Refine reader config (#266)

* refine reader config

demo/darts/README.md

@@ -42,6 +42,7 @@ python search.py                       # DARTS一阶近似搜索方法
 python search.py --unrolled=True       # DARTS的二阶近似搜索方法
 python search.py --method='PC-DARTS' --batch_size=256 --learning_rate=0.1 --arch_learning_rate=6e-4 --epochs_no_archopt=15   # PC-DARTS搜索方法
 ```
+如果您使用的是docker环境，请确保共享内存足够使用多进程的dataloader，如果碰到共享内存问题，请设置`--use_multiprocess=False`
 
 也可以使用多卡进行模型结构搜索，以4卡为例(GPU id: 0-3), 启动命令如下:
 

demo/darts/reader.py

@@ -140,32 +140,10 @@ def train_search(batch_size, train_portion, is_shuffle, args):
     split_point = int(np.floor(train_portion * len(datasets)))
     train_datasets = datasets[:split_point]
     val_datasets = datasets[split_point:]
-    train_readers = []
-    val_readers = []
-    n = int(math.ceil(len(train_datasets) // args.num_workers)
-            ) if args.use_multiprocess else len(train_datasets)
-    train_datasets_lists = [
-        train_datasets[i:i + n] for i in range(0, len(train_datasets), n)
-    ]
-    val_datasets_lists = [
-        val_datasets[i:i + n] for i in range(0, len(val_datasets), n)
-    ]
-
-    for pid in range(len(train_datasets_lists)):
-        train_readers.append(
-            reader_generator(train_datasets_lists[pid], batch_size, True, True,
-                             args))
-        val_readers.append(
-            reader_generator(val_datasets_lists[pid], batch_size, True, True,
-                             args))
-    if args.use_multiprocess:
     reader = [
-            paddle.reader.multiprocess_reader(train_readers, False),
-            paddle.reader.multiprocess_reader(val_readers, False)
+        reader_generator(train_datasets, batch_size, True, True, args),
+        reader_generator(val_datasets, batch_size, True, True, args)
     ]
-    else:
-        reader = [train_readers[0], val_readers[0]]
-
     return reader
 
 
@@ -174,18 +152,8 @@ def train_valid(batch_size, is_train, is_shuffle, args):
     datasets = cifar10_reader(
         paddle.dataset.common.download(CIFAR10_URL, 'cifar', CIFAR10_MD5),
         name, is_shuffle, args)
-    n = int(math.ceil(len(datasets) // args.
-                      num_workers)) if args.use_multiprocess else len(datasets)
-    datasets_lists = [datasets[i:i + n] for i in range(0, len(datasets), n)]
-    multi_readers = []
-    for pid in range(len(datasets_lists)):
-        multi_readers.append(
-            reader_generator(datasets_lists[pid], batch_size, is_train,
-                             is_shuffle, args))
-    if args.use_multiprocess:
-        reader = paddle.reader.multiprocess_reader(multi_readers, False)
-    else:
-        reader = multi_readers[0]
+
+    reader = reader_generator(datasets, batch_size, is_train, is_shuffle, args)
     return reader
 
 

demo/darts/search.py

@@ -35,8 +35,7 @@ add_arg = functools.partial(add_arguments, argparser=parser)
 
 # yapf: disable
 add_arg('log_freq',          int,   50,              "Log frequency.")
-add_arg('use_multiprocess',  bool,  False,            "Whether use multiprocess reader.")
-add_arg('num_workers',       int,   4,               "The multiprocess reader number.")
+add_arg('use_multiprocess',  bool,  True,            "Whether use multiprocess reader.")
 add_arg('data',              str,   'dataset/cifar10',"The dir of dataset.")
 add_arg('batch_size',        int,   64,              "Minibatch size.")
 add_arg('learning_rate',     float, 0.025,            "The start learning rate.")
@@ -88,6 +87,7 @@ def main(args):
             unrolled=args.unrolled,
             num_epochs=args.epochs,
             epochs_no_archopt=args.epochs_no_archopt,
+            use_multiprocess=args.use_multiprocess,
             use_data_parallel=args.use_data_parallel,
             save_dir=args.model_save_dir,
             log_freq=args.log_freq)

demo/darts/train.py

@@ -39,8 +39,7 @@ parser = argparse.ArgumentParser(description=__doc__)
 add_arg = functools.partial(add_arguments, argparser=parser)
 
 # yapf: disable
-add_arg('use_multiprocess',  bool,  False,            "Whether use multiprocess reader.")
-add_arg('num_workers',       int,   4,               "The multiprocess reader number.")
+add_arg('use_multiprocess',  bool,  True,            "Whether use multiprocess reader.")
 add_arg('data',              str,   'dataset/cifar10',"The dir of dataset.")
 add_arg('batch_size',        int,   96,              "Minibatch size.")
 add_arg('learning_rate',     float, 0.025,           "The start learning rate.")
@@ -170,17 +169,17 @@ def main(args):
             model = fluid.dygraph.parallel.DataParallel(model, strategy)
 
         train_loader = fluid.io.DataLoader.from_generator(
-            capacity=1024,
+            capacity=64,
             use_double_buffer=True,
             iterable=True,
             return_list=True,
-            use_multiprocess=True)
+            use_multiprocess=args.use_multiprocess)
         valid_loader = fluid.io.DataLoader.from_generator(
-            capacity=1024,
+            capacity=64,
             use_double_buffer=True,
             iterable=True,
             return_list=True,
-            use_multiprocess=True)
+            use_multiprocess=args.use_multiprocess)
 
         train_reader = reader.train_valid(
             batch_size=args.batch_size,

docs/zh_cn/api_cn/darts.rst

@@ -4,7 +4,7 @@
 DARTSearch
 ---------
 
-.. py:class:: paddleslim.nas.DARTSearch(model, train_reader, valid_reader, place, learning_rate=0.025, batchsize=64, num_imgs=50000, arch_learning_rate=3e-4, unrolled=False, num_epochs=50, epochs_no_archopt=0, use_data_parallel=False, save_dir='./', log_freq=50)
+.. py:class:: paddleslim.nas.DARTSearch(model, train_reader, valid_reader, place, learning_rate=0.025, batchsize=64, num_imgs=50000, arch_learning_rate=3e-4, unrolled=False, num_epochs=50, epochs_no_archopt=0, use_multiprocess=False, use_data_parallel=False, save_dir='./', log_freq=50)
 
 `源代码 <https://github.com/PaddlePaddle/PaddleSlim/blob/release/1.1.0/paddleslim/nas/darts/train_search.py>`_
 
@@ -18,11 +18,14 @@ DARTSearch
 - **place** (fluid.CPUPlace()|fluid.CUDAPlace(N))-该参数表示程序运行在何种设备上，这里的N为GPU对应的ID
 - **learning_rate** (float)-模型参数的初始学习率。默认值：0.025。
 - **batchsize** (int)-搜索过程数据的批大小。默认值：64。
+- **num_imgs** (int)-数据集总样本数。默认值：50000。
 - **arch_learning_rate** (float)-架构参数的学习率。默认值：3e-4。
 - **unrolled** (bool)-是否使用二阶搜索算法。默认值：False。
 - **num_epochs** (int)-搜索训练的轮数。默认值：50。
 - **epochs_no_archopt** (int)-跳过前若干轮的模型架构参数优化。默认值：0。
+- **use_multiprocess** (bool)-是否使用多进程的dataloader。默认值：False。
 - **use_data_parallel** (bool)-是否使用数据并行的多卡训练。默认值：False。
+- **save_dir** (str)-模型参数保存目录。默认值：'./'。
 - **log_freq** (int)-每多少步输出一条log。默认值：50。
 
 

paddleslim/nas/darts/train_search.py

@@ -59,6 +59,7 @@ class DARTSearch(object):
         unrolled(bool): Use one-step unrolled validation loss. Default: False.
         num_epochs(int): Epoch number. Default: 50.
         epochs_no_archopt(int): Epochs skip architecture optimize at begining. Default: 0.
+        use_multiprocess(bool): Whether to use multiprocess in dataloader. Default: False.
         use_data_parallel(bool): Whether to use data parallel mode. Default: False.
         log_freq(int): Log frequency. Default: 50.
 
@@ -76,6 +77,7 @@ class DARTSearch(object):
                  unrolled=False,
                  num_epochs=50,
                  epochs_no_archopt=0,
+                 use_multiprocess=False,
                  use_data_parallel=False,
                  save_dir='./',
                  log_freq=50):
@@ -90,6 +92,7 @@ class DARTSearch(object):
         self.unrolled = unrolled
         self.epochs_no_archopt = epochs_no_archopt
         self.num_epochs = num_epochs
+        self.use_multiprocess = use_multiprocess
         self.use_data_parallel = use_data_parallel
         self.save_dir = save_dir
         self.log_freq = log_freq
@@ -207,17 +210,17 @@ class DARTSearch(object):
                 self.valid_reader)
 
         train_loader = fluid.io.DataLoader.from_generator(
-            capacity=1024,
+            capacity=64,
             use_double_buffer=True,
             iterable=True,
             return_list=True,
-            use_multiprocess=True)
+            use_multiprocess=self.use_multiprocess)
         valid_loader = fluid.io.DataLoader.from_generator(
-            capacity=1024,
+            capacity=64,
             use_double_buffer=True,
             iterable=True,
             return_list=True,
-            use_multiprocess=True)
+            use_multiprocess=self.use_multiprocess)
 
         train_loader.set_batch_generator(self.train_reader, places=self.place)
         valid_loader.set_batch_generator(self.valid_reader, places=self.place)