# Copyright (c) 2022 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 import paddle import paddle.static from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest class TestBase(IPUOpTest): def setUp(self): self.set_atol() self.set_training() self.set_feed() self.set_op_attrs() def set_feed(self): data = np.random.uniform(size=[5, 4, 2, 3]) self.feed_fp32 = {'x': data.astype(np.float32)} self.feed_fp16 = {'x': data.astype(np.float16)} self.feed_shape = [x.shape for x in self.feed_fp32.values()] self.feed_list = list(self.feed_fp32.keys()) def set_op_attrs(self): self.attrs = {"pad": [1, 2, 3, 4]} @IPUOpTest.static_graph def build_model(self): x = paddle.static.data(name=self.feed_list[0], shape=self.feed_shape[0], dtype='float32') pad = paddle.nn.functional.pad(x, **self.attrs) self.fetch_list = [pad.name] def run_model(self, exec_mode): self.run_op_test(exec_mode) def test(self): for m in IPUOpTest.ExecutionMode: if not self.skip_mode(m): self.build_model() self.run_model(m) self.check() @unittest.skip("Do not support `pad` as a tensor") class TestCase1(TestBase): def set_op_attrs(self): self.attrs = {} @IPUOpTest.static_graph def build_model(self): x = paddle.static.data(name=self.feed_list[0], shape=self.feed_shape[0], dtype='float32') const_attrs = { 'name': 'y', 'shape': [4], 'dtype': 'int32', 'value': 2, } y = paddle.fluid.layers.fill_constant(**const_attrs) pad = paddle.nn.functional.pad(x, pad=y) self.fetch_list = [pad.name] class TestCase2(TestBase): def set_op_attrs(self): self.attrs = {"pad": [2, 5], "data_format": "NCL"} def set_feed(self): data = np.random.uniform(size=[4, 2, 3]) self.feed_fp32 = {'x': data.astype(np.float32)} self.feed_fp16 = {'x': data.astype(np.float16)} self.feed_shape = [x.shape for x in self.feed_fp32.values()] self.feed_list = list(self.feed_fp32.keys()) class TestCase3(TestBase): def set_op_attrs(self): self.attrs = {"pad": [2, 5, 2, 3, 6, 3], "data_format": "NCDHW"} def set_feed(self): data = np.random.uniform(size=[2, 3, 4, 2, 3]) self.feed_fp32 = {'x': data.astype(np.float32)} self.feed_fp16 = {'x': data.astype(np.float16)} self.feed_shape = [x.shape for x in self.feed_fp32.values()] self.feed_list = list(self.feed_fp32.keys()) class TestCase4(TestBase): def set_op_attrs(self): self.attrs = {"pad": [2, 2, 1, 1], "mode": "reflect"} @unittest.skip("replicate mode is not supported") class TestCase5(TestBase): def set_op_attrs(self): self.attrs = {"pad": [1, 2, 3, 4], "mode": "replicate"} @unittest.skip("circular mode is not supported") class TestCase6(TestBase): def set_op_attrs(self): self.attrs = {"pad": [1, 2, 3, 4], "mode": "circular"} @unittest.skip("Only support NCL, NCHW, NCDHW") class TestCase7(TestBase): def set_op_attrs(self): self.attrs = {"pad": [1, 2], "data_format": "NLC"} @unittest.skip("Only support NCL, NCHW, NCDHW") class TestCase7(TestBase): def set_op_attrs(self): self.attrs = {"pad": [1, 2, 3, 4], "data_format": "NHWC"} @unittest.skip("Only support NCL, NCHW, NCDHW") class TestCase7(TestBase): def set_op_attrs(self): self.attrs = {"pad": [1, 2, 3, 4, 1, 3], "data_format": "NDHWC"} if __name__ == "__main__": unittest.main()