wehub-resource-sync
150 lines
4.6 KiB
Python
150 lines
4.6 KiB
Python
# Copyright (c) 2022 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 get_test_cover_info import (
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XPUOpTestWrapper,
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create_test_class,
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get_xpu_op_support_types,
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)
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from op_test_xpu import XPUOpTest
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import paddle
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import paddle.nn.functional as F
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paddle.enable_static()
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np.random.seed(10)
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def temporal_shift(x, seg_num, shift_ratio, data_format):
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if data_format == "NHWC":
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x = np.transpose(x, (0, 3, 1, 2))
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shape = x.shape
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reshape_x = x.reshape((-1, seg_num, shape[1], shape[2], shape[3]))
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pad_x = np.pad(
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reshape_x, ((0, 0), (1, 1), (0, 0), (0, 0), (0, 0)), 'constant'
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)
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c1 = int(shape[1] * shift_ratio)
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c2 = int(shape[1] * 2 * shift_ratio)
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slice1 = pad_x[:, :seg_num, :c1, :, :]
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slice2 = pad_x[:, 2 : seg_num + 2, c1:c2, :, :]
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slice3 = pad_x[:, 1 : seg_num + 1, c2:, :, :]
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concat_x = np.concatenate([slice1, slice2, slice3], axis=2)
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out = concat_x.reshape(shape)
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if data_format == "NHWC":
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out = np.transpose(out, (0, 2, 3, 1))
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return out
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class XPUTestTemporalShiftOp(XPUOpTestWrapper):
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def __init__(self):
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self.op_name = "temporal_shift"
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self.use_dynamic_create_class = False
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class TestXPUTemporalShift(XPUOpTest):
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def setUp(self):
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self.initTestCase()
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self.op_type = 'temporal_shift'
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self.python_api = F.temporal_shift
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self.use_xpu = True
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x = np.random.random(self.x_shape).astype(self.dtype)
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self.attrs = {
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"seg_num": self.seg_num,
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"shift_ratio": self.shift_ratio,
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"data_format": self.data_format,
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}
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self.inputs = {
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"X": x,
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}
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output = temporal_shift(
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x, self.seg_num, self.shift_ratio, self.data_format
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)
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self.outputs = {"Out": output}
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self.python_out_sig = ["Out"]
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def test_check_output(self):
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self.check_output(check_dygraph=False)
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def test_check_grad(self):
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self.check_grad(['X'], 'Out', check_dygraph=False)
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def initTestCase(self):
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self.x_shape = (6, 4, 4, 4)
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self.seg_num = 3
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self.shift_ratio = 0.25
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self.dtype = 'float32'
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self.data_format = 'NCHW'
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class TestXPUTemporalShift2(TestXPUTemporalShift):
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def initTestCase(self):
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self.x_shape = (1, 1, 1, 1)
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self.seg_num = 1
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self.shift_ratio = 0.1
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self.dtype = 'float32'
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self.data_format = 'NCHW'
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class TestXPUTemporalShift3(TestXPUTemporalShift):
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def initTestCase(self):
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self.x_shape = (4, 9, 1, 1)
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self.seg_num = 2
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self.shift_ratio = 0.2
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self.dtype = 'float32'
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self.data_format = 'NCHW'
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class TestXPUTemporalShift4(TestXPUTemporalShift):
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def initTestCase(self):
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self.x_shape = (4, 1, 10, 10)
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self.seg_num = 2
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self.shift_ratio = 0.3
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self.dtype = 'float32'
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self.data_format = 'NCHW'
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class TestXPUTemporalShift5(TestXPUTemporalShift):
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def initTestCase(self):
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self.x_shape = (1, 1, 1, 1)
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self.seg_num = 1
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self.shift_ratio = 0.3
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self.dtype = 'float32'
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self.data_format = 'NHWC'
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class TestXPUTemporalShift6(TestXPUTemporalShift):
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def initTestCase(self):
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self.x_shape = (6, 5, 5, 1)
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self.seg_num = 3
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self.shift_ratio = 0.25
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self.dtype = 'float32'
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self.data_format = 'NHWC'
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class TestXPUTemporalShift7(TestXPUTemporalShift):
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def initTestCase(self):
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self.x_shape = (9, 1, 1, 4)
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self.seg_num = 3
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self.shift_ratio = 0.45
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self.dtype = 'float32'
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self.data_format = 'NHWC'
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support_types = get_xpu_op_support_types('temporal_shift')
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for stype in support_types:
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create_test_class(globals(), XPUTestTemporalShiftOp, stype)
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if __name__ == "__main__":
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paddle.enable_static()
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unittest.main()
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