fix numpyslice issue

mindspore/dataset/engine/datasets.py

@@ -3219,33 +3219,9 @@ class GeneratorDataset(MappableDataset):
     def __init__(self, source, column_names=None, column_types=None, schema=None, num_samples=None,
                  num_parallel_workers=1, shuffle=None, sampler=None, num_shards=None, shard_id=None):
         super().__init__(num_parallel_workers)
+        self.source = source
         self.sampler = _select_sampler(num_samples, sampler, shuffle, num_shards, shard_id)
-        if self.sampler is not None and hasattr(source, "__getitem__"):
-            if isinstance(self.sampler, (samplers.SequentialSampler, samplers.DistributedSampler,
-                                         samplers.RandomSampler, samplers.SubsetRandomSampler,
-                                         samplers.WeightedRandomSampler, samplers.Sampler)):
-                sampler_instance = self.sampler.create()
-                sampler_instance.set_num_rows(len(source))
-                sampler_instance.initialize()
-                if num_parallel_workers > 1:
-                    self.source = (lambda: _cpp_sampler_fn_mp(sampler_instance, source, num_parallel_workers))
-                else:
-                    self.source = (lambda: _cpp_sampler_fn(sampler_instance, source))
-            else:
-                if num_parallel_workers > 1:
-                    self.source = (lambda: _py_sampler_fn_mp(self.sampler, num_samples, source, num_parallel_workers))
-                else:
-                    self.source = (lambda: _py_sampler_fn(self.sampler, num_samples, source))
-        else:
-            try:
-                iter(source)
-            except TypeError:
-                # Use generator function if input callable
-                self.source = (lambda: _generator_fn(source, num_samples))
-            else:
-                # Use iterator function if input is iterable
-                # Random accessible input is also iterable
-                self.source = (lambda: _iter_fn(source, num_samples))
+        self.num_samples = num_samples
 
         if column_names is not None and not isinstance(column_names, list):
             column_names = [column_names]
@@ -3310,9 +3286,35 @@ class GeneratorDataset(MappableDataset):
         new_op.num_parallel_workers = copy.deepcopy(self.num_parallel_workers, memodict)
         new_op.column_types = copy.deepcopy(self.column_types, memodict)
         new_op.column_names = copy.deepcopy(self.column_names, memodict)
-
-        new_op.source = self.source
-        new_op.sampler = self.sampler
+        new_op.num_samples = copy.deepcopy(self.num_samples, memodict)
+
+        new_op.sampler = copy.deepcopy(self.sampler)
+        if new_op.sampler is not None and hasattr(self.source, "__getitem__"):
+            if isinstance(new_op.sampler, (samplers.SequentialSampler, samplers.DistributedSampler,
+                                           samplers.RandomSampler, samplers.SubsetRandomSampler,
+                                           samplers.WeightedRandomSampler, samplers.Sampler)):
+                sampler_instance = new_op.sampler.create()
+                sampler_instance.set_num_rows(len(self.source))
+                sampler_instance.initialize()
+                if new_op.num_parallel_workers > 1:
+                    new_op.source = (lambda: _cpp_sampler_fn_mp(sampler_instance, self.source, new_op.num_parallel_workers))
+                else:
+                    new_op.source = (lambda: _cpp_sampler_fn(sampler_instance, self.source))
+            else:
+                if new_op.num_parallel_workers > 1:
+                    new_op.source = (lambda: _py_sampler_fn_mp(new_op.sampler, new_op.num_samples, self.source, new_op.num_parallel_workers))
+                else:
+                    new_op.source = (lambda: _py_sampler_fn(new_op.sampler, new_op.num_samples, self.source))
+        else:
+            try:
+                iter(self.source)
+            except TypeError:
+                # Use generator function if input callable
+                new_op.source = (lambda: _generator_fn(self.source, new_op.num_samples))
+            else:
+                # Use iterator function if input is iterable
+                # Random accessible input is also iterable
+                new_op.source = (lambda: _iter_fn(self.source, new_op.num_samples))
 
         return new_op