Romove grid_sampler and refine example code (#27649)

* refine grid_sample and temporal_shift

python/paddle/fluid/layers/nn.py

@@ -13421,7 +13421,7 @@ def temporal_shift(x, seg_num, shift_ratio=0.25, name=None):
     ${comment}
 
     Args:
-        x(Variable): ${x_comment}
+        x(Tensor): ${x_comment}
         seg_num(int): ${seg_num_comment}
         shift_ratio(float): ${shift_ratio_comment}
         name(str, optional): For detailed information, please refer
@@ -13429,7 +13429,7 @@ def temporal_shift(x, seg_num, shift_ratio=0.25, name=None):
                              None by default.
 
     Returns:
-        out(Variable): The temporal shifting result is a tensor variable with the
+        out(Tensor): The temporal shifting result is a tensor with the
         same shape and same data type as the input.
 
     Raises:
@@ -13438,9 +13438,11 @@ def temporal_shift(x, seg_num, shift_ratio=0.25, name=None):
     Examples:
         .. code-block:: python
 
-            import paddle.fluid as fluid
-            input = fluid.data(name='input', shape=[None,4,2,2], dtype='float32')
-            out = fluid.layers.temporal_shift(x=input, seg_num=2, shift_ratio=0.2)
+            import paddle
+            import paddle.nn.functional as F
+
+            input = paddle.randn([6, 4, 2, 2])
+            out = F.temporal_shift(x=input, seg_num=2, shift_ratio=0.2)
     """
     helper = LayerHelper("temporal_shift", **locals())
     check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'temporal_shift')
@@ -13452,6 +13454,10 @@ def temporal_shift(x, seg_num, shift_ratio=0.25, name=None):
     if not isinstance(seg_num, int):
         raise TypeError("seg_num must be int type.")
 
+    if in_dygraph_mode():
+        return core.ops.temporal_shift(x, 'seg_num', seg_num, 'shift_ratio',
+                                       shift_ratio)
+
     helper.append_op(
         type="temporal_shift",
         inputs={"X": x},

python/paddle/fluid/tests/unittests/test_grid_sample_function.py

@@ -127,5 +127,15 @@ def load_tests(loader, standard_tests, pattern):
     return suite
 
 
+class TestGridSampleAPI(unittest.TestCase):
+    def test_errors(self):
+        with self.assertRaises(ValueError):
+            x = paddle.randn([1, 1, 3, 3])
+            F.grid_sample(x, 1.0)
+        with self.assertRaises(ValueError):
+            x = paddle.randn([1, 1, 3, 3])
+            F.grid_sample(1.0, x)
+
+
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/tests/unittests/test_temporal_shift_op.py

@@ -18,6 +18,7 @@ import unittest
 import numpy as np
 from op_test import OpTest
 
+import paddle
 from paddle.fluid import core
 
 
@@ -77,5 +78,12 @@ class TestTemporalShift3(TestTemporalShift):
         self.shift_ratio = 0.3
 
 
+class TestTemporalShiftAPI(unittest.TestCase):
+    def test_api(self):
+        input = paddle.randn([6, 4, 2, 2])
+        out = paddle.nn.functional.temporal_shift(
+            x=input, seg_num=2, shift_ratio=0.2)
+
+
 if __name__ == "__main__":
     unittest.main()

python/paddle/nn/functional/vision.py

@@ -34,7 +34,6 @@ from ...fluid.layers import distribute_fpn_proposals  #DEFINE_ALIAS
 from ...fluid.layers import generate_mask_labels  #DEFINE_ALIAS
 from ...fluid.layers import generate_proposal_labels  #DEFINE_ALIAS
 from ...fluid.layers import generate_proposals  #DEFINE_ALIAS
-from ...fluid.layers import grid_sampler  #DEFINE_ALIAS
 from ...fluid.layers import image_resize  #DEFINE_ALIAS
 from ...fluid.layers import prior_box  #DEFINE_ALIAS
 from ...fluid.layers import prroi_pool  #DEFINE_ALIAS
@@ -74,7 +73,7 @@ __all__ = [
     'generate_mask_labels',
     'generate_proposal_labels',
     'generate_proposals',
-    'grid_sampler',
+    'grid_sample',
     'image_resize',
     'image_resize_short',
     #       'multi_box_head',
@@ -205,25 +204,35 @@ def grid_sample(x,
     data x and y is indexing the 3rd dimension (in height dimension),
     finally results is the bilinear interpolation or nearest value of 4 nearest corner
     points. The output tensor shape will be [N, C, H, W].
+
+
+    Step 1:
+
+    Get (x, y) grid coordinates and scale to [0, H-1/W-1].
+
+    .. code-block:: text
+
+        grid_x = 0.5 * (grid[:, :, :, 0] + 1) * (W - 1)
+        grid_y = 0.5 * (grid[:, :, :, 1] + 1) * (H - 1)
+
+    Step 2:
+    
+    Indices input data X with grid (x, y) in each [H, W] area, and bilinear
+    interpolate point value by 4 nearest points or nearest interpolate point value
+    by nearest point.
+
     .. code-block:: text
-        Step 1:
-        Get (x, y) grid coordinates and scale to [0, H-1/W-1].
-        .. code-block:: text
-            grid_x = 0.5 * (grid[:, :, :, 0] + 1) * (W - 1)
-            grid_y = 0.5 * (grid[:, :, :, 1] + 1) * (H - 1)
-        Step 2:
-        Indices input data X with grid (x, y) in each [H, W] area, and bilinear
-        interpolate point value by 4 nearest points or nearest interpolate point value
-        by nearest point.
-          wn ------- y_n ------- en
-          |           |           |
-          |          d_n          |
-          |           |           |
-         x_w --d_w-- grid--d_e-- x_e
-          |           |           |
-          |          d_s          |
-          |           |           |
-          ws ------- y_s ------- wn
+
+        wn ------- y_n ------- en
+        |           |           |
+        |          d_n          |
+        |           |           |
+        x_w --d_w-- grid--d_e-- x_e
+        |           |           |
+        |          d_s          |
+        |           |           |
+        ws ------- y_s ------- wn
+
         For bilinear interpolation:
         x_w = floor(x)              // west side x coord
         x_e = x_w + 1               // east side x coord
@@ -237,8 +246,10 @@ def grid_sample(x,
         en = X[:, :, y_n, x_e]      // north-east point value
         ws = X[:, :, y_s, x_w]      // south-east point value
         es = X[:, :, y_s, x_w]      // north-east point value
+
         output = wn * d_e * d_s + en * d_w * d_s
-               + ws * d_e * d_n + es * d_w * d_n
+                + ws * d_e * d_n + es * d_w * d_n
+
     Args:
         x(Tensor): The input tensor, which is a 4-d tensor with shape
                      [N, C, H, W], N is the batch size, C is the channel
@@ -262,7 +273,9 @@ def grid_sample(x,
         Tensor, The shape of output is [N, C, grid_H, grid_W] in which `grid_H` is the height of grid and `grid_W` is the width of grid. The data type is same as input tensor.
 
     Examples:
+
         .. code-block:: python
+        
             import paddle
             import paddle.nn.functional as F
             import numpy as np
@@ -287,7 +300,7 @@ def grid_sample(x,
                             [ 0.7,  0.4],
                             [ 0.2,  0.8]]]]).astype("float64")
             
-            paddle.disable_static()
+            
             x = paddle.to_tensor(x)
             grid = paddle.to_tensor(grid)
             y_t = F.grid_sample(
@@ -304,13 +317,10 @@ def grid_sample(x,
             #    [ 0.596  0.38   0.52   0.24 ]]]]
     """
     helper = LayerHelper("grid_sample", **locals())
-    check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'grid_sampler')
+    check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'grid_sample')
     check_variable_and_dtype(grid, 'grid', ['float32', 'float64'],
-                             'grid_sampler')
-    if not isinstance(x, Variable):
-        raise ValueError("The x should be a Variable")
-    if not isinstance(grid, Variable):
-        raise ValueError("The grid should be a Variable")
+                             'grid_sample')
+
     _modes = ['bilinear', 'nearest']
     _padding_modes = ['zeros', 'reflection', 'border']
     if mode not in _modes: