101 lines
3.5 KiB
Python
101 lines
3.5 KiB
Python
# Copyright (C) 2018-2023 Intel Corporation
|
|
# SPDX-License-Identifier: Apache-2.0
|
|
|
|
import numpy as np
|
|
import pytest
|
|
import torch
|
|
|
|
from pytorch_layer_test_class import PytorchLayerTest
|
|
|
|
|
|
class TestRsub(PytorchLayerTest):
|
|
def _prepare_input(self):
|
|
return self.input_data
|
|
|
|
def create_model(self, second_type="float"):
|
|
class aten_rsub_float(torch.nn.Module):
|
|
|
|
def forward(self, x, y:float, alpha: float):
|
|
return torch.rsub(x, y, alpha=alpha)
|
|
|
|
class aten_rsub_int(torch.nn.Module):
|
|
|
|
def forward(self, x, y:int, alpha: float):
|
|
return torch.rsub(x, y, alpha=alpha)
|
|
|
|
model_cls = {
|
|
"float": aten_rsub_float,
|
|
"int": aten_rsub_int
|
|
}
|
|
model = model_cls[second_type]
|
|
|
|
|
|
ref_net = None
|
|
|
|
return model(), ref_net, "aten::rsub"
|
|
|
|
@pytest.mark.parametrize('input_data', [(np.random.randn(2, 3, 4).astype(np.float32),
|
|
np.array(5).astype(np.float32),
|
|
np.random.randn(1)),])
|
|
|
|
@pytest.mark.nightly
|
|
@pytest.mark.precommit
|
|
def test_rsub(self, ie_device, precision, ir_version, input_data):
|
|
self.input_data = input_data
|
|
self._test(*self.create_model(second_type="float"), ie_device, precision, ir_version)
|
|
|
|
@pytest.mark.parametrize('input_data', [(np.random.randn(2, 3, 4).astype(np.float32),
|
|
np.array(5).astype(int),
|
|
np.random.randn(1)),])
|
|
|
|
@pytest.mark.nightly
|
|
@pytest.mark.precommit
|
|
def test_rsub(self, ie_device, precision, ir_version, input_data):
|
|
self.input_data = input_data
|
|
self._test(*self.create_model(second_type="int"), ie_device, precision, ir_version)
|
|
|
|
|
|
class TestRsubTypes(PytorchLayerTest):
|
|
|
|
def _prepare_input(self):
|
|
return (torch.randn(self.lhs_shape).to(self.lhs_type).numpy(),
|
|
np.array([1]).astype(self.rhs_type))
|
|
|
|
def create_model(self, lhs_type, rhs_type):
|
|
|
|
class aten_rsub(torch.nn.Module):
|
|
def __init__(self, lhs_type, rhs_type):
|
|
super().__init__()
|
|
self.lhs_type = lhs_type
|
|
if rhs_type == np.int32:
|
|
self.forward = self.forward2
|
|
else:
|
|
self.forward = self.forward1
|
|
|
|
def forward1(self, lhs, rhs:float):
|
|
return torch.rsub(lhs.to(self.lhs_type), rhs, alpha=2)
|
|
|
|
def forward2(self, lhs, rhs:int):
|
|
return torch.rsub(lhs.to(self.lhs_type), rhs, alpha=2)
|
|
|
|
ref_net = None
|
|
|
|
return aten_rsub(lhs_type, rhs_type), ref_net, "aten::rsub"
|
|
|
|
@pytest.mark.parametrize(("lhs_type", "rhs_type"),
|
|
[[torch.int32, np.int32],
|
|
[torch.int32, np.float32],
|
|
[torch.int64, np.int32],
|
|
[torch.int64, np.float32],
|
|
[torch.float32, np.int32],
|
|
[torch.float32, np.float32],
|
|
])
|
|
@pytest.mark.parametrize(("lhs_shape"), [[2, 3], [3], [2, 3, 4]])
|
|
@pytest.mark.nightly
|
|
@pytest.mark.precommit
|
|
def test_rsub_types(self, ie_device, precision, ir_version, lhs_type, lhs_shape, rhs_type):
|
|
self.lhs_type = lhs_type
|
|
self.lhs_shape = lhs_shape
|
|
self.rhs_type = rhs_type
|
|
self._test(*self.create_model(lhs_type, rhs_type),
|
|
ie_device, precision, ir_version) |