# Copyright (c) 2018 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 from op_test import OpTest import paddle.fluid.core as core import paddle.fluid as fluid def nearest_neighbor_interp_np(X, out_h, out_w, 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: 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: 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 return out.astype(X.dtype) class TestNearestInterpOp(OpTest): def setUp(self): self.out_size = None self.actual_shape = None self.data_layout = 'NCHW' self.init_test_case() self.op_type = "nearest_interp" input_np = np.random.random(self.input_shape).astype("float32") 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] if self.scale > 0: out_h = int(in_h * self.scale) out_w = int(in_w * self.scale) else: out_h = self.out_h out_w = self.out_w output_np = nearest_neighbor_interp_np( input_np, out_h, out_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 self.attrs = { 'out_h': self.out_h, 'out_w': self.out_w, 'scale': self.scale, 'interp_method': self.interp_method, 'align_corners': self.align_corners, 'data_layout': self.data_layout } self.outputs = {'Out': output_np} def test_check_output(self): self.check_output() def test_check_grad(self): self.check_grad(['X'], 'Out', in_place=True) def init_test_case(self): self.interp_method = 'nearest' self.input_shape = [2, 3, 4, 4] 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, 7, 8] 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 TestNearestInterpOpUint8(OpTest): def setUp(self): self.out_size = None self.actual_shape = None self.init_test_case() self.op_type = "nearest_interp" input_np = np.random.randint( low=0, high=256, size=self.input_shape).astype("uint8") if self.scale > 0: out_h = int(self.input_shape[2] * self.scale) out_w = int(self.input_shape[3] * self.scale) else: out_h = self.out_h out_w = self.out_w output_np = nearest_neighbor_interp_np(input_np, out_h, out_w, self.out_size, self.actual_shape, self.align_corners) self.inputs = {'X': input_np} if self.out_size is not None: self.inputs['OutSize'] = self.out_size self.attrs = { 'out_h': self.out_h, 'out_w': self.out_w, 'scale': self.scale, 'interp_method': self.interp_method, 'align_corners': self.align_corners } self.outputs = {'Out': output_np} def test_check_output(self): self.check_output_with_place(place=core.CPUPlace(), atol=1) def init_test_case(self): self.interp_method = 'nearest' self.input_shape = [1, 3, 9, 6] self.out_h = 10 self.out_w = 9 self.scale = 0. self.align_corners = True class TestNearestNeighborInterpCase1Uint8(TestNearestInterpOpUint8): def init_test_case(self): self.interp_method = 'nearest' self.input_shape = [2, 3, 32, 64] self.out_h = 80 self.out_w = 40 self.scale = 0. self.align_corners = True class TestNearestNeighborInterpCase2Uint8(TestNearestInterpOpUint8): def init_test_case(self): self.interp_method = 'nearest' self.input_shape = [4, 1, 7, 8] self.out_h = 5 self.out_w = 13 self.scale = 0. self.out_size = np.array([6, 15]).astype("int32") self.align_corners = True 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 = 1. self.out_size = np.array([66, 40]).astype("int32") self.align_corners = True class TestNearestInterpOp_attr_tensor(OpTest): def setUp(self): self.out_size = None self.actual_shape = None self.init_test_case() self.op_type = "nearest_interp" 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 > 0: out_h = int(self.input_shape[2] * self.scale) out_w = int(self.input_shape[3] * self.scale) self.attrs['scale'] = self.scale 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 output_np = nearest_neighbor_interp_np(input_np, out_h, out_w, self.out_size, self.actual_shape, self.align_corners) self.outputs = {'Out': output_np} def test_check_output(self): self.check_output() def test_check_grad(self): self.check_grad(['X'], 'Out', in_place=True) def init_test_case(self): self.interp_method = 'nearest' self.input_shape = [2, 3, 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 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) 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): 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()