follow comments.

paddle/fluid/operators/reshape_op.cc

@@ -44,22 +44,22 @@ class ReshapeOp : public framework::OperatorWithKernel {
       ctx->SetOutputDim("Out", x_dims);
     } else {
       ctx->SetOutputDim("Out", framework::make_ddim(output_shape));
-
-      // FIXME(caoying): When shape of the output tensor is determined during
-      // runtime, LoD information of X will not passed to the output.
-      if (shape[0] == x_dims[0]) {
-        // Only pass LoD when the first dimension of output and Input(X)
-        // are the same.
-        ctx->ShareLoD("X", /*->*/ "Out");
-      }
     }
+
+    // NOTE: Reshape op cannot reshape an input sequence batch into an output
+    // sequence batch that has a different number of time steps.
+    // Here output always shares the LoD information with input. But if
+    // Attr(shape) contains 0 or -1, the actual output shape can only be
+    // determined during runtime. The check for wheather it is a valid output
+    // sequence batch is performed in runtime.
+    ctx->ShareLoD("X", /*->*/ "Out");
   }
 
  private:
   bool ValidateShape(const std::vector<int> &shape,
                      const framework::DDim &input_dim,
                      std::vector<int64_t> &output_shape) const {
-    // only one dimension canbe set to -1, whose size will be automatically
+    // only one dimension can be set to -1, whose size will be automatically
     // infered.
     const int64_t unknown_index = -1;
     const auto in_size = framework::product(input_dim);
@@ -82,7 +82,7 @@ class ReshapeOp : public framework::OperatorWithKernel {
     }
     PADDLE_ENFORCE_LE(
         neg_dims_idx.size(), 1,
-        "Only one input dimension of Attr(shape) may be unknown.");
+        "Only one input dimension of Attr(shape) can be unknown.");
 
     output_shape.resize(shape.size(), 0);
     std::transform(shape.begin(), shape.end(), output_shape.begin(),
@@ -113,22 +113,46 @@ class ReshapeOpMaker : public framework::OpProtoAndCheckerMaker {
     AddAttr<std::vector<int>>(
         "shape", "(std::vector<int>) Target shape of reshape operator.");
     AddAttr<bool>("inplace",
-                  "Change the source tensor's shape without copy memory.")
-        .SetDefault(true);
+                  "(default: false) Change the source tensor's shape without "
+                  "memory copy. When Attr(inplace) is set true, the output "
+                  "tensor shares memory with Input(X), otherwise, a new output "
+                  "tensor is created, and its data are copied from Input(x).")
+        .SetDefault(false);
     AddComment(R"DOC(
 Reshape Operator.
 
-Reshape Input(X) into the shape specified by Attr(shape).
+Reshape Input(X) into the shape specified by Attr(shape). The data in Input(X)
+are unchanged.
+
+Examples:
+
+1. Given a 3-D tensor Input(X) with a shape [2, 4, 6], and the target shape
+specified by Attr(shape) is [6, 8], the reshape operator will transform Input(X)
+into a 2-D tensor with shape [6, 8] and leaving Input(X)'s data unchanged.
+
+1. Given a 3-D tensor Input(X) with a shape [2, 4, 6], and the target shape
+specified by Attr(shape) is [2, 3, -1, 2], the reshape operator will transform
+Input(X) into a 4-D tensor with shape [2, 3, 4, 2] and leaving Input(X)'s data
+unchanged. In this case, one and only dimension of Attr(shape) can be set to -1,
+the value of this dimension is inferred from the total element number of
+Input(X) and remaining dimensions.
+
+1. Given a 3-D tensor Input(X) with a shape [2, 4, 6], and the target shape
+specified by Attr(shape) is [-1, 0, 3, 2], the reshape operator will transform
+Input(X) into a 4-D tensor with shape [2, 4, 3, 2] and leaving Input(X)'s data
+unchanged. In this case, besides -1, 0 means the actual dimension value is going
+to be copied from the corresponding dimension of Input(X).
 
-An example:
-Given a 2-D tensor X with 2 rows and 2 columns : [[1, 2], [3, 4]]
+Note:
 
-and target shape = [1, 4], the reshape operator will transform
-the tensor X into a 2-D tensor: [[1, 2, 3, 4]]
+1. One and only one dimension in Attr(shape) can be set -1. In this case,
+the actual dimension value will be infered from the total element number of
+Input(X) and remaining dimensions.
+1. More than one dimensions in Attr(shape) can be set to 0, which means the real
+dimension value will be copied from Input(X) at runtime. Note that the index of
+0 can not access Rank(X). For example, Input(X) is a 3-D tensor with shape
+[2, 3, 4], Attr(shape) = [2, 3, 2, 0] is an invalid input.
 
-One dimension in the target shape can be set -1, representing that its
-size is unknown. In this case, the real dimension will be infered from
-the original shape of Input(X) and other dimensions in the target shape.
 )DOC");
   }
 };

paddle/fluid/operators/reshape_op.h

@@ -24,11 +24,21 @@ template <typename DeviceContext, typename T>
 class ReshapeKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const {
-    auto* out = ctx.Output<framework::Tensor>("Out");
-    auto* in = ctx.Input<framework::Tensor>("X");
+    auto* out = ctx.Output<framework::LoDTensor>("Out");
+    auto* in = ctx.Input<framework::LoDTensor>("X");
 
     auto out_dims =
         ValidateShape(ctx.Attr<std::vector<int>>("shape"), in->dims());
+
+    if (!in->lod().empty()) {
+      PADDLE_ENFORCE_EQ(
+          out_dims[0], in->dims()[0],
+          "Reshape operator cannot reshape an input sequence batch "
+          "into an output sequence batch that has a different "
+          "number of time steps. Please consider using "
+          "sequence_reshape op.");
+    }
+
     bool inplace = ctx.Attr<bool>("inplace");
     if (!inplace) {
       out->mutable_data<T>(ctx.GetPlace());

python/paddle/fluid/layers/detection.py

@@ -130,9 +130,9 @@ def detection_output(loc,
         code_type='decode_center_size')
 
     old_shape = scores.shape
-    scores = ops.reshape(x=scores, shape=(-1, old_shape[-1]))
+    scores = nn.reshape(x=scores, shape=(-1, old_shape[-1]))
     scores = nn.softmax(input=scores)
-    scores = ops.reshape(x=scores, shape=old_shape)
+    scores = nn.reshape(x=scores, shape=old_shape)
     scores = nn.transpose(scores, perm=[0, 2, 1])
 
     nmsed_outs = helper.create_tmp_variable(dtype=decoded_box.dtype)

python/paddle/fluid/layers/nn.py

@@ -3299,13 +3299,35 @@ def autoincreased_step_counter(counter_name=None, begin=1, step=1):
 
 def reshape(x, shape, act=None, inplace=True, name=None):
     """
-    Gives a new shape to Tensor without changing its data.
-    This layer takes a tensor as input and the attribute shape specifying the
-    new shape. The shape attribute must be specified. At most one dimension of
-    the new shape can be -1. In this case, the value is inferred from the size
-    of the tensor and the remaining dimensions. A dimension could also be 0,
-    in which case the actual dimension value is going to be copied from the
-    input tensor.
+    Gives a new shape to the input Tensor without changing its data.
+
+    This layer takes a tensor and the attribute shape which specifies the
+    new shape as its inputs. The shape attribute must be given. It cannot be
+    empty. One and only one dimension of shape can be -1. More than one
+    dimension of shape can be 0.
+
+    -1 means the value of this dimension is inferred from the total element
+    number of x and remaining dimensions.
+
+    0 means the actual dimension value is going to be copied from the
+    corresponding dimension of x.
+
+    1. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape
+    specified by Attr(shape) is [6, 8], the reshape operator will transform x
+    into a 2-D tensor with shape [6, 8] and leaving x's data unchanged.
+
+    1. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape
+    specified by Attr(shape) is [2, 3, -1, 2], the reshape operator will
+    transform x into a 4-D tensor with shape [2, 3, 4, 2] and leaving x's data
+    unchanged. In this case, one and only dimension of Attr(shape) can be set
+    to -1, the value of this dimension is inferred from the total element number
+    of x and remaining dimensions.
+
+    1. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape
+    specified by Attr(shape) is [-1, 0, 3, 2], the reshape operator will
+    transform x into a 4-D tensor with shape [2, 4, 3, 2] and leaving x's data
+    unchanged. In this case, besides -1, 0 means the actual dimension value is
+    going to be copied from the corresponding dimension of x during runtime.
 
     Args:
         input(variable): The input tensor.
@@ -3320,18 +3342,10 @@ def reshape(x, shape, act=None, inplace=True, name=None):
 
     Examples:
         .. code-block:: python
-
-        Given a 2-D tensor X with shape [2 x 2], and the new shape: [1, 4].
-        The reshape layer will change tensor X into a 2-D tensor with
-        shape [1 x 4] with its data unchanged.
-
-        Given a 3-D tensor x with shape [2, 3, 4] and the new shape: [3, -1].
-        The reshape layer will change tensor X into a 2-D tensor with shape:
-        [3 x 8] with its data unchanged.
-
-        Given a 3-D tensor x with shape [2, 3, 8] and the new shape:
-        [-1, 0, 2, 2]. The reshape layer will change tensor X into a 4-D tensor
-        with shape [4, 3, 2, 2] with its data unchanged.
+            data = fluid.layers.data(name='data', shape=[2, 4, 6], dtype='float32')
+            reshaped = fluid.layers.reshape(
+                x=data, shape=[-1, 0, 3, 2], act='tanh', inplace=True
+            )
 
     """