[MD] Fix bugs in WeightedRandomSampler & SubsetRandomSampler

mindspore/ccsrc/minddata/dataset/api/samplers.cc

@@ -299,6 +299,24 @@ WeightedRandomSamplerObj::WeightedRandomSamplerObj(std::vector<double> weights,
     : weights_(std::move(weights)), num_samples_(num_samples), replacement_(replacement) {}
 
 bool WeightedRandomSamplerObj::ValidateParams() {
+  if (weights_.empty()) {
+    MS_LOG(ERROR) << "WeightedRandomSampler: weights vector must not be empty";
+    return false;
+  }
+  int32_t zero_elem = 0;
+  for (int32_t i = 0; i < weights_.size(); ++i) {
+    if (weights_[i] < 0) {
+      MS_LOG(ERROR) << "WeightedRandomSampler: weights vector must not contain negative number, got: " << weights_[i];
+      return false;
+    }
+    if (weights_[i] == 0.0) {
+      zero_elem++;
+    }
+  }
+  if (zero_elem == weights_.size()) {
+    MS_LOG(ERROR) << "WeightedRandomSampler: elements of weights vector must not be all zero";
+    return false;
+  }
   if (num_samples_ < 0) {
     MS_LOG(ERROR) << "WeightedRandomSampler: invalid num_samples: " << num_samples_;
     return false;

mindspore/ccsrc/minddata/dataset/engine/datasetops/source/sampler/subset_random_sampler.cc

@@ -103,7 +103,7 @@ Status SubsetRandomSampler::GetNextSample(std::unique_ptr<DataBuffer> *out_buffe
         RETURN_STATUS_UNEXPECTED(err_msg);
       }
 
-      int64_t sampled_id = indices_[sample_id_];
+      int64_t sampled_id = ((indices_[sample_id_] % num_rows_) + num_rows_) % num_rows_;
       if (HasChildSampler()) {
         RETURN_IF_NOT_OK(GetAssociatedChildId(&sampled_id, sampled_id));
       }

mindspore/dataset/engine/samplers.py

@@ -585,6 +585,15 @@ class WeightedRandomSampler(BuiltinSampler):
         if not isinstance(weights, list):
             weights = [weights]
 
+        if weights == []:
+            raise ValueError("weights size should not be 0")
+
+        if list(filter(lambda x: x < 0, weights)):
+            raise ValueError("weights should not contain negative numbers")
+
+        if list(filter(lambda x: x == 0, weights)) == weights:
+            raise ValueError("elements of weights should not be all zero")
+
         if num_samples is not None:
             if num_samples <= 0:
                 raise ValueError("num_samples should be a positive integer "

tests/ut/cpp/dataset/c_api_samplers_test.cc

@@ -99,3 +99,20 @@ TEST_F(MindDataTestPipeline, TestSamplersMoveParameters) {
   EXPECT_TRUE(indices.empty());
   EXPECT_NE(sampl2->Build(), nullptr);
 }
+
+TEST_F(MindDataTestPipeline, TestWeightedRandomSamplerFail) {
+  // weights is empty
+  std::vector<double> weights1 = {};
+  std::shared_ptr<SamplerObj> sampl1 = WeightedRandomSampler(weights1);
+  EXPECT_EQ(sampl1, nullptr);
+
+  // weights has negative number
+  std::vector<double> weights2 = {0.5, 0.2, -0.4};
+  std::shared_ptr<SamplerObj> sampl2 = WeightedRandomSampler(weights2);
+  EXPECT_EQ(sampl2, nullptr);
+
+  // weights elements are all zero
+  std::vector<double> weights3 = {0, 0, 0};
+  std::shared_ptr<SamplerObj> sampl3 = WeightedRandomSampler(weights3);
+  EXPECT_EQ(sampl3, nullptr);
+}