adjust infershape details

Author: chenwhql

paddle/fluid/operators/sequence_enumerate_op.cc

@@ -13,7 +13,6 @@
 // limitations under the License.
 
 #include "paddle/fluid/operators/sequence_enumerate_op.h"
-#include <vector>
 
 namespace paddle {
 namespace operators {
@@ -34,18 +33,12 @@ class SequenceEnumerateOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE_EQ(
         x_dims.size(), 2UL,
         "Input(X) of SequenceEnumerate operator's rank should be 2.");
+    PADDLE_ENFORCE_EQ(
+        x_dims[1], 1UL,
+        "Input(X) of SequenceEnumerate operator's 2nd dimension should be 1.");
 
     const auto win_size = ctx->Attrs().Get<int>("win_size");
-    // TODO(chenweihang): unittest doesn't has batch size, but test_layers has
-    auto first_dim = x_dims[0] == -1 ? x_dims[1] : x_dims[0];
-    PADDLE_ENFORCE(win_size <= first_dim,
-                   "The enumerate window size should be less than or equal to "
-                   "input sequence length.");
-
-    std::vector<int64_t> out_shape(x_dims.size() + 1, 0);
-    for (int i = 0; i < x_dims.size(); ++i) out_shape.emplace_back(x_dims[i]);
-    out_shape.emplace_back(win_size);
-    ctx->SetOutputDim("Out", framework::make_ddim(out_shape));
+    ctx->SetOutputDim("Out", {x_dims[0], win_size});
     ctx->ShareLoD("X", "Out");
   }
 };

python/paddle/fluid/layers/nn.py

@@ -5563,15 +5563,15 @@ def sequence_enumerate(input, win_size, pad_value, name=None):
             out = fluid.layers.sequence_enumerate(input=x, win_size=3, pad_value=0)
     """
     helper = LayerHelper('sequence_enumerate', **locals())
-    out = helper.create_tmp_variable(helper.input_dtype())
+    out = helper.create_tmp_variable(helper.input_dtype(), stop_gradient=True)
     helper.append_op(
         type='sequence_enumerate',
         inputs={'X': input},
         outputs={'Out': out},
         attrs={'win_size': win_size,
                'pad_value': pad_value})
 
-    
+
 def stack(x, axis=0):
     helper = LayerHelper('stack', **locals())
     axis = 0 if axis is None else axis

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

@@ -522,10 +522,8 @@ def test_prelu(self):
     def test_sequence_enumerate(self):
         program = Program()
         with program_guard(program):
-            x = layers.data(
-                name="input", shape=[30], dtype='int32', lod_level=1)
+            x = layers.data(name="input", shape=[1], dtype='int32', lod_level=1)
             out = layers.sequence_enumerate(input=x, win_size=2, pad_value=0)
-            self.assertIsNotNone(out)
         print(str(program))
 
 

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

@@ -19,7 +19,7 @@
 from op_test import OpTest
 
 
-def sequence_enumerate(input_seq, lod0, win_size, pad_value):
+def sequence_enumerate(input_seq, win_size, pad_value):
     out_seq = []
     for idx in range(0, len(input_seq)):
         single_seq = []
@@ -48,8 +48,7 @@ def init_test_case(self):
         self.lod = [[9, 4, 11, 6]]
         self.win_size = 2
         self.pad_value = 0
-        out_seq = sequence_enumerate(self.in_seq, self.lod[0], self.win_size,
-                                     self.pad_value)
+        out_seq = sequence_enumerate(self.in_seq, self.win_size, self.pad_value)
         self.out_seq = np.array(out_seq).astype("int32")
 
 
@@ -59,8 +58,7 @@ def init_test_case(self):
         self.lod = [[9, 4, 11, 6]]
         self.win_size = 2
         self.pad_value = 0
-        out_seq = sequence_enumerate(self.in_seq, self.lod[0], self.win_size,
-                                     self.pad_value)
+        out_seq = sequence_enumerate(self.in_seq, self.win_size, self.pad_value)
         self.out_seq = np.array(out_seq).astype("int64")
 
 
@@ -70,8 +68,7 @@ def init_test_case(self):
         self.lod = [[9, 4, 11, 6]]
         self.win_size = 30
         self.pad_value = 0
-        out_seq = sequence_enumerate(self.in_seq, self.lod[0], self.win_size,
-                                     self.pad_value)
+        out_seq = sequence_enumerate(self.in_seq, self.win_size, self.pad_value)
         self.out_seq = np.array(out_seq).astype("int32")