87 lines
2.3 KiB
Python
87 lines
2.3 KiB
Python
# Copyright (C) 2018-2024 Intel Corporation
|
|
# SPDX-License-Identifier: Apache-2.0
|
|
|
|
import numpy as np
|
|
import pytest
|
|
|
|
from pytorch_layer_test_class import PytorchLayerTest
|
|
|
|
|
|
class TestStack2D(PytorchLayerTest):
|
|
def _prepare_input(self):
|
|
return self.input_tensors
|
|
|
|
def create_model(self, dim):
|
|
import torch
|
|
|
|
class aten_stack(torch.nn.Module):
|
|
def __init__(self, dim):
|
|
super(aten_stack, self).__init__()
|
|
self.dim = dim
|
|
|
|
def forward(self, x, y):
|
|
inputs = [x, y]
|
|
return torch.stack(inputs, self.dim)
|
|
|
|
ref_net = None
|
|
|
|
return aten_stack(dim), ref_net, "aten::stack"
|
|
|
|
@pytest.mark.parametrize("input_shape",
|
|
[
|
|
[1, 3, 3],
|
|
[4, 4, 2],
|
|
[8, 1, 1, 9]
|
|
])
|
|
@pytest.mark.parametrize("dim", ([
|
|
0, 1, 2,
|
|
]))
|
|
@pytest.mark.nightly
|
|
@pytest.mark.precommit
|
|
def test_stack2D(self, input_shape, dim, ie_device, precision, ir_version):
|
|
self.input_tensors = [
|
|
np.random.randn(*input_shape).astype(np.float32),
|
|
np.random.randn(*input_shape).astype(np.float32),
|
|
]
|
|
self._test(*self.create_model(dim), ie_device, precision, ir_version)
|
|
|
|
|
|
class TestStack3D(PytorchLayerTest):
|
|
def _prepare_input(self):
|
|
return self.input_tensors
|
|
|
|
def create_model(self, dim):
|
|
import torch
|
|
|
|
class aten_stack(torch.nn.Module):
|
|
def __init__(self, dim):
|
|
super(aten_stack, self).__init__()
|
|
self.dim = dim
|
|
|
|
def forward(self, x, y, z):
|
|
inputs = [x, y, z]
|
|
return torch.stack(inputs, self.dim)
|
|
|
|
ref_net = None
|
|
|
|
return aten_stack(dim), ref_net, "aten::stack"
|
|
|
|
@pytest.mark.parametrize("input_shape",
|
|
[
|
|
[1, 3, 3],
|
|
[4, 4, 2],
|
|
[8, 1, 1, 9]
|
|
])
|
|
@pytest.mark.parametrize("dim", ([
|
|
0, 1, 2,
|
|
]))
|
|
@pytest.mark.nightly
|
|
@pytest.mark.precommit
|
|
def test_stack3D(self, input_shape, dim, ie_device, precision, ir_version):
|
|
self.input_tensors = [
|
|
np.random.randn(*input_shape).astype(np.float32),
|
|
np.random.randn(*input_shape).astype(np.float32),
|
|
np.random.randn(*input_shape).astype(np.float32)
|
|
]
|
|
self._test(*self.create_model(dim), ie_device, precision, ir_version)
|