plt update net instance, test=model

PaddlePaddle · Sep 4, 2024 · 25aaae1 · 25aaae1
1 parent b154952
commit 25aaae1
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diff --git a/...dleLT_new/layercase/sublayer1000/Clas_cases/DeiT_DeiT_tiny_distilled_patch16_224/SIR_4.py b/...dleLT_new/layercase/sublayer1000/Clas_cases/DeiT_DeiT_tiny_distilled_patch16_224/SIR_4.py
@@ -47,12 +47,12 @@ def forward(
         var_9 = var_5.matmul(var_8)
         var_10 = var_9.__mul__(0.125)
         var_11 = paddle.nn.functional.activation.softmax(var_10, axis=-1)
-        var_12 = paddle.nn.functional.common.dropout(var_11, p=0.0, axis=None, training=True, mode='upscale_in_train', name=None)
+        var_12 = paddle.nn.functional.common.dropout(var_11, p=0.0, axis=None, training=self.training, mode='upscale_in_train', name=None)
         var_13 = var_12.matmul(var_7)
         var_14 = var_13.transpose((0, 2, 1, 3,))
         var_15 = var_14.reshape((-1, 198, 192,))
         var_16 = paddle.nn.functional.common.linear(x=var_15, weight=self.parameter_2, bias=self.parameter_4, name=None)
-        var_17 = paddle.nn.functional.common.dropout(var_16, p=0.0, axis=None, training=True, mode='upscale_in_train', name=None)
+        var_17 = paddle.nn.functional.common.dropout(var_16, p=0.0, axis=None, training=self.training, mode='upscale_in_train', name=None)
         return var_17
 
 

diff --git a/...leLT_new/layercase/sublayer1000/Clas_cases/DeiT_DeiT_tiny_distilled_patch16_224/SIR_56.py b/...leLT_new/layercase/sublayer1000/Clas_cases/DeiT_DeiT_tiny_distilled_patch16_224/SIR_56.py
@@ -47,12 +47,12 @@ def forward(
         var_9 = var_5.matmul(var_8)
         var_10 = var_9.__mul__(0.125)
         var_11 = paddle.nn.functional.activation.softmax(var_10, axis=-1)
-        var_12 = paddle.nn.functional.common.dropout(var_11, p=0.0, axis=None, training=True, mode='upscale_in_train', name=None)
+        var_12 = paddle.nn.functional.common.dropout(var_11, p=0.0, axis=None, training=self.training, mode='upscale_in_train', name=None)
         var_13 = var_12.matmul(var_7)
         var_14 = var_13.transpose((0, 2, 1, 3,))
         var_15 = var_14.reshape((-1, 198, 192,))
         var_16 = paddle.nn.functional.common.linear(x=var_15, weight=self.parameter_1, bias=self.parameter_2, name=None)
-        var_17 = paddle.nn.functional.common.dropout(var_16, p=0.0, axis=None, training=True, mode='upscale_in_train', name=None)
+        var_17 = paddle.nn.functional.common.dropout(var_16, p=0.0, axis=None, training=self.training, mode='upscale_in_train', name=None)
         return var_17
 
 

diff --git a/...rk/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/DeiT_DeiT_tiny_patch16_224/SIR_4.py b/...rk/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/DeiT_DeiT_tiny_patch16_224/SIR_4.py
@@ -47,12 +47,12 @@ def forward(
         var_9 = var_5.matmul(var_8)
         var_10 = var_9.__mul__(0.125)
         var_11 = paddle.nn.functional.activation.softmax(var_10, axis=-1)
-        var_12 = paddle.nn.functional.common.dropout(var_11, p=0.0, axis=None, training=True, mode='upscale_in_train', name=None)
+        var_12 = paddle.nn.functional.common.dropout(var_11, p=0.0, axis=None, training=self.training, mode='upscale_in_train', name=None)
         var_13 = var_12.matmul(var_7)
         var_14 = var_13.transpose((0, 2, 1, 3,))
         var_15 = var_14.reshape((-1, 197, 192,))
         var_16 = paddle.nn.functional.common.linear(x=var_15, weight=self.parameter_3, bias=self.parameter_5, name=None)
-        var_17 = paddle.nn.functional.common.dropout(var_16, p=0.0, axis=None, training=True, mode='upscale_in_train', name=None)
+        var_17 = paddle.nn.functional.common.dropout(var_16, p=0.0, axis=None, training=self.training, mode='upscale_in_train', name=None)
         return var_17
 
 

diff --git a/...k/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/DeiT_DeiT_tiny_patch16_224/SIR_56.py b/...k/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/DeiT_DeiT_tiny_patch16_224/SIR_56.py
@@ -47,12 +47,12 @@ def forward(
         var_9 = var_5.matmul(var_8)
         var_10 = var_9.__mul__(0.125)
         var_11 = paddle.nn.functional.activation.softmax(var_10, axis=-1)
-        var_12 = paddle.nn.functional.common.dropout(var_11, p=0.0, axis=None, training=True, mode='upscale_in_train', name=None)
+        var_12 = paddle.nn.functional.common.dropout(var_11, p=0.0, axis=None, training=self.training, mode='upscale_in_train', name=None)
         var_13 = var_12.matmul(var_7)
         var_14 = var_13.transpose((0, 2, 1, 3,))
         var_15 = var_14.reshape((-1, 197, 192,))
         var_16 = paddle.nn.functional.common.linear(x=var_15, weight=self.parameter_0, bias=self.parameter_5, name=None)
-        var_17 = paddle.nn.functional.common.dropout(var_16, p=0.0, axis=None, training=True, mode='upscale_in_train', name=None)
+        var_17 = paddle.nn.functional.common.dropout(var_16, p=0.0, axis=None, training=self.training, mode='upscale_in_train', name=None)
         return var_17
 
 

diff --git a/...e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/EfficientNet_EfficientNetB0/SIR_140.py b/...e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/EfficientNet_EfficientNetB0/SIR_140.py
@@ -21,7 +21,7 @@ def forward(
     ):
         paddle.seed(33)
         var_1 = paddle.nn.functional.pooling.adaptive_avg_pool2d(var_0, output_size=1, data_format='NCHW', name=None)
-        var_2 = paddle.nn.functional.common.dropout(var_1, p=0.2, axis=None, training=True, mode='upscale_in_train', name=None)
+        var_2 = paddle.nn.functional.common.dropout(var_1, p=0.2, axis=None, training=self.training, mode='upscale_in_train', name=None)
         var_3 = paddle.tensor.manipulation.squeeze(var_2, axis=[2, 3])
         var_4 = paddle.nn.functional.common.linear(x=var_3, weight=self.parameter_1, bias=self.parameter_0, name=None)
         return var_4

diff --git a/.../e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/EfficientNet_EfficientNetB0/SIR_96.py b/.../e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/EfficientNet_EfficientNetB0/SIR_96.py
@@ -21,7 +21,7 @@ def forward(
     ):
         paddle.seed(33)
         var_1 = paddle.nn.functional.pooling.adaptive_avg_pool2d(var_0, output_size=1, data_format='NCHW', name=None)
-        var_2 = paddle.nn.functional.common.dropout(var_1, p=0.2, axis=None, training=True, mode='upscale_in_train', name=None)
+        var_2 = paddle.nn.functional.common.dropout(var_1, p=0.2, axis=None, training=self.training, mode='upscale_in_train', name=None)
         var_3 = paddle.tensor.manipulation.squeeze(var_2, axis=[2, 3])
         var_4 = paddle.nn.functional.common.linear(x=var_3, weight=self.parameter_1, bias=self.parameter_0, name=None)
         return var_4

diff --git a/framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/Inception_InceptionV4/SIR_39.py b/framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/Inception_InceptionV4/SIR_39.py
@@ -22,7 +22,7 @@ def forward(
         paddle.seed(33)
         var_1 = paddle.nn.functional.pooling.adaptive_avg_pool2d(var_0, output_size=1, data_format='NCHW', name=None)
         var_2 = paddle.tensor.manipulation.squeeze(var_1, axis=[2, 3])
-        var_3 = paddle.nn.functional.common.dropout(var_2, p=0.2, axis=None, training=True, mode='downscale_in_infer', name=None)
+        var_3 = paddle.nn.functional.common.dropout(var_2, p=0.2, axis=None, training=self.training, mode='downscale_in_infer', name=None)
         var_4 = paddle.nn.functional.common.linear(x=var_3, weight=self.parameter_0, bias=self.parameter_1, name=None)
         return var_4
 

diff --git a/framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/Inception_InceptionV4/SIR_40.py b/framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/Inception_InceptionV4/SIR_40.py
@@ -22,7 +22,7 @@ def forward(
         paddle.seed(33)
         var_1 = paddle.nn.functional.pooling.adaptive_avg_pool2d(var_0, output_size=1, data_format='NCHW', name=None)
         var_2 = paddle.tensor.manipulation.squeeze(var_1, axis=[2, 3])
-        var_3 = paddle.nn.functional.common.dropout(var_2, p=0.2, axis=None, training=True, mode='downscale_in_infer', name=None)
+        var_3 = paddle.nn.functional.common.dropout(var_2, p=0.2, axis=None, training=self.training, mode='downscale_in_infer', name=None)
         var_4 = paddle.nn.functional.common.linear(x=var_3, weight=self.parameter_1, bias=self.parameter_0, name=None)
         return var_4
 

diff --git a/...work/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/MobileViT_MobileViT_XXS/SIR_26.py b/...work/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/MobileViT_MobileViT_XXS/SIR_26.py
@@ -22,7 +22,7 @@ def forward(
         paddle.seed(33)
         var_1 = paddle.nn.functional.pooling.adaptive_avg_pool2d(var_0, output_size=1, data_format='NCHW', name=None)
         var_2 = var_1.reshape([43, 320])
-        var_3 = paddle.nn.functional.common.dropout(var_2, p=0.1, axis=None, training=True, mode='upscale_in_train', name=None)
+        var_3 = paddle.nn.functional.common.dropout(var_2, p=0.1, axis=None, training=self.training, mode='upscale_in_train', name=None)
         var_4 = paddle.nn.functional.common.linear(x=var_3, weight=self.parameter_0, bias=self.parameter_1, name=None)
         return var_4
 

diff --git a/...work/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/MobileViT_MobileViT_XXS/SIR_27.py b/...work/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/MobileViT_MobileViT_XXS/SIR_27.py
@@ -22,7 +22,7 @@ def forward(
         paddle.seed(33)
         var_1 = paddle.nn.functional.pooling.adaptive_avg_pool2d(var_0, output_size=1, data_format='NCHW', name=None)
         var_2 = var_1.reshape([11, 320])
-        var_3 = paddle.nn.functional.common.dropout(var_2, p=0.1, axis=None, training=True, mode='upscale_in_train', name=None)
+        var_3 = paddle.nn.functional.common.dropout(var_2, p=0.1, axis=None, training=self.training, mode='upscale_in_train', name=None)
         var_4 = paddle.nn.functional.common.linear(x=var_3, weight=self.parameter_0, bias=self.parameter_1, name=None)
         return var_4
 

diff --git a/framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/PeleeNet_PeleeNet/SIR_41.py b/framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/PeleeNet_PeleeNet/SIR_41.py
@@ -22,7 +22,7 @@ def forward(
         paddle.seed(33)
         var_1 = paddle.nn.functional.pooling.avg_pool2d(var_0, kernel_size=[7, 7])
         var_2 = var_1.flatten(1)
-        var_3 = paddle.nn.functional.common.dropout(var_2, p=0.05, training=True)
+        var_3 = paddle.nn.functional.common.dropout(var_2, p=0.05, training=self.training)
         var_4 = paddle.nn.functional.common.linear(x=var_3, weight=self.parameter_0, bias=self.parameter_1, name=None)
         return var_4
 

diff --git a/framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/PeleeNet_PeleeNet/SIR_42.py b/framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/PeleeNet_PeleeNet/SIR_42.py
@@ -22,7 +22,7 @@ def forward(
         paddle.seed(33)
         var_1 = paddle.nn.functional.pooling.avg_pool2d(var_0, kernel_size=[7, 7])
         var_2 = var_1.flatten(1)
-        var_3 = paddle.nn.functional.common.dropout(var_2, p=0.05, training=True)
+        var_3 = paddle.nn.functional.common.dropout(var_2, p=0.05, training=self.training)
         var_4 = paddle.nn.functional.common.linear(x=var_3, weight=self.parameter_1, bias=self.parameter_0, name=None)
         return var_4
 

diff --git a/framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/ReXNet_ReXNet_3_0/SIR_60.py b/framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/ReXNet_ReXNet_3_0/SIR_60.py
@@ -20,7 +20,7 @@ def forward(
         var_0,    # (shape: [22, 3840, 1, 1], dtype: paddle.float32, stop_gradient: False)
     ):
         paddle.seed(33)
-        var_1 = paddle.nn.functional.common.dropout(var_0, p=0.2, axis=None, training=True, mode='upscale_in_train', name=None)
+        var_1 = paddle.nn.functional.common.dropout(var_0, p=0.2, axis=None, training=self.training, mode='upscale_in_train', name=None)
         var_2 = paddle.nn.functional.conv._conv_nd(var_1, self.parameter_1, bias=self.parameter_0, stride=[1, 1], padding=[0, 0], padding_algorithm='EXPLICIT', dilation=[1, 1], groups=1, data_format='NCHW', channel_dim=1, op_type='conv2d', use_cudnn=True)
         var_3 = var_2.squeeze(axis=-1)
         var_4 = var_3.squeeze(axis=-1)

diff --git a/framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/ReXNet_ReXNet_3_0/SIR_61.py b/framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/ReXNet_ReXNet_3_0/SIR_61.py
@@ -20,7 +20,7 @@ def forward(
         var_0,    # (shape: [10, 3840, 1, 1], dtype: paddle.float32, stop_gradient: False)
     ):
         paddle.seed(33)
-        var_1 = paddle.nn.functional.common.dropout(var_0, p=0.2, axis=None, training=True, mode='upscale_in_train', name=None)
+        var_1 = paddle.nn.functional.common.dropout(var_0, p=0.2, axis=None, training=self.training, mode='upscale_in_train', name=None)
         var_2 = paddle.nn.functional.conv._conv_nd(var_1, self.parameter_0, bias=self.parameter_1, stride=[1, 1], padding=[0, 0], padding_algorithm='EXPLICIT', dilation=[1, 1], groups=1, data_format='NCHW', channel_dim=1, op_type='conv2d', use_cudnn=True)
         var_3 = var_2.squeeze(axis=-1)
         var_4 = var_3.squeeze(axis=-1)

diff --git a/...work/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/SqueezeNet_SqueezeNet1_0/SIR_0.py b/...work/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/SqueezeNet_SqueezeNet1_0/SIR_0.py
@@ -281,7 +281,7 @@ def forward(
         var_59 = paddle.nn.functional.conv._conv_nd(var_56, self.parameter_35, bias=self.parameter_48, stride=[1, 1], padding=[1, 1], padding_algorithm='EXPLICIT', dilation=[1, 1], groups=1, data_format='NCHW', channel_dim=1, op_type='conv2d', use_cudnn=True)
         var_60 = paddle.nn.functional.activation.relu(var_59)
         var_61 = paddle.tensor.manipulation.concat([var_58, var_60], axis=1)
-        var_62 = paddle.nn.functional.common.dropout(var_61, p=0.5, axis=None, training=True, mode='downscale_in_infer', name=None)
+        var_62 = paddle.nn.functional.common.dropout(var_61, p=0.5, axis=None, training=self.training, mode='downscale_in_infer', name=None)
         var_63 = paddle.nn.functional.conv._conv_nd(var_62, self.parameter_44, bias=self.parameter_1, stride=[1, 1], padding=[0, 0], padding_algorithm='EXPLICIT', dilation=[1, 1], groups=1, data_format='NCHW', channel_dim=1, op_type='conv2d', use_cudnn=True)
         var_64 = paddle.nn.functional.activation.relu(var_63)
         var_65 = paddle.nn.functional.pooling.adaptive_avg_pool2d(var_64, output_size=1, data_format='NCHW', name=None)

diff --git a/...work/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/SqueezeNet_SqueezeNet1_0/SIR_1.py b/...work/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/SqueezeNet_SqueezeNet1_0/SIR_1.py
@@ -281,7 +281,7 @@ def forward(
         var_59 = paddle.nn.functional.conv._conv_nd(var_56, self.parameter_39, bias=self.parameter_1, stride=[1, 1], padding=[1, 1], padding_algorithm='EXPLICIT', dilation=[1, 1], groups=1, data_format='NCHW', channel_dim=1, op_type='conv2d', use_cudnn=True)
         var_60 = paddle.nn.functional.activation.relu(var_59)
         var_61 = paddle.tensor.manipulation.concat([var_58, var_60], axis=1)
-        var_62 = paddle.nn.functional.common.dropout(var_61, p=0.5, axis=None, training=True, mode='downscale_in_infer', name=None)
+        var_62 = paddle.nn.functional.common.dropout(var_61, p=0.5, axis=None, training=self.training, mode='downscale_in_infer', name=None)
         var_63 = paddle.nn.functional.conv._conv_nd(var_62, self.parameter_15, bias=self.parameter_17, stride=[1, 1], padding=[0, 0], padding_algorithm='EXPLICIT', dilation=[1, 1], groups=1, data_format='NCHW', channel_dim=1, op_type='conv2d', use_cudnn=True)
         var_64 = paddle.nn.functional.activation.relu(var_63)
         var_65 = paddle.nn.functional.pooling.adaptive_avg_pool2d(var_64, output_size=1, data_format='NCHW', name=None)

diff --git a/framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/Twins_alt_gvt_base/SIR_108.py b/framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/Twins_alt_gvt_base/SIR_108.py
@@ -32,7 +32,7 @@ def forward(
         var_2 = var_1.flatten(2)
         var_3 = var_2.transpose([0, 2, 1])
         var_4 = paddle.nn.functional.norm.layer_norm(var_3, normalized_shape=[384], weight=self.parameter_2, bias=self.parameter_1, epsilon=1e-05)
-        var_5 = paddle.nn.functional.common.dropout(var_4, p=0.0, axis=None, training=True, mode='upscale_in_train', name=None)
+        var_5 = paddle.nn.functional.common.dropout(var_4, p=0.0, axis=None, training=self.training, mode='upscale_in_train', name=None)
         return var_5
 
 

diff --git a/framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/Twins_alt_gvt_base/SIR_136.py b/framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/Twins_alt_gvt_base/SIR_136.py
@@ -32,7 +32,7 @@ def forward(
         var_2 = var_1.flatten(2)
         var_3 = var_2.transpose([0, 2, 1])
         var_4 = paddle.nn.functional.norm.layer_norm(var_3, normalized_shape=[768], weight=self.parameter_0, bias=self.parameter_3, epsilon=1e-05)
-        var_5 = paddle.nn.functional.common.dropout(var_4, p=0.0, axis=None, training=True, mode='upscale_in_train', name=None)
+        var_5 = paddle.nn.functional.common.dropout(var_4, p=0.0, axis=None, training=self.training, mode='upscale_in_train', name=None)
         return var_5
 
 

diff --git a/framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/Twins_alt_gvt_base/SIR_14.py b/framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/Twins_alt_gvt_base/SIR_14.py
@@ -32,7 +32,7 @@ def forward(
         var_2 = var_1.flatten(2)
         var_3 = var_2.transpose([0, 2, 1])
         var_4 = paddle.nn.functional.norm.layer_norm(var_3, normalized_shape=[192], weight=self.parameter_3, bias=self.parameter_2, epsilon=1e-05)
-        var_5 = paddle.nn.functional.common.dropout(var_4, p=0.0, axis=None, training=True, mode='upscale_in_train', name=None)
+        var_5 = paddle.nn.functional.common.dropout(var_4, p=0.0, axis=None, training=self.training, mode='upscale_in_train', name=None)
         return var_5
 
 

diff --git a/framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/Twins_alt_gvt_base/SIR_2.py b/framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/Twins_alt_gvt_base/SIR_2.py
@@ -32,7 +32,7 @@ def forward(
         var_2 = var_1.flatten(2)
         var_3 = var_2.transpose([0, 2, 1])
         var_4 = paddle.nn.functional.norm.layer_norm(var_3, normalized_shape=[96], weight=self.parameter_1, bias=self.parameter_0, epsilon=1e-05)
-        var_5 = paddle.nn.functional.common.dropout(var_4, p=0.0, axis=None, training=True, mode='upscale_in_train', name=None)
+        var_5 = paddle.nn.functional.common.dropout(var_4, p=0.0, axis=None, training=self.training, mode='upscale_in_train', name=None)
         return var_5
 
 

diff --git a/framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/Twins_alt_gvt_base/SIR_26.py b/framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/Twins_alt_gvt_base/SIR_26.py
@@ -32,7 +32,7 @@ def forward(
         var_2 = var_1.flatten(2)
         var_3 = var_2.transpose([0, 2, 1])
         var_4 = paddle.nn.functional.norm.layer_norm(var_3, normalized_shape=[384], weight=self.parameter_0, bias=self.parameter_2, epsilon=1e-05)
-        var_5 = paddle.nn.functional.common.dropout(var_4, p=0.0, axis=None, training=True, mode='upscale_in_train', name=None)
+        var_5 = paddle.nn.functional.common.dropout(var_4, p=0.0, axis=None, training=self.training, mode='upscale_in_train', name=None)
         return var_5
 
 

diff --git a/framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/Twins_alt_gvt_base/SIR_70.py b/framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/Twins_alt_gvt_base/SIR_70.py
@@ -32,7 +32,7 @@ def forward(
         var_2 = var_1.flatten(2)
         var_3 = var_2.transpose([0, 2, 1])
         var_4 = paddle.nn.functional.norm.layer_norm(var_3, normalized_shape=[768], weight=self.parameter_3, bias=self.parameter_2, epsilon=1e-05)
-        var_5 = paddle.nn.functional.common.dropout(var_4, p=0.0, axis=None, training=True, mode='upscale_in_train', name=None)
+        var_5 = paddle.nn.functional.common.dropout(var_4, p=0.0, axis=None, training=self.training, mode='upscale_in_train', name=None)
         return var_5
 
 

diff --git a/framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/Twins_alt_gvt_base/SIR_84.py b/framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/Twins_alt_gvt_base/SIR_84.py
@@ -32,7 +32,7 @@ def forward(
         var_2 = var_1.flatten(2)
         var_3 = var_2.transpose([0, 2, 1])
         var_4 = paddle.nn.functional.norm.layer_norm(var_3, normalized_shape=[96], weight=self.parameter_2, bias=self.parameter_0, epsilon=1e-05)
-        var_5 = paddle.nn.functional.common.dropout(var_4, p=0.0, axis=None, training=True, mode='upscale_in_train', name=None)
+        var_5 = paddle.nn.functional.common.dropout(var_4, p=0.0, axis=None, training=self.training, mode='upscale_in_train', name=None)
         return var_5
 
 

diff --git a/framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/Twins_alt_gvt_base/SIR_96.py b/framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/Twins_alt_gvt_base/SIR_96.py
@@ -32,7 +32,7 @@ def forward(
         var_2 = var_1.flatten(2)
         var_3 = var_2.transpose([0, 2, 1])
         var_4 = paddle.nn.functional.norm.layer_norm(var_3, normalized_shape=[192], weight=self.parameter_1, bias=self.parameter_0, epsilon=1e-05)
-        var_5 = paddle.nn.functional.common.dropout(var_4, p=0.0, axis=None, training=True, mode='upscale_in_train', name=None)
+        var_5 = paddle.nn.functional.common.dropout(var_4, p=0.0, axis=None, training=self.training, mode='upscale_in_train', name=None)
         return var_5
 
 

diff --git a/.../e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/Xception_Xception65_deeplab/SIR_35.py b/.../e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/Xception_Xception65_deeplab/SIR_35.py
@@ -20,7 +20,7 @@ def forward(
         var_0,    # (shape: [22, 2048, 10, 10], dtype: paddle.float32, stop_gradient: False)
     ):
         paddle.seed(33)
-        var_1 = paddle.nn.functional.common.dropout(var_0, p=0.5, axis=None, training=True, mode='downscale_in_infer', name=None)
+        var_1 = paddle.nn.functional.common.dropout(var_0, p=0.5, axis=None, training=self.training, mode='downscale_in_infer', name=None)
         var_2 = paddle.nn.functional.pooling.adaptive_avg_pool2d(var_1, output_size=1, data_format='NCHW', name=None)
         var_3 = paddle.tensor.manipulation.squeeze(var_2, axis=[2, 3])
         var_4 = paddle.nn.functional.common.linear(x=var_3, weight=self.parameter_1, bias=self.parameter_0, name=None)

diff --git a/.../e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/Xception_Xception65_deeplab/SIR_55.py b/.../e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/Xception_Xception65_deeplab/SIR_55.py
@@ -20,7 +20,7 @@ def forward(
         var_0,    # (shape: [10, 2048, 10, 10], dtype: paddle.float32, stop_gradient: False)
     ):
         paddle.seed(33)
-        var_1 = paddle.nn.functional.common.dropout(var_0, p=0.5, axis=None, training=True, mode='downscale_in_infer', name=None)
+        var_1 = paddle.nn.functional.common.dropout(var_0, p=0.5, axis=None, training=self.training, mode='downscale_in_infer', name=None)
         var_2 = paddle.nn.functional.pooling.adaptive_avg_pool2d(var_1, output_size=1, data_format='NCHW', name=None)
         var_3 = paddle.tensor.manipulation.squeeze(var_2, axis=[2, 3])
         var_4 = paddle.nn.functional.common.linear(x=var_3, weight=self.parameter_1, bias=self.parameter_0, name=None)

diff --git a/...w/layercase/sublayer1000/Det_cases/faster_rcnn_faster_rcnn_swin_tiny_fpn_1x_coco/SIR_3.py b/...w/layercase/sublayer1000/Det_cases/faster_rcnn_faster_rcnn_swin_tiny_fpn_1x_coco/SIR_3.py
@@ -36,7 +36,7 @@ def forward(
         var_6 = var_5.reshape([-1, 96, 200, 304])
         var_7 = var_6.flatten(2)
         var_8 = var_7.transpose([0, 2, 1])
-        var_9 = paddle.nn.functional.common.dropout(var_8, p=0.0, axis=None, training=True, mode='upscale_in_train', name=None)
+        var_9 = paddle.nn.functional.common.dropout(var_8, p=0.0, axis=None, training=self.training, mode='upscale_in_train', name=None)
         return var_9
 
 

diff --git a/...w/layercase/sublayer1000/Det_cases/faster_rcnn_faster_rcnn_swin_tiny_fpn_2x_coco/SIR_3.py b/...w/layercase/sublayer1000/Det_cases/faster_rcnn_faster_rcnn_swin_tiny_fpn_2x_coco/SIR_3.py
@@ -36,7 +36,7 @@ def forward(
         var_6 = var_5.reshape([-1, 96, 136, 160])
         var_7 = var_6.flatten(2)
         var_8 = var_7.transpose([0, 2, 1])
-        var_9 = paddle.nn.functional.common.dropout(var_8, p=0.0, axis=None, training=True, mode='upscale_in_train', name=None)
+        var_9 = paddle.nn.functional.common.dropout(var_8, p=0.0, axis=None, training=self.training, mode='upscale_in_train', name=None)
         return var_9