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wassname
2024-06-02 18:52:57 +08:00
parent 61a43cc1d1
commit f853c03f4b
10 changed files with 866 additions and 63 deletions
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configs.yaml
+1 -1
View File
@@ -17,7 +17,7 @@ defaults:
compile: True
precision: 32
debug: False
video_pred_log: True
video_pred_log: False # broken install FIXME:
# Environment
task: 'dmc_walker_walk'
dreamer.py
+56 -48
View File
@@ -4,12 +4,12 @@ import os
import pathlib
import sys
os.environ["MUJOCO_GL"] = "osmesa"
# os.environ["MUJOCO_GL"] = "osmesa"
import numpy as np
import ruamel.yaml as yaml
sys.path.append(str(pathlib.Path(__file__).parent))
# sys.path.append(str(pathlib.Path(__file__).parent))
import exploration as expl
import models
@@ -21,15 +21,19 @@ import torch
from torch import nn
from torch import distributions as torchd
from loguru import logger
from tqdm.auto import tqdm
logger.remove()
logger.add(lambda msg: tqdm.write(msg, end=""), colorize=True)
to_np = lambda x: x.detach().cpu().numpy()
class Dreamer(nn.Module):
def __init__(self, obs_space, act_space, config, logger, dataset):
def __init__(self, obs_space, act_space, config, tlogger, dataset):
super(Dreamer, self).__init__()
self._config = config
self._logger = logger
self._logger = tlogger
self._should_log = tools.Every(config.log_every)
batch_steps = config.batch_size * config.batch_length
self._should_train = tools.Every(batch_steps / config.train_ratio)
@@ -38,7 +42,7 @@ class Dreamer(nn.Module):
self._should_expl = tools.Until(int(config.expl_until / config.action_repeat))
self._metrics = {}
# this is update step
self._step = logger.step // config.action_repeat
self._step = tlogger.step // config.action_repeat
self._update_count = 0
self._dataset = dataset
self._wm = models.WorldModel(obs_space, act_space, self._step, config)
@@ -63,7 +67,7 @@ class Dreamer(nn.Module):
if self._should_pretrain()
else self._should_train(step)
)
for _ in range(steps):
for _ in tqdm(range(steps), desc='minitrain'):
self._train(next(self._dataset))
self._update_count += 1
self._metrics["update_count"] = self._update_count
@@ -215,15 +219,15 @@ def main(config):
config.log_every //= config.action_repeat
config.time_limit //= config.action_repeat
print("Logdir", logdir)
logger.info(f"Logdir {logdir}")
logdir.mkdir(parents=True, exist_ok=True)
config.traindir.mkdir(parents=True, exist_ok=True)
config.evaldir.mkdir(parents=True, exist_ok=True)
step = count_steps(config.traindir)
# step in logger is environmental step
logger = tools.Logger(logdir, config.action_repeat * step)
tlogger = tools.Logger(logdir, config.action_repeat * step)
print("Create envs.")
logger.info("Create envs.")
if config.offline_traindir:
directory = config.offline_traindir.format(**vars(config))
else:
@@ -244,13 +248,13 @@ def main(config):
train_envs = [Damy(env) for env in train_envs]
eval_envs = [Damy(env) for env in eval_envs]
acts = train_envs[0].action_space
print("Action Space", acts)
logger.info(f"Action Space {acts}" )
config.num_actions = acts.n if hasattr(acts, "n") else acts.shape[0]
state = None
if not config.offline_traindir:
prefill = max(0, config.prefill - count_steps(config.traindir))
print(f"Prefill dataset ({prefill} steps).")
logger.info(f"Prefill dataset ({prefill} steps).")
if hasattr(acts, "discrete"):
random_actor = tools.OneHotDist(
torch.zeros(config.num_actions).repeat(config.envs, 1)
@@ -274,21 +278,21 @@ def main(config):
train_envs,
train_eps,
config.traindir,
logger,
tlogger,
limit=config.dataset_size,
steps=prefill,
)
logger.step += prefill * config.action_repeat
print(f"Logger: ({logger.step} steps).")
tlogger.step += prefill * config.action_repeat
logger.info(f"Logger: ({tlogger.step} steps).")
print("Simulate agent.")
logger.info("Simulate agent.")
train_dataset = make_dataset(train_eps, config)
eval_dataset = make_dataset(eval_eps, config)
agent = Dreamer(
train_envs[0].observation_space,
train_envs[0].action_space,
config,
logger,
tlogger,
train_dataset,
).to(config.device)
agent.requires_grad_(requires_grad=False)
@@ -299,39 +303,43 @@ def main(config):
agent._should_pretrain._once = False
# make sure eval will be executed once after config.steps
while agent._step < config.steps + config.eval_every:
logger.write()
if config.eval_episode_num > 0:
print("Start evaluation.")
eval_policy = functools.partial(agent, training=False)
tools.simulate(
eval_policy,
eval_envs,
eval_eps,
config.evaldir,
logger,
is_eval=True,
episodes=config.eval_episode_num,
with tqdm(total=config.steps + config.eval_every) as pbar:
while agent._step < config.steps + config.eval_every:
tlogger.write()
if config.eval_episode_num > 0:
logger.info("Start evaluation.")
eval_policy = functools.partial(agent, training=False)
tools.simulate(
eval_policy,
eval_envs,
eval_eps,
config.evaldir,
tlogger,
is_eval=True,
episodes=config.eval_episode_num,
video_pred_log=config.video_pred_log,
)
if config.video_pred_log:
video_pred = agent._wm.video_pred(next(eval_dataset))
tlogger.video("eval_openl", to_np(video_pred))
logger.info("Start training.")
state = tools.simulate(
agent,
train_envs,
train_eps,
config.traindir,
tlogger,
limit=config.dataset_size,
steps=config.eval_every,
state=state,
)
if config.video_pred_log:
video_pred = agent._wm.video_pred(next(eval_dataset))
logger.video("eval_openl", to_np(video_pred))
print("Start training.")
state = tools.simulate(
agent,
train_envs,
train_eps,
config.traindir,
logger,
limit=config.dataset_size,
steps=config.eval_every,
state=state,
)
items_to_save = {
"agent_state_dict": agent.state_dict(),
"optims_state_dict": tools.recursively_collect_optim_state_dict(agent),
}
torch.save(items_to_save, logdir / "latest.pt")
items_to_save = {
"agent_state_dict": agent.state_dict(),
"optims_state_dict": tools.recursively_collect_optim_state_dict(agent),
}
torch.save(items_to_save, logdir / "latest.pt")
logger.info(f"Saved model to {logdir / 'latest.pt'}")
pbar.update_to(agent._step)
for env in train_envs + eval_envs:
try:
env.close()
envs/crafter.py
+2 -2
View File
@@ -37,7 +37,7 @@ class Crafter:
@property
def action_space(self):
action_space = self._env.action_space
action_space.discrete = True
# action_space.discrete = True
return action_space
def step(self, action):
@@ -60,7 +60,7 @@ class Crafter:
def render(self):
return self._env.render()
def reset(self):
def reset(self, seed=None, options=None):
image = self._env.reset()
obs = {
"image": image,
envs/wrappers.py
+6 -6
View File
@@ -2,7 +2,7 @@ import datetime
import gymnasium as gym
import numpy as np
import uuid
import crafter
class TimeLimit(gym.Wrapper):
def __init__(self, env, duration):
@@ -21,9 +21,9 @@ class TimeLimit(gym.Wrapper):
self._step = None
return obs, reward, done, info
def reset(self):
def reset(self, *args, **kwargs):
self._step = 0
return self.env.reset()
return self.env.reset(*args, **kwargs)
class NormalizeActions(gym.Wrapper):
@@ -46,7 +46,7 @@ class NormalizeActions(gym.Wrapper):
class OneHotAction(gym.Wrapper):
def __init__(self, env):
assert isinstance(env.action_space, gym.spaces.Discrete)
assert isinstance(env.action_space, (gym.spaces.Discrete, crafter.env.DiscreteSpace))
super().__init__(env)
self._random = np.random.RandomState()
shape = (self.env.action_space.n,)
@@ -62,8 +62,8 @@ class OneHotAction(gym.Wrapper):
raise ValueError(f"Invalid one-hot action:\n{action}")
return self.env.step(index)
def reset(self):
return self.env.reset()
def reset(self, *args, **kwargs):
return self.env.reset(*args, **kwargs)
def _sample_action(self):
actions = self.env.action_space.n
justfile
+4
View File
@@ -0,0 +1,4 @@
main:
. ./.venv/bin/activate
python dreamer.py --configs crafter --task crafter_reward --logdir ./logdir/crafter
nbs/01_fix_video.ipynb
+80
View File
@@ -0,0 +1,80 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "not enough values to unpack (expected 5, got 1)",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[4], line 18\u001b[0m\n\u001b[1;32m 15\u001b[0m video \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mrandint(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m255\u001b[39m, size\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m1\u001b[39m, T, C, H, W))\u001b[38;5;241m.\u001b[39mastype(np\u001b[38;5;241m.\u001b[39muint8)\n\u001b[1;32m 16\u001b[0m \u001b[38;5;66;03m# value = np.clip(255 * value, 0, 255).astype(np.uint8)\u001b[39;00m\n\u001b[1;32m 17\u001b[0m \u001b[38;5;66;03m# value = value.transpose(1, 4, 2, 0, 3).reshape((1, T, C, H, B * W))\u001b[39;00m\n\u001b[0;32m---> 18\u001b[0m \u001b[43mwriter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madd_video\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstep\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m16\u001b[39;49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/media/wassname/SGIronWolf/projects5/worldmodels/dreamerv3-torch/.venv/lib/python3.9/site-packages/torch/utils/tensorboard/writer.py:821\u001b[0m, in \u001b[0;36mSummaryWriter.add_video\u001b[0;34m(self, tag, vid_tensor, global_step, fps, walltime)\u001b[0m\n\u001b[1;32m 805\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Add video data to summary.\u001b[39;00m\n\u001b[1;32m 806\u001b[0m \n\u001b[1;32m 807\u001b[0m \u001b[38;5;124;03mNote that this requires the ``moviepy`` package.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 817\u001b[0m \u001b[38;5;124;03m vid_tensor: :math:`(N, T, C, H, W)`. The values should lie in [0, 255] for type `uint8` or [0, 1] for type `float`.\u001b[39;00m\n\u001b[1;32m 818\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 819\u001b[0m torch\u001b[38;5;241m.\u001b[39m_C\u001b[38;5;241m.\u001b[39m_log_api_usage_once(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtensorboard.logging.add_video\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 820\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_file_writer()\u001b[38;5;241m.\u001b[39madd_summary(\n\u001b[0;32m--> 821\u001b[0m \u001b[43mvideo\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtag\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvid_tensor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfps\u001b[49m\u001b[43m)\u001b[49m, global_step, walltime\n\u001b[1;32m 822\u001b[0m )\n",
"File \u001b[0;32m/media/wassname/SGIronWolf/projects5/worldmodels/dreamerv3-torch/.venv/lib/python3.9/site-packages/torch/utils/tensorboard/summary.py:644\u001b[0m, in \u001b[0;36mvideo\u001b[0;34m(tag, tensor, fps)\u001b[0m\n\u001b[1;32m 642\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mvideo\u001b[39m(tag, tensor, fps\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m4\u001b[39m):\n\u001b[1;32m 643\u001b[0m tensor \u001b[38;5;241m=\u001b[39m make_np(tensor)\n\u001b[0;32m--> 644\u001b[0m tensor \u001b[38;5;241m=\u001b[39m \u001b[43m_prepare_video\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtensor\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 645\u001b[0m \u001b[38;5;66;03m# If user passes in uint8, then we don't need to rescale by 255\u001b[39;00m\n\u001b[1;32m 646\u001b[0m scale_factor \u001b[38;5;241m=\u001b[39m _calc_scale_factor(tensor)\n",
"File \u001b[0;32m/media/wassname/SGIronWolf/projects5/worldmodels/dreamerv3-torch/.venv/lib/python3.9/site-packages/torch/utils/tensorboard/_utils.py:49\u001b[0m, in \u001b[0;36m_prepare_video\u001b[0;34m(V)\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_prepare_video\u001b[39m(V):\n\u001b[1;32m 40\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 41\u001b[0m \u001b[38;5;124;03m Convert a 5D tensor into 4D tensor.\u001b[39;00m\n\u001b[1;32m 42\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 47\u001b[0m \u001b[38;5;124;03m e.g. Video with batchsize 16 will have a 4x4 grid.\u001b[39;00m\n\u001b[1;32m 48\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m---> 49\u001b[0m b, t, c, h, w \u001b[38;5;241m=\u001b[39m V\u001b[38;5;241m.\u001b[39mshape\n\u001b[1;32m 51\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m V\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m np\u001b[38;5;241m.\u001b[39muint8:\n\u001b[1;32m 52\u001b[0m V \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mfloat32(V) \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m255.0\u001b[39m\n",
"\u001b[0;31mValueError\u001b[0m: not enough values to unpack (expected 5, got 1)"
]
}
],
"source": [
"from torch.utils.tensorboard import SummaryWriter\n",
"import numpy as np\n",
"\n",
"value = 1\n",
"T = 2\n",
"C = 3\n",
"H = 32\n",
"H = 32\n",
"B = 4\n",
"W = 5\n",
"step = 6\n",
"name = 'name'\n",
"\n",
"writer = SummaryWriter()\n",
"video = np.random.randint(0, 255, size=(1, T, C, H, W)).astype(np.uint8)\n",
"# value = np.clip(255 * value, 0, 255).astype(np.uint8)\n",
"# value = value.transpose(1, 4, 2, 0, 3).reshape((1, T, C, H, B * W))\n",
"writer.add_video(name, value, step, 16)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.16"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
networks.py
+3 -3
View File
@@ -6,7 +6,7 @@ import torch
from torch import nn
import torch.nn.functional as F
from torch import distributions as torchd
from loguru import logger
import tools
@@ -320,8 +320,8 @@ class MultiEncoder(nn.Module):
for k, v in shapes.items()
if len(v) in (1, 2) and re.match(mlp_keys, k)
}
print("Encoder CNN shapes:", self.cnn_shapes)
print("Encoder MLP shapes:", self.mlp_shapes)
logger.info("Encoder CNN shapes:", self.cnn_shapes)
logger.info("Encoder MLP shapes:", self.mlp_shapes)
self.outdim = 0
if self.cnn_shapes:
poetry.lock
Generated
+706 -2
View File
@@ -11,6 +11,35 @@ files = [
{file = "absl_py-2.1.0-py3-none-any.whl", hash = "sha256:526a04eadab8b4ee719ce68f204172ead1027549089702d99b9059f129ff1308"},
]
[[package]]
name = "appnope"
version = "0.1.4"
description = "Disable App Nap on macOS >= 10.9"
optional = false
python-versions = ">=3.6"
files = [
{file = "appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c"},
{file = "appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee"},
]
[[package]]
name = "asttokens"
version = "2.4.1"
description = "Annotate AST trees with source code positions"
optional = false
python-versions = "*"
files = [
{file = "asttokens-2.4.1-py2.py3-none-any.whl", hash = "sha256:051ed49c3dcae8913ea7cd08e46a606dba30b79993209636c4875bc1d637bc24"},
{file = "asttokens-2.4.1.tar.gz", hash = "sha256:b03869718ba9a6eb027e134bfdf69f38a236d681c83c160d510768af11254ba0"},
]
[package.dependencies]
six = ">=1.12.0"
[package.extras]
astroid = ["astroid (>=1,<2)", "astroid (>=2,<4)"]
test = ["astroid (>=1,<2)", "astroid (>=2,<4)", "pytest"]
[[package]]
name = "beartype"
version = "0.18.5"
@@ -40,6 +69,70 @@ files = [
{file = "certifi-2024.6.2.tar.gz", hash = "sha256:3cd43f1c6fa7dedc5899d69d3ad0398fd018ad1a17fba83ddaf78aa46c747516"},
]
[[package]]
name = "cffi"
version = "1.16.0"
description = "Foreign Function Interface for Python calling C code."
optional = false
python-versions = ">=3.8"
files = [
{file = "cffi-1.16.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:6b3d6606d369fc1da4fd8c357d026317fbb9c9b75d36dc16e90e84c26854b088"},
{file = "cffi-1.16.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:ac0f5edd2360eea2f1daa9e26a41db02dd4b0451b48f7c318e217ee092a213e9"},
{file = "cffi-1.16.0-cp310-cp310-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7e61e3e4fa664a8588aa25c883eab612a188c725755afff6289454d6362b9673"},
{file = "cffi-1.16.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a72e8961a86d19bdb45851d8f1f08b041ea37d2bd8d4fd19903bc3083d80c896"},
{file = "cffi-1.16.0-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:5b50bf3f55561dac5438f8e70bfcdfd74543fd60df5fa5f62d94e5867deca684"},
{file = "cffi-1.16.0-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:7651c50c8c5ef7bdb41108b7b8c5a83013bfaa8a935590c5d74627c047a583c7"},
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@@ -631,6 +901,25 @@ files = [
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@@ -648,6 +937,60 @@ MarkupSafe = ">=2.0"
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@@ -1668,6 +2296,25 @@ files = [
{file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
]
[[package]]
name = "stack-data"
version = "0.6.3"
description = "Extract data from python stack frames and tracebacks for informative displays"
optional = false
python-versions = "*"
files = [
{file = "stack_data-0.6.3-py3-none-any.whl", hash = "sha256:d5558e0c25a4cb0853cddad3d77da9891a08cb85dd9f9f91b9f8cd66e511e695"},
{file = "stack_data-0.6.3.tar.gz", hash = "sha256:836a778de4fec4dcd1dcd89ed8abff8a221f58308462e1c4aa2a3cf30148f0b9"},
]
[package.dependencies]
asttokens = ">=2.1.0"
executing = ">=1.2.0"
pure-eval = "*"
[package.extras]
tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
[[package]]
name = "sympy"
version = "1.12.1"
@@ -1777,6 +2424,26 @@ type = "legacy"
url = "https://download.pytorch.org/whl/cu121"
reference = "pytorch"
[[package]]
name = "tornado"
version = "6.4"
description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
optional = false
python-versions = ">= 3.8"
files = [
{file = "tornado-6.4-cp38-abi3-macosx_10_9_universal2.whl", hash = "sha256:02ccefc7d8211e5a7f9e8bc3f9e5b0ad6262ba2fbb683a6443ecc804e5224ce0"},
{file = "tornado-6.4-cp38-abi3-macosx_10_9_x86_64.whl", hash = "sha256:27787de946a9cffd63ce5814c33f734c627a87072ec7eed71f7fc4417bb16263"},
{file = "tornado-6.4-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f7894c581ecdcf91666a0912f18ce5e757213999e183ebfc2c3fdbf4d5bd764e"},
{file = "tornado-6.4-cp38-abi3-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e43bc2e5370a6a8e413e1e1cd0c91bedc5bd62a74a532371042a18ef19e10579"},
{file = "tornado-6.4-cp38-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f0251554cdd50b4b44362f73ad5ba7126fc5b2c2895cc62b14a1c2d7ea32f212"},
{file = "tornado-6.4-cp38-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:fd03192e287fbd0899dd8f81c6fb9cbbc69194d2074b38f384cb6fa72b80e9c2"},
{file = "tornado-6.4-cp38-abi3-musllinux_1_1_i686.whl", hash = "sha256:88b84956273fbd73420e6d4b8d5ccbe913c65d31351b4c004ae362eba06e1f78"},
{file = "tornado-6.4-cp38-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:71ddfc23a0e03ef2df1c1397d859868d158c8276a0603b96cf86892bff58149f"},
{file = "tornado-6.4-cp38-abi3-win32.whl", hash = "sha256:6f8a6c77900f5ae93d8b4ae1196472d0ccc2775cc1dfdc9e7727889145c45052"},
{file = "tornado-6.4-cp38-abi3-win_amd64.whl", hash = "sha256:10aeaa8006333433da48dec9fe417877f8bcc21f48dda8d661ae79da357b2a63"},
{file = "tornado-6.4.tar.gz", hash = "sha256:72291fa6e6bc84e626589f1c29d90a5a6d593ef5ae68052ee2ef000dfd273dee"},
]
[[package]]
name = "tqdm"
version = "4.66.4"
@@ -1797,6 +2464,21 @@ notebook = ["ipywidgets (>=6)"]
slack = ["slack-sdk"]
telegram = ["requests"]
[[package]]
name = "traitlets"
version = "5.14.3"
description = "Traitlets Python configuration system"
optional = false
python-versions = ">=3.8"
files = [
{file = "traitlets-5.14.3-py3-none-any.whl", hash = "sha256:b74e89e397b1ed28cc831db7aea759ba6640cb3de13090ca145426688ff1ac4f"},
{file = "traitlets-5.14.3.tar.gz", hash = "sha256:9ed0579d3502c94b4b3732ac120375cda96f923114522847de4b3bb98b96b6b7"},
]
[package.extras]
docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"]
test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,<8.2)", "pytest-mock", "pytest-mypy-testing"]
[[package]]
name = "triton"
version = "2.3.0"
@@ -1863,6 +2545,17 @@ h2 = ["h2 (>=4,<5)"]
socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
zstd = ["zstandard (>=0.18.0)"]
[[package]]
name = "wcwidth"
version = "0.2.13"
description = "Measures the displayed width of unicode strings in a terminal"
optional = false
python-versions = "*"
files = [
{file = "wcwidth-0.2.13-py2.py3-none-any.whl", hash = "sha256:3da69048e4540d84af32131829ff948f1e022c1c6bdb8d6102117aac784f6859"},
{file = "wcwidth-0.2.13.tar.gz", hash = "sha256:72ea0c06399eb286d978fdedb6923a9eb47e1c486ce63e9b4e64fc18303972b5"},
]
[[package]]
name = "werkzeug"
version = "3.0.3"
@@ -1880,6 +2573,17 @@ MarkupSafe = ">=2.1.1"
[package.extras]
watchdog = ["watchdog (>=2.3)"]
[[package]]
name = "widgetsnbextension"
version = "4.0.11"
description = "Jupyter interactive widgets for Jupyter Notebook"
optional = false
python-versions = ">=3.7"
files = [
{file = "widgetsnbextension-4.0.11-py3-none-any.whl", hash = "sha256:55d4d6949d100e0d08b94948a42efc3ed6dfdc0e9468b2c4b128c9a2ce3a7a36"},
{file = "widgetsnbextension-4.0.11.tar.gz", hash = "sha256:8b22a8f1910bfd188e596fe7fc05dcbd87e810c8a4ba010bdb3da86637398474"},
]
[[package]]
name = "win32-setctime"
version = "1.1.0"
@@ -1912,4 +2616,4 @@ test = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-it
[metadata]
lock-version = "2.0"
python-versions = "^3.9"
content-hash = "ee7f63a25f80b16944749f5313ebee1bdd3d54af0497ed668225c8441a8a8237"
content-hash = "b62e06d233a29860e590f94b174bef24c4d1024e339420bfa870e8e709b8e6e4"
pyproject.toml
+5
View File
@@ -32,6 +32,11 @@ jaxtyping = "^0.2.2"
beartype = "^0.18.5"
tqdm = "^4.66.4"
loguru = "^0.7.2"
imageio-ffmpeg = "^0.5.0"
importlib = "^1.0.4"
imageio = "^2.34.1"
ipywidgets = "^8.1.3"
ipykernel = "^6.29.4"
[build-system]
requires = ["poetry-core"]
tools.py
+3 -1
View File
@@ -136,6 +136,7 @@ def simulate(
steps=0,
episodes=0,
state=None,
video_pred_log=True,
):
# initialize or unpack simulation state
if state is None:
@@ -233,7 +234,8 @@ def simulate(
score = sum(eval_scores) / len(eval_scores)
length = sum(eval_lengths) / len(eval_lengths)
logger.video(f"eval_policy", np.array(video)[None])
if video_pred_log:
logger.video(f"eval_policy", np.array(video)[None])
if len(eval_scores) >= episodes and not eval_done:
logger.scalar(f"eval_return", score)