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())