mirror of https://github.com/llvm/torch-mlir
130 lines
3.9 KiB
Python
130 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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import torchvision.models as models
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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 ResNet18Module(torch.nn.Module):
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def __init__(self):
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super().__init__()
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# Reset seed to make model deterministic.
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torch.manual_seed(0)
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self.resnet = models.resnet18()
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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, 3, -1, -1], torch.float32, True),
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])
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def forward(self, img):
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return self.resnet.forward(img)
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@register_test_case(module_factory=lambda: ResNet18Module())
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def ResNet18Module_basic(module, tu: TestUtils):
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module.forward(tu.rand(1, 3, 224, 224))
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class ResNet18StaticModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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# Reset seed to make model deterministic.
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torch.manual_seed(0)
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self.resnet = models.resnet18()
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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, 3, 224, 224], torch.float32, True),
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])
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def forward(self, img):
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return self.resnet.forward(img)
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@register_test_case(module_factory=lambda: ResNet18StaticModule())
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def ResNet18StaticModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(1, 3, 224, 224))
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class IouOfModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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@export
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@annotate_args([
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None,
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([-1, -1], torch.float32, True),
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([-1, -1], torch.float32, True),
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])
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def forward(self, bbox1, bbox2):
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area1 = (bbox1[:, 2] - bbox1[:, 0]) * (bbox1[:, 3] - bbox1[:, 1])
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area2 = (bbox2[:, 2] - bbox2[:, 0]) * (bbox2[:, 3] - bbox2[:, 1])
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lt = torch.maximum(bbox1[:, :2], bbox2[:, :2])
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rb = torch.minimum(bbox1[:, 2:], bbox2[:, 2:])
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overlap_coord = (rb - lt).clip(0)
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overlap = overlap_coord[:, 0] * overlap_coord[:, 1]
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union = area1 + area2 - overlap
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return overlap / union
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@register_test_case(module_factory=lambda: IouOfModule())
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def IouOfModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(1024, 4), tu.rand(1024, 4))
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class MobilenetV2Module(torch.nn.Module):
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def __init__(self):
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super().__init__()
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# Reset seed to make model deterministic.
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torch.manual_seed(0)
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self.mobilenetv2 = models.mobilenet_v2()
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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, 3, -1, -1], torch.float32, True),
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])
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def forward(self, img):
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return self.mobilenetv2.forward(img)
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# TODO (cathyzhyi) The runtime assertion for conv2d with group != 1 is exposed
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# after aten.hardtanh is implemented. Reenable once the the runtime assertion
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# is fixed.
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#@register_test_case(module_factory=lambda: MobilenetV2Module())
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def MobilenetV2Module_basic(module, tu: TestUtils):
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module.forward(tu.rand(1, 3, 224, 224))
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class MobilenetV3Module(torch.nn.Module):
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def __init__(self):
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super().__init__()
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# Reset seed to make model deterministic.
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torch.manual_seed(0)
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self.mobilenetv3 = models.mobilenet_v3_small()
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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, 3, -1, -1], torch.float32, True),
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])
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def forward(self, img):
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return self.mobilenetv3.forward(img)
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@register_test_case(module_factory=lambda: MobilenetV3Module())
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def MobilenetV3Module_basic(module, tu: TestUtils):
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module.forward(tu.rand(1, 3, 224, 224))
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