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import pytest
import numpy as np
from onnx import TensorProto, helper
import finn.core.onnx_exec as oxe
from finn.core.datatype import DataType
from finn.core.modelwrapper import ModelWrapper
from finn.custom_op.general.maxpoolnhwc import compute_pool_output_dim
from finn.transformation.infer_datatypes import InferDataTypes
from finn.transformation.infer_shapes import InferShapes
from finn.transformation.streamline.reorder import MoveMulPastMaxPool
from finn.util.basic import gen_finn_dt_tensor
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# input dimension
@pytest.mark.parametrize("ifm_dim", [4, 7])
# input channels
@pytest.mark.parametrize("ifm_ch", [1, 3])
# kernel size
@pytest.mark.parametrize("k", [2, 3])
# stride
@pytest.mark.parametrize("stride", [1, 2])
# padding
@pytest.mark.parametrize("pad", [0, 1])
# channelwise or scalar mul
@pytest.mark.parametrize("cw", [0, 1])
# negative mul
@pytest.mark.parametrize("negative", [0, 1])
def test_move_mul_past_maxpool(ifm_dim, ifm_ch, k, stride, pad, cw, negative):
if cw == 1:
mul_shape = [1, ifm_ch, 1, 1]
else:
mul_shape = [1, 1, 1, 1]
ofm_ch = ifm_ch
ofm_dim = compute_pool_output_dim(ifm_dim, k, stride, pad)
# set up onnx model
inp = helper.make_tensor_value_info(
"inp", TensorProto.FLOAT, [1, ifm_ch, ifm_dim, ifm_dim]
)
mul = helper.make_tensor_value_info("mul", TensorProto.FLOAT, mul_shape)
outp = helper.make_tensor_value_info(
"outp", TensorProto.FLOAT, [1, ofm_ch, ofm_dim, ofm_dim]
)
Mul_node = helper.make_node("Mul", ["inp", "mul"], ["mul_out"])
Maxpool_node = helper.make_node(
"MaxPool",
["mul_out"],
["outp"],
kernel_shape=[k, k],
pads=[pad, pad, pad, pad],
strides=[stride, stride],
)
graph = helper.make_graph(
nodes=[Mul_node, Maxpool_node],
name="mulpastmaxpool_graph",
inputs=[inp],
outputs=[outp],
value_info=[mul],
)
model = helper.make_model(graph, producer_name="mulpastmaxpool-model")
model = ModelWrapper(model)
inp_values = gen_finn_dt_tensor(DataType.INT2, [1, ifm_ch, ifm_dim, ifm_dim])
mul_values = np.random.random_sample(mul_shape).astype(np.float32)
if negative == 1:
mul_values = mul_values * (-1)
model.set_initializer("mul", mul_values)
model = model.transform(InferShapes())
model = model.transform(InferDataTypes())
idict = {"inp": inp_values}
odict = oxe.execute_onnx(model, idict, True)
out_before = odict["outp"]
# perform transformation
model_transformed = model.transform(MoveMulPastMaxPool())
odict = oxe.execute_onnx(model_transformed, idict, True)
out_after = odict["outp"]
assert (out_before == out_after).all()
if negative == 1:
assert model.graph.node[0].op_type == model_transformed.graph.node[0].op_type
assert model.graph.node[1].op_type == model_transformed.graph.node[1].op_type
else:
assert model.graph.node[0].op_type == model_transformed.graph.node[1].op_type
assert model.graph.node[1].op_type == model_transformed.graph.node[0].op_type