def experiment(variant):
cuda = True
from gym.envs.mujoco import HalfCheetahEnv
from mujoco_torch.core.bridge import MjCudaRender
R = 84
env = HalfCheetahEnv()
c = Convnet(6, output_activation=torch.tanh, input_channels=3)
if cuda:
c.cuda()
gt.stamp("start")
for i in range(100):
img = env.sim.render(R, R, device_id=1)
gt.stamp("warmstart")
for i in gt.timed_for(range(1000)):
env.step(np.random.rand(6))
gt.stamp('step')
img = env.sim.render(R, R, device_id=1)
gt.stamp('render')
x = np_to_var(img)
if cuda:
x = x.cuda()
torch.cuda.synchronize()
gt.stamp('transfer')
# cv2.imshow("img", img)
# cv2.waitKey(1)
gt.stamp("end")
print(img)
print(gt.report(include_itrs=False))
def experiment(variant):
from gym.envs.mujoco import HalfCheetahEnv
from mujoco_torch.core.bridge import MjCudaRender
renderer = MjCudaRender(84, 84)
env = HalfCheetahEnv()
gt.stamp("start")
for i in range(100):
tensor, img = renderer.get_cuda_tensor(env.sim, False)
gt.stamp("warmstart")
for i in range(1000):
env.step(np.random.rand(6))
tensor, img = renderer.get_cuda_tensor(env.sim, True)
x = np_to_var(img).cuda()
torch.cuda.synchronize()
# cv2.imshow("img", img)
# cv2.waitKey(1)
gt.stamp("end")
print(img)
print(gt.report())
def experiment(variant):
from gym.envs.mujoco import HalfCheetahEnv
from mujoco_torch.core.bridge import MjCudaRender
renderer = MjCudaRender(84, 84)
env = HalfCheetahEnv()
gt.stamp("start")
for i in range(100):
tensor, img = renderer.get_cuda_tensor(env.sim, False)
gt.stamp("warmstart")
for i in gt.timed_for(range(1000)):
env.step(np.random.rand(6))
gt.stamp('step')
tensor, img = renderer.get_cuda_tensor(env.sim, False)
gt.stamp('render')
# cv2.imshow("img", img)
# cv2.waitKey(1)
gt.stamp("end")
print(img)
print(gt.report(include_itrs=False))
