[Cleanup][B-11]Replace to_variable (#61532)

co63oc · web-flow · commit eae1c35db404 · 2024-02-04T12:39:19.000+08:00
diff --git a/test/legacy_test/test_imperative_ptb_rnn.py b/test/legacy_test/test_imperative_ptb_rnn.py
@@ -21,7 +21,6 @@
 import paddle
 from paddle import base
 from paddle.base import core, framework
-from paddle.base.dygraph.base import to_variable
 from paddle.nn import Embedding
 
 
@@ -285,10 +284,10 @@ def ptb_rnn_cpu_float32(self, is_sparse):
                 init_cell_data = np.zeros(
                     (num_layers, batch_size, hidden_size), dtype='float32'
                 )
-                x = to_variable(x_data)
-                y = to_variable(y_data)
-                init_hidden = to_variable(init_hidden_data)
-                init_cell = to_variable(init_cell_data)
+                x = paddle.to_tensor(x_data)
+                y = paddle.to_tensor(y_data)
+                init_hidden = paddle.to_tensor(init_hidden_data)
+                init_cell = paddle.to_tensor(init_cell_data)
 
                 outs = ptb_model(x, y, init_hidden, init_cell)
 
diff --git a/test/legacy_test/test_imperative_ptb_rnn_sorted_gradient.py b/test/legacy_test/test_imperative_ptb_rnn_sorted_gradient.py
@@ -21,7 +21,6 @@
 import paddle
 from paddle import base
 from paddle.base import core, framework
-from paddle.base.dygraph.base import to_variable
 
 
 class TestDygraphPtbRnnSortGradient(unittest.TestCase):
@@ -74,10 +73,10 @@ def ptb_rnn_sort_gradient_cpu_float32(self, is_sparse):
                 init_cell_data = np.zeros(
                     (num_layers, batch_size, hidden_size), dtype='float32'
                 )
-                x = to_variable(x_data)
-                y = to_variable(y_data)
-                init_hidden = to_variable(init_hidden_data)
-                init_cell = to_variable(init_cell_data)
+                x = paddle.to_tensor(x_data)
+                y = paddle.to_tensor(y_data)
+                init_hidden = paddle.to_tensor(init_hidden_data)
+                init_cell = paddle.to_tensor(init_cell_data)
                 dy_loss, last_hidden, last_cell = ptb_model(
                     x, y, init_hidden, init_cell
                 )
diff --git a/test/legacy_test/test_imperative_recurrent_usage.py b/test/legacy_test/test_imperative_recurrent_usage.py
@@ -20,7 +20,6 @@
 import paddle
 from paddle import base
 from paddle.base import core
-from paddle.base.dygraph.base import to_variable
 
 
 class RecurrentTest(paddle.nn.Layer):
@@ -40,8 +39,8 @@ def test_recurrent_feed(self):
         original_np2 = np.arange(5, 9).reshape(2, 2).astype("float32")
         with base.dygraph.guard():
             paddle.seed(seed)
-            original_in1 = to_variable(original_np1)
-            original_in2 = to_variable(original_np2)
+            original_in1 = paddle.to_tensor(original_np1)
+            original_in2 = paddle.to_tensor(original_np2)
             original_in1.stop_gradient = False
             original_in2.stop_gradient = False
             rt = RecurrentTest("RecurrentTest")
@@ -58,8 +57,8 @@ def test_recurrent_feed(self):
 
         with base.dygraph.guard():
             paddle.seed(seed)
-            original_in1 = to_variable(original_np1)
-            original_in2 = to_variable(original_np2)
+            original_in1 = paddle.to_tensor(original_np1)
+            original_in2 = paddle.to_tensor(original_np2)
             original_in1.stop_gradient = False
             original_in2.stop_gradient = False
             rt = RecurrentTest("RecurrentTest")
diff --git a/test/legacy_test/test_imperative_reinforcement.py b/test/legacy_test/test_imperative_reinforcement.py
@@ -63,18 +63,18 @@ def run_dygraph():
 
             policy = Policy(input_size=4)
 
-            dy_state = base.dygraph.base.to_variable(state)
+            dy_state = paddle.to_tensor(state)
             dy_state.stop_gradient = True
             loss_probs = policy(dy_state)
 
-            dy_mask = base.dygraph.base.to_variable(mask)
+            dy_mask = paddle.to_tensor(mask)
             dy_mask.stop_gradient = True
 
             loss_probs = paddle.log(loss_probs)
             loss_probs = paddle.multiply(loss_probs, dy_mask)
             loss_probs = paddle.sum(loss_probs, axis=-1)
 
-            dy_reward = base.dygraph.base.to_variable(reward)
+            dy_reward = paddle.to_tensor(reward)
             dy_reward.stop_gradient = True
 
             loss_probs = paddle.multiply(dy_reward, loss_probs)
diff --git a/test/legacy_test/test_imperative_resnet.py b/test/legacy_test/test_imperative_resnet.py
@@ -21,7 +21,6 @@
 import paddle
 from paddle import base
 from paddle.base import core
-from paddle.base.dygraph.base import to_variable
 from paddle.base.layer_helper import LayerHelper
 from paddle.nn import BatchNorm
 
@@ -293,8 +292,8 @@ def test_resnet_float32(self):
                     .reshape(batch_size, 1)
                 )
 
-                img = to_variable(dy_x_data)
-                label = to_variable(y_data)
+                img = paddle.to_tensor(dy_x_data)
+                label = paddle.to_tensor(y_data)
                 label.stop_gradient = True
 
                 out = None
diff --git a/test/legacy_test/test_imperative_resnet_sorted_gradient.py b/test/legacy_test/test_imperative_resnet_sorted_gradient.py
@@ -21,7 +21,6 @@
 import paddle
 from paddle import base
 from paddle.base import core
-from paddle.base.dygraph.base import to_variable
 
 batch_size = 8
 train_parameters = {
@@ -112,8 +111,8 @@ def test_resnet_sort_gradient_float32(self):
                     .reshape(batch_size, 1)
                 )
 
-                img = to_variable(dy_x_data)
-                label = to_variable(y_data)
+                img = paddle.to_tensor(dy_x_data)
+                label = paddle.to_tensor(y_data)
                 label.stop_gradient = True
 
                 out = resnet(img)
diff --git a/test/legacy_test/test_imperative_save_load_v2.py b/test/legacy_test/test_imperative_save_load_v2.py
@@ -21,7 +21,6 @@
 import paddle
 from paddle import base
 from paddle.base import core
-from paddle.base.dygraph.base import to_variable
 from paddle.nn import Embedding
 from paddle.optimizer import Adam
 from paddle.optimizer.lr import LRScheduler
@@ -298,10 +297,10 @@ def func_setUp(self):
                 init_cell_data = np.zeros(
                     (num_layers, batch_size, hidden_size), dtype='float32'
                 )
-                x = to_variable(x_data)
-                y = to_variable(y_data)
-                init_hidden = to_variable(init_hidden_data)
-                init_cell = to_variable(init_cell_data)
+                x = paddle.to_tensor(x_data)
+                y = paddle.to_tensor(y_data)
+                init_hidden = paddle.to_tensor(init_hidden_data)
+                init_cell = paddle.to_tensor(init_cell_data)
                 dy_loss, last_hidden, last_cell = ptb_model(
                     x, y, init_hidden, init_cell
                 )
@@ -401,10 +400,10 @@ def func_testLoadAndSetVarBase(self):
                 init_cell_data = np.zeros(
                     (num_layers, batch_size, hidden_size), dtype='float32'
                 )
-                x = to_variable(x_data)
-                y = to_variable(y_data)
-                init_hidden = to_variable(init_hidden_data)
-                init_cell = to_variable(init_cell_data)
+                x = paddle.to_tensor(x_data)
+                y = paddle.to_tensor(y_data)
+                init_hidden = paddle.to_tensor(init_hidden_data)
+                init_cell = paddle.to_tensor(init_cell_data)
                 dy_loss, last_hidden, last_cell = ptb_model(
                     x, y, init_hidden, init_cell
                 )
@@ -523,10 +522,10 @@ def func_testSetVariable(self):
                 init_cell_data = np.zeros(
                     (num_layers, batch_size, hidden_size), dtype='float32'
                 )
-                x = to_variable(x_data)
-                y = to_variable(y_data)
-                init_hidden = to_variable(init_hidden_data)
-                init_cell = to_variable(init_cell_data)
+                x = paddle.to_tensor(x_data)
+                y = paddle.to_tensor(y_data)
+                init_hidden = paddle.to_tensor(init_hidden_data)
+                init_cell = paddle.to_tensor(init_cell_data)
                 dy_loss, last_hidden, last_cell = ptb_model(
                     x, y, init_hidden, init_cell
                 )
@@ -641,10 +640,10 @@ def func_testSetNumpy(self):
                 init_cell_data = np.zeros(
                     (num_layers, batch_size, hidden_size), dtype='float32'
                 )
-                x = to_variable(x_data)
-                y = to_variable(y_data)
-                init_hidden = to_variable(init_hidden_data)
-                init_cell = to_variable(init_cell_data)
+                x = paddle.to_tensor(x_data)
+                y = paddle.to_tensor(y_data)
+                init_hidden = paddle.to_tensor(init_hidden_data)
+                init_cell = paddle.to_tensor(init_cell_data)
                 dy_loss, last_hidden, last_cell = ptb_model(
                     x, y, init_hidden, init_cell
                 )
@@ -760,10 +759,10 @@ def func_testSetVariableBeforeTrain(self):
                 init_cell_data = np.zeros(
                     (num_layers, batch_size, hidden_size), dtype='float32'
                 )
-                x = to_variable(x_data)
-                y = to_variable(y_data)
-                init_hidden = to_variable(init_hidden_data)
-                init_cell = to_variable(init_cell_data)
+                x = paddle.to_tensor(x_data)
+                y = paddle.to_tensor(y_data)
+                init_hidden = paddle.to_tensor(init_hidden_data)
+                init_cell = paddle.to_tensor(init_cell_data)
                 dy_loss, last_hidden, last_cell = ptb_model(
                     x, y, init_hidden, init_cell
                 )
@@ -860,10 +859,10 @@ def func_testLoadAndSetVarBaseBeforeTrain(self):
                 init_cell_data = np.zeros(
                     (num_layers, batch_size, hidden_size), dtype='float32'
                 )
-                x = to_variable(x_data)
-                y = to_variable(y_data)
-                init_hidden = to_variable(init_hidden_data)
-                init_cell = to_variable(init_cell_data)
+                x = paddle.to_tensor(x_data)
+                y = paddle.to_tensor(y_data)
+                init_hidden = paddle.to_tensor(init_hidden_data)
+                init_cell = paddle.to_tensor(init_cell_data)
                 dy_loss, last_hidden, last_cell = ptb_model(
                     x, y, init_hidden, init_cell
                 )
@@ -973,10 +972,10 @@ def func_testSetNumpyBeforeTrain(self):
                 init_cell_data = np.zeros(
                     (num_layers, batch_size, hidden_size), dtype='float32'
                 )
-                x = to_variable(x_data)
-                y = to_variable(y_data)
-                init_hidden = to_variable(init_hidden_data)
-                init_cell = to_variable(init_cell_data)
+                x = paddle.to_tensor(x_data)
+                y = paddle.to_tensor(y_data)
+                init_hidden = paddle.to_tensor(init_hidden_data)
+                init_cell = paddle.to_tensor(init_cell_data)
                 dy_loss, last_hidden, last_cell = ptb_model(
                     x, y, init_hidden, init_cell
                 )
diff --git a/test/legacy_test/test_imperative_selected_rows.py b/test/legacy_test/test_imperative_selected_rows.py
@@ -19,7 +19,6 @@
 import paddle
 from paddle import base
 from paddle.base import core
-from paddle.base.dygraph.base import to_variable
 
 
 class SimpleNet(paddle.nn.Layer):
@@ -91,7 +90,7 @@ def test_selectedrows_gradient2(self):
                     grad_clip = paddle.nn.ClipGradByGlobalNorm(5.0)
 
                     input_word = np.array([[1, 2], [2, 1]]).astype('int64')
-                    input = to_variable(input_word)
+                    input = paddle.to_tensor(input_word)
 
                     simplenet = SimpleNet(20, 32, "float32")
                     adam = paddle.optimizer.SGD(
diff --git a/test/legacy_test/test_imperative_selected_rows_to_lod_tensor.py b/test/legacy_test/test_imperative_selected_rows_to_lod_tensor.py
@@ -20,7 +20,6 @@
 import paddle
 from paddle import base
 from paddle.base import core, framework
-from paddle.base.dygraph.base import to_variable
 from paddle.nn import Embedding
 
 
@@ -142,8 +141,8 @@ def simple_net_float(self, is_sparse, dtype):
                         x_data = x_data.reshape((-1, num_steps))
                         y_data = y_data.reshape((-1, 1))
 
-                        x = to_variable(x_data)
-                        y = to_variable(y_data)
+                        x = paddle.to_tensor(x_data)
+                        y = paddle.to_tensor(y_data)
                         outs = simple_net(x, y)
                         dy_loss = outs
                         if i == 0:
diff --git a/test/legacy_test/test_imperative_star_gan_with_gradient_penalty.py b/test/legacy_test/test_imperative_star_gan_with_gradient_penalty.py
@@ -520,9 +520,9 @@ def clear_gradients(self):
             self.d_optimizer.clear_gradients()
 
     def run(self, image_real, label_org, label_trg):
-        image_real = base.dygraph.to_variable(image_real)
-        label_org = base.dygraph.to_variable(label_org)
-        label_trg = base.dygraph.to_variable(label_trg)
+        image_real = paddle.to_tensor(image_real)
+        label_org = paddle.to_tensor(label_org)
+        label_trg = paddle.to_tensor(label_trg)
 
         g_loss = get_generator_loss(
             image_real,
