wehub-resource-sync
514 lines
18 KiB
Python
514 lines
18 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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from op_test import OpTest
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import paddle
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from paddle import _C_ops
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paddle.enable_static()
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def python_api(
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logits,
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label,
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logits_length,
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labels_length,
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blank=0,
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fastemit_lambda=0.0,
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num_threads=1,
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):
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loss_out = _C_ops.warprnnt(
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logits,
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label,
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logits_length,
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labels_length,
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blank,
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fastemit_lambda,
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num_threads,
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)
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return loss_out
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class TestWarpRNNTOp(OpTest):
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def set_act(self):
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# logsoftmax
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self.acts = np.array(
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[
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[
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[
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[-1.40493705, -0.68276381, -1.38870219],
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[-1.25243963, -1.03148021, -1.02802034],
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[-1.19624572, -0.93786934, -1.18347801],
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],
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[
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[-1.03417513, -0.84465814, -1.53815849],
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[-0.96884241, -1.01432347, -1.35545407],
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[-0.82076925, -1.10135010, -1.48067081],
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],
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[
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[-1.43828803, -1.16579869, -0.79630424],
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[-1.38401855, -0.83654478, -1.15129927],
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[-1.05188255, -1.29604414, -0.97522265],
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],
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[
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[-1.34330978, -0.86678589, -1.14344457],
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[-0.72518815, -1.32106859, -1.39063758],
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[-1.09984781, -1.00059987, -1.20590993],
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],
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],
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[
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[
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[-1.02221057, -1.47617485, -0.88748174],
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[-1.18362952, -0.78488945, -1.43689575],
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[-1.00784739, -1.28566450, -1.02574476],
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],
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[
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[-1.02589709, -1.13153743, -1.14260096],
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[-1.09942215, -1.12238913, -1.07459704],
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[-1.09359647, -0.89829379, -1.35585602],
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],
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[
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[-1.07782876, -0.84361953, -1.47178440],
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[-1.23424792, -1.00248783, -1.07299990],
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[-0.96521771, -1.19895815, -1.14698912],
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],
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[
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[-1.50722446, -1.15380039, -0.76994115],
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[-1.19125975, -0.89919308, -1.24041594],
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[-0.91301359, -1.19665577, -1.21576258],
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],
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],
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[
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[
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[-1.02221057, -1.47617485, -0.88748174],
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[-1.18362952, -0.78488945, -1.43689575],
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[-1.00784739, -1.28566450, -1.02574476],
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],
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[
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[-1.02589709, -1.13153743, -1.14260096],
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[-1.09942215, -1.12238913, -1.07459704],
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[-1.09359647, -0.89829379, -1.35585602],
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],
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[
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[-1.07782876, -0.84361953, -1.47178440],
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[-1.23424792, -1.00248783, -1.07299990],
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[-0.96521771, -1.19895815, -1.14698912],
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],
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[
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[-1.50722446, -1.15380039, -0.76994115],
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[-1.19125975, -0.89919308, -1.24041594],
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[-0.91301359, -1.19665577, -1.21576258],
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],
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],
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],
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dtype=np.float32,
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)
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def set_gradient(self):
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self.gradient = np.array(
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[
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[
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[
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[-0.43222645, -0.56777355, 0.0],
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[-0.3656501, 0.0, -0.20212345],
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[-0.20212345, 0.0, 0.0],
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],
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[
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[-0.16521672, -0.26700973, 0.0],
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[-0.39436539, 0.0, -0.23829444],
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[-0.44041789, 0.0, 0.0],
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],
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[
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[-0.05212979, -0.11308693, 0.0],
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[-0.18313787, 0.0, -0.32431445],
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[-0.76473234, 0.0, 0.0],
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],
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[
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[0.0, -0.05212979, 0.0],
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[0.0, 0.0, -0.23526766],
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[-1.0, 0.0, 0.0],
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],
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],
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[
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[
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[-0.71614241, -0.28385759, 0.0],
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[-0.18382932, -0.10002826, 0.0],
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[-0.10002826, 0.0, 0.0],
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],
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[
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[-0.41121795, -0.30492447, 0.0],
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[-0.32957594, -0.15917785, 0.0],
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[-0.25920611, 0.0, 0.0],
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],
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[
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[-0.11607642, -0.29514153, 0.0],
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[-0.28653336, -0.3381841, 0.0],
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[-0.59739022, 0.0, 0.0],
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],
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[
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[0.0, -0.11607642, 0.0],
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[0.0, -0.40260978, 0.0],
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[-1.0, 0.0, 0.0],
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],
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],
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[
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[
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[-0.71614241, -0.28385759, 0.0],
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[-0.18382932, -0.10002826, 0.0],
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[-0.10002826, 0.0, 0.0],
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],
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[
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[-0.41121795, -0.30492447, 0.0],
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[-0.32957594, -0.15917785, 0.0],
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[-0.25920611, 0.0, 0.0],
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],
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[
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[-0.11607642, -0.29514153, 0.0],
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[-0.28653336, -0.3381841, 0.0],
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[-0.59739022, 0.0, 0.0],
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],
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[
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[0.0, -0.11607642, 0.0],
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[0.0, -0.40260978, 0.0],
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[-1.0, 0.0, 0.0],
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],
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],
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],
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dtype=np.float32,
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)
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def config(self):
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self.blank = 0
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self.fastemit_lambda = 0.0
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self.set_act()
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self.labels = np.array([[1, 2], [1, 1], [1, 1]], dtype=np.int32)
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self.logit_lens = np.array([4, 4, 4], dtype=np.int32)
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self.label_lens = np.array([2, 2, 2], dtype=np.int32)
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self.loss = np.array(
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[4.2806528590890736, 3.9384369822503591, 3.9384369822503591],
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dtype=np.float64,
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)
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self.set_gradient()
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def setUp(self):
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self.op_type = "warprnnt"
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self.config()
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self.python_api = python_api
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self.python_out_sig = ["loss"]
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self.inputs = {
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"input": self.acts,
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"label": self.labels,
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"input_lengths": self.logit_lens,
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"label_lengths": self.label_lens,
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}
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self.outputs = {"loss": self.loss}
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self.attrs = {
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"blank": self.blank,
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"fastemit_lambda": self.fastemit_lambda,
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"num_threads": 1,
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}
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def test_check_output(self):
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self.check_output(check_pir=True)
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def test_check_grad(self):
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self.outputs["warprnntgrad"] = self.gradient
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self.check_grad(
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["input"], "loss", numeric_grad_delta=0.009, check_pir=True
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)
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class TestWarpRNNTFP64Op(TestWarpRNNTOp):
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def test_check_output(self):
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self.acts.astype(np.float64)
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self.check_output(check_pir=True)
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def test_check_grad(self):
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self.acts.astype(np.float64)
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self.outputs["warprnntgrad"] = self.gradient
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self.check_grad(
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["input"], "loss", numeric_grad_delta=0.009, check_pir=True
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)
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class TestWarpRNNTOpError(unittest.TestCase):
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def test_errors1(self):
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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logits_length = paddle.static.data(
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name='logit_lengths', shape=[None], dtype='int32'
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)
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label = paddle.static.data(
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name='labels', shape=[16, 3], dtype='int32'
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)
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label_length = paddle.static.data(
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name='label_lengths', shape=[None], dtype='int32'
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)
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def test_logits_Variable():
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logits_data = paddle.static.data(
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name='logits_data', shape=[5, 16, 6], dtype='int32'
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)
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paddle.nn.functional.rnnt_loss(
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input=logits_data,
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label=label,
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input_lengths=logits_length,
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label_lengths=label_length,
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)
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self.assertRaises(TypeError, test_logits_Variable)
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def test_errors2(self):
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with (
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paddle.pir_utils.OldIrGuard(),
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paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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),
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):
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logits = paddle.static.data(
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name='input', shape=[5, 16, 6], dtype='float32'
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)
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logits_length = paddle.static.data(
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name='logit_lengths', shape=[None], dtype='int32'
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)
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label = paddle.static.data(
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name='labels', shape=[16, 3], dtype='int32'
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)
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label_length = paddle.static.data(
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name='label_lengths', shape=[None], dtype='int32'
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)
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def test_label_Variable():
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label_data = paddle.static.data(
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name='label_data', shape=[16, 3], dtype='int64'
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)
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paddle.nn.functional.rnnt_loss(
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input=logits,
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label=label_data,
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input_lengths=logits_length,
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label_lengths=label_length,
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)
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self.assertRaises(TypeError, test_label_Variable)
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def test_logits_len_Variable():
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logits_length_data = paddle.static.data(
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name='logits_length_data', shape=[None], dtype='int64'
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)
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paddle.nn.functional.rnnt_loss(
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input=logits,
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label=label,
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input_lengths=logits_length_data,
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label_lengths=label_length,
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)
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self.assertRaises(TypeError, test_logits_len_Variable)
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def test_label_len_Variable():
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label_length_data = paddle.static.data(
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name='label_length_data', shape=[None], dtype='int64'
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)
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paddle.nn.functional.rnnt_loss(
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input=logits,
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label=label,
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input_lengths=logits_length,
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label_lengths=label_length_data,
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)
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self.assertRaises(TypeError, test_label_len_Variable)
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def test_dygraph_errors(self):
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def test_dygraph_with_lod():
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print("test_dygraph_with_lod")
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logits = np.random.uniform(0.1, 1.0, [20, 15]).astype("float32")
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# labels should not be blank
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labels = np.random.randint(0, 15 - 1, [15, 1], dtype="int32")
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labels_len = np.random.randint(0, 15 - 1, [15, 1], dtype="int64")
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logits_len = np.random.randint(0, 15 - 1, [15, 1], dtype="int32")
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softmax = paddle.to_tensor(logits)
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labels = paddle.to_tensor(labels)
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logits_len = paddle.to_tensor(logits_len)
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labels_len = paddle.to_tensor(labels_len)
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paddle.nn.functional.rnnt_loss(
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input=softmax,
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label=labels,
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input_lengths=logits_len,
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label_lengths=labels_len,
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)
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paddle.disable_static()
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self.assertRaises(ValueError, test_dygraph_with_lod)
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paddle.enable_static()
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class TestRNNTLossAPICase(unittest.TestCase):
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def set_act(self):
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# logsoftmax
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self.acts = np.array(
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[
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[
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[
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[-1.40493705, -0.68276381, -1.38870219],
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[-1.25243963, -1.03148021, -1.02802034],
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[-1.19624572, -0.93786934, -1.18347801],
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],
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[
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[-1.03417513, -0.84465814, -1.53815849],
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[-0.96884241, -1.01432347, -1.35545407],
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[-0.82076925, -1.10135010, -1.48067081],
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],
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[
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[-1.43828803, -1.16579869, -0.79630424],
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[-1.38401855, -0.83654478, -1.15129927],
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[-1.05188255, -1.29604414, -0.97522265],
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],
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[
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[-1.34330978, -0.86678589, -1.14344457],
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[-0.72518815, -1.32106859, -1.39063758],
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[-1.09984781, -1.00059987, -1.20590993],
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],
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],
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[
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[
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[-1.02221057, -1.47617485, -0.88748174],
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[-1.18362952, -0.78488945, -1.43689575],
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[-1.00784739, -1.28566450, -1.02574476],
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],
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[
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[-1.02589709, -1.13153743, -1.14260096],
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[-1.09942215, -1.12238913, -1.07459704],
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[-1.09359647, -0.89829379, -1.35585602],
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],
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[
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[-1.07782876, -0.84361953, -1.47178440],
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[-1.23424792, -1.00248783, -1.07299990],
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[-0.96521771, -1.19895815, -1.14698912],
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],
|
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[
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[-1.50722446, -1.15380039, -0.76994115],
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[-1.19125975, -0.89919308, -1.24041594],
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[-0.91301359, -1.19665577, -1.21576258],
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],
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],
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|
[
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[
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[-1.02221057, -1.47617485, -0.88748174],
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[-1.18362952, -0.78488945, -1.43689575],
|
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[-1.00784739, -1.28566450, -1.02574476],
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],
|
|
[
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[-1.02589709, -1.13153743, -1.14260096],
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[-1.09942215, -1.12238913, -1.07459704],
|
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[-1.09359647, -0.89829379, -1.35585602],
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],
|
|
[
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[-1.07782876, -0.84361953, -1.47178440],
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[-1.23424792, -1.00248783, -1.07299990],
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|
[-0.96521771, -1.19895815, -1.14698912],
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],
|
|
[
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[-1.50722446, -1.15380039, -0.76994115],
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|
[-1.19125975, -0.89919308, -1.24041594],
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[-0.91301359, -1.19665577, -1.21576258],
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],
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],
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],
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dtype=np.float32,
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)
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def config(self):
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self.blank = 0
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self.fastemit_lambda = 0.0
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self.set_act()
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self.labels = np.array([[1, 2], [1, 1], [1, 1]], dtype=np.int32)
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self.logit_lens = np.array([4, 4, 4], dtype=np.int32)
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self.label_lens = np.array([2, 2, 2], dtype=np.int32)
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self.loss = np.array(
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[4.2806528590890736, 3.9384369822503591, 3.9384369822503591],
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dtype=np.float64,
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)
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def test_functional_api(self):
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self.config()
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paddle.disable_static()
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acts = paddle.to_tensor(self.acts)
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labels = paddle.to_tensor(self.labels)
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logit_lens = paddle.to_tensor(self.logit_lens)
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label_lens = paddle.to_tensor(self.label_lens)
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loss_pd_mean = paddle.nn.functional.rnnt_loss(
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acts,
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labels,
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logit_lens,
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label_lens,
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blank=self.blank,
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reduction='mean',
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fastemit_lambda=self.fastemit_lambda,
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)
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loss_pd_mean = loss_pd_mean.numpy()
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loss_pd_sum = paddle.nn.functional.rnnt_loss(
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acts,
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labels,
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logit_lens,
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label_lens,
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blank=self.blank,
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reduction='sum',
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|
fastemit_lambda=self.fastemit_lambda,
|
|
)
|
|
loss_pd_sum = loss_pd_sum.numpy()
|
|
|
|
paddle.enable_static()
|
|
B = self.loss.shape[0]
|
|
loss_np_mean = self.loss.sum() / B
|
|
loss_np_sum = self.loss.sum()
|
|
|
|
np.testing.assert_allclose(
|
|
loss_pd_mean, loss_np_mean, rtol=1e-05, atol=1
|
|
)
|
|
np.testing.assert_allclose(loss_pd_sum, loss_np_sum, rtol=1e-05, atol=1)
|
|
|
|
def test_class_api(self):
|
|
self.config()
|
|
|
|
paddle.disable_static()
|
|
|
|
acts = paddle.to_tensor(self.acts)
|
|
labels = paddle.to_tensor(self.labels)
|
|
logit_lens = paddle.to_tensor(self.logit_lens)
|
|
label_lens = paddle.to_tensor(self.label_lens)
|
|
|
|
loss_pd = paddle.nn.RNNTLoss(self.blank, self.fastemit_lambda, 'none')(
|
|
acts, labels, logit_lens, label_lens
|
|
)
|
|
loss_pd = loss_pd.numpy()
|
|
paddle.enable_static()
|
|
np.testing.assert_allclose(loss_pd, self.loss, rtol=1e-05, atol=1)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|