!13497 [MD] Fix error message of SoftDvpp Ops and some examples in docs

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

mindspore/ccsrc/minddata/dataset/kernels/image/soft_dvpp/soft_dvpp_decode_random_crop_resize_jpeg_op.cc

@@ -1,5 +1,5 @@
 /**
- * Copyright 2020 Huawei Technologies Co., Ltd
+ * Copyright 2020-2021 Huawei Technologies Co., Ltd
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -52,13 +52,14 @@ Status SoftDvppDecodeRandomCropResizeJpegOp::Compute(const std::shared_ptr<Tenso
                                                      std::shared_ptr<Tensor> *output) {
   IO_CHECK(input, output);
   if (!IsNonEmptyJPEG(input)) {
-    RETURN_STATUS_UNEXPECTED("SoftDvppDecodeRandomCropResizeJpeg only support process jpeg image.");
+    RETURN_STATUS_UNEXPECTED("SoftDvppDecodeRandomCropResizeJpeg: only support processing raw jpeg image.");
   }
   SoftDpCropInfo crop_info;
   RETURN_IF_NOT_OK(GetCropInfo(input, &crop_info));
   try {
     unsigned char *buffer = const_cast<unsigned char *>(input->GetBuffer());
-    CHECK_FAIL_RETURN_UNEXPECTED(buffer != nullptr, "The input image buffer is empty.");
+    CHECK_FAIL_RETURN_UNEXPECTED(buffer != nullptr,
+                                 "SoftDvppDecodeRandomCropResizeJpeg: the input image buffer is empty.");
     SoftDpProcsessInfo info;
     info.input_buffer = static_cast<uint8_t *>(buffer);
     info.input_buffer_size = input->SizeInBytes();
@@ -69,14 +70,14 @@ Status SoftDvppDecodeRandomCropResizeJpegOp::Compute(const std::shared_ptr<Tenso
     info.output_buffer_size = target_width_ * target_height_ * 3;
     info.is_v_before_u = true;
     int ret = DecodeAndCropAndResizeJpeg(&info, crop_info);
-    std::string error_info("Soft dvpp DecodeAndResizeJpeg failed with return code: ");
-    error_info += std::to_string(ret);
+    std::string error_info("SoftDvppDecodeRandomCropResizeJpeg: failed with return code: ");
+    error_info += std::to_string(ret) + ", please check the log information for more details.";
     CHECK_FAIL_RETURN_UNEXPECTED(ret == 0, error_info);
     std::shared_ptr<CVTensor> cv_tensor = nullptr;
     RETURN_IF_NOT_OK(CVTensor::CreateFromMat(out_rgb_img, &cv_tensor));
     *output = std::static_pointer_cast<Tensor>(cv_tensor);
   } catch (const cv::Exception &e) {
-    std::string error = "Error in SoftDvppDecodeRandomCropResizeJpegOp:" + std::string(e.what());
+    std::string error = "SoftDvppDecodeRandomCropResizeJpeg:" + std::string(e.what());
     RETURN_STATUS_UNEXPECTED(error);
   }
   return Status::OK();

mindspore/ccsrc/minddata/dataset/kernels/image/soft_dvpp/soft_dvpp_decode_resize_jpeg_op.cc

@@ -1,5 +1,5 @@
 /**
- * Copyright 2020 Huawei Technologies Co., Ltd
+ * Copyright 2020-2021 Huawei Technologies Co., Ltd
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -28,11 +28,11 @@ namespace dataset {
 Status SoftDvppDecodeResizeJpegOp::Compute(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output) {
   IO_CHECK(input, output);
   if (!IsNonEmptyJPEG(input)) {
-    RETURN_STATUS_UNEXPECTED("SoftDvppDecodeReiszeJpegOp only support process jpeg image.");
+    RETURN_STATUS_UNEXPECTED("SoftDvppDecodeReiszeJpeg: only support processing raw jpeg image.");
   }
   try {
     unsigned char *buffer = const_cast<unsigned char *>(input->GetBuffer());
-    CHECK_FAIL_RETURN_UNEXPECTED(buffer != nullptr, "The input image buffer is empty.");
+    CHECK_FAIL_RETURN_UNEXPECTED(buffer != nullptr, "SoftDvppDecodeReiszeJpeg: the input image buffer is empty.");
     SoftDpProcsessInfo info;
     info.input_buffer = static_cast<uint8_t *>(buffer);
     info.input_buffer_size = input->SizeInBytes();
@@ -43,11 +43,11 @@ Status SoftDvppDecodeResizeJpegOp::Compute(const std::shared_ptr<Tensor> &input,
 
     if (target_width_ == 0) {
       if (input_h < input_w) {
-        CHECK_FAIL_RETURN_UNEXPECTED(input_h != 0, "The input height is 0");
+        CHECK_FAIL_RETURN_UNEXPECTED(input_h != 0, "SoftDvppDecodeReiszeJpeg: the input height is 0.");
         info.output_height = target_height_;
         info.output_width = static_cast<int>(std::lround(static_cast<float>(input_w) / input_h * info.output_height));
       } else {
-        CHECK_FAIL_RETURN_UNEXPECTED(input_w != 0, "The input width is 0");
+        CHECK_FAIL_RETURN_UNEXPECTED(input_w != 0, "SoftDvppDecodeReiszeJpeg: the input width is 0.");
         info.output_width = target_height_;
         info.output_height = static_cast<int>(std::lround(static_cast<float>(input_h) / input_w * info.output_width));
       }
@@ -62,14 +62,14 @@ Status SoftDvppDecodeResizeJpegOp::Compute(const std::shared_ptr<Tensor> &input,
 
     info.is_v_before_u = true;
     int ret = DecodeAndResizeJpeg(&info);
-    std::string error_info("Soft dvpp DecodeAndResizeJpeg failed with return code: ");
-    error_info += std::to_string(ret);
+    std::string error_info("SoftDvppDecodeReiszeJpeg: failed with return code: ");
+    error_info += std::to_string(ret) + ", please check the log information for more details.";
     CHECK_FAIL_RETURN_UNEXPECTED(ret == 0, error_info);
     std::shared_ptr<CVTensor> cv_tensor = nullptr;
     RETURN_IF_NOT_OK(CVTensor::CreateFromMat(out_rgb_img, &cv_tensor));
     *output = std::static_pointer_cast<Tensor>(cv_tensor);
   } catch (const cv::Exception &e) {
-    std::string error = "Error in SoftDvppDecodeResizeJpegOp:" + std::string(e.what());
+    std::string error = "SoftDvppDecodeResizeJpeg:" + std::string(e.what());
     RETURN_STATUS_UNEXPECTED(error);
   }
   return Status::OK();
@@ -82,7 +82,7 @@ Status SoftDvppDecodeResizeJpegOp::OutputShape(const std::vector<TensorShape> &i
   TensorShape out({-1, -1, 3});  // we don't know what is output image size, but we know it should be 3 channels
   if (inputs[0].Rank() == 1) outputs.emplace_back(out);
   if (!outputs.empty()) return Status::OK();
-  return Status(StatusCode::kMDUnexpectedError, "Input has a wrong shape");
+  return Status(StatusCode::kMDUnexpectedError, "SoftDvppDecodeReiszeJpeg: input has a wrong shape.");
 }
 
 }  // namespace dataset

mindspore/ccsrc/minddata/dataset/kernels/ir/vision/vision_ir.cc

@@ -1512,6 +1512,14 @@ SoftDvppDecodeRandomCropResizeJpegOperation::SoftDvppDecodeRandomCropResizeJpegO
 Status SoftDvppDecodeRandomCropResizeJpegOperation::ValidateParams() {
   // size
   RETURN_IF_NOT_OK(ValidateVectorSize("SoftDvppDecodeRandomCropResizeJpeg", size_));
+  for (int32_t i = 0; i < size_.size(); i++) {
+    if (size_[i] % 2 == 1) {
+      std::string err_msg = "SoftDvppDecodeRandomCropResizeJpeg: size[" + std::to_string(i) +
+                            "] must be even values, got: " + std::to_string(size_[i]);
+      MS_LOG(ERROR) << err_msg;
+      RETURN_STATUS_SYNTAX_ERROR(err_msg);
+    }
+  }
   // scale
   RETURN_IF_NOT_OK(ValidateVectorScale("SoftDvppDecodeRandomCropResizeJpeg", scale_));
   // ratio
@@ -1554,6 +1562,14 @@ SoftDvppDecodeResizeJpegOperation::SoftDvppDecodeResizeJpegOperation(std::vector
 
 Status SoftDvppDecodeResizeJpegOperation::ValidateParams() {
   RETURN_IF_NOT_OK(ValidateVectorSize("SoftDvppDecodeResizeJpeg", size_));
+  for (int32_t i = 0; i < size_.size(); i++) {
+    if (size_[i] % 2 == 1) {
+      std::string err_msg = "SoftDvppDecodeResizeJpeg: size[" + std::to_string(i) +
+                            "] must be even values, got: " + std::to_string(size_[i]);
+      MS_LOG(ERROR) << err_msg;
+      RETURN_STATUS_SYNTAX_ERROR(err_msg);
+    }
+  }
   return Status::OK();
 }
 

mindspore/dataset/engine/samplers.py

@@ -25,6 +25,7 @@ import mindspore._c_dataengine as cde
 import mindspore.dataset as ds
 from ..core import validator_helpers as validator
 
+
 def select_sampler(num_samples, input_sampler, shuffle, num_shards, shard_id):
     """
     Create sampler based on user input.
@@ -615,8 +616,8 @@ class SubsetSampler(BuiltinSampler):
     Examples:
         >>> indices = [0, 1, 2, 3, 4, 5]
         >>>
-        >>> # creates a SubsetRandomSampler, will sample from the provided indices
-        >>> sampler = ds.SubsetRandomSampler(indices)
+        >>> # creates a SubsetSampler, will sample from the provided indices
+        >>> sampler = ds.SubsetSampler(indices)
         >>> dataset = ds.ImageFolderDataset(image_folder_dataset_dir,
         ...                                 num_parallel_workers=8,
         ...                                 sampler=sampler)
@@ -694,7 +695,7 @@ class SubsetRandomSampler(SubsetSampler):
     Samples the elements randomly from a sequence of indices.
 
     Args:
-        indices (list[int]): A sequence of indices.
+        indices (Any iterable python object but string): A sequence of indices.
         num_samples (int, optional): Number of elements to sample (default=None, all elements).
 
     Examples:
@@ -740,17 +741,16 @@ class IterSampler(Sampler):
         num_samples (int, optional): Number of elements to sample (default=None, all elements).
 
     Examples:
-        >>> class MySampler():
-        >>>     def __iter__(self):
-        >>>         for i in range(99, -1, -1):
-        >>>             yield i
+        >>> class MySampler:
+        ...     def __iter__(self):
+        ...         for i in range(99, -1, -1):
+        ...             yield i
 
         >>> # creates an IterSampler
         >>> sampler = ds.IterSampler(sampler=MySampler())
         >>> dataset = ds.ImageFolderDataset(image_folder_dataset_dir,
         ...                                 num_parallel_workers=8,
         ...                                 sampler=sampler)
-
      """
 
     def __init__(self, sampler, num_samples=None):