def test_pooling_3d(self, stride, pad, kernel, size, input_channels,
batch_size, order, op_type, engine, gc, dc):
assume(pad < kernel)
assume(size + pad + pad >= kernel)
# Currently MIOpen Pooling only supports pooling with NCHW order.
if hiputl.run_in_hip(gc, dc) and (workspace.GetHIPVersion() < 303
or order == "NHWC"):
assume(engine != "CUDNN")
# some case here could be calculated with global pooling, but instead
# calculated with general implementation, slower but should still
# be correct.
op = core.CreateOperator(
op_type,
["X"],
["Y"],
strides=[stride] * 3,
kernels=[kernel] * 3,
pads=[pad] * 6,
order=order,
engine=engine,
)
X = np.random.rand(batch_size, size, size, size,
input_channels).astype(np.float32)
if order == "NCHW":
X = utils.NHWC2NCHW(X)
self.assertDeviceChecks(dc, op, [X], [0], threshold=0.001)
if 'MaxPool' not in op_type:
self.assertGradientChecks(gc, op, [X], 0, [0], threshold=0.001)
def test_global_pooling_3d(self, kernel, size, input_channels,
batch_size, order, op_type, engine, gc, dc):
# Currently MIOpen Pooling only supports pooling with NCHW order.
if hiputl.run_in_hip(gc, dc) and (workspace.GetHIPVersion() < 303 or order == "NHWC"):
assume(engine != "CUDNN")
# pad and stride ignored because they will be inferred in global_pooling
op = core.CreateOperator(
op_type,
["X"],
["Y"],
kernels=[kernel] * 3,
order=order,
global_pooling=True,
engine=engine,
)
X = np.random.rand(
batch_size, size, size, size, input_channels).astype(np.float32)
if order == "NCHW":
X = utils.NHWC2NCHW(X)
self.assertDeviceChecks(dc, op, [X], [0], threshold=0.001)
if 'MaxPool' not in op_type:
self.assertGradientChecks(gc, op, [X], 0, [0], threshold=0.001)