chore: import upstream snapshot with attribution

quickstart/SemanticSegmentation/export.py

@@ -0,0 +1,70 @@
+#
+# SPDX-FileCopyrightText: Copyright (c) 1993-2024 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.
+#
+
+from PIL import Image
+from io import BytesIO
+import requests
+
+output_image = "input.ppm"
+
+# Read sample image input and save it in ppm format
+print("Exporting ppm image {}".format(output_image))
+response = requests.get("https://pytorch.org/assets/images/deeplab1.png")
+with Image.open(BytesIO(response.content)) as img:
+    ppm = Image.new("RGB", img.size, (255, 255, 255))
+    ppm.paste(img, mask=img.split()[3])
+    ppm.save(output_image)
+
+
+import torch
+import torch.nn as nn
+import torchvision.models.segmentation as segmentation
+
+output_onnx = "fcn-resnet101.onnx"
+
+
+# FC-ResNet101 pretrained model from torch-hub extended with argmax layer
+class FCN_ResNet101(nn.Module):
+    def __init__(self):
+        super(FCN_ResNet101, self).__init__()
+        self.model = segmentation.fcn_resnet101(pretrained=True)
+
+    def forward(self, inputs):
+        x = self.model(inputs)["out"]
+        x = x.argmax(1, keepdims=True)
+        return x
+
+
+model = FCN_ResNet101()
+model.eval()
+
+# Generate input tensor with random values
+input_tensor = torch.rand(4, 3, 224, 224)
+
+# Export torch model to ONNX
+print("Exporting ONNX model {}".format(output_onnx))
+torch.onnx.export(
+    model,
+    input_tensor,
+    output_onnx,
+    opset_version=12,
+    do_constant_folding=True,
+    input_names=["input"],
+    output_names=["output"],
+    dynamic_axes={"input": {0: "batch", 2: "height", 3: "width"}, "output": {0: "batch", 2: "height", 3: "width"}},
+    verbose=False,
+)