wehub-resource-sync
640 lines
23 KiB
Python
640 lines
23 KiB
Python
# Copyright (c) 2019 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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from functools import partial
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import numpy as np
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from op_test import get_device_place
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import paddle
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from paddle import base
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from paddle.base.backward import append_backward
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from paddle.base.framework import Program, program_guard
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paddle.enable_static()
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class TestAPICase(unittest.TestCase):
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def test_return_single_var(self):
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def fn_1():
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return paddle.tensor.fill_constant(
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shape=[4, 2], dtype='int32', value=1
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)
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def fn_2():
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return paddle.tensor.fill_constant(
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shape=[4, 2], dtype='int32', value=2
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)
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def fn_3():
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return paddle.tensor.fill_constant(
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shape=[4, 3], dtype='int32', value=3
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)
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main_program = paddle.static.Program()
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startup_program = paddle.static.Program()
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with paddle.static.program_guard(main_program, startup_program):
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x = paddle.tensor.fill_constant(
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shape=[1], dtype='float32', value=0.3
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)
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y = paddle.tensor.fill_constant(
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shape=[1], dtype='float32', value=0.1
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)
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z = paddle.tensor.fill_constant(
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shape=[1], dtype='float32', value=0.2
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)
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pred_2 = paddle.less_than(x, y) # false: 0.3 < 0.1
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pred_1 = paddle.less_than(z, x) # true: 0.2 < 0.3
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# call fn_1
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out_0 = paddle.static.nn.control_flow.case(
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pred_fn_pairs=[(pred_1, fn_1), (pred_1, fn_2)], default=fn_3
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)
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# call fn_2
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out_1 = paddle.static.nn.control_flow.case(
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pred_fn_pairs=[(pred_2, fn_1), (pred_1, fn_2)], default=fn_3
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)
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# call default fn_3
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out_2 = paddle.static.nn.control_flow.case(
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pred_fn_pairs=((pred_2, fn_1), (pred_2, fn_2)), default=fn_3
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)
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# no default, call fn_2
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out_3 = paddle.static.nn.control_flow.case(
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pred_fn_pairs=[(pred_1, fn_2)]
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)
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# no default, call fn_2. but pred_2 is false
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out_4 = paddle.static.nn.control_flow.case(
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pred_fn_pairs=[(pred_2, fn_2)]
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)
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place = get_device_place()
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exe = base.Executor(place)
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res = exe.run(
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main_program, fetch_list=[out_0, out_1, out_2, out_3, out_4]
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)
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np.testing.assert_allclose(res[0], 1, rtol=1e-05)
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np.testing.assert_allclose(res[1], 2, rtol=1e-05)
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np.testing.assert_allclose(res[2], 3, rtol=1e-05)
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np.testing.assert_allclose(res[3], 2, rtol=1e-05)
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np.testing.assert_allclose(res[4], 2, rtol=1e-05)
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def test_0d_tensor(self):
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def fn_1():
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return paddle.full(shape=[], dtype='int32', fill_value=1)
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def fn_2():
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return paddle.full(shape=[], dtype='int32', fill_value=2)
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def fn_3():
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return paddle.full(shape=[], dtype='int32', fill_value=3)
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main_program = paddle.static.Program()
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startup_program = paddle.static.Program()
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with paddle.static.program_guard(main_program, startup_program):
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x = paddle.full(shape=[], dtype='float32', fill_value=0.3)
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y = paddle.full(shape=[], dtype='float32', fill_value=0.1)
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z = paddle.full(shape=[], dtype='float32', fill_value=0.2)
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pred_2 = paddle.less_than(x, y) # false: 0.3 < 0.1
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pred_1 = paddle.less_than(z, x) # true: 0.2 < 0.3
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# call fn_1
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out_0 = paddle.static.nn.control_flow.case(
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pred_fn_pairs=[(pred_1, fn_1), (pred_1, fn_2)], default=fn_3
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)
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# call fn_2
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out_1 = paddle.static.nn.control_flow.case(
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pred_fn_pairs=[(pred_2, fn_1), (pred_1, fn_2)], default=fn_3
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)
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# call default fn_3
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out_2 = paddle.static.nn.control_flow.case(
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pred_fn_pairs=((pred_2, fn_1), (pred_2, fn_2)), default=fn_3
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)
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# no default, call fn_2
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out_3 = paddle.static.nn.control_flow.case(
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pred_fn_pairs=[(pred_1, fn_2)]
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)
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# no default, call fn_2. but pred_2 is false
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out_4 = paddle.static.nn.control_flow.case(
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pred_fn_pairs=[(pred_2, fn_2)]
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)
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place = get_device_place()
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exe = base.Executor(place)
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res = exe.run(
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main_program, fetch_list=[out_0, out_1, out_2, out_3, out_4]
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)
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np.testing.assert_allclose(res[0], 1, rtol=1e-05)
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self.assertEqual(res[0].shape, ())
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np.testing.assert_allclose(res[1], 2, rtol=1e-05)
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self.assertEqual(res[1].shape, ())
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np.testing.assert_allclose(res[2], 3, rtol=1e-05)
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self.assertEqual(res[2].shape, ())
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np.testing.assert_allclose(res[3], 2, rtol=1e-05)
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self.assertEqual(res[3].shape, ())
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np.testing.assert_allclose(res[4], 2, rtol=1e-05)
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self.assertEqual(res[4].shape, ())
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def test_0d_tensor_backward(self):
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main_program = paddle.static.Program()
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startup_program = paddle.static.Program()
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with paddle.static.program_guard(main_program, startup_program):
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x = paddle.full(shape=[], dtype='float32', fill_value=-2.0)
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x.stop_gradient = False
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x.persistable = True
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pred = paddle.full(shape=[], dtype='bool', fill_value=0)
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# pred is False, so out = -x
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out = paddle.static.nn.case(
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pred_fn_pairs=[(pred, lambda: x)], default=lambda: -x
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)
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grad_list = append_backward(out)
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place = get_device_place()
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exe = base.Executor(place)
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if paddle.framework.in_pir_mode():
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for p, g in grad_list:
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if p.is_same(x):
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dx = g
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res = exe.run(main_program, fetch_list=[out, dx])
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else:
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res = exe.run(main_program, fetch_list=[out.name, x.grad_name])
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np.testing.assert_allclose(
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np.asarray(res[0]), np.array(2.0), rtol=1e-05
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)
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self.assertEqual(res[0].shape, ())
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np.testing.assert_allclose(
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np.asarray(res[1]), np.array(-1.0), rtol=1e-05
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)
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self.assertEqual(res[1].shape, ())
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def test_0d_tensor_dygraph(self):
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paddle.disable_static()
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def fn_1():
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return paddle.full(shape=[], dtype='int32', fill_value=1)
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def fn_2():
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return paddle.full(shape=[], dtype='int32', fill_value=2)
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def fn_3():
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return paddle.full(shape=[], dtype='int32', fill_value=3)
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x = paddle.full(shape=[], dtype='float32', fill_value=0.3)
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y = paddle.full(shape=[], dtype='float32', fill_value=0.1)
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z = paddle.full(shape=[], dtype='float32', fill_value=0.2)
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pred_2 = paddle.less_than(x, y) # false: 0.3 < 0.1
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pred_1 = paddle.less_than(z, x) # true: 0.2 < 0.3
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# call fn_1
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out_0 = paddle.static.nn.control_flow.case(
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pred_fn_pairs=[(pred_1, fn_1), (pred_1, fn_2)], default=fn_3
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)
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# call fn_2
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out_1 = paddle.static.nn.control_flow.case(
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pred_fn_pairs=[(pred_2, fn_1), (pred_1, fn_2)], default=fn_3
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)
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# call default fn_3
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out_2 = paddle.static.nn.control_flow.case(
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pred_fn_pairs=((pred_2, fn_1), (pred_2, fn_2)), default=fn_3
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)
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# no default, call fn_2
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out_3 = paddle.static.nn.control_flow.case(
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pred_fn_pairs=[(pred_1, fn_2)]
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)
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# no default, call fn_2. but pred_2 is false
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out_4 = paddle.static.nn.control_flow.case(
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pred_fn_pairs=[(pred_2, fn_2)]
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)
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np.testing.assert_allclose(out_0, 1, rtol=1e-05)
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self.assertEqual(out_0.shape, [])
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np.testing.assert_allclose(out_1, 2, rtol=1e-05)
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self.assertEqual(out_1.shape, [])
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np.testing.assert_allclose(out_2, 3, rtol=1e-05)
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self.assertEqual(out_2.shape, [])
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np.testing.assert_allclose(out_3, 2, rtol=1e-05)
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self.assertEqual(out_3.shape, [])
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np.testing.assert_allclose(out_4, 2, rtol=1e-05)
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self.assertEqual(out_4.shape, [])
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paddle.enable_static()
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def test_return_var_tuple(self):
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def fn_1():
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return paddle.tensor.fill_constant(
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shape=[1, 2], dtype='int32', value=1
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), paddle.tensor.fill_constant(
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shape=[2, 3], dtype='float32', value=2
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)
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def fn_2():
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return paddle.tensor.fill_constant(
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shape=[3, 4], dtype='int32', value=3
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), paddle.tensor.fill_constant(
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shape=[4, 5], dtype='float32', value=4
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)
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def fn_3():
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return paddle.tensor.fill_constant(
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shape=[5, 6], dtype='int32', value=5
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), paddle.tensor.fill_constant(
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shape=[5, 6], dtype='float32', value=6
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)
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main_program = paddle.static.Program()
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startup_program = paddle.static.Program()
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with paddle.static.program_guard(main_program, startup_program):
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x = paddle.tensor.fill_constant(shape=[1], dtype='float32', value=1)
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y = paddle.tensor.fill_constant(shape=[1], dtype='float32', value=1)
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z = paddle.tensor.fill_constant(shape=[1], dtype='float32', value=3)
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pred_1 = paddle.equal(x, y) # true
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pred_2 = paddle.equal(x, z) # false
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out = paddle.static.nn.control_flow.case(
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((pred_1, fn_1), (pred_2, fn_2)), fn_3
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)
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place = get_device_place()
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exe = base.Executor(place)
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ret = exe.run(main_program, fetch_list=out)
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np.testing.assert_allclose(
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np.asarray(ret[0]), np.full((1, 2), 1, np.int32), rtol=1e-05
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)
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np.testing.assert_allclose(
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np.asarray(ret[1]), np.full((2, 3), 2, np.float32), rtol=1e-05
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)
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class TestAPICase_Nested(unittest.TestCase):
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def test_nested_case(self):
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def fn_1(x=1):
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var_5 = paddle.tensor.fill_constant(
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shape=[1], dtype='int32', value=5
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)
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var_6 = paddle.tensor.fill_constant(
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shape=[1], dtype='int32', value=6
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)
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out = paddle.static.nn.control_flow.case(
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pred_fn_pairs=[
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(
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var_5 < var_6,
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partial(
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paddle.tensor.fill_constant,
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shape=[1],
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dtype='int32',
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value=x,
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),
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),
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(
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var_5 == var_6,
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partial(
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paddle.tensor.fill_constant,
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shape=[2],
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dtype='int32',
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value=x,
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),
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),
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]
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)
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return out
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def fn_2(x=2):
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var_5 = paddle.tensor.fill_constant(
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shape=[1], dtype='int32', value=5
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)
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var_6 = paddle.tensor.fill_constant(
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shape=[1], dtype='int32', value=6
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)
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out = paddle.static.nn.control_flow.case(
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pred_fn_pairs=[
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(var_5 < var_6, partial(fn_1, x=x)),
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(
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var_5 == var_6,
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partial(
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paddle.tensor.fill_constant,
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shape=[2],
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dtype='int32',
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value=x,
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),
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),
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]
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)
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return out
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def fn_3():
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var_5 = paddle.tensor.fill_constant(
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shape=[1], dtype='int32', value=5
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)
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var_6 = paddle.tensor.fill_constant(
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shape=[1], dtype='int32', value=6
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)
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out = paddle.static.nn.control_flow.case(
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pred_fn_pairs=[
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(var_5 < var_6, partial(fn_2, x=3)),
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(
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var_5 == var_6,
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partial(
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paddle.tensor.fill_constant,
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shape=[2],
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dtype='int32',
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value=7,
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),
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),
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]
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)
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return out
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main_program = paddle.static.Program()
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startup_program = paddle.static.Program()
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with paddle.static.program_guard(main_program, startup_program):
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x = paddle.tensor.fill_constant(
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shape=[1], dtype='float32', value=0.3
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)
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y = paddle.tensor.fill_constant(
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shape=[1], dtype='float32', value=0.1
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)
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z = paddle.tensor.fill_constant(
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shape=[1], dtype='float32', value=0.2
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)
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pred_2 = paddle.less_than(x, y) # false: 0.3 < 0.1
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pred_1 = paddle.less_than(z, x) # true: 0.2 < 0.3
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out_1 = paddle.static.nn.control_flow.case(
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pred_fn_pairs=[(pred_1, fn_1), (pred_2, fn_2)], default=fn_3
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)
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out_2 = paddle.static.nn.control_flow.case(
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pred_fn_pairs=[(pred_2, fn_1), (pred_1, fn_2)], default=fn_3
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)
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out_3 = paddle.static.nn.control_flow.case(
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pred_fn_pairs=[(x == y, fn_1), (x == z, fn_2)], default=fn_3
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)
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place = get_device_place()
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exe = base.Executor(place)
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res = exe.run(main_program, fetch_list=[out_1, out_2, out_3])
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np.testing.assert_allclose(res[0], 1, rtol=1e-05)
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np.testing.assert_allclose(res[1], 2, rtol=1e-05)
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np.testing.assert_allclose(res[2], 3, rtol=1e-05)
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def test_nested_0d_tensor(self):
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def fn_1(x=1):
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var_5 = paddle.full(shape=[], dtype='int32', fill_value=5)
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var_6 = paddle.full(shape=[], dtype='int32', fill_value=6)
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out = paddle.static.nn.control_flow.case(
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pred_fn_pairs=[
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(
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var_5 < var_6,
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partial(
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paddle.full,
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shape=[],
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dtype='int32',
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fill_value=x,
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),
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),
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(
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var_5 == var_6,
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partial(
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paddle.full,
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shape=[],
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dtype='int32',
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fill_value=x,
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),
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),
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]
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)
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return out
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def fn_2(x=2):
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var_5 = paddle.full(shape=[], dtype='int32', fill_value=5)
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var_6 = paddle.full(shape=[], dtype='int32', fill_value=6)
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out = paddle.static.nn.control_flow.case(
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pred_fn_pairs=[
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(var_5 < var_6, partial(fn_1, x=x)),
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(
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var_5 == var_6,
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partial(
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paddle.full,
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shape=[],
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dtype='int32',
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fill_value=x,
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),
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),
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]
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)
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return out
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def fn_3():
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var_5 = paddle.full(shape=[], dtype='int32', fill_value=5)
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var_6 = paddle.full(shape=[], dtype='int32', fill_value=6)
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out = paddle.static.nn.control_flow.case(
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pred_fn_pairs=[
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(var_5 < var_6, partial(fn_2, x=3)),
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(
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var_5 == var_6,
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partial(
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paddle.full,
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shape=[],
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dtype='int32',
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fill_value=7,
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),
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),
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]
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)
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return out
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main_program = paddle.static.Program()
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startup_program = paddle.static.Program()
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with paddle.static.program_guard(main_program, startup_program):
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x = paddle.full(shape=[], dtype='float32', fill_value=0.3)
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y = paddle.full(shape=[], dtype='float32', fill_value=0.1)
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z = paddle.full(shape=[], dtype='float32', fill_value=0.2)
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pred_2 = paddle.less_than(x, y) # false: 0.3 < 0.1
|
|
pred_1 = paddle.less_than(z, x) # true: 0.2 < 0.3
|
|
|
|
out_1 = paddle.static.nn.control_flow.case(
|
|
pred_fn_pairs=[(pred_1, fn_1), (pred_2, fn_2)], default=fn_3
|
|
)
|
|
|
|
out_2 = paddle.static.nn.control_flow.case(
|
|
pred_fn_pairs=[(pred_2, fn_1), (pred_1, fn_2)], default=fn_3
|
|
)
|
|
|
|
out_3 = paddle.static.nn.control_flow.case(
|
|
pred_fn_pairs=[(x == y, fn_1), (x == z, fn_2)], default=fn_3
|
|
)
|
|
|
|
place = get_device_place()
|
|
exe = base.Executor(place)
|
|
|
|
res = exe.run(main_program, fetch_list=[out_1, out_2, out_3])
|
|
|
|
np.testing.assert_allclose(res[0], 1, rtol=1e-05)
|
|
self.assertEqual(res[0].shape, ())
|
|
np.testing.assert_allclose(res[1], 2, rtol=1e-05)
|
|
self.assertEqual(res[1].shape, ())
|
|
np.testing.assert_allclose(res[2], 3, rtol=1e-05)
|
|
self.assertEqual(res[2].shape, ())
|
|
|
|
|
|
class TestAPICase_Error(unittest.TestCase):
|
|
def test_error(self):
|
|
def fn_1():
|
|
return paddle.tensor.fill_constant(
|
|
shape=[4, 2], dtype='int32', value=1
|
|
)
|
|
|
|
main_program = Program()
|
|
startup_program = Program()
|
|
with program_guard(main_program, startup_program):
|
|
x = paddle.tensor.fill_constant(
|
|
shape=[1], dtype='float32', value=0.23
|
|
)
|
|
z = paddle.tensor.fill_constant(
|
|
shape=[1], dtype='float32', value=0.2
|
|
)
|
|
pred_1 = paddle.less_than(z, x) # true
|
|
|
|
# The type of 'pred_fn_pairs' in case must be list or tuple
|
|
def type_error_pred_fn_pairs():
|
|
paddle.static.nn.control_flow.case(
|
|
pred_fn_pairs=1, default=fn_1
|
|
)
|
|
|
|
self.assertRaises(TypeError, type_error_pred_fn_pairs)
|
|
|
|
# The elements' type of 'pred_fn_pairs' in Op(case) must be tuple
|
|
def type_error_pred_fn_1():
|
|
paddle.static.nn.control_flow.case(
|
|
pred_fn_pairs=[1], default=fn_1
|
|
)
|
|
|
|
self.assertRaises(TypeError, type_error_pred_fn_1)
|
|
|
|
# The tuple's size of 'pred_fn_pairs' in Op(case) must be 2
|
|
def type_error_pred_fn_2():
|
|
paddle.static.nn.control_flow.case(
|
|
pred_fn_pairs=[(1, 2, 3)], default=fn_1
|
|
)
|
|
|
|
self.assertRaises(TypeError, type_error_pred_fn_2)
|
|
|
|
# The pred's type of 'pred_fn_pairs' in Op(case) must be bool Variable
|
|
def type_error_pred():
|
|
paddle.static.nn.control_flow.case(
|
|
pred_fn_pairs=[(1, fn_1)], default=fn_1
|
|
)
|
|
|
|
self.assertRaises(TypeError, type_error_pred)
|
|
|
|
# The function of pred_fn_pairs in case must be callable
|
|
def type_error_fn():
|
|
paddle.static.nn.control_flow.case(
|
|
pred_fn_pairs=[(pred_1, 2)], default=fn_1
|
|
)
|
|
|
|
self.assertRaises(TypeError, type_error_fn)
|
|
|
|
# The default in Op(case) must be callable
|
|
def type_error_default():
|
|
paddle.static.nn.control_flow.case(
|
|
pred_fn_pairs=[(pred_1, fn_1)], default=fn_1()
|
|
)
|
|
|
|
self.assertRaises(TypeError, type_error_default)
|
|
|
|
|
|
# when optimizer in case
|
|
class TestMultiTask(unittest.TestCase):
|
|
def test_optimizer_in_case(self):
|
|
BATCH_SIZE = 1
|
|
INPUT_SIZE = 784
|
|
EPOCH_NUM = 2
|
|
main_program = paddle.static.Program()
|
|
startup_program = paddle.static.Program()
|
|
with paddle.static.program_guard(main_program, startup_program):
|
|
x = paddle.static.data(
|
|
name='x', shape=[BATCH_SIZE, INPUT_SIZE], dtype='float32'
|
|
)
|
|
y = paddle.static.data(
|
|
name='y', shape=[BATCH_SIZE, INPUT_SIZE], dtype='float32'
|
|
)
|
|
x.stop_gradient = False
|
|
y.stop_gradient = False
|
|
switch_id = paddle.static.data(
|
|
name='switch_id', shape=[1], dtype='int32'
|
|
)
|
|
|
|
one = paddle.tensor.fill_constant(shape=[1], dtype='int32', value=1)
|
|
adam = paddle.optimizer.Adam(learning_rate=0.001)
|
|
adagrad = paddle.optimizer.Adagrad(learning_rate=0.001)
|
|
|
|
def fn_1():
|
|
sum = paddle.multiply(x, y)
|
|
loss = paddle.mean(sum, name="f_1_loss")
|
|
adam.minimize(loss)
|
|
|
|
def fn_2():
|
|
sum = paddle.multiply(x, y)
|
|
loss = paddle.mean(sum, name="f_2_loss")
|
|
adagrad.minimize(loss)
|
|
|
|
paddle.static.nn.control_flow.case(
|
|
pred_fn_pairs=[(switch_id == one, fn_1)], default=fn_2
|
|
)
|
|
|
|
exe = base.Executor(base.CPUPlace())
|
|
exe.run(startup_program)
|
|
|
|
for epoch in range(EPOCH_NUM):
|
|
np.random.seed(epoch)
|
|
feed_image = np.random.random(
|
|
size=[BATCH_SIZE, INPUT_SIZE]
|
|
).astype('float32')
|
|
out = exe.run(
|
|
main_program,
|
|
feed={
|
|
'x': feed_image,
|
|
'y': feed_image,
|
|
'switch_id': np.array([epoch]).astype('int32'),
|
|
},
|
|
fetch_list=[],
|
|
)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|