replace datafeeder with dataloader

demo/quant/pact_quant_aware/train.py

@@ -15,6 +15,7 @@ from paddleslim.analysis import flops
 from paddleslim.quant import quant_aware, quant_post, convert
 import models
 from utility import add_arguments, print_arguments
+sys.path.append('./')
 from pact import *
 quantization_model_save_dir = './quantization_models/'
 
@@ -133,8 +134,8 @@ def compress(args):
         raise ValueError("{} is not supported.".format(args.data))
 
     image_shape = [int(m) for m in image_shape.split(",")]
-    assert args.model in model_list, "{} is not in lists: {}".format(
-        args.model, model_list)
+    assert args.model in model_list, "{} is not in lists: {}".format(args.model,
+                                                                     model_list)
     image = fluid.layers.data(name='image', shape=image_shape, dtype='float32')
     if args.use_pact:
         image.stop_gradient = False
@@ -196,8 +197,7 @@ def compress(args):
     if args.pretrained_model:
 
         def if_exist(var):
-            return os.path.exists(
-                os.path.join(args.pretrained_model, var.name))
+            return os.path.exists(os.path.join(args.pretrained_model, var.name))
 
         fluid.io.load_vars(exe, args.pretrained_model, predicate=if_exist)
 
@@ -205,20 +205,29 @@ def compress(args):
     train_reader = paddle.fluid.io.batch(
         train_reader, batch_size=args.batch_size, drop_last=True)
 
-    train_feeder = feeder = fluid.DataFeeder([image, label], place)
-    val_feeder = feeder = fluid.DataFeeder(
-        [image, label], place, program=val_program)
+    train_loader = fluid.io.DataLoader.from_generator(
+        feed_list=[image, label],
+        capacity=512,
+        use_double_buffer=True,
+        iterable=True)
+    valid_loader = fluid.io.DataLoader.from_generator(
+        feed_list=[image, label],
+        capacity=512,
+        use_double_buffer=True,
+        iterable=True)
+
+    places = fluid.cuda_places() if args.use_gpu else fluid.cpu_places()
+    train_loader.set_sample_list_generator(train_reader, places)
+    valid_loader.set_sample_list_generator(val_reader, place)
 
     def test(epoch, program):
         batch_id = 0
         acc_top1_ns = []
         acc_top5_ns = []
-        for data in val_reader():
+        for data in valid_loader():
             start_time = time.time()
             acc_top1_n, acc_top5_n = exe.run(
-                program,
-                feed=train_feeder.feed(data),
-                fetch_list=[acc_top1.name, acc_top5.name])
+                program, feed=data, fetch_list=[acc_top1.name, acc_top5.name])
             end_time = time.time()
             if batch_id % args.log_period == 0:
                 _logger.info(
@@ -230,20 +239,19 @@ def compress(args):
             acc_top5_ns.append(np.mean(acc_top5_n))
             batch_id += 1
 
-        _logger.info("Final eval epoch[{}] - acc_top1: {}; acc_top5: {}".
-                     format(epoch,
-                            np.mean(np.array(acc_top1_ns)),
-                            np.mean(np.array(acc_top5_ns))))
+        _logger.info("Final eval epoch[{}] - acc_top1: {}; acc_top5: {}".format(
+            epoch,
+            np.mean(np.array(acc_top1_ns)), np.mean(np.array(acc_top5_ns))))
         return np.mean(np.array(acc_top1_ns))
 
     def train(epoch, compiled_train_prog):
 
         batch_id = 0
-        for data in train_reader():
+        for data in train_loader():
             start_time = time.time()
             loss_n, acc_top1_n, acc_top5_n = exe.run(
                 compiled_train_prog,
-                feed=train_feeder.feed(data),
+                feed=data,
                 fetch_list=[avg_cost.name, acc_top1.name, acc_top5.name])
             end_time = time.time()
             loss_n = np.mean(loss_n)
@@ -259,8 +267,8 @@ def compress(args):
                 threshold = {}
                 for var in val_program.list_vars():
                     if 'pact' in var.name:
-                        array = np.array(fluid.global_scope().find_var(
-                            var.name).get_tensor())
+                        array = np.array(fluid.global_scope().find_var(var.name)
+                                         .get_tensor())
                         threshold[var.name] = array[0]
                 print(threshold)