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!15070 fix code review alarmsfix code review alarms 

From: @shibeiji
Reviewed-by: @wuxuejian,@liangchenghui
Signed-off-by: @wuxuejian
This commit is contained in:
mindspore-ci-bot 2021-04-13 19:23:33 +08:00 committed by Gitee
parent 4df8a6188d 5cfab3b1a8
commit e2112c55f9
3 changed files with 16 additions and 15 deletions
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9
mindspore/ccsrc/backend/kernel_compiler/cpu/minimum_grad_cpu_kernel.cc
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@ -83,10 +83,11 @@ bool MinimumGradCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inputs,
} }
template <typename T> template <typename T>
void MinimumGradRecTask(T *x, T *y, T *dout, T *dx, T *dy, size_t dim, size_t x_index, size_t y_index, void MinimumGradRecTask(const T *x, const T *y, const T *dout, T *dx, T *dy, const size_t dim, const size_t x_index,
size_t dout_index, const std::vector<size_t> &x_cargo, const std::vector<size_t> &y_cargo, const size_t y_index, const size_t dout_index, const std::vector<size_t> &x_cargo,
const std::vector<size_t> &dout_cargo, const std::vector<size_t> &x_shape, const std::vector<size_t> &y_cargo, const std::vector<size_t> &dout_cargo,
const std::vector<size_t> &y_shape, const std::vector<size_t> &dout_shape) { const std::vector<size_t> &x_shape, const std::vector<size_t> &y_shape,
const std::vector<size_t> &dout_shape) {
for (size_t i = 0; i < dout_shape[dim]; i++) { for (size_t i = 0; i < dout_shape[dim]; i++) {
size_t x_i = x_shape[dim] == dout_shape[dim] ? i * x_cargo[dim] : 0; size_t x_i = x_shape[dim] == dout_shape[dim] ? i * x_cargo[dim] : 0;
size_t y_i = y_shape[dim] == dout_shape[dim] ? i * y_cargo[dim] : 0; size_t y_i = y_shape[dim] == dout_shape[dim] ? i * y_cargo[dim] : 0;

4
tests/st/ops/cpu/test_minimum_grad_op.py
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@ -64,7 +64,7 @@ def gen_data(inputA_np, inputB_np, grad_=None):
@pytest.mark.level0 @pytest.mark.level0
@pytest.mark.platform_x86_cpu_training @pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard @pytest.mark.env_onecard
def test_min_tensor_grad_4d(): def test_min_tensor_grad_4d():
inputA_np = np.random.randn(1, 3, 2, 2).astype(np.float32) inputA_np = np.random.randn(1, 3, 2, 2).astype(np.float32)
@ -76,7 +76,7 @@ def test_min_tensor_grad_4d():
@pytest.mark.level0 @pytest.mark.level0
@pytest.mark.platform_x86_cpu_training @pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard @pytest.mark.env_onecard
def test_min_tensor_grad_result(): def test_min_tensor_grad_result():
inputA = np.array([[[[0.659578], [0.49113268], [0.75909054], [0.71681815], [0.30421826]]], inputA = np.array([[[[0.659578], [0.49113268], [0.75909054], [0.71681815], [0.30421826]]],

18
tests/st/ops/cpu/test_minimum_op.py
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@ -42,7 +42,7 @@ class TwoTensorsMinimum(Cell):
@pytest.mark.level0 @pytest.mark.level0
@pytest.mark.platform_x86_cpu_training @pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard @pytest.mark.env_onecard
def test_minimum_constScalar_tensor_int(): def test_minimum_constScalar_tensor_int():
x = Tensor(np.array([[2, 3, 4], [100, 200, 300]]).astype(np.int32)) x = Tensor(np.array([[2, 3, 4], [100, 200, 300]]).astype(np.int32))
@ -55,7 +55,7 @@ def test_minimum_constScalar_tensor_int():
@pytest.mark.level0 @pytest.mark.level0
@pytest.mark.platform_x86_cpu_training @pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard @pytest.mark.env_onecard
def test_minimum_two_tensors_Not_Broadcast_int(): def test_minimum_two_tensors_Not_Broadcast_int():
prop = 100 if np.random.random() > 0.5 else -100 prop = 100 if np.random.random() > 0.5 else -100
@ -70,7 +70,7 @@ def test_minimum_two_tensors_Not_Broadcast_int():
@pytest.mark.level0 @pytest.mark.level0
@pytest.mark.platform_x86_cpu_training @pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard @pytest.mark.env_onecard
def test_minimum_two_tensors_Broadcast_int(): def test_minimum_two_tensors_Broadcast_int():
prop = 100 if np.random.random() > 0.5 else -100 prop = 100 if np.random.random() > 0.5 else -100
@ -85,7 +85,7 @@ def test_minimum_two_tensors_Broadcast_int():
@pytest.mark.level0 @pytest.mark.level0
@pytest.mark.platform_x86_cpu_training @pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard @pytest.mark.env_onecard
def test_minimum_two_tensors_Broadcast_oneDimension_int(): def test_minimum_two_tensors_Broadcast_oneDimension_int():
prop = 100 if np.random.random() > 0.5 else -100 prop = 100 if np.random.random() > 0.5 else -100
@ -100,7 +100,7 @@ def test_minimum_two_tensors_Broadcast_oneDimension_int():
@pytest.mark.level0 @pytest.mark.level0
@pytest.mark.platform_x86_cpu_training @pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard @pytest.mark.env_onecard
def test_minimum_two_tensors_notBroadcast_all_oneDimension_int(): def test_minimum_two_tensors_notBroadcast_all_oneDimension_int():
x = Tensor(np.array([[2]]).astype(np.int32)) x = Tensor(np.array([[2]]).astype(np.int32))
@ -114,7 +114,7 @@ def test_minimum_two_tensors_notBroadcast_all_oneDimension_int():
@pytest.mark.level0 @pytest.mark.level0
@pytest.mark.platform_x86_cpu_training @pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard @pytest.mark.env_onecard
def test_minimum_two_tensors_notBroadcast_float32(): def test_minimum_two_tensors_notBroadcast_float32():
prop = 100 if np.random.random() > 0.5 else -100 prop = 100 if np.random.random() > 0.5 else -100
@ -132,7 +132,7 @@ def test_minimum_two_tensors_notBroadcast_float32():
@pytest.mark.level0 @pytest.mark.level0
@pytest.mark.platform_x86_cpu_training @pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard @pytest.mark.env_onecard
def test_minimum_two_tensors_notBroadcast_float16(): def test_minimum_two_tensors_notBroadcast_float16():
prop = 100 if np.random.random() > 0.5 else -100 prop = 100 if np.random.random() > 0.5 else -100
@ -150,7 +150,7 @@ def test_minimum_two_tensors_notBroadcast_float16():
@pytest.mark.level0 @pytest.mark.level0
@pytest.mark.platform_x86_cpu_training @pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard @pytest.mark.env_onecard
def test_minimum_two_tensors_Broadcast_float16(): def test_minimum_two_tensors_Broadcast_float16():
prop = 100 if np.random.random() > 0.5 else -100 prop = 100 if np.random.random() > 0.5 else -100
@ -168,7 +168,7 @@ def test_minimum_two_tensors_Broadcast_float16():
@pytest.mark.level0 @pytest.mark.level0
@pytest.mark.platform_x86_cpu_training @pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard @pytest.mark.env_onecard
def test_minimum_two_tensors_notBroadcast_float64(): def test_minimum_two_tensors_notBroadcast_float64():
prop = 100 if np.random.random() > 0.5 else -100 prop = 100 if np.random.random() > 0.5 else -100