diff --git a/dygraph/benchmark/deeplabv3p.py b/dygraph/benchmark/deeplabv3p.py deleted file mode 100644 index 48a03e8cc8ee6381caede26659eea9c2a23f8d70..0000000000000000000000000000000000000000 --- a/dygraph/benchmark/deeplabv3p.py +++ /dev/null @@ -1,206 +0,0 @@ -# Copyright (c) 2020 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 argparse - -import paddle.fluid as fluid -from paddle.fluid.dygraph.parallel import ParallelEnv - -from dygraph.datasets import DATASETS -import dygraph.transforms as T -#from dygraph.models import MODELS -from dygraph.cvlibs import manager -from dygraph.utils import get_environ_info -from dygraph.utils import logger -from dygraph.core import train - - -def parse_args(): - parser = argparse.ArgumentParser(description='Model training') - - # params of model - parser.add_argument( - '--model_name', - dest='model_name', - help='Model type for training, which is one of {}'.format( - str(list(manager.MODELS.components_dict.keys()))), - type=str, - default='UNet') - - # params of dataset - parser.add_argument( - '--dataset', - dest='dataset', - help="The dataset you want to train, which is one of {}".format( - str(list(DATASETS.keys()))), - type=str, - default='OpticDiscSeg') - parser.add_argument( - '--dataset_root', - dest='dataset_root', - help="dataset root directory", - type=str, - default=None) - - # params of training - parser.add_argument( - "--input_size", - dest="input_size", - help="The image size for net inputs.", - nargs=2, - default=[512, 512], - type=int) - parser.add_argument( - '--iters', - dest='iters', - help='iters for training', - type=int, - default=10000) - parser.add_argument( - '--batch_size', - dest='batch_size', - help='Mini batch size of one gpu or cpu', - type=int, - default=2) - parser.add_argument( - '--learning_rate', - dest='learning_rate', - help='Learning rate', - type=float, - default=0.01) - parser.add_argument( - '--pretrained_model', - dest='pretrained_model', - help='The path of pretrained model', - type=str, - default=None) - parser.add_argument( - '--resume_model', - dest='resume_model', - help='The path of resume model', - type=str, - default=None) - parser.add_argument( - '--save_interval_iters', - dest='save_interval_iters', - help='The interval iters for save a model snapshot', - type=int, - default=1000) - parser.add_argument( - '--save_dir', - dest='save_dir', - help='The directory for saving the model snapshot', - type=str, - default='./output') - parser.add_argument( - '--num_workers', - dest='num_workers', - help='Num workers for data loader', - type=int, - default=0) - parser.add_argument( - '--do_eval', - dest='do_eval', - help='Eval while training', - action='store_true') - parser.add_argument( - '--log_iters', - dest='log_iters', - help='Display logging information at every log_iters', - default=10, - type=int) - parser.add_argument( - '--use_vdl', - dest='use_vdl', - help='Whether to record the data to VisualDL during training', - action='store_true') - - return parser.parse_args() - - -def main(args): - env_info = get_environ_info() - info = ['{}: {}'.format(k, v) for k, v in env_info.items()] - info = '\n'.join(['\n', format('Environment Information', '-^48s')] + info + - ['-' * 48]) - logger.info(info) - - places = fluid.CUDAPlace(ParallelEnv().dev_id) \ - if env_info['Paddle compiled with cuda'] and env_info['GPUs used'] \ - else fluid.CPUPlace() - - if args.dataset not in DATASETS: - raise Exception('`--dataset` is invalid. it should be one of {}'.format( - str(list(DATASETS.keys())))) - dataset = DATASETS[args.dataset] - - with fluid.dygraph.guard(places): - # Creat dataset reader - train_transforms = T.Compose([ - T.RandomHorizontalFlip(0.5), - T.ResizeStepScaling(0.5, 2.0, 0.25), - T.RandomPaddingCrop(args.input_size), - T.RandomDistort(), - T.Normalize(), - ]) - train_dataset = dataset( - dataset_root=args.dataset_root, - transforms=train_transforms, - mode='train') - - eval_dataset = None - if args.do_eval: - eval_transforms = T.Compose( - [T.Padding((2049, 1025)), - T.Normalize()]) - eval_dataset = dataset( - dataset_root=args.dataset_root, - transforms=eval_transforms, - mode='val') - - model = manager.MODELS[args.model_name]( - num_classes=train_dataset.num_classes) - - # Creat optimizer - # todo, may less one than len(loader) - num_iters_each_epoch = len(train_dataset) // ( - args.batch_size * ParallelEnv().nranks) - lr_decay = fluid.layers.polynomial_decay( - args.learning_rate, args.iters, end_learning_rate=0, power=0.9) - optimizer = fluid.optimizer.Momentum( - lr_decay, - momentum=0.9, - parameter_list=model.parameters(), - regularization=fluid.regularizer.L2Decay(regularization_coeff=4e-5)) - - train( - model, - train_dataset, - places=places, - eval_dataset=eval_dataset, - optimizer=optimizer, - save_dir=args.save_dir, - iters=args.iters, - batch_size=args.batch_size, - resume_model=args.resume_model, - save_interval_iters=args.save_interval_iters, - log_iters=args.log_iters, - num_classes=train_dataset.num_classes, - num_workers=args.num_workers, - use_vdl=args.use_vdl) - - -if __name__ == '__main__': - args = parse_args() - main(args) diff --git a/dygraph/benchmark/deeplabv3p.yml b/dygraph/benchmark/deeplabv3p.yml new file mode 100644 index 0000000000000000000000000000000000000000..a299bee30dbea80768d766e3473c6a7f6eb48254 --- /dev/null +++ b/dygraph/benchmark/deeplabv3p.yml @@ -0,0 +1,50 @@ +batch_size: 2 +iters: 500 + +train_dataset: + type: Cityscapes + dataset_root: data/cityscapes + transforms: + - type: ResizeStepScaling + min_scale_factor: 0.5 + max_scale_factor: 2.0 + scale_step_size: 0.25 + - type: RandomPaddingCrop + crop_size: [1024, 512] + - type: RandomHorizontalFlip + - type: RandomDistort + - type: Normalize + mode: train + +val_dataset: + type: Cityscapes + dataset_root: data/cityscapes + transforms: + - type: Normalize + mode: val + +model: + type: DeepLabV3P + backbone: + type: ResNet50_vd + output_stride: 8 + num_classes: 19 + backbone_indices: [0, 3] + aspp_ratios: [1, 12, 24, 36] + +optimizer: + type: sgd + weight_decay: 0.00004 + +learning_rate: + value: 0.01 + decay: + type: poly + power: 0.9 + end_lr: 0.0 + +loss: + types: + - type: CrossEntropyLoss + ignore_index: 255 + coef: [1] diff --git a/dygraph/benchmark/hrnet.py b/dygraph/benchmark/hrnet.py deleted file mode 100644 index 4e07856283906f361d9ceac0d03f7cef1fee518e..0000000000000000000000000000000000000000 --- a/dygraph/benchmark/hrnet.py +++ /dev/null @@ -1,205 +0,0 @@ -# Copyright (c) 2020 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 argparse - -import paddle.fluid as fluid -from paddle.fluid.dygraph.parallel import ParallelEnv - -from dygraph.datasets import DATASETS -import dygraph.transforms as T -#from dygraph.models import MODELS -from dygraph.cvlibs import manager -from dygraph.utils import get_environ_info -from dygraph.utils import logger -from dygraph.core import train - - -def parse_args(): - parser = argparse.ArgumentParser(description='Model training') - - # params of model - parser.add_argument( - '--model_name', - dest='model_name', - help='Model type for training, which is one of {}'.format( - str(list(manager.MODELS.components_dict.keys()))), - type=str, - default='UNet') - - # params of dataset - parser.add_argument( - '--dataset', - dest='dataset', - help="The dataset you want to train, which is one of {}".format( - str(list(DATASETS.keys()))), - type=str, - default='OpticDiscSeg') - parser.add_argument( - '--dataset_root', - dest='dataset_root', - help="dataset root directory", - type=str, - default=None) - - # params of training - parser.add_argument( - "--input_size", - dest="input_size", - help="The image size for net inputs.", - nargs=2, - default=[512, 512], - type=int) - parser.add_argument( - '--iters', - dest='iters', - help='iters for training', - type=int, - default=10000) - parser.add_argument( - '--batch_size', - dest='batch_size', - help='Mini batch size of one gpu or cpu', - type=int, - default=2) - parser.add_argument( - '--learning_rate', - dest='learning_rate', - help='Learning rate', - type=float, - default=0.01) - parser.add_argument( - '--pretrained_model', - dest='pretrained_model', - help='The path of pretrained model', - type=str, - default=None) - parser.add_argument( - '--resume_model', - dest='resume_model', - help='The path of resume model', - type=str, - default=None) - parser.add_argument( - '--save_interval_iters', - dest='save_interval_iters', - help='The interval iters for save a model snapshot', - type=int, - default=1000) - parser.add_argument( - '--save_dir', - dest='save_dir', - help='The directory for saving the model snapshot', - type=str, - default='./output') - parser.add_argument( - '--num_workers', - dest='num_workers', - help='Num workers for data loader', - type=int, - default=0) - parser.add_argument( - '--do_eval', - dest='do_eval', - help='Eval while training', - action='store_true') - parser.add_argument( - '--log_iters', - dest='log_iters', - help='Display logging information at every log_iters', - default=10, - type=int) - parser.add_argument( - '--use_vdl', - dest='use_vdl', - help='Whether to record the data to VisualDL during training', - action='store_true') - - return parser.parse_args() - - -def main(args): - env_info = get_environ_info() - info = ['{}: {}'.format(k, v) for k, v in env_info.items()] - info = '\n'.join(['\n', format('Environment Information', '-^48s')] + info + - ['-' * 48]) - logger.info(info) - - places = fluid.CUDAPlace(ParallelEnv().dev_id) \ - if env_info['Paddle compiled with cuda'] and env_info['GPUs used'] \ - else fluid.CPUPlace() - - if args.dataset not in DATASETS: - raise Exception('`--dataset` is invalid. it should be one of {}'.format( - str(list(DATASETS.keys())))) - dataset = DATASETS[args.dataset] - - with fluid.dygraph.guard(places): - # Creat dataset reader - train_transforms = T.Compose([ - T.RandomHorizontalFlip(0.5), - T.ResizeStepScaling(0.5, 2.0, 0.25), - T.RandomPaddingCrop(args.input_size), - T.RandomDistort(), - T.Normalize(), - ]) - train_dataset = dataset( - dataset_root=args.dataset_root, - transforms=train_transforms, - mode='train') - - eval_dataset = None - if args.do_eval: - eval_transforms = T.Compose([T.Normalize()]) - eval_dataset = dataset( - dataset_root=args.dataset_root, - transforms=eval_transforms, - mode='val') - - model = manager.MODELS[args.model_name]( - num_classes=train_dataset.num_classes, - pretrained_model=args.pretrained_model) - - # Creat optimizer - # todo, may less one than len(loader) - num_iters_each_epoch = len(train_dataset) // ( - args.batch_size * ParallelEnv().nranks) - lr_decay = fluid.layers.polynomial_decay( - args.learning_rate, args.iters, end_learning_rate=0, power=0.9) - optimizer = fluid.optimizer.Momentum( - lr_decay, - momentum=0.9, - parameter_list=model.parameters(), - regularization=fluid.regularizer.L2Decay(regularization_coeff=4e-5)) - - train( - model, - train_dataset, - places=places, - eval_dataset=eval_dataset, - optimizer=optimizer, - save_dir=args.save_dir, - iters=args.iters, - batch_size=args.batch_size, - resume_model=args.resume_model, - save_interval_iters=args.save_interval_iters, - log_iters=args.log_iters, - num_classes=train_dataset.num_classes, - num_workers=args.num_workers, - use_vdl=args.use_vdl) - - -if __name__ == '__main__': - args = parse_args() - main(args) diff --git a/dygraph/benchmark/hrnet.yml b/dygraph/benchmark/hrnet.yml new file mode 100644 index 0000000000000000000000000000000000000000..6bbfda73c116bf962dae78ee38a284bd44a7de67 --- /dev/null +++ b/dygraph/benchmark/hrnet.yml @@ -0,0 +1,48 @@ +batch_size: 2 +iters: 500 + +train_dataset: + type: Cityscapes + dataset_root: data/cityscapes + transforms: + - type: ResizeStepScaling + min_scale_factor: 0.5 + max_scale_factor: 2.0 + scale_step_size: 0.25 + - type: RandomPaddingCrop + crop_size: [1024, 512] + - type: RandomHorizontalFlip + - type: RandomDistort + - type: Normalize + mode: train + +val_dataset: + type: Cityscapes + dataset_root: data/cityscapes + transforms: + - type: Normalize + mode: val + +model: + type: FCN + backbone: + type: HRNet_W18 + num_classes: 19 + backbone_indices: [-1] + +optimizer: + type: sgd + weight_decay: 0.0005 + +learning_rate: + value: 0.01 + decay: + type: poly + power: 0.9 + end_lr: 0.0 + +loss: + types: + - type: CrossEntropyLoss + ignore_index: 255 + coef: [1] diff --git a/dygraph/configs/fcn_hrnet/fcn_hrnetw18_cityscapes_1024x512_100k.yml b/dygraph/configs/fcn_hrnet/fcn_hrnetw18_cityscapes_1024x512_80k.yml similarity index 74% rename from dygraph/configs/fcn_hrnet/fcn_hrnetw18_cityscapes_1024x512_100k.yml rename to dygraph/configs/fcn_hrnet/fcn_hrnetw18_cityscapes_1024x512_80k.yml index 031a831cccf5fb8b1e048e3417622ea8fff88a1f..410f938eae74af048d973ab34bba21b98e2ac015 100644 --- a/dygraph/configs/fcn_hrnet/fcn_hrnetw18_cityscapes_1024x512_100k.yml +++ b/dygraph/configs/fcn_hrnet/fcn_hrnetw18_cityscapes_1024x512_80k.yml @@ -4,10 +4,12 @@ model: type: FCN backbone: type: HRNet_W18 - pretrained: pretrained_model/hrnet_w18_imagenet + pretrained: pretrained_model/hrnet_w18_ssld num_classes: 19 pretrained: Null backbone_indices: [-1] optimizer: weight_decay: 0.0005 + +iters: 80000