anirudh2290 commented on a change in pull request #12427: Improved test for
BilinearSampler
URL: https://github.com/apache/incubator-mxnet/pull/12427#discussion_r215379100
##########
File path: tests/python/gpu/test_operator_gpu.py
##########
@@ -1918,6 +1918,65 @@ def test_softmax_activation():
assert_almost_equal(cpu_a.grad.asnumpy(), gpu_a.grad.asnumpy(),
atol = 1e-3, rtol = 1e-3)
+
+@with_seed()
+def test_bilinear_sampler_versions():
+ data = mx.sym.Variable('data')
+ grid = mx.sym.Variable('grid')
+ sym1 = mx.sym.BilinearSampler(data=data, grid=grid)
+ sym2 = mx.sym.BilinearSampler(data=data, grid=grid, cudnn_off=True)
+ sym3 = mx.sym.BilinearSampler(data=data, grid=grid)
+
+ test_cases = [[(1,3,15,16),(1,2,10,10)],
+ [(1,6,7,16),(1,2,10,4)],
+ [(1,7,3,16),(1,2,8,11)],
+ [(1,9,50,50),(1,2,50,50)]]
+
+ for item in test_cases:
+ data_shape, grid_shape = item
+ # kWriteTo
+ exe_cpu = sym1.simple_bind(data=data_shape, grid=grid_shape,
ctx=mx.cpu(), grad_req='write')
+ exe_gpu = sym2.simple_bind(data=data_shape, grid=grid_shape,
ctx=default_context(), grad_req='write')
+ exe_cudnn = sym3.simple_bind(data=data_shape, grid=grid_shape,
ctx=default_context(), grad_req='write')
+ exe_list = [exe_cpu, exe_gpu, exe_cudnn]
+ ref_idx = 0
+ test_data = np.random.uniform(low=-0.1,
high=0.1,size=data_shape).astype(np.float32)
+ test_grid = np.random.uniform(low=-2, high=2,
size=grid_shape).astype(np.float32)
+ for exe in exe_list:
+ exe.arg_dict['data'][:] = test_data
+ exe.arg_dict['grid'][:] = test_grid
+ exe.forward(is_train=True)
+ assert_almost_equal(exe_list[0].outputs[0].asnumpy(),
exe.outputs[0].asnumpy(), rtol=1e-3, atol=1e-5)
+
+ out_grad = np.random.uniform(low=-0.01, high=0.01,size=data_shape[:2]
+ grid_shape[2:]).astype(np.float32)
+ for exe in exe_list:
+ exe.backward(mx.nd.array(out_grad))
+ assert_almost_equal(exe.grad_dict['data'].asnumpy(),
exe_list[ref_idx].grad_dict['data'].asnumpy(), rtol=1e-3, atol=1e-5)
+ assert_almost_equal(exe.grad_dict['grid'].asnumpy(),
exe_list[ref_idx].grad_dict['grid'].asnumpy(), rtol=1e-3, atol=1e-5)
+
+ data_grad = exe_list[ref_idx].grad_dict['data'].asnumpy()
+ grid_grad = exe_list[ref_idx].grad_dict['grid'].asnumpy()
+
+ # kAddTo
+ exe_cpu_addto = sym1.simple_bind(data=data_shape, grid=grid_shape,
ctx=mx.cpu(), grad_req='add')
+ exe_gpu_addto = sym2.simple_bind(data=data_shape, grid=grid_shape,
ctx=default_context(), grad_req='add')
+ exe_cudnn_addto = sym3.simple_bind(data=data_shape, grid=grid_shape,
ctx=default_context(), grad_req='add')
+ exe_list = [exe_cpu_addto, exe_gpu_addto, exe_cudnn_addto]
+ data_initial_grad =
np.random.normal(size=exe_list[ref_idx].grad_dict['data'].shape).astype(np.float32)
+ grid_initial_grad =
np.random.normal(size=exe_list[ref_idx].grad_dict['grid'].shape).astype(np.float32)
+ for exe in exe_list:
+ exe.arg_dict['data'][:] = test_data
+ exe.arg_dict['grid'][:] = test_grid
+ exe.grad_dict['data'][:] = data_initial_grad
+ exe.grad_dict['grid'][:] = grid_initial_grad
+ exe.forward(is_train=True)
+ exe.backward(mx.nd.array(out_grad))
+ assert_almost_equal(exe.grad_dict['data'].asnumpy(),
exe_list[ref_idx].grad_dict['data'].asnumpy(), rtol=1e-3, atol=1e-5)
+ assert_almost_equal(exe.grad_dict['grid'].asnumpy(),
exe_list[ref_idx].grad_dict['grid'].asnumpy(), rtol=1e-3, atol=1e-5)
+ assert_almost_equal(exe_list[ref_idx].grad_dict['data'].asnumpy(),
data_grad + data_initial_grad, rtol=1e-3, atol=1e-5)
+ assert_almost_equal(exe_list[ref_idx].grad_dict['grid'].asnumpy(),
grid_grad + grid_initial_grad, rtol=1e-3, atol=1e-5)
Review comment:
Is my understanding correct that we are doing only consistency checks
between cpu , gpu and cudnn runs for the bilinear sampler and removing the
correctness tests that we had earlier (bilinear sampler forward and backward
computation using numpy ops) ? This should be fine assuming the correctness of
cudnn op.
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