wehub-resource-sync
380 lines
12 KiB
Python
380 lines
12 KiB
Python
# Copyright (c) 2020 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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import numpy as np
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from op_test import (
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OpTest,
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convert_float_to_uint16,
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get_device_place,
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get_places,
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is_custom_device,
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)
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import paddle
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from paddle.base import core
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def np_masked_select(x, mask):
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result = np.empty(shape=(0), dtype=x.dtype)
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x, mask = np.broadcast_arrays(x, mask)
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if x.size != 0:
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for ele, ma in zip(np.nditer(x), np.nditer(mask)):
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if ma:
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result = np.append(result, ele)
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return result.flatten()
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class TestMaskedSelectOp(OpTest):
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def setUp(self):
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self.init()
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self.op_type = "masked_select"
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self.prim_op_type = "prim"
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self.python_api = paddle.masked_select
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self.public_python_api = paddle.masked_select
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x = np.random.random(self.shape).astype("float64")
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mask = np.array(np.random.randint(2, size=self.mask_shape, dtype=bool))
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out = np_masked_select(x, mask)
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self.inputs = {'X': x, 'Mask': mask}
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self.outputs = {'Y': out}
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def test_check_output(self):
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self.check_output(check_pir=True, check_symbol_infer=False)
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def test_check_grad(self):
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self.check_grad(['X'], 'Y', check_pir=True, check_prim_pir=True)
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def init(self):
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self.shape = (50, 3)
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self.mask_shape = self.shape
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class TestMaskedSelectOp1(TestMaskedSelectOp):
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def init(self):
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self.shape = (6, 8, 9, 18)
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self.mask_shape = self.shape
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class TestMaskedSelectOp2(TestMaskedSelectOp):
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def init(self):
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self.shape = (168,)
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self.mask_shape = self.shape
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class TestMaskedSelectFP16Op(OpTest):
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def setUp(self):
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self.init()
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self.op_type = "masked_select"
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self.prim_op_type = "prim"
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self.dtype = np.float16
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self.python_api = paddle.masked_select
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self.public_python_api = paddle.masked_select
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x = np.random.random(self.shape).astype("float16")
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mask = np.array(np.random.randint(2, size=self.shape, dtype=bool))
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out = np_masked_select(x, mask)
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self.inputs = {'X': x, 'Mask': mask}
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self.outputs = {'Y': out}
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def test_check_output(self):
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self.check_output(check_pir=True, check_symbol_infer=False)
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def test_check_grad(self):
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self.check_grad(['X'], 'Y', check_pir=True, check_prim_pir=True)
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def init(self):
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self.shape = (50, 3)
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class TestMaskedSelectFP16Op1(TestMaskedSelectFP16Op):
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def init(self):
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self.shape = (6, 8, 9, 18)
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class TestMaskedSelectFP16Op2(TestMaskedSelectFP16Op):
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def init(self):
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self.shape = (168,)
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or not core.is_bfloat16_supported(get_device_place()),
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"core is not compiled with CUDA or not support bfloat16",
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)
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class TestMaskedSelectBF16Op(OpTest):
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def setUp(self):
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self.init()
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self.op_type = "masked_select"
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self.prim_op_type = "prim"
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self.dtype = np.uint16
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self.python_api = paddle.masked_select
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self.public_python_api = paddle.masked_select
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x = np.random.random(self.shape).astype("float32")
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mask = np.array(np.random.randint(2, size=self.shape, dtype=bool))
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out = np_masked_select(x, mask)
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self.inputs = {'X': convert_float_to_uint16(x), 'Mask': mask}
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self.outputs = {'Y': convert_float_to_uint16(out)}
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def test_check_output(self):
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self.check_output_with_place(
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get_device_place(), check_pir=True, check_symbol_infer=False
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)
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def test_check_grad(self):
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self.check_grad_with_place(
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get_device_place(), ['X'], 'Y', check_pir=True, check_prim_pir=True
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)
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def init(self):
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self.shape = (50, 3)
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class TestMaskedSelectBF16Op1(TestMaskedSelectBF16Op):
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def init(self):
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self.shape = (6, 8, 9, 2)
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class TestMaskedSelectBF16Op2(TestMaskedSelectBF16Op):
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def init(self):
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self.shape = (168,)
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class TestMaskedSelectAPI(unittest.TestCase):
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def test_imperative_mode(self):
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paddle.disable_static()
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shape = (88, 6, 8)
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np_x = np.random.random(shape).astype('float32')
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np_mask = np.array(np.random.randint(2, size=shape, dtype=bool))
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x = paddle.to_tensor(np_x)
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mask = paddle.to_tensor(np_mask)
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out = paddle.masked_select(x, mask)
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np_out = np_masked_select(np_x, np_mask)
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np.testing.assert_allclose(out.numpy(), np_out, rtol=1e-05)
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paddle.enable_static()
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def test_static_mode(self):
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shape = [8, 9, 6]
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x = paddle.static.data(shape=shape, dtype='float32', name='x')
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mask = paddle.static.data(shape=shape, dtype='bool', name='mask')
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np_x = np.random.random(shape).astype('float32')
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np_mask = np.array(np.random.randint(2, size=shape, dtype=bool))
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out = paddle.masked_select(x, mask)
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np_out = np_masked_select(np_x, np_mask)
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exe = paddle.static.Executor(place=paddle.CPUPlace())
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(res,) = exe.run(
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paddle.static.default_main_program(),
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feed={"x": np_x, "mask": np_mask},
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fetch_list=[out],
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)
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np.testing.assert_allclose(res, np_out, rtol=1e-05)
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class TestMaskedSelectError(unittest.TestCase):
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def setUp(self):
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paddle.enable_static()
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def test_error(self):
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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shape = [8, 9, 6]
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x = paddle.static.data(shape=shape, dtype='float32', name='x')
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mask = paddle.static.data(shape=shape, dtype='bool', name='mask')
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mask_float = paddle.static.data(
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shape=shape, dtype='float32', name='mask_float'
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)
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np_x = np.random.random(shape).astype('float32')
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np_mask = np.array(np.random.randint(2, size=shape, dtype=bool))
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def test_x_type():
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paddle.masked_select(np_x, mask)
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self.assertRaises(TypeError, test_x_type)
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def test_mask_type():
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paddle.masked_select(x, np_mask)
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self.assertRaises(TypeError, test_mask_type)
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def test_mask_dtype():
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paddle.masked_select(x, mask_float)
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self.assertRaises(TypeError, test_mask_dtype)
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class TestMaskedSelectBroadcast(unittest.TestCase):
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def setUp(self):
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paddle.disable_static()
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def test_broadcast(self):
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shape = (3, 4)
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np_x = np.random.random(shape).astype('float32')
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np_mask = np.array([[True], [False], [False]])
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x = paddle.to_tensor(np_x)
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mask = paddle.to_tensor(np_mask)
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out = paddle.masked_select(x, mask)
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np_out = np_x[0]
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np.testing.assert_allclose(out.numpy(), np_out, rtol=1e-05)
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def test_broadcast_grad(self):
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shape = (3, 4)
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np_x = np.random.random(shape).astype('float32')
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np_mask = np.array([[True], [False], [False]])
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x = paddle.to_tensor(np_x, stop_gradient=False)
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mask = paddle.to_tensor(np_mask)
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out = paddle.masked_select(x, mask)
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out.sum().backward()
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np_out = np.zeros(shape)
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np_out[0] = 1.0
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np.testing.assert_allclose(x.grad.numpy(), np_out, rtol=1e-05)
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def test_broadcast_zerodim(self):
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shape = (3, 4)
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np_x = np.random.random(shape).astype('float32')
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x = paddle.to_tensor(np_x)
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mask = paddle.to_tensor(True)
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out = paddle.masked_select(x, mask)
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np_out = np_x.reshape(-1)
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np.testing.assert_allclose(out.numpy(), np_out, rtol=1e-05)
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def test_broadcast_zerodim_grad(self):
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shape = (3, 4)
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np_x = np.random.random(shape).astype('float32')
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np_mask = np.array(True)
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x = paddle.to_tensor(np_x, stop_gradient=False)
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mask = paddle.to_tensor(np_mask)
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out = paddle.masked_select(x, mask)
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out.sum().backward()
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np_out = np.ones(shape)
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np.testing.assert_allclose(x.grad.numpy(), np_out, rtol=1e-05)
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class TestMaskedSelectOpBroadcast(TestMaskedSelectOp):
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def init(self):
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self.shape = (3, 40)
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self.mask_shape = (3, 1)
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class TestMaskedSelectOpBroadcast2(TestMaskedSelectOp):
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def init(self):
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self.shape = (300, 1)
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self.mask_shape = (300, 40)
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class TestMaskedSelectOpBroadcast3(TestMaskedSelectOp):
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def init(self):
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self.shape = (120,)
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self.mask_shape = (300, 120)
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class TestMaskedSelectOpBroadcast4(TestMaskedSelectOp):
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def init(self):
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self.shape = (300, 40)
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self.mask_shape = 40
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class TestMaskedSelectOpBroadcast_ZeroSize(TestMaskedSelectOp):
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def init(self):
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self.shape = (0, 40)
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self.mask_shape = 40
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class TestMaskedSelectOpBroadcast_ZeroSize2(TestMaskedSelectOp):
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def init(self):
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self.shape = (0, 0)
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self.mask_shape = 0
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class TestMaskedSelectOp_ZeroSize3(unittest.TestCase):
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def setUp(self):
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self.place = get_places()
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def _test_out_0size(self, place):
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paddle.disable_static(place)
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x = paddle.to_tensor([1, 2], dtype='float32')
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x.stop_gradient = False
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y = paddle.to_tensor([False, False], dtype='bool')
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z = x.masked_select(y)
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np.testing.assert_allclose(z.shape, [0])
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z.sum().backward()
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np.testing.assert_allclose(x.grad.numpy(), [0, 0])
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paddle.enable_static()
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def test_out_0size(self):
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for place in self.place:
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self._test_out_0size(place)
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class TestMaskedSelectAPI_Compatibility(unittest.TestCase):
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def test_imperative_mode(self):
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paddle.disable_static()
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shape = (88, 6, 8)
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np_x = np.random.random(shape).astype('float32')
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np_mask = np.array(np.random.randint(2, size=shape, dtype=bool))
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np_out = np_masked_select(np_x, np_mask)
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paddle_dygraph_out = []
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x = paddle.to_tensor(np_x)
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mask = paddle.to_tensor(np_mask)
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out1 = paddle.masked_select(x, mask)
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paddle_dygraph_out.append(out1)
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out2 = paddle.masked_select(x=x, mask=mask)
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paddle_dygraph_out.append(out2)
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out3 = paddle.masked_select(input=x, mask=mask)
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paddle_dygraph_out.append(out3)
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# test out
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out4 = paddle.empty(np_out.shape, dtype=paddle.float32)
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out5 = paddle.masked_select(x, mask, out=out4)
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paddle_dygraph_out.append(out4)
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paddle_dygraph_out.append(out5)
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for out in paddle_dygraph_out:
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np.testing.assert_allclose(out.numpy(), np_out, rtol=1e-05)
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paddle.enable_static()
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def test_static_mode(self):
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shape = [8, 9, 6]
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x = paddle.static.data(shape=shape, dtype='float32', name='x')
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mask = paddle.static.data(shape=shape, dtype='bool', name='mask')
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np_x = np.random.random(shape).astype('float32')
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np_mask = np.array(np.random.randint(2, size=shape, dtype=bool))
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np_out = np_masked_select(np_x, np_mask)
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out1 = paddle.masked_select(x, mask)
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out2 = paddle.masked_select(x=x, mask=mask)
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out3 = paddle.masked_select(input=x, mask=mask)
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exe = paddle.static.Executor(place=paddle.CPUPlace())
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fetches = exe.run(
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paddle.static.default_main_program(),
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feed={"x": np_x, "mask": np_mask},
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fetch_list=[out1, out2, out3],
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)
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for out in fetches:
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np.testing.assert_allclose(out, np_out, rtol=1e-05)
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if __name__ == '__main__':
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paddle.enable_static()
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unittest.main()
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