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test_listunpack.py
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# Copyright (C) 2018-2025 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
from typing import List
import numpy as np
import pytest
import torch
from pytorch_layer_test_class import PytorchLayerTest
class TestListUnpack(PytorchLayerTest):
def _prepare_input(self):
return (
np.random.randn(8, 3, 512, 512).astype(np.float32),
np.random.randn(1, 3, 224, 224).astype(np.float32),
np.random.randn(10, 1, 8, 8).astype(np.float32),
np.random.randn(1, 1, 1, 1).astype(np.float32),
)
def create_model_size_listunpack(self):
class prim_listunpack(torch.nn.Module):
def forward(self, in1, in2, in3, in4):
a, b, c, d = in1.size()
return a, b, c, d
ref_net = None
return (
prim_listunpack(),
ref_net,
"prim::ListUnpack",
)
def create_model_size_slice_listunpack(self, slices):
class prim_listunpack(torch.nn.Module):
def __init__(self, slices):
self.start = slices[0]
self.stop = slices[1]
self.step = slices[2]
super(prim_listunpack, self).__init__()
def forward(self, in1, in2, in3, in4):
a, b = in1.size()[self.start: self.stop: self.step]
return a, b
ref_net = None
return prim_listunpack(slices), ref_net, "prim::ListUnpack"
def create_model_listconstruct_append_listunpack(self):
class prim_listunpack(torch.nn.Module):
def forward(self, in1, in2, in3, in4):
in_list = [in1, in2]
in_list.append(in3)
in_list.append(in4)
a, b, c, d = in_list
return a, b, c, d
ref_net = None
return prim_listunpack(), ref_net, "prim::ListUnpack"
def create_model_listconstruct_getitem_listunpack(self, idx):
class prim_listunpack(torch.nn.Module):
def __init__(self, idx):
self.idx = idx
super(prim_listunpack, self).__init__()
def forward(self, in1, in2, in3, in4):
items: List[List[int]] = []
items.append(in1.size())
items.append(in2.size())
items.append(in3.size())
items.append(in4.size())
getitem_0 = items[self.idx]
a, b, c, d = getitem_0
return a, b, c, d
ref_net = None
return prim_listunpack(idx), ref_net, "prim::ListUnpack"
@pytest.mark.nightly
@pytest.mark.precommit
def test_size_listunpack(self, ie_device, precision, ir_version):
self._test(
*self.create_model_size_listunpack(), ie_device, precision, ir_version
)
@pytest.mark.parametrize(
"slices", [(0, 2, 1), (0, 4, 2), (-1, -3, -1), (-3, -1, 1)]
)
@pytest.mark.nightly
@pytest.mark.precommit
def test_size_slice_listunpack(self, slices, ie_device, precision, ir_version):
self._test(
*self.create_model_size_slice_listunpack(slices),
ie_device,
precision,
ir_version
)
@pytest.mark.nightly
@pytest.mark.precommit
def test_listconstruct_append_listunpack(self, ie_device, precision, ir_version):
self._test(
*self.create_model_listconstruct_append_listunpack(),
ie_device,
precision,
ir_version
)
@pytest.mark.parametrize("idx", [-4, -3, -2, -1, 0, 1, 2, 3])
@pytest.mark.nightly
@pytest.mark.precommit
def test_listconstruct_getitem_listunpack(
self, idx, ie_device, precision, ir_version
):
self._test(
*self.create_model_listconstruct_getitem_listunpack(idx),
ie_device,
precision,
ir_version,
use_convert_model=True,
)
class TestMeshgridListUnpack(PytorchLayerTest):
def _prepare_input(self):
return (
np.random.randn(3),
np.random.randn(5),
np.random.randn(7),
np.random.randn(11),
)
def create_model_meshgrid_listunpack_1_in(self, idx):
class prim_listunpack(torch.nn.Module):
def __init__(self, idx):
self.idx = idx
super(prim_listunpack, self).__init__()
def forward(self, in1, in2, in3, in4):
(
a,
) = torch.meshgrid(in1, indexing=self.idx)
return a
ref_net = None
return prim_listunpack(idx), ref_net, ["aten::meshgrid", "prim::ListUnpack"]
def create_model_meshgrid_listunpack_2_in(self, idx):
class prim_listunpack(torch.nn.Module):
def __init__(self, idx):
self.idx = idx
super(prim_listunpack, self).__init__()
def forward(self, in1, in2, in3, in4):
(
a,
b,
) = torch.meshgrid(in1, in2, indexing=self.idx)
return a, b
ref_net = None
return prim_listunpack(idx), ref_net, ["aten::meshgrid", "prim::ListUnpack"]
def create_model_meshgrid_listunpack_3_in(self, idx):
class prim_listunpack(torch.nn.Module):
def __init__(self, idx):
self.idx = idx
super(prim_listunpack, self).__init__()
def forward(self, in1, in2, in3, in4):
a, b, c = torch.meshgrid(in1, in2, in3, indexing=self.idx)
return a, b, c
ref_net = None
return prim_listunpack(idx), ref_net, ["aten::meshgrid", "prim::ListUnpack"]
def create_model_meshgrid_listunpack_4_in(self, idx):
class prim_listunpack(torch.nn.Module):
def __init__(self, idx):
self.idx = idx
super(prim_listunpack, self).__init__()
def forward(self, in1, in2, in3, in4):
a, b, c, d = torch.meshgrid(
in1, in2, in3, in4, indexing=self.idx)
return a, b, c, d
ref_net = None
return prim_listunpack(idx), ref_net, ["aten::meshgrid", "prim::ListUnpack"]
@pytest.mark.parametrize("idx", ["ij", "xy"])
@pytest.mark.parametrize("inp", [1, 2, 3, 4])
@pytest.mark.nightly
@pytest.mark.precommit
def test_meshgrid_listunpack(self, idx, inp, ie_device, precision, ir_version):
func = getattr(self, f"create_model_meshgrid_listunpack_{inp}_in")
self._test(*func(idx), ie_device, precision, ir_version)
class TestMeshgridListUnpackStack(PytorchLayerTest):
def _prepare_input(self):
return (
np.random.randn(28, 28),
)
def create_model(self):
class meshgrid_model(torch.nn.Module):
def forward(self, x):
h, w = x.shape
coords1, coords2 = torch.meshgrid(
torch.arange(h), torch.arange(w), indexing="ij")
coords = torch.stack([coords2, coords1], dim=0)
return coords.float()
return meshgrid_model(), None, ["aten::meshgrid", "aten::stack", "prim::ListUnpack"]
@pytest.mark.nightly
@pytest.mark.precommit
def test_meshgrid_subgraph(self, ie_device, precision, ir_version):
self._test(*self.create_model(), ie_device, precision, ir_version)
class TestListUnpackParameterSingle(PytorchLayerTest):
def _prepare_input(self):
def tensor_gen():
return np.random.uniform(0, 50, (1, 2, 10)).astype(np.float32)
return ((tensor_gen(), tensor_gen()), )
def create_model(self):
import torch
from typing import List
class model(torch.nn.Module):
def forward(self, x: List[torch.Tensor]):
x1, x2 = x
return x1, x2
return model(), None, ["prim::ListUnpack"]
@pytest.mark.nightly
def test(self, ie_device, precision, ir_version):
self._test(*self.create_model(), ie_device, precision, ir_version)
class TestListUnpackParameterSingleMixed(PytorchLayerTest):
def _prepare_input(self):
def tensor_gen():
return np.random.uniform(0, 50, (1, 2, 10)).astype(np.float32)
# generate tensor with a different shape for easier mismatch detection in case of mixed input order
def tensor_gen_2():
return np.random.uniform(0, 50, (2, 3)).astype(np.float32)
return (tensor_gen_2(), (tensor_gen(), tensor_gen()), tensor_gen_2())
def create_model(self):
import torch
from typing import List
class model(torch.nn.Module):
def forward(self, y1, x: List[torch.Tensor], y2):
x1, x2 = x
return x1, x2, y1, y2
return model(), None, ["prim::ListUnpack"]
@pytest.mark.nightly
def test(self, ie_device, precision, ir_version):
self._test(*self.create_model(), ie_device, precision, ir_version)
class TestListUnpackParameterNested(PytorchLayerTest):
def _prepare_input(self):
def tensor_gen():
return np.random.uniform(0, 50, (1, 2, 10)).astype(np.float32)
return (((tensor_gen(), tensor_gen()), (tensor_gen(), tensor_gen())), )
def create_model(self):
import torch
from typing import List
class model(torch.nn.Module):
def forward(self, x: List[List[torch.Tensor]]):
x1, x2 = x
y1, y2 = x1
y3, y4 = x2
return y1, y2, y3, y4
return model(), None, ["prim::ListUnpack"]
@pytest.mark.nightly
def test(self, ie_device, precision, ir_version):
self._test(*self.create_model(), ie_device, precision, ir_version)
class TestListUnpackParameterMultiple(PytorchLayerTest):
def _prepare_input(self):
def tensor_gen():
return np.random.uniform(0, 50, (1, 2, 10)).astype(np.float32)
return ((tensor_gen(), tensor_gen()), (tensor_gen(), tensor_gen()))
def create_model(self):
import torch
from typing import List
class model(torch.nn.Module):
def forward(self, x: List[torch.Tensor], y: List[torch.Tensor]):
z1, z2 = x
z3, z4 = y
return z1, z2, z3, z4
return model(), None, ["prim::ListUnpack"]
@pytest.mark.nightly
def test(self, ie_device, precision, ir_version):
self._test(*self.create_model(), ie_device, precision, ir_version)