def test_densityOverflowRandom(self):
dtype = np.float32
xx = np.array([1.0, 4.6]).astype(dtype)
yy = np.array([0.5, 4.1]).astype(dtype)
node_size_x = np.array([0.5, 1.0]).astype(dtype)
node_size_y = np.array([1.0, 1.0]).astype(dtype)
#xx = np.array([2.0]).astype(dtype)
#yy = np.array([1.5]).astype(dtype)
#node_size_x = np.array([1.0]).astype(dtype)
#node_size_y = np.array([1.0]).astype(dtype)
num_nodes = len(xx)
scale_factor = 1.0
xl = 1.0
yl = 1.0
xh = 5.0
yh = 5.0
num_movable_nodes = len(xx)
num_terminals = 0
num_filler_nodes = 0
# test cpu
custom = move_boundary.MoveBoundary(
torch.from_numpy(node_size_x),
torch.from_numpy(node_size_y),
xl=xl,
yl=yl,
xh=xh,
yh=yh,
num_movable_nodes=num_movable_nodes,
num_filler_nodes=num_filler_nodes)
pos = Variable(torch.from_numpy(np.concatenate([xx, yy])))
result = custom.forward(pos)
print("custom_result = ", result)
#result.retain_grad()
#result.sum().backward()
#print("custom_result.grad = ", result.grad)
# test cuda
if torch.cuda.device_count():
custom_cuda = move_boundary.MoveBoundary(
torch.from_numpy(node_size_x).cuda(),
torch.from_numpy(node_size_y).cuda(),
xl=xl,
yl=yl,
xh=xh,
yh=yh,
num_movable_nodes=num_movable_nodes,
num_filler_nodes=num_filler_nodes)
pos = Variable(torch.from_numpy(np.concatenate([xx, yy]))).cuda()
result_cuda = custom_cuda.forward(pos)
print("custom_result = ", result_cuda.data.cpu())
np.testing.assert_allclose(result, result_cuda.data.cpu())
def build_move_boundary(self, params, placedb, data_collections, device):
"""
@brief bound nodes into layout region
@param params parameters
@param placedb placement database
@param data_collections a collection of all data and variables required for constructing the ops
@param device cpu or cuda
"""
return move_boundary.MoveBoundary(
data_collections.node_size_x, data_collections.node_size_y,
xl=placedb.xl, yl=placedb.yl, xh=placedb.xh, yh=placedb.yh,
num_movable_nodes=placedb.num_movable_nodes,
num_filler_nodes=placedb.num_filler_nodes
)
