# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest import numpy as np from op_test import ( OpTest, convert_float_to_uint16, get_device_place, is_custom_device, ) import paddle from paddle.base import core def temporal_shift(x, seg_num, shift_ratio, data_format): if data_format == "NHWC": x = np.transpose(x, (0, 3, 1, 2)) shape = x.shape reshape_x = x.reshape((-1, seg_num, shape[1], shape[2], shape[3])) pad_x = np.pad( reshape_x, ((0, 0), (1, 1), (0, 0), (0, 0), (0, 0)), 'constant' ) c1 = int(shape[1] * shift_ratio) c2 = int(shape[1] * 2 * shift_ratio) slice1 = pad_x[:, :seg_num, :c1, :, :] slice2 = pad_x[:, 2 : seg_num + 2, c1:c2, :, :] slice3 = pad_x[:, 1 : seg_num + 1, c2:, :, :] concat_x = np.concatenate([slice1, slice2, slice3], axis=2) out = concat_x.reshape(shape) if data_format == "NHWC": out = np.transpose(out, (0, 2, 3, 1)) return out def wrapper_temporal_shift(x, seg_num, shift_ratio=0.25, data_format="NCHW"): return paddle._C_ops.temporal_shift(x, seg_num, shift_ratio, data_format) class TestTemporalShift(OpTest): def setUp(self): self.initTestCase() self.init_dtype() self.op_type = 'temporal_shift' self.python_api = wrapper_temporal_shift x = np.random.random(self.x_shape).astype(self.dtype) self.attrs = { "seg_num": self.seg_num, "shift_ratio": self.shift_ratio, "data_format": self.data_format, } self.inputs = { "X": x, } output = temporal_shift( x, self.seg_num, self.shift_ratio, self.data_format ) self.outputs = {"Out": output} self.python_out_sig = ["Out"] def init_dtype(self): self.dtype = 'float64' def test_check_output(self): self.check_output(check_pir=True) def test_check_grad_ignore_uv(self): self.check_grad(['X'], 'Out', check_pir=True) def initTestCase(self): self.x_shape = (6, 4, 4, 4) self.seg_num = 3 self.shift_ratio = 0.25 self.data_format = 'NCHW' class TestTemporalShift2(TestTemporalShift): def initTestCase(self): self.x_shape = (4, 9, 7, 7) self.seg_num = 2 self.shift_ratio = 0.2 self.data_format = 'NCHW' class TestTemporalShift3(TestTemporalShift): def initTestCase(self): self.x_shape = (3, 10, 5, 5) self.seg_num = 1 self.shift_ratio = 0.3 self.data_format = 'NCHW' class TestTemporalShift4(TestTemporalShift): def initTestCase(self): self.x_shape = (6, 5, 5, 4) self.seg_num = 3 self.shift_ratio = 0.25 self.data_format = 'NHWC' class TestTemporalShift_ZeroSize(TestTemporalShift): def initTestCase(self): self.x_shape = (0, 9, 7, 7) self.seg_num = 2 self.shift_ratio = 0.2 self.data_format = 'NCHW' @unittest.skipIf( not (core.is_compiled_with_cuda() or is_custom_device()), "core is not compiled with CUDA", ) class TestTemporalShiftFP16(TestTemporalShift): def initTestCase(self): self.x_shape = (3, 10, 5, 5) self.seg_num = 1 self.shift_ratio = 0.3 self.dtype = 'float16' self.data_format = 'NCHW' def test_check_output(self): place = get_device_place() if core.is_float16_supported(place): self.check_output_with_place(place, check_pir=True) def test_check_grad_ignore_uv(self): place = get_device_place() if core.is_float16_supported(place): self.check_grad_with_place(place, ['X'], 'Out', check_pir=True) class TestTemporalShiftAPI(unittest.TestCase): def test_api(self): input = paddle.randn([6, 4, 2, 2]) out_from_function = paddle.nn.functional.temporal_shift( x=input, seg_num=2, shift_ratio=0.2 ) # dygraph with paddle.base.dygraph.guard(): input = paddle.randn([6, 4, 2, 2]) out = paddle.nn.functional.temporal_shift( x=input, seg_num=2, shift_ratio=0.2 ) def test_static_fp16_gpu(self): if paddle.base.core.is_compiled_with_cuda() or is_custom_device(): place = get_device_place() with paddle.static.program_guard( paddle.static.Program(), paddle.static.Program() ): input = np.random.random([4, 4, 112, 112]).astype("float16") x = paddle.static.data( name="x", shape=[4, 4, 112, 112], dtype="float16" ) y = paddle.nn.functional.temporal_shift( x=x, seg_num=2, shift_ratio=0.2 ) exe = paddle.static.Executor(place) res = exe.run( paddle.static.default_main_program(), feed={ "x": input, }, fetch_list=[y], ) def test_error(self): def attr_data_format(): input = paddle.randn([6, 4, 2, 2]) out = paddle.nn.functional.temporal_shift( x=input, seg_num=2, shift_ratio=0.2, data_format="HWC" ) self.assertRaises(ValueError, attr_data_format) class TestTemporalShiftFP16OP(TestTemporalShift): def init_dtype(self): self.dtype = np.float16 @unittest.skipIf( not (core.is_compiled_with_cuda() or is_custom_device()) or not core.is_bfloat16_supported(get_device_place()), "core is not compiled with CUDA and not support the bfloat16", ) class TestTemporalShiftBF16(OpTest): def initTestCase(self): self.x_shape = (3, 10, 5, 5) self.seg_num = 1 self.shift_ratio = 0.3 self.dtype = np.uint16 self.data_format = 'NCHW' def setUp(self): self.initTestCase() self.op_type = 'temporal_shift' self.python_api = wrapper_temporal_shift x = np.random.random(self.x_shape).astype(np.float32) self.attrs = { "seg_num": self.seg_num, "shift_ratio": self.shift_ratio, "data_format": self.data_format, } self.inputs = { "X": convert_float_to_uint16(x), } output = temporal_shift( x, self.seg_num, self.shift_ratio, self.data_format ) self.outputs = {"Out": convert_float_to_uint16(output)} self.python_out_sig = ["Out"] def test_check_output(self): place = get_device_place() self.check_output_with_place(place, check_pir=True) def test_check_grad_ignore_uv(self): place = get_device_place() self.check_grad_with_place(place, ['X'], 'Out', check_pir=True) if __name__ == "__main__": paddle.enable_static() unittest.main()