[Cleanup][B-3] clean some paddle.base.dygraph.to_variable for test

test/dygraph_to_static/test_bmn.py

@@ -27,9 +27,7 @@
 from predictor_utils import PredictorTools
 
 import paddle
-from paddle import base
 from paddle.base import ParamAttr
-from paddle.base.dygraph import to_variable
 from paddle.base.framework import unique_name
 from paddle.jit.translated_layer import INFER_MODEL_SUFFIX, INFER_PARAMS_SUFFIX
 
@@ -220,7 +218,7 @@ def __init__(self, cfg):
             self.num_sample,
             self.num_sample_perbin,
         )
-        self.sample_mask = base.dygraph.base.to_variable(sample_mask)
+        self.sample_mask = paddle.to_tensor(sample_mask)
         self.sample_mask.stop_gradient = True
 
         self.p_conv3d1 = paddle.nn.Conv3D(
@@ -604,10 +602,10 @@ def val_bmn(model, args):
         gt_start = np.array([item[2] for item in data]).astype(DATATYPE)
         gt_end = np.array([item[3] for item in data]).astype(DATATYPE)
 
-        x_data = to_variable(video_feat)
-        gt_iou_map = to_variable(gt_iou_map)
-        gt_start = to_variable(gt_start)
-        gt_end = to_variable(gt_end)
+        x_data = paddle.to_tensor(video_feat)
+        gt_iou_map = paddle.to_tensor(gt_iou_map)
+        gt_start = paddle.to_tensor(gt_start)
+        gt_end = paddle.to_tensor(gt_end)
         gt_iou_map.stop_gradient = True
         gt_start.stop_gradient = True
         gt_end.stop_gradient = True
@@ -679,10 +677,10 @@ def train_bmn(self, args, to_static):
                         DATATYPE
                     )
 
-                    x_data = to_variable(video_feat)
-                    gt_iou_map = to_variable(gt_iou_map)
-                    gt_start = to_variable(gt_start)
-                    gt_end = to_variable(gt_end)
+                    x_data = paddle.to_tensor(video_feat)
+                    gt_iou_map = paddle.to_tensor(gt_iou_map)
+                    gt_start = paddle.to_tensor(gt_start)
+                    gt_end = paddle.to_tensor(gt_end)
                     gt_iou_map.stop_gradient = True
                     gt_start.stop_gradient = True
                     gt_end.stop_gradient = True
@@ -826,7 +824,7 @@ def predict_dygraph(self, data):
             bmn.set_dict(model_dict)
             bmn.eval()
 
-            x = to_variable(data)
+            x = paddle.to_tensor(data)
             pred_res = bmn(x)
             pred_res = [var.numpy() for var in pred_res]
 
@@ -857,7 +855,7 @@ def predict_dygraph_jit(self, data):
         bmn = paddle.jit.load(self.model_save_prefix)
         bmn.eval()
 
-        x = to_variable(data)
+        x = paddle.to_tensor(data)
         pred_res = bmn(x)
         pred_res = [var.numpy() for var in pred_res]
 

test/dygraph_to_static/test_cast.py

@@ -22,26 +22,25 @@
 )
 
 import paddle
-from paddle.base.dygraph import to_variable
 
 SEED = 2020
 np.random.seed(SEED)
 
 
 def test_bool_cast(x):
-    x = to_variable(x)
+    x = paddle.to_tensor(x)
     x = bool(x)
     return x
 
 
 def test_int_cast(x):
-    x = to_variable(x)
+    x = paddle.to_tensor(x)
     x = int(x)
     return x
 
 
 def test_float_cast(x):
-    x = to_variable(x)
+    x = paddle.to_tensor(x)
     x = float(x)
     return x
 
@@ -52,7 +51,7 @@ def test_not_var_cast(x):
 
 
 def test_mix_cast(x):
-    x = to_variable(x)
+    x = paddle.to_tensor(x)
     x = int(x)
     x = float(x)
     x = bool(x)

test/legacy_test/test_batch_norm_op.py

@@ -974,7 +974,7 @@ def compute(x, is_test, trainable_statistics):
                         is_test=is_test,
                         trainable_statistics=trainable_statistics,
                     )
-                    y = bn(base.dygraph.to_variable(x))
+                    y = bn(paddle.to_tensor(x))
                 return y.numpy()
 
             x = np.random.randn(*shape).astype("float32")

test/legacy_test/test_bicubic_interp_op.py

@@ -351,7 +351,7 @@ def test_case(self):
                 np.testing.assert_allclose(res, expect_res, rtol=1e-05)
 
         with base.dygraph.guard():
-            x = base.dygraph.to_variable(x_data)
+            x = paddle.to_tensor(x_data)
             interp = interpolate(
                 x, size=[12, 12], mode='bicubic', align_corners=False
             )

test/legacy_test/test_bicubic_interp_v2_op.py

@@ -661,7 +661,7 @@ def test_case(self):
                 np.testing.assert_allclose(res, expect_res, rtol=1e-05)
 
         with base.dygraph.guard():
-            x = base.dygraph.to_variable(x_data)
+            x = paddle.to_tensor(x_data)
             interp = interpolate(
                 x, size=[12, 12], mode='bicubic', align_corners=False
             )

test/legacy_test/test_bincount_op.py

@@ -63,7 +63,7 @@ def test_static_graph(self):
     def test_dygraph(self):
         with base.dygraph.guard():
             inputs_np = np.array([0, 1, 1, 3, 2, 1, 7]).astype(np.int64)
-            inputs = base.dygraph.to_variable(inputs_np)
+            inputs = paddle.to_tensor(inputs_np)
             actual = paddle.bincount(inputs)
             expected = np.bincount(inputs)
             self.assertTrue(

test/legacy_test/test_bmm_op.py

@@ -129,8 +129,8 @@ def test_out(self):
             ]
         )
         with base.dygraph.guard():
-            x = base.dygraph.to_variable(input1)
-            y = base.dygraph.to_variable(input2)
+            x = paddle.to_tensor(input1)
+            y = paddle.to_tensor(input2)
             out = paddle.bmm(x, y)
             out_np = out.numpy()
         expected_result = np.matmul(input1, input2)

test/legacy_test/test_cholesky_op.py

@@ -209,7 +209,7 @@ def test_dygraph(self):
                         [[10, 11, 12], [13, 14, 15], [16, 17, 18]],
                     ]
                 ).astype("float64")
-                input = base.dygraph.to_variable(input_np)
+                input = paddle.to_tensor(input_np)
                 try:
                     result = paddle.cholesky(input)
                 except RuntimeError as ex:

test/legacy_test/test_chunk_op.py

@@ -112,7 +112,7 @@ def test_out1(self):
         with base.dygraph.guard():
             input_1 = np.random.random([4, 6, 6]).astype("int32")
             # input is a variable which shape is [4, 6, 6]
-            input = base.dygraph.to_variable(input_1)
+            input = paddle.to_tensor(input_1)
             x0, x1, x2 = paddle.chunk(input, chunks=3, axis=1)
             x0_out = x0.numpy()
             x1_out = x1.numpy()
@@ -126,7 +126,7 @@ def test_out2(self):
         with base.dygraph.guard():
             input_1 = np.random.random([4, 6, 6]).astype("bool")
             # input is a variable which shape is [4, 6, 6]
-            input = base.dygraph.to_variable(input_1)
+            input = paddle.to_tensor(input_1)
             x0, x1, x2 = paddle.chunk(input, chunks=3, axis=1)
             x0_out = x0.numpy()
             x1_out = x1.numpy()
@@ -140,7 +140,7 @@ def test_axis_tensor_input(self):
         with base.dygraph.guard():
             input_1 = np.random.random([4, 6, 6]).astype("int32")
             # input is a variable which shape is [4, 6, 6]
-            input = base.dygraph.to_variable(input_1)
+            input = paddle.to_tensor(input_1)
             num1 = paddle.full(shape=[1], fill_value=1, dtype='int32')
             x0, x1, x2 = paddle.chunk(input, chunks=3, axis=num1)
             x0_out = x0.numpy()

test/legacy_test/test_coalesce_tensor_op.py

@@ -116,29 +116,32 @@ def init_output(self, input_list, set_constant, constant):
     def verify_output(self, place):
         with base.dygraph.base.guard(place=place):
             tensor_input = [
-                base.dygraph.base.to_variable(value=data[1])
-                for data in self.inputs["Input"]
+                paddle.to_tensor(data[1]) for data in self.inputs["Input"]
             ]
             eager_outputs, eager_fused_output = coalesce_tensor_eager_api(
                 tensor_input,
                 datatype=self.attrs["dtype"],
-                copy_data=self.attrs["copy_data"]
-                if "copy_data" in self.attrs
-                else False,
-                set_constant=self.attrs["set_constant"]
-                if "set_constant" in self.attrs
-                else False,
+                copy_data=(
+                    self.attrs["copy_data"]
+                    if "copy_data" in self.attrs
+                    else False
+                ),
+                set_constant=(
+                    self.attrs["set_constant"]
+                    if "set_constant" in self.attrs
+                    else False
+                ),
                 persist_output=False,
-                constant=self.attrs["constant"]
-                if "constant" in self.attrs
-                else 0.0,
+                constant=(
+                    self.attrs["constant"] if "constant" in self.attrs else 0.0
+                ),
                 use_align=True,
                 align_size=-1,
-                user_defined_size_of_dtype=self.attrs[
-                    "user_defined_size_of_dtype"
-                ]
-                if "user_defined_size_of_dtype" in self.attrs
-                else -1,
+                user_defined_size_of_dtype=(
+                    self.attrs["user_defined_size_of_dtype"]
+                    if "user_defined_size_of_dtype" in self.attrs
+                    else -1
+                ),
                 concated_shapes=[],
                 concated_ranks=[],
             )