remove imperative data loader place limit, test=develop (#24641)

python/paddle/fluid/reader.py

@@ -18,7 +18,7 @@ import six
 import numpy as np
 import threading
 import paddle
-from .framework import Program, Variable, program_guard, default_main_program, default_startup_program, in_dygraph_mode, cpu_places
+from .framework import Program, Variable, program_guard, default_main_program, default_startup_program, in_dygraph_mode, cpu_places, _current_expected_place
 from .executor import global_scope
 from .data_feeder import DataFeeder, BatchedTensorProvider
 from .multiprocess_utils import multiprocess_queue_set, CleanupFuncRegistrar, _cleanup_mmap, _cleanup, _set_SIGCHLD_handler
@@ -671,12 +671,12 @@ class DygraphGeneratorLoader(DataLoaderBase):
 
         if not iterable:
             logging.warning(
-                "Please NOTE: dygraph can support iterable mode only. Change to iterable mode."
+                "Please NOTE: imperative mode can support iterable mode only. Change to iterable mode."
             )
         self._iterable = True
         if not return_list:
             logging.warning(
-                "Please NOTE: dygraph can support return as list only. Change to return as list."
+                "Please NOTE: imperative mode can support return as list only. Change to return as list."
             )
         self._return_list = True
 
@@ -941,10 +941,11 @@ class DygraphGeneratorLoader(DataLoaderBase):
 
     def set_batch_generator(self, reader, places=None):
         self._batch_reader = reader
-        assert places is not None, "Places cannot be None when DataLoader is iterable"
+        if places is None:
+            places = _current_expected_place()
         self._places = _convert_places(places)
         assert len(self._places) == 1, \
-            "Number of places must be 1 in dygraph mode"
+            "Number of places must be 1 in imperative mode"
         return self
 
 

python/paddle/fluid/tests/unittests/test_imperative_data_loader_base.py

@@ -41,6 +41,14 @@ class TestDygraphDataLoader(unittest.TestCase):
         self.epoch_num = 1
         self.capacity = 5
 
+    def iter_loader_data(self, loader):
+        for _ in range(self.epoch_num):
+            for image, label in loader():
+                relu = fluid.layers.relu(image)
+                self.assertEqual(image.shape, [self.batch_size, 784])
+                self.assertEqual(label.shape, [self.batch_size, 1])
+                self.assertEqual(relu.shape, [self.batch_size, 784])
+
     def test_single_process_loader(self):
         with fluid.dygraph.guard():
             loader = fluid.io.DataLoader.from_generator(
@@ -49,12 +57,7 @@ class TestDygraphDataLoader(unittest.TestCase):
                 sample_generator_creator(self.batch_size, self.batch_num),
                 batch_size=self.batch_size,
                 places=fluid.CPUPlace())
-            for _ in range(self.epoch_num):
-                for image, label in loader():
-                    relu = fluid.layers.relu(image)
-                    self.assertEqual(image.shape, [self.batch_size, 784])
-                    self.assertEqual(label.shape, [self.batch_size, 1])
-                    self.assertEqual(relu.shape, [self.batch_size, 784])
+            self.iter_loader_data(loader)
 
     def test_multi_process_loader(self):
         with fluid.dygraph.guard():
@@ -64,12 +67,15 @@ class TestDygraphDataLoader(unittest.TestCase):
                 sample_generator_creator(self.batch_size, self.batch_num),
                 batch_size=self.batch_size,
                 places=fluid.CPUPlace())
-            for _ in range(self.epoch_num):
-                for image, label in loader():
-                    relu = fluid.layers.relu(image)
-                    self.assertEqual(image.shape, [self.batch_size, 784])
-                    self.assertEqual(label.shape, [self.batch_size, 1])
-                    self.assertEqual(relu.shape, [self.batch_size, 784])
+            self.iter_loader_data(loader)
+
+    def test_generator_no_places(self):
+        with fluid.dygraph.guard():
+            loader = fluid.io.DataLoader.from_generator(capacity=self.capacity)
+            loader.set_sample_generator(
+                sample_generator_creator(self.batch_size, self.batch_num),
+                batch_size=self.batch_size)
+            self.iter_loader_data(loader)
 
 
 if __name__ == '__main__':