[Cleanup][B-16]Replace to_variable (#61538)

test/dygraph_to_static/test_reinforcement_learning.py

@@ -26,7 +26,6 @@
 
 import paddle
 import paddle.nn.functional as F
-from paddle.base.dygraph import to_variable
 from paddle.nn import Layer
 
 SEED = 2020
@@ -112,14 +111,14 @@ def choose_best_action(probs):
             return idx, np.array([mask]).astype("float32")
 
         def select_action(state):
-            state = to_variable(state)
+            state = paddle.to_tensor(state)
             state.stop_gradient = True
             loss_probs = policy(state)
 
             probs = loss_probs.numpy()
 
             action, _mask = sample_action(probs[0])
-            mask = to_variable(_mask)
+            mask = paddle.to_tensor(_mask)
             mask.stop_gradient = True
 
             loss_probs = paddle.log(loss_probs)
@@ -148,7 +147,7 @@ def finish_episode():
 
                 R_numpy = np.ones_like(log_prob_numpy).astype("float32")
                 _R = -1 * R * R_numpy
-                _R = to_variable(_R)
+                _R = paddle.to_tensor(_R)
                 _R.stop_gradient = True
                 cur_loss = paddle.multiply(_R, log_prob)
                 policy_loss.append(cur_loss)

test/dygraph_to_static/test_save_load.py

@@ -73,7 +73,7 @@ def test_save_load_same_result(self):
         x_data = np.random.randn(30, 10, 32).astype('float32')
         batch_num = 3
 
-        x = base.dygraph.to_variable(x_data)
+        x = paddle.to_tensor(x_data)
         net = Linear(32, 64)
         adam = Adam(learning_rate=0.1, parameters=net.parameters())
 
@@ -100,7 +100,7 @@ def test_save_load_same_result(self):
         # Switch into eval mode.
         dygraph_net.eval()
 
-        x = base.dygraph.to_variable(x_data)
+        x = paddle.to_tensor(x_data)
         # predict output
         with enable_to_static_guard(False):
             dygraph_out, dygraph_loss = dygraph_net(x)

test/dygraph_to_static/test_se_resnet.py

@@ -494,8 +494,8 @@ def predict_dygraph(self, data):
                 se_resnext.eval()
 
                 label = np.random.random([1, 1]).astype("int64")
-                img = base.dygraph.to_variable(data)
-                label = base.dygraph.to_variable(label)
+                img = paddle.to_tensor(data)
+                label = paddle.to_tensor(label)
                 pred_res, _, _, _ = se_resnext(img, label)
 
                 return pred_res.numpy()

test/dygraph_to_static/test_sentiment.py

@@ -20,7 +20,6 @@
 
 import paddle
 from paddle import base
-from paddle.base.dygraph import to_variable
 from paddle.nn import Embedding, Linear
 
 SEED = 2020
@@ -172,7 +171,7 @@ def __init__(self, dict_dim, batch_size, seq_len):
             sparse=False,
         )
         h_0 = np.zeros((self.batch_size, self.hid_dim), dtype="float32")
-        h_0 = to_variable(h_0)
+        h_0 = paddle.to_tensor(h_0)
         self._fc1 = Linear(self.hid_dim, self.hid_dim * 3)
         self._fc2 = Linear(self.hid_dim, self.fc_hid_dim)
         self._fc_prediction = Linear(self.fc_hid_dim, self.class_dim)
@@ -219,7 +218,7 @@ def __init__(self, dict_dim, batch_size, seq_len):
             sparse=False,
         )
         h_0 = np.zeros((self.batch_size, self.hid_dim), dtype="float32")
-        h_0 = to_variable(h_0)
+        h_0 = paddle.to_tensor(h_0)
         self._fc1 = Linear(self.hid_dim, self.hid_dim * 3)
         self._fc2 = Linear(self.hid_dim * 2, self.fc_hid_dim)
         self._fc_prediction = Linear(self.fc_hid_dim, self.class_dim)

test/legacy_test/test_repeat_interleave_op.py

@@ -236,16 +236,16 @@ def test_dygraph_api(self):
         index_x = np.array([1, 1, 2, 1, 2, 2]).astype('int32')
 
         with base.dygraph.guard():
-            x = base.dygraph.to_variable(input_x)
-            index = base.dygraph.to_variable(index_x)
+            x = paddle.to_tensor(input_x)
+            index = paddle.to_tensor(index_x)
             z = paddle.repeat_interleave(x, index, None)
             np_z = z.numpy()
         expect_out = np.repeat(input_x, index_x, axis=None)
         np.testing.assert_allclose(expect_out, np_z, rtol=1e-05)
 
         # case repeats int
         with base.dygraph.guard():
-            x = base.dygraph.to_variable(input_x)
+            x = paddle.to_tensor(input_x)
             index = 2
             z = paddle.repeat_interleave(x, index, None)
             np_z = z.numpy()
@@ -254,16 +254,16 @@ def test_dygraph_api(self):
 
         # case 1:
         with base.dygraph.guard():
-            x = base.dygraph.to_variable(self.data_x)
-            index = base.dygraph.to_variable(self.data_index)
+            x = paddle.to_tensor(self.data_x)
+            index = paddle.to_tensor(self.data_index)
             z = paddle.repeat_interleave(x, index, -1)
             np_z = z.numpy()
         expect_out = np.repeat(self.data_x, self.data_index, axis=-1)
         np.testing.assert_allclose(expect_out, np_z, rtol=1e-05)
 
         with base.dygraph.guard():
-            x = base.dygraph.to_variable(self.data_x)
-            index = base.dygraph.to_variable(self.data_index)
+            x = paddle.to_tensor(self.data_x)
+            index = paddle.to_tensor(self.data_index)
             z = paddle.repeat_interleave(x, index, 1)
             np_z = z.numpy()
         expect_out = np.repeat(self.data_x, self.data_index, axis=1)
@@ -272,16 +272,16 @@ def test_dygraph_api(self):
         # case 2:
         index_x = np.array([1, 2, 1]).astype('int32')
         with base.dygraph.guard():
-            x = base.dygraph.to_variable(self.data_x)
-            index = base.dygraph.to_variable(index_x)
+            x = paddle.to_tensor(self.data_x)
+            index = paddle.to_tensor(index_x)
             z = paddle.repeat_interleave(x, index, axis=0)
             np_z = z.numpy()
         expect_out = np.repeat(self.data_x, index, axis=0)
         np.testing.assert_allclose(expect_out, np_z, rtol=1e-05)
 
         # case 3 zero_dim:
         with base.dygraph.guard():
-            x = base.dygraph.to_variable(self.data_zero_dim_x)
+            x = paddle.to_tensor(self.data_zero_dim_x)
             index = 2
             z = paddle.repeat_interleave(x, index, None)
             np_z = z.numpy()
@@ -290,8 +290,8 @@ def test_dygraph_api(self):
 
         # case 4 zero_dim_index
         with base.dygraph.guard():
-            x = base.dygraph.to_variable(self.data_zero_dim_x)
-            index = base.dygraph.to_variable(self.data_zero_dim_index)
+            x = paddle.to_tensor(self.data_zero_dim_x)
+            index = paddle.to_tensor(self.data_zero_dim_index)
             z = paddle.repeat_interleave(x, index, None)
             np_z = z.numpy()
         expect_out = np.repeat(

test/legacy_test/test_roll_op.py

@@ -237,7 +237,7 @@ def test_dygraph_api(self):
         self.input_data()
         # case 1:
         with base.dygraph.guard():
-            x = base.dygraph.to_variable(self.data_x)
+            x = paddle.to_tensor(self.data_x)
             z = paddle.roll(x, shifts=1)
             np_z = z.numpy()
         expect_out = np.array(
@@ -247,7 +247,7 @@ def test_dygraph_api(self):
 
         # case 2:
         with base.dygraph.guard():
-            x = base.dygraph.to_variable(self.data_x)
+            x = paddle.to_tensor(self.data_x)
             z = paddle.roll(x, shifts=1, axis=0)
             np_z = z.numpy()
         expect_out = np.array(

test/legacy_test/test_rot90_op.py

@@ -312,7 +312,7 @@ def run5():
     def test_dygraph(self):
         img = np.array([[1, 2, 3], [4, 5, 6]]).astype(np.float32)
         with base.dygraph.guard():
-            inputs = base.dygraph.to_variable(img)
+            inputs = paddle.to_tensor(img)
 
             ret = paddle.rot90(inputs, k=1, axes=[0, 1])
             ret = ret.rot90(1, axes=[0, 1])

test/legacy_test/test_scatter_nd_op.py

@@ -559,7 +559,7 @@ class TestDygraph(unittest.TestCase):
     def test_dygraph(self):
         with base.dygraph.guard(base.CPUPlace()):
             index_data = np.array([[1, 1], [0, 1], [1, 3]]).astype(np.int64)
-            index = base.dygraph.to_variable(index_data)
+            index = paddle.to_tensor(index_data)
             updates = paddle.rand(shape=[3, 9, 10], dtype='float32')
             shape = [3, 5, 9, 10]
             output = paddle.scatter_nd(index, updates, shape)
@@ -569,7 +569,7 @@ def test_dygraph_1(self):
             x = paddle.rand(shape=[3, 5, 9, 10], dtype='float32')
             updates = paddle.rand(shape=[3, 9, 10], dtype='float32')
             index_data = np.array([[1, 1], [0, 1], [1, 3]]).astype(np.int64)
-            index = base.dygraph.to_variable(index_data)
+            index = paddle.to_tensor(index_data)
             output = paddle.scatter_nd_add(x, index, updates)
 
 

test/legacy_test/test_scatter_op.py

@@ -676,9 +676,9 @@ def test_dygraph(self):
                     [[1, 1], [2, 2], [3, 3], [4, 4]]
                 ).astype(np.float64)
 
-                x = base.dygraph.to_variable(x_data)
-                index = base.dygraph.to_variable(index_data)
-                updates = base.dygraph.to_variable(updates_data)
+                x = paddle.to_tensor(x_data)
+                index = paddle.to_tensor(index_data)
+                updates = paddle.to_tensor(updates_data)
 
                 output1 = self.scatter(x, index, updates, overwrite=False)
                 self.assertEqual(

test/legacy_test/test_slice_op.py

@@ -985,7 +985,7 @@ class TestImperativeVarBaseGetItem(unittest.TestCase):
     def test_getitem_with_long(self):
         with base.dygraph.guard():
             data = np.random.random((2, 80, 16128)).astype('float32')
-            var = base.dygraph.to_variable(data)
+            var = paddle.to_tensor(data)
             sliced = var[:, 10:, : var.shape[1]]  # var.shape[1] is 80L here
             self.assertEqual(sliced.shape, [2, 70, 80])
 
@@ -996,15 +996,15 @@ def test_getitem_with_float(self):
         def test_float_in_slice_item():
             with base.dygraph.guard():
                 data = np.random.random((2, 80, 16128)).astype('float32')
-                var = base.dygraph.to_variable(data)
+                var = paddle.to_tensor(data)
                 sliced = var[:, 1.1:, : var.shape[1]]
 
         self.assertRaises(Exception, test_float_in_slice_item)
 
         def test_float_in_index():
             with base.dygraph.guard():
                 data = np.random.random((2, 80, 16128)).astype('float32')
-                var = base.dygraph.to_variable(data)
+                var = paddle.to_tensor(data)
                 sliced = var[1.1]
 
         self.assertRaises(Exception, test_float_in_index)