tqdm progress bar improvements (openvinotoolkit#2114)

### Changes

1. Fixed an issue with wrong `tqdm` bar length in the case when
calibration dataset length is less than `subset_size`.
Reproducer:
nikita-savelyevv@f0951c1
**Before:**
`Statistics collection: 34%|██████ | 101/300 [00:03<00:06, 28.66it/s]`
**After:**
When dataset has `__len__`:
`Statistics collection: 100%|██████████████████| 101/101 [00:03<00:00,
28.20it/s]`
When dataset doesn't have `__len__`:
`Statistics collection: 34%|██████ | 101/300 [00:03<00:06, 29.45it/s]`

2. Improved progress bar GUI when ran from notebooks.
**Before:**
<img width="704" alt="Screenshot 2023-09-06 091857"
src="https://github.com/openvinotoolkit/nncf/assets/23343961/9851cb8d-00f1-4297-af50-14697e86e961">

or (in some browsers progress bar takes up multiple lines):


![image](https://github.com/openvinotoolkit/nncf/assets/23343961/99fa9629-2869-4d8f-872e-97ef59bc092e)
**After:**
<img width="706" alt="Screenshot 2023-09-06 105453"
src="https://github.com/openvinotoolkit/nncf/assets/23343961/58e75cc9-2507-4c5b-8c3c-cac44eefcb79">

In console the progress bar is the same.

### Reason for changes

User experience improvement.

### Related tickets

112627

### Tests

<!--- How was the correctness of changes tested and whether new tests
were added -->

nncf/common/tensor_statistics/aggregator.py

@@ -13,7 +13,7 @@
 from itertools import islice
 from typing import Any, Dict, TypeVar
 
-from tqdm import tqdm
+from tqdm.auto import tqdm
 
 from nncf.common import factory
 from nncf.common.graph.graph import NNCFGraph
@@ -54,9 +54,15 @@ def collect_statistics(self, model: TModel, graph: NNCFGraph) -> None:
         model_with_outputs = model_transformer.transform(transformation_layout)
         engine = factory.EngineFactory.create(model_with_outputs)
 
+        dataset_length = self.dataset.get_length()
+        total = (
+            min(dataset_length or self.stat_subset_size, self.stat_subset_size)
+            if self.stat_subset_size is not None
+            else None
+        )
         for input_data in tqdm(
             islice(self.dataset.get_inference_data(), self.stat_subset_size),
-            total=self.stat_subset_size,
+            total=total,
             desc="Statistics collection",
         ):
             outputs = engine.infer(input_data)

nncf/data/dataset.py

@@ -72,6 +72,15 @@ def get_inference_data(self, indices: Optional[List[int]] = None) -> Iterable[Mo
         """
         return DataProvider(self._data_source, self._transform_func, indices)
 
+    def get_length(self) -> Optional[int]:
+        """
+        Tries to fetch length of the underlying dataset.
+        :return: The length of the data_source if __len__() is implemented for it, and None otherwise.
+        """
+        if hasattr(self._data_source, "__len__"):
+            return self._data_source.__len__()
+        return None
+
 
 class DataProvider(Generic[DataItem, ModelInput]):
     def __init__(

nncf/quantization/algorithms/bias_correction/algorithm.py

@@ -13,7 +13,7 @@
 from typing import Any, Dict, List, Optional, Tuple, TypeVar
 
 import numpy as np
-from tqdm import tqdm
+from tqdm.auto import tqdm
 
 from nncf import Dataset
 from nncf import nncf_logger

nncf/quantization/algorithms/channel_alignment/algorithm.py

@@ -12,7 +12,7 @@
 from typing import Any, Dict, List, Optional, Tuple, TypeVar
 
 import numpy as np
-from tqdm import tqdm
+from tqdm.auto import tqdm
 
 from nncf import Dataset
 from nncf.common.factory import CommandCreatorFactory

nncf/quantization/algorithms/fast_bias_correction/algorithm.py

@@ -11,7 +11,7 @@
 
 from typing import Any, Dict, List, Optional, Tuple, TypeVar, Union
 
-from tqdm import tqdm
+from tqdm.auto import tqdm
 
 from nncf import Dataset
 from nncf.common.factory import EngineFactory

nncf/quantization/algorithms/smooth_quant/algorithm.py

@@ -24,7 +24,7 @@
 from copy import deepcopy
 from typing import Dict, List, Optional, Tuple, TypeVar
 
-from tqdm import tqdm
+from tqdm.auto import tqdm
 
 from nncf import Dataset
 from nncf.common.factory import ModelTransformerFactory

tests/common/test_dataset.py

@@ -0,0 +1,63 @@
+# Copyright (c) 2023 Intel Corporation
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#      http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from nncf import Dataset
+
+
+def test_dataset():
+    raw_data = list(range(50))
+    dataset = Dataset(raw_data)
+
+    data_provider = dataset.get_data()
+    retrieved_data_items = list(data_provider)
+    assert all(raw_data[i] == retrieved_data_items[i] for i in range(len(raw_data)))
+
+
+def test_dataset_with_transform_func():
+    raw_data = list(range(50))
+    dataset = Dataset(raw_data, transform_func=lambda it: 2 * it)
+
+    data_provider = dataset.get_inference_data()
+    retrieved_data_items = list(data_provider)
+    assert all(2 * raw_data[i] == retrieved_data_items[i] for i in range(len(raw_data)))
+
+
+def test_dataset_with_indices():
+    raw_data = list(range(50))
+    dataset = Dataset(raw_data)
+
+    data_provider = dataset.get_data(indices=list(range(0, 50, 2)))
+    retrieved_data_items = list(data_provider)
+    assert all(raw_data[2 * i] == retrieved_data_items[i] for i in range(len(raw_data) // 2))
+
+
+def test_dataset_with_transform_func_with_indices():
+    raw_data = list(range(50))
+    dataset = Dataset(raw_data, transform_func=lambda it: 2 * it)
+
+    data_provider = dataset.get_inference_data(indices=list(range(0, 50, 2)))
+    retrieved_data_items = list(data_provider)
+    assert all(2 * raw_data[2 * i] == retrieved_data_items[i] for i in range(len(raw_data) // 2))
+
+
+def test_dataset_without_length():
+    raw_data = list(range(50))
+    dataset_with_length = Dataset(raw_data)
+    dataset_without_length = Dataset(iter(raw_data))
+    assert dataset_with_length.get_length() == 50
+    assert dataset_without_length.get_length() is None
+
+    data_provider = dataset_with_length.get_data()
+    retrieved_data_items = list(data_provider)
+    assert all(raw_data[i] == retrieved_data_items[i] for i in range(len(raw_data)))
+
+    data_provider = dataset_without_length.get_data()
+    retrieved_data_items = list(data_provider)
+    assert all(raw_data[i] == retrieved_data_items[i] for i in range(len(raw_data)))