增加pytest.mark.torch or paddle标记

tests/core/collators/padders/test_get_padder.py

@@ -15,6 +15,8 @@ def test_get_element_shape_dtype():
 
 
 @pytest.mark.parametrize('backend', ['raw', None, 'numpy', 'torch', 'jittor', 'paddle'])
+@pytest.mark.torch
+@pytest.mark.paddle
 def test_get_padder_run(backend):
     if not _NEED_IMPORT_TORCH and backend == 'torch':
         pytest.skip("No torch")
@@ -100,6 +102,7 @@ def test_numpy_padder():
         padder = get_padder(batch_field, pad_val=0, backend=backend, dtype=int, field_name='test')
 
 
+@pytest.mark.torch
 def test_torch_padder():
     if not _NEED_IMPORT_TORCH:
         pytest.skip("No torch.")

tests/core/collators/padders/test_numpy_padder.py

@@ -14,6 +14,7 @@ class TestNumpyNumberPadder:
         assert (padder(a) == np.array(a)).sum() == 3
 
 
+@pytest.mark.torch
 class TestNumpySequencePadder:
     def test_run(self):
         padder = NumpySequencePadder(ele_dtype=int, dtype=int, pad_val=-1)

tests/core/collators/padders/test_torch_padder.py

@@ -9,6 +9,7 @@ if _NEED_IMPORT_TORCH:
     import torch
 
 
+@pytest.mark.torch
 class TestTorchNumberPadder:
     def test_run(self):
         padder = TorchNumberPadder(ele_dtype=int, dtype=int, pad_val=-1)
@@ -18,6 +19,7 @@ class TestTorchNumberPadder:
         assert (t_a == torch.LongTensor(a)).sum() == 3
 
 
+@pytest.mark.torch
 class TestTorchSequencePadder:
     def test_run(self):
         padder = TorchSequencePadder(ele_dtype=int, dtype=int, pad_val=-1)
@@ -40,7 +42,7 @@ class TestTorchSequencePadder:
         padder = TorchSequencePadder(ele_dtype=np.zeros(2).dtype, dtype=None, pad_val=-1)
 
 
-
+@pytest.mark.torch
 class TestTorchTensorPadder:
     def test_run(self):
         padder = TorchTensorPadder(ele_dtype=torch.zeros(3).dtype, dtype=int, pad_val=-1)

tests/core/collators/padders/test_utils.py

@@ -45,6 +45,7 @@ def test_get_padded_nest_list():
     assert np.shape(a) == (2, 3, 2)
 
 
+@pytest.mark.torch
 def test_is_number_or_numpy_number():
     assert is_number_or_numpy_number(type(3)) is True
     assert is_number_or_numpy_number(type(3.1)) is True
@@ -60,6 +61,7 @@ def test_is_number_or_numpy_number():
         assert is_number_or_numpy_number(dtype) is False
 
 
+@pytest.mark.torch
 def test_is_number():
     assert is_number(type(3)) is True
     assert is_number(type(3.1)) is True
@@ -75,6 +77,7 @@ def test_is_number():
         assert is_number(dtype) is False
 
 
+@pytest.mark.torch
 def test_is_numpy_number():
     assert is_numpy_number_dtype(type(3)) is False
     assert is_numpy_number_dtype(type(3.1)) is False

tests/core/collators/test_new_collator.py

@@ -42,6 +42,8 @@ def findListDiff(d1, d2):
 
 
 class TestCollator:
+
+    @pytest.mark.torch
     def test_run(self):
         dict_batch = [{
             'str': '1',

tests/core/dataloaders/paddle_dataloader/test_fdl.py

@@ -17,6 +17,7 @@ class RandomDataset(Dataset):
         return 10
 
 
+@pytest.mark.paddle
 class TestPaddle:
 
     def test_init(self):

tests/core/dataloaders/torch_dataloader/test_fdl.py

@@ -5,6 +5,7 @@ from fastNLP.core.dataset import DataSet
 from fastNLP.io.data_bundle import DataBundle
 
 
+@pytest.mark.torch
 class TestFdl:
 
     def test_init_v1(self):

tests/core/metrics/test_accuracy_torch.py

@@ -69,6 +69,7 @@ def pre_process():
     pool.join()
 
 
+@pytest.mark.torch
 @pytest.mark.parametrize('dataset', [
     DataSet({'pred': np.random.randint(low=0, high=1, size=(36, 32)),
              'target': np.random.randint(low=0, high=1, size=(36, 32))}),

tests/core/metrics/test_accutacy_paddle.py

@@ -8,11 +8,13 @@ import paddle.distributed.fleet as fleet
 from fastNLP.core.metrics import Accuracy
 from fastNLP.core.drivers.paddle_driver.fleet_launcher import FleetLauncher
 
+
 ############################################################################
 #
 # 测试 单机单卡情况下的Accuracy
 #
 ############################################################################
+@pytest.mark.paddle
 def test_accuracy_single():
     pred = paddle.to_tensor([[1.19812393, -0.82041764, -0.53517765, -0.73061031, -1.45006669,
                               0.46514302],
@@ -56,4 +58,3 @@ def test_accuracy_ddp():
         pass
     elif fleet.is_worker():
         print(os.getenv("PADDLE_TRAINER_ID"))
-

tests/core/metrics/test_classify_f1_pre_rec_metric_torch.py

@@ -29,6 +29,7 @@ def _test(local_rank: int, world_size: int, device: torch.device,
         np.allclose(my_result[keys], metric_result[keys], atol=0.000001)
 
 
+@pytest.mark.torch
 class TestClassfiyFPreRecMetric:
     def test_case_1(self):
         pred = torch.tensor([[-0.4375, -0.1779, -1.0985, -1.1592, 0.4910],

tests/core/metrics/test_span_f1_rec_acc_torch.py

@@ -66,6 +66,7 @@ def _test(local_rank: int,
     assert my_result == sklearn_metric
 
 
+@pytest.mark.torch
 class TestSpanFPreRecMetric:
 
     def test_case1(self):