wassname/pytorch-soft-actor-critic
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wassname
2021-01-17 11:53:18 +08:00
parent e4fd67f3b5
commit 9ffe9fa9a2
5 changed files with 57 additions and 50 deletions
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Makefile
+5 -4
View File
@@ -1,11 +1,12 @@
SHELL=/bin/bash
python=/home/wassname/anaconda/envs/diygym3/bin/python
date=2021-01-03_13-30-07
LOGURU_LEVEL=INFO
# ulimit -S -m 35000000
# ulimit -S -v 35000000
run:
LOGURU_LEVEL=INFO ${python} -m pdb main.py --cuda --automatic_entropy_tuning true --replay_size 15000 --load auto
ulimit -S -m 65000000
ulimit -S -v 65000000
LOGURU_LEVEL=INFO ${python} main.py --cuda --automatic_entropy_tuning true --replay_size 50000 --load auto
# ${python} -m pdb main.py --cuda --automatic_entropy_tuning true --replay_size 10000 --load auto --start_steps 200
# LOGURU_LEVEL=INFO ${python} main.py --demonstrations data/demonstrations --cuda --automatic_entropy_tuning true --replay_size 20000 --load auto
# LOGURU_LEVEL=INFO ${python} main.py --demonstrations data/demonstrations --cuda --updates_per_step 2 --load auto --alpha 0.1 --tau 1 --target_update_interval 1000
# LOGURU_LEVEL=INFO ${python} main.py --demonstrations data/demonstrations --cuda --updates_per_step 2 --load auto --tau 1 --target_update_interval 1000 --policy Deterministic
main.py
+7 -41
View File
@@ -13,26 +13,12 @@ import apple_gym.env
import pickle
from process_obs import ProcessObservation
# from torchinfo import summary
from tqdm.auto import tqdm
from torch.utils.tensorboard import SummaryWriter
from progress import RichTQDM
from loguru import logger
from rich import print
from rich.logging import RichHandler
from rich.progress import (
ProgressColumn,
BarColumn,
DownloadColumn,
TextColumn,
TransferSpeedColumn,
TimeRemainingColumn,
Progress,
TaskID,
TimeElapsedColumn,
SpinnerColumn,
Text
)
logging.basicConfig(level=logging.INFO, handlers=[RichHandler(rich_tracebacks=True, markup=True)])
logger.configure(handlers=[{"sink": RichHandler(markup=True),
"format": "{message}"}])
@@ -156,32 +142,11 @@ if args.demonstrations:
total_numsteps = 0
updates = 0
class SpeedColumn(ProgressColumn):
"""Renders human readable transfer speed."""
def render(self, task: "Task") -> Text:
"""Show data transfer speed."""
speed = task.speed
if speed is None:
return Text("?", style="progress.data.speed")
return Text(f"{speed:2.2f} it/s", style="progress.data.speed")
with Progress(
SpinnerColumn(),
"[progress.description]{task.description}",
BarColumn(),
TextColumn("{task.completed}/{task.total}"),
"[",
TimeElapsedColumn(),
"<",
TimeRemainingColumn(),
',',
SpeedColumn(),
']',
refresh_per_second=1, speed_estimate_period=360
) as prog:
with RichTQDM() as prog:
task1 = prog.add_task("[red]steps", total=args.num_steps)
task2 = prog.add_task("[red]updates", total=args.num_steps)
task3 = prog.add_task("[red]test", total=args.num_steps)
for i_episode in itertools.count(0):
print('1')
episode_reward = 0
@@ -234,7 +199,7 @@ with Progress(
logger.info("\nEpisode: {}, total numsteps: {}, episode steps: {}, reward: {}, updates: {}".format(i_episode, total_numsteps, episode_steps, round(episode_reward, 2), updates))
prog.desc = "e: {}, r: {}, u: {}, m: {}".format(i_episode, round(episode_reward, 2), updates, len(memory))
if i_episode % 10 == 0 and args.eval is True:
if (i_episode % 100 == 0) and (args.eval is True) and i_episode>0:
avg_reward = 0.
episodes = 10
for _ in range(episodes):
@@ -243,6 +208,7 @@ with Progress(
done = False
while not done:
action = agent.select_action(state, evaluate=True)
prog.update(task3, advance=1)
next_state, reward, done, _ = env.step(action)
episode_reward += reward
@@ -266,7 +232,7 @@ with Progress(
if total_numsteps >= args.num_steps:
break
if args.train:
save(save_dir)
if args.train:
save(save_dir)
env.close()
process_obs.py
+3 -2
View File
@@ -122,7 +122,7 @@ class ProcessObservation(nn.Module):
os.path.dirname(os.path.abspath(__file__)),
'data/nets/cornell-randsplit-rgbd-grconvnet3-drop1-ch16/epoch_30_iou_0.97.pt'
)
self.feature_extractor = GenerativeResnet3Headless()#.half()
self.feature_extractor = GenerativeResnet3Headless().half()
self.feature_extractor.load_state_dict(state_dict=torch.load(grconvnet3_path), strict=False)
old_img_size = (res[0], res[1], 8)
@@ -145,7 +145,8 @@ class ProcessObservation(nn.Module):
# make a batch
x = torch.cat([base_rgbd, arm_rgbd], 0)
x = x.permute((0, 3, 1, 2)) # to ((-1, 4, x, y))
x = x.permute((0, 3, 1, 2)) # to ((-1, 4, x, y))
x = x.half()
h = self.feature_extractor(x)
# undo fake batch
progress.py
+39
View File
@@ -0,0 +1,39 @@
from rich.progress import (
ProgressColumn,
BarColumn,
DownloadColumn,
TextColumn,
TransferSpeedColumn,
TimeRemainingColumn,
Progress,
TaskID,
TimeElapsedColumn,
SpinnerColumn,
Text
)
class SpeedColumn(ProgressColumn):
"""Renders human readable transfer speed."""
def render(self, task: "Task") -> Text:
"""Show data transfer speed."""
speed = task.speed
if speed is None:
return Text("?", style="progress.data.speed")
return Text(f"{speed:2.2f} it/s", style="progress.data.speed")
def RichTQDM():
return Progress(
SpinnerColumn(),
"[progress.description]{task.description}",
BarColumn(),
TextColumn("{task.completed}/{task.total}"),
"[",
TimeElapsedColumn(),
"<",
TimeRemainingColumn(),
',',
SpeedColumn(),
']',
refresh_per_second=.1, speed_estimate_period=30
)
replay_memory.py
+3 -3
View File
@@ -21,8 +21,8 @@ def unpack(data):
return data
class ReplayMemory2:
def __init__(self, capacity, seed):
class ReplayMemory:
def __init__(self, capacity, seed, *args, **kwargs):
random.seed(seed)
self.capacity = capacity
self.buffer = []
@@ -56,7 +56,7 @@ class ReplayMemory2:
self.position = len(self.buffer)
class ReplayMemory:
class ReplayMemory2:
def __init__(self, capacity, seed, observation_dim, action_dim):
random.seed(seed)
self.capacity = capacity