Renaming Dataloader into Dataset

tests/onnx/quantization/test_classification_models_graph.py

@@ -63,7 +63,7 @@
 REFERENCE_GRAPHS_TEST_ROOT = 'data/reference_graphs/quantization'
 
 
-class TestDataloader(Dataset):
+class TestDataset(Dataset):
     def __init__(self, input_shape):
         super().__init__()
         self.input_shape = input_shape
@@ -88,10 +88,10 @@ def test_min_max_quantization_graph(tmp_path, model, path_ref_graph, input_shape
 
     original_model = onnx.load(onnx_model_path)
 
-    dataloader = TestDataloader(input_shape)
+    dataset = TestDataset(input_shape)
     builder = CompressionBuilder()
     builder.add_algorithm(ONNXMinMaxQuantization(MinMaxQuantizationParameters(number_samples=1)))
-    quantized_model = builder.apply(original_model, dataloader)
+    quantized_model = builder.apply(original_model, dataset)
 
     nncf_graph = GraphConverter.create_nncf_graph(quantized_model)
     nx_graph = nncf_graph.get_graph_for_structure_analysis(extended=True)
@@ -115,10 +115,10 @@ def test_post_training_quantization_graph(tmp_path, model, path_ref_graph, input
 
     original_model = onnx.load(onnx_model_path)
 
-    dataloader = TestDataloader(input_shape)
+    dataset = TestDataset(input_shape)
     builder = CompressionBuilder()
     builder.add_algorithm(PostTrainingQuantization(PostTrainingQuantizationParameters(number_samples=1)))
-    quantized_model = builder.apply(original_model, dataloader)
+    quantized_model = builder.apply(original_model, dataset)
 
     nncf_graph = GraphConverter.create_nncf_graph(quantized_model)
     nx_graph = nncf_graph.get_graph_for_structure_analysis(extended=True)

tests/onnx/quantization/test_detection_models_graph.py

@@ -21,7 +21,7 @@
 from tests.common.helpers import TEST_ROOT
 from tests.onnx.test_nncf_graph_builder import check_nx_graph
 
-from tests.onnx.quantization.test_classification_models_graph import TestDataloader
+from tests.onnx.quantization.test_classification_models_graph import TestDataset
 
 from nncf.experimental.post_training.compression_builder import CompressionBuilder
 from nncf.experimental.onnx.algorithms.quantization.min_max_quantization import ONNXMinMaxQuantization
@@ -66,10 +66,10 @@ def test_min_max_quantization_graph(tmp_path, model_name, model_url, path_ref_gr
 
     original_model = onnx.load(onnx_model_path)
 
-    dataloader = TestDataloader(input_shape)
+    dataset = TestDataset(input_shape)
     builder = CompressionBuilder()
     builder.add_algorithm(ONNXMinMaxQuantization(MinMaxQuantizationParameters(number_samples=1)))
-    quantized_model = builder.apply(original_model, dataloader)
+    quantized_model = builder.apply(original_model, dataset)
 
     nncf_graph = GraphConverter.create_nncf_graph(quantized_model)
     nx_graph = nncf_graph.get_graph_for_structure_analysis(extended=True)

tests/onnx/test_samplers.py

@@ -29,7 +29,7 @@
                    (100 * np.ones(INPUT_SHAPE), 2)]
 
 
-class TestDataloader(Dataset):
+class TestDataset(Dataset):
     def __init__(self, samples: List[Tuple[np.ndarray, int]]):
         super().__init__(shuffle=False)
         self.samples = samples
@@ -43,9 +43,9 @@ def __len__(self):
 
 @pytest.mark.parametrize("batch_size", (1, 2, 3))
 def test_batch_sampler(batch_size):
-    dataloader = TestDataloader(DATASET_SAMPLES)
-    dataloader.batch_size = batch_size
-    sampler = ONNXBatchSampler(dataloader)
+    dataset = TestDataset(DATASET_SAMPLES)
+    dataset.batch_size = batch_size
+    sampler = ONNXBatchSampler(dataset)
     for i, sample in enumerate(sampler):
         ref_sample = []
         ref_target = []
@@ -61,9 +61,9 @@ def test_batch_sampler(batch_size):
 @pytest.mark.parametrize("batch_size", (1, 2, 3))
 def test_random_batch_sampler(batch_size):
     np.random.seed(0)
-    dataloader = TestDataloader(DATASET_SAMPLES)
-    dataloader.batch_size = batch_size
-    sampler = ONNXRandomBatchSampler(dataloader)
+    dataset = TestDataset(DATASET_SAMPLES)
+    dataset.batch_size = batch_size
+    sampler = ONNXRandomBatchSampler(dataset)
     random_permuated_indices = [0, 2, 1]
     for i, sample in enumerate(sampler):
         ref_sample = []

tests/onnx/test_sanity_sample.py

@@ -47,7 +47,7 @@
 ]
 
 
-class TestDataloader(Dataset):
+class TestDataset(Dataset):
     def __init__(self, samples: List[Tuple[np.ndarray, int]]):
         super().__init__(shuffle=False)
         self.samples = samples
@@ -59,14 +59,14 @@ def __len__(self):
         return 1
 
 
-def mock_dataloader_creator(dataset_path, input_shape, batch_size, shuffle):
-    return TestDataloader([(np.zeros(input_shape[1:]), 0), ])
+def mock_dataset_creator(dataset_path, input_shape, batch_size, shuffle):
+    return TestDataset([(np.zeros(input_shape[1:]), 0), ])
 
 
 @pytest.mark.parametrize(("model, input_shape"),
                          zip(MODELS, INPUT_SHAPES))
 @patch('examples.experimental.onnx.onnx_ptq_classification.create_imagenet_torch_dataset',
-       new=mock_dataloader_creator)
+       new=mock_dataset_creator)
 def test_sanity_quantize_sample(tmp_path, model, input_shape):
     model_name = str(model.__class__)
     onnx_model_dir = str(TEST_ROOT.joinpath('onnx', 'data', 'models'))

tests/onnx/test_statistics_aggregator.py

@@ -22,7 +22,7 @@
 from nncf.experimental.post_training.algorithms.quantization.min_max_quantization import RangeType
 
 from tests.onnx.models import OneConvolutionalModel
-from tests.onnx.test_samplers import TestDataloader
+from tests.onnx.test_samplers import TestDataset
 
 INPUT_SHAPE = [3, 10, 10]
 
@@ -59,7 +59,7 @@ def __init__(self, number_samples, activation_float_range, weight_float_range):
 def test_statistics_aggregator(range_type, test_parameters):
     model = OneConvolutionalModel().onnx_model
 
-    dataloader = TestDataloader(DATASET_SAMPLES)
+    dataset = TestDataset(DATASET_SAMPLES)
     compression_builder = CompressionBuilder()
 
     quantization = ONNXMinMaxQuantization(MinMaxQuantizationParameters(
@@ -68,7 +68,7 @@ def test_statistics_aggregator(range_type, test_parameters):
     ))
 
     compression_builder.add_algorithm(quantization)
-    quantized_model = compression_builder.apply(model, dataloader)
+    quantized_model = compression_builder.apply(model, dataset)
 
     onnx_graph = ONNXGraph(quantized_model)
     num_q = 0