# Copyright 2020 Huawei Technologies Co., Ltd # # 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 numpy as np import pytest import mindspore.context as context import mindspore.nn as nn from mindspore import Tensor from mindspore.ops import operations as P from mindspore.common import dtype as mstype from mindspore.ops.composite import GradOperation class Net(nn.Cell): def __init__(self): super(Net, self).__init__() self.loss = P.CTCLossV2() self.div = P.RealDiv() self.cast = P.Cast() self.mean = P.ReduceMean() def construct(self, probs, label, input_length, label_length): x, _ = self.loss(probs, label, input_length, label_length) x = self.div(x, self.cast(label_length, mstype.float32)) x = self.mean(x) return x class GradData(nn.Cell): def __init__(self, network): super(GradData, self).__init__() self.grad = GradOperation(get_all=True, sens_param=False) self.network = network def construct(self, probs, labels, input_lengths, label_lengths): return self.grad(self.network)(probs, labels, input_lengths, label_lengths) @pytest.mark.level0 @pytest.mark.platform_x86_gpu_training @pytest.mark.env_onecard def test_ctcloss(): probs = Tensor([[[-4.4131, -4.6093, -3.4333, -3.9268, -2.8917, -3.4093, -4.2243, -1.1379, -7.1046, -0.6902], [-2.5109, -3.3397, -4.9384, -1.2723, -1.1443, -2.4683, -2.6768, -4.1282, -2.7062, -3.1906], [-2.5092, -1.6392, -2.0864, -4.0059, -1.5610, -2.3223, -2.4816, -2.9922, -3.1412, -2.3311]], [[-2.1243, -3.5773, -3.1108, -4.4253, -2.7080, -1.9653, -2.0499, -2.4418, -1.8620, -1.5229], [-2.2479, -3.5128, -1.4189, -2.8701, -1.8562, -2.2752, -2.7019, -2.1865, -2.5634, -2.9869], [-3.2144, -1.3986, -3.1083, -3.9634, -3.5131, -3.2317, -2.6200, -1.7938, -1.8159, -1.7255]], [[-3.1301, -2.1649, -0.9286, -2.9452, -2.5992, -2.0263, -2.9201, -3.2155, -2.8302, -3.3636], [-1.4661, -3.6311, -2.4781, -4.6180, -2.7308, -1.7019, -1.5570, -2.6012, -4.0788, -2.3073], [-2.6833, -1.5033, -3.6922, -2.6360, -2.6974, -2.6847, -2.7579, -2.1396, -1.4093, -2.9630]], [[-2.0094, -2.3024, -3.3673, -1.0220, -2.8326, -2.2613, -3.0535, -2.9879, -3.7015, -2.4510], [-1.9071, -3.2603, -2.3229, -2.0572, -4.3450, -2.1284, -2.6306, -1.3824, -2.9815, -2.5061], [-2.7931, -3.7631, -3.2440, -4.3887, -1.0271, -3.8851, -1.2418, -4.5123, -2.2993, -2.4607]], [[-1.5763, -2.7539, -3.6941, -3.8166, -1.2599, -2.6903, -2.5826, -4.8208, -2.9562, -1.6321], [-3.3031, -3.0087, -1.9982, -1.9081, -3.8731, -2.8764, -2.2485, -2.3808, -1.4283, -2.1625], [-2.4516, -3.2394, -4.2053, -4.3541, -2.5229, -4.0717, -1.4894, -2.3151, -1.1098, -2.3465]]], dtype=mstype.float32) labels = Tensor([9, 4, 6, 4, 7, 1, 4, 6, 6, 8], dtype=mstype.int32) input_lengths = Tensor([5, 5, 5], dtype=mstype.int32) label_lengths = Tensor([3, 3, 4], dtype=mstype.int32) context.set_context(mode=context.GRAPH_MODE, device_target="GPU") net = Net() ctc_loss = net(probs, labels, input_lengths, label_lengths) expect_loss = [2.4099] assert np.allclose(ctc_loss.asnumpy(), expect_loss) grad = GradData(net)(probs, labels, input_lengths, label_lengths) expect_grad = [[[8.8442e-05, 1.1065e-03, 3.5867e-03, 2.1896e-03, 6.1646e-03, 3.6738e-03, 1.6262e-03, 3.5610e-02, 9.1258e-05, -5.4134e-02], [-3.7523e-03, 3.9386e-03, 7.9623e-04, 3.1132e-02, -6.2954e-02, 9.4143e-03, 7.6425e-03, 1.7902e-03, 7.4211e-03, 4.5719e-03], [6.7778e-03, 1.6178e-02, 1.0344e-02, 1.5173e-03, -6.5840e-02, 8.1707e-03, 6.9674e-03, 4.1814e-03, 3.6026e-03, 8.0991e-03]], [[-1.2581e-02, 3.1057e-03, 4.9517e-03, 1.3301e-03, -2.6320e-02, 1.5568e-02, 1.4305e-02, 9.6671e-03, 1.7262e-02, -2.7292e-02], [-1.5566e-02, 3.3126e-03, 2.6887e-02, 6.2993e-03, -3.9716e-02, 1.1420e-02, 7.4531e-03, -1.4252e-02, 8.5603e-03, 5.6048e-03], [3.3483e-03, 2.0579e-02, 3.7231e-03, 1.5832e-03, 2.4837e-03, 3.2909e-03, -7.7267e-02, 1.3861e-02, 1.3558e-02, 1.4840e-02]], [[-8.0007e-03, 1.2751e-02, 4.3901e-02, 5.8435e-03, -7.2627e-02, 1.4647e-02, -8.0584e-03, 4.4595e-03, 6.5557e-03, 5.2891e-04], [-3.6006e-02, 1.5308e-03, 9.3225e-03, 1.0969e-03, -2.5098e-03, 2.0260e-02, 2.3419e-02, -3.0053e-02, 1.8809e-03, 1.1059e-02], [-7.7639e-02, 1.8533e-02, 2.0764e-03, 5.9706e-03, 5.6150e-03, 5.6868e-03, 5.2854e-03, 9.8085e-03, 2.0360e-02, 4.3053e-03]], [[-2.6776e-02, 1.1113e-02, 3.8314e-03, 3.9986e-02, -1.6020e-02, 1.1579e-02, -4.1635e-02, 5.5992e-03, 2.7429e-03, 9.5786e-03], [-6.8619e-03, -6.4066e-03, 1.0888e-02, 1.4201e-02, 1.4413e-03, 1.3225e-02, 8.0039e-03, -4.9191e-02, 5.6352e-03, 9.0651e-03], [5.1026e-03, 1.9343e-03, 3.2506e-03, 1.0347e-03, 2.9837e-02, 1.7121e-03, -5.9261e-02, 9.1443e-04, 8.3608e-03, 7.1146e-03]], [[-2.0848e-02, 7.0754e-03, 2.7633e-03, 2.4447e-03, 3.1520e-02, 7.5401e-03, -5.8895e-02, 8.9559e-04, 5.7796e-03, 2.1724e-02], [-1.3499e-03, -1.0019e-01, 1.5064e-02, 1.6485e-02, 2.3104e-03, 6.2597e-03, 1.1729e-02, 1.0275e-02, 2.6635e-02, 1.2782e-02], [7.1796e-03, 3.2656e-03, 1.2430e-03, 1.0712e-03, 6.6856e-03, 1.4207e-03, 1.8792e-02, 8.2297e-03, -5.5865e-02, 7.9753e-03]]] assert np.allclose(grad[0].asnumpy(), expect_grad, atol=1e-5)