def render(env_name):
from many_world import IS_PATCHED
assert IS_PATCHED, "need the patched gym"
import gym, numpy as np
from tqdm import tqdm
from ml_logger import logger
logger.prefix = env_name
env = gym.make(env_name)
env.reset()
frames = []
y = 0
args = [
dict(x=x, y=y, filename=f"images/{x:0.3f},{y:0.3f}.png")
for x in np.linspace(-0.25, 0.25, 128)
]
logger.log_data(args, 'index.pkl')
for p in tqdm(args):
x, y, filename = p['x'], p['y'], p['filename']
env.set_state(np.array([x, y, 0, 0]), np.array([0, 0, 0, 0]))
# env.do_simulation([0, 0], 1) # PointMass does not need this.
image = env.render('grey', width=20, height=20)
frames.append(image)
logger.log_image(image, filename)
print('saving video')
logger.log_video(frames, f"{env_name}.mp4")
print('done')
def test_video(setup):
import numpy as np
def im(x, y):
canvas = np.zeros((200, 200))
for i in range(200):
for j in range(200):
if x - 5 < i < x + 5 and y - 5 < j < y + 5:
canvas[i, j] = 1
return canvas
frames = [im(100 + i, 80) for i in range(20)]
logger.log_video(frames, "test_video.mp4")
def test_video(setup):
import numpy as np
def im(x, y):
canvas = np.ones((640, 480), dtype=np.float32) * 0.001
for i in range(200):
for j in range(200):
if x - 5 < i < x + 5 and y - 5 < j < y + 5:
canvas[i, j] = 1
return canvas
frames = [im(100 + i, 80) for i in range(20)]
logger.log_video(frames, "test_video.mp4")
def main(env_id="Reacher-v2"):
# env_name = "PointMass-v0"
samples = dynamics_data_gen(env_name=env_id,
start_seed=0,
timesteps=50,
n_parallel_envs=5,
width=28,
height=28)
for k, v in samples.items():
print(k, v.shape)
video = np.swapaxes(samples['views'], 0, 1).reshape(-1, 28, 28, 3)
from ml_logger import logger
logger.log_video(video, 'test.mp4')