chore: import upstream snapshot with attribution

2026-07-13 13:36:55 +08:00
commit c8a779b1bb
1887 changed files with 3245738 additions and 0 deletions
@@ -0,0 +1,92 @@
+#
+# 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.
+#
+"""Tests of calibrators"""
+import inspect
+import pytest
+import numpy as np
+
+import torch
+
+from pytorch_quantization import enable_onnx_export
+from pytorch_quantization import utils as quant_utils
+from pytorch_quantization import calib
+from pytorch_quantization import nn as quant_nn
+import tests.utils as test_utils
+from examples.torchvision.models.classification import *
+from tests.fixtures import verbose
+from tests.fixtures.models import QuantLeNet
+
+np.random.seed(12345)
+torch.manual_seed(12345)
+
+# pylint:disable=missing-docstring, no-self-use
+
+
+class TestExampleModels():
+
+    def test_resnet50(self):
+        model = resnet50(pretrained=True, quantize=True)
+        model.eval()
+
+        for name, module in model.named_modules():
+            if name.endswith('_quantizer'):
+                module.amax = 2.50
+
+        model.cuda()
+        dummy_input = torch.randn(1, 3, 224, 224, device='cuda')
+        with enable_onnx_export():
+            if "enable_onnx_checker" in inspect.signature(torch.onnx.export).parameters:
+                torch.onnx.export(model,
+                                  dummy_input,
+                                  "/tmp/resnet50.onnx",
+                                  verbose=False,
+                                  opset_version=13,
+                                  enable_onnx_checker=False,
+                                  do_constant_folding=True)
+            else:
+                torch.onnx.export(model,
+                                  dummy_input,
+                                  "/tmp/resnet50.onnx",
+                                  verbose=False,
+                                  opset_version=13,
+                                  do_constant_folding=True)
+
+    def test_resnet50_cpu(self):
+        model = resnet50(pretrained=True, quantize=True)
+        model.eval()
+
+        for name, module in model.named_modules():
+            if name.endswith('_quantizer'):
+                module.amax = 2.50
+
+        dummy_input = torch.randn(1, 3, 224, 224)
+        with enable_onnx_export():
+            if "enable_onnx_checker" in inspect.signature(torch.onnx.export).parameters:
+                torch.onnx.export(model,
+                                  dummy_input,
+                                  "/tmp/resnet50_cpu.onnx",
+                                  verbose=False,
+                                  opset_version=13,
+                                  enable_onnx_checker=False,
+                                  do_constant_folding=True)
+            else:
+                torch.onnx.export(model,
+                                  dummy_input,
+                                  "/tmp/resnet50.onnx",
+                                  verbose=False,
+                                  opset_version=13,
+                                  do_constant_folding=True)