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Fix save_checkpoint_fn return value #659

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Jun 2, 2022
Merged

Fix save_checkpoint_fn return value #659

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e01bbe9
fix save_checkpoint_fn return value
Trinkle23897 Jun 2, 2022
866d085
fix wrong link
Trinkle23897 Jun 2, 2022
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2 changes: 1 addition & 1 deletion docs/tutorials/cheatsheet.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -48,7 +48,7 @@ And to successfully resume from a checkpoint:
1. Load everything needed in the training process **before trainer initialization**, i.e., policy, optim, buffer;
2. Set ``resume_from_log=True`` with trainer;

We provide an example to show how these steps work: checkout `test_c51.py <https://github.com/thu-ml/tianshou/blob/master/test/discrete/test_c51.py>`_, `test_ppo.py <https://github.com/thu-ml/tianshou/blob/master/test/continuous/test_ppo.py>`_ or `test_il_bcq.py <https://github.com/thu-ml/tianshou/blob/master/test/discrete/test_il_bcq.py>`_ by running
We provide an example to show how these steps work: checkout `test_c51.py <https://github.com/thu-ml/tianshou/blob/master/test/discrete/test_c51.py>`_, `test_ppo.py <https://github.com/thu-ml/tianshou/blob/master/test/continuous/test_ppo.py>`_ or `test_discrete_bcq.py <https://github.com/thu-ml/tianshou/blob/master/test/offline/test_discrete_bcq.py>`_ by running

.. code-block:: console

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2 changes: 1 addition & 1 deletion examples/atari/atari_dqn.py
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Expand Up @@ -192,7 +192,7 @@ def test_fn(epoch, env_step):

def save_checkpoint_fn(epoch, env_step, gradient_step):
# see also: https://pytorch.org/tutorials/beginner/saving_loading_models.html
ckpt_path = os.path.join(log_path, "checkpoint.pth")
ckpt_path = os.path.join(log_path, f"checkpoint_{epoch}.pth")
torch.save({"model": policy.state_dict()}, ckpt_path)
return ckpt_path

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2 changes: 1 addition & 1 deletion examples/atari/atari_ppo.py
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Expand Up @@ -222,7 +222,7 @@ def stop_fn(mean_rewards):

def save_checkpoint_fn(epoch, env_step, gradient_step):
# see also: https://pytorch.org/tutorials/beginner/saving_loading_models.html
ckpt_path = os.path.join(log_path, "checkpoint.pth")
ckpt_path = os.path.join(log_path, f"checkpoint_{epoch}.pth")
torch.save({"model": policy.state_dict()}, ckpt_path)
return ckpt_path

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28 changes: 16 additions & 12 deletions test/continuous/test_ppo.py
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Expand Up @@ -117,41 +117,45 @@ def dist(*logits):
dual_clip=args.dual_clip,
value_clip=args.value_clip,
gae_lambda=args.gae_lambda,
action_space=env.action_space
action_space=env.action_space,
)
# collector
train_collector = Collector(
policy, train_envs, VectorReplayBuffer(args.buffer_size, len(train_envs))
)
test_collector = Collector(policy, test_envs)
# log
log_path = os.path.join(args.logdir, args.task, 'ppo')
log_path = os.path.join(args.logdir, args.task, "ppo")
writer = SummaryWriter(log_path)
logger = TensorboardLogger(writer, save_interval=args.save_interval)

def save_best_fn(policy):
torch.save(policy.state_dict(), os.path.join(log_path, 'policy.pth'))
torch.save(policy.state_dict(), os.path.join(log_path, "policy.pth"))

def stop_fn(mean_rewards):
return mean_rewards >= args.reward_threshold

def save_checkpoint_fn(epoch, env_step, gradient_step):
# see also: https://pytorch.org/tutorials/beginner/saving_loading_models.html
ckpt_path = os.path.join(log_path, "checkpoint.pth")
# Example: saving by epoch num
# ckpt_path = os.path.join(log_path, f"checkpoint_{epoch}.pth")
torch.save(
{
'model': policy.state_dict(),
'optim': optim.state_dict(),
}, os.path.join(log_path, 'checkpoint.pth')
"model": policy.state_dict(),
"optim": optim.state_dict(),
}, ckpt_path
)
return ckpt_path

if args.resume:
# load from existing checkpoint
print(f"Loading agent under {log_path}")
ckpt_path = os.path.join(log_path, 'checkpoint.pth')
ckpt_path = os.path.join(log_path, "checkpoint.pth")
if os.path.exists(ckpt_path):
checkpoint = torch.load(ckpt_path, map_location=args.device)
policy.load_state_dict(checkpoint['model'])
optim.load_state_dict(checkpoint['optim'])
policy.load_state_dict(checkpoint["model"])
optim.load_state_dict(checkpoint["optim"])
print("Successfully restore policy and optim.")
else:
print("Fail to restore policy and optim.")
Expand All @@ -171,7 +175,7 @@ def save_checkpoint_fn(epoch, env_step, gradient_step):
save_best_fn=save_best_fn,
logger=logger,
resume_from_log=args.resume,
save_checkpoint_fn=save_checkpoint_fn
save_checkpoint_fn=save_checkpoint_fn,
)

for epoch, epoch_stat, info in trainer:
Expand All @@ -181,7 +185,7 @@ def save_checkpoint_fn(epoch, env_step, gradient_step):

assert stop_fn(info["best_reward"])

if __name__ == '__main__':
if __name__ == "__main__":
pprint.pprint(info)
# Let's watch its performance!
env = gym.make(args.task)
Expand All @@ -197,5 +201,5 @@ def test_ppo_resume(args=get_args()):
test_ppo(args)


if __name__ == '__main__':
if __name__ == "__main__":
test_ppo()
42 changes: 22 additions & 20 deletions test/discrete/test_c51.py
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Expand Up @@ -85,7 +85,7 @@ def test_c51(args=get_args()):
hidden_sizes=args.hidden_sizes,
device=args.device,
softmax=True,
num_atoms=args.num_atoms
num_atoms=args.num_atoms,
)
optim = torch.optim.Adam(net.parameters(), lr=args.lr)
policy = C51Policy(
Expand All @@ -96,15 +96,15 @@ def test_c51(args=get_args()):
args.v_min,
args.v_max,
args.n_step,
target_update_freq=args.target_update_freq
target_update_freq=args.target_update_freq,
).to(args.device)
# buffer
if args.prioritized_replay:
buf = PrioritizedVectorReplayBuffer(
args.buffer_size,
buffer_num=len(train_envs),
alpha=args.alpha,
beta=args.beta
beta=args.beta,
)
else:
buf = VectorReplayBuffer(args.buffer_size, buffer_num=len(train_envs))
Expand All @@ -114,12 +114,12 @@ def test_c51(args=get_args()):
# policy.set_eps(1)
train_collector.collect(n_step=args.batch_size * args.training_num)
# log
log_path = os.path.join(args.logdir, args.task, 'c51')
log_path = os.path.join(args.logdir, args.task, "c51")
writer = SummaryWriter(log_path)
logger = TensorboardLogger(writer, save_interval=args.save_interval)

def save_best_fn(policy):
torch.save(policy.state_dict(), os.path.join(log_path, 'policy.pth'))
torch.save(policy.state_dict(), os.path.join(log_path, "policy.pth"))

def stop_fn(mean_rewards):
return mean_rewards >= args.reward_threshold
Expand All @@ -140,29 +140,31 @@ def test_fn(epoch, env_step):

def save_checkpoint_fn(epoch, env_step, gradient_step):
# see also: https://pytorch.org/tutorials/beginner/saving_loading_models.html
ckpt_path = os.path.join(log_path, "checkpoint.pth")
# Example: saving by epoch num
# ckpt_path = os.path.join(log_path, f"checkpoint_{epoch}.pth")
torch.save(
{
'model': policy.state_dict(),
'optim': optim.state_dict(),
}, os.path.join(log_path, 'checkpoint.pth')
)
pickle.dump(
train_collector.buffer,
open(os.path.join(log_path, 'train_buffer.pkl'), "wb")
"model": policy.state_dict(),
"optim": optim.state_dict(),
}, ckpt_path
)
buffer_path = os.path.join(log_path, "train_buffer.pkl")
pickle.dump(train_collector.buffer, open(buffer_path, "wb"))
return ckpt_path

if args.resume:
# load from existing checkpoint
print(f"Loading agent under {log_path}")
ckpt_path = os.path.join(log_path, 'checkpoint.pth')
ckpt_path = os.path.join(log_path, "checkpoint.pth")
if os.path.exists(ckpt_path):
checkpoint = torch.load(ckpt_path, map_location=args.device)
policy.load_state_dict(checkpoint['model'])
policy.optim.load_state_dict(checkpoint['optim'])
policy.load_state_dict(checkpoint["model"])
policy.optim.load_state_dict(checkpoint["optim"])
print("Successfully restore policy and optim.")
else:
print("Fail to restore policy and optim.")
buffer_path = os.path.join(log_path, 'train_buffer.pkl')
buffer_path = os.path.join(log_path, "train_buffer.pkl")
if os.path.exists(buffer_path):
train_collector.buffer = pickle.load(open(buffer_path, "rb"))
print("Successfully restore buffer.")
Expand All @@ -186,11 +188,11 @@ def save_checkpoint_fn(epoch, env_step, gradient_step):
save_best_fn=save_best_fn,
logger=logger,
resume_from_log=args.resume,
save_checkpoint_fn=save_checkpoint_fn
save_checkpoint_fn=save_checkpoint_fn,
)
assert stop_fn(result['best_reward'])
assert stop_fn(result["best_reward"])

if __name__ == '__main__':
if __name__ == "__main__":
pprint.pprint(result)
# Let's watch its performance!
env = gym.make(args.task)
Expand All @@ -214,5 +216,5 @@ def test_pc51(args=get_args()):
test_c51(args)


if __name__ == '__main__':
if __name__ == "__main__":
test_c51(get_args())
38 changes: 20 additions & 18 deletions test/discrete/test_rainbow.py
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Original file line number Diff line number Diff line change
Expand Up @@ -98,7 +98,7 @@ def noisy_linear(x, y):
"linear_layer": noisy_linear
}, {
"linear_layer": noisy_linear
})
}),
)
optim = torch.optim.Adam(net.parameters(), lr=args.lr)
policy = RainbowPolicy(
Expand All @@ -109,7 +109,7 @@ def noisy_linear(x, y):
args.v_min,
args.v_max,
args.n_step,
target_update_freq=args.target_update_freq
target_update_freq=args.target_update_freq,
).to(args.device)
# buffer
if args.prioritized_replay:
Expand All @@ -118,7 +118,7 @@ def noisy_linear(x, y):
buffer_num=len(train_envs),
alpha=args.alpha,
beta=args.beta,
weight_norm=True
weight_norm=True,
)
else:
buf = VectorReplayBuffer(args.buffer_size, buffer_num=len(train_envs))
Expand All @@ -128,12 +128,12 @@ def noisy_linear(x, y):
# policy.set_eps(1)
train_collector.collect(n_step=args.batch_size * args.training_num)
# log
log_path = os.path.join(args.logdir, args.task, 'rainbow')
log_path = os.path.join(args.logdir, args.task, "rainbow")
writer = SummaryWriter(log_path)
logger = TensorboardLogger(writer, save_interval=args.save_interval)

def save_best_fn(policy):
torch.save(policy.state_dict(), os.path.join(log_path, 'policy.pth'))
torch.save(policy.state_dict(), os.path.join(log_path, "policy.pth"))

def stop_fn(mean_rewards):
return mean_rewards >= args.reward_threshold
Expand Down Expand Up @@ -164,29 +164,31 @@ def test_fn(epoch, env_step):

def save_checkpoint_fn(epoch, env_step, gradient_step):
# see also: https://pytorch.org/tutorials/beginner/saving_loading_models.html
ckpt_path = os.path.join(log_path, "checkpoint.pth")
# Example: saving by epoch num
# ckpt_path = os.path.join(log_path, f"checkpoint_{epoch}.pth")
torch.save(
{
'model': policy.state_dict(),
'optim': optim.state_dict(),
}, os.path.join(log_path, 'checkpoint.pth')
)
pickle.dump(
train_collector.buffer,
open(os.path.join(log_path, 'train_buffer.pkl'), "wb")
"model": policy.state_dict(),
"optim": optim.state_dict(),
}, ckpt_path
)
buffer_path = os.path.join(log_path, "train_buffer.pkl")
pickle.dump(train_collector.buffer, open(buffer_path, "wb"))
return ckpt_path

if args.resume:
# load from existing checkpoint
print(f"Loading agent under {log_path}")
ckpt_path = os.path.join(log_path, 'checkpoint.pth')
ckpt_path = os.path.join(log_path, "checkpoint.pth")
if os.path.exists(ckpt_path):
checkpoint = torch.load(ckpt_path, map_location=args.device)
policy.load_state_dict(checkpoint['model'])
policy.optim.load_state_dict(checkpoint['optim'])
print("Successfully restore policy and optim.")
else:
print("Fail to restore policy and optim.")
buffer_path = os.path.join(log_path, 'train_buffer.pkl')
buffer_path = os.path.join(log_path, "train_buffer.pkl")
if os.path.exists(buffer_path):
train_collector.buffer = pickle.load(open(buffer_path, "rb"))
print("Successfully restore buffer.")
Expand All @@ -210,11 +212,11 @@ def save_checkpoint_fn(epoch, env_step, gradient_step):
save_best_fn=save_best_fn,
logger=logger,
resume_from_log=args.resume,
save_checkpoint_fn=save_checkpoint_fn
save_checkpoint_fn=save_checkpoint_fn,
)
assert stop_fn(result['best_reward'])
assert stop_fn(result["best_reward"])

if __name__ == '__main__':
if __name__ == "__main__":
pprint.pprint(result)
# Let's watch its performance!
env = gym.make(args.task)
Expand All @@ -238,5 +240,5 @@ def test_prainbow(args=get_args()):
test_rainbow(args)


if __name__ == '__main__':
if __name__ == "__main__":
test_rainbow(get_args())
30 changes: 17 additions & 13 deletions test/offline/test_discrete_bcq.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,7 +25,7 @@
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument("--task", type=str, default="CartPole-v0")
parser.add_argument('--reward-threshold', type=float, default=None)
parser.add_argument("--reward-threshold", type=float, default=None)
parser.add_argument("--seed", type=int, default=1626)
parser.add_argument("--eps-test", type=float, default=0.001)
parser.add_argument("--lr", type=float, default=3e-4)
Expand All @@ -37,7 +37,7 @@ def get_args():
parser.add_argument("--epoch", type=int, default=5)
parser.add_argument("--update-per-epoch", type=int, default=2000)
parser.add_argument("--batch-size", type=int, default=64)
parser.add_argument('--hidden-sizes', type=int, nargs='*', default=[64, 64])
parser.add_argument("--hidden-sizes", type=int, nargs="*", default=[64, 64])
parser.add_argument("--test-num", type=int, default=100)
parser.add_argument("--logdir", type=str, default="log")
parser.add_argument("--render", type=float, default=0.)
Expand Down Expand Up @@ -104,33 +104,37 @@ def test_discrete_bcq(args=get_args()):
# collector
test_collector = Collector(policy, test_envs, exploration_noise=True)

log_path = os.path.join(args.logdir, args.task, 'discrete_bcq')
log_path = os.path.join(args.logdir, args.task, "discrete_bcq")
writer = SummaryWriter(log_path)
logger = TensorboardLogger(writer, save_interval=args.save_interval)

def save_best_fn(policy):
torch.save(policy.state_dict(), os.path.join(log_path, 'policy.pth'))
torch.save(policy.state_dict(), os.path.join(log_path, "policy.pth"))

def stop_fn(mean_rewards):
return mean_rewards >= args.reward_threshold

def save_checkpoint_fn(epoch, env_step, gradient_step):
# see also: https://pytorch.org/tutorials/beginner/saving_loading_models.html
ckpt_path = os.path.join(log_path, "checkpoint.pth")
# Example: saving by epoch num
# ckpt_path = os.path.join(log_path, f"checkpoint_{epoch}.pth")
torch.save(
{
'model': policy.state_dict(),
'optim': optim.state_dict(),
}, os.path.join(log_path, 'checkpoint.pth')
"model": policy.state_dict(),
"optim": optim.state_dict(),
}, ckpt_path
)
return ckpt_path

if args.resume:
# load from existing checkpoint
print(f"Loading agent under {log_path}")
ckpt_path = os.path.join(log_path, 'checkpoint.pth')
ckpt_path = os.path.join(log_path, "checkpoint.pth")
if os.path.exists(ckpt_path):
checkpoint = torch.load(ckpt_path, map_location=args.device)
policy.load_state_dict(checkpoint['model'])
optim.load_state_dict(checkpoint['optim'])
policy.load_state_dict(checkpoint["model"])
optim.load_state_dict(checkpoint["optim"])
print("Successfully restore policy and optim.")
else:
print("Fail to restore policy and optim.")
Expand All @@ -147,11 +151,11 @@ def save_checkpoint_fn(epoch, env_step, gradient_step):
save_best_fn=save_best_fn,
logger=logger,
resume_from_log=args.resume,
save_checkpoint_fn=save_checkpoint_fn
save_checkpoint_fn=save_checkpoint_fn,
)
assert stop_fn(result['best_reward'])
assert stop_fn(result["best_reward"])

if __name__ == '__main__':
if __name__ == "__main__":
pprint.pprint(result)
# Let's watch its performance!
env = gym.make(args.task)
Expand Down
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