[Typing] 修理部分示例中的类型缺失 var-annotated (#65609)

megemini · web-flow · commit 683f41da00d4 · 2024-07-02T10:02:20.000+08:00
diff --git a/python/paddle/base/executor.py b/python/paddle/base/executor.py
@@ -3133,7 +3133,7 @@ def infer_from_dataset(
                 >>> dataset.set_use_var([x, y])
                 >>> dataset.set_thread(1)
                 >>> # you should set your own filelist, e.g. filelist = ["dataA.txt"]
-                >>> filelist = []
+                >>> filelist = [] # type: ignore[var-annotated]
                 >>> dataset.set_filelist(filelist)
                 >>> exe.run(paddle.static.default_startup_program())
                 >>> exe.infer_from_dataset(program=paddle.static.default_main_program(),
@@ -3255,7 +3255,7 @@ def train_from_dataset(
                 >>> dataset.set_use_var([x, y])
                 >>> dataset.set_thread(1)
                 >>> # you should set your own filelist, e.g. filelist = ["dataA.txt"]
-                >>> filelist = []
+                >>> filelist = [] # type: ignore[var-annotated]
                 >>> dataset.set_filelist(filelist)
                 >>> exe.run(paddle.static.default_startup_program())
                 >>> exe.train_from_dataset(program=paddle.static.default_main_program(),
diff --git a/python/paddle/distributed/communication/stream/all_to_all.py b/python/paddle/distributed/communication/stream/all_to_all.py
@@ -157,7 +157,7 @@ def alltoall(
             >>> import paddle.distributed as dist
 
             >>> dist.init_parallel_env()
-            >>> out_tensor_list = []
+            >>> out_tensor_list = [] # type: ignore[var-annotated]
             >>> if dist.get_rank() == 0:
             ...     data1 = paddle.to_tensor([[1, 2, 3], [4, 5, 6]])
             ...     data2 = paddle.to_tensor([[7, 8, 9], [10, 11, 12]])
diff --git a/python/paddle/incubate/optimizer/lbfgs.py b/python/paddle/incubate/optimizer/lbfgs.py
@@ -82,10 +82,10 @@ class LBFGS(Optimizer):
 
             >>> paddle.disable_static()
             >>> np.random.seed(0)
-            >>> np_w = np.random.rand(1).astype(np.float32)
-            >>> np_x = np.random.rand(1).astype(np.float32)
+            >>> np_w = np.random.rand(1).astype(np.float32) # type: ignore[var-annotated]
+            >>> np_x = np.random.rand(1).astype(np.float32) # type: ignore[var-annotated]
 
-            >>> inputs = [np.random.rand(1).astype(np.float32) for i in range(10)]
+            >>> inputs = [np.random.rand(1).astype(np.float32) for i in range(10)] # type: ignore[var-annotated]
             >>> # y = 2x
             >>> targets = [2 * x for x in inputs]
 
@@ -110,9 +110,9 @@ class LBFGS(Optimizer):
             ...     opt.step(closure)
 
             >>> for input, target in zip(inputs, targets):
-            ...     input = paddle.to_tensor(input)
-            ...     target = paddle.to_tensor(target)
-            ...     train_step(input, target)
+            ...     input_tensor = paddle.to_tensor(input)
+            ...     target_tensor = paddle.to_tensor(target)
+            ...     train_step(input_tensor, target_tensor)
 
     """
 
diff --git a/python/paddle/static/input.py b/python/paddle/static/input.py
@@ -103,7 +103,7 @@ def data(
 
             # In this example, we will feed x and y with np-ndarray "1"
             # and fetch z, like implementing "1 + 1 = 2" in PaddlePaddle
-            >>> feed_data = np.ones(shape=[3, 2, 1], dtype=np.float32)
+            >>> feed_data = np.ones(shape=[3, 2, 1], dtype=np.float32) # type: ignore[var-annotated]
 
             >>> exe = paddle.static.Executor(paddle.framework.CPUPlace())
             >>> out = exe.run(paddle.static.default_main_program(),
@@ -297,7 +297,7 @@ def from_numpy(
                 >>> import numpy as np
                 >>> from paddle.static import InputSpec
 
-                >>> x = np.ones([2, 2], np.float32)
+                >>> x = np.ones([2, 2], np.float32) # type: ignore[var-annotated]
                 >>> x_spec = InputSpec.from_numpy(x, name='x')
                 >>> print(x_spec)
                 InputSpec(shape=(2, 2), dtype=paddle.float32, name=x, stop_gradient=False)
diff --git a/python/paddle/static/io.py b/python/paddle/static/io.py
@@ -837,7 +837,7 @@ def load_inference_model(path_prefix, executor, **kwargs):
 
             >>> [inference_program, feed_target_names, fetch_targets] = (
             ...     paddle.static.load_inference_model(path_prefix, exe))
-            >>> tensor_img = np.array(np.random.random((64, 784)), dtype=np.float32)
+            >>> tensor_img = np.array(np.random.random((64, 784)), dtype=np.float32) # type: ignore[var-annotated]
             >>> results = exe.run(inference_program,
             ...               feed={feed_target_names[0]: tensor_img},
             ...               fetch_list=fetch_targets)
diff --git a/python/paddle/static/nn/common.py b/python/paddle/static/nn/common.py
@@ -3787,7 +3787,7 @@ def embedding(
             >>> exe = paddle.static.Executor(place)
             >>> exe.run(paddle.static.default_startup_program())
 
-            >>> x = np.array([[7, 2, 4, 5],[4, 3, 2, 9]], dtype=np.int64)
+            >>> x = np.array([[7, 2, 4, 5],[4, 3, 2, 9]], dtype=np.int64) # type: ignore[var-annotated]
             >>> out, = exe.run(paddle.static.default_main_program(), feed={'x':x}, fetch_list=[output])
             >>> print(out)
             [[[1. 1. 1.]
diff --git a/python/paddle/static/nn/static_pylayer.py b/python/paddle/static/nn/static_pylayer.py
@@ -330,7 +330,7 @@ def static_pylayer(forward_fn, inputs, backward_fn=None, name=None):
                 ...     data_grad = paddle.static.gradients([ret], data)[0]
 
                 >>> exe.run(start_program)
-                >>> x = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32)
+                >>> x = np.array([[1.0, 2.0, 3.0, 4.0, 5.0]], dtype=np.float32) # type: ignore[var-annotated]
                 >>> x, x_grad, y = exe.run(
                 ...     main_program,
                 ...     feed={"X": x},
diff --git a/python/paddle/static/pir_io.py b/python/paddle/static/pir_io.py
@@ -827,7 +827,7 @@ def load_inference_model_pir(path_prefix, executor, **kwargs):
 
             >>> [inference_program, feed_target_names, fetch_targets] = (
             ...     paddle.static.load_inference_model(path_prefix, exe))
-            >>> tensor_img = np.array(np.random.random((64, 784)), dtype=np.float32)
+            >>> tensor_img = np.array(np.random.random((64, 784)), dtype=np.float32) # type: ignore[var-annotated]
             >>> results = exe.run(inference_program,
             ...               feed={feed_target_names[0]: tensor_img},
             ...               fetch_list=fetch_targets)
