!48367 fix cpu dropoutnd

Merge pull request !48367 from 王禹程/drop_ratio

mindspore/ccsrc/plugin/device/cpu/kernel/dropout_nd_cpu_kernel.cc

@@ -135,9 +135,9 @@ bool DropoutNdCpuKernelMod::LaunchKernel(const std::vector<AddressPtr> &inputs,
     auto per_output = output + start * inner_size;
     auto per_mask = mask + start * inner_size;
     for (size_t i = start; i < end; ++i) {
-      bool drop = static_cast<float>(distribution_(generator_)) <= keep_prob_;
-      if (drop) {
-        std::fill(per_mask, per_mask + inner_size, drop);
+      bool keep = distribution_(generator_);
+      if (keep) {
+        std::fill(per_mask, per_mask + inner_size, keep);
         if constexpr (std::is_same<T, float>::value) {
           DropoutFp32(per_input, scale_, SizeToInt(inner_size), per_output);
         } else {

tests/st/ops/cpu/test_dropout_op.py

@@ -16,6 +16,7 @@
 import numpy as np
 import pytest
 
+import mindspore
 import mindspore.context as context
 import mindspore.nn as nn
 from mindspore import Tensor
@@ -132,6 +133,40 @@ def test_op1():
     assert np.allclose(mask1.asnumpy(), mask2.asnumpy())
 
 
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_op2():
+    """
+    Feature: test Dropout2D.
+    Description: dropout.
+    Expectation: No exception.
+    """
+    input_np = np.ones((1000, 1000, 20, 5)).astype(np.float32)
+    input_x = Tensor(input_np, mindspore.float32)
+    data_size = 1000 * 1000 * 20 * 5
+
+    dropout = ops.Dropout2D(keep_prob=0.0)
+    output_ms, _ = dropout(input_x)
+    ans = np.sum(np.where(output_ms.asnumpy(), 0, 1))
+    assert ans == data_size
+
+    dropout = ops.Dropout2D(keep_prob=0.2)
+    output_ms, _ = dropout(input_x)
+    ans = np.sum(np.where(output_ms.asnumpy(), 0, 1))
+    assert data_size * 0.75 <= ans <= data_size * 0.85
+
+    dropout = ops.Dropout2D(keep_prob=0.8)
+    output_ms, _ = dropout(input_x)
+    ans = np.sum(np.where(output_ms.asnumpy(), 0, 1))
+    assert data_size * 0.15 <= ans <= data_size * 0.25
+
+    dropout = ops.Dropout2D(keep_prob=1.0)
+    output_ms, _ = dropout(input_x)
+    ans = np.sum(np.where(output_ms.asnumpy(), 0, 1))
+    assert ans == 0
+
+
 if __name__ == '__main__':
     test_net()
     test_net1()