删除不必要的测试文件

tests/core/drivers/paddle_driver/test.py

@@ -1,25 +0,0 @@
-import sys
-import os
-import warnings
-warnings.filterwarnings("ignore")
-os.environ["FASTNLP_BACKEND"] = "torch"
-sys.path.append("../../../../")
-
-import paddle
-from fastNLP.core.samplers import RandomSampler
-from fastNLP.core.drivers.paddle_driver.utils import replace_sampler, replace_batch_sampler
-from tests.helpers.datasets.paddle_data import PaddleNormalDataset
-
-dataset = PaddleNormalDataset(20)
-batch_sampler = paddle.io.BatchSampler(dataset=dataset, batch_size=2)
-batch_sampler.sampler = RandomSampler(dataset, True)
-dataloader = paddle.io.DataLoader(
-    dataset,
-    batch_sampler=batch_sampler
-)
-
-forward_steps = 9
-iter_dataloader = iter(dataloader)
-for _ in range(forward_steps):
-    print(next(iter_dataloader))
-print(dataloader.batch_sampler.sampler.during_iter)

tests/core/drivers/paddle_driver/test2.py

@@ -1,21 +0,0 @@
-import torch
-# from torch.utils.data import DataLoader, Dataset
-import paddle
-from paddle.io import Dataset, DataLoader
-paddle.device.set_device("cpu")
-class NormalDataset(Dataset):
-    def __init__(self, num_of_data=1000):
-        self.num_of_data = num_of_data
-        self._data = list(range(num_of_data))
-
-    def __len__(self):
-        return self.num_of_data
-
-    def __getitem__(self, item):
-        return self._data[item]
-dataset = NormalDataset(20)
-dataloader = DataLoader(dataset, batch_size=2, use_buffer_reader=False)
-for i, b in enumerate(dataloader):
-    print(b)
-    if i >= 2:
-        break