Remove fluid API (#1783)

Author: wanghaoshuang

ce_tests/dygraph/quant/src/save_quant_model.py

@@ -22,7 +22,7 @@
 import numpy as np
 import time
 import paddle
-from paddle.fluid.framework import IrGraph
+from paddle.framework import IrGraph
 from paddle.static.quantization import Quant2Int8MkldnnPass
 from paddle.framework import core
 
@@ -45,7 +45,8 @@ def parse_args():
         '--ops_to_quantize',
         type=str,
         default='',
-        help='A comma separated list of operators to quantize. Only quantizable operators are taken into account. If the option is not used, an attempt to quantize all quantizable operators will be made.'
+        help=
+        'A comma separated list of operators to quantize. Only quantizable operators are taken into account. If the option is not used, an attempt to quantize all quantizable operators will be made.'
     )
     parser.add_argument(
         '--op_ids_to_skip',
@@ -70,8 +71,9 @@ def transform_and_save_int8_model(original_path, save_path):
 
     with paddle.static.scope_guard(inference_scope):
         if os.path.exists(os.path.join(original_path, '__model__')):
-            [inference_program, feed_target_names, fetch_targets
-             ] = paddle.static.load_inference_model(original_path, exe)
+            [inference_program, feed_target_names,
+             fetch_targets] = paddle.static.load_inference_model(
+                 original_path, exe)
         else:
             [inference_program, feed_target_names,
              fetch_targets] = paddle.static.load_inference_model(

demo/darts/README.md

@@ -16,8 +16,6 @@
 │
 ├── search.py 模型结构搜索入口
 │
-├── train.py CIFAR10数据集评估训练入口
-│
 ├── train_imagenet.py ImageNet数据集评估训练入口
 │
 ├── visualize.py 模型结构可视化入口
@@ -67,14 +65,12 @@ python -m paddle.distributed.launch --selected_gpus=0,1,2,3 --log_dir ./mylog se
 在得到搜索结构Genotype之后，可以对其进行评估训练，从而获得它在特定数据集上的真实性能
 
 ```bash
-python train.py --arch='PC_DARTS'            # 在CIFAR10数据集上对搜索到的结构评估训练
 python train_imagenet.py --arch='PC_DARTS'   # 在ImageNet数据集上对搜索得到的结构评估训练
 ```
 
 同样，也支持用多卡进行评估训练, 以4卡为例(GPU id: 0-3), 启动命令如下：
 
 ```bash
-python -m paddle.distributed.launch --selected_gpus=0,1,2,3  --log_dir ./mylog train.py --use_data_parallel 1 --arch='DARTS_V2'
 python -m paddle.distributed.launch --selected_gpus=0,1,2,3  --log_dir ./mylog train_imagenet.py --use_data_parallel 1 --arch='DARTS_V2'
 ```
 

demo/darts/train.py

@@ -24,7 +24,6 @@
 import functools
 
 import paddle
-import paddle.fluid as fluid
 from paddleslim.common import AvgrageMeter, get_logger
 from paddleslim.nas.darts import count_parameters_in_MB
 
@@ -166,8 +165,8 @@ def main(args):
 
     device_num = paddle.distributed.parallel.ParallelEnv().nranks
     step_per_epoch = int(args.trainset_num / (args.batch_size * device_num))
-    learning_rate = paddle.optimizer.lr.ExponentialDecay(args.learning_rate,
-                                                         args.decay_rate)
+    learning_rate = paddle.optimizer.lr.ExponentialDecay(
+        args.learning_rate, args.decay_rate)
 
     clip = paddle.nn.ClipGradByGlobalNorm(args.grad_clip)
     optimizer = paddle.optimizer.Momentum(
@@ -181,24 +180,29 @@ def main(args):
         strategy = paddle.distributed.init_parallel_env()
         model = paddle.DataParallel(model, strategy)
 
-    train_loader = paddle.io.DataLoader.from_generator(
-        capacity=64, use_double_buffer=True, iterable=True, return_list=True)
-    valid_loader = paddle.io.DataLoader.from_generator(
-        capacity=64, use_double_buffer=True, iterable=True, return_list=True)
-
-    train_reader = paddle.batch(
-        reader.imagenet_reader(args.data_dir, 'train'),
+    train_dataset = reader.imagenet_reader(args.data_dir, 'train')
+    valid_dataset = reader.imagenet_reader(args.data_dir, 'val')
+    valid_loader = paddle.io.DataLoader(
+        valid_dataset,
         batch_size=args.batch_size,
-        drop_last=True)
-    valid_reader = paddle.batch(
-        reader.imagenet_reader(args.data_dir, 'val'),
-        batch_size=args.batch_size)
-    if args.use_data_parallel:
-        train_reader = fluid.contrib.reader.distributed_batch_reader(
-            train_reader)
-
-    train_loader.set_sample_list_generator(train_reader, places=place)
-    valid_loader.set_sample_list_generator(valid_reader, places=place)
+        use_buffer_reader=True,
+        return_list=True)
+
+    if not args.use_data_parallel:
+        train_loader = paddle.io.DataLoader(
+            train_dataset,
+            batch_size=args.batch_size,
+            use_buffer_reader=True,
+            return_list=True,
+            drop_last=True)
+    else:
+        sampler = paddle.io.DistributedBatchSampler(
+            train_dataset, args.batch_size, drop_last=True)
+        train_loader = paddle.io.DataLoader(
+            train_dataset,
+            batch_sampler=sampler,
+            use_buffer_reader=True,
+            return_list=True)
 
     save_parameters = (not args.use_data_parallel) or (
         args.use_data_parallel and