[ms][pynative][lenet]fix training bug

[ms][pynative][lenet]fix training bug

[ms][pynative][lenet]fix training bug

[ms][pynative][lenet]fix training bug

[ms][pynative][lenet]fix training bug

[ms][pynative][lenet]fix training bug

[ms][pynative][lenet]fix training bug

[ms][pynative][lenet]fix training bug

[ms][pynative][lenet]fix training bug

[ms][pynative][lenet]fix training bug

[ms][pynative][lenet]fix training bug

[ms][pynative][lenet]fix training bug

[ms][pynative][lenet]fix training bug

[ms][pynative][lenet]fix training bug

mindspore/python/mindspore/ops/_op_impl/_custom_op/fake_quant_perchannel_grad.py

@@ -144,7 +144,7 @@ def fake_quant_perchannel_grad_param(x, min_val, max_val, channel_axis,
 
     shape_c = [1] * len(x_shape)
     shape_c[channel_axis_] = min_val.get("ori_shape")[0]
-    if x_format == "NC1HWC0" and channel_axis_ == 1:
+    if shape_c[channel_axis_] != x_shape[channel_axis_]:
         shape_c = min_val.get("shape")
     return x_shape, shape_c, x_dtype
 

tests/st/quantization/lenet_quant/test_lenet_quant.py

@@ -182,3 +182,18 @@ def test_lenet_quant_ascend():
     train_lenet_quant(optim_option="LEARNED_SCALE")
     eval_quant(optim_option="LEARNED_SCALE")
     export_lenet(optim_option="LEARNED_SCALE", file_format="AIR")
+
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_lenet_quant_ascend_pynative():
+    """
+    test_lenet_quant_ascend_pynative
+    Features: test_lenet_quant_ascend_pynative
+    Description: test_lenet_quant_ascend_pynative pynative mode
+    Expectation: None
+    """
+    context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
+    train_lenet_quant(optim_option="QAT")

tests/st/quantization/mobilenetv2_quant/test_mobilenetv2_quant.py

@@ -55,13 +55,8 @@ config_ascend_quant = ed({
 dataset_path = "/home/workspace/mindspore_dataset/cifar-10-batches-bin/"
 
 
-@pytest.mark.level0
-@pytest.mark.platform_arm_ascend_training
-@pytest.mark.platform_x86_ascend_training
-@pytest.mark.env_single
-def test_mobilenetv2_quant():
-    set_seed(1)
-    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+def train():
+    """train"""
     config = config_ascend_quant
     print("training configure: {}".format(config))
 
@@ -121,5 +116,38 @@ def test_mobilenetv2_quant():
     assert avg_step_loss < expect_avg_step_loss
 
 
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_single
+def test_mobilenetv2_quant():
+    """
+    test_mobilenetv2_quant
+    Features: test_mobilenetv2_quant
+    Description: test_mobilenetv2_quant graph mode
+    Expectation: None
+    """
+    set_seed(1)
+    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+    train()
+
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_single
+def test_mobilenetv2_quant_pynative():
+    """
+    test_mobilenetv2_quant_pynative
+    Features: test_mobilenetv2_quant_pynative
+    Description: test_mobilenetv2_quant_pynative pynative mode
+    Expectation: None
+    """
+    set_seed(1)
+    context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
+    train()
+
+
 if __name__ == '__main__':
     test_mobilenetv2_quant()
+    test_mobilenetv2_quant_pynative()