!9825 [MD] fix pad, normalize in c_trasnform

From: @luoyang42
Reviewed-by: @heleiwang,@liucunwei
Signed-off-by: @liucunwei

mindspore/ccsrc/minddata/dataset/api/python/bindings/dataset/include/execute_binding.cc

@@ -30,6 +30,11 @@ PYBIND_REGISTER(Execute, 0, ([](const py::module *m) {
                     }))
                     .def("__call__", [](Execute &self, std::shared_ptr<Tensor> in) {
                       std::shared_ptr<Tensor> out = self(in);
+                      if (out == nullptr) {
+                        THROW_IF_ERROR([]() {
+                          RETURN_STATUS_UNEXPECTED("Failed to execute op in eager mode, please check ERROR log above.");
+                        }());
+                      }
                       return out;
                     });
                 }));

mindspore/ccsrc/minddata/dataset/kernels/image/image_utils.cc

@@ -73,7 +73,7 @@ Status Flip(std::shared_ptr<Tensor> input, std::shared_ptr<Tensor> *output, int
       *output = std::static_pointer_cast<Tensor>(output_cv);
       return Status::OK();
     } catch (const cv::Exception &e) {
-      RETURN_STATUS_UNEXPECTED("Error in flip op.");
+      RETURN_STATUS_UNEXPECTED("Error in flip op: " + std::string(e.what()));
     }
   } else {
     RETURN_STATUS_UNEXPECTED("Could not convert to CV Tensor, the input data is null");
@@ -118,7 +118,7 @@ Status Resize(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *out
     *output = std::static_pointer_cast<Tensor>(output_cv);
     return Status::OK();
   } catch (const cv::Exception &e) {
-    RETURN_STATUS_UNEXPECTED("Error in image resize.");
+    RETURN_STATUS_UNEXPECTED("Error in image resize: " + std::string(e.what()));
   }
 }
 
@@ -153,7 +153,7 @@ Status DecodeCv(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *o
     *output = std::static_pointer_cast<Tensor>(output_cv);
     return Status::OK();
   } catch (const cv::Exception &e) {
-    RETURN_STATUS_UNEXPECTED("Error in image Decode");
+    RETURN_STATUS_UNEXPECTED("Error in image Decode: " + std::string(e.what()));
   }
 }
 
@@ -350,7 +350,7 @@ Status Rescale(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *ou
     input_image.convertTo(output_cv->mat(), CV_32F, rescale, shift);
     *output = std::static_pointer_cast<Tensor>(output_cv);
   } catch (const cv::Exception &e) {
-    RETURN_STATUS_UNEXPECTED("Error in image rescale");
+    RETURN_STATUS_UNEXPECTED("Error in image rescale: " + std::string(e.what()));
   }
   return Status::OK();
 }
@@ -382,7 +382,7 @@ Status Crop(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *outpu
     *output = std::static_pointer_cast<Tensor>(output_cv);
     return Status::OK();
   } catch (const cv::Exception &e) {
-    RETURN_STATUS_UNEXPECTED("Unexpected error in crop.");
+    RETURN_STATUS_UNEXPECTED("Unexpected error in crop: " + std::string(e.what()));
   }
 }
 
@@ -417,7 +417,7 @@ Status HwcToChw(std::shared_ptr<Tensor> input, std::shared_ptr<Tensor> *output)
     *output = std::move(output_cv);
     return Status::OK();
   } catch (const cv::Exception &e) {
-    RETURN_STATUS_UNEXPECTED("Unexpected error in ChannelSwap.");
+    RETURN_STATUS_UNEXPECTED("Unexpected error in ChannelSwap: " + std::string(e.what()));
   }
 }
 
@@ -503,7 +503,7 @@ Status SwapRedAndBlue(std::shared_ptr<Tensor> input, std::shared_ptr<Tensor> *ou
     *output = std::static_pointer_cast<Tensor>(output_cv);
     return Status::OK();
   } catch (const cv::Exception &e) {
-    RETURN_STATUS_UNEXPECTED("Unexpected error in ChangeMode.");
+    RETURN_STATUS_UNEXPECTED("Unexpected error in ChangeMode: " + std::string(e.what()));
   }
 }
 
@@ -537,7 +537,7 @@ Status CropAndResize(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tenso
     *output = std::static_pointer_cast<Tensor>(cvt_out);
     return Status::OK();
   } catch (const cv::Exception &e) {
-    RETURN_STATUS_UNEXPECTED("Unexpected error in CropAndResize.");
+    RETURN_STATUS_UNEXPECTED("Unexpected error in CropAndResize: " + std::string(e.what()));
   }
 }
 
@@ -584,7 +584,7 @@ Status Rotate(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *out
     }
     *output = std::static_pointer_cast<Tensor>(output_cv);
   } catch (const cv::Exception &e) {
-    RETURN_STATUS_UNEXPECTED("Error in image rotation");
+    RETURN_STATUS_UNEXPECTED("Error in image rotation: " + std::string(e.what()));
   }
   return Status::OK();
 }
@@ -626,7 +626,7 @@ Status Normalize(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *
     *output = std::static_pointer_cast<Tensor>(output_cv);
     return Status::OK();
   } catch (const cv::Exception &e) {
-    RETURN_STATUS_UNEXPECTED("Unexpected error in Normalize");
+    RETURN_STATUS_UNEXPECTED("Unexpected error in Normalize: " + std::string(e.what()));
   }
 }
 
@@ -646,7 +646,7 @@ Status AdjustBrightness(const std::shared_ptr<Tensor> &input, std::shared_ptr<Te
     output_cv->mat() = input_img * alpha;
     *output = std::static_pointer_cast<Tensor>(output_cv);
   } catch (const cv::Exception &e) {
-    RETURN_STATUS_UNEXPECTED("Error in adjust brightness");
+    RETURN_STATUS_UNEXPECTED("Error in adjust brightness: " + std::string(e.what()));
   }
   return Status::OK();
 }
@@ -673,7 +673,7 @@ Status AdjustContrast(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tens
     output_cv->mat() = output_img * (1.0 - alpha) + input_img * alpha;
     *output = std::static_pointer_cast<Tensor>(output_cv);
   } catch (const cv::Exception &e) {
-    RETURN_STATUS_UNEXPECTED("Error in adjust contrast");
+    RETURN_STATUS_UNEXPECTED("Error in adjust contrast: " + std::string(e.what()));
   }
   return Status::OK();
 }
@@ -751,7 +751,7 @@ Status AutoContrast(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor
     (*output) = std::static_pointer_cast<Tensor>(output_cv);
     (*output)->Reshape(input->shape());
   } catch (const cv::Exception &e) {
-    RETURN_STATUS_UNEXPECTED("Error in auto contrast");
+    RETURN_STATUS_UNEXPECTED("Error in auto contrast: " + std::string(e.what()));
   }
   return Status::OK();
 }
@@ -776,7 +776,7 @@ Status AdjustSaturation(const std::shared_ptr<Tensor> &input, std::shared_ptr<Te
     output_cv->mat() = output_img * (1.0 - alpha) + input_img * alpha;
     *output = std::static_pointer_cast<Tensor>(output_cv);
   } catch (const cv::Exception &e) {
-    RETURN_STATUS_UNEXPECTED("Error in adjust saturation");
+    RETURN_STATUS_UNEXPECTED("Error in adjust saturation: " + std::string(e.what()));
   }
   return Status::OK();
 }
@@ -812,7 +812,7 @@ Status AdjustHue(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *
     cv::cvtColor(output_img, output_cv->mat(), CV_HSV2RGB_FULL);
     *output = std::static_pointer_cast<Tensor>(output_cv);
   } catch (const cv::Exception &e) {
-    RETURN_STATUS_UNEXPECTED("Error in adjust hue");
+    RETURN_STATUS_UNEXPECTED("Error in adjust hue: " + std::string(e.what()));
   }
   return Status::OK();
 }
@@ -853,7 +853,7 @@ Status Equalize(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *o
     (*output) = std::static_pointer_cast<Tensor>(output_cv);
     (*output)->Reshape(input->shape());
   } catch (const cv::Exception &e) {
-    RETURN_STATUS_UNEXPECTED("Error in equalize.");
+    RETURN_STATUS_UNEXPECTED("Error in equalize: " + std::string(e.what()));
   }
   return Status::OK();
 }
@@ -915,7 +915,7 @@ Status Erase(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *outp
     *output = std::static_pointer_cast<Tensor>(input);
     return Status::OK();
   } catch (const cv::Exception &e) {
-    RETURN_STATUS_UNEXPECTED("Error in erasing");
+    RETURN_STATUS_UNEXPECTED("Error in erasing: " + std::string(e.what()));
   }
 }
 
@@ -943,7 +943,7 @@ Status Pad(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output
     *output = std::static_pointer_cast<Tensor>(output_cv);
     return Status::OK();
   } catch (const cv::Exception &e) {
-    RETURN_STATUS_UNEXPECTED("Unexpected error in pad");
+    RETURN_STATUS_UNEXPECTED("Unexpected error in pad: " + std::string(e.what()));
   }
 }
 
@@ -962,7 +962,7 @@ Status RgbaToRgb(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *
     *output = std::static_pointer_cast<Tensor>(output_cv);
     return Status::OK();
   } catch (const cv::Exception &e) {
-    RETURN_STATUS_UNEXPECTED("Unexpected error in RgbaToRgb.");
+    RETURN_STATUS_UNEXPECTED("Unexpected error in RgbaToRgb: " + std::string(e.what()));
   }
 }
 
@@ -981,7 +981,7 @@ Status RgbaToBgr(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *
     *output = std::static_pointer_cast<Tensor>(output_cv);
     return Status::OK();
   } catch (const cv::Exception &e) {
-    RETURN_STATUS_UNEXPECTED("Unexpected error in RgbaToBgr.");
+    RETURN_STATUS_UNEXPECTED("Unexpected error in RgbaToBgr: " + std::string(e.what()));
   }
 }
 

mindspore/dataset/core/validator_helpers.py

@@ -112,6 +112,14 @@ def check_pos_int64(value, arg_name=""):
     check_value(value, [UINT64_MIN, INT64_MAX])
 
 
+def check_float32(value, arg_name=""):
+    check_value(value, [FLOAT_MIN_INTEGER, FLOAT_MAX_INTEGER], arg_name)
+
+
+def check_float64(value, arg_name=""):
+    check_value(value, [DOUBLE_MIN_INTEGER, DOUBLE_MAX_INTEGER], arg_name)
+
+
 def check_pos_float32(value, arg_name=""):
     check_value(value, [UINT32_MIN, FLOAT_MAX_INTEGER], arg_name)
 

mindspore/dataset/vision/c_transforms.py

@@ -263,7 +263,7 @@ class Normalize(cde.NormalizeOp):
 
     Args:
         mean (sequence): List or tuple of mean values for each channel, with respect to channel order.
-            The mean values must be in range (0.0, 255.0].
+            The mean values must be in range [0.0, 255.0].
         std (sequence): List or tuple of standard deviations for each channel, with respect to channel order.
             The standard deviation values must be in range (0.0, 255.0].
 
@@ -278,6 +278,10 @@ class Normalize(cde.NormalizeOp):
 
     @check_normalize_c
     def __init__(self, mean, std):
+        if len(mean) == 1:
+            mean = [mean[0]] * 3
+        if len(std) == 1:
+            std = [std[0]] * 3
         self.mean = mean
         self.std = std
         super().__init__(*mean, *std)
@@ -1193,9 +1197,11 @@ class Pad(cde.PadOp):
             with the first value and (right and bottom) with the second value.
             If 4 values are provided as a list or tuple,
             it pads the left, top, right and bottom respectively.
-        fill_value (Union[int, tuple], optional): The pixel intensity of the borders if
+        fill_value (Union[int, tuple], optional): The pixel intensity of the borders, only valid for
-            the padding_mode is Border.CONSTANT (default=0). If it is a 3-tuple, it is used to
+            padding_mode Border.CONSTANT (default=0).
-            fill R, G, B channels respectively.
+            If it is an integer, it is used for all RGB channels.
+            If it is a 3-tuple, it is used to fill R, G, B channels respectively.
+            The fill_value values must be in range [0, 255].
         padding_mode (Border mode, optional): The method of padding (default=Border.CONSTANT). Can be any of
             [Border.CONSTANT, Border.EDGE, Border.REFLECT, Border.SYMMETRIC].
 

mindspore/dataset/vision/py_transforms.py

@@ -199,7 +199,7 @@ class Normalize:
 
     Args:
         mean (sequence): List or tuple of mean values for each channel, with respect to channel order.
-            The mean values must be in the range (0.0, 1.0].
+            The mean values must be in the range [0.0, 1.0].
         std (sequence): List or tuple of standard deviations for each channel, w.r.t. channel order.
             The standard deviation values must be in the range (0.0, 1.0].
 
@@ -783,8 +783,9 @@ class Pad:
             with the first value and the right and bottom with the second value.
             If 4 values are provided as a list or tuple,
             pad the left, top, right and bottom respectively.
-        fill_value (Union[int, tuple], optional): Filling value for the pixel intensity
+        fill_value (Union[int, tuple], optional): The pixel intensity of the borders, only valid for
-            of the borders if the padding_mode is Border.CONSTANT (Default=0).
+            padding_mode Border.CONSTANT (default=0).
+            If it is an integer, it is used for all RGB channels.
             If it is a 3-tuple, it is used to fill R, G, B channels respectively.
         padding_mode (Border mode, optional): The method of padding (default=Border.CONSTANT).
             It can be any of [Border.CONSTANT, Border.EDGE, Border.REFLECT, Border.SYMMETRIC].

mindspore/dataset/vision/validators.py

@@ -20,7 +20,7 @@ import numpy as np
 from mindspore._c_dataengine import TensorOp
 
 from mindspore.dataset.core.validator_helpers import check_value, check_uint8, FLOAT_MAX_INTEGER, check_pos_float32, \
-    check_2tuple, check_range, check_positive, INT32_MAX, parse_user_args, type_check, type_check_list, \
+    check_float32, check_2tuple, check_range, check_positive, INT32_MAX, parse_user_args, type_check, type_check_list, \
     check_tensor_op, UINT8_MAX, check_value_normalize_std
 from .utils import Inter, Border, ImageBatchFormat
 
@@ -78,12 +78,14 @@ def check_mix_up_batch_c(method):
 
 
 def check_normalize_c_param(mean, std):
+    type_check(mean, (list, tuple), "mean")
+    type_check(std, (list, tuple), "std")
     if len(mean) != len(std):
         raise ValueError("Length of mean and std must be equal.")
     for mean_value in mean:
-        check_pos_float32(mean_value)
+        check_value(mean_value, [0, 255], "mean_value")
     for std_value in std:
-        check_pos_float32(std_value)
+        check_value_normalize_std(std_value, [0, 255], "std_value")
 
 
 def check_normalize_py_param(mean, std):
@@ -548,8 +550,10 @@ def check_rescale(method):
     @wraps(method)
     def new_method(self, *args, **kwargs):
         [rescale, shift], _ = parse_user_args(method, *args, **kwargs)
-        check_pos_float32(rescale)
+        type_check(rescale, (numbers.Number,), "rescale")
         type_check(shift, (numbers.Number,), "shift")
+        check_float32(rescale)
+        check_float32(shift)
 
         return method(self, *args, **kwargs)
 

tests/ut/python/dataset/test_eager_vision.py

@@ -92,6 +92,7 @@ def test_eager_exceptions():
     except TypeError as e:
         assert "Input should be NumPy or PIL image" in str(e)
 
+
 if __name__ == '__main__':
     test_eager_resize()
     test_eager_rescale()

tests/ut/python/dataset/test_normalizeOp.py

@@ -245,6 +245,24 @@ def test_normalize_exception_unequal_size_c():
         assert str(e) == "Length of mean and std must be equal."
 
 
+def test_normalize_exception_out_of_range_c():
+    """
+    Test Normalize in c transformation: mean, std out of range
+    expected to raise ValueError
+    """
+    logger.info("test_normalize_exception_out_of_range_c")
+    try:
+        _ = c_vision.Normalize([256, 250, 125], [50, 75, 75])
+    except ValueError as e:
+        logger.info("Got an exception in DE: {}".format(str(e)))
+        assert "not within the required interval" in str(e)
+    try:
+        _ = c_vision.Normalize([255, 250, 125], [0, 75, 75])
+    except ValueError as e:
+        logger.info("Got an exception in DE: {}".format(str(e)))
+        assert "not within the required interval" in str(e)
+
+
 def test_normalize_exception_unequal_size_py():
     """
     Test Normalize in python transformation: len(mean) != len(std)