You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
221 lines
8.3 KiB
221 lines
8.3 KiB
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
from __future__ import print_function
|
|
|
|
import unittest
|
|
from paddle.fluid.framework import default_main_program, Program, convert_np_dtype_to_dtype_, in_dygraph_mode
|
|
import paddle.fluid as fluid
|
|
import paddle.fluid.layers as layers
|
|
import paddle.fluid.core as core
|
|
import numpy as np
|
|
|
|
|
|
class TestVarBase(unittest.TestCase):
|
|
def setUp(self):
|
|
self.shape = [512, 1234]
|
|
self.dtype = np.float32
|
|
self.array = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
|
|
|
|
def test_to_variable(self):
|
|
with fluid.dygraph.guard():
|
|
var = fluid.dygraph.to_variable(self.array, name="abc")
|
|
self.assertTrue(np.array_equal(var.numpy(), self.array))
|
|
self.assertEqual(var.name, 'abc')
|
|
# default value
|
|
self.assertEqual(var.persistable, False)
|
|
self.assertEqual(var.stop_gradient, True)
|
|
self.assertEqual(var.shape, self.shape)
|
|
self.assertEqual(var.dtype, core.VarDesc.VarType.FP32)
|
|
self.assertEqual(var.type, core.VarDesc.VarType.LOD_TENSOR)
|
|
# The type of input must be 'ndarray' or 'Variable', it will raise TypeError
|
|
with self.assertRaises(TypeError):
|
|
var = fluid.dygraph.to_variable("test", name="abc")
|
|
# test to_variable of LayerObjectHelper(LayerHelperBase)
|
|
with self.assertRaises(TypeError):
|
|
linear = fluid.dygraph.Linear(32, 64)
|
|
var = linear._helper.to_variable("test", name="abc")
|
|
|
|
def test_write_property(self):
|
|
with fluid.dygraph.guard():
|
|
var = fluid.dygraph.to_variable(self.array)
|
|
|
|
self.assertEqual(var.name, 'generated_var_0')
|
|
var.name = 'test'
|
|
self.assertEqual(var.name, 'test')
|
|
|
|
self.assertEqual(var.persistable, False)
|
|
var.persistable = True
|
|
self.assertEqual(var.persistable, True)
|
|
|
|
self.assertEqual(var.stop_gradient, True)
|
|
var.stop_gradient = False
|
|
self.assertEqual(var.stop_gradient, False)
|
|
|
|
# test some patched methods
|
|
def test_set_value(self):
|
|
with fluid.dygraph.guard():
|
|
var = fluid.dygraph.to_variable(self.array)
|
|
tmp1 = np.random.uniform(0.1, 1, [2, 2, 3]).astype(self.dtype)
|
|
self.assertRaises(AssertionError, var.set_value, tmp1)
|
|
|
|
tmp2 = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
|
|
var.set_value(tmp2)
|
|
self.assertTrue(np.array_equal(var.numpy(), tmp2))
|
|
|
|
def test_to_string(self):
|
|
with fluid.dygraph.guard():
|
|
var = fluid.dygraph.to_variable(self.array)
|
|
self.assertTrue(isinstance(str(var.to_string(True)), str))
|
|
|
|
def test_backward(self):
|
|
with fluid.dygraph.guard():
|
|
var = fluid.dygraph.to_variable(self.array)
|
|
var.stop_gradient = False
|
|
loss = fluid.layers.relu(var)
|
|
loss.backward()
|
|
grad_var = var._grad_ivar()
|
|
self.assertEqual(grad_var.shape, self.shape)
|
|
|
|
def test_gradient(self):
|
|
with fluid.dygraph.guard():
|
|
var = fluid.dygraph.to_variable(self.array)
|
|
var.stop_gradient = False
|
|
loss = fluid.layers.relu(var)
|
|
loss.backward()
|
|
grad_var = var.gradient()
|
|
self.assertEqual(grad_var.shape, self.array.shape)
|
|
|
|
def test_block(self):
|
|
with fluid.dygraph.guard():
|
|
var = fluid.dygraph.to_variable(self.array)
|
|
self.assertEqual(var.block,
|
|
fluid.default_main_program().global_block())
|
|
|
|
def _test_slice(self):
|
|
w = fluid.dygraph.to_variable(
|
|
np.random.random((784, 100, 100)).astype('float64'))
|
|
|
|
for i in range(3):
|
|
nw = w[i]
|
|
self.assertEqual((100, 100), tuple(nw.shape))
|
|
|
|
nw = w[:]
|
|
self.assertEqual((784, 100, 100), tuple(nw.shape))
|
|
|
|
nw = w[:, :]
|
|
self.assertEqual((784, 100, 100), tuple(nw.shape))
|
|
|
|
nw = w[:, :, -1]
|
|
self.assertEqual((784, 100), tuple(nw.shape))
|
|
|
|
nw = w[1, 1, 1]
|
|
|
|
self.assertEqual(len(nw.shape), 1)
|
|
self.assertEqual(nw.shape[0], 1)
|
|
|
|
nw = w[:, :, :-1]
|
|
self.assertEqual((784, 100, 99), tuple(nw.shape))
|
|
|
|
tensor_array = np.array(
|
|
[[[1, 2, 3], [4, 5, 6], [7, 8, 9]],
|
|
[[10, 11, 12], [13, 14, 15], [16, 17, 18]],
|
|
[[19, 20, 21], [22, 23, 24], [25, 26, 27]]]).astype('float32')
|
|
var = fluid.dygraph.to_variable(tensor_array)
|
|
var1 = var[0, 1, 1]
|
|
var2 = var[1:]
|
|
var3 = var[0:1]
|
|
var4 = var[::-1]
|
|
var5 = var[1, 1:, 1:]
|
|
var_reshape = fluid.layers.reshape(var, [3, -1, 3])
|
|
var6 = var_reshape[:, :, -1]
|
|
var7 = var[:, :, :-1]
|
|
var8 = var[:1, :1, :1]
|
|
var9 = var[:-1, :-1, :-1]
|
|
var10 = var[::-1, :1, :-1]
|
|
var11 = var[:-1, ::-1, -1:]
|
|
var12 = var[1:2, 2:, ::-1]
|
|
var13 = var[2:10, 2:, -2:-1]
|
|
var14 = var[1:-1, 0:2, ::-1]
|
|
var15 = var[::-1, ::-1, ::-1]
|
|
|
|
vars = [
|
|
var, var1, var2, var3, var4, var5, var6, var7, var8, var9, var10,
|
|
var11, var12, var13, var14, var15
|
|
]
|
|
local_out = [var.numpy() for var in vars]
|
|
|
|
self.assertTrue(np.array_equal(local_out[1], tensor_array[0, 1, 1:2]))
|
|
self.assertTrue(np.array_equal(local_out[2], tensor_array[1:]))
|
|
self.assertTrue(np.array_equal(local_out[3], tensor_array[0:1]))
|
|
self.assertTrue(np.array_equal(local_out[4], tensor_array[::-1]))
|
|
self.assertTrue(np.array_equal(local_out[5], tensor_array[1, 1:, 1:]))
|
|
self.assertTrue(
|
|
np.array_equal(local_out[6],
|
|
tensor_array.reshape((3, -1, 3))[:, :, -1]))
|
|
self.assertTrue(np.array_equal(local_out[7], tensor_array[:, :, :-1]))
|
|
self.assertTrue(np.array_equal(local_out[8], tensor_array[:1, :1, :1]))
|
|
self.assertTrue(
|
|
np.array_equal(local_out[9], tensor_array[:-1, :-1, :-1]))
|
|
self.assertTrue(
|
|
np.array_equal(local_out[10], tensor_array[::-1, :1, :-1]))
|
|
self.assertTrue(
|
|
np.array_equal(local_out[11], tensor_array[:-1, ::-1, -1:]))
|
|
self.assertTrue(
|
|
np.array_equal(local_out[12], tensor_array[1:2, 2:, ::-1]))
|
|
self.assertTrue(
|
|
np.array_equal(local_out[13], tensor_array[2:10, 2:, -2:-1]))
|
|
self.assertTrue(
|
|
np.array_equal(local_out[14], tensor_array[1:-1, 0:2, ::-1]))
|
|
self.assertTrue(
|
|
np.array_equal(local_out[15], tensor_array[::-1, ::-1, ::-1]))
|
|
|
|
def test_slice(self):
|
|
with fluid.dygraph.guard():
|
|
self._test_slice()
|
|
|
|
var = fluid.dygraph.to_variable(self.array)
|
|
self.assertTrue(np.array_equal(var[1, :].numpy(), self.array[1, :]))
|
|
self.assertTrue(np.array_equal(var[::-1].numpy(), self.array[::-1]))
|
|
|
|
def test_var_base_to_np(self):
|
|
with fluid.dygraph.guard():
|
|
var = fluid.dygraph.to_variable(self.array)
|
|
self.assertTrue(
|
|
np.array_equal(var.numpy(),
|
|
fluid.framework._var_base_to_np(var)))
|
|
|
|
def test_if(self):
|
|
with fluid.dygraph.guard():
|
|
var1 = fluid.dygraph.to_variable(np.array([[[0]]]))
|
|
var2 = fluid.dygraph.to_variable(np.array([[[1]]]))
|
|
|
|
var1_bool = False
|
|
var2_bool = False
|
|
|
|
if var1:
|
|
var1_bool = True
|
|
|
|
if var2:
|
|
var2_bool = True
|
|
|
|
assert var1_bool == False, "if var1 should be false"
|
|
assert var2_bool == True, "if var2 should be true"
|
|
assert bool(var1) == False, "bool(var1) is False"
|
|
assert bool(var2) == True, "bool(var2) is True"
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|