[Fluid Clean] Clean image_resize, resize_bilinear, resize_trilinear and resize_nearest. (#48691)

* clean fluild resize_trilinear

* clean fluild resize_bilinear

* clean fluild resize_nearest

* clean fluid image_resize

* fix test_trt_nearest_interp_op.py

* fix yolov3.py

* fix yolov3.py

python/paddle/fluid/tests/unittests/dygraph_to_static/yolov3.py

@@ -211,9 +211,10 @@ class Upsample(fluid.dygraph.Layer):
         out_shape.stop_gradient = True
 
         # reisze by actual_shape
-        out = fluid.layers.resize_nearest(
-            input=inputs, scale=self.scale, actual_shape=out_shape
+        out = paddle.nn.functional.interpolate(
+            x=inputs, size=out_shape, mode='nearest'
         )
+
         return out
 
 

python/paddle/fluid/tests/unittests/ir/inference/test_trt_nearest_interp_op.py

@@ -17,6 +17,7 @@ import unittest
 import numpy as np
 from inference_pass_test import InferencePassTest
 
+import paddle
 import paddle.fluid as fluid
 import paddle.fluid.core as core
 from paddle.fluid.core import AnalysisConfig, PassVersionChecker
@@ -81,16 +82,14 @@ class TRTNearestInterpTest(InferencePassTest):
 
     def append_nearest_interp(self, data):
         if self.scale > 0.0:
-            return fluid.layers.resize_nearest(
+            return paddle.nn.functional.interpolate(
                 data,
-                scale=self.scale,
-                align_corners=self.align_corners,
+                scale_factor=self.scale,
                 data_format=self.data_layout,
             )
-        return fluid.layers.resize_nearest(
+        return paddle.nn.functional.interpolate(
             data,
-            out_shape=self.resize_shape,
-            align_corners=self.align_corners,
+            size=self.resize_shape,
             data_format=self.data_layout,
         )
 

python/paddle/fluid/tests/unittests/mlu/test_bilinear_interp_v2_op_mlu.py

@@ -511,57 +511,6 @@ class TestBilinearInterp_attr_tensor_Case3(TestBilinearInterpOp_attr_tensor):
         self.scale_by_1Dtensor = True
 
 
-class TestBilinearInterpOpAPI(unittest.TestCase):
-    def test_case(self):
-        x = fluid.data(name="x", shape=[2, 3, 6, 6], dtype="float32")
-
-        dim = fluid.data(name="dim", shape=[1], dtype="int32")
-        shape_tensor = fluid.data(name="shape_tensor", shape=[2], dtype="int32")
-        actual_size = fluid.data(name="actual_size", shape=[2], dtype="int32")
-        scale_tensor = fluid.data(
-            name="scale_tensor", shape=[1], dtype="float32"
-        )
-
-        out1 = fluid.layers.resize_bilinear(x, out_shape=[12, 12])
-        out2 = fluid.layers.resize_bilinear(x, out_shape=[12, dim])
-        out3 = fluid.layers.resize_bilinear(x, out_shape=shape_tensor)
-        out4 = fluid.layers.resize_bilinear(
-            x, out_shape=[4, 4], actual_shape=actual_size
-        )
-        out5 = fluid.layers.resize_bilinear(x, scale=scale_tensor)
-
-        x_data = np.random.random((2, 3, 6, 6)).astype("float32")
-        dim_data = np.array([12]).astype("int32")
-        shape_data = np.array([12, 12]).astype("int32")
-        actual_size_data = np.array([12, 12]).astype("int32")
-        scale_data = np.array([2.0]).astype("float32")
-
-        if core.is_compiled_with_mlu():
-            place = paddle.device.MLUPlace(0)
-        else:
-            place = core.CPUPlace()
-        exe = fluid.Executor(place)
-        exe.run(fluid.default_startup_program())
-        results = exe.run(
-            fluid.default_main_program(),
-            feed={
-                "x": x_data,
-                "dim": dim_data,
-                "shape_tensor": shape_data,
-                "actual_size": actual_size_data,
-                "scale_tensor": scale_data,
-            },
-            fetch_list=[out1, out2, out3, out4, out5],
-            return_numpy=True,
-        )
-
-        expect_res = bilinear_interp_np(
-            x_data, out_h=12, out_w=12, align_corners=True
-        )
-        for res in results:
-            np.testing.assert_allclose(res, expect_res, rtol=1e-6)
-
-
 class TestBilinearInterpOpAPI_dy(unittest.TestCase):
     def test_case(self):
         import paddle

python/paddle/fluid/tests/unittests/mlu/test_nearest_interp_v2_op_mlu.py

@@ -546,101 +546,5 @@ class TestNearestInterp_attr_tensor_Case3(TestNearestInterpOp_attr_tensor):
         self.scale_by_1Dtensor = True
 
 
-class TestNearestAPI(unittest.TestCase):
-    def test_case(self):
-        x = fluid.data(name="x", shape=[2, 3, 6, 6], dtype="float32")
-        y = fluid.data(name="y", shape=[2, 6, 6, 3], dtype="float32")
-
-        dim = fluid.data(name="dim", shape=[1], dtype="int32")
-        shape_tensor = fluid.data(name="shape_tensor", shape=[2], dtype="int32")
-        actual_size = fluid.data(name="actual_size", shape=[2], dtype="int32")
-        scale_tensor = fluid.data(
-            name="scale_tensor", shape=[1], dtype="float32"
-        )
-
-        out1 = fluid.layers.resize_nearest(
-            y, out_shape=[12, 12], data_format='NHWC', align_corners=False
-        )
-        out2 = fluid.layers.resize_nearest(
-            x, out_shape=[12, dim], align_corners=False
-        )
-        out3 = fluid.layers.resize_nearest(
-            x, out_shape=shape_tensor, align_corners=False
-        )
-        out4 = fluid.layers.resize_nearest(
-            x, out_shape=[4, 4], actual_shape=actual_size, align_corners=False
-        )
-        out5 = fluid.layers.resize_nearest(
-            x, scale=scale_tensor, align_corners=False
-        )
-
-        x_data = np.random.random((2, 3, 6, 6)).astype("float32")
-        dim_data = np.array([12]).astype("int32")
-        shape_data = np.array([12, 12]).astype("int32")
-        actual_size_data = np.array([12, 12]).astype("int32")
-        scale_data = np.array([2.0]).astype("float32")
-
-        place = paddle.MLUPlace(0)
-        exe = fluid.Executor(place)
-        exe.run(fluid.default_startup_program())
-        results = exe.run(
-            fluid.default_main_program(),
-            feed={
-                "x": x_data,
-                "y": np.transpose(x_data, (0, 2, 3, 1)),
-                "dim": dim_data,
-                "shape_tensor": shape_data,
-                "actual_size": actual_size_data,
-                "scale_tensor": scale_data,
-            },
-            fetch_list=[out1, out2, out3, out4, out5],
-            return_numpy=True,
-        )
-
-        expect_res = nearest_neighbor_interp_np(
-            x_data, out_h=12, out_w=12, align_corners=False
-        )
-        np.testing.assert_allclose(
-            results[0], np.transpose(expect_res, (0, 2, 3, 1))
-        )
-        for i in range(len(results) - 1):
-            np.testing.assert_allclose(results[i + 1], expect_res)
-
-
-class TestNearestInterpException(unittest.TestCase):
-    def test_exception(self):
-        import paddle
-
-        input = fluid.data(name="input", shape=[1, 3, 6, 6], dtype="float32")
-
-        def attr_data_format():
-            # for 4-D input, data_format can only be NCHW or NHWC
-            out = fluid.layers.resize_nearest(
-                input, out_shape=[4, 8], data_format='NDHWC'
-            )
-
-        def attr_scale_type():
-            out = fluid.layers.resize_nearest(input, scale='scale')
-
-        def attr_scale_value():
-            out = fluid.layers.resize_nearest(input, scale=-0.3)
-
-        def input_shape_error():
-            x = paddle.randn([1, 3])
-            out = paddle.nn.functional.interpolate(x, scale_factor='scale')
-
-        def mode_error():
-            x = paddle.randn([1, 3])
-            out = paddle.nn.functional.interpolate(
-                x, scale_factor='scale', mode="BILINEAR"
-            )
-
-        self.assertRaises(ValueError, attr_data_format)
-        self.assertRaises(TypeError, attr_scale_type)
-        self.assertRaises(ValueError, attr_scale_value)
-        self.assertRaises(ValueError, input_shape_error)
-        self.assertRaises(ValueError, mode_error)
-
-
 if __name__ == "__main__":
     unittest.main()

python/paddle/fluid/tests/unittests/npu/test_nearest_interp_op_npu.py

@@ -404,87 +404,5 @@ class TestNearestInterp_attr_tensor_Case3(TestNearestInterpOp_attr_tensor):
         self.scale_by_1Dtensor = True
 
 
-class TestNearestAPI(unittest.TestCase):
-    def test_case(self):
-        x = fluid.data(name="x", shape=[2, 3, 6, 6], dtype="float32")
-        y = fluid.data(name="y", shape=[2, 6, 6, 3], dtype="float32")
-
-        dim = fluid.data(name="dim", shape=[1], dtype="int32")
-        shape_tensor = fluid.data(name="shape_tensor", shape=[2], dtype="int32")
-        actual_size = fluid.data(name="actual_size", shape=[2], dtype="int32")
-        scale_tensor = fluid.data(
-            name="scale_tensor", shape=[1], dtype="float32"
-        )
-
-        out1 = fluid.layers.resize_nearest(
-            y, out_shape=[12, 12], data_format='NHWC', align_corners=False
-        )
-        out2 = fluid.layers.resize_nearest(
-            x, out_shape=[12, dim], align_corners=False
-        )
-        out3 = fluid.layers.resize_nearest(
-            x, out_shape=shape_tensor, align_corners=False
-        )
-        out4 = fluid.layers.resize_nearest(
-            x, out_shape=[4, 4], actual_shape=actual_size, align_corners=False
-        )
-        out5 = fluid.layers.resize_nearest(
-            x, scale=scale_tensor, align_corners=False
-        )
-
-        x_data = np.random.random((2, 3, 6, 6)).astype("float32")
-        dim_data = np.array([12]).astype("int32")
-        shape_data = np.array([12, 12]).astype("int32")
-        actual_size_data = np.array([12, 12]).astype("int32")
-        scale_data = np.array([2.0]).astype("float32")
-
-        place = paddle.NPUPlace(0)
-        exe = fluid.Executor(place)
-        exe.run(fluid.default_startup_program())
-        results = exe.run(
-            fluid.default_main_program(),
-            feed={
-                "x": x_data,
-                "y": np.transpose(x_data, (0, 2, 3, 1)),
-                "dim": dim_data,
-                "shape_tensor": shape_data,
-                "actual_size": actual_size_data,
-                "scale_tensor": scale_data,
-            },
-            fetch_list=[out1, out2, out3, out4, out5],
-            return_numpy=True,
-        )
-
-        expect_res = nearest_neighbor_interp_np(
-            x_data, out_h=12, out_w=12, align_corners=False
-        )
-        np.testing.assert_allclose(
-            results[0], np.transpose(expect_res, (0, 2, 3, 1))
-        )
-        for i in range(len(results) - 1):
-            np.testing.assert_allclose(results[i + 1], expect_res)
-
-
-class TestNearestInterpException(unittest.TestCase):
-    def test_exception(self):
-        input = fluid.data(name="input", shape=[1, 3, 6, 6], dtype="float32")
-
-        def attr_data_format():
-            # for 4-D input, data_format can only be NCHW or NHWC
-            out = fluid.layers.resize_nearest(
-                input, out_shape=[4, 8], data_format='NDHWC'
-            )
-
-        def attr_scale_type():
-            out = fluid.layers.resize_nearest(input, scale='scale')
-
-        def attr_scale_value():
-            out = fluid.layers.resize_nearest(input, scale=-0.3)
-
-        self.assertRaises(ValueError, attr_data_format)
-        self.assertRaises(TypeError, attr_scale_type)
-        self.assertRaises(ValueError, attr_scale_value)
-
-
 if __name__ == "__main__":
     unittest.main()

python/paddle/fluid/tests/unittests/test_bilinear_interp_op.py

@@ -18,7 +18,6 @@ import numpy as np
 from op_test import OpTest
 
 import paddle
-import paddle.fluid as fluid
 import paddle.fluid.core as core
 
 paddle.enable_static()
@@ -529,56 +528,5 @@ class TestBilinearInterp_attr_tensor_Case3(TestBilinearInterpOp_attr_tensor):
         self.scale_by_1Dtensor = True
 
 
-class TestBilinearInterpOpAPI(unittest.TestCase):
-    def test_case(self):
-        x = fluid.data(name="x", shape=[2, 3, 6, 6], dtype="float32")
-
-        dim = fluid.data(name="dim", shape=[1], dtype="int32")
-        shape_tensor = fluid.data(name="shape_tensor", shape=[2], dtype="int32")
-        actual_size = fluid.data(name="actual_size", shape=[2], dtype="int32")
-        scale_tensor = fluid.data(
-            name="scale_tensor", shape=[1], dtype="float32"
-        )
-
-        out1 = fluid.layers.resize_bilinear(x, out_shape=[12, 12])
-        out2 = fluid.layers.resize_bilinear(x, out_shape=[12, dim])
-        out3 = fluid.layers.resize_bilinear(x, out_shape=shape_tensor)
-        out4 = fluid.layers.resize_bilinear(
-            x, out_shape=[4, 4], actual_shape=actual_size
-        )
-        out5 = fluid.layers.resize_bilinear(x, scale=scale_tensor)
-
-        x_data = np.random.random((2, 3, 6, 6)).astype("float32")
-        dim_data = np.array([12]).astype("int32")
-        shape_data = np.array([12, 12]).astype("int32")
-        actual_size_data = np.array([12, 12]).astype("int32")
-        scale_data = np.array([2.0]).astype("float32")
-
-        if core.is_compiled_with_cuda():
-            place = core.CUDAPlace(0)
-        else:
-            place = core.CPUPlace()
-        exe = fluid.Executor(place)
-        exe.run(fluid.default_startup_program())
-        results = exe.run(
-            fluid.default_main_program(),
-            feed={
-                "x": x_data,
-                "dim": dim_data,
-                "shape_tensor": shape_data,
-                "actual_size": actual_size_data,
-                "scale_tensor": scale_data,
-            },
-            fetch_list=[out1, out2, out3, out4, out5],
-            return_numpy=True,
-        )
-
-        expect_res = bilinear_interp_np(
-            x_data, out_h=12, out_w=12, align_corners=True
-        )
-        for res in results:
-            np.testing.assert_allclose(res, expect_res, rtol=1e-05)
-
-
 if __name__ == "__main__":
     unittest.main()

python/paddle/fluid/tests/unittests/test_bilinear_interp_v2_op.py

@@ -638,57 +638,6 @@ class TestBilinearInterp_attr_tensor_Case3(TestBilinearInterpOp_attr_tensor):
         self.scale_by_1Dtensor = True
 
 
-class TestBilinearInterpOpAPI(unittest.TestCase):
-    def test_case(self):
-        x = fluid.data(name="x", shape=[2, 3, 6, 6], dtype="float32")
-
-        dim = fluid.data(name="dim", shape=[1], dtype="int32")
-        shape_tensor = fluid.data(name="shape_tensor", shape=[2], dtype="int32")
-        actual_size = fluid.data(name="actual_size", shape=[2], dtype="int32")
-        scale_tensor = fluid.data(
-            name="scale_tensor", shape=[1], dtype="float32"
-        )
-
-        out1 = fluid.layers.resize_bilinear(x, out_shape=[12, 12])
-        out2 = fluid.layers.resize_bilinear(x, out_shape=[12, dim])
-        out3 = fluid.layers.resize_bilinear(x, out_shape=shape_tensor)
-        out4 = fluid.layers.resize_bilinear(
-            x, out_shape=[4, 4], actual_shape=actual_size
-        )
-        out5 = fluid.layers.resize_bilinear(x, scale=scale_tensor)
-
-        x_data = np.random.random((2, 3, 6, 6)).astype("float32")
-        dim_data = np.array([12]).astype("int32")
-        shape_data = np.array([12, 12]).astype("int32")
-        actual_size_data = np.array([12, 12]).astype("int32")
-        scale_data = np.array([2.0]).astype("float32")
-
-        if core.is_compiled_with_cuda():
-            place = core.CUDAPlace(0)
-        else:
-            place = core.CPUPlace()
-        exe = fluid.Executor(place)
-        exe.run(fluid.default_startup_program())
-        results = exe.run(
-            fluid.default_main_program(),
-            feed={
-                "x": x_data,
-                "dim": dim_data,
-                "shape_tensor": shape_data,
-                "actual_size": actual_size_data,
-                "scale_tensor": scale_data,
-            },
-            fetch_list=[out1, out2, out3, out4, out5],
-            return_numpy=True,
-        )
-
-        expect_res = bilinear_interp_np(
-            x_data, out_h=12, out_w=12, align_corners=True
-        )
-        for res in results:
-            np.testing.assert_allclose(res, expect_res, rtol=1e-05)
-
-
 class TestBilinearInterpOpAPI_dy(unittest.TestCase):
     def test_case(self):
         import paddle

python/paddle/fluid/tests/unittests/test_layers.py

@@ -3187,94 +3187,6 @@ class TestBook(LayerTest):
             return values
             return indices
 
-    def make_resize_bilinear(self):
-        with program_guard(
-            fluid.default_main_program(), fluid.default_startup_program()
-        ):
-            x = self._get_data(name='x', shape=[3, 9, 6], dtype="float32")
-            output = layers.resize_bilinear(x, out_shape=[12, 12])
-            return output
-
-    def make_resize_bilinear_by_scale(self):
-        with program_guard(
-            fluid.default_main_program(), fluid.default_startup_program()
-        ):
-            x = self._get_data(name='x', shape=[3, 9, 6], dtype="float32")
-            output = layers.resize_bilinear(x, scale=1.5)
-            return output
-
-    def make_resize_nearest(self):
-        try:
-            with program_guard(
-                fluid.default_main_program(), fluid.default_startup_program()
-            ):
-                x = self._get_data(name='x1', shape=[3, 9, 6], dtype="float32")
-                output = layers.resize_nearest(x, out_shape=[12, 12])
-        except ValueError:
-            pass
-
-        try:
-            with program_guard(
-                fluid.default_main_program(), fluid.default_startup_program()
-            ):
-                x = self._get_data(
-                    name='x2', shape=[3, 9, 6, 7], dtype="float32"
-                )
-                output = layers.resize_nearest(x, out_shape=[12, 12, 12])
-        except ValueError:
-            pass
-
-        with program_guard(
-            fluid.default_main_program(), fluid.default_startup_program()
-        ):
-            x = self._get_data(name='x', shape=[3, 9, 6], dtype="float32")
-            output = layers.resize_nearest(x, out_shape=[12, 12])
-            return output
-
-    def make_resize_nearest_by_scale(self):
-        with program_guard(
-            fluid.default_main_program(), fluid.default_startup_program()
-        ):
-            x = self._get_data(name='x1', shape=[3, 9, 6], dtype="float32")
-            output = layers.resize_nearest(x, scale=1.8)
-            return output
-
-    def make_resize_trilinear(self):
-        try:
-            with program_guard(
-                fluid.default_main_program(), fluid.default_startup_program()
-            ):
-                x = self._get_data(name='x2', shape=[3, 9, 6], dtype="float32")
-                output = layers.resize_trilinear(x, out_shape=[12, 12, 12])
-        except ValueError:
-            pass
-
-        try:
-            with program_guard(
-                fluid.default_main_program(), fluid.default_startup_program()
-            ):
-                x = self._get_data(
-                    name='x', shape=[3, 9, 6, 7], dtype="float32"
-                )
-                output = layers.resize_trilinear(x, out_shape=[12, 12])
-        except ValueError:
-            pass
-
-        with program_guard(
-            fluid.default_main_program(), fluid.default_startup_program()
-        ):
-            x = self._get_data(name='x', shape=[3, 9, 6, 7], dtype="float32")
-            output = layers.resize_trilinear(x, out_shape=[12, 12, 12])
-            return output
-
-    def make_resize_trilinear_by_scale(self):
-        with program_guard(
-            fluid.default_main_program(), fluid.default_startup_program()
-        ):
-            x = self._get_data(name='x', shape=[3, 9, 6, 7], dtype="float32")
-            output = layers.resize_trilinear(x, scale=2.1)
-            return output
-
     def make_polygon_box_transform(self):
         with program_guard(
             fluid.default_main_program(), fluid.default_startup_program()

python/paddle/fluid/tests/unittests/test_nearest_interp_op.py

@@ -17,7 +17,6 @@ import unittest
 import numpy as np
 from op_test import OpTest
 
-import paddle.fluid as fluid
 import paddle.fluid.core as core
 
 
@@ -459,85 +458,6 @@ class TestNearestInterp_attr_tensor_Case3(TestNearestInterpOp_attr_tensor):
         self.scale_by_1Dtensor = True
 
 
-class TestNearestAPI(unittest.TestCase):
-    def test_case(self):
-        x = fluid.data(name="x", shape=[2, 3, 6, 6], dtype="float32")
-        y = fluid.data(name="y", shape=[2, 6, 6, 3], dtype="float32")
-
-        dim = fluid.data(name="dim", shape=[1], dtype="int32")
-        shape_tensor = fluid.data(name="shape_tensor", shape=[2], dtype="int32")
-        actual_size = fluid.data(name="actual_size", shape=[2], dtype="int32")
-        scale_tensor = fluid.data(
-            name="scale_tensor", shape=[1], dtype="float32"
-        )
-
-        out1 = fluid.layers.resize_nearest(
-            y, out_shape=[12, 12], data_format='NHWC'
-        )
-        out2 = fluid.layers.resize_nearest(x, out_shape=[12, dim])
-        out3 = fluid.layers.resize_nearest(x, out_shape=shape_tensor)
-        out4 = fluid.layers.resize_nearest(
-            x, out_shape=[4, 4], actual_shape=actual_size
-        )
-        out5 = fluid.layers.resize_nearest(x, scale=scale_tensor)
-
-        x_data = np.random.random((2, 3, 6, 6)).astype("float32")
-        dim_data = np.array([12]).astype("int32")
-        shape_data = np.array([12, 12]).astype("int32")
-        actual_size_data = np.array([12, 12]).astype("int32")
-        scale_data = np.array([2.0]).astype("float32")
-
-        if core.is_compiled_with_cuda():
-            place = core.CUDAPlace(0)
-        else:
-            place = core.CPUPlace()
-        exe = fluid.Executor(place)
-        exe.run(fluid.default_startup_program())
-        results = exe.run(
-            fluid.default_main_program(),
-            feed={
-                "x": x_data,
-                "y": np.transpose(x_data, (0, 2, 3, 1)),
-                "dim": dim_data,
-                "shape_tensor": shape_data,
-                "actual_size": actual_size_data,
-                "scale_tensor": scale_data,
-            },
-            fetch_list=[out1, out2, out3, out4, out5],
-            return_numpy=True,
-        )
-
-        expect_res = nearest_neighbor_interp_np(
-            x_data, out_h=12, out_w=12, align_corners=True
-        )
-        np.testing.assert_allclose(
-            results[0], np.transpose(expect_res, (0, 2, 3, 1)), rtol=1e-05
-        )
-        for i in range(len(results) - 1):
-            np.testing.assert_allclose(results[i + 1], expect_res, rtol=1e-05)
-
-
-class TestNearestInterpException(unittest.TestCase):
-    def test_exception(self):
-        input = fluid.data(name="input", shape=[1, 3, 6, 6], dtype="float32")
-
-        def attr_data_format():
-            # for 4-D input, data_format can only be NCHW or NHWC
-            out = fluid.layers.resize_nearest(
-                input, out_shape=[4, 8], data_format='NDHWC'
-            )
-
-        def attr_scale_type():
-            out = fluid.layers.resize_nearest(input, scale='scale')
-
-        def attr_scale_value():
-            out = fluid.layers.resize_nearest(input, scale=-0.3)
-
-        self.assertRaises(ValueError, attr_data_format)
-        self.assertRaises(TypeError, attr_scale_type)
-        self.assertRaises(ValueError, attr_scale_value)
-
-
 if __name__ == "__main__":
     import paddle
 

python/paddle/fluid/tests/unittests/test_nearest_interp_v2_op.py

@@ -684,64 +684,6 @@ class TestNearestInterp_attr_tensor_Case3(TestNearestInterpOp_attr_tensor):
         self.scale_by_1Dtensor = True
 
 
-class TestNearestAPI(unittest.TestCase):
-    def test_case(self):
-        x = fluid.data(name="x", shape=[2, 3, 6, 6], dtype="float32")
-        y = fluid.data(name="y", shape=[2, 6, 6, 3], dtype="float32")
-
-        dim = fluid.data(name="dim", shape=[1], dtype="int32")
-        shape_tensor = fluid.data(name="shape_tensor", shape=[2], dtype="int32")
-        actual_size = fluid.data(name="actual_size", shape=[2], dtype="int32")
-        scale_tensor = fluid.data(
-            name="scale_tensor", shape=[1], dtype="float32"
-        )
-
-        out1 = fluid.layers.resize_nearest(
-            y, out_shape=[12, 12], data_format='NHWC'
-        )
-        out2 = fluid.layers.resize_nearest(x, out_shape=[12, dim])
-        out3 = fluid.layers.resize_nearest(x, out_shape=shape_tensor)
-        out4 = fluid.layers.resize_nearest(
-            x, out_shape=[4, 4], actual_shape=actual_size
-        )
-        out5 = fluid.layers.resize_nearest(x, scale=scale_tensor)
-
-        x_data = np.random.random((2, 3, 6, 6)).astype("float32")
-        dim_data = np.array([12]).astype("int32")
-        shape_data = np.array([12, 12]).astype("int32")
-        actual_size_data = np.array([12, 12]).astype("int32")
-        scale_data = np.array([2.0]).astype("float32")
-
-        if core.is_compiled_with_cuda():
-            place = core.CUDAPlace(0)
-        else:
-            place = core.CPUPlace()
-        exe = fluid.Executor(place)
-        exe.run(fluid.default_startup_program())
-        results = exe.run(
-            fluid.default_main_program(),
-            feed={
-                "x": x_data,
-                "y": np.transpose(x_data, (0, 2, 3, 1)),
-                "dim": dim_data,
-                "shape_tensor": shape_data,
-                "actual_size": actual_size_data,
-                "scale_tensor": scale_data,
-            },
-            fetch_list=[out1, out2, out3, out4, out5],
-            return_numpy=True,
-        )
-
-        expect_res = nearest_neighbor_interp_np(
-            x_data, out_h=12, out_w=12, align_corners=True
-        )
-        np.testing.assert_allclose(
-            results[0], np.transpose(expect_res, (0, 2, 3, 1)), rtol=1e-05
-        )
-        for i in range(len(results) - 1):
-            np.testing.assert_allclose(results[i + 1], expect_res, rtol=1e-05)
-
-
 class TestNearestInterpOpAPI_dy(unittest.TestCase):
     def test_case(self):
         import paddle
@@ -793,41 +735,6 @@ class TestNearestInterp3DOpAPI_dy(unittest.TestCase):
             np.testing.assert_allclose(out.numpy(), expect_res, rtol=1e-05)
 
 
-class TestNearestInterpException(unittest.TestCase):
-    def test_exception(self):
-        import paddle
-
-        input = fluid.data(name="input", shape=[1, 3, 6, 6], dtype="float32")
-
-        def attr_data_format():
-            # for 4-D input, data_format can only be NCHW or NHWC
-            out = fluid.layers.resize_nearest(
-                input, out_shape=[4, 8], data_format='NDHWC'
-            )
-
-        def attr_scale_type():
-            out = fluid.layers.resize_nearest(input, scale='scale')
-
-        def attr_scale_value():
-            out = fluid.layers.resize_nearest(input, scale=-0.3)
-
-        def input_shape_error():
-            x = paddle.randn([1, 3])
-            out = paddle.nn.functional.interpolate(x, scale_factor='scale')
-
-        def mode_error():
-            x = paddle.randn([1, 3])
-            out = paddle.nn.functional.interpolate(
-                x, scale_factor='scale', mode="BILINEAR"
-            )
-
-        self.assertRaises(ValueError, attr_data_format)
-        self.assertRaises(TypeError, attr_scale_type)
-        self.assertRaises(ValueError, attr_scale_value)
-        self.assertRaises(ValueError, input_shape_error)
-        self.assertRaises(ValueError, mode_error)
-
-
 @unittest.skipIf(
     not fluid.core.is_compiled_with_cuda(), "core is not compiled with CUDA"
 )

python/paddle/fluid/tests/unittests/test_trilinear_interp_op.py

@@ -17,9 +17,7 @@ import unittest
 import numpy as np
 from op_test import OpTest
 
-import paddle.fluid as fluid
 import paddle.fluid.core as core
-from paddle.nn.functional import interpolate
 
 
 def trilinear_interp_np(
@@ -623,85 +621,5 @@ class TestTrilinearInterp_attr_tensor_Case3(TestTrilinearInterpOp_attr_tensor):
         self.scale_by_1Dtensor = True
 
 
-class TestTrilinearInterpAPI(unittest.TestCase):
-    def test_case(self):
-        x = fluid.data(name="x", shape=[2, 3, 6, 9, 4], dtype="float32")
-        y = fluid.data(name="y", shape=[2, 6, 9, 4, 3], dtype="float32")
-
-        dim = fluid.data(name="dim", shape=[1], dtype="int32")
-        shape_tensor = fluid.data(name="shape_tensor", shape=[3], dtype="int32")
-        actual_size = fluid.data(name="actual_size", shape=[3], dtype="int32")
-        scale_tensor = fluid.data(
-            name="scale_tensor", shape=[1], dtype="float32"
-        )
-
-        out1 = fluid.layers.resize_trilinear(
-            y, out_shape=[12, 18, 8], data_format='NDHWC'
-        )
-        out2 = fluid.layers.resize_trilinear(x, out_shape=[12, dim, 8])
-        out3 = fluid.layers.resize_trilinear(x, out_shape=shape_tensor)
-        out4 = fluid.layers.resize_trilinear(
-            x, out_shape=[4, 4, 8], actual_shape=actual_size
-        )
-        out5 = fluid.layers.resize_trilinear(x, scale=scale_tensor)
-        out6 = interpolate(
-            x, scale_factor=scale_tensor, mode='trilinear', data_format="NCDHW"
-        )
-        out7 = interpolate(
-            x, size=[4, 4, 8], mode='trilinear', data_format="NCDHW"
-        )
-        out8 = interpolate(
-            x, size=shape_tensor, mode='trilinear', data_format="NCDHW"
-        )
-
-        x_data = np.random.random((2, 3, 6, 9, 4)).astype("float32")
-        dim_data = np.array([18]).astype("int32")
-        shape_data = np.array([12, 18, 8]).astype("int32")
-        actual_size_data = np.array([12, 18, 8]).astype("int32")
-        scale_data = np.array([2.0]).astype("float32")
-
-        if core.is_compiled_with_cuda():
-            place = core.CUDAPlace(0)
-        else:
-            place = core.CPUPlace()
-        exe = fluid.Executor(place)
-        exe.run(fluid.default_startup_program())
-        results = exe.run(
-            fluid.default_main_program(),
-            feed={
-                "x": x_data,
-                "y": np.transpose(x_data, (0, 2, 3, 4, 1)),
-                "dim": dim_data,
-                "shape_tensor": shape_data,
-                "actual_size": actual_size_data,
-                "scale_tensor": scale_data,
-            },
-            fetch_list=[out1, out2, out3, out4, out5],
-            return_numpy=True,
-        )
-
-        expect_res = trilinear_interp_np(
-            x_data, out_d=12, out_h=18, out_w=8, align_mode=1
-        )
-        np.testing.assert_allclose(
-            results[0], np.transpose(expect_res, (0, 2, 3, 4, 1)), rtol=1e-05
-        )
-        for i in range(len(results) - 1):
-            np.testing.assert_allclose(results[i + 1], expect_res, rtol=1e-05)
-
-
-class TestTrilinearInterpOpException(unittest.TestCase):
-    def test_exception(self):
-        input = fluid.data(name="input", shape=[2, 3, 6, 9, 4], dtype="float32")
-
-        def attr_data_format():
-            # for 5-D input, data_format only can be NCDHW or NDHWC
-            out = fluid.layers.resize_trilinear(
-                input, out_shape=[4, 8, 4], data_format='NHWC'
-            )
-
-        self.assertRaises(ValueError, attr_data_format)
-
-
 if __name__ == "__main__":
     unittest.main()

python/paddle/fluid/tests/unittests/test_trilinear_interp_v2_op.py

@@ -20,7 +20,6 @@ from op_test import OpTest
 import paddle
 import paddle.fluid as fluid
 import paddle.fluid.core as core
-from paddle.fluid.framework import _test_eager_guard
 from paddle.nn.functional import interpolate
 
 np.random.seed(123)
@@ -741,100 +740,6 @@ class TestTrilinearInterp_attr_tensor_Case3(TestTrilinearInterpOp_attr_tensor):
         self.scale_by_1Dtensor = True
 
 
-class TestTrilinearInterpAPI(unittest.TestCase):
-    def test_imperative_case(self):
-        with _test_eager_guard():
-            self.func_case()
-        self.func_case()
-
-    def func_case(self):
-        x = fluid.data(name="x", shape=[2, 3, 6, 9, 4], dtype="float32")
-        y = fluid.data(name="y", shape=[2, 6, 9, 4, 3], dtype="float32")
-
-        dim = fluid.data(name="dim", shape=[1], dtype="int32")
-        shape_tensor = fluid.data(name="shape_tensor", shape=[3], dtype="int32")
-        actual_size = fluid.data(name="actual_size", shape=[3], dtype="int32")
-        scale_tensor = fluid.data(
-            name="scale_tensor", shape=[1], dtype="float32"
-        )
-
-        out1 = fluid.layers.resize_trilinear(
-            y, out_shape=[12, 18, 8], data_format='NDHWC'
-        )
-        out2 = fluid.layers.resize_trilinear(x, out_shape=[12, dim, 8])
-        out3 = fluid.layers.resize_trilinear(x, out_shape=shape_tensor)
-        out4 = fluid.layers.resize_trilinear(
-            x, out_shape=[4, 4, 8], actual_shape=actual_size
-        )
-        out5 = fluid.layers.resize_trilinear(x, scale=scale_tensor)
-        out6 = interpolate(
-            x, scale_factor=scale_tensor, mode='trilinear', data_format="NCDHW"
-        )
-        out7 = interpolate(
-            x, size=[4, 4, 8], mode='trilinear', data_format="NCDHW"
-        )
-        out8 = interpolate(
-            x, size=shape_tensor, mode='trilinear', data_format="NCDHW"
-        )
-
-        x_data = np.random.random((2, 3, 6, 9, 4)).astype("float32")
-        dim_data = np.array([18]).astype("int32")
-        shape_data = np.array([12, 18, 8]).astype("int32")
-        actual_size_data = np.array([12, 18, 8]).astype("int32")
-        scale_data = np.array([2.0]).astype("float32")
-
-        if core.is_compiled_with_cuda():
-            place = core.CUDAPlace(0)
-        else:
-            place = core.CPUPlace()
-        exe = fluid.Executor(place)
-        exe.run(fluid.default_startup_program())
-        results = exe.run(
-            fluid.default_main_program(),
-            feed={
-                "x": x_data,
-                "y": np.transpose(x_data, (0, 2, 3, 4, 1)),
-                "dim": dim_data,
-                "shape_tensor": shape_data,
-                "actual_size": actual_size_data,
-                "scale_tensor": scale_data,
-            },
-            fetch_list=[out1, out2, out3, out4, out5],
-            return_numpy=True,
-        )
-
-        expect_res = trilinear_interp_np(
-            x_data, out_d=12, out_h=18, out_w=8, align_mode=1
-        )
-        np.testing.assert_allclose(
-            results[0], np.transpose(expect_res, (0, 2, 3, 4, 1)), rtol=1e-05
-        )
-        for i in range(len(results) - 1):
-            np.testing.assert_allclose(results[i + 1], expect_res, rtol=1e-05)
-
-        # Follow the calculation of preceding out6, out7, out8.
-        # To pass CI-coverage, calculate out9 without verifying accuracy.
-        # Preceding PR link: https://github.com/PaddlePaddle/Paddle/pull/26520/files#diff-ee0c2b73d08659e90a8f3ac48451a6588d35e1613742f864f9aad4394e12c290
-        with fluid.dygraph.guard():
-            x = fluid.dygraph.to_variable(x_data)
-            out9 = interpolate(
-                x, size=[12, 18, 8], mode='trilinear', data_format="NCDHW"
-            )
-
-
-class TestTrilinearInterpOpException(unittest.TestCase):
-    def test_exception(self):
-        input = fluid.data(name="input", shape=[2, 3, 6, 9, 4], dtype="float32")
-
-        def attr_data_format():
-            # for 5-D input, data_format only can be NCDHW or NDHWC
-            out = fluid.layers.resize_trilinear(
-                input, out_shape=[4, 8, 4], data_format='NHWC'
-            )
-
-        self.assertRaises(ValueError, attr_data_format)
-
-
 @unittest.skipIf(
     not fluid.core.is_compiled_with_cuda(), "core is not compiled with CUDA"
 )