mirror of https://github.com/llvm/torch-mlir
128 lines
3.7 KiB
Python
128 lines
3.7 KiB
Python
# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
|
|
# See https://llvm.org/LICENSE.txt for license information.
|
|
# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
|
|
# Also available under a BSD-style license. See LICENSE.
|
|
|
|
import torch
|
|
import torchvision.models as models
|
|
|
|
from torch_mlir_e2e_test.torchscript.framework import TestUtils
|
|
from torch_mlir_e2e_test.torchscript.registry import register_test_case
|
|
from torch_mlir_e2e_test.torchscript.annotations import annotate_args, export
|
|
|
|
# ==============================================================================
|
|
|
|
|
|
class ResNet18Module(torch.nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
# Reset seed to make model deterministic.
|
|
torch.manual_seed(0)
|
|
self.resnet = models.resnet18()
|
|
self.train(False)
|
|
|
|
@export
|
|
@annotate_args([
|
|
None,
|
|
([-1, 3, -1, -1], torch.float32, True),
|
|
])
|
|
def forward(self, img):
|
|
return self.resnet.forward(img)
|
|
|
|
|
|
@register_test_case(module_factory=lambda: ResNet18Module())
|
|
def ResNet18Module_basic(module, tu: TestUtils):
|
|
module.forward(tu.rand(1, 3, 224, 224))
|
|
|
|
|
|
class ResNet18StaticModule(torch.nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
# Reset seed to make model deterministic.
|
|
torch.manual_seed(0)
|
|
self.resnet = models.resnet18()
|
|
self.train(False)
|
|
|
|
@export
|
|
@annotate_args([
|
|
None,
|
|
([1, 3, 224, 224], torch.float32, True),
|
|
])
|
|
def forward(self, img):
|
|
return self.resnet.forward(img)
|
|
|
|
|
|
@register_test_case(module_factory=lambda: ResNet18StaticModule())
|
|
def ResNet18StaticModule_basic(module, tu: TestUtils):
|
|
module.forward(tu.rand(1, 3, 224, 224))
|
|
|
|
|
|
class IouOfModule(torch.nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
|
|
@export
|
|
@annotate_args([
|
|
None,
|
|
([-1, -1], torch.float32, True),
|
|
([-1, -1], torch.float32, True),
|
|
])
|
|
def forward(self, bbox1, bbox2):
|
|
area1 = (bbox1[:, 2] - bbox1[:, 0]) * (bbox1[:, 3] - bbox1[:, 1])
|
|
area2 = (bbox2[:, 2] - bbox2[:, 0]) * (bbox2[:, 3] - bbox2[:, 1])
|
|
lt = torch.maximum(bbox1[:, :2], bbox2[:, :2])
|
|
rb = torch.minimum(bbox1[:, 2:], bbox2[:, 2:])
|
|
|
|
overlap_coord = (rb - lt).clip(0)
|
|
overlap = overlap_coord[:, 0] * overlap_coord[:, 1]
|
|
union = area1 + area2 - overlap
|
|
|
|
return overlap / union
|
|
|
|
|
|
@register_test_case(module_factory=lambda: IouOfModule())
|
|
def IouOfModule_basic(module, tu: TestUtils):
|
|
module.forward(tu.rand(1024, 4), tu.rand(1024, 4))
|
|
|
|
class MobilenetV2Module(torch.nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
# Reset seed to make model deterministic.
|
|
torch.manual_seed(0)
|
|
self.mobilenetv2 = models.mobilenet_v2()
|
|
self.train(False)
|
|
|
|
@export
|
|
@annotate_args([
|
|
None,
|
|
([-1, 3, -1, -1], torch.float32, True),
|
|
])
|
|
def forward(self, img):
|
|
return self.mobilenetv2.forward(img)
|
|
|
|
|
|
@register_test_case(module_factory=lambda: MobilenetV2Module())
|
|
def MobilenetV2Module_basic(module, tu: TestUtils):
|
|
module.forward(tu.rand(1, 3, 224, 224))
|
|
|
|
class MobilenetV3Module(torch.nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
# Reset seed to make model deterministic.
|
|
torch.manual_seed(0)
|
|
self.mobilenetv3 = models.mobilenet_v3_small()
|
|
self.train(False)
|
|
|
|
@export
|
|
@annotate_args([
|
|
None,
|
|
([-1, 3, -1, -1], torch.float32, True),
|
|
])
|
|
def forward(self, img):
|
|
return self.mobilenetv3.forward(img)
|
|
|
|
|
|
@register_test_case(module_factory=lambda: MobilenetV3Module())
|
|
def MobilenetV3Module_basic(module, tu: TestUtils):
|
|
module.forward(tu.rand(1, 3, 224, 224))
|