8d0381b0fe
* [PT FE] Implement custom op for types alignment * Fix code style * Fix inplace ops * Fix layer tests * Remove no longer needed change * Fix ovc tests * Fix fe tests
126 lines
5.1 KiB
Python
126 lines
5.1 KiB
Python
# Copyright (C) 2018-2023 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import numpy as np
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import pytest
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import torch
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from pytorch_layer_test_class import PytorchLayerTest
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@pytest.mark.parametrize('test_input', [(np.array([[1, 2], [3, 4]], dtype=np.float32),
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np.array([[1, 1], [2, 2]], dtype=np.float32),),
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(np.array([[1, 2], [3, 4]], dtype=np.float32),
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np.array([2, 3], dtype=np.float32),),
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(np.array([[1, 2], [3, 4]], dtype=np.float32),
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np.array([2], dtype=np.float32),),
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(np.array([5, 6], dtype=np.float32),
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np.array([[1, 2], [3, 4]], dtype=np.float32),),
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(np.array([5], dtype=np.float32),
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np.array([[1, 2], [3, 4]], dtype=np.float32),)])
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class TestPow(PytorchLayerTest):
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"""
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Input test data contains five test cases - elementwise power, broadcast exponent, one exponent,
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broadcast base, one base.
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"""
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def _prepare_input(self):
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return self.test_input
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def create_model(self):
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class aten_pow(torch.nn.Module):
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def forward(self, input_data, exponent):
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return torch.pow(input_data, exponent)
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ref_net = None
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return aten_pow(), ref_net, "aten::pow"
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@pytest.mark.nightly
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@pytest.mark.precommit
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def test_pow(self, ie_device, precision, ir_version, test_input):
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self.test_input = test_input
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self._test(*self.create_model(), ie_device, precision, ir_version, use_convert_model=True)
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class TestPowMixedTypes(PytorchLayerTest):
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def _prepare_input(self):
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if len(self.lhs_shape) == 0:
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return (torch.randn(self.rhs_shape) * 2 + 0.6).to(self.rhs_type).numpy(),
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elif len(self.rhs_shape) == 0:
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return (torch.randint(1, 3, self.lhs_shape).to(self.lhs_type).numpy(),)
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return (torch.randint(1, 3, self.lhs_shape).to(self.lhs_type).numpy(),
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(torch.randn(self.rhs_shape) * 2 + 0.6).to(self.rhs_type).numpy())
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def create_model(self, lhs_type, lhs_shape, rhs_type, rhs_shape):
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class aten_pow(torch.nn.Module):
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def __init__(self, lhs_type, lhs_shape, rhs_type, rhs_shape):
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super().__init__()
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self.lhs_type = lhs_type
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self.rhs_type = rhs_type
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if len(lhs_shape) == 0:
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self.forward = self.forward1
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elif len(rhs_shape) == 0:
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self.forward = self.forward2
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else:
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self.forward = self.forward3
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def forward1(self, rhs):
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return torch.pow(torch.tensor(3).to(self.lhs_type), rhs.to(self.rhs_type))
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def forward2(self, lhs):
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return torch.pow(lhs.to(self.lhs_type), torch.tensor(3).to(self.rhs_type))
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def forward3(self, lhs, rhs):
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return torch.pow(lhs.to(self.lhs_type), rhs.to(self.rhs_type))
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ref_net = None
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return aten_pow(lhs_type, lhs_shape, rhs_type, rhs_shape), ref_net, "aten::pow"
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@pytest.mark.parametrize(("lhs_type", "rhs_type"),
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[[torch.int32, torch.int64],
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[torch.int32, torch.float32],
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[torch.int32, torch.float64],
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[torch.int64, torch.int32],
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[torch.int64, torch.float32],
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[torch.int64, torch.float64],
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[torch.float32, torch.int32],
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[torch.float32, torch.int64],
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[torch.float32, torch.float64],
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])
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@pytest.mark.parametrize(("lhs_shape", "rhs_shape"), [([2, 3], [2, 3]),
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([2, 3], []),
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([], [2, 3]),
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])
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@pytest.mark.nightly
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@pytest.mark.precommit
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def test_pow_mixed_types(self, ie_device, precision, ir_version, lhs_type, lhs_shape, rhs_type, rhs_shape):
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self.lhs_type = lhs_type
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self.lhs_shape = lhs_shape
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self.rhs_type = rhs_type
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self.rhs_shape = rhs_shape
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self._test(*self.create_model(lhs_type, lhs_shape, rhs_type, rhs_shape),
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ie_device, precision, ir_version)
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class TestPowMixedTypesScalars(PytorchLayerTest):
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def _prepare_input(self):
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return (torch.randn([1,2,3,4]).numpy(),)
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def create_model(self):
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class aten_pow(torch.nn.Module):
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def forward(self, x):
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return torch.pow(x.size(2), -0.5)
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ref_net = None
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return aten_pow(), ref_net, "aten::pow"
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@pytest.mark.nightly
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@pytest.mark.precommit
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def test_pow_mixed_types(self, ie_device, precision, ir_version):
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self._test(*self.create_model(), ie_device, precision, ir_version)
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