Use a new scope for inference in python unittest to avoid changing the value...

Use a new scope for inference in python unittest to avoid changing the value of variables for training.

python/paddle/v2/fluid/tests/book/notest_rnn_encoder_decoer.py

@@ -228,6 +228,8 @@ def infer(use_cuda, save_dirname=None):
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
     exe = fluid.Executor(place)
 
+    inference_scope = fluid.core.Scope()
+    with fluid.scope_guard(inference_scope):
         # Use fluid.io.load_inference_model to obtain the inference program desc,
         # the feed_target_names (the names of variables that will be feeded
         # data using feed operators), and the fetch_targets (variables that

python/paddle/v2/fluid/tests/book/test_fit_a_line.py

@@ -72,6 +72,8 @@ def infer(use_cuda, save_dirname=None):
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
     exe = fluid.Executor(place)
 
+    inference_scope = fluid.core.Scope()
+    with fluid.scope_guard(inference_scope):
         # Use fluid.io.load_inference_model to obtain the inference program desc,
         # the feed_target_names (the names of variables that will be feeded
         # data using feed operators), and the fetch_targets (variables that
@@ -82,7 +84,8 @@ def infer(use_cuda, save_dirname=None):
         # The input's dimension should be 2-D and the second dim is 13
         # The input data should be >= 0
         batch_size = 10
-    tensor_x = numpy.random.uniform(0, 10, [batch_size, 13]).astype("float32")
+        tensor_x = numpy.random.uniform(0, 10,
+                                        [batch_size, 13]).astype("float32")
         assert feed_target_names[0] == 'x'
         results = exe.run(inference_program,
                           feed={feed_target_names[0]: tensor_x},

python/paddle/v2/fluid/tests/book/test_image_classification.py

@@ -174,6 +174,8 @@ def infer(use_cuda, save_dirname=None):
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
     exe = fluid.Executor(place)
 
+    inference_scope = fluid.core.Scope()
+    with fluid.scope_guard(inference_scope):
         # Use fluid.io.load_inference_model to obtain the inference program desc,
         # the feed_target_names (the names of variables that will be feeded
         # data using feed operators), and the fetch_targets (variables that

python/paddle/v2/fluid/tests/book/test_label_semantic_roles.py

@@ -252,6 +252,8 @@ def infer(use_cuda, save_dirname=None):
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
     exe = fluid.Executor(place)
 
+    inference_scope = fluid.core.Scope()
+    with fluid.scope_guard(inference_scope):
         # Use fluid.io.load_inference_model to obtain the inference program desc,
         # the feed_target_names (the names of variables that will be feeded
         # data using feed operators), and the fetch_targets (variables that
@@ -260,14 +262,22 @@ def infer(use_cuda, save_dirname=None):
          fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
 
         lod = [0, 4, 10]
-    word = create_random_lodtensor(lod, place, low=0, high=word_dict_len - 1)
-    pred = create_random_lodtensor(lod, place, low=0, high=pred_dict_len - 1)
-    ctx_n2 = create_random_lodtensor(lod, place, low=0, high=word_dict_len - 1)
-    ctx_n1 = create_random_lodtensor(lod, place, low=0, high=word_dict_len - 1)
-    ctx_0 = create_random_lodtensor(lod, place, low=0, high=word_dict_len - 1)
-    ctx_p1 = create_random_lodtensor(lod, place, low=0, high=word_dict_len - 1)
-    ctx_p2 = create_random_lodtensor(lod, place, low=0, high=word_dict_len - 1)
-    mark = create_random_lodtensor(lod, place, low=0, high=mark_dict_len - 1)
+        word = create_random_lodtensor(
+            lod, place, low=0, high=word_dict_len - 1)
+        pred = create_random_lodtensor(
+            lod, place, low=0, high=pred_dict_len - 1)
+        ctx_n2 = create_random_lodtensor(
+            lod, place, low=0, high=word_dict_len - 1)
+        ctx_n1 = create_random_lodtensor(
+            lod, place, low=0, high=word_dict_len - 1)
+        ctx_0 = create_random_lodtensor(
+            lod, place, low=0, high=word_dict_len - 1)
+        ctx_p1 = create_random_lodtensor(
+            lod, place, low=0, high=word_dict_len - 1)
+        ctx_p2 = create_random_lodtensor(
+            lod, place, low=0, high=word_dict_len - 1)
+        mark = create_random_lodtensor(
+            lod, place, low=0, high=mark_dict_len - 1)
 
         # Construct feed as a dictionary of {feed_target_name: feed_target_data}
         # and results will contain a list of data corresponding to fetch_targets.

python/paddle/v2/fluid/tests/book/test_recognize_digits.py

@@ -165,6 +165,8 @@ def infer(use_cuda, save_dirname=None, param_filename=None):
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
     exe = fluid.Executor(place)
 
+    inference_scope = fluid.core.Scope()
+    with fluid.scope_guard(inference_scope):
         # Use fluid.io.load_inference_model to obtain the inference program desc,
         # the feed_target_names (the names of variables that will be feeded
         # data using feed operators), and the fetch_targets (variables that
@@ -175,8 +177,8 @@ def infer(use_cuda, save_dirname=None, param_filename=None):
         # The input's dimension of conv should be 4-D or 5-D.
         # Use normilized image pixels as input data, which should be in the range [-1.0, 1.0].
         batch_size = 1
-    tensor_img = numpy.random.uniform(-1.0, 1.0,
-                                      [batch_size, 1, 28, 28]).astype("float32")
+        tensor_img = numpy.random.uniform(
+            -1.0, 1.0, [batch_size, 1, 28, 28]).astype("float32")
 
         # Construct feed as a dictionary of {feed_target_name: feed_target_data}
         # and results will contain a list of data corresponding to fetch_targets.

python/paddle/v2/fluid/tests/book/test_recommender_system.py

@@ -251,13 +251,6 @@ def infer(use_cuda, save_dirname=None):
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
     exe = fluid.Executor(place)
 
-    # Use fluid.io.load_inference_model to obtain the inference program desc,
-    # the feed_target_names (the names of variables that will be feeded
-    # data using feed operators), and the fetch_targets (variables that
-    # we want to obtain data from using fetch operators).
-    [inference_program, feed_target_names,
-     fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
-
     def create_lod_tensor(data, lod=None):
         tensor = fluid.LoDTensor()
         if lod is None:
@@ -275,6 +268,15 @@ def infer(use_cuda, save_dirname=None):
         tensor.set(flattened_data, place)
         return tensor
 
+    inference_scope = fluid.core.Scope()
+    with fluid.scope_guard(inference_scope):
+        # Use fluid.io.load_inference_model to obtain the inference program desc,
+        # the feed_target_names (the names of variables that will be feeded
+        # data using feed operators), and the fetch_targets (variables that
+        # we want to obtain data from using fetch operators).
+        [inference_program, feed_target_names,
+         fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
+
         # Use the first data from paddle.dataset.movielens.test() as input
         assert feed_target_names[0] == "user_id"
         user_id = create_lod_tensor([[1]])

python/paddle/v2/fluid/tests/book/test_understand_sentiment.py

@@ -144,6 +144,8 @@ def infer(word_dict, use_cuda, save_dirname=None):
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
     exe = fluid.Executor(place)
 
+    inference_scope = fluid.core.Scope()
+    with fluid.scope_guard(inference_scope):
         # Use fluid.io.load_inference_model to obtain the inference program desc,
         # the feed_target_names (the names of variables that will be feeded
         # data using feed operators), and the fetch_targets (variables that

python/paddle/v2/fluid/tests/book/test_word2vec.py

@@ -138,6 +138,8 @@ def infer(use_cuda, save_dirname=None):
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
     exe = fluid.Executor(place)
 
+    inference_scope = fluid.core.Scope()
+    with fluid.scope_guard(inference_scope):
         # Use fluid.io.load_inference_model to obtain the inference program desc,
         # the feed_target_names (the names of variables that will be feeded
         # data using feed operators), and the fetch_targets (variables that
@@ -150,10 +152,14 @@ def infer(use_cuda, save_dirname=None):
 
         # Setup inputs, by creating 4 words, the lod of which should be [0, 1]
         lod = [0, 1]
-    first_word = create_random_lodtensor(lod, place, low=0, high=dict_size - 1)
-    second_word = create_random_lodtensor(lod, place, low=0, high=dict_size - 1)
-    third_word = create_random_lodtensor(lod, place, low=0, high=dict_size - 1)
-    fourth_word = create_random_lodtensor(lod, place, low=0, high=dict_size - 1)
+        first_word = create_random_lodtensor(
+            lod, place, low=0, high=dict_size - 1)
+        second_word = create_random_lodtensor(
+            lod, place, low=0, high=dict_size - 1)
+        third_word = create_random_lodtensor(
+            lod, place, low=0, high=dict_size - 1)
+        fourth_word = create_random_lodtensor(
+            lod, place, low=0, high=dict_size - 1)
 
         assert feed_target_names[0] == 'firstw'
         assert feed_target_names[1] == 'secondw'