The num_samples and numRows in schema for TFRecordDataset are conflict

mindspore/ccsrc/dataset/engine/datasetops/source/storage_client.cc

@@ -162,7 +162,11 @@ Status StorageClient::numRowsFromFile(uint32_t &num_rows) const {
     std::ifstream in(schemaFile);
     nlohmann::json js;
     in >> js;
-    num_rows = js.value("numRows", 0);
+    if (js.find("numRows") == js.end()) {
+      num_rows = MAX_INTEGER_INT32;
+    } else {
+      num_rows = js.value("numRows", 0);
+    }
     if (num_rows == 0) {
       std::string err_msg =
         "Storage client has not properly done dataset "

mindspore/ccsrc/dataset/engine/datasetops/source/tf_reader_op.cc

@@ -163,6 +163,9 @@ Status TFReaderOp::Init() {
   if (total_rows_ == 0) {
     total_rows_ = data_schema_->num_rows();
   }
+  if (total_rows_ < 0) {
+    RETURN_STATUS_UNEXPECTED("The num_sample or numRows for TFRecordDataset should be greater than 0");
+  }
 
   // Build the index with our files such that each file corresponds to a key id.
   RETURN_IF_NOT_OK(filename_index_->insert(dataset_files_list_));

mindspore/dataset/engine/datasets.py

@@ -1455,7 +1455,7 @@ class StorageDataset(SourceDataset):
 
     Args:
         dataset_files (list[str]): List of files to be read.
-        schema (str): Path to the json schema file.
+        schema (str): Path to the json schema file. If numRows(parsed from schema) is not exist, read the full dataset.
         distribution (str, optional): Path of distribution config file (default="").
         columns_list (list[str], optional): List of columns to be read (default=None, read all columns).
         num_parallel_workers (int, optional): Number of parallel working threads (default=None).
@@ -2193,7 +2193,10 @@ class TFRecordDataset(SourceDataset):
         schema (str or Schema, optional): Path to the json schema file or schema object (default=None).
             If the schema is not provided, the meta data from the TFData file is considered the schema.
         columns_list (list[str], optional): List of columns to be read (default=None, read all columns)
-        num_samples (int, optional): number of samples(rows) to read (default=None, reads the full dataset).
+        num_samples (int, optional): number of samples(rows) to read (default=None).
+            If num_samples is None and numRows(parsed from schema) is not exist, read the full dataset;
+            If num_samples is None and numRows(parsed from schema) is greater than 0, read numRows rows;
+            If both num_samples and numRows(parsed from schema) are greater than 0, read num_samples rows.
         num_parallel_workers (int, optional): number of workers to read the data
             (default=None, number set in the config).
         shuffle (bool, Shuffle level, optional): perform reshuffling of the data every epoch (default=Shuffle.GLOBAL).
@@ -2711,10 +2714,10 @@ class Schema:
     """
 
     def __init__(self, schema_file=None):
+        self.num_rows = None
         if schema_file is None:
             self.columns = []
             self.dataset_type = ''
-            self.num_rows = 0
         else:
             if not os.path.isfile(schema_file) or not os.access(schema_file, os.R_OK):
                 raise ValueError("The file %s does not exist or permission denied!" % schema_file)
@@ -2859,6 +2862,9 @@ class Schema:
             raise RuntimeError("DatasetType field is missing.")
         if self.columns is None:
             raise RuntimeError("Columns are missing.")
+        if self.num_rows is not None:
+            if not isinstance(self.num_rows, int) or self.num_rows <= 0:
+                raise ValueError("numRows must be greater than 0")
 
     def __str__(self):
         return self.to_json()

tests/ut/data/dataset/testTFTestAllTypes/datasetSchemaNoRow.json

@@ -0,0 +1,45 @@
+{
+  "datasetType": "TF",
+  "columns": {
+    "col_sint16": {
+      "type": "int16",
+      "rank": 1,
+      "shape": [1]
+    },
+    "col_sint32": {
+      "type": "int32",
+      "rank": 1,
+      "shape": [1]
+    },
+    "col_sint64": {
+      "type": "int64",
+      "rank": 1,
+      "shape": [1]
+    },
+    "col_float": {
+      "type": "float32",
+      "rank": 1,
+      "shape": [1]
+    },
+    "col_1d": {
+      "type": "int64",
+      "rank": 1,
+      "shape": [2]
+    },
+    "col_2d": {
+      "type": "int64",
+      "rank": 2,
+      "shape": [2, 2]
+    },
+    "col_3d": {
+      "type": "int64",
+      "rank": 3,
+      "shape": [2, 2, 2]
+    },
+    "col_binary": {
+      "type": "uint8",
+      "rank": 1,
+      "shape": [1]
+    }
+  }
+}

tests/ut/data/dataset/test_tf_file_3_images/datasetNoRowsSchema.json

@@ -0,0 +1,15 @@
+{
+  "datasetType": "TF",
+  "columns": {
+    "image": {
+        "type": "uint8",
+        "rank": 1,
+        "t_impl": "cvmat"
+    },
+    "label" : {
+      "type": "uint64",
+      "rank": 1,
+      "t_impl": "flex"
+    }
+  }
+}

tests/ut/python/dataset/test_storage.py

@@ -37,3 +37,15 @@ def test_case_storage():
 
     filename = "storage_result.npz"
     save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN)
+
+
+def test_case_no_rows():
+    DATA_DIR = ["../data/dataset/test_tf_file_3_images/train-0000-of-0001.data"]
+    SCHEMA_DIR = "../data/dataset/test_tf_file_3_images/datasetNoRowsSchema.json"
+
+    dataset = ds.StorageDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"])
+    assert dataset.get_dataset_size() == 3
+    count = 0
+    for data in dataset.create_tuple_iterator():
+        count += 1
+    assert count == 3

tests/ut/python/dataset/test_tfreader_op.py

@@ -37,6 +37,36 @@ def test_case_tf_shape():
     assert (len(output_shape[-1]) == 1)
 
 
+def test_case_tf_read_all_dataset():
+    schema_file = "../data/dataset/testTFTestAllTypes/datasetSchemaNoRow.json"
+    ds1 = ds.TFRecordDataset(FILES, schema_file)
+    assert ds1.get_dataset_size() == 12
+    count = 0
+    for data in ds1.create_tuple_iterator():
+        count += 1
+    assert count == 12
+
+
+def test_case_num_samples():
+    schema_file = "../data/dataset/testTFTestAllTypes/datasetSchema7Rows.json"
+    ds1 = ds.TFRecordDataset(FILES, schema_file, num_samples=8)
+    assert ds1.get_dataset_size() == 8
+    count = 0
+    for data in ds1.create_dict_iterator():
+        count += 1
+    assert count == 8
+
+
+def test_case_num_samples2():
+    schema_file = "../data/dataset/testTFTestAllTypes/datasetSchema7Rows.json"
+    ds1 = ds.TFRecordDataset(FILES, schema_file)
+    assert ds1.get_dataset_size() == 7
+    count = 0
+    for data in ds1.create_dict_iterator():
+        count += 1
+    assert count == 7
+
+
 def test_case_tf_shape_2():
     ds1 = ds.TFRecordDataset(FILES, SCHEMA_FILE)
     ds1 = ds1.batch(2)