From f853c03f4b3af901b28b93b1aa0394f6a6dd028f Mon Sep 17 00:00:00 2001 From: wassname Date: Sun, 2 Jun 2024 18:52:57 +0800 Subject: [PATCH] wip --- configs.yaml | 2 +- dreamer.py | 104 +++--- envs/crafter.py | 4 +- envs/wrappers.py | 12 +- justfile | 4 + nbs/01_fix_video.ipynb | 80 +++++ networks.py | 6 +- poetry.lock | 708 ++++++++++++++++++++++++++++++++++++++++- pyproject.toml | 5 + tools.py | 4 +- 10 files changed, 866 insertions(+), 63 deletions(-) create mode 100644 justfile create mode 100644 nbs/01_fix_video.ipynb diff --git a/configs.yaml b/configs.yaml index 8d8d7ae..64f2865 100644 --- a/configs.yaml +++ b/configs.yaml @@ -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' diff --git a/dreamer.py b/dreamer.py index 7384845..d632d2f 100644 --- a/dreamer.py +++ b/dreamer.py @@ -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() diff --git a/envs/crafter.py b/envs/crafter.py index 351bce6..57bf1a7 100644 --- a/envs/crafter.py +++ b/envs/crafter.py @@ -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, diff --git a/envs/wrappers.py b/envs/wrappers.py index 4927b78..a4e292b 100644 --- a/envs/wrappers.py +++ b/envs/wrappers.py @@ -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 diff --git a/justfile b/justfile new file mode 100644 index 0000000..ad10460 --- /dev/null +++ b/justfile @@ -0,0 +1,4 @@ + +main: + . ./.venv/bin/activate + python dreamer.py --configs crafter --task crafter_reward --logdir ./logdir/crafter diff --git a/nbs/01_fix_video.ipynb b/nbs/01_fix_video.ipynb new file mode 100644 index 0000000..e130bd1 --- /dev/null +++ b/nbs/01_fix_video.ipynb @@ -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 +} diff --git a/networks.py b/networks.py index c5d7f6a..72a4940 100644 --- a/networks.py +++ b/networks.py @@ -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: diff --git a/poetry.lock b/poetry.lock index a1438cb..d3d9725 100644 --- a/poetry.lock +++ b/poetry.lock @@ -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 = 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logger.scalar(f"eval_return", score)