paddlepaddle--paddle
wehub-resource-sync
127 行
3.9 KiB
Python
127 行
3.9 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_ipu import IPUOpTest
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import paddle
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import paddle.static
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class TestBase(IPUOpTest):
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def setUp(self):
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self.set_atol()
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self.set_training()
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self.set_data_feed()
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self.set_feed_attr()
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self.set_op_attrs()
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def set_training(self):
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# ctc_loss only support training currently.
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self.is_training = True
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self.epoch = 1
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def set_data_feed(self):
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self.batch_size = 16
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self.max_seq_length = 5
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self.max_label_length = 3
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self.num_classes = 5
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self.logits_length = np.array(
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[self.max_seq_length] * self.batch_size, dtype=np.int64
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)
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self.labels_length = np.array(
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[self.max_label_length] * self.batch_size, dtype=np.int64
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)
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self.blank = self.num_classes - 1
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self.norm_by_times = False
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logits = np.random.uniform(
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0.1, 1.0, [self.max_seq_length, self.batch_size, self.num_classes]
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).astype("float32")
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labels = np.random.randint(
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0,
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self.num_classes - 1,
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[self.batch_size, self.max_label_length],
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dtype="int32",
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)
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self.feed_fp32 = {
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"Logits": logits,
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"Label": labels,
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"input_length": self.logits_length.astype("int64"),
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"label_length": self.labels_length.astype("int64"),
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}
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self.feed_fp16 = {
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"Logits": logits.astype(np.float16),
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"Label": labels,
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"input_length": self.logits_length.astype("int64"),
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"label_length": self.labels_length.astype("int64"),
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}
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def set_feed_attr(self):
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self.feed_shape = [x.shape for x in self.feed_fp32.values()]
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self.feed_list = list(self.feed_fp32.keys())
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def set_op_attrs(self):
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self.attrs = {
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"blank": self.blank,
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"norm_by_times": self.norm_by_times,
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}
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@IPUOpTest.static_graph
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def build_model(self):
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data = paddle.static.data(
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name=self.feed_list[0], shape=self.feed_shape[0], dtype="float32"
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)
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logits = paddle.nn.Linear(
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self.num_classes, self.num_classes, bias_attr=False
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)(data)
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labels = paddle.static.data(
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name=self.feed_list[1], shape=self.feed_shape[1], dtype='int32'
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)
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input_length = paddle.static.data(
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name=self.feed_list[2], shape=self.feed_shape[2], dtype='int64'
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)
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label_length = paddle.static.data(
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name=self.feed_list[3], shape=self.feed_shape[3], dtype='int64'
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)
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out = paddle.nn.functional.ctc_loss(
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logits,
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labels,
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input_length=input_length,
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label_length=label_length,
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reduction='mean',
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**self.attrs,
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)
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loss = paddle.mean(out)
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adam = paddle.optimizer.Adam(learning_rate=1e-2)
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adam.minimize(loss)
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self.fetch_list = [loss.name, out.name]
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def run_model(self, exec_mode):
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self.run_op_test(exec_mode)
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def test(self):
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for m in IPUOpTest.ExecutionMode:
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if not self.skip_mode(m):
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self.build_model()
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self.run_model(m)
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self.check()
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if __name__ == "__main__":
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unittest.main()
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