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115 lines
4.3 KiB
115 lines
4.3 KiB
# Copyright (c) 2018 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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from __future__ import print_function
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import unittest
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from paddle.fluid.framework import default_main_program, Program, convert_np_dtype_to_dtype_, in_dygraph_mode
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import paddle.fluid as fluid
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import paddle.fluid.layers as layers
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import paddle.fluid.core as core
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import numpy as np
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class TestVarBase(unittest.TestCase):
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def setUp(self):
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self.shape = [512, 1234]
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self.dtype = np.float32
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self.array = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
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def test_to_variable(self):
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with fluid.dygraph.guard():
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var = fluid.dygraph.to_variable(self.array, name="abc")
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self.assertTrue(np.array_equal(var.numpy(), self.array))
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self.assertEqual(var.name, 'abc')
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# default value
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self.assertEqual(var.persistable, False)
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self.assertEqual(var.stop_gradient, True)
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self.assertEqual(var.shape, self.shape)
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self.assertEqual(var.dtype, core.VarDesc.VarType.FP32)
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self.assertEqual(var.type, core.VarDesc.VarType.LOD_TENSOR)
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def test_write_property(self):
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with fluid.dygraph.guard():
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var = fluid.dygraph.to_variable(self.array)
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self.assertEqual(var.name, 'generated_var_0')
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var.name = 'test'
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self.assertEqual(var.name, 'test')
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self.assertEqual(var.persistable, False)
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var.persistable = True
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self.assertEqual(var.persistable, True)
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self.assertEqual(var.stop_gradient, True)
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var.stop_gradient = False
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self.assertEqual(var.stop_gradient, False)
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# test some patched methods
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def test_set_value(self):
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with fluid.dygraph.guard():
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var = fluid.dygraph.to_variable(self.array)
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tmp1 = np.random.uniform(0.1, 1, [2, 2, 3]).astype(self.dtype)
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self.assertRaises(AssertionError, var.set_value, tmp1)
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tmp2 = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
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var.set_value(tmp2)
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self.assertTrue(np.array_equal(var.numpy(), tmp2))
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def test_to_string(self):
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with fluid.dygraph.guard():
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var = fluid.dygraph.to_variable(self.array)
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self.assertTrue(isinstance(str(var.to_string(True)), str))
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def test_backward(self):
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with fluid.dygraph.guard():
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var = fluid.dygraph.to_variable(self.array)
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var.stop_gradient = False
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loss = fluid.layers.relu(var)
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loss.backward()
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grad_var = var._grad_ivar()
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self.assertEqual(grad_var.shape, self.shape)
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def test_gradient(self):
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with fluid.dygraph.guard():
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var = fluid.dygraph.to_variable(self.array)
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var.stop_gradient = False
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loss = fluid.layers.relu(var)
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loss.backward()
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grad_var = var.gradient()
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self.assertEqual(grad_var.shape, self.array.shape)
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def test_block(self):
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with fluid.dygraph.guard():
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var = fluid.dygraph.to_variable(self.array)
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self.assertEqual(var.block,
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fluid.default_main_program().global_block())
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def test_slice(self):
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with fluid.dygraph.guard():
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var = fluid.dygraph.to_variable(self.array)
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self.assertTrue(np.array_equal(var[1, :].numpy(), self.array[1, :]))
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self.assertTrue(np.array_equal(var[::-1].numpy(), self.array[::-1]))
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def test_var_base_to_np(self):
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with fluid.dygraph.guard():
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var = fluid.dygraph.to_variable(self.array)
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self.assertTrue(
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np.array_equal(var.numpy(),
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fluid.framework._var_base_to_np(var)))
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if __name__ == '__main__':
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unittest.main()
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