wehub-resource-sync
1212 lines
44 KiB
Python
1212 lines
44 KiB
Python
# 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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import contextlib
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import inspect
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import sys
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import unittest
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from op_test import get_device_place, is_custom_device
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sys.path.append("../../legacy_test")
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import numpy as np
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from test_imperative_base import new_program_scope
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import paddle
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import paddle.nn.functional as F
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from paddle import base
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from paddle.base import core, dygraph
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from paddle.tensor import random
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class LayerTest(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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cls.seed = 111
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@classmethod
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def tearDownClass(cls):
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pass
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def _get_place(self, force_to_use_cpu=False):
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# this option for ops that only have cpu kernel
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if force_to_use_cpu:
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return core.CPUPlace()
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else:
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if core.is_compiled_with_cuda() or is_custom_device():
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return get_device_place()
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return core.CPUPlace()
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@contextlib.contextmanager
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def static_graph(self):
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paddle.seed(self.seed)
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if paddle.framework.use_pir_api():
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with paddle.pir_utils.OldIrGuard():
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paddle.framework.random._manual_program_seed(self.seed)
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paddle.framework.random._manual_program_seed(self.seed)
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else:
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paddle.framework.random._manual_program_seed(self.seed)
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with new_program_scope():
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yield
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def get_static_graph_result(
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self, feed, fetch_list, with_lod=False, force_to_use_cpu=False
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):
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exe = base.Executor(self._get_place(force_to_use_cpu))
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exe.run(paddle.static.default_startup_program())
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return exe.run(
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paddle.static.default_main_program(),
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feed=feed,
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fetch_list=fetch_list,
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return_numpy=(not with_lod),
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)
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@contextlib.contextmanager
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def dynamic_graph(self, force_to_use_cpu=False):
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paddle.seed(self.seed)
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if paddle.framework.use_pir_api():
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with paddle.pir_utils.OldIrGuard():
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paddle.framework.random._manual_program_seed(self.seed)
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paddle.framework.random._manual_program_seed(self.seed)
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else:
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paddle.framework.random._manual_program_seed(self.seed)
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with base.dygraph.guard(
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self._get_place(force_to_use_cpu=force_to_use_cpu)
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):
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yield
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class TestLayer(LayerTest):
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def test_custom_layer_with_kwargs(self):
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class CustomLayer(paddle.nn.Layer):
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def __init__(self, input_size, linear1_size=4):
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super().__init__()
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self.linear1 = paddle.nn.Linear(
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input_size, linear1_size, bias_attr=False
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)
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self.linear2 = paddle.nn.Linear(
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linear1_size, 1, bias_attr=False
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)
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def forward(self, x, do_linear2=False):
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ret = self.linear1(x)
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if do_linear2:
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ret = self.linear2(ret)
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return ret
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with self.dynamic_graph():
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inp = np.ones([3, 3], dtype='float32')
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x = paddle.to_tensor(inp)
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custom = CustomLayer(input_size=3, linear1_size=2)
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ret = custom(x, do_linear2=False)
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np.testing.assert_array_equal(ret.numpy().shape, [3, 2])
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ret = custom(x, do_linear2=True)
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np.testing.assert_array_equal(ret.numpy().shape, [3, 1])
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def test_dropout(self):
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inp = np.ones([3, 32, 32], dtype='float32')
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with self.static_graph():
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t = paddle.static.data(
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name='data',
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shape=[3, 32, 32],
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dtype='float32',
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)
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dropout = paddle.nn.Dropout(p=0.35)
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ret = dropout(t)
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ret2 = paddle.nn.functional.dropout(t, p=0.35)
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static_ret, static_ret2 = self.get_static_graph_result(
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feed={'data': inp}, fetch_list=[ret, ret2]
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)
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with self.dynamic_graph():
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t = paddle.to_tensor(inp)
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dropout = paddle.nn.Dropout(p=0.35)
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dy_ret = dropout(t)
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dy_ret2 = paddle.nn.functional.dropout(t, p=0.35)
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dy_ret_value = dy_ret.numpy()
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dy_ret2_value = dy_ret2.numpy()
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np.testing.assert_array_equal(static_ret, static_ret2)
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np.testing.assert_array_equal(dy_ret_value, dy_ret2_value)
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np.testing.assert_array_equal(static_ret, dy_ret_value)
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def test_linear(self):
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inp = np.ones([3, 32, 32], dtype='float32')
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with self.static_graph():
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t = paddle.static.data(
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name='data', shape=[3, 32, 32], dtype='float32'
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)
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linear = paddle.nn.Linear(
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32,
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4,
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bias_attr=paddle.nn.initializer.Constant(value=1),
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)
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ret = linear(t)
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static_ret = self.get_static_graph_result(
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feed={'data': inp}, fetch_list=[ret]
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)[0]
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with self.dynamic_graph():
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t = paddle.to_tensor(inp)
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linear = paddle.nn.Linear(
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32,
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4,
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bias_attr=paddle.nn.initializer.Constant(value=1),
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)
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dy_ret = linear(t)
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dy_ret_value = dy_ret.numpy()
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np.testing.assert_array_equal(static_ret, dy_ret_value)
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with self.static_graph():
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# the input of Linear must be Variable.
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def test_Variable():
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inp = np.ones([3, 32, 32], dtype='float32')
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linear = paddle.nn.Linear(
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32,
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4,
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bias_attr=paddle.nn.initializer.Constant(value=1),
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)
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linear_ret1 = linear(inp)
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self.assertRaises(TypeError, test_Variable)
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# the input dtype of Linear must be float16 or float32 or float64
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# float16 only can be set on GPU place
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def test_type():
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inp = np.ones([3, 32, 32], dtype='int32')
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linear = paddle.nn.Linear(
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32,
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4,
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bias_attr=paddle.nn.initializer.Constant(value=1),
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)
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linear_ret2 = linear(inp)
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self.assertRaises(TypeError, test_type)
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def test_Flatten(self):
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inp = np.ones([3, 4, 4, 5], dtype='float32')
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with self.static_graph():
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t = paddle.static.data(
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name='data', shape=[3, 4, 4, 5], dtype='float32'
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)
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flatten = paddle.nn.Flatten()
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ret = flatten(t)
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static_ret = self.get_static_graph_result(
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feed={'data': inp}, fetch_list=[ret]
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)[0]
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with self.dynamic_graph():
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t = paddle.to_tensor(inp)
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flatten = paddle.nn.Flatten()
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dy_ret = flatten(t)
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dy_ret_value = dy_ret.numpy()
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np.testing.assert_array_equal(static_ret, dy_ret_value)
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with self.static_graph():
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# the input of Linear must be Variable.
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def test_Variable():
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inp = np.ones([3, 32, 32], dtype='float32')
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linear = paddle.nn.Linear(
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32,
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4,
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bias_attr=paddle.nn.initializer.Constant(value=1),
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)
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linear_ret1 = linear(inp)
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self.assertRaises(TypeError, test_Variable)
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# the input dtype of Linear must be float16 or float32 or float64
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# float16 only can be set on GPU place
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def test_type():
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inp = np.ones([3, 32, 32], dtype='int32')
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linear = paddle.nn.Linear(
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32,
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4,
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bias_attr=paddle.nn.initializer.Constant(value=1),
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)
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linear_ret2 = linear(inp)
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self.assertRaises(TypeError, test_type)
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def test_SyncBatchNorm(self):
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if core.is_compiled_with_cuda() or is_custom_device():
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with self.static_graph():
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t = paddle.static.data(
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name='t', shape=[-1, 3, 5, 5], dtype='float32'
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)
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my_sync_bn = paddle.nn.SyncBatchNorm(3)
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ret = my_sync_bn(t)
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static_ret = self.get_static_graph_result(
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feed={'t': np.ones([3, 3, 5, 5], dtype='float32')},
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fetch_list=[ret],
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)[0]
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with self.dynamic_graph():
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t = np.ones([3, 3, 5, 5], dtype='float32')
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my_syncbn = paddle.nn.SyncBatchNorm(3)
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dy_ret = my_syncbn(paddle.to_tensor(t))
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dy_ret_value = dy_ret.numpy()
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np.testing.assert_array_equal(static_ret, dy_ret_value)
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def test_relu(self):
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with self.static_graph():
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t = paddle.static.data(name='t', shape=[-1, 3, 3], dtype='float32')
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ret = F.relu(t)
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static_ret = self.get_static_graph_result(
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feed={'t': np.ones([3, 3], dtype='float32')}, fetch_list=[ret]
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)[0]
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with self.dynamic_graph():
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t = np.ones([3, 3], dtype='float32')
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dy_ret = F.relu(paddle.to_tensor(t))
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dy_ret_value = dy_ret.numpy()
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np.testing.assert_allclose(static_ret, dy_ret_value, rtol=1e-05)
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def test_matmul(self):
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with self.static_graph():
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t = paddle.static.data(name='t', shape=[-1, 3, 3], dtype='float32')
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t2 = paddle.static.data(
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name='t2', shape=[-1, 3, 3], dtype='float32'
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)
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ret = paddle.matmul(t, t2)
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static_ret = self.get_static_graph_result(
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feed={
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't': np.ones([3, 3], dtype='float32'),
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't2': np.ones([3, 3], dtype='float32'),
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},
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fetch_list=[ret],
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)[0]
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with self.dynamic_graph():
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t = np.ones([3, 3], dtype='float32')
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t2 = np.ones([3, 3], dtype='float32')
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dy_ret = paddle.matmul(paddle.to_tensor(t), paddle.to_tensor(t2))
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dy_ret_value = dy_ret.numpy()
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np.testing.assert_allclose(static_ret, dy_ret_value, rtol=1e-05)
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def test_elementwise_math(self):
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n = np.ones([3, 3], dtype='float32')
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n2 = np.ones([3, 3], dtype='float32') * 1.1
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n3 = np.ones([3, 3], dtype='float32') * 2
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n4 = np.ones([3, 3], dtype='float32') * 3
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n5 = np.ones([3, 3], dtype='float32') * 4
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n6 = np.ones([3, 3], dtype='float32') * 5
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with self.static_graph():
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t = paddle.static.data(name='t', shape=[-1, 3, 3], dtype='float32')
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t2 = paddle.static.data(
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name='t2', shape=[-1, 3, 3], dtype='float32'
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)
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t3 = paddle.static.data(
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name='t3', shape=[-1, 3, 3], dtype='float32'
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)
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t4 = paddle.static.data(
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name='t4', shape=[-1, 3, 3], dtype='float32'
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)
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t5 = paddle.static.data(
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name='t5', shape=[-1, 3, 3], dtype='float32'
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)
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t6 = paddle.static.data(
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name='t6', shape=[-1, 3, 3], dtype='float32'
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)
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ret = paddle.add(t, t2)
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ret = paddle.pow(ret, t3)
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ret = paddle.divide(ret, t4)
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ret = paddle.subtract(ret, t5)
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ret = paddle.multiply(ret, t6)
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static_ret = self.get_static_graph_result(
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feed={'t': n, 't2': n2, 't3': n3, 't4': n4, 't5': n5, 't6': n6},
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fetch_list=[ret],
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)[0]
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with self.dynamic_graph():
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ret = paddle.add(paddle.to_tensor(n), paddle.to_tensor(n2))
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ret = paddle.pow(ret, paddle.to_tensor(n3))
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ret = paddle.divide(ret, paddle.to_tensor(n4))
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ret = paddle.subtract(ret, paddle.to_tensor(n5))
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dy_ret = paddle.multiply(ret, paddle.to_tensor(n6))
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dy_ret_value = dy_ret.numpy()
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np.testing.assert_allclose(static_ret, dy_ret_value, rtol=1e-05)
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def test_elementwise_minmax(self):
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n = np.ones([3, 3], dtype='float32')
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n2 = np.ones([3, 3], dtype='float32') * 2
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with self.dynamic_graph():
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min_ret = paddle.minimum(paddle.to_tensor(n), paddle.to_tensor(n2))
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max_ret = paddle.maximum(paddle.to_tensor(n), paddle.to_tensor(n2))
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min_ret_value = min_ret.numpy()
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max_ret_value = max_ret.numpy()
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np.testing.assert_allclose(n, min_ret_value, rtol=1e-05)
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np.testing.assert_allclose(n2, max_ret_value, rtol=1e-05)
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def test_one_hot(self):
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with self.dynamic_graph():
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label = paddle.to_tensor(np.array([[1], [1], [3], [0]]))
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one_hot_label1 = paddle.nn.functional.one_hot(label, 4)
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one_hot_label2 = paddle.nn.functional.one_hot(
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label, paddle.to_tensor(np.array([4]))
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)
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np.testing.assert_array_equal(
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one_hot_label1.numpy(), one_hot_label2.numpy()
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)
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def test_split(self):
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with self.dynamic_graph():
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input = paddle.to_tensor(np.random.random((3, 8, 5)))
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x0, x1 = paddle.split(input, num_or_sections=2, axis=1)
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x00, x11 = paddle.split(
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input,
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num_or_sections=2,
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axis=paddle.to_tensor(np.array([1])),
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)
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np.testing.assert_array_equal(x0.numpy(), x00.numpy())
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np.testing.assert_array_equal(x1.numpy(), x11.numpy())
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def test_topk(self):
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with self.dynamic_graph():
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input = paddle.to_tensor(np.random.random((13, 11)))
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top5_values1, top5_indices1 = paddle.topk(input, k=5)
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top5_values2, top5_indices2 = paddle.topk(
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input, k=paddle.to_tensor(np.array([5]))
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)
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np.testing.assert_array_equal(
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top5_values1.numpy(), top5_values2.numpy()
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)
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np.testing.assert_array_equal(
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top5_indices1.numpy(), top5_indices2.numpy()
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)
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def test_compare(self):
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value_a = np.arange(3)
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value_b = np.arange(3)
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# less than
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with self.static_graph():
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a = paddle.static.data(name='a', shape=[-1, 1], dtype='int64')
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b = paddle.static.data(name='b', shape=[-1, 1], dtype='int64')
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cond = paddle.less_than(x=a, y=b)
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cond_ = paddle.less(x=a, y=b)
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static_ret = self.get_static_graph_result(
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feed={"a": value_a, "b": value_b}, fetch_list=[cond]
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)[0]
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with self.dynamic_graph():
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da = paddle.to_tensor(value_a)
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db = paddle.to_tensor(value_b)
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dcond = paddle.less_than(x=da, y=db)
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dcond_ = paddle.less(x=da, y=db)
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for i in range(len(static_ret)):
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self.assertTrue(dcond.numpy()[i] == static_ret[i])
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self.assertTrue(dcond_.numpy()[i] == static_ret[i])
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# less equal
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with self.static_graph():
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a1 = paddle.static.data(name='a1', shape=[-1, 1], dtype='int64')
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b1 = paddle.static.data(name='b1', shape=[-1, 1], dtype='int64')
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cond1 = paddle.less_equal(x=a1, y=b1)
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static_ret1 = self.get_static_graph_result(
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feed={"a1": value_a, "b1": value_b}, fetch_list=[cond1]
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)[0]
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with self.dynamic_graph():
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da1 = paddle.to_tensor(value_a)
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db1 = paddle.to_tensor(value_b)
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dcond1 = paddle.less_equal(x=da1, y=db1)
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for i in range(len(static_ret1)):
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self.assertTrue(dcond1.numpy()[i] == static_ret1[i])
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# greater than
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with self.static_graph():
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a2 = paddle.static.data(name='a2', shape=[-1, 1], dtype='int64')
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b2 = paddle.static.data(name='b2', shape=[-1, 1], dtype='int64')
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cond2 = paddle.greater_than(x=a2, y=b2)
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static_ret2 = self.get_static_graph_result(
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feed={"a2": value_a, "b2": value_b}, fetch_list=[cond2]
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)[0]
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with self.dynamic_graph():
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da2 = paddle.to_tensor(value_a)
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db2 = paddle.to_tensor(value_b)
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dcond2 = paddle.greater_than(x=da2, y=db2)
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for i in range(len(static_ret2)):
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self.assertTrue(dcond2.numpy()[i] == static_ret2[i])
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# greater equal
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with self.static_graph():
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a3 = paddle.static.data(name='a3', shape=[-1, 1], dtype='int64')
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b3 = paddle.static.data(name='b3', shape=[-1, 1], dtype='int64')
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cond3 = paddle.greater_equal(x=a3, y=b3)
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static_ret3 = self.get_static_graph_result(
|
|
feed={"a3": value_a, "b3": value_b}, fetch_list=[cond3]
|
|
)[0]
|
|
with self.dynamic_graph():
|
|
da3 = paddle.to_tensor(value_a)
|
|
db3 = paddle.to_tensor(value_b)
|
|
dcond3 = paddle.greater_equal(x=da3, y=db3)
|
|
|
|
for i in range(len(static_ret3)):
|
|
self.assertTrue(dcond3.numpy()[i] == static_ret3[i])
|
|
|
|
# equal
|
|
with self.static_graph():
|
|
a4 = paddle.static.data(name='a4', shape=[-1, 1], dtype='int64')
|
|
b4 = paddle.static.data(name='b4', shape=[-1, 1], dtype='int64')
|
|
cond4 = paddle.equal(x=a4, y=b4)
|
|
static_ret4 = self.get_static_graph_result(
|
|
feed={"a4": value_a, "b4": value_b}, fetch_list=[cond4]
|
|
)[0]
|
|
with self.dynamic_graph():
|
|
da4 = paddle.to_tensor(value_a)
|
|
db4 = paddle.to_tensor(value_b)
|
|
dcond4 = paddle.equal(x=da4, y=db4)
|
|
|
|
for i in range(len(static_ret4)):
|
|
self.assertTrue(dcond4.numpy()[i] == static_ret4[i])
|
|
|
|
# not equal
|
|
with self.static_graph():
|
|
a5 = paddle.static.data(name='a5', shape=[-1, 1], dtype='int64')
|
|
b5 = paddle.static.data(name='b5', shape=[-1, 1], dtype='int64')
|
|
cond5 = paddle.equal(x=a5, y=b5)
|
|
static_ret5 = self.get_static_graph_result(
|
|
feed={"a5": value_a, "b5": value_b}, fetch_list=[cond5]
|
|
)[0]
|
|
with self.dynamic_graph():
|
|
da5 = paddle.to_tensor(value_a)
|
|
db5 = paddle.to_tensor(value_b)
|
|
dcond5 = paddle.equal(x=da5, y=db5)
|
|
|
|
for i in range(len(static_ret5)):
|
|
self.assertTrue(dcond5.numpy()[i] == static_ret5[i])
|
|
|
|
def test_crop_tensor(self):
|
|
with self.static_graph():
|
|
x = paddle.static.data(
|
|
name="x1", shape=[-1, 6, 5, 8], dtype="float32"
|
|
)
|
|
|
|
dim1 = paddle.static.data(name="dim1", shape=[1], dtype="int32")
|
|
dim2 = paddle.static.data(name="dim2", shape=[1], dtype="int32")
|
|
crop_shape1 = (1, 2, 4, 4)
|
|
crop_shape2 = paddle.static.data(
|
|
name="crop_shape", shape=[4], dtype="float32"
|
|
)
|
|
crop_shape3 = [-1, dim1, dim2, 4]
|
|
crop_offsets1 = [0, 0, 1, 0]
|
|
crop_offsets2 = paddle.static.data(
|
|
name="crop_offset", shape=[4], dtype="float32"
|
|
)
|
|
crop_offsets3 = [0, dim1, dim2, 0]
|
|
|
|
out1 = paddle.crop(x, shape=crop_shape1, offsets=crop_offsets1)
|
|
out2 = paddle.crop(x, shape=crop_shape2, offsets=crop_offsets2)
|
|
out3 = paddle.crop(x, shape=crop_shape3, offsets=crop_offsets3)
|
|
|
|
self.assertIsNotNone(out1)
|
|
self.assertIsNotNone(out2)
|
|
self.assertIsNotNone(out3)
|
|
|
|
def test_shard_index(self):
|
|
with self.static_graph():
|
|
x = paddle.static.data(
|
|
name="label", shape=[-1, 4, 1], dtype='int64'
|
|
)
|
|
shard_label = paddle.shard_index(
|
|
input=x, index_num=20, nshards=2, shard_id=0
|
|
)
|
|
|
|
self.assertIsNotNone(shard_label)
|
|
|
|
def test_accuracy(self):
|
|
x = np.random.rand(3, 32, 32).astype("float32")
|
|
y = np.array([[1], [0], [1]])
|
|
with self.static_graph():
|
|
data = paddle.static.data(
|
|
name="input", shape=[-1, 32, 32], dtype="float32"
|
|
)
|
|
label = paddle.static.data(name="label", shape=[-1, 1], dtype="int")
|
|
data_new = paddle.reshape(data, [3, 32 * 32])
|
|
fc_out = paddle.nn.Linear(32 * 32, 10)(data_new)
|
|
predict = paddle.nn.functional.softmax(fc_out)
|
|
result = paddle.static.accuracy(input=predict, label=label, k=5)
|
|
place = base.CPUPlace()
|
|
exe = base.Executor(place)
|
|
|
|
exe.run(base.default_startup_program())
|
|
# x = np.random.rand(3, 32, 32).astype("float32")
|
|
# y = np.array([[1], [0], [1]])
|
|
|
|
static_out = exe.run(
|
|
feed={"input": x, "label": y}, fetch_list=result
|
|
)
|
|
|
|
with self.dynamic_graph(force_to_use_cpu=True):
|
|
data = paddle.to_tensor(x)
|
|
label = paddle.to_tensor(y)
|
|
data_new = paddle.reshape(data, [3, 32 * 32])
|
|
fc_out = paddle.nn.Linear(32 * 32, 10)(data_new)
|
|
predict = paddle.nn.functional.softmax(fc_out)
|
|
dynamic_out = paddle.static.accuracy(
|
|
input=predict, label=label, k=5
|
|
)
|
|
|
|
np.testing.assert_array_equal(static_out[0], dynamic_out.numpy())
|
|
|
|
|
|
class TestBook(LayerTest):
|
|
def setUp(self):
|
|
self.only_static_set = set({"make_word_embedding"})
|
|
self.not_compare_static_dygraph_set = set(
|
|
{
|
|
"make_gaussian_random",
|
|
"make_kldiv_loss",
|
|
"make_uniform_random_batch_size_like",
|
|
}
|
|
)
|
|
self.all_close_compare = set({"make_spectral_norm"})
|
|
|
|
def test_all_layers(self):
|
|
attrs = (getattr(self, name) for name in dir(self))
|
|
methods = filter(inspect.ismethod, attrs)
|
|
for method in methods:
|
|
if not method.__name__.startswith('make_'):
|
|
continue
|
|
self._low_data_bound = 0
|
|
self._high_data_bound = 2
|
|
self._batch_size = 2
|
|
self._feed_dict = {}
|
|
self._force_to_use_cpu = False
|
|
with self.static_graph():
|
|
static_var = method()
|
|
if isinstance(static_var, tuple):
|
|
static_var = static_var[0]
|
|
|
|
if static_var is not None:
|
|
static_result = self.get_static_graph_result(
|
|
feed=self._feed_dict,
|
|
fetch_list=[static_var],
|
|
force_to_use_cpu=self._force_to_use_cpu,
|
|
)
|
|
|
|
else:
|
|
continue
|
|
if method.__name__ in self.only_static_set:
|
|
continue
|
|
|
|
with self.dynamic_graph(self._force_to_use_cpu):
|
|
dy_result = method()
|
|
if isinstance(dy_result, tuple):
|
|
dy_result = dy_result[0]
|
|
dy_result_value = dy_result.numpy()
|
|
|
|
if method.__name__ in self.all_close_compare:
|
|
np.testing.assert_allclose(
|
|
static_result[0],
|
|
dy_result_value,
|
|
rtol=1e-05,
|
|
atol=0,
|
|
err_msg=f'Result of function [{method.__name__}] compare failed',
|
|
)
|
|
continue
|
|
|
|
if method.__name__ not in self.not_compare_static_dygraph_set:
|
|
np.testing.assert_array_equal(
|
|
static_result[0],
|
|
dy_result_value,
|
|
err_msg=f'Result of function [{method.__name__}] not equal',
|
|
)
|
|
|
|
def _get_np_data(self, shape, dtype, append_batch_size=True):
|
|
np.random.seed(self.seed)
|
|
if append_batch_size:
|
|
shape = [self._batch_size, *shape]
|
|
if dtype == 'float32':
|
|
return np.random.random(shape).astype(dtype)
|
|
elif dtype == 'float64':
|
|
return np.random.random(shape).astype(dtype)
|
|
elif dtype == 'int32':
|
|
return np.random.randint(
|
|
self._low_data_bound, self._high_data_bound, shape
|
|
).astype(dtype)
|
|
elif dtype == 'int64':
|
|
return np.random.randint(
|
|
self._low_data_bound, self._high_data_bound, shape
|
|
).astype(dtype)
|
|
|
|
def _get_data(
|
|
self, name, shape, dtype, set_feed_dict=True, append_batch_size=True
|
|
):
|
|
if dygraph.base.enabled():
|
|
return paddle.to_tensor(
|
|
self._get_np_data(shape, dtype, append_batch_size),
|
|
)
|
|
else:
|
|
if set_feed_dict:
|
|
self._feed_dict[name] = self._get_np_data(
|
|
shape, dtype, append_batch_size
|
|
)
|
|
if append_batch_size:
|
|
shape = [-1, *shape]
|
|
data = paddle.static.data(
|
|
name=name,
|
|
shape=shape,
|
|
dtype=dtype,
|
|
)
|
|
if not paddle.framework.use_pir_api():
|
|
data.desc.set_need_check_feed(False)
|
|
return data
|
|
|
|
def make_fit_a_line(self):
|
|
with base.program_guard(
|
|
base.default_main_program(),
|
|
startup_program=base.default_startup_program(),
|
|
):
|
|
x = self._get_data(name='x', shape=[13], dtype='float32')
|
|
y_predict = paddle.nn.Linear(13, 1)(x)
|
|
y = self._get_data(name='y', shape=[1], dtype='float32')
|
|
cost = paddle.nn.functional.square_error_cost(
|
|
input=y_predict, label=y
|
|
)
|
|
avg_cost = paddle.mean(cost)
|
|
return avg_cost
|
|
|
|
def make_recognize_digits_mlp(self):
|
|
with base.program_guard(
|
|
base.default_main_program(), base.default_startup_program()
|
|
):
|
|
# Change g_program, so the rest layers use `g_program`
|
|
images = self._get_data(name='pixel', shape=[784], dtype='float32')
|
|
label = self._get_data(name='label', shape=[1], dtype='int64')
|
|
hidden1 = paddle.nn.Linear(784, 128)(images)
|
|
hidden1 = paddle.nn.functional.relu(hidden1)
|
|
hidden2 = paddle.nn.Linear(128, 64)(hidden1)
|
|
hidden2 = paddle.nn.functional.relu(hidden2)
|
|
hidden1 = paddle.nn.Linear(128, 10, "sftmax.w1")(hidden1)
|
|
hidden2 = paddle.nn.Linear(64, 10, "sftmax.w2")(hidden2)
|
|
hidden = hidden1 + hidden2
|
|
predict = paddle.nn.functional.softmax(hidden)
|
|
cost = paddle.nn.functional.cross_entropy(
|
|
input=predict, label=label, reduction='none', use_softmax=False
|
|
)
|
|
avg_cost = paddle.mean(cost)
|
|
return avg_cost
|
|
|
|
def make_pool2d(self):
|
|
with base.program_guard(
|
|
base.default_main_program(), base.default_startup_program()
|
|
):
|
|
x = self._get_data(name='x', shape=[3, 224, 224], dtype='float32')
|
|
return paddle.nn.functional.max_pool2d(
|
|
x, kernel_size=[5, 3], stride=[1, 2], padding=(2, 1)
|
|
)
|
|
|
|
def make_pool2d_infershape(self):
|
|
with base.program_guard(
|
|
base.default_main_program(), base.default_startup_program()
|
|
):
|
|
theta = self._get_data("theta", shape=[2, 3], dtype='float32')
|
|
x = paddle.nn.functional.affine_grid(
|
|
theta, out_shape=[2, 3, 244, 244]
|
|
)
|
|
return paddle.nn.functional.max_pool2d(
|
|
x, kernel_size=[5, 3], stride=[1, 2], padding=(2, 1)
|
|
)
|
|
|
|
def make_softmax(self):
|
|
with base.program_guard(
|
|
base.default_main_program(), base.default_startup_program()
|
|
):
|
|
data = self._get_data(name='data', shape=[10], dtype='float32')
|
|
hid = paddle.nn.Linear(10, 20)(data)
|
|
return paddle.nn.functional.softmax(hid, axis=1)
|
|
|
|
def make_multiplex(self):
|
|
with base.program_guard(
|
|
base.default_main_program(), base.default_startup_program()
|
|
):
|
|
x1 = self._get_data(name='x1', shape=[4], dtype='float32')
|
|
x2 = self._get_data(name='x2', shape=[4], dtype='float32')
|
|
index = self._get_data(name='index', shape=[1], dtype='int32')
|
|
out = paddle.multiplex(inputs=[x1, x2], index=index)
|
|
return out
|
|
|
|
def make_softmax_with_cross_entropy(self):
|
|
with base.program_guard(
|
|
base.default_main_program(), base.default_startup_program()
|
|
):
|
|
x = self._get_data(name='x', shape=[16], dtype='float32')
|
|
y = self._get_data(name='label', shape=[1], dtype='int64')
|
|
loss, softmax = paddle.nn.functional.softmax_with_cross_entropy(
|
|
x, y, return_softmax=True
|
|
)
|
|
self.assertIsNotNone(loss)
|
|
self.assertIsNotNone(softmax)
|
|
|
|
loss = paddle.nn.functional.softmax_with_cross_entropy(x, y)
|
|
self.assertIsNotNone(loss)
|
|
|
|
x1 = self._get_data(name='x1', shape=[16, 32, 64], dtype='float32')
|
|
y1 = self._get_data(name='label1', shape=[1, 32, 64], dtype='int64')
|
|
y2 = self._get_data(name='label2', shape=[16, 1, 64], dtype='int64')
|
|
y3 = self._get_data(name='label3', shape=[16, 32, 1], dtype='int64')
|
|
loss1 = paddle.nn.functional.softmax_with_cross_entropy(
|
|
x1, y1, axis=1
|
|
)
|
|
loss2 = paddle.nn.functional.softmax_with_cross_entropy(
|
|
x1, y2, axis=2
|
|
)
|
|
loss3 = paddle.nn.functional.softmax_with_cross_entropy(
|
|
x1, y3, axis=3
|
|
)
|
|
loss4 = paddle.nn.functional.softmax_with_cross_entropy(
|
|
x1, y3, axis=-1
|
|
)
|
|
self.assertIsNotNone(loss1)
|
|
self.assertIsNotNone(loss2)
|
|
self.assertIsNotNone(loss3)
|
|
self.assertIsNotNone(loss4)
|
|
return loss4
|
|
|
|
def make_scatter(self):
|
|
with base.program_guard(
|
|
base.default_main_program(), base.default_startup_program()
|
|
):
|
|
x = self._get_data(
|
|
name='x', shape=[3, 3], append_batch_size=False, dtype='float32'
|
|
)
|
|
idx = self._get_data(
|
|
name='idx', shape=[2], append_batch_size=False, dtype='int32'
|
|
)
|
|
updates = self._get_data(
|
|
name='updates',
|
|
shape=[2, 3],
|
|
dtype='float32',
|
|
append_batch_size=False,
|
|
)
|
|
out = paddle.scatter(x, index=idx, updates=updates)
|
|
return out
|
|
|
|
def make_one_hot(self):
|
|
with base.framework._dygraph_place_guard(place=base.CPUPlace()):
|
|
label = self._get_data(name="label", shape=[1], dtype="int32")
|
|
one_hot_label = paddle.nn.functional.one_hot(label, 10)
|
|
return one_hot_label
|
|
|
|
def make_label_smooth(self):
|
|
# TODO(minqiyang): support gpu ut
|
|
self._force_to_use_cpu = True
|
|
with base.framework._dygraph_place_guard(place=base.CPUPlace()):
|
|
label = self._get_data(name="label", shape=[1], dtype="int32")
|
|
one_hot_label = paddle.nn.functional.one_hot(label, 10)
|
|
smooth_label = F.label_smooth(label=one_hot_label, epsilon=0.1)
|
|
return smooth_label
|
|
|
|
def make_topk(self):
|
|
with base.program_guard(
|
|
base.default_main_program(), base.default_startup_program()
|
|
):
|
|
data = self._get_data(name="label", shape=[200], dtype="float32")
|
|
values, indices = paddle.topk(data, k=5)
|
|
return values
|
|
return indices
|
|
|
|
def make_l2_normalize(self):
|
|
with base.program_guard(
|
|
base.default_main_program(), base.default_startup_program()
|
|
):
|
|
x = self._get_data(name='x', shape=[8, 7, 10], dtype="float32")
|
|
output = paddle.nn.functional.normalize(x, axis=1)
|
|
return output
|
|
|
|
def make_shape(self):
|
|
with base.program_guard(
|
|
base.default_main_program(), base.default_startup_program()
|
|
):
|
|
input = self._get_data(
|
|
name="input", shape=[3, 100, 100], dtype="float32"
|
|
)
|
|
out = paddle.shape(input)
|
|
return out
|
|
|
|
def make_pad2d(self):
|
|
with base.program_guard(
|
|
base.default_main_program(), base.default_startup_program()
|
|
):
|
|
input = self._get_data(
|
|
name="input", shape=[3, 100, 100], dtype="float32"
|
|
)
|
|
|
|
tmp_pad = paddle.nn.Pad2D(
|
|
padding=[1, 2, 3, 4],
|
|
mode='reflect',
|
|
data_format='NCHW',
|
|
name="shape",
|
|
)
|
|
out = tmp_pad(input)
|
|
return out
|
|
|
|
def make_mish(self):
|
|
with base.program_guard(
|
|
base.default_main_program(), base.default_startup_program()
|
|
):
|
|
input = self._get_data(name="input", shape=[16], dtype="float32")
|
|
out = paddle.nn.functional.mish(input, name='mish')
|
|
return out
|
|
|
|
def make_cross_entropy(self):
|
|
with base.program_guard(
|
|
base.default_main_program(), base.default_startup_program()
|
|
):
|
|
x = self._get_data(name="x", shape=[30, 10], dtype="float32")
|
|
label = self._get_data(name="label", shape=[30, 1], dtype="int64")
|
|
mode = 'channel'
|
|
out = paddle.nn.functional.cross_entropy(
|
|
x,
|
|
label,
|
|
soft_label=False,
|
|
ignore_index=4,
|
|
reduction='none',
|
|
use_softmax=False,
|
|
)
|
|
return out
|
|
|
|
def make_gaussian_random(self):
|
|
with base.program_guard(
|
|
base.default_main_program(), base.default_startup_program()
|
|
):
|
|
out = random.gaussian(shape=[20, 30])
|
|
return out
|
|
|
|
def make_sum(self):
|
|
with base.program_guard(
|
|
base.default_main_program(), base.default_startup_program()
|
|
):
|
|
input = self._get_data(
|
|
name="input", shape=[13, 11], dtype='float32'
|
|
)
|
|
|
|
out = paddle.add_n(input)
|
|
return out
|
|
|
|
def make_slice(self):
|
|
starts = [1, 0, 2]
|
|
ends = [3, 3, 4]
|
|
axes = [0, 1, 2]
|
|
|
|
with base.program_guard(
|
|
base.default_main_program(), base.default_startup_program()
|
|
):
|
|
input = self._get_data(
|
|
name="input", shape=[3, 4, 5, 6], dtype='float32'
|
|
)
|
|
|
|
out = paddle.slice(input, axes=axes, starts=starts, ends=ends)
|
|
return out
|
|
|
|
def make_scale_variable(self):
|
|
with base.program_guard(
|
|
base.default_main_program(), base.default_startup_program()
|
|
):
|
|
input = self._get_data(
|
|
name="input", shape=[3, 4, 5, 6], dtype='float32'
|
|
)
|
|
scale_var = self._get_data(
|
|
name="scale",
|
|
shape=[1],
|
|
dtype='float32',
|
|
append_batch_size=False,
|
|
)
|
|
out = paddle.scale(input, scale=scale_var)
|
|
return out
|
|
|
|
def make_range(self):
|
|
with base.program_guard(
|
|
base.default_main_program(), base.default_startup_program()
|
|
):
|
|
paddle.arange(0, 10, 2, 'int32')
|
|
paddle.arange(0.1, 10.0, 0.2, 'float32')
|
|
paddle.arange(0.1, 10.0, 0.2, 'float64')
|
|
start = paddle.tensor.fill_constant(
|
|
shape=[1], value=0.1, dtype="float32"
|
|
)
|
|
end = paddle.tensor.fill_constant(
|
|
shape=[1], value=10.0, dtype="float32"
|
|
)
|
|
step = paddle.tensor.fill_constant(
|
|
shape=[1], value=0.2, dtype="float32"
|
|
)
|
|
y = paddle.arange(start, end, step, 'float64')
|
|
return y
|
|
|
|
def make_kldiv_loss(self):
|
|
with base.program_guard(
|
|
base.default_main_program(), base.default_startup_program()
|
|
):
|
|
x = self._get_data(
|
|
name='x',
|
|
shape=[32, 128, 128],
|
|
dtype="float32",
|
|
append_batch_size=False,
|
|
)
|
|
target = self._get_data(
|
|
name='target',
|
|
shape=[32, 128, 128],
|
|
dtype="float32",
|
|
append_batch_size=False,
|
|
)
|
|
loss = paddle.nn.functional.kl_div(
|
|
input=x, label=target, reduction='batchmean'
|
|
)
|
|
return loss
|
|
|
|
def make_pixel_shuffle(self):
|
|
with base.program_guard(
|
|
base.default_main_program(), base.default_startup_program()
|
|
):
|
|
x = self._get_data(name="X", shape=[9, 4, 4], dtype="float32")
|
|
out = paddle.nn.functional.pixel_shuffle(x, upscale_factor=3)
|
|
return out
|
|
|
|
def make_mse_loss(self):
|
|
with base.program_guard(
|
|
base.default_main_program(), base.default_startup_program()
|
|
):
|
|
x = self._get_data(name="X", shape=[1], dtype="float32")
|
|
y = self._get_data(name="Y", shape=[1], dtype="float32")
|
|
out = paddle.nn.functional.mse_loss(input=x, label=y)
|
|
return out
|
|
|
|
def make_square_error_cost(self):
|
|
with base.program_guard(
|
|
base.default_main_program(), base.default_startup_program()
|
|
):
|
|
x = self._get_data(name="X", shape=[1], dtype="float32")
|
|
y = self._get_data(name="Y", shape=[1], dtype="float32")
|
|
out = paddle.nn.functional.square_error_cost(input=x, label=y)
|
|
return out
|
|
|
|
def test_affine_grid(self):
|
|
with self.static_graph():
|
|
data = paddle.static.data(
|
|
name='data', shape=[-1, 2, 3, 3], dtype="float32"
|
|
)
|
|
out = paddle.argsort(x=data, axis=1)
|
|
|
|
theta = paddle.static.data(
|
|
name="theta", shape=[-1, 2, 3], dtype="float32"
|
|
)
|
|
out_shape = paddle.static.data(
|
|
name="out_shape", shape=[-1], dtype="int32"
|
|
)
|
|
data_0 = paddle.nn.functional.affine_grid(theta, out_shape)
|
|
data_1 = paddle.nn.functional.affine_grid(theta, [5, 3, 28, 28])
|
|
|
|
self.assertIsNotNone(data_0)
|
|
self.assertIsNotNone(data_1)
|
|
|
|
def test_stridedslice(self):
|
|
axes = [0, 1, 2]
|
|
starts = [1, 0, 2]
|
|
ends = [3, 3, 4]
|
|
strides = [1, 1, 1]
|
|
with self.static_graph():
|
|
x = paddle.static.data(
|
|
name="x", shape=[-1, 245, 30, 30], dtype="float32"
|
|
)
|
|
out = paddle.strided_slice(
|
|
x, axes=axes, starts=starts, ends=ends, strides=strides
|
|
)
|
|
return out
|
|
|
|
def test_squeeze(self):
|
|
# TODO(minqiyang): dygraph do not support layers with param now
|
|
with self.static_graph():
|
|
x = paddle.static.data(
|
|
name='x', shape=[-1, 1, 1, 4], dtype='float32'
|
|
)
|
|
out = paddle.squeeze(x, axis=[2])
|
|
return out
|
|
|
|
def test_flatten(self):
|
|
# TODO(minqiyang): dygraph do not support op without kernel now
|
|
with self.static_graph():
|
|
x = paddle.static.data(
|
|
name='x',
|
|
shape=[4, 4, 3],
|
|
dtype="float32",
|
|
)
|
|
out = paddle.flatten(x, 1, -1, name="flatten")
|
|
return out
|
|
|
|
def test_linspace(self):
|
|
program = base.Program()
|
|
with base.program_guard(program):
|
|
out = paddle.linspace(20, 10, 5, 'float64')
|
|
self.assertIsNotNone(out)
|
|
print(str(program))
|
|
|
|
def test_unfold(self):
|
|
with self.static_graph():
|
|
x = paddle.static.data(
|
|
name='x', shape=[-1, 3, 20, 20], dtype='float32'
|
|
)
|
|
out = paddle.nn.functional.unfold(x, [3, 3], 1, 1, 1)
|
|
return out
|
|
|
|
def test_addmm(self):
|
|
with base.program_guard(
|
|
base.default_main_program(), base.default_startup_program()
|
|
):
|
|
input = paddle.static.data(
|
|
name='input_data',
|
|
shape=[3, 3],
|
|
dtype='float32',
|
|
)
|
|
x = paddle.static.data(name='x', shape=[3, 2], dtype='float32')
|
|
y = paddle.static.data(name='y', shape=[2, 3], dtype='float32')
|
|
|
|
out = paddle.addmm(input=input, x=x, y=y)
|
|
return out
|
|
|
|
def test_warpctc_with_padding(self):
|
|
# TODO(minqiyang): dygraph do not support lod now
|
|
with self.static_graph():
|
|
input_length = paddle.static.data(
|
|
name='logits_length', shape=[12], dtype='int64'
|
|
)
|
|
label_length = paddle.static.data(
|
|
name='labels_length', shape=[12], dtype='int64'
|
|
)
|
|
label = paddle.static.data(
|
|
name='label', shape=[12, 1], dtype='int32'
|
|
)
|
|
predict = paddle.static.data(
|
|
name='predict', shape=[4, 12, 8], dtype='float32'
|
|
)
|
|
output = paddle.nn.functional.ctc_loss(
|
|
log_probs=predict,
|
|
labels=label,
|
|
input_lengths=input_length,
|
|
label_lengths=label_length,
|
|
reduction='none',
|
|
)
|
|
return output
|
|
|
|
|
|
class ExampleNet(paddle.nn.Layer):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.weight = self.create_parameter(
|
|
shape=[1, 1], attr=paddle.ParamAttr(trainable=False)
|
|
)
|
|
|
|
def forward(self):
|
|
# only for test parameter trainable attr
|
|
pass
|
|
|
|
|
|
class TestLayerParameterTrainableSet(unittest.TestCase):
|
|
def test_layer_parameter_set(self):
|
|
with base.dygraph.guard():
|
|
net = ExampleNet()
|
|
self.assertFalse(net.weight.trainable)
|
|
|
|
|
|
class TestLayerTrainingAttribute(unittest.TestCase):
|
|
def test_set_train_eval_in_dynamic_mode(self):
|
|
with base.dygraph.guard():
|
|
net = paddle.nn.Dropout()
|
|
net.train()
|
|
self.assertTrue(net.training)
|
|
net.eval()
|
|
self.assertFalse(net.training)
|
|
|
|
def test_set_train_eval_in_static_mode(self):
|
|
net = paddle.nn.Dropout()
|
|
net.train()
|
|
self.assertTrue(net.training)
|
|
net.eval()
|
|
self.assertFalse(net.training)
|
|
|
|
|
|
class MyLayer(paddle.nn.Layer):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self._linear = paddle.nn.Linear(1, 1)
|
|
self._dropout = paddle.nn.Dropout(p=0.5)
|
|
|
|
def forward(self, input):
|
|
temp = self._linear(input)
|
|
temp = self._dropout(temp)
|
|
return temp
|
|
|
|
|
|
class MySuperLayer(paddle.nn.Layer):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self._mylayer = MyLayer()
|
|
|
|
def forward(self, input):
|
|
temp = self._mylayer(input)
|
|
return temp
|
|
|
|
|
|
class TestSubLayerCount(unittest.TestCase):
|
|
def test_sublayer(self):
|
|
with base.dygraph.guard():
|
|
mySuperlayer = MySuperLayer()
|
|
self.assertTrue(len(mySuperlayer.sublayers()) == 3)
|
|
self.assertTrue(len(mySuperlayer.sublayers(include_self=True)) == 4)
|
|
|
|
|
|
class TestExcludedLayersSupportBool(unittest.TestCase):
|
|
def test_support_tuple(self):
|
|
with base.dygraph.guard():
|
|
model = MyLayer()
|
|
model.float16(excluded_layers=[paddle.nn.Linear])
|
|
self.assertTrue(model._linear.weight.dtype == paddle.float32)
|
|
model.bfloat16(excluded_layers=(paddle.nn.Linear))
|
|
self.assertTrue(model._linear.weight.dtype == paddle.float32)
|
|
|
|
|
|
class TestLayerClearGradientSetToZero(unittest.TestCase):
|
|
def test_layer_clear_gradient_set_to_zero_true(self):
|
|
with base.dygraph.guard():
|
|
net = MyLayer()
|
|
inputs = paddle.randn([10, 1])
|
|
outputs = net(inputs)
|
|
outputs.backward()
|
|
net.clear_gradients()
|
|
self.assertTrue(
|
|
net._linear.weight.grad.numpy() == np.array([[0.0]])
|
|
)
|
|
|
|
def test_layer_clear_gradient_set_to_zero_false(self):
|
|
with base.dygraph.guard():
|
|
net = MyLayer()
|
|
inputs = paddle.randn([10, 1])
|
|
outputs = net(inputs)
|
|
outputs.backward()
|
|
net.clear_gradients(set_to_zero=False)
|
|
self.assertTrue(net._linear.weight.grad is None)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
paddle.enable_static()
|
|
unittest.main()
|