【Hackathon 9th No.105】基于Paddle实现CoNFiLD流场生成模型 #1230

ADream-ki · 2025-10-19T15:01:01Z

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添加CoNFILD案例

…in confild.py, enhancing code documentation and usability.

paddle-bot · 2025-10-19T15:01:08Z

Thanks for your contribution!

…andling and loss logging. Update _update_ema function to work with parameter dictionaries. Enhance log_loss_dict to control loss aggregation during validation. Modify GaussianDiffusion class to ensure loss calculation includes valid_mse and vb terms correctly.

…dability and consistency. Update variable names for clarity, enhance gradient handling, and streamline loss logging. Fix tensor type conversions and ensure proper handling of model parameters during training and evaluation.

fix

…irectory exists before saving model parameters. This change improves file management during training by organizing saved models into a specified output directory.

…for dynamic adjustment while defaulting to 15698. This enhances flexibility in model evaluation.

HydrogenSulfate · 2025-11-17T04:32:51Z

docs/zh/examples/confild.md

+git clone https://github.com/PaddlePaddle/PaddleScience.git
+cd PaddleScience/examples/confild
+python confild.py mode=train


Suggested change

git clone https://github.com/PaddlePaddle/PaddleScience.git

cd PaddleScience/examples/confild

python confild.py mode=train

python confild.py mode=train

HydrogenSulfate · 2025-11-17T04:34:16Z

docs/zh/examples/confild.md

+```python
+class Normalizer_ts(object):
+    def __init__(self, params=[], method="-11", dim=None):
+        self.params = params
+        self.method = method
+        self.dim = dim
+
+    def fit_normalize(self, data):
+        assert type(data) == paddle.Tensor
+        if len(self.params) == 0:
+            if self.method == "-11" or self.method == "01":
+                if self.dim is None:
+                    self.params = paddle.max(x=data), paddle.min(x=data)
+                else:
+                    self.params = (
+                        paddle.max(keepdim=True, x=data, axis=self.dim),
+                        paddle.argmax(keepdim=True, x=data, axis=self.dim),
+                    )[0], (
+                        paddle.min(keepdim=True, x=data, axis=self.dim),
+                        paddle.argmin(keepdim=True, x=data, axis=self.dim),
+                    )[
+                        0
+                    ]
+            elif self.method == "ms":
+                if self.dim is None:
+                    self.params = paddle.mean(x=data, axis=self.dim), paddle.std(
+                        x=data, axis=self.dim
+                    )
+                else:
+                    self.params = paddle.mean(
+                        x=data, axis=self.dim, keepdim=True
+                    ), paddle.std(x=data, axis=self.dim, keepdim=True)
+            elif self.method == "none":
+                self.params = None
+        return self.fnormalize(data, self.params, self.method)
+
+    def normalize(self, new_data):
+        if not new_data.place == self.params[0].place:
+            self.params = self.params[0].to(new_data.place), self.params[1].to(
+                new_data.place
+            )
+        return self.fnormalize(new_data, self.params, self.method)
+
+    def denormalize(self, new_data_norm):
+        if not new_data_norm.place == self.params[0].place:
+            self.params = self.params[0].to(new_data_norm.place), self.params[1].to(
+                new_data_norm.place
+            )
+        return self.fdenormalize(new_data_norm, self.params, self.method)
+
+    def get_params(self):
+        if self.method == "ms":
+            print("returning mean and std")
+        elif self.method == "01":
+            print("returning max and min")
+        elif self.method == "-11":
+            print("returning max and min")
+        elif self.method == "none":
+            print("do nothing")
+        return self.params
+
+    @staticmethod
+    def fnormalize(data, params, method):
+        if method == "-11":
+            return (data - params[1].to(data.place)) / (
+                params[0].to(data.place) - params[1].to(data.place)
+            ) * 2 - 1
+        elif method == "01":
+            return (data - params[1].to(data.place)) / (
+                params[0].to(data.place) - params[1].to(data.place)
+            )
+        elif method == "ms":
+            return (data - params[0].to(data.place)) / params[1].to(data.place)
+        elif method == "none":
+            return data
+
+    @staticmethod
+    def fdenormalize(data_norm, params, method):
+        if method == "-11":
+            return (data_norm + 1) / 2 * (
+                params[0].to(data_norm.place) - params[1].to(data_norm.place)
+            ) + params[1].to(data_norm.place)
+        elif method == "01":
+            return data_norm * (
+                params[0].to(data_norm.place) - params[1].to(data_norm.place)
+            ) + params[1].to(data_norm.place)
+        elif method == "ms":
+            return data_norm * params[1].to(data_norm.place) + params[0].to(
+                data_norm.place
+            )
+        elif method == "none":
+            return data_norm
+```


文档内的代码片段可以使用引用语法，引用.py文件里的代码

HydrogenSulfate · 2025-11-17T04:34:38Z

docs/zh/examples/confild.md

+class SIRENAutodecoder_film(paddle.nn.Layer):
+    """
+    siren network with author decoding
+
+    Args:
+        input_keys (Tuple[str,...], optional): Key to get the input tensor from the dict.
+        output_keys (Tuple[str,...], optional): Key to save the output tensor into the dict.
+        in_coord_features (int, optional): Number of input coordinates features
+        in_latent_features (int, optional): Number of input latent features
+        out_features (int, optional): Number of output features
+        num_hidden_layers (int, optional): Number of hidden layers
+        hidden_features (int, optional): Number of hidden features
+        outermost_linear (bool, optional): Whether to use linear layer at the end. Defaults to False.
+        nonlinearity (str, optional): Nonlinearity to use. Defaults to "sine".
+        weight_init (Callable, optional): Weight initialization function. Defaults to None.
+        bias_init (Callable, optional): Bias initialization function. Defaults to None.
+        premap_mode (str, optional): Feature mapping mode. Defaults to None.
+
+    Examples:
+        >>> model = ppsci.arch.SIRENAutodecoder_film(
+                input_keys=["input1", "input2"],
+                output_keys=("output",),
+                in_coord_features=2,
+                in_latent_features=128,
+                out_features=3,
+                num_hidden_layers=10,
+                hidden_features=128,
+            )
+        >>> input_data = {"input1": paddle.randn([10, 2]), "input2": paddle.randn([10, 128])}
+        >>> out_dict = model(input_data)
+        >>> for k, v in out_dict.items():
+        ...     print(k, v.shape)
+        output [22, 918, 3]
+    """
+
+    def __init__(
+        self,
+        input_keys,
+        output_keys,
+        in_coord_features,
+        in_latent_features,
+        out_features,
+        num_hidden_layers,
+        hidden_features,
+        outermost_linear=False,
+        nonlinearity="sine",
+        weight_init=None,
+        bias_init=None,
+        premap_mode=None,
+        **kwargs,
+    ):
+        super().__init__()
+        self.input_keys = input_keys
+        self.output_keys = output_keys
+
+        self.premap_mode = premap_mode
+        if self.premap_mode is not None:
+            self.premap_layer = FeatureMapping(
+                in_coord_features, mode=premap_mode, **kwargs
+            )
+            in_coord_features = self.premap_layer.dim
+        self.first_layer_init = None
+        self.nl, nl_weight_init, first_layer_init = NLS_AND_INITS[nonlinearity]
+        if weight_init is not None:
+            self.weight_init = weight_init
+        else:
+            self.weight_init = nl_weight_init
+        self.net1 = paddle.nn.LayerList(
+            sublayers=[BatchLinear(in_coord_features, hidden_features)]
+            + [
+                BatchLinear(hidden_features, hidden_features)
+                for i in range(num_hidden_layers)
+            ]
+            + [BatchLinear(hidden_features, out_features)]
+        )
+        self.net2 = paddle.nn.LayerList(
+            sublayers=[
+                BatchLinear(in_latent_features, hidden_features, bias_attr=False)
+                for i in range(num_hidden_layers + 1)
+            ]
+        )
+        if self.weight_init is not None:
+            self.net1.apply(self.weight_init)
+            self.net2.apply(self.weight_init)
+        if first_layer_init is not None:
+            self.net1[0].apply(first_layer_init)
+            self.net2[0].apply(first_layer_init)
+        if bias_init is not None:
+            self.net2.apply(bias_init)
+
+    def forward(self, input_data):
+        coords = input_data[self.input_keys[0]]
+        latents = input_data[self.input_keys[1]]
+        if self.premap_mode is not None:
+            x = self.premap_layer(coords)
+        else:
+            x = coords
+
+        for i in range(len(self.net1) - 1):
+            x = self.net1[i](x) + self.net2[i](latents)
+            x = self.nl(x)
+        x = self.net1[-1](x)
+        return {self.output_keys[0]: x}
+
+    def disable_gradient(self):
+        for param in self.parameters():
+            param.stop_gradient = not False


同上，引用

HydrogenSulfate · 2025-11-17T04:34:41Z

docs/zh/examples/confild.md

+4.3 模型训练、评估
+完成上述设置之后，只需要将上述实例化的对象按照文档进行组合，然后启动训练、评估。
+```python
+def signal_train(cfg, normed_coords, normed_fois, spatio_axis, out_normalizer):


同上，引用

HydrogenSulfate · 2025-11-17T04:34:49Z

docs/zh/examples/confild.md

+```
+
+## 5. 实验结果
+


实验结果没有？

HydrogenSulfate · 2025-11-17T04:37:58Z

examples/confild/confild.py

+    # 显示图形
+    plt.show()


.show可以删除，图片保存即可

Suggested change

# 显示图形

plt.show()

HydrogenSulfate · 2025-11-17T04:45:43Z

ppsci/arch/confild.py

commit之前请安装pre-commit，并且逐个对上传的文件调用pre-commit run进行格式化

HydrogenSulfate · 2025-11-17T04:46:21Z

ppsci/arch/__init__.py

    "LatentNO",
    "LatentNO_time",
    "LNO",
+    "LossType",


XXType不需要作为公开接口，可以删除

HydrogenSulfate · 2025-11-17T04:48:41Z

ppsci/arch/confild.py

+class BatchLinear(paddle.nn.Linear):
+    """
+    Batch-wise linear transformation layer that supports manual parameter injection.
+    
+    This layer extends paddle.nn.Linear to allow passing parameters explicitly,
+    which is useful for meta-learning and hypernetwork applications.
+    
+    Args:
+        in_features (int): Size of input features.
+        out_features (int): Size of output features.
+        
+    Note:
+        - Weight shape: (out_features, in_features)
+        - Bias shape: (out_features,)
+    """
+
+    __doc__ = paddle.nn.Linear.__doc__
+
+    def forward(self, input, params=None):
+        """
+        Forward pass with optional external parameters.
+        
+        Args:
+            input (paddle.Tensor): Input tensor of shape (..., in_features).
+            params (OrderedDict, optional): External parameters dict containing 'weight' and optionally 'bias'.
+                                           If None, uses internal parameters. Defaults to None.
+        
+        Returns:
+            paddle.Tensor: Output tensor of shape (..., out_features).
+        """
+        if params is None:
+            params = OrderedDict(self.named_parameters())
+        bias = params.get("bias", None)
+        weight = params["weight"]
+
+        output = paddle.matmul(x=input, y=weight)
+        if bias is not None:
+            output += bias.unsqueeze(axis=-2)
+        return output
+


BatchLinear跟nn.Linear的区别是什么，如果没区别是不是可以删掉

HydrogenSulfate · 2025-11-17T04:50:38Z

ppsci/arch/confild.py

+            self.centers = paddle.base.framework.EagerParamBase.from_tensor(
+                tensor=paddle.empty(
+                    shape=(rbf_out_features, in_features), dtype="float32"
+                )
+            )


较新的paddle可以直接用torch类似的语法：self.centers = nn.Paramter(...)

ADream-ki added 6 commits September 21, 2025 18:34

【Hackathon 9th No.105】CoNFiLD 论文复现

b19d520

Add UNet model implementation and Gaussian diffusion methods

ed154e5

update unet model

119f02c

delete duplicate key

411ca7c

fix

645cb4c

Add detailed docstrings for activation functions, layers, and models …

9bf9f56

…in confild.py, enhancing code documentation and usability.

paddle-bot bot added the contributor label Oct 19, 2025

luotao1 mentioned this pull request Oct 21, 2025

【Hackathon 9th】开源贡献个人挑战赛 PaddlePaddle/Paddle#74773

Open

ADream-ki and others added 7 commits November 2, 2025 11:00

fix

f34c982

Merge branch 'H9NO105' into add_err_func

c4c25e8

Merge pull request #1 from wangguan1995/add_err_func

32e7bf7

fix

Enhance model saving functionality in confild.py by ensuring output d…

931b8ee

…irectory exists before saving model parameters. This change improves file management during training by organizing saved models into a specified output directory.

Make t_std configurable in evaluate function of confild.py, allowing …

46a5260

…for dynamic adjustment while defaulting to 15698. This enhances flexibility in model evaluation.

HydrogenSulfate requested changes Nov 17, 2025

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【Hackathon 9th No.105】基于Paddle实现CoNFiLD流场生成模型 #1230

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		```

		## 5. 实验结果

【Hackathon 9th No.105】基于Paddle实现CoNFiLD流场生成模型 #1230

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