From d1a53649596d0eb0a8920ffe46bab007e890c124 Mon Sep 17 00:00:00 2001 From: Chenxiao Niu Date: Fri, 24 Jun 2022 10:42:35 +0800 Subject: [PATCH] add UTs for mlu interp_v2(nearest). (#43709) --- .../mlu/test_nearest_interp_v2_op_mlu.py | 628 ++++++++++++++++++ 1 file changed, 628 insertions(+) create mode 100644 python/paddle/fluid/tests/unittests/mlu/test_nearest_interp_v2_op_mlu.py diff --git a/python/paddle/fluid/tests/unittests/mlu/test_nearest_interp_v2_op_mlu.py b/python/paddle/fluid/tests/unittests/mlu/test_nearest_interp_v2_op_mlu.py new file mode 100644 index 00000000000..e9235e62a79 --- /dev/null +++ b/python/paddle/fluid/tests/unittests/mlu/test_nearest_interp_v2_op_mlu.py @@ -0,0 +1,628 @@ +# 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() -- GitLab