mirror of https://github.com/llvm/torch-mlir
136 lines
3.9 KiB
Python
136 lines
3.9 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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# Also available under a BSD-style license. See LICENSE.
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import torch
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from torch_mlir_e2e_test.torchscript.framework import TestUtils
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from torch_mlir_e2e_test.torchscript.registry import register_test_case
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from torch_mlir_e2e_test.torchscript.annotations import annotate_args, export
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# ==============================================================================
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class Conv2dNoPaddingModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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torch.manual_seed(0)
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self.conv = torch.nn.Conv2d(2, 10, 3, bias=False)
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self.train(False)
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@export
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@annotate_args([
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None,
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([-1, -1, -1, -1], torch.float32, True),
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])
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def forward(self, x):
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return self.conv(x)
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@register_test_case(module_factory=lambda: Conv2dNoPaddingModule())
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def Conv2dNoPaddingModule_basic(module, tu: TestUtils):
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t = tu.rand(5, 2, 10, 20)
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module.forward(t)
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class Conv2dBiasNoPaddingModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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torch.manual_seed(0)
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self.conv = torch.nn.Conv2d(2, 10, 3, bias=True)
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self.train(False)
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@export
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@annotate_args([
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None,
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([-1, -1, -1, -1], torch.float32, True),
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])
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def forward(self, x):
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return self.conv(x)
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@register_test_case(module_factory=lambda: Conv2dBiasNoPaddingModule())
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def Conv2dBiasNoPaddingModule_basic(module, tu: TestUtils):
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t = tu.rand(5, 2, 10, 20)
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module.forward(t)
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class Conv2dWithPaddingModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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torch.manual_seed(0)
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self.conv = torch.nn.Conv2d(2, 10, 3, bias=False, padding=3)
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self.train(False)
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@export
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@annotate_args([
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None,
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([-1, -1, -1, -1], torch.float32, True),
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])
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def forward(self, x):
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return self.conv(x)
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@register_test_case(module_factory=lambda: Conv2dWithPaddingModule())
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def Conv2dWithPaddingModule_basic(module, tu: TestUtils):
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t = tu.rand(5, 2, 10, 20)
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module.forward(t)
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class Conv2dWithPaddingDilationStrideModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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torch.manual_seed(0)
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self.conv = torch.nn.Conv2d(in_channels=2,
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out_channels=10,
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kernel_size=3,
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padding=3,
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stride=2,
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dilation=3,
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bias=False)
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self.train(False)
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@export
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@annotate_args([
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None,
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([-1, -1, -1, -1], torch.float32, True),
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])
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def forward(self, x):
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return self.conv(x)
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@register_test_case(
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module_factory=lambda: Conv2dWithPaddingDilationStrideModule())
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def Conv2dWithPaddingDilationStrideModule_basic(module, tu: TestUtils):
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t = tu.rand(5, 2, 10, 20)
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module.forward(t)
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class Conv2dWithPaddingDilationStrideStaticModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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torch.manual_seed(0)
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self.conv = torch.nn.Conv2d(in_channels=2,
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out_channels=10,
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kernel_size=3,
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padding=3,
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stride=2,
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dilation=3,
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bias=False)
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self.train(False)
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@export
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@annotate_args([
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None,
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([5, 2, 10, 20], torch.float32, True),
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])
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def forward(self, x):
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return self.conv(x)
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@register_test_case(
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module_factory=lambda: Conv2dWithPaddingDilationStrideStaticModule())
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def Conv2dWithPaddingDilationStrideStaticModule_basic(module, tu: TestUtils):
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t = tu.rand(5, 2, 10, 20)
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module.forward(t)
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