huangfu/InteractionImitation
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getting hyperparameter tunning with ray tune working. updating default network with optimization and general parameters.

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This commit is contained in:
Arec
2021-07-27 14:30:28 -07:00
parent ca80fa19eb
commit 87aa19b86b
6 changed files with 184 additions and 60 deletions
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12
config/networks.json5
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@@ -1,5 +1,5 @@
{
ego_state: {
ego_encoder: {
input_dim: 5, // number of state vars
hidden_n: 0,
hidden_dim: 5,
@@ -30,5 +30,13 @@
hidden_dim: 50,
output_dim: 1, // number of outputs e.g. number of actions, or just one
final_activation: 'sigmoid',
}
},
optim: {
optimizer: 'adam',
lr: 1e-3,
weight_decay: 0.1,
},
train_epochs: 200,
train_batch_size: 32,
loss: 'huber',
}

1
requirements.txt
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@@ -5,3 +5,4 @@ pytest
json5
tqdm
tensorboard
ray[tune]

2
src/bc/__init__.py
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@@ -1 +1 @@
from src.bc.bc import BehaviorCloningPolicy, train
from src.bc.bc import BehaviorCloningPolicy, train, bc_config

75
src/bc/bc.py
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@@ -7,10 +7,12 @@ from torch.utils.tensorboard import SummaryWriter
from src.policies import DeepSetsPolicy
from src.util.transform import MinMaxScaler
from tqdm import tqdm
import json5
from ray import tune
def bc_config(ray_config):
config = {
'ego_state': {'input_dim': 5, 'hidden_n': 0, 'output_dim': 0},
'ego_encoder': {'input_dim': 5, 'hidden_n': 0, 'hidden_dim':0, 'output_dim': 0},
'deepsets': {
'input_dim': 5,
'phi': {
@@ -21,7 +23,7 @@ def bc_config(ray_config):
'rho': {'hidden_n': 0, 'hidden_dim': 10},
'output_dim': 0
},
'path_encoder': {'input_dim': 40, 'hidden_n': 0, 'output_dim': 0},
'path_encoder': {'input_dim': 40, 'hidden_n': 0, 'hidden_dim': 0, 'output_dim': 0},
'head': {
'input_dim': 0, # computed in constructor
'hidden_n': ray_config['head_hidden_n'],
@@ -34,8 +36,8 @@ def bc_config(ray_config):
'lr':ray_config['lr'],
'weight_decay':ray_config['weight_decay']
},
'train_epochs':1000,
'train_batch_size': ray_config['batch_size'],
'train_epochs': 100,
'train_batch_size': ray_config['train_batch_size'],
'loss': ray_config['loss'],
}
@@ -99,7 +101,7 @@ class BehaviorCloningPolicy():
return action
@classmethod
def load_model(cls, config: dict, filestr: str):
def load_model(cls, filestr: str, config: dict = None):
"""
Load a model from a file prefix
Args:
@@ -108,6 +110,9 @@ class BehaviorCloningPolicy():
Returns
model (BehaviorCloningPolicy): loaded model
"""
if not config:
with open(filestr+'_config.json', 'r') as cfg:
config = json5.load(cfg)
transforms = pickle.load(open(filestr+'_transforms.pkl', 'rb'))
model = cls(config, transforms=transforms)
model._policy.load_state_dict(torch.load(filestr+'_model.pt'))
@@ -119,13 +124,17 @@ class BehaviorCloningPolicy():
def parameters(self):
return self._policy.parameters()
def save_model(self, filestr, save_transforms=True):
def save_model(self, filestr, save_config=True, save_transforms=True):
"""
Save transforms and state_dict to a location specificed by filestr
Args:
filestr (str): string prefix to save model to
save_transforms (bool): whether to save transforms
save_config (bool): whether to save the config file (as a json)
save_transforms (bool): whether to save transforms (as a pickle)
"""
if save_config:
with open(filestr+'_config.json', 'w') as cfg:
json5.dump(self._config, cfg)
if save_transforms:
pickle.dump(self._transforms, open(filestr+'_transforms.pkl', 'wb'))
torch.save(self._policy.state_dict(), filestr+'_model.pt')
@@ -148,15 +157,19 @@ def generate_transforms(dataset):
return transforms
def train(train_dataset, cv_dataset, policy, filestr, **kwargs):
def train(config, policy, train_dataset, cv_dataset, filestr, **kwargs):
using_ray = kwargs.get('ray', False)
# hyperparams
train_epochs = 1000
train_batch_size = 64
learning_rate = 1e-3
weight_decay = 0.1
loss_type = config['loss']
train_epochs = config['train_epochs']
train_batch_size = config['train_batch_size']
optimizer_type = config['optim']['optimizer']
learning_rate = config['optim']['lr']
weight_decay = config['optim']['weight_decay']
cv_every = 10
cv_every = 5
print_epoch_every = 1000
print_cv_every = 1000
checkpoint_every = 100
@@ -176,22 +189,30 @@ def train(train_dataset, cv_dataset, policy, filestr, **kwargs):
policy.policy = policy.policy.type(train_dataset[0]['state'].dtype)
# generate loss function, optimizer
cv_loss_fn = nn.MSELoss(reduction='sum')
if loss_type == 'huber':
loss_fn = nn.HuberLoss(reduction='sum')
elif loss_type == 'mse':
loss_fn = nn.MSELoss(reduction='sum')
else:
raise NotImplementedError
if optimizer_type == 'adam':
optimizer = torch.optim.Adam(policy.parameters(), lr=learning_rate, weight_decay=weight_decay)
else:
raise NotImplementedError
# generate tensorboard writer
if not using_ray:
writer = SummaryWriter(filestr)
for i in tqdm(range(train_epochs)):
# train
epoch_loss = 0
for (batch_idx, batch) in enumerate(training_loader):
# sample mini-batch and run through policy
pred_action = policy(batch)
if i == 0 and batch_idx==0:
pickle.dump(batch, open(filestr+'_test_batch.pkl', 'wb'))
policy.save_model(filestr)
loss = loss_fn(pred_action, batch['action'])
# compute loss and step optimizer
@@ -202,24 +223,34 @@ def train(train_dataset, cv_dataset, policy, filestr, **kwargs):
epoch_loss += loss.item() / len(train_dataset)
# Write epoch loss
if using_ray:
tune.report(training_loss=epoch_loss, training_iteration=i)
else:
writer.add_scalar('training loss',epoch_loss, i)
if i % print_epoch_every == 0:
print('Epoch: {}, Training Loss: {}'.format(i, epoch_loss))
# if i % print_epoch_every == 0:
# print('Epoch: {}, Training Loss: {}'.format(i, epoch_loss))
# measure cv loss
if i % cv_every == 0:
with torch.no_grad():
cv_loss = 0.
for (batch_idx, batch) in enumerate(cv_loader):
pred_action = policy(batch)
loss = loss_fn(pred_action, batch['action'])
loss = cv_loss_fn(pred_action, batch['action'])
cv_loss += loss.item() / len(cv_dataset)
if using_ray:
tune.report(cv_loss=cv_loss, cv_epoch=i)
else:
writer.add_scalar('cv loss', cv_loss, i)
if i % print_cv_every == 0:
print('Epoch: {}, CV Loss: {}'.format(i, cv_loss))
# if i % print_cv_every == 0:
# print('Epoch: {}, CV Loss: {}'.format(i, cv_loss))
# save model checkpoints
if i % checkpoint_every == 0:
policy.save_model(filestr + '_epoch%04i'%(i) )
policy.save_model(filestr)

130
src/main.py
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@@ -6,6 +6,7 @@ import json5
import os
opj = os.path.join
from tqdm import tqdm
from functools import partial
from src import InteractionDatasetSingleAgent, metrics
from intersim.utils import get_map_path, get_svt
@@ -20,10 +21,11 @@ def basestr(**kwargs):
"""
return 'base'
def main(method='bc', train=False, test=False, loc=0, config_path=None, **kwargs):
def main(config, method='bc', train=False, test=False, loc=0, datadir='./expert_data', filestr='', **kwargs):
"""
Main loop for training and testing different imitation models
Args:
config (dict): configuration dictionary for model
train (bool): whether to run train loop
test (bool): whether to run test loop
method (str): the method to try for imitation
@@ -35,19 +37,6 @@ def main(method='bc', train=False, test=False, loc=0, config_path=None, **kwargs
seed = kwargs.get('seed',0)
torch.manual_seed(seed)
# make prefix of output files
outdir = opj('output',method,'loc%02i'%(loc))
if not os.path.isdir(outdir):
os.makedirs(outdir)
filestr = opj(outdir, basestr(**kwargs))
# load config
if config_path:
with open(config_path, 'r') as cfg:
config = json5.load(cfg)
else:
raise Exception('No config path specified')
# method-based training
if method=='bc':
from src import bc
@@ -61,9 +50,9 @@ def main(method='bc', train=False, test=False, loc=0, config_path=None, **kwargs
# make policy, train and test datasets, and send to
policy = policy_class(config)
train_dataset = InteractionDatasetSingleAgent(loc=loc, tracks=[0,1,2])
cv_dataset = InteractionDatasetSingleAgent(loc=loc, tracks=[3])
train_fn(train_dataset, cv_dataset, policy, filestr, **kwargs)
train_dataset = InteractionDatasetSingleAgent(output_dir=datadir, loc=loc, tracks=[0])#,1,2])
cv_dataset = InteractionDatasetSingleAgent(output_dir=datadir, loc=loc, tracks=[3])
train_fn(config, policy, train_dataset, cv_dataset, filestr, **kwargs)
if test:
@@ -76,7 +65,7 @@ def main(method='bc', train=False, test=False, loc=0, config_path=None, **kwargs
simulate_policy(policy, loc=loc, track=track, filestr=filestr, nframes=500)
# run test metrics
test_dataset = InteractionDatasetSingleAgent(loc=loc, tracks=[track])
test_dataset = InteractionDatasetSingleAgent(output_dir=datadir, loc=loc, tracks=[track])
metrics(filestr, test_dataset, policy)
@@ -90,8 +79,9 @@ def simulate_policy(policy, loc=0, track=0, filestr='', nframes=float('inf')):
filestr (str): path prefix to save simulation to
"""
# animate from environment
svt, svt_path = get_svt(base='InteractionSimulator', loc=loc, track=track)
osm = get_map_path(base='InteractionSimulator', loc=loc)
basepath = os.path.abspath('./InteractionSimulator')
svt, svt_path = get_svt(base=basepath, loc=loc, track=track)
osm = get_map_path(base=basepath, loc=loc)
env = gym.make('intersim:intersim-v0', svt=svt, map_path=osm,
min_acc=-np.inf, max_acc=np.inf)
# env = gym.make('intersim:intersim-v0', loc=loc, track=track,
@@ -134,12 +124,14 @@ def parse_args():
help='location (default 0)')
parser.add_argument("--train", help="train model",
action="store_true")
parser.add_argument("--all-runs", help="use ray tune to run multiple experiments",
action="store_true")
parser.add_argument("--test", help="test model",
action="store_true")
parser.add_argument("--method", help="modeling method",
choices=['bc', 'gail', 'advil'], default='bc')
parser.add_argument("--config", help="config file path",
default='config/networks.json5', type=str)
default=None, type=str)
parser.add_argument('--seed', default=0, type=int,
help='seed')
args = parser.parse_args()
@@ -149,11 +141,103 @@ def parse_args():
'method':args.method,
'loc':args.loc,
'config_path':args.config,
'seed':args.seed
'seed':args.seed,
'all_runs':args.all_runs
}
return kwargs
def main_wrapper(**kwargs):
if kwargs['all_runs']:
pass
else:
main(**kwargs)
def get_full_config(ray_config:dict, method:str)->dict:
"""
Get full model configuration from ray config and method string
Args:
ray_config (dict): ray config
method (str): method to get full configuration for
"""
if method == 'bc':
from src.bc import bc_config
config = bc_config(ray_config)
else:
raise NotImplementedError
return config
def get_ray_config(method:str)->dict:
"""
Get configuration for ray based on method.
Args:
method (str): method to get configuration for
Returns:
ray_config (dict): configuration for ray
"""
if method == 'bc':
ray_config = {
"lr": tune.choice([1e-4, 1e-3, 1e-2, 1e-1]),
"weight_decay": tune.choice([0.001, 0.01, 0.1, 0.5, 0.9]),
"loss": tune.choice(['huber', 'mse']),
"train_batch_size": tune.choice([16,32,64]),
"deepsets_phi_hidden_n": tune.choice([1,2,3]),
"deepsets_phi_hidden_dim": tune.choice([16,32,64]),
"deepsets_latent_dim": tune.choice([16,32,64]),
"deepsets_rho_hidden_n": tune.choice([0,1,2]),
"deepsets_rho_hidden_dim": tune.choice([16,32,64]),
"deepsets_output_dim": tune.choice([8,16,32,64]),
"head_hidden_n": tune.choice([0,1,2]),
"head_hidden_dim": tune.choice([16,32,64]),
"head_final_activation": tune.choice(['sigmoid', None]),
}
else:
raise NotImplementedError
return ray_config
if __name__ == '__main__':
kwargs = parse_args()
main(**kwargs)
# make prefix of output files
outdir = opj('output',kwargs['method'],'loc%02i'%(kwargs['loc']))
if kwargs['config_path']:
# load config
with open(kwargs['config_path'], 'r') as cfg:
config = json5.load(cfg)
if not os.path.isdir(outdir):
os.makedirs(outdir)
filestr = opj(outdir, basestr(**kwargs))
main(config, filestr=filestr, **kwargs)
elif kwargs['all_runs'] and kwargs['train']:
def ray_train(config, datadir=None):
full_config = get_full_config(config, kwargs['method'])
main(full_config, filestr='exp', datadir=datadir, ray=True, **kwargs)
# set up ray tune
from ray import tune
from ray.tune.schedulers import ASHAScheduler
datadir = os.path.abspath('./expert_data')
ray_config = get_ray_config(kwargs['method'])
custom_scheduler = ASHAScheduler(
metric='cv_loss',
mode="min",
grace_period=25,
)
analysis = tune.run(
partial(ray_train, datadir=datadir),
config=ray_config,
scheduler=custom_scheduler,
local_dir=outdir,
resources_per_trial={"cpu": 2},
num_samples=20,
)
else:
raise Exception('No valid config found')

8
src/policies/policy.py
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@@ -14,13 +14,13 @@ class DeepSetsPolicy(Policy, nn.Module):
config (dict): dictionary for configuring the deep sets policy
"""
super(DeepSetsPolicy, self).__init__()
ego_config = config['ego_state']
ego_config = config['ego_encoder']
deepsets_config = config['deepsets']
pathnet_config = config['path_encoder']
self.ego_net = Phi.from_config(ego_config) if ego_config else lambda x: x
self.deepsets_net = DeepSetsModule.from_config(deepsets_config) if deepsets_config else lambda x: x
self.path_net = Phi.from_config(pathnet_config) if pathnet_config else lambda x: x
self.ego_net = Phi.from_config(ego_config)
self.deepsets_net = DeepSetsModule.from_config(deepsets_config)
self.path_net = Phi.from_config(pathnet_config)
cat_dim = self.ego_net.output_dim + self.deepsets_net.output_dim + self.path_net.output_dim
# head has number of concatenated features as input