Fix test team issue: ToNumber in eager mode.

And support multiple tensor as input in call method

Signed-off-by: alex-yuyue <yue.yu1@huawei.com>

mindspore/ccsrc/minddata/dataset/kernels/tensor_op.cc

@@ -40,6 +40,8 @@ Status TensorOp::Compute(const std::shared_ptr<Tensor> &input, std::shared_ptr<T
 Status TensorOp::Compute(const TensorRow &input, TensorRow *output) {
   IO_CHECK_VECTOR(input, output);
   if (OneToOne()) {
+    if (input.size() != 1)
+      return Status(StatusCode::kMDUnexpectedError, "The op is OneToOne, can only accept one tensor as input.");
     output->resize(1);
     return Compute(input[0], &(*output)[0]);
   }

mindspore/dataset/text/transforms.py

@@ -63,28 +63,23 @@ class TextTensorOperation(TensorOperation):
     """
     Base class of Text Tensor Ops
     """
-
-    def __call__(self, input_tensor):
-        if not isinstance(input_tensor, list):
-            input_list = [input_tensor]
-        else:
-            input_list = input_tensor
-        tensor_list = []
-        for tensor in input_list:
-            if not isinstance(tensor, str):
-                raise TypeError("Input should be string or list of strings, got {}.".format(type(tensor)))
-            tensor_list.append(cde.Tensor(np.asarray(tensor)))
+    def __call__(self, *tensor_list):
+        tensor_array = []
+        output_list = []
+        # Combine input tensor_list to a TensorRow
+        for input_tensor in tensor_list:
+            if not isinstance(input_tensor, (str, list)):
+                raise TypeError("Input should be string or list of strings, got {}.".format(type(input_tensor)))
+            tensor_array.append(cde.Tensor(np.asarray(input_tensor)))
         callable_op = cde.Execute(self.parse())
-        output_list = callable_op(tensor_list)
+        output_list = callable_op(tensor_array)
         for i, element in enumerate(output_list):
             arr = element.as_array()
             if arr.dtype.char == 'S':
-                output_list[i] = to_str(arr)
+                output_list[i] = np.char.decode(arr)
             else:
                 output_list[i] = arr
-        if not isinstance(input_tensor, list) and len(output_list) == 1:
-            output_list = output_list[0]
-        return output_list
+        return output_list[0] if len(output_list) == 1 else output_list
 
     def parse(self):
         raise NotImplementedError("TextTensorOperation has to implement parse() method.")

mindspore/dataset/vision/c_transforms.py

@@ -62,28 +62,24 @@ class ImageTensorOperation(TensorOperation):
     """
     Base class of Image Tensor Ops
     """
-
-    def __call__(self, input_tensor):
-        if not isinstance(input_tensor, list):
-            input_list = [input_tensor]
-        else:
-            input_list = input_tensor
-        tensor_list = []
-        for tensor in input_list:
-            if not isinstance(tensor, (np.ndarray, Image.Image)):
-                raise TypeError("Input should be NumPy or PIL image, got {}.".format(type(tensor)))
-            tensor_list.append(cde.Tensor(np.asarray(tensor)))
+    def __call__(self, *tensor_list):
+        tensor_array = []
+        output_list = []
+        # Combine input tensor_list to a TensorRow
+        for input_tensor in tensor_list:
+            if not isinstance(input_tensor, (np.ndarray, Image.Image)):
+                raise TypeError("Input should be NumPy or PIL image, got {}.".format(type(input_tensor)))
+            tensor_array.append(cde.Tensor(np.asarray(input_tensor)))
         callable_op = cde.Execute(self.parse())
-        output_list = callable_op(tensor_list)
+        output_list = callable_op(tensor_array)
+
         for i, element in enumerate(output_list):
             arr = element.as_array()
             if arr.dtype.char == 'S':
                 output_list[i] = np.char.decode(arr)
             else:
                 output_list[i] = arr
-        if not isinstance(input_tensor, list) and len(output_list) == 1:
-            output_list = output_list[0]
-        return output_list
+        return output_list[0] if len(output_list) == 1 else output_list
 
     def parse(self):
         raise NotImplementedError("ImageTensorOperation has to implement parse() method.")

tests/ut/python/dataset/test_HWC2CHW.py

@@ -16,6 +16,7 @@
 Testing HWC2CHW op in DE
 """
 import numpy as np
+import pytest
 import mindspore.dataset as ds
 import mindspore.dataset.transforms.py_transforms
 import mindspore.dataset.vision.c_transforms as c_vision
@@ -36,11 +37,23 @@ def test_HWC2CHW_callable():
     logger.info("Test HWC2CHW callable")
     img = np.fromfile("../data/dataset/apple.jpg", dtype=np.uint8)
     logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.shape))
-
     img = c_vision.Decode()(img)
-    img = c_vision.HWC2CHW()(img)
-    logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.shape))
-    assert img.shape == (3, 2268, 4032)
+    assert img.shape == (2268, 4032, 3)
+
+    # test one tensor
+    img1 = c_vision.HWC2CHW()(img)
+    assert img1.shape == (3, 2268, 4032)
+
+    # test input multiple tensors
+    with pytest.raises(RuntimeError) as info:
+        imgs = [img, img]
+        _ = c_vision.HWC2CHW()(*imgs)
+    assert "The op is OneToOne, can only accept one tensor as input." in str(info.value)
+
+    with pytest.raises(RuntimeError) as info:
+        _ = c_vision.HWC2CHW()(img, img)
+    assert "The op is OneToOne, can only accept one tensor as input." in str(info.value)
+
 
 
 def test_HWC2CHW(plot=False):

tests/ut/python/dataset/test_random_crop_and_resize.py

@@ -44,10 +44,12 @@ def test_random_crop_and_resize_callable():
 
     decode_op = c_vision.Decode()
     img = decode_op(img)
+    assert img.shape == (2268, 4032, 3)
 
-    random_crop_and_resize_op = c_vision.RandomResizedCrop((256, 512), (2, 2), (1, 3))
-    img = random_crop_and_resize_op(img)
-    assert np.shape(img) == (256, 512, 3)
+    # test one tensor
+    random_crop_and_resize_op1 = c_vision.RandomResizedCrop((256, 512), (2, 2), (1, 3))
+    img1 = random_crop_and_resize_op1(img)
+    assert img1.shape == (256, 512, 3)
 
 
 def test_random_crop_and_resize_op_c(plot=False):

tests/ut/python/dataset/test_text_jieba_tokenizer.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 # ==============================================================================
 import numpy as np
+import pytest
 import mindspore.dataset as ds
 from mindspore.dataset.text import JiebaTokenizer
 from mindspore.dataset.text import JiebaMode, to_str
@@ -33,14 +34,19 @@ def test_jieba_callable():
     jieba_op1 = JiebaTokenizer(HMM_FILE, MP_FILE, mode=JiebaMode.MP)
     jieba_op2 = JiebaTokenizer(HMM_FILE, MP_FILE, mode=JiebaMode.HMM)
 
+    # test one tensor
     text1 = "今天天气太好了我们一起去外面玩吧"
     text2 = "男默女泪市长江大桥"
     assert np.array_equal(jieba_op1(text1), ['今天天气', '太好了', '我们', '一起', '去', '外面', '玩吧'])
     assert np.array_equal(jieba_op2(text1), ['今天', '天气', '太', '好', '了', '我们', '一起', '去', '外面', '玩', '吧'])
-
     jieba_op1.add_word("男默女泪")
     assert np.array_equal(jieba_op1(text2), ['男默女泪', '市', '长江大桥'])
 
+    # test input multiple tensors
+    with pytest.raises(RuntimeError) as info:
+        _ = jieba_op1(text1, text2)
+    assert "JiebaTokenizer: input only support one column data." in str(info.value)
+
 
 def test_jieba_1():
     """Test jieba tokenizer with MP mode"""

tests/ut/python/dataset/test_to_number_op.py

@@ -33,6 +33,24 @@ def string_dataset_generator(strings):
         yield (np.array(string, dtype='S'),)
 
 
+def test_to_number_eager():
+    """
+    Test ToNumber op is callable
+    """
+    input_strings = [["1", "2", "3"], ["4", "5", "6"]]
+    op = text.ToNumber(mstype.int8)
+
+    # test input_strings as one 2D tensor
+    result1 = op(input_strings) # np array: [[1 2 3] [4 5 6]]
+    assert np.array_equal(result1, np.array([[1, 2, 3], [4, 5, 6]], dtype='i'))
+
+    # test input multiple tensors
+    with pytest.raises(RuntimeError) as info:
+        # test input_strings as two 1D tensor. It's error because to_number is an OneToOne op
+        _ = op(*input_strings)
+    assert "The op is OneToOne, can only accept one tensor as input." in str(info.value)
+
+
 def test_to_number_typical_case_integral():
     input_strings = [["-121", "14"], ["-2219", "7623"], ["-8162536", "162371864"],
                      ["-1726483716", "98921728421"]]
@@ -194,6 +212,7 @@ def test_to_number_invalid_type():
 
 
 if __name__ == '__main__':
+    test_to_number_eager()
     test_to_number_typical_case_integral()
     test_to_number_typical_case_non_integral()
     test_to_number_boundaries_integral()

tests/ut/python/dataset/test_uniform_augment.py

@@ -38,12 +38,13 @@ def test_uniform_augment_callable(num_ops=2):
 
     decode_op = C.Decode()
     img = decode_op(img)
+    assert img.shape == (2268, 4032, 3)
 
     transforms_ua = [C.RandomCrop(size=[400, 400], padding=[32, 32, 32, 32]),
                      C.RandomCrop(size=[400, 400], padding=[32, 32, 32, 32])]
     uni_aug = C.UniformAugment(transforms=transforms_ua, num_ops=num_ops)
-    img = uni_aug([img, img])
-    assert ((np.shape(img) == (2, 2268, 4032, 3)) or (np.shape(img) == (1, 400, 400, 3)))
+    img = uni_aug(img)
+    assert img.shape == (2268, 4032, 3) or img.shape == (400, 400, 3)
 
 
 def test_uniform_augment(plot=False, num_ops=2):