[Cleanup][B-2] clean some paddle.base.dygraph.to_variable for test

test/dygraph_to_static/seq2seq_dygraph_model.py

@@ -18,7 +18,6 @@
 import paddle
 from paddle import base
 from paddle.base import ParamAttr
-from paddle.base.dygraph.base import to_variable
 from paddle.nn import Embedding, Layer
 
 INF = 1.0 * 1e5
@@ -211,10 +210,10 @@ def forward(self, inputs):
 
         # NOTE: modify model code about `enc_hidden` and `enc_cell` to transforme dygraph code successfully.
         # Because nested list can't be transformed now.
-        enc_hidden_0 = to_variable(
+        enc_hidden_0 = paddle.to_tensor(
             np.zeros((self.batch_size, self.hidden_size), dtype='float32')
         )
-        enc_cell_0 = to_variable(
+        enc_cell_0 = paddle.to_tensor(
             np.zeros((self.batch_size, self.hidden_size), dtype='float32')
         )
         zero = paddle.zeros(shape=[1], dtype="int64")
@@ -312,10 +311,10 @@ def beam_search(self, inputs):
             self.batch_size = src.shape[0]
 
         src_emb = self.src_embeder(self._transpose_batch_time(src))
-        enc_hidden_0 = to_variable(
+        enc_hidden_0 = paddle.to_tensor(
             np.zeros((self.batch_size, self.hidden_size), dtype='float32')
         )
-        enc_cell_0 = to_variable(
+        enc_cell_0 = paddle.to_tensor(
             np.zeros((self.batch_size, self.hidden_size), dtype='float32')
         )
         zero = paddle.zeros(shape=[1], dtype="int64")
@@ -367,20 +366,20 @@ def beam_search(self, inputs):
 
         # beam search
         batch_beam_shape = (self.batch_size, self.beam_size)
-        vocab_size_tensor = to_variable(
+        vocab_size_tensor = paddle.to_tensor(
             np.full((1), self.tar_vocab_size)
         ).astype("int64")
-        start_token_tensor = to_variable(
+        start_token_tensor = paddle.to_tensor(
             np.full(batch_beam_shape, self.beam_start_token, dtype='int64')
         )
-        end_token_tensor = to_variable(
+        end_token_tensor = paddle.to_tensor(
             np.full(batch_beam_shape, self.beam_end_token, dtype='int64')
         )
         step_input = self.tar_embeder(start_token_tensor)
-        beam_finished = to_variable(
+        beam_finished = paddle.to_tensor(
             np.full(batch_beam_shape, 0, dtype='float32')
         )
-        beam_state_log_probs = to_variable(
+        beam_state_log_probs = paddle.to_tensor(
             np.array(
                 [[0.0] + [-self.kinf] * (self.beam_size - 1)], dtype="float32"
             )
@@ -395,15 +394,17 @@ def beam_search(self, inputs):
 
         batch_pos = paddle.expand(
             paddle.unsqueeze(
-                to_variable(np.arange(0, self.batch_size, 1, dtype="int64")),
+                paddle.to_tensor(
+                    np.arange(0, self.batch_size, 1, dtype="int64")
+                ),
                 [1],
             ),
             [-1, self.beam_size],
         )
         predicted_ids = []
         parent_ids = []
 
-        for step_idx in range(paddle.to_tensor(self.beam_max_step_num)):
+        for step_idx in range(paddle.paddle.to_tensor(self.beam_max_step_num)):
             if paddle.sum(1 - beam_finished) == 0:
                 break
             step_input = self._merge_batch_beams(step_input)
@@ -437,7 +438,7 @@ def beam_search(self, inputs):
             )
             noend_array = [-self.kinf] * self.tar_vocab_size
             noend_array[self.beam_end_token] = 0
-            noend_mask_tensor = to_variable(
+            noend_mask_tensor = paddle.to_tensor(
                 np.array(noend_array, dtype='float32')
             )
 
@@ -726,11 +727,11 @@ def forward(self, inputs):
 
         # NOTE: modify model code about `enc_hidden` and `enc_cell` to transform dygraph code successfully.
         # Because nested list can't be transformed now.
-        enc_hidden_0 = to_variable(
+        enc_hidden_0 = paddle.to_tensor(
             np.zeros((self.batch_size, self.hidden_size), dtype='float32')
         )
         enc_hidden_0.stop_gradient = True
-        enc_cell_0 = to_variable(
+        enc_cell_0 = paddle.to_tensor(
             np.zeros((self.batch_size, self.hidden_size), dtype='float32')
         )
         enc_hidden_0.stop_gradient = True
@@ -789,7 +790,7 @@ def forward(self, inputs):
         enc_outputs = self._transpose_batch_time(enc_outputs)
 
         # train
-        input_feed = to_variable(
+        input_feed = paddle.to_tensor(
             np.zeros((self.batch_size, self.hidden_size), dtype='float32')
         )
         # NOTE: set stop_gradient here, otherwise grad var is null

test/dygraph_to_static/simnet_dygraph_model.py

@@ -1,4 +1,4 @@
-#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+x  #   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -298,14 +298,14 @@ class FC(paddle.nn.Layer):
 
     Examples:
         .. code-block:: python
-          from paddle.base.dygraph.base import to_variable
+          import paddle
           import paddle.base as base
           from paddle.base.dygraph import FC
           import numpy as np
           data = np.random.uniform(-1, 1, [30, 10, 32]).astype('float32')
           with base.dygraph.guard():
               fc = FC("fc", 64, num_flatten_dims=2)
-              data = to_variable(data)
+              data = paddle.to_tensor(data)
               conv = fc(data)
     """
 

test/dygraph_to_static/test_basic_api_transformation.py

@@ -21,27 +21,15 @@
 )
 
 import paddle
-from paddle import base, to_tensor
-from paddle.base import dygraph
-from paddle.base.dygraph import to_variable
+from paddle import to_tensor
 from paddle.jit.api import to_static
 
 SEED = 2020
 np.random.seed(SEED)
 
 
 def dyfunc_to_variable(x):
-    res = base.dygraph.to_variable(x, name=None, zero_copy=None)
-    return res
-
-
-def dyfunc_to_variable_2(x):
-    res = dygraph.to_variable(value=np.zeros(shape=(1), dtype=np.int32))
-    return res
-
-
-def dyfunc_to_variable_3(x):
-    res = to_variable(x, name=None, zero_copy=None)
+    res = paddle.to_tensor(x)
     return res
 
 
@@ -73,15 +61,7 @@ def setUp(self):
             dyfunc_bool_to_tensor,
             dyfunc_int_to_tensor,
             dyfunc_float_to_tensor,
-            dyfunc_to_variable,
-            dyfunc_to_variable_2,
-            dyfunc_to_variable_3,
         ]
-        self.place = (
-            paddle.CUDAPlace(0)
-            if paddle.is_compiled_with_cuda()
-            else paddle.CPUPlace()
-        )
 
     def get_dygraph_output(self):
         res = self.dygraph_func(self.input).numpy()

test/legacy_test/test_activation_op.py

@@ -370,7 +370,7 @@ def test_out_name(self):
     def test_dygraph(self):
         with base.dygraph.guard():
             np_x = np.array([0.1])
-            x = base.dygraph.to_variable(np_x)
+            x = paddle.to_tensor(np_x)
             z = eval("paddle.%s(x).numpy()" % self.op_type)
             z_expected = eval("np.%s(np_x)" % self.op_type)
             np.testing.assert_allclose(z, z_expected, rtol=1e-05)
@@ -963,7 +963,7 @@ def test_out_name(self):
     def test_dygraph(self):
         with base.dygraph.guard():
             np_x = np.array([0.1])
-            x = base.dygraph.to_variable(np_x)
+            x = paddle.to_tensor(np_x)
             z = paddle.atan(x).numpy()
             z_expected = np.arctan(np_x)
             self.assertEqual(z, z_expected)
@@ -1036,7 +1036,7 @@ class TestSinhAPI(unittest.TestCase):
     def test_dygraph(self):
         with base.dygraph.guard():
             np_x = np.array([0.1])
-            x = base.dygraph.to_variable(np_x)
+            x = paddle.to_tensor(np_x)
             z = paddle.sinh(x).numpy()
             z_expected = np.sinh(np_x)
             np.testing.assert_allclose(z, z_expected, rtol=1e-05)
@@ -1075,7 +1075,7 @@ def test_backward(self):
             input_x = np.random.uniform(0.1, 1, test_data_shape).astype(
                 "float32"
             )
-            var = base.dygraph.to_variable(input_x)
+            var = paddle.to_tensor(input_x)
             var.stop_gradient = False
             loss = paddle.sinh(var)
             loss.backward()
@@ -1168,7 +1168,7 @@ class TestCoshAPI(unittest.TestCase):
     def test_dygraph(self):
         with base.dygraph.guard():
             np_x = np.array([0.1])
-            x = base.dygraph.to_variable(np_x)
+            x = paddle.to_tensor(np_x)
             z = paddle.cosh(x).numpy()
             z_expected = np.cosh(np_x)
             np.testing.assert_allclose(z, z_expected, rtol=1e-05)
@@ -1206,7 +1206,7 @@ def test_backward(self):
             input_x = np.random.uniform(0.1, 1, test_data_shape).astype(
                 "float32"
             )
-            var = base.dygraph.to_variable(input_x)
+            var = paddle.to_tensor(input_x)
             var.stop_gradient = False
             loss = paddle.cosh(var)
             loss.backward()
@@ -4067,7 +4067,7 @@ def test_api(self):
         # dygraph
         with base.dygraph.guard():
             np_x = np.random.uniform(0.1, 1, [11, 17]).astype("float64")
-            data_x = base.dygraph.to_variable(np_x)
+            data_x = paddle.to_tensor(np_x)
             z = paddle.log1p(data_x)
             np_z = z.numpy()
             z_expected = np.array(np.log1p(np_x))

test/legacy_test/test_adam_op.py

@@ -725,7 +725,7 @@ def test_pir_adam_op(self):
     def test_adam_op_dygraph(self):
         paddle.disable_static()
         value = np.arange(26).reshape(2, 13).astype("float32")
-        a = base.dygraph.to_variable(value)
+        a = paddle.to_tensor(value)
         linear = paddle.nn.Linear(13, 5)
 
         adam = paddle.optimizer.Adam(
@@ -773,7 +773,7 @@ def test_adam_op_with_state_dict(self):
     def test_adam_with_grad_clip(self):
         paddle.disable_static()
         value = np.arange(26).reshape(2, 13).astype("float32")
-        a = base.dygraph.to_variable(value)
+        a = paddle.to_tensor(value)
         linear = paddle.nn.Linear(13, 5)
         clip = paddle.nn.ClipGradByGlobalNorm(clip_norm=1.0)
         adam = paddle.optimizer.Adam(

test/legacy_test/test_adaptive_avg_pool1d.py

@@ -87,7 +87,7 @@ def setUp(self):
     def check_adaptive_avg_dygraph_results(self, place):
         with base.dygraph.guard(place):
             input_np = np.random.random([2, 3, 32]).astype("float32")
-            input = base.dygraph.to_variable(input_np)
+            input = paddle.to_tensor(input_np)
             result = F.adaptive_avg_pool1d(input, output_size=16)
             result_np = avg_pool1D_forward_naive(
                 input_np, ksize=[16], strides=[0], paddings=[0], adaptive=True

test/legacy_test/test_adaptive_max_pool1d.py

@@ -78,7 +78,7 @@ def setUp(self):
     def check_adaptive_max_dygraph_results(self, place):
         with base.dygraph.guard(place):
             input_np = np.random.random([2, 3, 32]).astype("float32")
-            input = base.dygraph.to_variable(input_np)
+            input = paddle.to_tensor(input_np)
             result = F.adaptive_max_pool1d(input, output_size=16)
 
             result_np = max_pool1D_forward_naive(

test/legacy_test/test_addmm_op.py

@@ -324,9 +324,9 @@ def test_api_with_dygraph(self):
         np_y = np.random.random((6, 30)).astype(np.float32)
 
         with base.dygraph.guard():
-            input = base.dygraph.to_variable(np_input)
-            x = base.dygraph.to_variable(np_x)
-            y = base.dygraph.to_variable(np_y)
+            input = paddle.to_tensor(np_input)
+            x = paddle.to_tensor(np_x)
+            y = paddle.to_tensor(np_y)
             out = paddle.tensor.addmm(input, x, y)
             np.testing.assert_allclose(
                 np_input + np.dot(np_x, np_y), out.numpy(), rtol=1e-5, atol=1e-8

test/legacy_test/test_affine_grid_function.py

@@ -122,10 +122,12 @@ def test_static_api(self):
     def paddle_dygraph_layer(self):
         paddle.disable_static()
         theta_var = (
-            dg.to_variable(self.theta) if not self.invalid_theta else "invalid"
+            paddle.to_tensor(self.theta)
+            if not self.invalid_theta
+            else "invalid"
         )
         output_shape = (
-            dg.to_variable(self.output_shape)
+            paddle.to_tensor(self.output_shape)
             if self.variable_output_shape
             else self.output_shape
         )

test/legacy_test/test_array_read_write_op.py

@@ -106,9 +106,9 @@ def test_read_write(self):
         self.assertAlmostEqual(1.0, g_out_sum, delta=0.1)
 
         with base.dygraph.guard(place):
-            tensor1 = base.dygraph.to_variable(tensor)
-            tensor2 = base.dygraph.to_variable(tensor)
-            tensor3 = base.dygraph.to_variable(tensor)
+            tensor1 = paddle.to_tensor(tensor)
+            tensor2 = paddle.to_tensor(tensor)
+            tensor3 = paddle.to_tensor(tensor)
             x_dygraph = [tensor1, tensor2, tensor3]
             for each_x in x_dygraph:
                 each_x.stop_gradient = False