【PPSCI Doc No.23、25-29、31-34、97】 (#840)

* ppsci.equation.PDE.parameters/state_dict/set_state_dict api fix

* ppsci.equation.PDE.parameters/state_dict/set_state_dict api fix

* fix api docs in the timedomain

* fix api docs of timedomain

* fix api docs of timedomain

* ppsci api docs fixed

* ppsci api docs fixed

* ppsci api docs fixed

---------

Co-authored-by: krp <2934631798@qq.com>
Co-authored-by: HydrogenSulfate <490868991@qq.com>

Author: 3 people

ppsci/arch/afno.py

@@ -414,6 +414,11 @@ class AFNONet(base.Arch):
     Examples:
         >>> import ppsci
         >>> model = ppsci.arch.AFNONet(("input", ), ("output", ))
+        >>> input_data = {"input": paddle.randn([1, 20, 720, 1440])}
+        >>> output_data = model(input_data)
+        >>> for k, v in output_data.items():
+        ...     print(k, v.shape)
+        output [1, 20, 720, 1440]
     """
 
     def __init__(
@@ -577,6 +582,11 @@ class PrecipNet(base.Arch):
         >>> import ppsci
         >>> wind_model = ppsci.arch.AFNONet(("input", ), ("output", ))
         >>> model = ppsci.arch.PrecipNet(("input", ), ("output", ), wind_model)
+        >>> data = paddle.randn([1, 20, 720, 1440])
+        >>> data_dict = {"input": data}
+        >>> output = model.forward(data_dict)
+        >>> print(output['output'].shape)
+        [1, 1, 720, 1440]
     """
 
     def __init__(

ppsci/arch/embedding_koopman.py

@@ -285,8 +285,18 @@ class CylinderEmbedding(base.Arch):
         drop (float, optional):  Probability of dropout the units. Defaults to 0.0.
 
     Examples:
+        >>> import paddle
         >>> import ppsci
-        >>> model = ppsci.arch.CylinderEmbedding(("x", "y"), ("u", "v"))
+        >>> model = ppsci.arch.CylinderEmbedding(("states", "visc"), ("pred_states", "recover_states"))
+        >>> states_shape = [32, 10, 3, 64, 128]
+        >>> visc_shape = [32, 1]
+        >>> input_dict = {"states" : paddle.rand(states_shape),
+        ...               "visc" : paddle.rand(visc_shape)}
+        >>> out_dict = model(input_dict)
+        >>> print(out_dict["pred_states"].shape)
+        [32, 9, 3, 64, 128]
+        >>> print(out_dict["recover_states"].shape)
+        [32, 10, 3, 64, 128]
     """
 
     def __init__(

ppsci/arch/gan.py

@@ -286,6 +286,21 @@ class Discriminator(base.Arch):
         >>> acts = ("leaky_relu", "leaky_relu", "leaky_relu", "leaky_relu", None)
         >>> output_keys_disc = ("out_1", "out_2", "out_3", "out_4", "out_5", "out_6", "out_7", "out_8", "out_9", "out_10")
         >>> model = ppsci.arch.Discriminator(("in_1","in_2"), output_keys_disc, in_channel, out_channels, fc_channel, kernel_sizes, strides, use_bns, acts)
+        >>> input_data = [paddle.to_tensor(paddle.randn([1, in_channel, 128, 128])),paddle.to_tensor(paddle.randn([1, in_channel, 128, 128]))]
+        >>> input_dict = {"in_1": input_data[0],"in_2": input_data[1]}
+        >>> out_dict = model(input_dict)
+        >>> for k, v in out_dict.items():
+        ...     print(k, v.shape)
+        out_1 [1, 32, 64, 64]
+        out_2 [1, 64, 32, 32]
+        out_3 [1, 128, 16, 16]
+        out_4 [1, 256, 16, 16]
+        out_5 [1, 1]
+        out_6 [1, 32, 64, 64]
+        out_7 [1, 64, 32, 32]
+        out_8 [1, 128, 16, 16]
+        out_9 [1, 256, 16, 16]
+        out_10 [1, 1]
     """
 
     def __init__(

ppsci/arch/model_list.py

@@ -28,10 +28,19 @@ class ModelList(base.Arch):
         model_list (Tuple[base.Arch, ...]): Model(s) nested in tuple.
 
     Examples:
+        >>> import paddle
         >>> import ppsci
         >>> model1 = ppsci.arch.MLP(("x", "y"), ("u", "v"), 10, 128)
         >>> model2 = ppsci.arch.MLP(("x", "y"), ("w", "p"), 5, 128)
         >>> model = ppsci.arch.ModelList((model1, model2))
+        >>> input_dict = {"x": paddle.rand([64, 64, 1]),"y": paddle.rand([64, 64, 1])}
+        >>> output_dict = model(input_dict)
+        >>> for k, v in output_dict.items():
+        ...     print(k, v.shape)
+        u [64, 64, 1]
+        v [64, 64, 1]
+        w [64, 64, 1]
+        p [64, 64, 1]
     """
 
     def __init__(

ppsci/arch/nowcastnet.py

@@ -36,6 +36,11 @@ class NowcastNet(base.Arch):
     Examples:
         >>> import ppsci
         >>> model = ppsci.arch.NowcastNet(("input", ), ("output", ))
+        >>> input_data = paddle.rand([1, 9, 512, 512, 2])
+        >>> input_dict = {"input": input_data}
+        >>> output_dict = model(input_dict)
+        >>> print(output_dict["output"].shape)
+        [1, 20, 512, 512, 1]
     """
 
     def __init__(

ppsci/arch/physx_transformer.py

@@ -256,8 +256,14 @@ class PhysformerGPT2(base.Arch):
             output data to the physical space. Defaults to None.
 
     Examples:
+        >>> import paddle
         >>> import ppsci
         >>> model = ppsci.arch.PhysformerGPT2(("embeds", ), ("pred_embeds", ), 6, 16, 128, 4)
+        >>> data = paddle.to_tensor(paddle.randn([10, 16, 128]))
+        >>> inputs = {"embeds": data}
+        >>> outputs = model(inputs)
+        >>> print(outputs["pred_embeds"].shape)
+        [10, 16, 128]
     """
 
     def __init__(

ppsci/autodiff/ad.py

@@ -283,6 +283,8 @@ def __call__(
             >>> x.stop_gradient = False
             >>> y = (x * x).sin()
             >>> dy_dxx = ppsci.autodiff.hessian(y, x, component=0)
+            >>> print(dy_dxx.shape)
+            [4, 1]
         """
         key = (ys, xs, component)
         if key not in self.Hs:
@@ -299,6 +301,24 @@ def _clear(self):
 
 
 def clear():
-    """Clear cached Jacobians and Hessians."""
+    """Clear cached Jacobians and Hessians.
+
+    Args:
+        None.
+
+    Returns:
+        None.
+
+    Examples:
+        >>> import paddle
+        >>> import ppsci
+        >>> x = paddle.randn([4, 3])
+        >>> x.stop_gradient = False
+        >>> y = (x * x).sin()
+        >>> dy_dxx = ppsci.autodiff.hessian(y, x, component=0)
+        >>> ppsci.autodiff.clear()
+        >>> print(ppsci.autodiff.hessian.Hs)
+        {}
+    """
     jacobian._clear()
     hessian._clear()

ppsci/utils/misc.py

@@ -303,6 +303,22 @@ def all_gather(
 
     Returns:
         Union[paddle.Tensor, List[paddle.Tensor]]: Gathered Tensors.
+
+    Examples:
+        >>> import paddle
+        >>> import ppsci
+        >>> import paddle.distributed as dist
+        >>> dist.init_parallel_env()      # doctest: +SKIP
+        >>> if dist.get_rank() == 0:      # doctest: +SKIP
+        ...     data = paddle.to_tensor([[1, 2, 3], [4, 5, 6]])
+        ... else:
+        ...     data = paddle.to_tensor([[7, 8, 9], [10, 11, 12]])
+        >>> result = ppsci.utils.misc.all_gather(data)    # doctest: +SKIP
+        >>> print(result.numpy())     # doctest: +SKIP
+        [[ 1  2  3]
+         [ 4  5  6]
+         [ 7  8  9]
+         [10 11 12]]
     """
     result: List[paddle.Tensor] = []