chore: import upstream snapshot with attribution

tools/pytorch-quantization/tests/print_test.py

@@ -0,0 +1,60 @@
+#
+# SPDX-FileCopyrightText: Copyright (c) 1993-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# 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.
+#
+
+
+"""test for str and repr
+Make sure things can print and in a nice form. Put all the print tests together so that running this test file alone
+can inspect all the print messages in the project
+"""
+
+import torch
+from torch import nn
+
+from pytorch_quantization import calib
+from pytorch_quantization import tensor_quant
+from pytorch_quantization import nn as quant_nn
+from pytorch_quantization.nn.modules.tensor_quantizer import TensorQuantizer
+
+# pylint:disable=missing-docstring, no-self-use
+
+class TestPrint():
+
+    def test_print_descriptor(self):
+        test_desc = tensor_quant.QUANT_DESC_8BIT_CONV2D_WEIGHT_PER_CHANNEL
+        print(test_desc)
+
+    def test_print_tensor_quantizer(self):
+        test_quantizer = TensorQuantizer()
+        print(test_quantizer)
+
+    def test_print_module(self):
+        class _TestModule(nn.Module):
+            def __init__(self):
+                super(_TestModule, self).__init__()
+                self.conv = nn.Conv2d(33, 65, 3)
+                self.quant_conv = quant_nn.Conv2d(33, 65, 3)
+                self.linear = nn.Linear(33, 65)
+                self.quant_linear = quant_nn.Linear(33, 65)
+
+        test_module = _TestModule()
+        print(test_module)
+
+    def test_print_calibrator(self):
+        print(calib.MaxCalibrator(7, 1, False))
+        hist_calibrator = calib.HistogramCalibrator(8, None, True)
+        hist_calibrator.collect(torch.rand(10))
+        print(hist_calibrator)