add yaml of benchmark and hrnet_w18_cityscape (#416)

* update hrnet yaml

* add benchmark yaml

* update deeplabv3p.yml

dygraph/benchmark/deeplabv3p.py

-# 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)

dygraph/benchmark/deeplabv3p.yml

+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]

dygraph/benchmark/hrnet.py

-# 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)

dygraph/benchmark/hrnet.yml

+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]

dygraph/configs/fcn_hrnet/fcn_hrnetw18_cityscapes_1024x512_100k.yml → 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