add UTs for mlu interp_v2(nearest). (#43709)

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

+#   Copyright (c) 2022 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.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+import sys
+
+sys.path.append('..')
+from op_test import OpTest
+import paddle.fluid.core as core
+import paddle.fluid as fluid
+import paddle.nn as nn
+import paddle
+from paddle.nn.functional import interpolate
+
+paddle.enable_static()
+
+
+def nearest_neighbor_interp_np(X,
+                               out_h,
+                               out_w,
+                               scale_h=0,
+                               scale_w=0,
+                               out_size=None,
+                               actual_shape=None,
+                               align_corners=True,
+                               data_layout='NCHW'):
+    """nearest neighbor interpolation implement in shape [N, C, H, W]"""
+    if data_layout == "NHWC":
+        X = np.transpose(X, (0, 3, 1, 2))  # NHWC => NCHW
+    if out_size is not None:
+        out_h = out_size[0]
+        out_w = out_size[1]
+    if actual_shape is not None:
+        out_h = actual_shape[0]
+        out_w = actual_shape[1]
+    n, c, in_h, in_w = X.shape
+
+    ratio_h = ratio_w = 0.0
+    if (out_h > 1):
+        if (align_corners):
+            ratio_h = (in_h - 1.0) / (out_h - 1.0)
+        else:
+            if scale_h > 0:
+                ratio_h = 1.0 / scale_h
+            else:
+                ratio_h = 1.0 * in_h / out_h
+    if (out_w > 1):
+        if (align_corners):
+            ratio_w = (in_w - 1.0) / (out_w - 1.0)
+        else:
+            if scale_w > 0:
+                ratio_w = 1.0 / scale_w
+            else:
+                ratio_w = 1.0 * in_w / out_w
+    out = np.zeros((n, c, out_h, out_w))
+
+    if align_corners:
+        for i in range(out_h):
+            in_i = int(ratio_h * i + 0.5)
+            for j in range(out_w):
+                in_j = int(ratio_w * j + 0.5)
+                out[:, :, i, j] = X[:, :, in_i, in_j]
+    else:
+        for i in range(out_h):
+            in_i = int(ratio_h * i)
+            for j in range(out_w):
+                in_j = int(ratio_w * j)
+                out[:, :, i, j] = X[:, :, in_i, in_j]
+
+    if data_layout == "NHWC":
+        out = np.transpose(out, (0, 2, 3, 1))  # NCHW => NHWC
+    # out = np.expand_dims(out, 2)
+    return out.astype(X.dtype)
+
+
+def nearest_neighbor_interp3d_np(X,
+                                 out_d,
+                                 out_h,
+                                 out_w,
+                                 scale_d=0,
+                                 scale_h=0,
+                                 scale_w=0,
+                                 out_size=None,
+                                 actual_shape=None,
+                                 align_corners=True,
+                                 data_layout='NCHW'):
+    """nearest neighbor interpolation implement in shape [N, C, H, W]"""
+    if data_layout == "NHWC":
+        X = np.transpose(X, (0, 4, 1, 2, 3))  # NDHWC => NCDHW
+    if out_size is not None:
+        out_d = out_size[0]
+        out_h = out_size[1]
+        out_w = out_size[2]
+    if actual_shape is not None:
+        out_d = actual_shape[0]
+        out_h = actual_shape[1]
+        out_w = actual_shape[2]
+    n, c, in_d, in_h, in_w = X.shape
+
+    ratio_d = ratio_h = ratio_w = 0.0
+    if (out_d > 1):
+        if (align_corners):
+            ratio_d = (in_d - 1.0) / (out_d - 1.0)
+        else:
+            if scale_d > 0:
+                ratio_d = 1.0 / scale_d
+            else:
+                ratio_d = 1.0 * in_d / out_d
+    if (out_h > 1):
+        if (align_corners):
+            ratio_h = (in_h - 1.0) / (out_h - 1.0)
+        else:
+            if scale_h > 0:
+                ratio_h = 1.0 / scale_h
+            else:
+                ratio_h = 1.0 * in_h / out_h
+    if (out_w > 1):
+        if (align_corners):
+            ratio_w = (in_w - 1.0) / (out_w - 1.0)
+        else:
+            if scale_w > 0:
+                ratio_w = 1.0 / scale_w
+            else:
+                ratio_w = 1.0 * in_w / out_w
+    out = np.zeros((n, c, out_d, out_h, out_w))
+
+    if align_corners:
+        for d in range(out_d):
+            in_d = int(ratio_d * d + 0.5)
+            for i in range(out_h):
+                in_i = int(ratio_h * i + 0.5)
+                for j in range(out_w):
+                    in_j = int(ratio_w * j + 0.5)
+                    out[:, :, d, i, j] = X[:, :, in_d, in_i, in_j]
+    else:
+        for d in range(out_d):
+            in_d = int(ratio_d * d)
+            for i in range(out_h):
+                in_i = int(ratio_h * i)
+                for j in range(out_w):
+                    in_j = int(ratio_w * j)
+                    out[:, :, d, i, j] = X[:, :, in_d, in_i, in_j]
+
+    if data_layout == "NDHWC":
+        out = np.transpose(out, (0, 2, 3, 4, 1))  # NCDHW => NDHWC
+    return out.astype(X.dtype)
+
+
+class TestNearestInterpOp(OpTest):
+
+    def setUp(self):
+        self.place = paddle.device.MLUPlace(0)
+        self.__class__.use_mlu = True
+        self.out_size = None
+        self.actual_shape = None
+        self.init_test_case()
+        self.data_layout = 'NCHW' if len(self.input_shape) == 4 else 'NCDHW'
+        self.op_type = "nearest_interp_v2"
+        input_np = np.random.random(self.input_shape).astype("float32")
+
+        if self.data_layout == "NCHW" and len(self.input_shape) == 4:
+            in_d = 1
+            in_h = self.input_shape[2]
+            in_w = self.input_shape[3]
+        else:
+            in_d = 1
+            in_h = self.input_shape[1]
+            in_w = self.input_shape[2]
+
+        if self.data_layout == "NCDHW" and len(self.input_shape) == 5:
+            in_d = self.input_shape[2]
+            in_h = self.input_shape[3]
+            in_w = self.input_shape[4]
+        else:
+            in_d = self.input_shape[1]
+            in_h = self.input_shape[2]
+            in_w = self.input_shape[3]
+        scale_d = 0
+        scale_h = 0
+        scale_w = 0
+        if self.scale:
+            if isinstance(self.scale, float) or isinstance(self.scale, int):
+                if self.scale > 0:
+                    scale_d = scale_h = scale_w = float(self.scale)
+            if isinstance(self.scale, list) and len(self.scale) == 1:
+                scale_d = scale_w = scale_h = self.scale[0]
+            elif isinstance(self.scale, list) and len(self.scale) > 1:
+                if len(self.scale) == 5:
+                    scale_w = self.scale[2]
+                    scale_h = self.scale[1]
+                    scale_d = self.scale[0]
+                else:
+                    scale_w = self.scale[1]
+                    scale_h = self.scale[0]
+
+            out_h = int(in_h * scale_h)
+            out_w = int(in_w * scale_w)
+            out_d = int(in_d * scale_d)
+        else:
+            if len(self.input_shape) == 5:
+                out_d = self.out_d
+            out_h = self.out_h
+            out_w = self.out_w
+
+        if len(self.input_shape) == 4:
+            output_np = nearest_neighbor_interp_np(
+                input_np, out_h, out_w, scale_h, scale_w, self.out_size,
+                self.actual_shape, self.align_corners, self.data_layout)
+        elif len(self.input_shape) == 5:
+            output_np = nearest_neighbor_interp3d_np(input_np, out_d, out_h,
+                                                     out_w, scale_d, scale_h,
+                                                     scale_w, self.out_size,
+                                                     self.actual_shape,
+                                                     self.align_corners,
+                                                     self.data_layout)
+        self.inputs = {'X': input_np}
+        if self.out_size is not None:
+            self.inputs['OutSize'] = self.out_size
+        if self.actual_shape is not None:
+            self.inputs['OutSize'] = self.actual_shape
+        if len(self.input_shape) == 5:
+            self.attrs = {
+                'out_d': self.out_d,
+                'out_h': self.out_h,
+                'out_w': self.out_w,
+                'interp_method': self.interp_method,
+                'align_corners': self.align_corners,
+                'data_layout': self.data_layout
+            }
+        else:
+            self.attrs = {
+                'out_h': self.out_h,
+                'out_w': self.out_w,
+                'interp_method': self.interp_method,
+                'align_corners': self.align_corners,
+                'data_layout': self.data_layout
+            }
+        if self.scale:
+            if isinstance(self.scale, float) or isinstance(self.scale, int):
+                if self.scale > 0:
+                    self.scale = [self.scale]
+            if isinstance(self.scale, list) and len(self.scale) == 1:
+                self.scale = [self.scale[0], self.scale[0]]
+            self.attrs['scale'] = self.scale
+        self.outputs = {'Out': output_np}
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place)
+
+    def test_check_grad(self):
+        self.check_grad_with_place(self.place, ['X'], 'Out', in_place=True)
+
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [2, 3, 4, 5]
+        self.out_h = 2
+        self.out_w = 2
+        self.scale = 0.
+        self.out_size = np.array([3, 3]).astype("int32")
+        self.align_corners = True
+
+
+# class TestNearestNeighborInterpCase1(TestNearestInterpOp):
+#     def init_test_case(self):
+#         self.interp_method = 'nearest'
+#         self.input_shape = [4, 1, 1, 7, 8]
+#         self.out_d = 1
+#         self.out_h = 1
+#         self.out_w = 1
+#         self.scale = 0.
+#         self.align_corners = True
+
+
+class TestNearestNeighborInterpCase2(TestNearestInterpOp):
+
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [3, 3, 9, 6]
+        self.out_h = 12
+        self.out_w = 12
+        self.scale = 0.
+        self.align_corners = True
+
+
+class TestNearestNeighborInterpCase3(TestNearestInterpOp):
+
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [1, 1, 32, 64]
+        self.out_h = 64
+        self.out_w = 32
+        self.scale = 0.
+        self.align_corners = True
+
+
+class TestNearestNeighborInterpCase4(TestNearestInterpOp):
+
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [4, 1, 7, 8]
+        self.out_h = 1
+        self.out_w = 1
+        self.scale = 0.
+        self.out_size = np.array([2, 2]).astype("int32")
+        self.align_corners = True
+
+
+class TestNearestNeighborInterpCase5(TestNearestInterpOp):
+
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [3, 3, 9, 6]
+        self.out_h = 12
+        self.out_w = 12
+        self.scale = 0.
+        self.out_size = np.array([11, 11]).astype("int32")
+        self.align_corners = True
+
+
+class TestNearestNeighborInterpCase6(TestNearestInterpOp):
+
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [1, 1, 32, 64]
+        self.out_h = 64
+        self.out_w = 32
+        self.scale = 0.
+        self.out_size = np.array([65, 129]).astype("int32")
+        self.align_corners = True
+
+
+class TestNearestNeighborInterpSame(TestNearestInterpOp):
+
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [2, 3, 32, 64]
+        self.out_h = 32
+        self.out_w = 64
+        self.scale = 0.
+        self.align_corners = True
+
+
+class TestNearestNeighborInterpActualShape(TestNearestInterpOp):
+
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [3, 2, 32, 16]
+        self.out_h = 64
+        self.out_w = 32
+        self.scale = 0.
+        self.out_size = np.array([66, 40]).astype("int32")
+        self.align_corners = True
+
+
+class TestNearestNeighborInterpDataLayout(TestNearestInterpOp):
+
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [2, 4, 4, 5]
+        self.out_h = 2
+        self.out_w = 2
+        self.scale = 0.
+        self.out_size = np.array([3, 8]).astype("int32")
+        self.align_corners = True
+        self.data_layout = "NHWC"
+
+
+class TestNearestInterpWithoutCorners(TestNearestInterpOp):
+
+    def set_align_corners(self):
+        self.align_corners = False
+
+
+class TestNearestNeighborInterpScale1(TestNearestInterpOp):
+
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [3, 2, 7, 5]
+        self.out_h = 64
+        self.out_w = 32
+        self.scale = 2.
+        self.out_size = np.array([66, 40]).astype("int32")
+        self.align_corners = True
+
+
+class TestNearestNeighborInterpScale2(TestNearestInterpOp):
+
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [3, 2, 5, 7]
+        self.out_h = 64
+        self.out_w = 32
+        self.scale = 1.5
+        self.out_size = np.array([66, 40]).astype("int32")
+        self.align_corners = True
+
+
+class TestNearestNeighborInterpScale3(TestNearestInterpOp):
+
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [3, 2, 7, 5]
+        self.out_h = 64
+        self.out_w = 32
+        self.scale = [2.0, 3.0]
+        self.out_size = np.array([66, 40]).astype("int32")
+        self.align_corners = True
+
+
+class TestNearestInterpOp_attr_tensor(OpTest):
+
+    def setUp(self):
+        self.place = paddle.device.MLUPlace(0)
+        self.__class__.use_mlu = True
+        self.out_size = None
+        self.actual_shape = None
+        self.init_test_case()
+        self.op_type = "nearest_interp_v2"
+        self.shape_by_1Dtensor = False
+        self.scale_by_1Dtensor = False
+        self.attrs = {
+            'interp_method': self.interp_method,
+            'align_corners': self.align_corners,
+        }
+
+        input_np = np.random.random(self.input_shape).astype("float32")
+        self.inputs = {'X': input_np}
+
+        if self.scale_by_1Dtensor:
+            self.inputs['Scale'] = np.array([self.scale]).astype("float32")
+        elif self.scale:
+            if isinstance(self.scale, float) or isinstance(self.scale, int):
+                if self.scale > 0:
+                    scale_h = scale_w = float(self.scale)
+            if isinstance(self.scale, list) and len(self.scale) == 1:
+                scale_w = scale_h = self.scale[0]
+            elif isinstance(self.scale, list) and len(self.scale) > 1:
+                scale_w = self.scale[1]
+                scale_h = self.scale[0]
+            out_h = int(self.input_shape[2] * scale_h)
+            out_w = int(self.input_shape[3] * scale_w)
+        else:
+            out_h = self.out_h
+            out_w = self.out_w
+
+        if self.shape_by_1Dtensor:
+            self.inputs['OutSize'] = self.out_size
+        elif self.out_size is not None:
+            size_tensor = []
+            for index, ele in enumerate(self.out_size):
+                size_tensor.append(("x" + str(index), np.ones(
+                    (1)).astype('int32') * ele))
+            self.inputs['SizeTensor'] = size_tensor
+
+        self.attrs['out_h'] = self.out_h
+        self.attrs['out_w'] = self.out_w
+        if self.scale:
+            if isinstance(self.scale, float) or isinstance(self.scale, int):
+                if self.scale > 0:
+                    self.scale = [self.scale]
+            if isinstance(self.scale, list) and len(self.scale) == 1:
+                self.scale = [self.scale[0], self.scale[0]]
+            self.attrs['scale'] = self.scale
+        output_np = nearest_neighbor_interp_np(input_np, out_h, out_w, 0, 0,
+                                               self.out_size, self.actual_shape,
+                                               self.align_corners)
+        self.outputs = {'Out': output_np}
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place)
+
+    def test_check_grad(self):
+        self.check_grad_with_place(self.place, ['X'], 'Out', in_place=True)
+
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [2, 5, 4, 4]
+        self.out_h = 3
+        self.out_w = 3
+        self.scale = 0.
+        self.out_size = [3, 3]
+        self.align_corners = True
+
+
+# out_size is a tensor list
+class TestNearestInterp_attr_tensor_Case1(TestNearestInterpOp_attr_tensor):
+
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [3, 3, 9, 6]
+        self.out_h = 12
+        self.out_w = 12
+        self.scale = 0.
+        self.out_size = [8, 12]
+        self.align_corners = True
+
+
+# out_size is a 1-D tensor
+class TestNearestInterp_attr_tensor_Case2(TestNearestInterpOp_attr_tensor):
+
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [3, 2, 32, 16]
+        self.out_h = 64
+        self.out_w = 32
+        self.scale = 0.
+        self.out_size = np.array([66, 40]).astype("int32")
+        self.align_corners = True
+        self.shape_by_1Dtensor = True
+
+
+# scale is a 1-D tensor
+class TestNearestInterp_attr_tensor_Case3(TestNearestInterpOp_attr_tensor):
+
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [3, 2, 32, 16]
+        self.out_h = 64
+        self.out_w = 32
+        self.scale = 2.0
+        self.out_size = None
+        self.align_corners = True
+        self.scale_by_1Dtensor = True
+
+
+#TODO: comment this test for now until nearest_interp_op added.
+# 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)
+#         self.assertTrue(
+#             np.allclose(results[0], np.transpose(expect_res, (0, 2, 3, 1))))
+#         for i in range(len(results) - 1):
+#             self.assertTrue(np.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()