More torch2hpvm README Path fixes

hpvm/projects/torch2hpvm/README.md

@@ -26,9 +26,9 @@ First, prepare 2 datasets for autotuning and testing.
 
 ```python
 from torch2hpvm import BinDataset
-from pathlib import Path, PosixPath
+from pathlib import Path
 
-data_dir = Path(__file__).parent / "model_params/resnet18_cifar10"
+data_dir = Path(__file__).parent / "../model_params/resnet18_cifar10"
 dataset_shape = 5000, 3, 32, 32
 tuneset = BinDataset(data_dir / "tune_input.bin", data_dir / "tune_labels.bin", dataset_shape)
 testset = BinDataset(data_dir / "test_input.bin", data_dir / "test_labels.bin", dataset_shape)
@@ -52,7 +52,7 @@ from torch.nn import Module
 import dnn  # Defined at `hpvm/test/dnn_benchmarks/pytorch`
 
 model: Module = dnn.ResNet18()
-checkpoint = Path(__file__).parent.absolute()  /  PosixPath("../model_params/resnet18_cifar10.pth.tar")
+checkpoint = Path(__file__).parent / "../model_params/resnet18_cifar10.pth.tar"
 model.load_state_dict(torch.load(checkpoint))
 ```
 
@@ -108,3 +108,4 @@ This choice of operators is largely constrained by backend (tensor_runtime) supp
 1. Expand the list of operators supported in the frontend.
    - Most ideally, create a high-level description of operators that can tie
      HPVM-C intrinsics and the frontend list of operators together.
+