def op_function(x: tp.Numpy.Placeholder(input.shape, dtype=value_type)):
with flow.scope.placement(device_type, "0:0"):
x += flow.get_variable(
name="input",
shape=input.shape,
dtype=value_type,
initializer=flow.zeros_initializer(),
)
out = flow.reflection_pad2d(x, padding)
flow.optimizer.SGD(
flow.optimizer.PiecewiseConstantScheduler([], [0]), momentum=0
).minimize(out)
flow.watch_diff(x, _compare_diff)
return out
def op_function(x: tp.Numpy.Placeholder(input.shape, dtype=flow.float32)):
with flow.scope.placement(device_type, "0:0"):
x_var = flow.get_variable(
name="input",
shape=input.shape,
dtype=flow.float32,
initializer=flow.constant_initializer(0),
)
x_var = flow.cast_to_current_logical_view(x_var)
input_x = x_var + x
x_fp32 = flow.cast(input_x, flow.float32)
x_fp16 = flow.cast(input_x, dtype=flow.float16)
y_fp16 = flow.reflection_pad2d(x_fp16, padding)
y_fp32 = flow.cast(y_fp16, dtype=flow.float32)
flow.optimizer.SGD(
flow.optimizer.PiecewiseConstantScheduler([], [0]), momentum=0
).minimize(y_fp32)
flow.watch_diff(x_fp32, _compare_diff)
return y_fp32