small

tests/core/callbacks/test_checkpoint_callback_torch.py

@@ -16,7 +16,7 @@ from fastNLP.envs import FASTNLP_LAUNCH_TIME, FASTNLP_DISTRIBUTED_CHECK
 from tests.helpers.utils import magic_argv_env_context
 from fastNLP.core import rank_zero_rm
 from tests.helpers.models.torch_model import TorchNormalModel_Classification_1
-from tests.helpers.datasets.torch_data import TorchArgMaxDatset
+from tests.helpers.datasets.torch_data import TorchArgMaxDataset
 from torchmetrics import Accuracy
 from fastNLP.core.log import logger
 
@@ -53,7 +53,7 @@ def model_and_optimizers(request):
         feature_dimension=ArgMaxDatasetConfig.feature_dimension
     )
     trainer_params.optimizers = SGD(trainer_params.model.parameters(), lr=0.001)
-    dataset = TorchArgMaxDatset(
+    dataset = TorchArgMaxDataset(
         feature_dimension=ArgMaxDatasetConfig.feature_dimension,
         data_num=ArgMaxDatasetConfig.data_num,
         seed=ArgMaxDatasetConfig.seed

tests/core/callbacks/test_load_best_model_callback_torch.py

@@ -19,7 +19,7 @@ from fastNLP.core import Evaluator
 from fastNLP.core.utils.utils import safe_rm
 from fastNLP.core.drivers.torch_driver import TorchSingleDriver
 from tests.helpers.models.torch_model import TorchNormalModel_Classification_1
-from tests.helpers.datasets.torch_data import TorchArgMaxDatset
+from tests.helpers.datasets.torch_data import TorchArgMaxDataset
 from tests.helpers.utils import magic_argv_env_context
 
 
@@ -55,7 +55,7 @@ def model_and_optimizers(request):
         feature_dimension=ArgMaxDatasetConfig.feature_dimension
     )
     trainer_params.optimizers = optim.SGD(trainer_params.model.parameters(), lr=0.01)
-    dataset = TorchArgMaxDatset(
+    dataset = TorchArgMaxDataset(
         feature_dimension=ArgMaxDatasetConfig.feature_dimension,
         data_num=ArgMaxDatasetConfig.data_num,
         seed=ArgMaxDatasetConfig.seed

tests/core/callbacks/test_more_evaluate_callback.py

@@ -24,7 +24,7 @@ from fastNLP.envs import FASTNLP_LAUNCH_TIME, FASTNLP_DISTRIBUTED_CHECK
 from tests.helpers.utils import magic_argv_env_context
 from fastNLP.core import rank_zero_rm
 from tests.helpers.models.torch_model import TorchNormalModel_Classification_1
-from tests.helpers.datasets.torch_data import TorchArgMaxDatset
+from tests.helpers.datasets.torch_data import TorchArgMaxDataset
 from torchmetrics import Accuracy
 from fastNLP.core.metrics import Metric
 from fastNLP.core.log import logger
@@ -64,7 +64,7 @@ def model_and_optimizers(request):
         feature_dimension=ArgMaxDatasetConfig.feature_dimension
     )
     trainer_params.optimizers = SGD(trainer_params.model.parameters(), lr=0.001)
-    dataset = TorchArgMaxDatset(
+    dataset = TorchArgMaxDataset(
         feature_dimension=ArgMaxDatasetConfig.feature_dimension,
         data_num=ArgMaxDatasetConfig.data_num,
         seed=ArgMaxDatasetConfig.seed

tests/core/controllers/test_trainer_w_evaluator_torch.py

@@ -11,7 +11,7 @@ from torchmetrics import Accuracy
 
 from fastNLP.core.controllers.trainer import Trainer
 from tests.helpers.models.torch_model import TorchNormalModel_Classification_1
-from tests.helpers.datasets.torch_data import TorchNormalDataset_Classification, TorchArgMaxDatset
+from tests.helpers.datasets.torch_data import TorchNormalDataset_Classification, TorchArgMaxDataset
 from tests.helpers.callbacks.helper_callbacks import RecordLossCallback, RecordMetricCallback
 from tests.helpers.utils import magic_argv_env_context
 
@@ -80,7 +80,7 @@ def model_and_optimizers(request):
             feature_dimension=ArgMaxDatasetConfig.feature_dimension
         )
         trainer_params.optimizers = SGD(trainer_params.model.parameters(), lr=0.001)
-        dataset = TorchArgMaxDatset(
+        dataset = TorchArgMaxDataset(
             feature_dimension=ArgMaxDatasetConfig.feature_dimension,
             data_num=ArgMaxDatasetConfig.data_num,
             seed=ArgMaxDatasetConfig.seed

tests/core/drivers/torch_driver/test_single_device.py

@@ -6,7 +6,7 @@ from pathlib import Path
 from fastNLP.core.drivers.torch_driver.single_device import TorchSingleDriver
 from fastNLP.core.samplers import RandomBatchSampler, RandomSampler
 from tests.helpers.models.torch_model import TorchNormalModel_Classification_1
-from tests.helpers.datasets.torch_data import TorchNormalDataset, TorchArgMaxDatset
+from tests.helpers.datasets.torch_data import TorchNormalDataset, TorchArgMaxDataset
 from tests.helpers.datasets.paddle_data import PaddleNormalDataset
 from tests.helpers.models.paddle_model import PaddleNormalModel_Classification_1
 from fastNLP.core import rank_zero_rm
@@ -17,7 +17,7 @@ import paddle
 
 def dataloader_with_randombatchsampler(dataset, batch_size, shuffle, drop_last):
     """
-    建立一个 batch_samper 为 RandomBatchSampler 的 dataloader
+    建立一个 batch_sampler 为 RandomBatchSampler 的 dataloader
     """
     if shuffle:
         sampler = torch.utils.data.RandomSampler(dataset)
@@ -38,7 +38,7 @@ def dataloader_with_randombatchsampler(dataset, batch_size, shuffle, drop_last):
 
 def dataloader_with_randomsampler(dataset, batch_size, shuffle, drop_last, seed=0):
     """
-    建立一个 samper 为 RandomSampler 的 dataloader
+    建立一个 sampler 为 RandomSampler 的 dataloader
     """
     dataloader = DataLoader(
         dataset,
@@ -531,7 +531,7 @@ def generate_random_driver(features, labels, fp16=False, device="cpu"):
 
 @pytest.fixture
 def prepare_test_save_load():
-    dataset = TorchArgMaxDatset(10, 40)
+    dataset = TorchArgMaxDataset(10, 40)
     dataloader = DataLoader(dataset, batch_size=4)
     driver1, driver2 = generate_random_driver(10, 10), generate_random_driver(10, 10)
     return driver1, driver2, dataloader
@@ -566,7 +566,7 @@ def test_save_and_load_with_randombatchsampler(only_state_dict, fp16):
 
     try:
         path = "model.ckp"
-        dataset = TorchArgMaxDatset(10, 40)
+        dataset = TorchArgMaxDataset(10, 40)
         dataloader = dataloader_with_randombatchsampler(dataset, 4, True, False)
         driver1, driver2 = generate_random_driver(10, 10, fp16, "cuda"), generate_random_driver(10, 10, False, "cuda")
 
@@ -636,7 +636,7 @@ def test_save_and_load_with_randomsampler(only_state_dict, fp16):
         path = "model.ckp"
 
         driver1, driver2 = generate_random_driver(10, 10, fp16, "cuda"), generate_random_driver(10, 10, False, "cuda")
-        dataset = TorchArgMaxDatset(10, 40)
+        dataset = TorchArgMaxDataset(10, 40)
         dataloader = dataloader_with_randomsampler(dataset, 4, True, False)
         num_consumed_batches = 2
 

tests/helpers/datasets/torch_data.py

@@ -38,7 +38,7 @@ class TorchNormalDataset_Classification(Dataset):
         return {"x": self.x[item], "y": self.y[item]}
 
 
-class TorchArgMaxDatset(Dataset):
+class TorchArgMaxDataset(Dataset):
     def __init__(self, feature_dimension=10, data_num=1000, seed=0):
         self.num_labels = feature_dimension
         self.feature_dimension = feature_dimension