mirror of https://github.com/llvm/torch-mlir
76 lines
2.9 KiB
Python
76 lines
2.9 KiB
Python
# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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# See https://llvm.org/LICENSE.txt for license information.
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# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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import torch
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from npcomp_torchscript.e2e_test.framework import TestUtils
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from npcomp_torchscript.e2e_test.registry import register_test_case
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from npcomp_torchscript.annotations import annotate_args, export
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# ==============================================================================
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class BatchNorm1DModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.bn1d = torch.nn.BatchNorm1d(4)
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self.bn1d.eval()
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self.bn1d.running_mean = torch.tensor([0.5, 0.4, 0.3, 0.6])
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self.bn1d.running_var = torch.tensor([3.0, 2.0, 4.0, 5.0])
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self.bn1d.weight = torch.nn.Parameter(torch.tensor([3.0, 2.0, 4.0, 5.0]))
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self.bn1d.bias = torch.nn.Parameter(torch.tensor([0.5, 0.4, 0.3, 0.6]))
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@export
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@annotate_args([
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None,
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([10, 4, 3], torch.float32, True),
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])
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def forward(self, x):
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return self.bn1d(x)
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@register_test_case(module_factory=lambda: BatchNorm1DModule())
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def BatchNorm1DModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(10, 4, 3))
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# ==============================================================================
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class BatchNorm2DModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.bn2d = torch.nn.BatchNorm2d(2)
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self.bn2d.eval()
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self.bn2d.running_mean = torch.tensor([0.5, 0.4])
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self.bn2d.running_var = torch.tensor([3.0, 2.0])
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self.bn2d.weight = torch.nn.Parameter(torch.tensor([3.0, 2.0]))
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self.bn2d.bias = torch.nn.Parameter(torch.tensor([0.5, 0.4]))
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@export
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@annotate_args([
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None,
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([10, 2, 3, 3], torch.float32, True),
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])
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def forward(self, x):
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return self.bn2d(x)
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@register_test_case(module_factory=lambda: BatchNorm2DModule())
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def BatchNorm2DModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(10, 2, 3, 3))
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# ==============================================================================
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class BatchNorm3DModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.bn3d = torch.nn.BatchNorm3d(5)
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self.bn3d.eval()
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self.bn3d.running_mean = torch.tensor([0.5, 0.4, 0.3, 0.2, 0.4])
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self.bn3d.running_var = torch.tensor([3.0, 2.0, 4.0, 2.0, 3.0])
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self.bn3d.weight = torch.nn.Parameter(torch.tensor([3.0, 2.0, 4.0, 2.0, 3.0]))
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self.bn3d.bias = torch.nn.Parameter(torch.tensor([0.5, 0.4, 0.3, 0.2, 0.4]))
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@export
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@annotate_args([
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None,
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([2, 5, 3, 6, 4], torch.float32, True),
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])
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def forward(self, x):
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return self.bn3d(x)
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@register_test_case(module_factory=lambda: BatchNorm3DModule())
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def BatchNorm3DModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(2, 5, 3, 6, 4))
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