chore: import upstream snapshot with attribution

tools/pytorch-quantization/tests/fixtures/__init__.py

@@ -0,0 +1,22 @@
+#
+# 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.
+#
+
+
+import pytest
+@pytest.fixture
+def verbose(request):
+    return request.config.getoption("verbose")

tools/pytorch-quantization/tests/fixtures/models.py

@@ -0,0 +1,69 @@
+#
+# 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.
+#
+
+
+"""Model used for tests"""
+
+import pytest
+import torch.nn as nn
+import torch.nn.functional as F
+from pytorch_quantization.nn import QuantConv2d, QuantLinear
+from pytorch_quantization.tensor_quant import QuantDescriptor
+
+class LeNet(nn.Module):
+    def __init__(self, **kwargs):
+        super(LeNet, self).__init__()
+        self.conv1 = nn.Conv2d(1, 10, kernel_size=5, **kwargs)
+        self.conv2 = nn.Conv2d(10, 20, kernel_size=5, **kwargs)
+        self.fc1 = nn.Linear(320, 50, **kwargs)
+        self.fc2 = nn.Linear(50, 10, **kwargs)
+
+    def forward(self, x):
+        x = F.relu(F.max_pool2d(self.conv1(x), 2))
+        x = F.relu(F.max_pool2d(self.conv2(x), 2))
+        x = x.view(-1, 320)
+        x = F.relu(self.fc1(x))
+        x = F.dropout(x, training=self.training)
+        x = self.fc2(x)
+        return F.log_softmax(x, dim=1)
+
+
+class QuantLeNet(nn.Module):
+    def __init__(self, **kwargs):
+        super(QuantLeNet, self).__init__()
+        self.conv1 = QuantConv2d(1, 10, kernel_size=5, **kwargs)
+        self.conv2 = QuantConv2d(10, 20, kernel_size=5, **kwargs)
+        self.fc1 = QuantLinear(320, 50, **kwargs)
+        self.fc2 = QuantLinear(50, 10, **kwargs)
+
+    def forward(self, x):
+        x = F.relu(F.max_pool2d(self.conv1(x), 2))
+        x = F.relu(F.max_pool2d(self.conv2(x), 2))
+        x = x.view(-1, 320)
+        x = F.relu(self.fc1(x))
+        x = F.dropout(x, training=self.training)
+        x = self.fc2(x)
+        return F.log_softmax(x, dim=1)
+
+@pytest.fixture
+def resnet18():
+    import torchvision
+    return torchvision.models.resnet18()
+
+@pytest.fixture
+def quant_lenet():
+    return QuantLeNet(quant_desc_input=QuantDescriptor(), quant_desc_weight=QuantDescriptor())