wehub-resource-sync
251 lines
7.3 KiB
Python
251 lines
7.3 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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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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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 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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def wrapper_temporal_shift(x, seg_num, shift_ratio=0.25, data_format="NCHW"):
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return paddle._C_ops.temporal_shift(x, seg_num, shift_ratio, data_format)
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class TestTemporalShift(OpTest):
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def setUp(self):
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self.initTestCase()
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self.init_dtype()
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self.op_type = 'temporal_shift'
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self.python_api = wrapper_temporal_shift
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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 init_dtype(self):
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self.dtype = 'float64'
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def test_check_output(self):
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self.check_output(check_pir=True)
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def test_check_grad_ignore_uv(self):
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self.check_grad(['X'], 'Out', check_pir=True)
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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.data_format = 'NCHW'
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class TestTemporalShift2(TestTemporalShift):
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def initTestCase(self):
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self.x_shape = (4, 9, 7, 7)
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self.seg_num = 2
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self.shift_ratio = 0.2
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self.data_format = 'NCHW'
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class TestTemporalShift3(TestTemporalShift):
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def initTestCase(self):
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self.x_shape = (3, 10, 5, 5)
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self.seg_num = 1
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self.shift_ratio = 0.3
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self.data_format = 'NCHW'
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class TestTemporalShift4(TestTemporalShift):
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def initTestCase(self):
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self.x_shape = (6, 5, 5, 4)
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self.seg_num = 3
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self.shift_ratio = 0.25
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self.data_format = 'NHWC'
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class TestTemporalShift_ZeroSize(TestTemporalShift):
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def initTestCase(self):
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self.x_shape = (0, 9, 7, 7)
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self.seg_num = 2
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self.shift_ratio = 0.2
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self.data_format = 'NCHW'
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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"core is not compiled with CUDA",
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)
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class TestTemporalShiftFP16(TestTemporalShift):
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def initTestCase(self):
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self.x_shape = (3, 10, 5, 5)
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self.seg_num = 1
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self.shift_ratio = 0.3
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self.dtype = 'float16'
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self.data_format = 'NCHW'
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def test_check_output(self):
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place = get_device_place()
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if core.is_float16_supported(place):
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self.check_output_with_place(place, check_pir=True)
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def test_check_grad_ignore_uv(self):
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place = get_device_place()
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if core.is_float16_supported(place):
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self.check_grad_with_place(place, ['X'], 'Out', check_pir=True)
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class TestTemporalShiftAPI(unittest.TestCase):
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def test_api(self):
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input = paddle.randn([6, 4, 2, 2])
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out_from_function = paddle.nn.functional.temporal_shift(
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x=input, seg_num=2, shift_ratio=0.2
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)
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# dygraph
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with paddle.base.dygraph.guard():
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input = paddle.randn([6, 4, 2, 2])
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out = paddle.nn.functional.temporal_shift(
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x=input, seg_num=2, shift_ratio=0.2
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)
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def test_static_fp16_gpu(self):
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if paddle.base.core.is_compiled_with_cuda() or is_custom_device():
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place = get_device_place()
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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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input = np.random.random([4, 4, 112, 112]).astype("float16")
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x = paddle.static.data(
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name="x", shape=[4, 4, 112, 112], dtype="float16"
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)
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y = paddle.nn.functional.temporal_shift(
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x=x, seg_num=2, shift_ratio=0.2
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)
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exe = paddle.static.Executor(place)
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res = exe.run(
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paddle.static.default_main_program(),
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feed={
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"x": input,
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},
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fetch_list=[y],
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)
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def test_error(self):
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def attr_data_format():
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input = paddle.randn([6, 4, 2, 2])
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out = paddle.nn.functional.temporal_shift(
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x=input, seg_num=2, shift_ratio=0.2, data_format="HWC"
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)
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self.assertRaises(ValueError, attr_data_format)
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class TestTemporalShiftFP16OP(TestTemporalShift):
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def init_dtype(self):
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self.dtype = np.float16
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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 and not support the bfloat16",
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)
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class TestTemporalShiftBF16(OpTest):
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def initTestCase(self):
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self.x_shape = (3, 10, 5, 5)
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self.seg_num = 1
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self.shift_ratio = 0.3
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self.dtype = np.uint16
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self.data_format = 'NCHW'
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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 = wrapper_temporal_shift
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x = np.random.random(self.x_shape).astype(np.float32)
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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": convert_float_to_uint16(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": convert_float_to_uint16(output)}
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self.python_out_sig = ["Out"]
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def test_check_output(self):
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place = get_device_place()
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self.check_output_with_place(place, check_pir=True)
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def test_check_grad_ignore_uv(self):
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place = get_device_place()
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self.check_grad_with_place(place, ['X'], 'Out', check_pir=True)
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if __name__ == "__main__":
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paddle.enable_static()
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unittest.main()
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