Simplify multi_box_head API in detection.py and remove assign op. (#18310) (#18388)

* Simplify multi_box_head API in detection.py and remove assign op.

Author: qingqing01

paddle/fluid/operators/detection/mine_hard_examples_op.cc

@@ -195,25 +195,31 @@ class MineHardExamplesOp : public framework::OperatorWithKernel {
       auto loc_loss_dims = ctx->GetInputDim("LocLoss");
       PADDLE_ENFORCE_EQ(loc_loss_dims.size(), 2UL,
                         "The shape of LocLoss is [N, Np].");
-      PADDLE_ENFORCE_EQ(cls_loss_dims[0], loc_loss_dims[0],
-                        "Batch size of ClsLoss and LocLoss must be the same.");
-      PADDLE_ENFORCE_EQ(
-          cls_loss_dims[1], loc_loss_dims[1],
-          "Prior box number of ClsLoss and LocLoss must be the same.");
+      if (ctx->IsRuntime()) {
+        PADDLE_ENFORCE_EQ(
+            cls_loss_dims[0], loc_loss_dims[0],
+            "Batch size of ClsLoss and LocLoss must be the same.");
+        PADDLE_ENFORCE_EQ(
+            cls_loss_dims[1], loc_loss_dims[1],
+            "Prior box number of ClsLoss and LocLoss must be the same.");
+      }
     }
 
-    PADDLE_ENFORCE_EQ(
-        cls_loss_dims[0], idx_dims[0],
-        "Batch size of ClsLoss and MatchIndices must be the same.");
-    PADDLE_ENFORCE_EQ(
-        cls_loss_dims[1], idx_dims[1],
-        "Prior box number of ClsLoss and MatchIndices must be the same.");
-
-    PADDLE_ENFORCE_EQ(cls_loss_dims[0], dis_dims[0],
-                      "Batch size of ClsLoss and MatchDist must be the same.");
-    PADDLE_ENFORCE_EQ(
-        cls_loss_dims[1], idx_dims[1],
-        "Prior box number of ClsLoss and MatchDist must be the same.");
+    if (ctx->IsRuntime()) {
+      PADDLE_ENFORCE_EQ(
+          cls_loss_dims[0], idx_dims[0],
+          "Batch size of ClsLoss and MatchIndices must be the same.");
+      PADDLE_ENFORCE_EQ(
+          cls_loss_dims[1], idx_dims[1],
+          "Prior box number of ClsLoss and MatchIndices must be the same.");
+
+      PADDLE_ENFORCE_EQ(
+          cls_loss_dims[0], dis_dims[0],
+          "Batch size of ClsLoss and MatchDist must be the same.");
+      PADDLE_ENFORCE_EQ(
+          cls_loss_dims[1], idx_dims[1],
+          "Prior box number of ClsLoss and MatchDist must be the same.");
+    }
 
     auto mining_type =
         GetMiningType(ctx->Attrs().Get<std::string>("mining_type"));

python/paddle/fluid/layers/detection.py

@@ -1393,8 +1393,10 @@ def __reshape_to_2d(var):
     # 3. Mining hard examples
     actual_shape = nn.slice(conf_shape, axes=[0], starts=[0], ends=[2])
     actual_shape.stop_gradient = True
+    # shape=(-1, 0) is set for compile-time, the correct shape is set by
+    # actual_shape in runtime.
     conf_loss = nn.reshape(
-        x=conf_loss, shape=(num, num_prior), actual_shape=actual_shape)
+        x=conf_loss, shape=(-1, 0), actual_shape=actual_shape)
     conf_loss.stop_gradient = True
     neg_indices = helper.create_variable_for_type_inference(dtype='int32')
     dtype = matched_indices.dtype
@@ -1464,7 +1466,9 @@ def __reshape_to_2d(var):
     # 5.3 Compute overall weighted loss.
     loss = conf_loss_weight * conf_loss + loc_loss_weight * loc_loss
     # reshape to [N, Np], N is the batch size and Np is the prior box number.
-    loss = nn.reshape(x=loss, shape=(num, num_prior), actual_shape=actual_shape)
+    # shape=(-1, 0) is set for compile-time, the correct shape is set by
+    # actual_shape in runtime.
+    loss = nn.reshape(x=loss, shape=(-1, 0), actual_shape=actual_shape)
     loss = nn.reduce_sum(loss, dim=1, keep_dim=True)
     if normalize:
         normalizer = nn.reduce_sum(target_loc_weight)
@@ -1927,13 +1931,7 @@ def _is_list_or_tuple_and_equal(data, length, err_info):
             stride=stride)
 
         mbox_loc = nn.transpose(mbox_loc, perm=[0, 2, 3, 1])
-        compile_shape = [
-            mbox_loc.shape[0], cpt.floor_division(
-                mbox_loc.shape[1] * mbox_loc.shape[2] * mbox_loc.shape[3], 4), 4
-        ]
-        run_shape = tensor.assign(numpy.array([0, -1, 4]).astype("int32"))
-        mbox_loc_flatten = nn.reshape(
-            mbox_loc, shape=compile_shape, actual_shape=run_shape)
+        mbox_loc_flatten = nn.flatten(mbox_loc, axis=1)
         mbox_locs.append(mbox_loc_flatten)
 
         # get conf
@@ -1945,16 +1943,7 @@ def _is_list_or_tuple_and_equal(data, length, err_info):
             padding=pad,
             stride=stride)
         conf_loc = nn.transpose(conf_loc, perm=[0, 2, 3, 1])
-        new_shape = [0, -1, num_classes]
-        compile_shape = [
-            conf_loc.shape[0],
-            cpt.floor_division(conf_loc.shape[1] * conf_loc.shape[2] *
-                               conf_loc.shape[3], num_classes), num_classes
-        ]
-        run_shape = tensor.assign(
-            numpy.array([0, -1, num_classes]).astype("int32"))
-        conf_loc_flatten = nn.reshape(
-            conf_loc, shape=compile_shape, actual_shape=run_shape)
+        conf_loc_flatten = nn.flatten(conf_loc, axis=1)
         mbox_confs.append(conf_loc_flatten)
 
     if len(box_results) == 1:
@@ -1972,7 +1961,10 @@ def _is_list_or_tuple_and_equal(data, length, err_info):
         box = tensor.concat(reshaped_boxes)
         var = tensor.concat(reshaped_vars)
         mbox_locs_concat = tensor.concat(mbox_locs, axis=1)
+        mbox_locs_concat = nn.reshape(mbox_locs_concat, shape=[0, -1, 4])
         mbox_confs_concat = tensor.concat(mbox_confs, axis=1)
+        mbox_confs_concat = nn.reshape(
+            mbox_confs_concat, shape=[0, -1, num_classes])
 
     box.stop_gradient = True
     var.stop_gradient = True