update benchmark

dygraph/benchmark/deeplabv3p.py

@@ -13,22 +13,14 @@
 # limitations under the License.
 
 import argparse
-import os
-import sys
 
 import paddle.fluid as fluid
 from paddle.fluid.dygraph.parallel import ParallelEnv
-from paddle.fluid.io import DataLoader
-from paddle.incubate.hapi.distributed import DistributedBatchSampler
 
-from datasets import OpticDiscSeg, Cityscapes
+from datasets import DATASETS
 import transforms as T
 from models import MODELS
-import utils.logging as logging
 from utils import get_environ_info
-from utils import load_pretrained_model
-from utils import resume
-from utils import Timer, calculate_eta
 from core import train
 
 
@@ -48,10 +40,16 @@ def parse_args():
     parser.add_argument(
         '--dataset',
         dest='dataset',
-        help=
-        "The dataset you want to train, which is one of ('OpticDiscSeg', 'Cityscapes')",
+        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(
@@ -135,36 +133,38 @@ def main(args):
         if env_info['place'] == 'cuda' and fluid.is_compiled_with_cuda() \
         else fluid.CPUPlace()
 
-    if args.dataset.lower() == 'opticdiscseg':
-        dataset = OpticDiscSeg
-    elif args.dataset.lower() == 'cityscapes':
-        dataset = Cityscapes
-    else:
-        raise Exception(
-            "The --dataset set wrong. It should be one of ('OpticDiscSeg', 'Cityscapes')"
-        )
+    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.RandomHorizontalFlip(),
-            T.Normalize()
+            T.RandomDistort(),
+            T.Normalize(),
         ])
-        train_dataset = dataset(transforms=train_transforms, mode='train')
+        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(transforms=eval_transforms, mode='eval')
+                 T.Normalize()])
+            eval_dataset = dataset(
+                dataset_root=args.dataset_root,
+                transforms=eval_transforms,
+                mode='val')
 
         if args.model_name not in MODELS:
             raise Exception(
-                '--model_name is invalid. it should be one of {}'.format(
+                '`--model_name` is invalid. it should be one of {}'.format(
                     str(list(MODELS.keys()))))
         model = MODELS[args.model_name](num_classes=train_dataset.num_classes)
 
@@ -174,17 +174,13 @@ def main(args):
             args.batch_size * ParallelEnv().nranks)
         decay_step = args.num_epochs * num_steps_each_epoch
         lr_decay = fluid.layers.polynomial_decay(
-            args.learning_rate, decay_step, end_learning_rate=0.00001, power=0.9)
-        
-        
+            args.learning_rate, decay_step, end_learning_rate=0, power=0.9)
         optimizer = fluid.optimizer.Momentum(
             lr_decay,
             momentum=0.9,
             parameter_list=model.parameters(),
-            #parameter_list=filter(lambda p: p.trainable, model.parameters()),
             regularization=fluid.regularizer.L2Decay(regularization_coeff=4e-5))
 
-
         train(
             model,
             train_dataset,

dygraph/benchmark/hrnet_w18_benchmark.py → dygraph/benchmark/hrnet.py

@@ -13,22 +13,15 @@
 # limitations under the License.
 
 import argparse
-import os
 
 import paddle.fluid as fluid
 from paddle.fluid.dygraph.parallel import ParallelEnv
-from paddle.fluid.io import DataLoader
-from paddle.incubate.hapi.distributed import DistributedBatchSampler
 
-from datasets import OpticDiscSeg, Cityscapes
+from datasets import DATASETS
 import transforms as T
 from models import MODELS
-import utils.logging as logging
 from utils import get_environ_info
-from utils import load_pretrained_model
-from utils import resume
-from utils import Timer, calculate_eta
-from core import train, evaluate
+from core import train
 
 
 def parse_args():
@@ -47,10 +40,16 @@ def parse_args():
     parser.add_argument(
         '--dataset',
         dest='dataset',
-        help=
-        "The dataset you want to train, which is one of ('OpticDiscSeg', 'Cityscapes')",
+        help="The dataset you want to train, which is one of {}".format(
+            str(list(DATASETS.keys()))),
         type=str,
-        default='Cityscapes')
+        default='OpticDiscSeg')
+    parser.add_argument(
+        '--dataset_root',
+        dest='dataset_root',
+        help="dataset root directory",
+        type=str,
+        default=None)
 
     # params of training
     parser.add_argument(
@@ -58,14 +57,14 @@ def parse_args():
         dest="input_size",
         help="The image size for net inputs.",
         nargs=2,
-        default=[1024, 512],
+        default=[512, 512],
         type=int)
     parser.add_argument(
         '--num_epochs',
         dest='num_epochs',
         help='Number epochs for training',
         type=int,
-        default=500)
+        default=100)
     parser.add_argument(
         '--batch_size',
         dest='batch_size',
@@ -107,7 +106,7 @@ def parse_args():
         dest='num_workers',
         help='Num workers for data loader',
         type=int,
-        default=2)
+        default=0)
     parser.add_argument(
         '--do_eval',
         dest='do_eval',
@@ -134,14 +133,10 @@ def main(args):
         if env_info['place'] == 'cuda' and fluid.is_compiled_with_cuda() \
         else fluid.CPUPlace()
 
-    if args.dataset.lower() == 'opticdiscseg':
-        dataset = OpticDiscSeg
-    elif args.dataset.lower() == 'cityscapes':
-        dataset = Cityscapes
-    else:
-        raise Exception(
-            "The --dataset set wrong. It should be one of ('OpticDiscSeg', 'Cityscapes')"
-        )
+    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
@@ -152,16 +147,22 @@ def main(args):
             T.RandomDistort(),
             T.Normalize(),
         ])
-        train_dataset = dataset(transforms=train_transforms, mode='train')
+        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(transforms=eval_transforms, mode='eval')
+            eval_dataset = dataset(
+                dataset_root=args.dataset_root,
+                transforms=eval_transforms,
+                mode='val')
 
         if args.model_name not in MODELS:
             raise Exception(
-                '--model_name is invalid. it should be one of {}'.format(
+                '`--model_name` is invalid. it should be one of {}'.format(
                     str(list(MODELS.keys()))))
         model = MODELS[args.model_name](num_classes=train_dataset.num_classes)
 
@@ -176,7 +177,8 @@ def main(args):
             lr_decay,
             momentum=0.9,
             parameter_list=model.parameters(),
-            regularization=fluid.regularizer.L2Decay(regularization_coeff=5e-4))
+            regularization=fluid.regularizer.L2Decay(regularization_coeff=4e-5))
+
         train(
             model,
             train_dataset,