From a0363d180500be25d9c3a7412a6bd7e18883d661 Mon Sep 17 00:00:00 2001 From: Chenxiao Niu Date: Mon, 13 Jun 2022 23:52:53 +0800 Subject: [PATCH] [MLU] add UTs for mlu interp_v2(bilinear). (#43386) --- .../mlu/test_bilinear_interp_v2_op_mlu.py | 663 ++++++++++++++++++ 1 file changed, 663 insertions(+) create mode 100644 python/paddle/fluid/tests/unittests/mlu/test_bilinear_interp_v2_op_mlu.py diff --git a/python/paddle/fluid/tests/unittests/mlu/test_bilinear_interp_v2_op_mlu.py b/python/paddle/fluid/tests/unittests/mlu/test_bilinear_interp_v2_op_mlu.py new file mode 100644 index 0000000000..b8c3157809 --- /dev/null +++ b/python/paddle/fluid/tests/unittests/mlu/test_bilinear_interp_v2_op_mlu.py @@ -0,0 +1,663 @@ +# 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 +from paddle.nn.functional import interpolate +import paddle + +paddle.enable_static() + + +def bilinear_interp_np(input, + out_h, + out_w, + scale_w=0, + scale_h=0, + out_size=None, + actual_shape=None, + align_corners=True, + align_mode=0, + data_layout='NCHW'): + """bilinear interpolation implement in shape [N, C, H, W]""" + if data_layout == "NHWC": + input = np.transpose(input, (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] + batch_size, channel, in_h, in_w = input.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((batch_size, channel, out_h, out_w)) + + for i in range(out_h): + if (align_mode == 0 and not align_corners): + h = int(ratio_h * (i + 0.5) - 0.5) + else: + h = int(ratio_h * i) + + h = max(0, h) + hid = 1 if h < in_h - 1 else 0 + if (align_mode == 0 and not align_corners): + idx_src_h = max(ratio_h * (i + 0.5) - 0.5, 0) + h1lambda = idx_src_h - h + else: + h1lambda = ratio_h * i - h + h2lambda = 1.0 - h1lambda + for j in range(out_w): + if (align_mode == 0 and not align_corners): + w = int(ratio_w * (j + 0.5) - 0.5) + else: + w = int(ratio_w * j) + w = max(0, w) + wid = 1 if w < in_w - 1 else 0 + if (align_mode == 0 and not align_corners): + idx_src_w = max(ratio_w * (j + 0.5) - 0.5, 0) + w1lambda = idx_src_w - w + else: + w1lambda = ratio_w * j - w + w2lambda = 1.0 - w1lambda + + out[:, :, i, j] = h2lambda*(w2lambda*input[:, :, h, w] + + w1lambda*input[:, :, h, w+wid]) + \ + h1lambda*(w2lambda*input[:, :, h+hid, w] + + w1lambda*input[:, :, h+hid, w+wid]) + + if data_layout == "NHWC": + out = np.transpose(out, (0, 2, 3, 1)) # NCHW => NHWC + + return out.astype(input.dtype) + + +class TestBilinearInterpOp(OpTest): + + def setUp(self): + self.place = paddle.device.MLUPlace(0) + self.__class__.use_mlu = True + self.out_size = None + self.actual_shape = None + self.data_layout = 'NCHW' + self.init_test_case() + self.dtype = "float32" + self.op_type = "bilinear_interp_v2" + input_np = np.random.random(self.input_shape).astype(self.dtype) + + if self.data_layout == "NCHW": + in_h = self.input_shape[2] + in_w = self.input_shape[3] + else: + in_h = self.input_shape[1] + in_w = self.input_shape[2] + scale_h = 0 + scale_w = 0 + if 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(in_h * scale_h) + out_w = int(in_w * scale_w) + else: + out_h = self.out_h + out_w = self.out_w + + output_np = bilinear_interp_np(input_np, out_h, out_w, 0, 0, + self.out_size, self.actual_shape, + self.align_corners, self.align_mode, + 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 + + self.attrs = { + 'out_h': self.out_h, + 'out_w': self.out_w, + 'interp_method': self.interp_method, + 'align_corners': self.align_corners, + 'align_mode': self.align_mode, + '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 = 'bilinear' + self.input_shape = [2, 3, 5, 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 + self.align_mode = 1 + + +class TestBilinearInterpCase1(TestBilinearInterpOp): + + def init_test_case(self): + self.interp_method = 'bilinear' + self.input_shape = [4, 1, 7, 8] + self.out_h = 1 + self.out_w = 1 + self.scale = 0. + self.align_corners = True + self.align_mode = 1 + + +class TestBilinearInterpCase2(TestBilinearInterpOp): + + def init_test_case(self): + self.interp_method = 'bilinear' + self.input_shape = [3, 3, 9, 6] + self.out_h = 12 + self.out_w = 12 + self.scale = 0. + self.align_corners = True + self.align_mode = 1 + + +class TestBilinearInterpCase3(TestBilinearInterpOp): + + def init_test_case(self): + self.interp_method = 'bilinear' + self.input_shape = [1, 1, 32, 64] + self.out_h = 64 + self.out_w = 32 + self.scale = 0. + self.align_corners = True + self.align_mode = 1 + + def test_check_output(self): + self.check_output_with_place(self.place, atol=1e-5) + + +class TestBilinearInterpCase4(TestBilinearInterpOp): + + def init_test_case(self): + self.interp_method = 'bilinear' + 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 + self.align_mode = 1 + + +class TestBilinearInterpCase5(TestBilinearInterpOp): + + def init_test_case(self): + self.interp_method = 'bilinear' + 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 + self.align_mode = 1 + + +class TestBilinearInterpCase6(TestBilinearInterpOp): + + def init_test_case(self): + self.interp_method = 'bilinear' + self.input_shape = [1, 1, 32, 64] + self.out_h = 64 + self.out_w = 32 + self.scale = 0. + self.out_size = np.array([65, 33]).astype("int32") + self.align_corners = True + self.align_mode = 1 + + +class TestBilinearInterpCase7(TestBilinearInterpOp): + + def init_test_case(self): + self.interp_method = 'bilinear' + self.input_shape = [1, 1, 32, 64] + self.out_h = 64 + self.out_w = 32 + self.scale = [2.0, 0.5] + self.align_corners = False + self.align_mode = 1 + + +class TestBilinearInterpSame(TestBilinearInterpOp): + + def init_test_case(self): + self.interp_method = 'bilinear' + self.input_shape = [2, 3, 32, 64] + self.out_h = 32 + self.out_w = 64 + self.scale = 0. + self.align_corners = True + self.align_mode = 1 + + +class TestBilinearInterpActualShape(TestBilinearInterpOp): + + def init_test_case(self): + self.interp_method = 'bilinear' + 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.align_mode = 1 + + +class TestBilinearInterpDataLayout(TestBilinearInterpOp): + + def init_test_case(self): + self.interp_method = 'bilinear' + self.input_shape = [2, 5, 5, 3] + self.out_h = 2 + self.out_w = 2 + self.scale = 0. + self.out_size = np.array([3, 3]).astype("int32") + self.align_corners = True + self.align_mode = 1 + self.data_layout = "NHWC" + + +class TestBilinearInterpOtherMethod1(TestBilinearInterpOp): + + def set_align_mode(self): + self.align_corners = False + self.align_mode = 1 + + +class TestBilinearInterpWithMethod2(TestBilinearInterpOp): + + def set_align_mode(self): + self.align_corners = False + self.align_mode = 0 + + +class TestBilinearInterpWithMethod3(TestBilinearInterpOp): + + def set_align_mode(self): + self.align_corners = True + self.align_mode = 0 + + +class TestBilinearInterpScale1(TestBilinearInterpOp): + + def init_test_case(self): + self.interp_method = 'bilinear' + self.input_shape = [2, 3, 5, 7] + self.out_h = 60 + self.out_w = 25 + self.scale = 2. + self.align_corners = True + self.align_mode = 1 + + +class TestBilinearInterpScale2(TestBilinearInterpOp): + + def init_test_case(self): + self.interp_method = 'bilinear' + self.input_shape = [2, 3, 5, 7] + self.out_h = 60 + self.out_w = 25 + self.scale = 1. + self.align_corners = True + self.align_mode = 1 + + +class TestBilinearInterpScale3(TestBilinearInterpOp): + + def init_test_case(self): + self.interp_method = 'bilinear' + self.input_shape = [2, 3, 5, 7] + self.out_h = 60 + self.out_w = 25 + self.scale = 1.5 + self.align_corners = True + self.align_mode = 1 + + +class TestBilinearInterpScale4(TestBilinearInterpOp): + + def init_test_case(self): + self.interp_method = 'bilinear' + self.input_shape = [2, 3, 5, 7] + self.out_h = 60 + self.out_w = 25 + self.scale = [1.5, 0.5] + self.align_corners = True + self.align_mode = 1 + + +class TestBilinearInterpZero(TestBilinearInterpOp): + + def init_test_case(self): + self.interp_method = 'bilinear' + self.input_shape = [2, 3, 5, 7] + self.out_h = 60 + self.out_w = 25 + self.scale = 0.2 + self.align_corners = False + self.align_mode = 0 + + +class TestBilinearInterpOp_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 = "bilinear_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 = bilinear_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 = 'bilinear' + self.input_shape = [2, 3, 5, 5] + self.out_h = 3 + self.out_w = 3 + self.scale = 0. + self.out_size = [3, 3] + self.align_corners = True + + +# out_size is a 1-D tensor +class TestBilinearInterp_attr_tensor_Case1(TestBilinearInterpOp_attr_tensor): + + def init_test_case(self): + self.interp_method = 'bilinear' + 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 + + +# scale is a 1-D tensor +class TestBilinearInterp_attr_tensor_Case2(TestBilinearInterpOp_attr_tensor): + + def init_test_case(self): + self.interp_method = 'bilinear' + 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 TestBilinearInterp_attr_tensor_Case3(TestBilinearInterpOp_attr_tensor): + + def init_test_case(self): + self.interp_method = 'bilinear' + 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 bilinear_interp_op added. +# 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: +# self.assertTrue(np.allclose(res, expect_res)) + + +class TestBilinearInterpOpAPI_dy(unittest.TestCase): + + def test_case(self): + import paddle + if core.is_compiled_with_mlu(): + place = paddle.device.MLUPlace(0) + else: + place = core.CPUPlace() + with fluid.dygraph.guard(place): + input_data = np.random.random((2, 3, 6, 6)).astype("float32") + input_data = np.load('input.npy').astype("float32") + # print(input_data) + input_x = paddle.to_tensor(input_data) + expect_res = bilinear_interp_np(input_data, + out_h=12, + out_w=12, + align_corners=False) + out = interpolate(x=input_x, + size=[12, 12], + mode="bilinear", + align_corners=False) + self.assertTrue(np.allclose(out.numpy(), expect_res)) + + +class TestBilinearInterpOpAPI_dy2(unittest.TestCase): + + def test_case(self): + import paddle + if core.is_compiled_with_mlu(): + place = paddle.device.MLUPlace(0) + else: + place = core.CPUPlace() + with fluid.dygraph.guard(place): + input_data = np.random.random((2, 3, 6, 6)).astype("float32") + size_np = np.array([12, 12]).astype("int64") + input_x = paddle.to_tensor(input_data) + size = paddle.to_tensor(size_np) + expect_res = bilinear_interp_np(input_data, + out_h=12, + out_w=12, + align_corners=False) + out = interpolate(x=input_x, + size=size, + mode="bilinear", + align_corners=False) + self.assertTrue(np.allclose(out.numpy(), expect_res)) + + +class TestBilinearInterpOpAPI_dy3(unittest.TestCase): + + def test_case(self): + import paddle + if core.is_compiled_with_mlu(): + place = paddle.device.MLUPlace(0) + else: + place = core.CPUPlace() + with fluid.dygraph.guard(place): + input_data = np.random.random((2, 3, 6, 6)).astype("float32") + size_1 = np.array([12]).astype("int64") + input_x = paddle.to_tensor(input_data) + size = paddle.to_tensor(size_1) + expect_res = bilinear_interp_np(input_data, + out_h=12, + out_w=12, + align_corners=False) + out = interpolate(x=input_x, + size=[size, size], + mode="bilinear", + align_corners=False) + self.assertTrue(np.allclose(out.numpy(), expect_res)) + + +class TestBilinearInterpOpAPI_dy4(unittest.TestCase): + + def test_case(self): + import paddle + if core.is_compiled_with_mlu(): + place = paddle.device.MLUPlace(0) + else: + place = core.CPUPlace() + with fluid.dygraph.guard(place): + input_data = np.random.random((2, 3, 6, 6)).astype("float32") + scale_np = np.array([2, 2]).astype("int64") + input_x = paddle.to_tensor(input_data) + scale = paddle.to_tensor(scale_np) + expect_res = bilinear_interp_np(input_data, + out_h=12, + out_w=12, + align_corners=False) + out = interpolate(x=input_x, + scale_factor=scale, + mode="bilinear", + align_corners=False) + + self.assertTrue(np.allclose(out.numpy(), expect_res)) + + +if __name__ == "__main__": + unittest.main() -- GitLab