wehub-resource-sync
92 lines
3.3 KiB
Python
92 lines
3.3 KiB
Python
# Copyright (c) 2020 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 get_device_place, is_custom_device
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from simple_nets import simple_fc_net, simple_fc_net_with_inputs
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import paddle
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from paddle import base
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class TestFetchDenseTensorArray(unittest.TestCase):
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def build_program(self, main_program, startup_program):
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with (
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base.unique_name.guard(),
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base.program_guard(main_program, startup_program),
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):
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i = paddle.zeros(shape=[1], dtype='int64')
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img = paddle.static.data(
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name='image', shape=[-1, 784], dtype='float32'
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)
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label = paddle.static.data(
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name='label', shape=[-1, 1], dtype='int64'
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)
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loss = simple_fc_net_with_inputs(img, label, class_num=10)
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loss = simple_fc_net()
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opt = paddle.optimizer.SGD(learning_rate=0.001)
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opt.minimize(loss)
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array = paddle.tensor.array_write(x=img, i=i)
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i = paddle.increment(i)
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paddle.tensor.array_write(x=label, i=i, array=array)
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i = paddle.increment(i)
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paddle.tensor.array_write(x=loss, i=i, array=array)
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return loss, array
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def check_network(self, use_cuda=True):
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main_program = base.Program()
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startup_program = base.Program()
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loss, array = self.build_program(main_program, startup_program)
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batch_size = 32
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image = np.random.normal(size=(batch_size, 784)).astype('float32')
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label = np.random.randint(0, 10, (batch_size, 1), dtype="int64")
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place = get_device_place() if use_cuda else base.CPUPlace()
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exe = base.Executor(place)
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exe.run(startup_program)
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feed_dict = {'image': image, 'label': label}
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if not paddle.base.framework.use_pir_api():
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build_strategy = base.BuildStrategy()
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binary = base.CompiledProgram(
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main_program, build_strategy=build_strategy
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)
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else:
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binary = main_program
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for _ in range(3):
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loss_v, array_v = exe.run(
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binary, feed=feed_dict, fetch_list=[loss, array]
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)
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self.assertEqual(loss_v.shape, ())
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self.assertEqual(array_v[0].shape, (batch_size, 784))
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self.assertEqual(array_v[1].shape, (batch_size, 1))
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self.assertEqual(array_v[2].shape, ())
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np.testing.assert_allclose(loss_v, array_v[2], rtol=1e-05)
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def test_fetch_dense_tensor_array(self):
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if base.core.is_compiled_with_cuda() or is_custom_device():
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self.check_network(use_cuda=True)
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self.check_network(use_cuda=False)
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if __name__ == '__main__':
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paddle.enable_static()
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unittest.main()
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