# 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 import paddle.fluid.core as core from op_test import OpTest import paddle.fluid as fluid from paddle.fluid import Program, program_guard class TestSimilarityFocusOp(OpTest): def setUp(self): self.op_type = "similarity_focus" batch_size = 2 x_dim, y_dim, z_dim = 3, 2, 2 self.inputs = { 'X': np.array([[[[0.8, 0.1], [0.4, 0.5]], [[0.9, 0.7], [0.9, 0.9]], [[0.8, 0.9], [0.1, 0.2]]], [[[0.2, 0.5], [0.3, 0.4]], [[0.9, 0.7], [0.8, 0.4]], [[0.0, 0.2], [0.4, 0.7]]]]), } self.attrs = { 'axis': 1, 'indexes': [0], } output = None for batch in range(batch_size): res = np.zeros((1, y_dim, z_dim)).astype("float32").reshape(-1) for index in self.attrs['indexes']: channel = self.inputs['X'][batch, index, :, :].reshape(-1).copy( ) tag1 = [0 for i in range(y_dim)] tag2 = [0 for i in range(z_dim)] cnt = 0 for i in range(channel.size): index = channel.argmax() idx1 = index // z_dim idx2 = index % z_dim if tag1[idx1] + tag2[idx2] == 0: tag1[idx1] = 1 tag2[idx2] = 1 res[index] = 1 cnt += 1 if cnt == min(y_dim, z_dim): break channel[index] = -1 res = res.reshape(1, y_dim, z_dim).repeat([x_dim], axis=0) res = res.reshape(1, x_dim, y_dim, z_dim) if output is not None: output = np.concatenate((output, res), axis=0) else: output = res self.outputs = {'Out': output} def test_check_output(self): self.check_output() class TestSimilarityFocusOp_axis1(OpTest): def setUp(self): self.op_type = "similarity_focus" batch_size = 3 x_dim, y_dim, z_dim = 4, 5, 6 self.inputs = { 'X': np.random.random( (batch_size, x_dim, y_dim, z_dim)).astype("float32"), } self.attrs = { 'axis': 1, 'indexes': [0, 3], } output = None for batch in range(batch_size): res = np.zeros((1, y_dim, z_dim)).astype("float32").reshape(-1) for index in self.attrs['indexes']: channel = self.inputs['X'][batch, index, :, :].reshape(-1).copy( ) tag1 = [0 for i in range(y_dim)] tag2 = [0 for i in range(z_dim)] cnt = 0 for i in range(channel.size): index = channel.argmax() idx1 = index // z_dim idx2 = index % z_dim if tag1[idx1] + tag2[idx2] == 0: tag1[idx1] = 1 tag2[idx2] = 1 res[index] = 1 cnt += 1 if cnt == min(y_dim, z_dim): break channel[index] = -1 res = res.reshape(1, y_dim, z_dim) res = res.repeat([x_dim], axis=0) res = res.reshape(1, x_dim, y_dim, z_dim) if output is not None: output = np.concatenate((output, res), axis=0) else: output = res self.outputs = {'Out': output} def test_check_output(self): self.check_output() class TestSimilarityFocusOp_axis2(OpTest): def setUp(self): self.op_type = "similarity_focus" batch_size = 6 x_dim, y_dim, z_dim = 7, 8, 9 self.inputs = { 'X': np.random.random( (batch_size, x_dim, y_dim, z_dim)).astype("float32"), } self.attrs = { 'axis': 2, 'indexes': [0, 3, 5], } output = None for batch in range(batch_size): res = np.zeros((x_dim, 1, z_dim)).astype("float32").reshape(-1) for index in self.attrs['indexes']: channel = self.inputs['X'][batch, :, index, :].reshape(-1).copy( ) tag1 = [0 for i in range(x_dim)] tag2 = [0 for i in range(z_dim)] cnt = 0 for i in range(channel.size): index = channel.argmax() idx1 = index // z_dim idx2 = index % z_dim if tag1[idx1] + tag2[idx2] == 0: tag1[idx1] = 1 tag2[idx2] = 1 res[index] = 1 cnt += 1 if cnt == min(x_dim, z_dim): break channel[index] = -1 res = res.reshape(x_dim, 1, z_dim) res = res.repeat([y_dim], axis=1) res = res.reshape(1, x_dim, y_dim, z_dim) if output is not None: output = np.concatenate((output, res), axis=0) else: output = res self.outputs = {'Out': output} def test_check_output(self): self.check_output() class TestSimilarityFocusOp_axis3(OpTest): def setUp(self): self.op_type = "similarity_focus" batch_size = 64 x_dim, y_dim, z_dim = 48, 48, 13 self.inputs = { 'X': np.random.random( (batch_size, x_dim, y_dim, z_dim)).astype("float32"), } self.attrs = { 'axis': 3, 'indexes': [0, 2, 7, 9], } output = None for batch in range(batch_size): res = np.zeros((x_dim, y_dim, 1)).astype("float32").reshape(-1) for index in self.attrs['indexes']: channel = self.inputs['X'][batch, :, :, index].reshape(-1).copy( ) tag1 = [0 for i in range(x_dim)] tag2 = [0 for i in range(y_dim)] cnt = 0 for i in range(channel.size): index = channel.argmax() idx1 = index // y_dim idx2 = index % y_dim if tag1[idx1] + tag2[idx2] == 0: tag1[idx1] = 1 tag2[idx2] = 1 res[index] = 1 cnt += 1 if cnt == min(x_dim, y_dim): break channel[index] = -1 res = res.reshape(x_dim, y_dim, 1) res = res.repeat([z_dim], axis=2) res = res.reshape(1, x_dim, y_dim, z_dim) if output is not None: output = np.concatenate((output, res), axis=0) else: output = res self.outputs = {'Out': output} def test_check_output(self): self.check_output() class TestSimilarityFocusOpError(unittest.TestCase): def test_errors(self): with program_guard(Program(), Program()): data = fluid.data(name='data', shape=[16, 3, 2, 2], dtype='float32') def test_input_Variable(): input = np.random.rand(16, 3, 2, 2).astype("float32") out = fluid.layers.similarity_focus( input=input, axis=1, indexes=[0]) self.assertRaises(TypeError, test_input_Variable) def test_axis_Int(): axis = 1.0 out = fluid.layers.similarity_focus( input=data, axis=axis, indexes=[0]) self.assertRaises(TypeError, test_axis_Int) def test_indexes_List(): indexes = 0 out = fluid.layers.similarity_focus( input=data, axis=1, indexes=indexes) self.assertRaises(TypeError, test_indexes_List) if __name__ == "__main__": unittest.main()