:")
for d, (sum_corr, n) in enumerate(layer_avgs):
if n > 0:
log.info(
f"[{d}] Mean of the best corr: {sum_corr/n:.3f} [{n}]")
log.info(f"json_str: {json.dumps(json_result)}")
# Training
stats.reset()
for batch in tqdm.tqdm(loader, total=int(len(loader))):
optimizer.zero_grad()
d = batch2dict(batch)
f, _, _ = model(d["x"])
f_pos, _, _ = model(d["x_pos"])
f_neg, _, _ = model(d["x_neg"])
pos_loss = loss_func(f, f_pos)
neg_loss = loss_func(f, f_neg)
# import pdb
# pdb.set_trace()
if args.loss == "nce":
loss = -(-pos_loss / args.temp).exp() / (
(-pos_loss / args.temp).exp() +
(-neg_loss / args.temp).exp())
elif args.loss == "subtract":
loss = pos_loss - neg_loss
else:
raise NotImplementedError(f"{args.loss} is unknown")
#loss = pos_loss.exp() / ( pos_loss.exp() + neg_loss.exp())
stats["train_loss"].feed(loss.detach().item())
loss.backward()
optimizer.step()
log.info("\n" + stats.summary(i))
'''
measures = generator.check(model.linear1.weight.detach())
for k, v in measures.items():
for vv in v:
stats["stats_" + k].feed(vv)
'''
# log.info(f"\n{best_corrs}\n")
torch.save(model, args.save_file)