Return -1 as unknown batch size for callable batch_size param

mindspore/ccsrc/minddata/dataset/engine/consumers/tree_consumer.cc

@@ -786,7 +786,7 @@ Status TreeGetters::GetBatchSize(int64_t *batch_size) {
   std::shared_ptr<DatasetOp> root = std::shared_ptr<DatasetOp>(tree_adapter_->GetRoot());
   RETURN_UNEXPECTED_IF_NULL(root);
   *batch_size = root->GetTreeBatchSize();
-  CHECK_FAIL_RETURN_UNEXPECTED(*batch_size != -1, "GetBatchSize: Failed to find the batch size in Dataset pipeline.");
+  CHECK_FAIL_RETURN_UNEXPECTED(*batch_size != 0, "GetBatchSize: Failed to find the batch size in Dataset pipeline.");
   return Status::OK();
 }
 

tests/ut/python/dataset/test_var_batch_map.py

@@ -171,6 +171,26 @@ def test_variable_size_batch():
         4, 4, False, add_one_by_epoch, 2, 2)), "\nMULTIPROC2 MAP FAILED\n"
 
 
+def test_get_batchsize_on_callable_batchsize():
+    """
+    Feature: Test get_batch_size() func which can get batch size of pipeline.
+    Description: Use get_batch_size() func on dataset pipeline with dynamic batch size.
+    Expectation: Return -1 which means the batch size is unknown.
+    """
+    def gen(num):
+        for i in range(num):
+            yield (np.array([i]),)
+
+    def add_one_by_batch_num(batch_info):
+        return batch_info.get_batch_num() + 1
+
+    data1 = ds.GeneratorDataset((lambda: gen(10)), ["num"])
+    data1 = data1.batch(batch_size=add_one_by_batch_num, drop_remainder=True)
+
+    batchsize = data1.get_batch_size()
+    assert batchsize == -1
+
+
 def test_basic_batch_map():
     def check_res(arr1, arr2):
         for ind, _ in enumerate(arr1):
@@ -275,9 +295,9 @@ def test_var_batch_multi_col_map():
         return (batchInfo.get_batch_num() + 1) * (batchInfo.get_epoch_num() + 1)
 
     # multiply first col by batch_num, multiply second col by -batch_num
-    def map_func(col1, col2, batchInfo):
+    def map_func(col1, col2, batch_info):
-        return ([np.copy((1 + batchInfo.get_batch_num()) * arr) for arr in col1],
+        return ([np.copy((1 + batch_info.get_batch_num()) * arr) for arr in col1],
-                [np.copy(-(1 + batchInfo.get_batch_num()) * arr) for arr in col2])
+                [np.copy(-(1 + batch_info.get_batch_num()) * arr) for arr in col2])
 
     def batch_map_config(num, r, fbatch, fmap, col_names, res):
         data1 = ds.GeneratorDataset((lambda: gen_3_cols(num)), ["col1", "col2", "col3"]) \
@@ -445,6 +465,9 @@ if __name__ == '__main__':
     logger.info("Running test_var_batch_map.py test_variable_size_batch() function")
     test_variable_size_batch()
 
+    logger.info("Running test_var_batch_map.py test_get_batchsize_on_callable_batchsize() function")
+    test_get_batchsize_on_callable_batchsize()
+
     logger.info("Running test_var_batch_map.py test_basic_batch_map() function")
     test_basic_batch_map()