def __init__(self, pretrained=True, include_top=False, freeze=True):
super().__init__()
backbone = vision.resnet18(pretrained=pretrained,
include_top=include_top,
freeze=freeze)
output_size = backbone.get_output_size()
head = nn.Linear(output_size, num_classes)
self.model = torch.nn.Sequential(backbone, head)
def test_resnet18_ipex(self):
resnet18 = vision.resnet18(pretrained=False,
include_top=False,
freeze=True)
train_with_linear_top_layer(resnet18,
batch_size,
num_workers,
data_dir,
use_orca_lite_trainer=True)
def test_resnet18_quantitrain_image_folder_pngze(self):
resnet18 = vision.resnet18(pretrained=False,
include_top=False,
freeze=True)
train_torch_lightning(resnet18, root_dir1, batch_size)
train_torch_lightning(resnet18, root_dir2, batch_size)
def test_resnet18_ipex(self):
resnet18 = vision.resnet18(
pretrained=False, include_top=False, freeze=True)
train_with_linear_top_layer(
resnet18, batch_size, num_workers, data_dir,
accelerator=IPEXAccelerator())