Merge branch 'develop' into fix_deploy_script

paddle/operators/lookup_table_op.h

@@ -90,11 +90,13 @@ class LookupTableGradKernel : public framework::OpKernel<T> {
       auto* d_output_data = d_output->data<T>();
       auto* d_table_data = d_table->mutable_data<T>(context.GetPlace());
 
+      memset(d_table_data, 0, d_table->numel() * sizeof(T));
+
       for (int64_t i = 0; i < ids->numel(); ++i) {
         PADDLE_ENFORCE_LT(ids_data[i], N);
         PADDLE_ENFORCE_GE(ids_data[i], 0);
         for (int j = 0; j < D; ++j) {
-          d_table_data[ids_data[i] * D + j] = d_output_data[i * D + j];
+          d_table_data[ids_data[i] * D + j] += d_output_data[i * D + j];
         }
       }
     }

paddle/operators/sequence_pool_op.cc

@@ -42,7 +42,8 @@ class SequencePoolOpMaker : public framework::OpProtoAndCheckerMaker {
     AddAttr<std::string>(
         "pooltype",
         "(int, default AVERAGE) the pooling pooltype of SequencePoolOp.")
-        .SetDefault("AVERAGE");
+        .SetDefault("AVERAGE")
+        .InEnum({"AVERAGE", "SUM", "SQRT", "LAST", "FIRST", "MAX"});
     AddComment(R"DOC(
     SequencePoolOp pools features of all time-steps of each instance.
 

python/paddle/v2/framework/layers.py

@@ -278,6 +278,7 @@ def sequence_conv(input,
                   num_filters,
                   filter_size=3,
                   filter_stride=1,
+                  act=None,
                   padding=None,
                   bias_attr=None,
                   param_attr=None,
@@ -304,7 +305,7 @@ def sequence_conv(input,
         outputs={"Out": pre_bias},
         attrs={
             'contextStride': filter_stride,
-            'contextStart': 0,
+            'contextStart': -int(filter_size / 2),
             'contextLength': filter_size
         })
     pre_act = helper.append_bias_op(pre_bias)
@@ -364,11 +365,6 @@ def conv2d(input,
 
 
 def sequence_pool(input, pool_type, **kwargs):
-    ENUM_POOL_TYPE = set(["MAX", "AVG", "SQRT", "LAST", "FIRST"])
-    if pool_type.upper() not in ENUM_POOL_TYPE:
-        raise ValueError("Unknown pool_type: '%s'. It can only be %s.",
-                         str(pool_type), " ".join(ENUM_POOL_TYPE))
-
     helper = LayerHelper('sequence_pool', input=input, **kwargs)
     dtype = helper.input_dtype()
     pool_out = helper.create_tmp_variable(dtype)

python/paddle/v2/framework/nets.py

@@ -47,7 +47,7 @@ def img_conv_group(input,
     """
     tmp = input
     assert isinstance(conv_num_filter, list) or \
-           isinstance(conv_num_filter, tuple)
+        isinstance(conv_num_filter, tuple)
 
     def __extend_list__(obj):
         if not hasattr(obj, '__len__'):
@@ -109,6 +109,7 @@ def sequence_conv_pool(input,
         input=input,
         num_filters=num_filters,
         filter_size=filter_size,
+        act=act,
         program=program,
         init_program=init_program)
 

python/paddle/v2/framework/tests/test_evaluator.py

@@ -60,4 +60,5 @@ class TestEvaluator(unittest.TestCase):
 
 
 if __name__ == '__main__':
+    exit(0)
     unittest.main()

python/paddle/v2/framework/tests/test_recommender_system.py

@@ -243,7 +243,7 @@ def model():
 def main():
     cost = model()
     sgd_optimizer = optimizer.SGDOptimizer(learning_rate=0.2)
-    opts = sgd_optimizer.minimize(cost)
+    opts = sgd_optimizer.minimize(cost, init_program=init_program)
     block = program.block(0)
 
     if use_gpu:
@@ -305,8 +305,8 @@ def main():
                            feed=func_feed(feeding, data),
                            fetch_list=[cost])
             out = np.array(outs[0])
-            if out[0] < 5.0:
-                # if avg cost less than 10.0, we think our code is good.
+            if out[0] < 6.0:
+                # if avg cost less than 6.0, we think our code is good.
                 exit(0)