support intermediate outputs on python 2.7 platform (#1764)

tools/python/fluidtools/run.py

@@ -160,7 +160,10 @@ def load_feed_kv():
         data = data.reshape(feed_shape).astype("float32")
         
         if is_lod:
-            data = data.reshape((1, *feed_shape)).astype("float32")
+            data_shape = [1]
+            for dim in feed_shape:
+                data_shape.append(dim)
+            data = data.reshape(data_shape).astype("float32")
             tensor = fluid.LoDTensor()
             seq_lens = [len(seq) for seq in data]
             cur_len = 0
@@ -203,17 +206,32 @@ def get_feed_var_shape(var_name):
     # return [1, 3, 224, 224]
     return get_var_shape(var_name)
 
+persistable_cache = []
+# 所有var，全部变成持久化
+def force_all_vars_to_persistable():
+    global persistable_cache
+    for var_name in vars.keys():
+        var_name = str(var_name)
+        v = fluid.framework._get_var(var_name, prog)
+        persistable = v.persistable
+        if not persistable:
+            persistable_cache.append(var_name)
+            v.persistable = True
+
+# 恢复持久化属性
+def restore_all_vars_persistable():
+    global persistable_cache
+    for var_name in vars.keys():
+        var_name = str(var_name)
+        v = fluid.framework._get_var(var_name, prog)
+        persistable = v.persistable
+        if var_name in persistable_cache:
+            v.persistable = False
+    persistable_cache = []
+
 # 获取var的数据
 def get_var_data(var_name, feed_kv=None):
-    # 强制var为可持久化
-    v = fluid.framework._get_var(var_name, prog)
-    persistable = v.persistable
-    if not persistable:
-        v.persistable = True
-    # outputs = run_model(feed_kv=feed_kv)
-    output = np.array(fluid.global_scope().find_var(var_name).get_tensor())
-    # 恢复var的可持久化属性
-    v.persistable = persistable
+    output = np.array(fluid.global_scope().var(var_name).get_tensor())
     return output
 
 output_var_cache = {}
@@ -223,6 +241,7 @@ def tensor_sample(tensor):
     else:
         step = math.floor(len(tensor) / sample_num)
     step = max(step, 1)
+    step = int(step)
     sample = []
     for i in range(0, len(tensor), step):
         sample.append(tensor[i])
@@ -231,6 +250,8 @@ def tensor_sample(tensor):
 op_cache = {}
 # 获取每层输出的数据
 def save_all_op_output(feed_kv=None):
+    force_all_vars_to_persistable()
+    outputs = run_model(feed_kv=feed_kv)
     if not os.path.exists(output_path):
         os.mkdir(output_path)
     ops = prog.current_block().ops
@@ -281,6 +302,7 @@ def save_all_op_output(feed_kv=None):
         except:
             pass
     pp_green("all the op outputs are saved into directory 【{}】".format(output_path), 1)
+    restore_all_vars_persistable()
 
 ops = prog.current_block().ops
 vars = prog.current_block().vars