How to use the taichi.root.place function in taichi
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yuanming-hu / taichi / tests / python / test_sparse_parallel.py View on Github 
def place():
ti.root.dense(ti.i, n).pointer().dense(ti.i, n).place(x)
ti.root.place(s)def place():
ti.root.dense(ti.i, n).place(x)
ti.root.place(loss)
ti.root.lazy_grad()def place(self, root):
ti.root.dense(ti.i, self.n).place(self.x)
ti.root.place(self.total)def place():
ti.root.place(
arr) # Place an object. Make sure you defined place for that obj
ti.root.place(double_total)
ti.root.lazy_grad()def data():
ti.root.dense(ti.i, n).place(x, y, x.grad, y.grad) # place gradient tensors
ti.root.place(L, L.grad)def place():
ti.root.dense(ti.l, max_steps).dense(ti.i, n_objects).place(x, v, v_inc)
ti.root.dense(ti.i, n_springs).place(spring_anchor_a, spring_anchor_b,
spring_length, spring_stiffness,
spring_actuation)
ti.root.dense(ti.ij, (n_hidden, n_input_states())).place(weights1)
ti.root.dense(ti.i, n_hidden).place(bias1)
ti.root.dense(ti.ij, (n_springs, n_hidden)).place(weights2)
ti.root.dense(ti.i, n_springs).place(bias2)
ti.root.dense(ti.ij, (max_steps, n_hidden)).place(hidden)
ti.root.dense(ti.ij, (max_steps, n_springs)).place(act)
ti.root.dense(ti.i, max_steps).place(center)
ti.root.place(loss, goal)
ti.root.lazy_grad()def place():
ti.root.dense(ti.l, max_steps).dense(ti.i, n_objects).place(x, v, v_inc)
ti.root.dense(ti.i, n_springs).place(spring_anchor_a, spring_anchor_b,
spring_length, spring_stiffness,
spring_actuation)
ti.root.dense(ti.ij, (n_hidden, n_input_states())).place(weights1)
ti.root.dense(ti.i, n_hidden).place(bias1)
ti.root.dense(ti.ij, (n_springs, n_hidden)).place(weights2)
ti.root.dense(ti.i, n_springs).place(bias2)
ti.root.dense(ti.ij, (max_steps, n_hidden)).place(hidden)
ti.root.dense(ti.ij, (max_steps, n_springs)).place(act)
ti.root.dense(ti.i, max_steps).place(center, target_v)
ti.root.place(loss, goal)
ti.root.lazy_grad()def tensors():
ti.root.dense(ti.i, 16).place(x, y, z)
ti.root.place(loss)
ti.root.lazy_grad()def place():
ti.root.dense(ti.ij, (n_actuators, n_sin_waves)).place(weights)
ti.root.dense(ti.i, n_actuators).place(bias)
ti.root.dense(ti.ij, (max_steps, n_actuators)).place(actuation)
ti.root.dense(ti.i, n_particles).place(actuator_id, particle_type)
ti.root.dense(ti.l, max_steps).dense(ti.k, n_particles).place(x, v, C, F)
ti.root.dense(ti.ijk, n_grid).place(grid_v_in, grid_m_in, grid_v_out)
ti.root.place(loss, x_avg)
ti.root.dense(ti.ij,
(visualize_resolution, visualize_resolution)).place(screen)
ti.root.lazy_grad()def place():
ti.root.dense(ti.l, max_steps).dense(ti.ij, n_grid).place(p)
ti.root.dense(ti.l, max_steps).dense(ti.ij, n_grid).place(p.grad)
ti.root.dense(ti.ij, n_grid).place(target)
ti.root.dense(ti.ij, n_grid).place(target.grad)
ti.root.dense(ti.ij, n_grid).place(initial)
ti.root.dense(ti.ij, n_grid).place(initial.grad)
ti.root.place(loss)
ti.root.place(loss.grad)
