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_pyplot(setup):
import scipy.misc
import matplotlib
matplotlib.use('TKAgg')
import matplotlib.pyplot as plt
import numpy as np
face = scipy.misc.face()
logger.log_image(face, "face.png")
fig = plt.figure(figsize=(4, 2))
xs = np.linspace(0, 5, 1000)
plt.plot(xs, np.cos(xs))
logger.savefig("face_02.png", fig=fig)
plt.close()
fig = plt.figure(figsize=(4, 2))
xs = np.linspace(0, 5, 1000)
plt.plot(xs, np.cos(xs))
logger.savefig('sine.pdf')
def test_image(setup):
import scipy.misc
import numpy as np
image_bw = np.zeros((64, 64, 1), dtype=np.uint8)
image_bw_2 = scipy.misc.face(gray=True)[::4, ::4]
image_rgb = np.zeros((64, 64, 3), dtype=np.uint8)
image_rgba = scipy.misc.face()[::4, ::4, :]
logger.log_image(image_bw, "black_white.png")
logger.log_image(image_bw_2, "bw_face.png")
logger.log_image(image_rgb, 'rgb.png')
logger.log_image(image_rgba, f'rgba_face_{100}.png')
logger.log_image(image_bw, f"bw_{100}.png")
logger.log_image(image_rgba, f"rbga_{100}.png")
def test_image(setup):
import scipy.misc
import numpy as np
image_bw = np.zeros((64, 64, 1), dtype=np.uint8)
image_bw_2 = scipy.misc.face(gray=True)[::4, ::4]
image_rgb = np.zeros((64, 64, 3), dtype=np.uint8)
image_rgba = scipy.misc.face()[::4, ::4, :]
logger.log_image(image_bw, "black_white.png")
logger.log_image(image_bw_2, "bw_face.png")
logger.log_image(image_rgb, 'rgb.png')
logger.log_image(image_rgba, f'rgba_face_{100}.png')
logger.log_image(image_bw, f"bw_{100}.png")
logger.log_image(image_rgba, f"rbga_{100}.png")
logger.log_image(image_bw[:, :, 0].astype(np.float32),
"black_white_individual.png",
normalize='individual')
logger.log_image(np.ones([64, 64]),
"black_white_grid.png",
normalize='grid')
logger.remove(prefix)
logger.configure(prefix=prefix)
logger.log_params(Args=dict(lr=10**(-2 - i),
weight_decay=0.001,
gradient_clip=0.9,
env_id="GoalMassDiscreteIdLess-v0",
seed=int(i * 100)))
for ep in range(500 + 1):
logger.log_metrics(epoch=ep,
sine=fn(ep),
slow_sine=fn_1(ep))
logger.flush()
if ep % 10 == 0:
logger.log_image(face('gray'),
f"figures/gray_{ep:04d}.png")
logger.log_image(face('rgb'),
f"figures/rgb_{ep:04d}.png")
logger.log_image(face('gray'), "figures/face_gray.png")
logger.log_image(face('rgb'), "figures/face_rgb.png")
with logger.PrefixContext(f"runs/{username}/{project}"):
logger.log_line("# Root Files\n", file="RAEDME.md")
# import numpy as np
# import matplotlib.pyplot as plt
#
# xs = np.arange(500)
# ys = (1 + 0.001 * xs) * np.sin(xs * 0.1 / np.pi)
# ys += np.random.rand(*ys.shape) * 1
# obs['goal_img'] = self.render('grey', width=self.width, height=self.height).transpose(0, 1)[None, ...]
# self.set_state(curr_qpos, self.sim.data.qvel)
# return obs
from gym.envs import register
if __name__ == "__main__":
import gym
env = gym.make('GoalMassDiscrete-v0')
env.reset()
frame = env.render('rgb', width=200, height=200)
from os.path import basename
from ml_logger import logger
logger.log_image(frame,
f"../figures/{basename(__file__)}:{env.spec.id}.png")
# to show thy human.
from PIL import Image
im = Image.fromarray(frame)
im.show()
else:
# note: kwargs are not passed in to the constructor when entry_point is a function.
register(id="ManyWorld-v0",
entry_point=ManyWorldEnv,
kwargs=dict(n_objs=1, ),
max_episode_steps=50)
import os
import scipy.misc
import matplotlib
matplotlib.use('TKAgg')
import matplotlib.pyplot as plt
import numpy as np
from ml_logger import logger
logger.configure(os.path.realpath('.'))
face = scipy.misc.face()
logger.log_image(0, test_image=face)
fig = plt.figure(figsize=(4, 2))
xs = np.linspace(0, 5, 1000)
plt.plot(xs, np.cos(xs))
logger.log_pyplot(0, fig)
plt.close()