wehub-resource-sync
148 lines
4.1 KiB
Python
148 lines
4.1 KiB
Python
# Copyright (c) 2021 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_data_feed(self):
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x = np.random.uniform(size=[3, 7])
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label = np.arange(3).reshape([3, 1])
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self.feed_fp32 = {
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"x": x.astype(np.float32),
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"label": label.astype(np.int64),
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}
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self.feed_fp16 = {
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"x": x.astype(np.float16),
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"label": label.astype(np.int32),
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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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'soft_label': False,
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}
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@IPUOpTest.static_graph
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def build_model(self, on_ipu):
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x = 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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if on_ipu:
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label = 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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else:
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label = paddle.static.data(
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name=self.feed_list[1], shape=self.feed_shape[1], dtype='int64'
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)
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out = paddle.nn.functional.cross_entropy(
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input=x,
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label=label,
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reduction='none',
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use_softmax=False,
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**self.attrs,
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)
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self.fetch_list = [out.name]
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def run_model(self, exec_mode):
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if self.is_ipu_mode(exec_mode):
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self.feed_fp32['label'] = self.feed_fp32['label'].astype(np.int32)
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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(self.is_ipu_mode(m))
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self.run_model(m)
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self.check()
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class TestCase1(TestBase):
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def set_op_attrs(self):
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self.attrs = {
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'soft_label': False,
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'ignore_index': 1,
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}
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class TestCase2(TestBase):
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def set_data_feed(self):
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x = np.random.uniform(size=[30, 70])
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label = np.arange(30).reshape([30, 1])
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self.feed_fp32 = {
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"x": x.astype(np.float32),
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"label": label.astype(np.int64),
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}
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self.feed_fp16 = {
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"x": x.astype(np.float16),
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"label": label.astype(np.int32),
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}
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@unittest.skip("soft_label=True is not supported")
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class TestCase3(TestBase):
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def set_op_attrs(self):
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self.attrs = {
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'soft_label': True,
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}
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class TestCase4(TestBase):
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def set_data_feed(self):
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x = np.random.uniform(size=[3, 5, 7])
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label = np.random.randint(0, 7, [3, 5, 1], dtype='int64')
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self.feed_fp32 = {
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"x": x.astype(np.float32),
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"label": label.astype(np.int64),
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}
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self.feed_fp16 = {
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"x": x.astype(np.float16),
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"label": label.astype(np.int32),
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}
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class TestCase5(TestBase):
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def set_data_feed(self):
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x = np.random.uniform(size=[3, 5, 6, 7])
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label = np.random.randint(0, 7, [3, 5, 6], dtype='int64')
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self.feed_fp32 = {
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"x": x.astype(np.float32),
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"label": label.astype(np.int64),
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}
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self.feed_fp16 = {
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"x": x.astype(np.float16),
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"label": label.astype(np.int32),
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}
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if __name__ == "__main__":
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unittest.main()
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