Remove unused imports from Python tests. (#5405)

onnxruntime/test/python/onnxruntime_test_ort_trainer_with_mixed_precision.py

@@ -2,10 +2,8 @@
 # Licensed under the MIT License.
 
 import unittest
-import pytest
 from numpy.testing import assert_allclose, assert_array_equal
 
-import torch
 from onnxruntime_test_ort_trainer import runBertTrainingTest
 
 class TestOrtTrainer(unittest.TestCase):

onnxruntime/test/python/onnxruntime_test_python_backend.py

@@ -3,15 +3,10 @@
 
 # -*- coding: UTF-8 -*-
 import unittest
-import os
-import sys
 import numpy as np
 from numpy.testing import assert_allclose
 import onnxruntime as onnxrt
-from onnxruntime import datasets
 import onnxruntime.backend as backend
-from onnxruntime.backend.backend import OnnxRuntimeBackend as ort_backend
-from onnx import load
 from helper import get_name
 
 class TestBackend(unittest.TestCase):

onnxruntime/test/python/onnxruntime_test_python_backend_mlops.py

@@ -3,11 +3,7 @@
 
 # -*- coding: UTF-8 -*-
 import unittest
-import os
-import sys
 import numpy as np
-from numpy.testing import assert_allclose
-import onnxruntime as onnxrt
 from onnxruntime import datasets
 import onnxruntime.backend as backend
 from onnxruntime.backend.backend import OnnxRuntimeBackend as ort_backend

onnxruntime/test/python/onnxruntime_test_python_iobinding.py

@@ -1,9 +1,7 @@
 import numpy as np
-import onnx
 import onnxruntime
 import unittest
 
-from onnx import numpy_helper
 from helper import get_name
 
 class TestIOBinding(unittest.TestCase):

onnxruntime/test/python/onnxruntime_test_python_keras.py

@@ -4,8 +4,6 @@
 # -*- coding: UTF-8 -*-
 # Taken from https://github.com/onnx/onnxmltools/blob/master/tests/end2end/test_custom_op.py.
 import unittest
-import os
-import sys
 import numpy as np
 import onnxmltools
 import onnxruntime as onnxrt

onnxruntime/test/python/onnxruntime_test_python_mlops.py

@@ -3,10 +3,8 @@
 
 # -*- coding: UTF-8 -*-
 import unittest
-import os
 import numpy as np
 import onnxruntime as onnxrt
-import threading
 from helper import get_name
 
 

onnxruntime/test/python/onnxruntime_test_python_nuphar.py

@@ -8,7 +8,6 @@ from onnx import helper, numpy_helper
 import onnxruntime as onnxrt
 import os
 from onnxruntime.nuphar.rnn_benchmark import perf_test, generate_model
-from pathlib import Path
 import shutil
 import sys
 import subprocess
@@ -532,7 +531,6 @@ class TestNuphar(unittest.TestCase):
         assert np.allclose(first_lstm_data_output, scan_batch_data_output)
 
     def test_gemm_to_matmul(self):
-        model_cnt = 0
         gemm_model_name_prefix = "gemm_model"
         matmul_model_name_prefix = "matmul_model"
         common_config = {
@@ -582,7 +580,6 @@ class TestNuphar(unittest.TestCase):
             assert np.allclose(expected_y, actual_y)
 
     def test_gemm_to_matmul_with_scan(self):
-        model_cnt = 0
         gemm_model_name_prefix = "gemm_scan_model"
         matmul_model_name_prefix = "matmul_scan_model"
 

onnxruntime/test/python/onnxruntime_test_python_symbolic_shape_infer.py

@@ -6,7 +6,6 @@ import onnx
 import os
 from onnxruntime.tools.symbolic_shape_infer import SymbolicShapeInference
 from pathlib import Path
-import sys
 import unittest
 
 class TestSymbolicShapeInference(unittest.TestCase):

onnxruntime/test/python/onnxruntime_test_training_unit_tests.py

@@ -2,21 +2,15 @@
 # Licensed under the MIT License.
 
 import unittest
-import pytest
-import sys
-import copy
-from numpy.testing import assert_allclose, assert_array_equal
+from numpy.testing import assert_allclose
 
-import onnx
 import torch
 import torch.nn as nn
-import torch.nn.functional as F
 
 from onnxruntime_test_ort_trainer import map_optimizer_attributes, ort_trainer_learning_rate_description
-from helper import get_name
 import onnxruntime
 from onnxruntime_test_training_unittest_utils import process_dropout
-from onnxruntime.capi.ort_trainer import ORTTrainer, IODescription, ModelDescription, LossScaler, generate_sample
+from onnxruntime.capi.ort_trainer import ORTTrainer, IODescription, ModelDescription
 
 torch.manual_seed(1)
 onnxruntime.set_seed(1)
@@ -66,7 +60,7 @@ class TestTrainingDropout(unittest.TestCase):
 
         eval_output = model.eval_step(input)
         assert_allclose(expected_eval_output, eval_output.item(), rtol=rtol, err_msg="dropout eval loss mismatch")
- 
+
         # Do another train step to make sure it's using original ratios
         train_output_2 = model.train_step(*input_args)
         assert_allclose(expected_training_output, train_output_2.item(), rtol=rtol, err_msg="dropout training loss 2 mismatch")

onnxruntime/test/python/onnxruntime_test_training_unittest_utils.py

@@ -1,4 +1,3 @@
-import sys
 import numpy as np
 from onnx import numpy_helper