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Merge branch 'develop' of https://github.com/PaddlePaddle/GraphNet into develop
2 parents fc865be + ae2af30 commit 42a1337

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README.md

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@@ -98,11 +98,11 @@ Once you have packaged these extracted computation graphs, submit them to the Gr
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<table>
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<tr>
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<td align="center">
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<img width="200" src="https://github.com/user-attachments/assets/eef90a3e-ce83-4757-94ea-b0c0757785f0" />
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<img width="200" src="https://github.com/user-attachments/assets/3ea4794b-1aed-4096-bd1c-3184832b98f3" />
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</td>
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<td align="center">
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<img width="150" src="https://cdn.prod.website-files.com/6257adef93867e50d84d30e2/67d00cf7266d2c75571aebde_Example.svg" />
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<p><a href="https://discord.gg/43XQ3tf7">Channel</a> is also available.</p>
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<p><a href="https://discord.gg/FCZQVCkC">Channel</a> is also available.</p>
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</td>
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</tr>
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</table>

graph_net/paddle/utils.py

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@@ -180,9 +180,10 @@ def replay_tensor(info):
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if "data" in info and info["data"] is not None:
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return paddle.reshape(info["data"], shape).to(dtype).to(device)
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elif dtype == paddle.int32 or dtype == paddle.int64:
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# for some ops(binary_cross_entropy), label data can only be set 0 or 1.
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return paddle.cast(
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paddle.randint(low=0, high=2, shape=shape, dtype="int64"),
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paddle.randint(
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low=min_value, high=max_value + 1, shape=shape, dtype="int64"
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),
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dtype,
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).to(device)
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elif dtype == paddle.bool:
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).to(device)
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else:
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std = info["info"]["std"]
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# return paddle.randn(shape).to(dtype).to(device) * std * 1e-3 + 1e-2
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return (
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paddle.uniform(shape, dtype="float32", min=min_value, max=max_value)
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.to(dtype)
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b30ca9883649d991bc8c4ba04da935a9b4b73a36613bcc8529ef9a0a2fb8b1d3
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{
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"framework": "paddle",
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"num_devices_required": 1,
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"num_nodes_required": 1
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}

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