Maxim Vafin
48dec1000e
* Support operations on aliases of tensors * Add tests * Fix issue with convnd * Fix code style * Fix issue with tensor index of mutated tensor * Fix if types alignment * Fix issues in keypoint detectron2 * Fix issue with masks in detectron2 * Fix acuracy issue in mobilevitv2 models * Remove unused includes * Return upsample case in lictconstruct replacer * Fix types, apply review feedback * Apply feedback * Revert change of not using shared_from_this for getitem * Fix issue in prim::device transformation * Fix layer tests * Apply review feedback * Fix issue with not existing alias to tensor
114 lines
3.7 KiB
Python
114 lines
3.7 KiB
Python
# Copyright (C) 2018-2023 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import pytest
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import torch
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import torch.nn.functional as F
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from pytorch_layer_test_class import PytorchLayerTest
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class unary_op_net(torch.nn.Module):
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def __init__(self, op, dtype):
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super(unary_op_net, self).__init__()
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self.dtype = dtype
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self.op = op
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def forward(self, x):
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x1 = x.to(self.dtype)
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y = self.op(x1)
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return y, x1
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class TestUnaryOp(PytorchLayerTest):
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def _prepare_input(self):
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# random number in range [1, 11)
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x = torch.rand(2, 10) * 10 + 1
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return (x.to(self.dtype).numpy(),)
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@pytest.mark.nightly
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@pytest.mark.precommit
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@pytest.mark.parametrize("dtype", [torch.float32, torch.float64, torch.int8, torch.uint8, torch.int32, torch.int64])
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@pytest.mark.parametrize("op,op_type", [
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(torch.rsqrt, "aten::rsqrt"),
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(torch.sqrt, "aten::sqrt"),
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(torch.exp, "aten::exp"),
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(torch.relu, "aten::relu"),
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(torch.relu_, "aten::relu_"),
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(torch.ceil, "aten::ceil"),
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(torch.ceil_, "aten::ceil_"),
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(torch.floor, "aten::floor"),
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(torch.floor_, "aten::floor_"),
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(torch.sigmoid, "aten::sigmoid"),
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# trigonometry
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(torch.cos, "aten::cos"),
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(torch.sin, "aten::sin"),
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(torch.tan, "aten::tan"),
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(torch.cosh, "aten::cosh"),
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(torch.sinh, "aten::sinh"),
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(torch.tanh, "aten::tanh"),
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(torch.acos, "aten::acos"),
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(torch.asin, "aten::asin"),
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(torch.atan, "aten::atan"),
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(torch.acosh, "aten::acosh"),
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(torch.asinh, "aten::asinh"),
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(torch.atanh, "aten::atanh"),
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])
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def test_unary_op(self, op, op_type, dtype, ie_device, precision, ir_version):
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self.dtype = dtype
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self._test(unary_op_net(op, dtype), None, op_type,
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ie_device, precision, ir_version)
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@pytest.mark.nightly
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@pytest.mark.precommit
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@pytest.mark.parametrize("dtype", [torch.float32, torch.float64])
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@pytest.mark.parametrize("op,op_type", [
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# some pytorch inplace ops do not support int
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(torch.exp_, "aten::exp_"),
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(torch.sigmoid_, "aten::sigmoid_"),
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# trigonometry
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(torch.cos_, "aten::cos_"),
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(torch.sin_, "aten::sin_"),
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(torch.tan_, "aten::tan_"),
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(torch.cosh_, "aten::cosh_"),
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(torch.sinh_, "aten::sinh_"),
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(torch.tanh_, "aten::tanh_"),
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(torch.acos_, "aten::acos_"),
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(torch.asin_, "aten::asin_"),
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(torch.atan_, "aten::atan_"),
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(torch.acosh_, "aten::acosh_"),
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(torch.asinh_, "aten::asinh_"),
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(torch.atanh_, "aten::atanh_"),
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])
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def test_unary_op_float(self, op, op_type, dtype, ie_device, precision, ir_version):
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self.dtype = dtype
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self._test(unary_op_net(op, dtype), None, op_type,
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ie_device, precision, ir_version)
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class glu_net(torch.nn.Module):
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def __init__(self, dim, dtype):
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super(glu_net, self).__init__()
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self.dtype = dtype
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self.dim = dim
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def forward(self, x):
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y = F.glu(x.to(self.dtype), dim=self.dim)
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return y
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class TestGluOp(PytorchLayerTest):
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def _prepare_input(self):
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# random number in range [1, 11)
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x = torch.rand(2, 4, 10, 10) * 10 + 1
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return (x.to(self.dtype).numpy(),)
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@pytest.mark.nightly
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@pytest.mark.precommit
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@pytest.mark.parametrize("dim", [0, 1, 2, 3, -1, -2])
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@pytest.mark.parametrize("dtype", [torch.float32, torch.float64])
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def test_glu(self, dim, dtype, ie_device, precision, ir_version):
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self.dtype = dtype
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self._test(glu_net(dim, dtype), None, "aten::glu",
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ie_device, precision, ir_version)
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