# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve. # # 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. import unittest import numpy as np from op_test import OpTest def norm(input, scale, epsilon): s0, s1, s2, s3 = input.shape x_square = input * input for i in xrange(s0): input_batch = input[i:i + 1, :, :, :] input_batch = input_batch.reshape(s1, s2 * s3) x_square_batch = x_square[i:i + 1, :, :, :] x_square_batch = x_square_batch.reshape(s1, s2 * s3) square_colsum = x_square_batch.sum(axis=0) + epsilon tmp = pow(square_colsum, 0.5) tmp = np.reciprocal(tmp) tmp_tile = np.tile(tmp, s1) tmp_tile = tmp_tile.reshape(s1, s2 * s3) scale_tile = np.tile(scale, (1, s2 * s3)) scale_tile = scale_tile.reshape(s1, s2 * s3) out_batch = input_batch * tmp_tile * scale_tile out_batch = out_batch.reshape(1, s1, s2, s3) if i == 0: out = out_batch else: out = np.concatenate((out, out_batch), 0) out.reshape(s0, s1, s2, s3) return out class TestNormOp(OpTest): def setUp(self): self.op_type = "norm" self.init_test_case() input = np.random.random(self.shape).astype("float32") scale = np.array([10, 10, 10]) self.inputs = { 'X': input.astype('float32'), 'Scale': scale.astype('float32') } self.attrs = {'epsilon': self.epsilon} output = norm(input, scale, self.epsilon) self.outputs = {'Out': output.astype('float32')} def test_check_output(self): self.check_output() def test_check_grad(self): self.check_grad(['X'], 'Out') def init_test_case(self): self.shape = [2, 3, 2, 2] self.epsilon = 1e-6 if __name__ == '__main__': unittest.main()