92649105ed
* Add eltwise types resolving. Support big int constants. * Update src/bindings/python/src/openvino/frontend/pytorch/decoder.py * Small fix * Fix some cases * Add tests for add in different types * Add tests for mul * Add tests for sub and div * Small fixes * Return list handling (needed for empty lists) * Add test for empty list * Update src/frontends/pytorch/src/op/mul.cpp Co-authored-by: Roman Kazantsev <roman.kazantsev@intel.com> * Use refs instead of ptrs * Apply suggestions from code review Co-authored-by: Roman Kazantsev <roman.kazantsev@intel.com> * Apply code review suggestions * Fix code style * Add more eltwise ops --------- Co-authored-by: Roman Kazantsev <roman.kazantsev@intel.com>
135 lines
4.7 KiB
Python
135 lines
4.7 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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from pytorch_layer_test_class import PytorchLayerTest
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class TestComp(PytorchLayerTest):
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def _prepare_input(self):
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import numpy as np
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return (np.random.randn(1, 3, 24, 24).astype(np.float32), np.random.randn(1, 3, 24, 24).astype(np.float32))
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def create_model(self, op_type):
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class aten_eq(torch.nn.Module):
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def forward(self, x, y):
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return x == y
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class aten_ne(torch.nn.Module):
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def forward(self, x, y):
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return x != y
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class aten_lt(torch.nn.Module):
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def forward(self, x, y):
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return x < y
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class aten_gt(torch.nn.Module):
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def forward(self, x, y):
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return x > y
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class aten_le(torch.nn.Module):
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def forward(self, x, y):
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return x <= y
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class aten_ge(torch.nn.Module):
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def forward(self, x, y):
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return x >= y
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ops = {
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"eq": aten_eq,
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"ne": aten_ne,
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"lt": aten_lt,
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"gt": aten_gt,
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"ge": aten_ge,
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"le": aten_le
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}
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model_cls = ops[op_type]
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ref_net = None
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return model_cls(), ref_net, f"aten::{op_type}"
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@pytest.mark.parametrize("op", ["eq", "ne", "lt", "gt", "le", "ge"])
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@pytest.mark.nightly
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@pytest.mark.precommit
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def test_comp(self, op, ie_device, precision, ir_version):
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self._test(*self.create_model(op), ie_device, precision, ir_version)
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class TestCompMixedTypes(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.randint(0, 3, self.rhs_shape).to(self.rhs_type).numpy(),)
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elif len(self.rhs_shape) == 0:
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return (torch.randint(0, 3, self.lhs_shape).to(self.lhs_type).numpy(),)
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return (torch.randint(0, 3, self.lhs_shape).to(self.lhs_type).numpy(),
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torch.randint(0, 3, self.rhs_shape).to(self.rhs_type).numpy())
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def create_model(self, lhs_type, lhs_shape, rhs_type, rhs_shape, op):
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ops = {
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"eq": torch.eq,
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"ne": torch.ne,
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"lt": torch.lt,
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"gt": torch.gt,
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"ge": torch.ge,
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"le": torch.le
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}
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op_fn = ops[op]
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class aten_comp(torch.nn.Module):
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def __init__(self, lhs_type, lhs_shape, rhs_type, rhs_shape, op_fn):
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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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self.op_fn = op_fn
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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 self.op_fn(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 self.op_fn(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 self.op_fn(lhs.to(self.lhs_type), rhs.to(self.rhs_type))
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ref_net = None
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return aten_comp(lhs_type, lhs_shape, rhs_type, rhs_shape, op_fn), ref_net, f"aten::{op}"
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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.parametrize("op", ["eq", "ne", "lt", "gt", "le", "ge"])
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@pytest.mark.nightly
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@pytest.mark.precommit
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def test_eq_mixed_types(self, ie_device, precision, ir_version, lhs_type, lhs_shape, rhs_type, rhs_shape, op):
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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, op),
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ie_device, precision, ir_version)
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