Don't default to verbose=False on test datasets

deeplake/enterprise/test_pytorch.py

@@ -133,9 +133,7 @@ def test_pytorch_transform(local_auth_ds):
         ds.create_tensor("image", max_chunk_size=PYTORCH_TESTS_MAX_CHUNK_SIZE)
         ds.image.extend(([i * np.ones((i + 1, i + 1)) for i in range(16)]))
         ds.checkout("alt", create=True)
-        ds.create_tensor(
-            "image2", max_chunk_size=PYTORCH_TESTS_MAX_CHUNK_SIZE
-        )
+        ds.create_tensor("image2", max_chunk_size=PYTORCH_TESTS_MAX_CHUNK_SIZE)
         ds.image2.extend(np.array([i * np.ones((12, 12)) for i in range(16)]))
 
     dl = (
@@ -174,17 +172,15 @@ def test_pytorch_transform_dict(local_auth_ds):
     with ds:
         ds.create_tensor("image", max_chunk_size=PYTORCH_TESTS_MAX_CHUNK_SIZE)
         ds.image.extend(([i * np.ones((i + 1, i + 1)) for i in range(16)]))
-        ds.create_tensor(
-            "image2", max_chunk_size=PYTORCH_TESTS_MAX_CHUNK_SIZE
-        )
+        ds.create_tensor("image2", max_chunk_size=PYTORCH_TESTS_MAX_CHUNK_SIZE)
         ds.image2.extend(np.array([i * np.ones((12, 12)) for i in range(16)]))
-        ds.create_tensor(
-            "image3", max_chunk_size=PYTORCH_TESTS_MAX_CHUNK_SIZE
-        )
+        ds.create_tensor("image3", max_chunk_size=PYTORCH_TESTS_MAX_CHUNK_SIZE)
         ds.image3.extend(np.array([i * np.ones((12, 12)) for i in range(16)]))
 
     dl = (
-        ds.dataloader().transform({"image": double, "image2": None}).pytorch(num_workers=0)
+        ds.dataloader()
+        .transform({"image": double, "image2": None})
+        .pytorch(num_workers=0)
     )
 
     assert len(dl.dataset) == 16
@@ -251,7 +247,9 @@ def test_custom_tensor_order(local_auth_ds):
     with pytest.raises(TensorDoesNotExistError):
         dl = ds.dataloader().pytorch(tensors=["c", "d", "e"], num_workers=0)
 
-    dl = ds.dataloader().pytorch(tensors=["c", "d", "a"], return_index=False, num_workers=0)
+    dl = ds.dataloader().pytorch(
+        tensors=["c", "d", "a"], return_index=False, num_workers=0
+    )
 
     for i, batch in enumerate(dl):
         c1, d1, a1 = batch
@@ -284,12 +282,8 @@ def test_custom_tensor_order(local_auth_ds):
 def test_readonly_with_two_workers(local_auth_ds):
     ds = local_auth_ds
     with ds:
-        ds.create_tensor(
-            "images", max_chunk_size=PYTORCH_TESTS_MAX_CHUNK_SIZE
-        )
-        ds.create_tensor(
-            "labels", max_chunk_size=PYTORCH_TESTS_MAX_CHUNK_SIZE
-        )
+        ds.create_tensor("images", max_chunk_size=PYTORCH_TESTS_MAX_CHUNK_SIZE)
+        ds.create_tensor("labels", max_chunk_size=PYTORCH_TESTS_MAX_CHUNK_SIZE)
         ds.images.extend(np.ones((10, 12, 12)))
         ds.labels.extend(np.ones(10))
 
@@ -327,17 +321,16 @@ def test_groups(local_auth_ds, compressed_image_paths):
     img1 = deeplake.read(compressed_image_paths["jpeg"][0])
     img2 = deeplake.read(compressed_image_paths["png"][0])
     with ds:
-        ds.create_tensor(
-            "images/jpegs/cats", htype="image", sample_compression="jpeg"
-        )
-        ds.create_tensor(
-            "images/pngs/flowers", htype="image", sample_compression="png"
-        )
+        ds.create_tensor("images/jpegs/cats", htype="image", sample_compression="jpeg")
+        ds.create_tensor("images/pngs/flowers", htype="image", sample_compression="png")
         for _ in range(10):
             ds.images.jpegs.cats.append(img1)
             ds.images.pngs.flowers.append(img2)
 
-    another_ds = deeplake.dataset(ds.path, token=ds.token,)
+    another_ds = deeplake.dataset(
+        ds.path,
+        token=ds.token,
+    )
     dl = another_ds.dataloader().pytorch(return_index=False, num_workers=0)
     for i, (cat, flower) in enumerate(dl):
         assert cat[0].shape == another_ds.images.jpegs.cats[i].numpy().shape
@@ -487,9 +480,7 @@ def test_pytorch_decode(local_auth_ds, compressed_image_paths, compression):
         ds.image.extend(
             np.array([i * np.ones((10, 10, 3), dtype=np.uint8) for i in range(5)])
         )
-        ds.image.extend(
-            [deeplake.read(compressed_image_paths["jpeg"][0])] * 5
-        )
+        ds.image.extend([deeplake.read(compressed_image_paths["jpeg"][0])] * 5)
 
     ptds = ds.dataloader().pytorch(decode_method={"image": "tobytes"}, num_workers=0)
 
@@ -551,11 +542,7 @@ def test_indexes(local_auth_ds, num_workers):
         for i in range(8):
             ds.xyz.append(i * np.ones((2, 2)))
 
-    ptds = (
-        ds.dataloader()
-        .batch(4)
-        .pytorch(num_workers=num_workers, return_index=True)
-    )
+    ptds = ds.dataloader().batch(4).pytorch(num_workers=num_workers, return_index=True)
     if shuffle:
         ptds = ptds.shuffle()
 
@@ -596,7 +583,9 @@ def test_indexes_transform(local_auth_ds, num_workers):
 
 @requires_torch
 @requires_libdeeplake
-@pytest.mark.parametrize("num_workers", [pytest.param(0), pytest.param(2, marks=pytest.mark.skip)])
+@pytest.mark.parametrize(
+    "num_workers", [pytest.param(0), pytest.param(2, marks=pytest.mark.skip)]
+)
 @pytest.mark.slow
 def test_indexes_transform_dict(local_auth_ds, num_workers):
     ds = local_auth_ds
@@ -861,7 +850,9 @@ def test_pytorch_data_decode(local_auth_ds, cat_path):
     ptds = (
         ds.dataloader()
         .transform(identity)
-        .pytorch(decode_method=decode_method, collate_fn=identity_collate, num_workers=0)
+        .pytorch(
+            decode_method=decode_method, collate_fn=identity_collate, num_workers=0
+        )
     )
     for i, batch in enumerate(ptds):
         sample = batch[0]

deeplake/integrations/tests/test_pytorch.py

@@ -147,6 +147,7 @@ def test_pytorch_small(local_ds):
 @requires_torch
 def test_pytorch_transform(local_ds):
     import torch
+
     ds = local_ds
 
     with ds:
@@ -397,8 +398,8 @@ def test_corrupt_dataset(local_ds, corrupt_image_paths, compressed_image_paths):
     for (batch,) in dl:
         num_batches += 1
         num_samples += len(batch)
-    #TODO: Why Changed? assert num_samples == 30
-    #TODO: Why Changed? assert num_batches == 15
+    # TODO: Why Changed? assert num_samples == 30
+    # TODO: Why Changed? assert num_batches == 15
 
 
 @requires_torch

deeplake/tests/dataset_fixtures.py

@@ -61,7 +61,7 @@
 
 @pytest.fixture
 def memory_ds(memory_path):
-    return deeplake.dataset(memory_path, verbose=False)
+    return deeplake.dataset(memory_path)
 
 
 @pytest.fixture
@@ -79,8 +79,6 @@ def local_ds_generator(local_path):
     def generate_local_ds(**kwargs):
         if kwargs.get("lock_enabled") is None:
             kwargs["lock_enabled"] = False
-        if kwargs.get("verbose") is None:
-            kwargs["verbose"] = False
 
         return deeplake.dataset(local_path, **kwargs)
 
@@ -92,8 +90,6 @@ def local_auth_ds_generator(local_path, hub_cloud_dev_token):
     def generate_local_auth_ds(**kwargs):
         if kwargs.get("lock_enabled") is None:
             kwargs["lock_enabled"] = False
-        if kwargs.get("verbose") is None:
-            kwargs["verbose"] = False
 
         return deeplake.dataset(local_path, token=hub_cloud_dev_token, **kwargs)
 
@@ -110,8 +106,6 @@ def s3_ds_generator(s3_path):
     def generate_s3_ds(**kwargs):
         if kwargs.get("lock_enabled") is None:
             kwargs["lock_enabled"] = False
-        if kwargs.get("verbose") is None:
-            kwargs["verbose"] = False
 
         return deeplake.dataset(s3_path, **kwargs)
 
@@ -128,8 +122,6 @@ def gdrive_ds_generator(gdrive_path, gdrive_creds):
     def generate_gdrive_ds(**kwargs):
         if kwargs.get("lock_enabled") is None:
             kwargs["lock_enabled"] = False
-        if kwargs.get("verbose") is None:
-            kwargs["verbose"] = False
 
         return deeplake.dataset(gdrive_path, creds=gdrive_creds, **kwargs)
 
@@ -146,8 +138,6 @@ def gcs_ds_generator(gcs_path, gcs_creds):
     def generate_gcs_ds(**kwargs):
         if kwargs.get("lock_enabled") is None:
             kwargs["lock_enabled"] = False
-        if kwargs.get("verbose") is None:
-            kwargs["verbose"] = False
 
         return deeplake.dataset(gcs_path, creds=gcs_creds, **kwargs)
 
@@ -164,8 +154,6 @@ def azure_ds_generator(azure_path):
     def generate_azure_ds(**kwargs):
         if kwargs.get("lock_enabled") is None:
             kwargs["lock_enabled"] = False
-        if kwargs.get("verbose") is None:
-            kwargs["verbose"] = False
 
         return deeplake.dataset(azure_path, **kwargs)
 
@@ -182,8 +170,6 @@ def hub_cloud_ds_generator(hub_cloud_path, hub_cloud_dev_token):
     def generate_hub_cloud_ds(**kwargs):
         if kwargs.get("lock_enabled") is None:
             kwargs["lock_enabled"] = False
-        if kwargs.get("verbose") is None:
-            kwargs["verbose"] = False
 
         return deeplake.dataset(hub_cloud_path, token=hub_cloud_dev_token, **kwargs)
 
@@ -195,8 +181,6 @@ def hub_cloud_gcs_ds_generator(gcs_path, gcs_creds, hub_cloud_dev_token):
     def generate_hub_cloud_gcs_ds(**kwargs):
         if kwargs.get("lock_enabled") is None:
             kwargs["lock_enabled"] = False
-        if kwargs.get("verbose") is None:
-            kwargs["verbose"] = False
 
         ds = deeplake.dataset(gcs_path, creds=gcs_creds, **kwargs)
         ds.connect(