Add python wrapper for gather op. (#11033)

* Add python wrapper for gather op.

* Add unitest for 'rank==1' and fix comments.

* Fix comments.

doc/fluid/api/layers.rst

@@ -1009,3 +1009,9 @@ ____
 ..  autofunction:: paddle.fluid.layers.upsampling_bilinear2d
     :noindex:
 
+gather
+____
+
+..  autofunction:: paddle.fluid.layers.gather
+    :noindex:
+

paddle/fluid/operators/gather_op.cc

@@ -33,7 +33,6 @@ class GatherOp : public framework::OperatorWithKernel {
     auto index_dims = ctx->GetInputDim("Index");
     PADDLE_ENFORCE(index_dims.size() == 1);
     int batch_size = ctx->GetInputDim("Index")[0];
-    PADDLE_ENFORCE_GE(batch_size, 0, "Batch size must be >0");
     framework::DDim output_dims(ctx->GetInputDim("X"));
     output_dims[0] = batch_size;
     ctx->SetOutputDim("Out", output_dims);

python/paddle/fluid/layers/nn.py

@@ -82,6 +82,7 @@ __all__ = [
     'roi_pool',
     'dice_loss',
     'upsampling_bilinear2d',
+    'gather',
     'random_crop',
 ]
 
@@ -3889,7 +3890,6 @@ def roi_pool(input, rois, pooled_height=1, pooled_width=1, spatial_scale=1.0):
 
 def dice_loss(input, label, epsilon=0.00001):
     """
-    **Dice loss Layer**
     Dice loss for comparing the similarity of two batch of data,
     usually is used for binary image segmentation i.e. labels are binary.
     The dice loss can be defined as below equation:
@@ -3999,6 +3999,55 @@ def upsampling_bilinear2d(input, out_shape=None, scale=None, name=None):
     return out
 
 
+def gather(input, index):
+    """
+    Output is obtained by gathering entries of the outer-most dimension 
+    of X indexed by `index` and concatenate them together.
+
+    .. math::
+
+	Out = X[Index]
+
+
+    .. code-block:: text
+
+
+                Given:
+
+    		X = [[1, 2],
+         	     [3, 4],
+                     [5, 6]]
+
+                Index = [1, 2]
+
+                Then:
+
+                Out = [[3, 4],
+                       [5, 6]]
+
+    Args:
+        input (Variable): The source input with rank>=1. 
+        index (Variable): The index input with rank=1.
+
+    Returns:
+        output (Variable): The output is a tensor with the same rank as input.
+
+    Examples:
+        .. code-block:: python
+
+            output = fluid.layers.gather(x, index)
+    """
+    helper = LayerHelper('gather', **locals())
+    dtype = helper.input_dtype()
+    out = helper.create_tmp_variable(dtype)
+    helper.append_op(
+        type="gather",
+        inputs={"X": input,
+                "Index": index},
+        outputs={"Out": out})
+    return out
+
+
 def random_crop(input, shape, seed=1):
     helper = LayerHelper("random_crop", **locals())
     dtype = helper.input_dtype()

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

@@ -20,8 +20,9 @@ from op_test import OpTest
 class TestGatherOp(OpTest):
     def setUp(self):
         self.op_type = "gather"
-        xnp = np.random.random((10, 20)).astype("float32")
-        self.inputs = {'X': xnp, 'Index': np.array([1, 3, 5]).astype("int32")}
+        self.config()
+        xnp = np.random.random(self.x_shape).astype("float32")
+        self.inputs = {'X': xnp, 'Index': np.array(self.index).astype("int32")}
         self.outputs = {'Out': self.inputs["X"][self.inputs["Index"]]}
 
     def test_check_output(self):
@@ -30,6 +31,16 @@ class TestGatherOp(OpTest):
     def test_check_grad(self):
         self.check_grad(['X'], 'Out')
 
+    def config(self):
+        self.x_shape = (10, 20)
+        self.index = [1, 3, 5]
+
+
+class TestCase1(TestGatherOp):
+    def config(self):
+        self.x_shape = (10)
+        self.index = [1, 3, 5]
+
 
 if __name__ == "__main__":
     unittest.main()