# Copyright (c) 2021 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. from __future__ import print_function import unittest import numpy as np import paddle paddle.enable_static() import paddle.fluid.core as core import paddle.fluid as fluid from op_test import OpTest from paddle.fluid import Program, program_guard class TestConv2DAPI(unittest.TestCase): def test_api(self): input_NHWC = fluid.layers.data(name="input_NHWC", shape=[2, 5, 5, 3], append_batch_size=False, dtype="float32") input_NCHW = fluid.layers.data(name="input_NCHW", shape=[2, 3, 5, 5], append_batch_size=False, dtype="float32") fluid.layers.conv2d(input=input_NHWC, num_filters=3, filter_size=[3, 3], stride=[1, 1], padding=0, dilation=[1, 1], groups=1, data_format="NCHW") fluid.layers.conv2d(input=input_NCHW, num_filters=3, filter_size=[3, 3], stride=[1, 1], padding=[1, 2, 1, 0], dilation=[1, 1], groups=1, data_format="NCHW") fluid.layers.conv2d(input=input_NCHW, num_filters=3, filter_size=[3, 3], stride=[1, 1], padding=[[0, 0], [0, 0], [1, 1], [1, 1]], dilation=[1, 1], groups=1, data_format="NCHW") fluid.layers.conv2d(input=input_NHWC, num_filters=3, filter_size=[3, 3], stride=[1, 1], padding=[[0, 0], [1, 1], [1, 1], [0, 0]], dilation=[1, 1], groups=1, data_format="NHWC") fluid.layers.conv2d(input=input_NCHW, num_filters=3, filter_size=[3, 3], stride=[1, 1], padding="SAME", dilation=[1, 1], groups=1, data_format="NCHW") fluid.layers.conv2d(input=input_NCHW, num_filters=3, filter_size=[3, 3], stride=[1, 1], padding="VALID", dilation=[1, 1], groups=1, data_format="NCHW") def test_depthwise_conv2d(self): x_var = paddle.uniform((2, 8, 8, 4), dtype='float32', min=-1., max=1.) conv = paddle.nn.Conv2D(in_channels=4, out_channels=4, kernel_size=(3, 3), groups=4, data_format='NHWC') y_var = conv(x_var) class TestConv2DAPI_Error(unittest.TestCase): def test_api(self): input = fluid.layers.data(name="input", shape=[2, 5, 5, 5], append_batch_size=False, dtype="float32") # ValueError: cudnn def run_1(): fluid.layers.conv2d(input=input, num_filters=3, filter_size=[3, 3], stride=[1, 1], padding=0, dilation=[1, 1], groups=1, use_cudnn=[0], data_format="NCHW") self.assertRaises(ValueError, run_1) # ValueError: data_format def run_2(): fluid.layers.conv2d(input=input, num_filters=3, filter_size=[3, 3], stride=[1, 1], padding=0, dilation=[1, 1], groups=1, use_cudnn=False, data_format="NCHWC") self.assertRaises(ValueError, run_2) # ValueError: padding def run_3(): fluid.layers.conv2d(input=input, num_filters=3, filter_size=[3, 3], stride=[1, 1], padding="SAMEE", dilation=[1, 1], groups=1, use_cudnn=False, data_format="NCHW") self.assertRaises(ValueError, run_3) def run_4(): fluid.layers.conv2d(input=input, num_filters=3, filter_size=[3, 3], stride=[1, 1], padding=[[0, 1], [0, 1], [0, 1], [0, 1]], dilation=[1, 1], groups=1, use_cudnn=False, data_format="NCHW") self.assertRaises(ValueError, run_4) def run_5(): fluid.layers.conv2d(input=input, num_filters=3, filter_size=[3, 3], stride=[1, 1], padding=[[0, 1], [0, 1], [0, 1], [0, 1]], dilation=[1, 1], groups=1, use_cudnn=False, data_format="NHWC") self.assertRaises(ValueError, run_5) # ValueError: channel dimmention x = fluid.layers.data(name="x", shape=[2, 5, 5, -1], append_batch_size=False, dtype="float32") def run_6(): fluid.layers.conv2d(input=x, num_filters=3, filter_size=[3, 3], stride=[1, 1], padding=0, dilation=[1, 1], groups=1, use_cudnn=False, data_format="NHWC") self.assertRaises(ValueError, run_6) # ValueError: groups def run_7(): fluid.layers.conv2d(input=input, num_filters=3, filter_size=[3, 3], stride=[1, 1], padding=0, dilation=[1, 1], groups=3, use_cudnn=False, data_format="NHWC") self.assertRaises(ValueError, run_7) # ValueError: filter num def run_8(): fluid.layers.conv2d(input=input, num_filters=0, filter_size=0, stride=0, padding=0, dilation=0, groups=1, use_cudnn=False, data_format="NCHW") self.assertRaises(ValueError, run_8) # ValueError: groups def run_9(): fluid.layers.conv2d(input=input, num_filters=0, filter_size=0, stride=0, padding=0, dilation=0, groups=0, use_cudnn=False, data_format="NCHW") self.assertRaises(ValueError, run_9) # ValueError: stride def run_10(): fluid.layers.conv2d(input=input, num_filters=1, filter_size=1, stride=0, padding=0, dilation=0, groups=1, use_cudnn=False, data_format="NCHW") self.assertRaises(ValueError, run_10) def test_api_with_error_input(self): input = fluid.layers.data(name="error_input", shape=[1], append_batch_size=False, dtype="float32") # ValueError: cudnn def run_1(): fluid.layers.conv2d(input=input, num_filters=0, filter_size=0, stride=0, padding=0, dilation=0, groups=0, use_cudnn=False, data_format="NCHW") self.assertRaises(ValueError, run_1) # --------- test environment variable ------ @unittest.skipIf( not (core.is_compiled_with_cuda() or core.is_compiled_with_rocm()), "core is not compiled with CUDA or ROCM") class TestConv2DEnviron(unittest.TestCase): def run1(self, place): with fluid.program_guard(fluid.Program(), fluid.Program()): inputs = fluid.layers.data(shape=[2, 3, 5, 5], append_batch_size=False, name="inputs", dtype="float32") result = fluid.layers.conv2d(input=inputs, num_filters=4, filter_size=[3, 3], stride=[1, 1], padding=0, dilation=[1, 1], groups=1, data_format="NCHW") exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) fetches = exe.run(fluid.default_main_program(), feed={"inputs": self.input_np}, fetch_list=[result]) def run2(self, place): with fluid.dygraph.guard(place): inputs = fluid.dygraph.to_variable(self.input_np) conv = paddle.nn.Conv2D(in_channels=3, out_channels=4, kernel_size=(3, 3), data_format="NCHW") result = conv(inputs) def run3(self, place): with fluid.dygraph.guard(place): inputs = fluid.dygraph.to_variable(self.input_np) conv = paddle.fluid.dygraph.nn.Conv2D( num_channels=3, num_filters=4, filter_size=(3, 3), ) result = conv(inputs) def run_all(self, place): self.run1(place) self.run2(place) self.run3(place) def test_environ(self): self.input_np = np.random.random([2, 3, 5, 5]).astype("float32") for place in [paddle.CPUPlace(), paddle.CUDAPlace(0)]: fluid.set_flags({'FLAGS_conv2d_disable_cudnn': False}) self.run_all(place) fluid.set_flags({'FLAGS_conv2d_disable_cudnn': True}) self.run_all(place) if __name__ == '__main__': unittest.main()