# 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_tensor_to_variable(self): with fluid.dygraph.guard(): t = fluid.Tensor() t.set(np.random.random((1024, 1024)), fluid.CPUPlace()) var = fluid.dygraph.to_variable(t) self.assertTrue(np.array_equal(t, var.numpy())) 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_for_var(self): np_value = np.random.random((30, 100, 100)).astype('float32') w = fluid.dygraph.to_variable(np_value) for i, e in enumerate(w): self.assertTrue(np.array_equal(e.numpy(), np_value[i])) def test_slice(self): with fluid.dygraph.guard(): self._test_slice() self._test_for_var() 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])) with self.assertRaises(IndexError): y = var[self.shape[0]] 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" def test_to_static_var(self): with fluid.dygraph.guard(): # Convert VarBase into Variable or Parameter var_base = fluid.dygraph.to_variable(self.array, name="var_base_1") static_var = var_base._to_static_var() self._assert_to_static(var_base, static_var) var_base = fluid.dygraph.to_variable(self.array, name="var_base_2") static_param = var_base._to_static_var(to_parameter=True) self._assert_to_static(var_base, static_param, True) # Convert ParamBase into Parameter fc = fluid.dygraph.Linear( 10, 20, param_attr=fluid.ParamAttr( learning_rate=0.001, do_model_average=True, regularizer=fluid.regularizer.L1Decay())) weight = fc.parameters()[0] static_param = weight._to_static_var() self._assert_to_static(weight, static_param, True) def _assert_to_static(self, var_base, static_var, is_param=False): if is_param: self.assertTrue(isinstance(static_var, fluid.framework.Parameter)) self.assertTrue(static_var.persistable, True) if isinstance(var_base, fluid.framework.ParamBase): for attr in ['trainable', 'is_distributed', 'do_model_average']: self.assertEqual( getattr(var_base, attr), getattr(static_var, attr)) self.assertEqual(static_var.optimize_attr['learning_rate'], 0.001) self.assertTrue( isinstance(static_var.regularizer, fluid.regularizer.L1Decay)) else: self.assertTrue(isinstance(static_var, fluid.framework.Variable)) attr_keys = ['block', 'dtype', 'type', 'name'] for attr in attr_keys: self.assertEqual(getattr(var_base, attr), getattr(static_var, attr)) self.assertListEqual(list(var_base.shape), list(static_var.shape)) if __name__ == '__main__': unittest.main()