# 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 def bilinear_interp_np(input, out_h, out_w, out_size=None, actual_shape=None): """bilinear interpolation implement in shape [N, C, H, W]""" 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 if out_h > 1: ratio_h = (in_h - 1.0) / (out_h - 1.0) else: ratio_h = 0.0 if out_w > 1: ratio_w = (in_w - 1.0) / (out_w - 1.0) else: ratio_w = 0.0 out = np.zeros((batch_size, channel, out_h, out_w)) for i in range(out_h): h = int(ratio_h * i) hid = 1 if h < in_h - 1 else 0 h1lambda = ratio_h * i - h h2lambda = 1.0 - h1lambda for j in range(out_w): w = int(ratio_w * j) wid = 1 if w < in_w - 1 else 0 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]) return out.astype(input.dtype) class TestBilinearInterpOp(OpTest): def setUp(self): self.out_size = None self.actual_shape = None self.init_test_case() self.op_type = "bilinear_interp" input_np = np.random.random(self.input_shape).astype("float32") output_np = bilinear_interp_np(input_np, self.out_h, self.out_w, self.out_size, self.actual_shape) 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 } 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 = 'bilinear' self.input_shape = [2, 3, 4, 4] self.out_h = 2 self.out_w = 2 self.out_size = np.array([3, 3]).astype("int32") 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 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 class TestBilinearInterpCase3(TestBilinearInterpOp): def init_test_case(self): self.interp_method = 'bilinear' self.input_shape = [1, 1, 128, 64] self.out_h = 64 self.out_w = 128 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.out_size = np.array([2, 2]).astype("int32") 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.out_size = np.array([11, 11]).astype("int32") class TestBilinearInterpCase6(TestBilinearInterpOp): def init_test_case(self): self.interp_method = 'bilinear' self.input_shape = [1, 1, 128, 64] self.out_h = 64 self.out_w = 128 self.out_size = np.array([65, 129]).astype("int32") 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.out_size = np.array([66, 40]).astype("int32") class TestBilinearInterpOpUint8(OpTest): def setUp(self): self.out_size = None self.actual_shape = None self.init_test_case() self.op_type = "bilinear_interp" input_np = np.random.randint( low=0, high=256, size=self.input_shape).astype("uint8") output_np = bilinear_interp_np(input_np, self.out_h, self.out_w, self.out_size, self.actual_shape) 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, 'interp_method': self.interp_method } 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 = 'bilinear' self.input_shape = [1, 3, 9, 6] self.out_h = 10 self.out_w = 9 class TestBilinearInterpCase1Uint8(TestBilinearInterpOpUint8): def init_test_case(self): self.interp_method = 'bilinear' self.input_shape = [2, 3, 128, 64] self.out_h = 120 self.out_w = 50 class TestBilinearInterpCase2Uint8(TestBilinearInterpOpUint8): def init_test_case(self): self.interp_method = 'bilinear' self.input_shape = [4, 1, 7, 8] self.out_h = 5 self.out_w = 13 self.out_size = np.array([6, 15]).astype("int32") if __name__ == "__main__": unittest.main()