VedantDave/tia
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Adding Files

Browse Source
This commit is contained in:
Vedant Dave 2023-07-16 22:08:57 +02:00
parent a8b9de1e7e
commit bd4410e9d0
8 changed files with 74 additions and 160 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

189
Dreamer/dmc2gym/natural_imgsource.py
View File

@ -1,36 +1,19 @@
# This code provides the class that is used to generate backgrounds for the natural background setting
# the class is used inside an environment wrapper and will be called each time the env generates an observation
# the code is largely based on https://github.com/facebookresearch/deep_bisim4control
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import cv2
import skvideo.io
import random
import tqdm
class BackgroundMatting(object):
"""
Produce a mask by masking the given color. This is a simple strategy
but effective for many games.
"""
def __init__(self, color):
"""
Args:
color: a (r, g, b) tuple or single value for grayscale
"""
self._color = color
def get_mask(self, img):
return img == self._color
import cv2
import numpy as np
import skvideo.io
class ImageSource(object):
"""
Source of natural images to be added to a simulated environment.
"""
def get_image(self):
"""
Returns:
@ -43,141 +26,57 @@ class ImageSource(object):
pass
class FixedColorSource(ImageSource):
def __init__(self, shape, color):
"""
Args:
shape: [h, w]
color: a 3-tuple
"""
self.arr = np.zeros((shape[0], shape[1], 3))
self.arr[:, :] = color
def get_image(self):
return self.arr
class RandomColorSource(ImageSource):
def __init__(self, shape):
"""
Args:
shape: [h, w]
"""
self.shape = shape
self.arr = None
self.reset()
def reset(self):
self._color = np.random.randint(0, 256, size=(3,))
self.arr = np.zeros((self.shape[0], self.shape[1], 3))
self.arr[:, :] = self._color
def get_image(self):
return self.arr
class NoiseSource(ImageSource):
def __init__(self, shape, strength=255):
"""
Args:
shape: [h, w]
strength (int): the strength of noise, in range [0, 255]
"""
self.shape = shape
self.strength = strength
def get_image(self):
return np.random.randn(self.shape[0], self.shape[1], 3) * self.strength
class RandomImageSource(ImageSource):
def __init__(self, shape, filelist, total_frames=None, grayscale=False):
"""
Args:
shape: [h, w]
filelist: a list of image files
"""
self.grayscale = grayscale
self.total_frames = total_frames
self.shape = shape
self.filelist = filelist
self.build_arr()
self.current_idx = 0
self.reset()
def build_arr(self):
self.total_frames = self.total_frames if self.total_frames else len(self.filelist)
self.arr = np.zeros((self.total_frames, self.shape[0], self.shape[1]) + ((3,) if not self.grayscale else (1,)))
for i in range(self.total_frames):
# if i % len(self.filelist) == 0: random.shuffle(self.filelist)
fname = self.filelist[i % len(self.filelist)]
if self.grayscale: im = cv2.imread(fname, cv2.IMREAD_GRAYSCALE)[..., None]
else: im = cv2.imread(fname, cv2.IMREAD_COLOR)
self.arr[i] = cv2.resize(im, (self.shape[1], self.shape[0])) ## THIS IS NOT A BUG! cv2 uses (width, height)
def reset(self):
self._loc = np.random.randint(0, self.total_frames)
def get_image(self):
return self.arr[self._loc]
class RandomVideoSource(ImageSource):
def __init__(self, shape, filelist, total_frames=None, grayscale=False):
def __init__(self, shape, filelist, random_bg=False, max_videos=100, grayscale=False):
"""
Args:
shape: [h, w]
filelist: a list of video files
"""
self.grayscale = grayscale
self.total_frames = total_frames
self.shape = shape
self.filelist = filelist
self.build_arr()
random.shuffle(self.filelist)
self.filelist = self.filelist[:max_videos]
self.max_videos = max_videos
self.random_bg = random_bg
self.current_idx = 0
self._current_vid = None
self.reset()
def build_arr(self):
if not self.total_frames:
self.total_frames = 0
self.arr = None
random.shuffle(self.filelist)
for fname in tqdm.tqdm(self.filelist, desc="Loading videos for natural", position=0):
if self.grayscale: frames = skvideo.io.vread(fname, outputdict={"-pix_fmt": "gray"})
else: frames = skvideo.io.vread(fname)
local_arr = np.zeros((frames.shape[0], self.shape[0], self.shape[1]) + ((3,) if not self.grayscale else (1,)))
for i in tqdm.tqdm(range(frames.shape[0]), desc="video frames", position=1):
local_arr[i] = cv2.resize(frames[i], (self.shape[1], self.shape[0])) ## THIS IS NOT A BUG! cv2 uses (width, height)
if self.arr is None:
self.arr = local_arr
else:
self.arr = np.concatenate([self.arr, local_arr], 0)
self.total_frames += local_arr.shape[0]
else:
self.arr = np.zeros((self.total_frames, self.shape[0], self.shape[1]) + ((3,) if not self.grayscale else (1,)))
total_frame_i = 0
file_i = 0
with tqdm.tqdm(total=self.total_frames, desc="Loading videos for natural") as pbar:
while total_frame_i < self.total_frames:
if file_i % len(self.filelist) == 0: random.shuffle(self.filelist)
file_i += 1
fname = self.filelist[file_i % len(self.filelist)]
if self.grayscale: frames = skvideo.io.vread(fname, outputdict={"-pix_fmt": "gray"})
else: frames = skvideo.io.vread(fname)
for frame_i in range(frames.shape[0]):
if total_frame_i >= self.total_frames: break
if self.grayscale:
self.arr[total_frame_i] = cv2.resize(frames[frame_i], (self.shape[1], self.shape[0]))[..., None] ## THIS IS NOT A BUG! cv2 uses (width, height)
else:
self.arr[total_frame_i] = cv2.resize(frames[frame_i], (self.shape[1], self.shape[0]))
pbar.update(1)
total_frame_i += 1
def load_video(self, vid_id):
fname = self.filelist[vid_id]
if self.grayscale:
frames = skvideo.io.vread(fname, outputdict={"-pix_fmt": "gray"})
else:
frames = skvideo.io.vread(fname, num_frames=1000)
img_arr = np.zeros((frames.shape[0], self.shape[0], self.shape[1]) + ((3,) if not self.grayscale else (1,)))
for i in range(frames.shape[0]):
if self.grayscale:
img_arr[i] = cv2.resize(frames[i], (self.shape[1], self.shape[0]))[..., None] # THIS IS NOT A BUG! cv2 uses (width, height)
else:
img_arr[i] = cv2.resize(frames[i], (self.shape[1], self.shape[0]))
return img_arr
def reset(self):
self._loc = np.random.randint(0, self.total_frames)
del self._current_vid
self._video_id = np.random.randint(0, len(self.filelist))
self._current_vid = self.load_video(self._video_id)
while True:
try:
self._video_id = np.random.randint(0, len(self.filelist))
self._current_vid = self.load_video(self._video_id)
break
except Exception:
continue
self._loc = np.random.randint(0, len(self._current_vid))
def get_image(self):
img = self.arr[self._loc % self.total_frames]
self._loc += 1
return img
if self.random_bg:
self._loc = np.random.randint(0, len(self._current_vid))
else:
self._loc += 1
img = self._current_vid[self._loc % len(self._current_vid)]
return img

21
Dreamer/dmc2gym/wrappers.py
View File

@ -8,6 +8,11 @@ import skimage.io
from dmc2gym import natural_imgsource
high_noise = False
def set_global_var(set_high_noise):
global high_noise
high_noise = set_high_noise
def _spec_to_box(spec):
def extract_min_max(s):
@ -63,7 +68,6 @@ class DMCWrapper(core.Env):
self._camera_id = camera_id
self._frame_skip = frame_skip
self._img_source = img_source
self._resource_files = resource_files
# create task
self._env = suite.load(
@ -109,13 +113,16 @@ class DMCWrapper(core.Env):
self._bg_source = natural_imgsource.NoiseSource(shape2d)
else:
files = glob.glob(os.path.expanduser(resource_files))
self.files = files
self.total_frames = total_frames
self.shape2d = shape2d
assert len(files), "Pattern {} does not match any files".format(
resource_files
)
if img_source == "images":
self._bg_source = natural_imgsource.RandomImageSource(shape2d, files, grayscale=True, total_frames=total_frames)
self._bg_source = natural_imgsource.RandomImageSource(shape2d, files, grayscale=False, max_videos=100, random_bg=False)
elif img_source == "video":
self._bg_source = natural_imgsource.RandomVideoSource(shape2d, files, grayscale=True, total_frames=total_frames)
self._bg_source = natural_imgsource.RandomVideoSource(shape2d, files, grayscale=False,max_videos=100, random_bg=False)
else:
raise Exception("img_source %s not defined." % img_source)
@ -136,9 +143,7 @@ class DMCWrapper(core.Env):
mask = np.logical_and((obs[:, :, 2] > obs[:, :, 1]), (obs[:, :, 2] > obs[:, :, 0])) # hardcoded for dmc
bg = self._bg_source.get_image()
obs[mask] = bg[mask]
# obs = obs.transpose(2, 0, 1).copy()
# CHW to HWC for tensorflow
obs = obs.copy()
obs = obs.transpose(2, 0, 1).copy()
else:
obs = _flatten_obs(time_step.observation)
return obs
@ -188,6 +193,8 @@ class DMCWrapper(core.Env):
def reset(self):
time_step = self._env.reset()
self._bg_source.reset()
#self._bg_source = natural_imgsource.RandomVideoSource(self.shape2d, self.files, grayscale=True, total_frames=self.total_frames, high_noise=high_noise)
obs = self._get_obs(time_step)
return obs
@ -198,4 +205,4 @@ class DMCWrapper(core.Env):
camera_id = camera_id or self._camera_id
return self._env.physics.render(
height=height, width=width, camera_id=camera_id
)
)

3
Dreamer/dreamers.py
View File

@ -96,11 +96,12 @@ class Dreamer(tools.Module):
@tf.function()
def train(self, data, log_images=False):
self._strategy.experimental_run_v2(
self._strategy.run(
self._train, args=(data, log_images))
def _train(self, data, log_images):
with tf.GradientTape() as model_tape:
data["image"] = tf.transpose(data["image"], perm=[0, 1, 3, 4, 2])
embed = self._encode(data)
post, prior = self._dynamics.observe(embed, data['action'])
feat = self._dynamics.get_feat(post)

2
Dreamer/local_dm_control_suite/cheetah.xml
View File

@ -21,7 +21,7 @@
<option timestep="0.01"/>
<worldbody>
<geom name="ground" type="plane" conaffinity="1" pos="98 0 0" size="100 .8 .5" material="grid"/>
<geom name="ground" type="plane" conaffinity="1" pos="98 0 0" size="100 .8 .5" rgba="0.8 0.9 0.8 0" material="grid"/>
<body name="torso" pos="0 0 .7" childclass="cheetah">
<light name="light" pos="0 0 2" mode="trackcom"/>
<camera name="side" pos="0 -3 0" quat="0.707 0.707 0 0" mode="trackcom"/>

4
Dreamer/run.py
View File

@ -62,10 +62,10 @@ def main(method, config):
str(config.logdir), max_queue=1000, flush_millis=20000)
writer.set_as_default()
train_envs = [wrappers.Async(lambda: make_env(
config, writer, 'train', datadir, config.video_dir, store=True), config.parallel)
config, writer, 'train', datadir, config.video_dir_train, store=True), config.parallel)
for _ in range(config.envs)]
test_envs = [wrappers.Async(lambda: make_env(
config, writer, 'test', datadir, config.video_dir, store=False), config.parallel)
config, writer, 'test', datadir, config.video_dir_test, store=False), config.parallel)
for _ in range(config.envs)]
actspace = train_envs[0].action_space

5
Dreamer/tools.py
View File

@ -86,7 +86,11 @@ def video_summary(name, video, step=None, fps=20):
def encode_gif(frames, fps):
from subprocess import Popen, PIPE
print(frames[0].shape)
if frames[0].shape[-1] != 3:
frames = np.transpose(frames, [0, 2, 3, 1])
h, w, c = frames[0].shape
print(h,w,c)
pxfmt = {1: 'gray', 3: 'rgb24'}[c]
cmd = ' '.join([
f'ffmpeg -y -f rawvideo -vcodec rawvideo',
@ -123,6 +127,7 @@ def simulate(agent, envs, steps=0, episodes=0, state=None):
# Step agents.
# if use augmentation, need to modify dreamer.policy or here.
obs = {k: np.stack([o[k] for o in obs]) for k in obs[0]}
obs['image'] = tf.transpose(obs['image'], [0, 3, 2, 1])
action, agent_state = agent(obs, done, agent_state)
action = np.array(action)
assert len(action) == len(envs)

5
Dreamer/train_configs/dreamer.yaml
View File

@ -1,7 +1,8 @@
dmc:
logdir: ./
video_dir: ./
logdir: /home/vedant/tia/Dreamer/logdir
video_dir_train: /media/vedant/cpsDataStorageWK/Vedant/natural_video_setting/train/
video_dir_test: /media/vedant/cpsDataStorageWK/Vedant/natural_video_setting/test/
debug: False
seed: 0
steps: 1000000.0

5
Dreamer/train_configs/tia.yaml
View File

@ -1,7 +1,8 @@
dmc:
logdir: ./
video_dir: ./
logdir: /home/vedant/tia/Dreamer/logdir
video_dir_train: /media/vedant/cpsDataStorageWK/Vedant/natural_video_setting/train/
video_dir_test: /media/vedant/cpsDataStorageWK/Vedant/natural_video_setting/test/
debug: False
seed: 0
steps: 1000000.0