wehub-resource-sync
71 lines
2.1 KiB
Python
71 lines
2.1 KiB
Python
#
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# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from PIL import Image
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from io import BytesIO
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import requests
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output_image = "input.ppm"
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# Read sample image input and save it in ppm format
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print("Exporting ppm image {}".format(output_image))
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response = requests.get("https://pytorch.org/assets/images/deeplab1.png")
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with Image.open(BytesIO(response.content)) as img:
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ppm = Image.new("RGB", img.size, (255, 255, 255))
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ppm.paste(img, mask=img.split()[3])
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ppm.save(output_image)
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import torch
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import torch.nn as nn
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import torchvision.models.segmentation as segmentation
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output_onnx = "fcn-resnet101.onnx"
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# FC-ResNet101 pretrained model from torch-hub extended with argmax layer
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class FCN_ResNet101(nn.Module):
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def __init__(self):
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super(FCN_ResNet101, self).__init__()
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self.model = segmentation.fcn_resnet101(pretrained=True)
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def forward(self, inputs):
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x = self.model(inputs)["out"]
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x = x.argmax(1, keepdims=True)
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return x
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model = FCN_ResNet101()
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model.eval()
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# Generate input tensor with random values
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input_tensor = torch.rand(4, 3, 224, 224)
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# Export torch model to ONNX
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print("Exporting ONNX model {}".format(output_onnx))
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torch.onnx.export(
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model,
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input_tensor,
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output_onnx,
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opset_version=12,
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do_constant_folding=True,
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input_names=["input"],
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output_names=["output"],
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dynamic_axes={"input": {0: "batch", 2: "height", 3: "width"}, "output": {0: "batch", 2: "height", 3: "width"}},
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verbose=False,
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)
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