def sample_program_configs(self, draw):
elementwise_type = draw(
st.sampled_from(
["elementwise_add", "elementwise_sub", "elementwise_mul"]))
in_shape_x = draw(
st.lists(st.integers(min_value=1, max_value=20),
min_size=2,
max_size=5))
in_shape_y = draw(
st.lists(st.integers(min_value=1, max_value=20),
min_size=2,
max_size=5))
axis = draw(
st.integers(min_value=-1,
max_value=max(len(in_shape_x), len(in_shape_y))))
assume(
check_input_shape_available(in_shape_x=in_shape_x,
in_shape_y=in_shape_y,
axis=axis) == True)
elementwise_op = OpConfig(type=elementwise_type,
inputs={
"X": ["input_data_x"],
"Y": ["input_data_y"]
},
outputs={"Out": ["elementwise_output_data"]},
attrs={
"data_format": 'nchw',
"axis": axis
})
act_type = draw(st.sampled_from(['relu']))
def generate_act_attrs(act_type_str):
attrs = {}
if act_type_str == 'relu':
attrs = {}
return attrs
active_op = OpConfig(type=act_type,
inputs={"X": ["elementwise_output_data"]},
outputs={"Out": ["output_data"]},
attrs=generate_act_attrs(act_type))
ops = [elementwise_op, active_op]
self.ops = ops
program_config = ProgramConfig(ops=ops,
weights={},
inputs={
"input_data_x":
TensorConfig(shape=in_shape_x),
"input_data_y":
TensorConfig(shape=in_shape_y)
},
outputs=["output_data"])
return program_config
def sample_program_configs(self, draw):
in_shape_x = draw(
st.lists(
st.integers(
min_value=1, max_value=20), min_size=2, max_size=5))
in_shape_y = draw(
st.lists(
st.integers(
min_value=1, max_value=20), min_size=2, max_size=5))
axis = draw(
st.integers(
min_value=-1, max_value=max(len(in_shape_x), len(in_shape_y))))
assume(
check_input_shape_available(
in_shape_x=in_shape_x, in_shape_y=in_shape_y, axis=axis) ==
True)
#scale param
scale = draw(st.floats(min_value=0.5, max_value=5))
bias = draw(st.floats(min_value=0, max_value=1))
bias_after_scale = draw(st.sampled_from([False, True]))
elementwise_op = OpConfig(
type='elementwise_mul',
inputs={"X": ["input_data_x"],
"Y": ["input_data_y"]},
outputs={"Out": ["elementwise_output_data"]},
attrs={"data_format": 'nchw',
"axis": axis})
scale_op = OpConfig(
type='scale',
inputs={"X": ["elementwise_output_data"]},
outputs={"Out": ["output_data"]},
attrs={
"scale": scale,
"bias": bias,
"bias_after_scale": bias_after_scale
})
ops = [elementwise_op, scale_op]
program_config = ProgramConfig(
ops=ops,
weights={},
inputs={
"input_data_x": TensorConfig(shape=in_shape_x),
"input_data_y": TensorConfig(shape=in_shape_y)
},
outputs=["output_data"])
return program_config
def sample_program_configs(self, draw):
in_shape = draw(
st.lists(st.integers(min_value=1, max_value=20),
min_size=2,
max_size=5))
fill_constant_shape = draw(
st.lists(st.integers(min_value=1, max_value=20),
min_size=2,
max_size=5))
axis = draw(
st.integers(min_value=-1,
max_value=max(len(in_shape),
len(fill_constant_shape))))
out_shape = []
assume(
check_input_shape_available(in_shape_x=in_shape,
in_shape_y=fill_constant_shape,
axis=axis,
out_shape=out_shape) == True)
assume(out_shape == in_shape)
threshold = draw(st.floats(min_value=0, max_value=1))
scale = draw(st.floats(min_value=0.5, max_value=5))
offset = draw(st.floats(min_value=0, max_value=1))
hard_swish_op0 = OpConfig(type="hard_swish",
inputs={"X": ["input_data"]},
outputs={"Out": ["hard_swish_output_data"]},
attrs={
"threshold": threshold,
"scale": scale,
"offset": offset
})
fill_constant_op = OpConfig(
type="fill_constant",
inputs={},
outputs={"Out": ["fill_constant_output_data"]},
attrs={
"dtype": 5,
"shape": fill_constant_shape,
"value": 1.,
"force_cpu": False,
"place_type": -1
})
elementwise_mul_op = OpConfig(
type="elementwise_mul",
inputs={
"X": ["hard_swish_output_data"],
"Y": ["fill_constant_output_data"]
},
outputs={"Out": ["elementwise_mul_output_data"]},
attrs={"axis": axis})
hard_swish_op1 = OpConfig(
type="hard_swish",
inputs={"X": ["elementwise_mul_output_data"]},
outputs={"Out": ["output_data"]},
attrs={
"threshold": threshold,
"scale": scale,
"offset": offset
})
ops = [
hard_swish_op0, fill_constant_op, elementwise_mul_op,
hard_swish_op1
]
program_config = ProgramConfig(
ops=ops,
weights={},
inputs={"input_data": TensorConfig(shape=in_shape)},
outputs=["output_data"])
return program_config