[PaddleSlim] [Cherry pick ] make slim reader to support dataloader (#20625)

* cherry-pick refine slim reader to support dataloader test=release/1.6

* cherry-pick add test for reader test=release/1.6

* cherry-pick rm checkpoint path test=release/1.6

* cherry-pick fix details test=release/1.6

* cherry-pick fix details test=release/1.6

python/paddle/fluid/contrib/slim/core/compressor.py

@@ -20,7 +20,7 @@ from .... import profiler
 from .... import scope_guard
 from ....data_feeder import DataFeeder
 from ....log_helper import get_logger
-from ....reader import PyReader
+from ....reader import DataLoaderBase
 from ..graph import *
 from .config import ConfigFactory
 import numpy as np
@@ -194,8 +194,8 @@ class Context(object):
             reader = cached_reader(reader, sampled_rate, self.cache_path,
                                    cached_id)
 
-        if isinstance(reader, Variable) or (isinstance(reader, PyReader) and
-                                            (not reader.iterable)):
+        if isinstance(reader, Variable) or (
+                isinstance(reader, DataLoaderBase) and (not reader.iterable)):
             reader.start()
             try:
                 while True:
@@ -488,8 +488,8 @@ class Compressor(object):
                     build_strategy=build_strategy)
 
         if isinstance(context.train_reader, Variable) or (
-                isinstance(context.train_reader,
-                           PyReader) and (not context.train_reader.iterable)):
+                isinstance(context.train_reader, DataLoaderBase) and
+            (not context.train_reader.iterable)):
             context.train_reader.start()
             try:
                 while True:

python/paddle/fluid/contrib/slim/graph/executor.py

@@ -42,7 +42,10 @@ class SlimGraphExecutor(object):
         """
         assert isinstance(graph, GraphWrapper)
         feed = None
-        if data is not None:
+        if data is not None and isinstance(data[0], dict):
+            # return list = False
+            feed = data
+        elif data is not None:
             feeder = DataFeeder(
                 feed_list=list(graph.in_nodes.values()),
                 place=self.place,

python/paddle/fluid/contrib/slim/tests/test_reader.py

+#   copyright (c) 2019 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 os
+import shutil
+import paddle
+import unittest
+import paddle.fluid as fluid
+from mobilenet import MobileNet
+from paddle.fluid.contrib.slim.core import Compressor
+from paddle.fluid.contrib.slim.graph import GraphWrapper
+
+
+class TestReader(unittest.TestCase):
+    """
+    Test API of quantization strategy.
+    """
+
+    def set_train_reader(self, image, label, place):
+        train_reader = paddle.batch(
+            paddle.dataset.mnist.train(), batch_size=128)
+        return train_reader
+
+    def set_val_reader(self, image, label, place):
+        val_reader = paddle.batch(paddle.dataset.mnist.test(), batch_size=128)
+        return val_reader
+
+    def set_feed_list(self, image, label):
+        return [('img', image.name), ('label', label.name)]
+
+    def quan(self, config_file):
+        if os.path.exists('./checkpoints_quan'):
+            shutil.rmtree('./checkpoints_quan')
+
+        if not fluid.core.is_compiled_with_cuda():
+            return
+        class_dim = 10
+        image_shape = [1, 28, 28]
+
+        train_program = fluid.Program()
+        startup_program = fluid.Program()
+        val_program = fluid.Program()
+
+        with fluid.program_guard(train_program, startup_program):
+            with fluid.unique_name.guard():
+                image = fluid.layers.data(
+                    name='image', shape=image_shape, dtype='float32')
+                image.stop_gradient = False
+                label = fluid.layers.data(
+                    name='label', shape=[1], dtype='int64')
+                out = MobileNet(name='quan').net(input=image,
+                                                 class_dim=class_dim)
+                print("out: {}".format(out.name))
+                acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1)
+                acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5)
+                cost = fluid.layers.cross_entropy(input=out, label=label)
+                avg_cost = fluid.layers.mean(x=cost)
+        optimizer = fluid.optimizer.Momentum(
+            momentum=0.9,
+            learning_rate=0.01,
+            regularization=fluid.regularizer.L2Decay(4e-5))
+
+        val_program = train_program.clone(for_test=False)
+
+        place = fluid.CUDAPlace(0)
+        exe = fluid.Executor(place)
+        exe.run(startup_program)
+
+        val_reader = self.set_val_reader(image, label, place)
+
+        val_feed_list = self.set_feed_list(image, label)
+        val_fetch_list = [('acc_top1', acc_top1.name), ('acc_top5',
+                                                        acc_top5.name)]
+
+        train_reader = self.set_train_reader(image, label, place)
+        train_feed_list = self.set_feed_list(image, label)
+        train_fetch_list = [('loss', avg_cost.name)]
+
+        com_pass = Compressor(
+            place,
+            fluid.global_scope(),
+            train_program,
+            train_reader=train_reader,
+            train_feed_list=train_feed_list,
+            train_fetch_list=train_fetch_list,
+            eval_program=val_program,
+            eval_reader=val_reader,
+            eval_feed_list=val_feed_list,
+            eval_fetch_list=val_fetch_list,
+            train_optimizer=optimizer)
+        com_pass.config(config_file)
+        eval_graph = com_pass.run()
+
+
+class TestReader1(TestReader):
+    def set_train_reader(self, image, label, place):
+        loader = fluid.io.DataLoader.from_generator(
+            feed_list=[image, label], capacity=16, iterable=True)
+        loader.set_sample_generator(
+            paddle.dataset.mnist.train(), batch_size=128, places=place)
+        return loader
+
+    def set_val_reader(self, image, label, place):
+        loader = fluid.io.DataLoader.from_generator(
+            feed_list=[image, label], capacity=16, iterable=True)
+        loader.set_sample_generator(
+            paddle.dataset.mnist.test(), batch_size=128, places=place)
+        return loader
+
+    def test_compression(self):
+        self.quan("./quantization/compress_1.yaml")
+
+
+if __name__ == '__main__':
+    unittest.main()