plt update net instance, test=model

framework/e2e/PaddleLT_new/layerApicase/nn_sublayer/dropout2d_0_func.py

@@ -15,7 +15,7 @@ def forward(self, x, ):
         """
         forward
         """
-        out = paddle.nn.functional.dropout2d(x,  p=paddle.to_tensor([0.5], dtype='float32', stop_gradient=False), training=True, )
+        out = paddle.nn.functional.dropout2d(x,  p=paddle.to_tensor([0.5], dtype='float32', stop_gradient=False), training=self.training, )
         return out
 
 

framework/e2e/PaddleLT_new/layercase/perf245/Seg_cases/espnet_espnet_cityscapes_1024x512_120k/SIR_143.py

@@ -19,7 +19,7 @@ def forward(
     ):
         var_3 = paddle.nn.functional.common.interpolate(var_0, scale_factor=2, mode='bilinear', align_corners=True)
         var_4 = paddle.tensor.manipulation.concat([var_1, var_3], axis=1)
-        var_5 = paddle.nn.functional.common.dropout2d(var_4, p=0.0, training=True, data_format='NCHW', name=None)
+        var_5 = paddle.nn.functional.common.dropout2d(var_4, p=0.0, training=self.training, data_format='NCHW', name=None)
         var_6 = paddle.nn.functional.conv._conv_nd(var_5, self.parameter_0, bias=None, 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_7 = paddle.nn.functional.common.interpolate(var_6, scale_factor=2, mode='bilinear', align_corners=True)
         var_8 = paddle.nn.functional.common.interpolate(var_2, scale_factor=2, mode='bilinear', align_corners=True)

framework/e2e/PaddleLT_new/layercase/sublayer160/Clas_cases/DeiT_DeiT_tiny_distilled_patch16_224/SIR_4.py

@@ -46,12 +46,12 @@ 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_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
 
 

framework/e2e/PaddleLT_new/layercase/sublayer160/Clas_cases/EfficientNet_EfficientNetB0/SIR_96.py

@@ -20,7 +20,7 @@ def forward(
         var_0,    # (shape: [43, 1280, 7, 7], dtype: paddle.float32, stop_gradient: False)
     ):
         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

framework/e2e/PaddleLT_new/layercase/sublayer160/Clas_cases/Inception_InceptionV4/SIR_40.py

@@ -21,7 +21,7 @@ def forward(
     ):
         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
 

framework/e2e/PaddleLT_new/layercase/sublayer160/Clas_cases/MobileViT_MobileViT_XXS/SIR_26.py

@@ -21,7 +21,7 @@ def forward(
     ):
         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_1, bias=self.parameter_0, name=None)
         return var_4
 

framework/e2e/PaddleLT_new/layercase/sublayer160/Clas_cases/PeleeNet_PeleeNet/SIR_41.py

@@ -21,7 +21,7 @@ def forward(
     ):
         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
 

framework/e2e/PaddleLT_new/layercase/sublayer160/Clas_cases/ReXNet_ReXNet_3_0/SIR_60.py

@@ -19,7 +19,7 @@ def forward(
         self,
         var_0,    # (shape: [22, 3840, 1, 1], dtype: paddle.float32, stop_gradient: False)
     ):
-        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)

framework/e2e/PaddleLT_new/layercase/sublayer160/Clas_cases/SqueezeNet_SqueezeNet1_0/SIR_0.py

@@ -280,7 +280,7 @@ def forward(
         var_59 = paddle.nn.functional.conv._conv_nd(var_56, self.parameter_2, bias=self.parameter_30, 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_29, bias=self.parameter_44, 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)

framework/e2e/PaddleLT_new/layercase/sublayer160/Clas_cases/Twins_alt_gvt_base/SIR_84.py

@@ -31,7 +31,7 @@ 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_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
 
 

framework/e2e/PaddleLT_new/layercase/sublayer160/Clas_cases/Xception_Xception65_deeplab/SIR_35.py

@@ -19,7 +19,7 @@ def forward(
         self,
         var_0,    # (shape: [22, 2048, 10, 10], dtype: paddle.float32, stop_gradient: False)
     ):
-        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)

framework/e2e/PaddleLT_new/layercase/sublayer160/Det_cases/faster_rcnn_faster_rcnn_swin_tiny_fpn_1x_coco/SIR_3.py

@@ -35,7 +35,7 @@ def forward(
         var_6 = var_5.reshape([-1, 96, 160, 240])
         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
 
 

framework/e2e/PaddleLT_new/layercase/sublayer160/Det_cases/smalldet_ppyoloe_plus_sod_crn_l_80e_coco/SIR_127.py

@@ -44,14 +44,14 @@ def forward(
         var_0,    # (shape: [1, 361, 1024], dtype: paddle.float32, stop_gradient: False)
         var_1,    # (shape: [1, 361, 1024], dtype: paddle.float32, stop_gradient: False)
     ):
-        var_2 = paddle.nn.functional.common.dropout(var_0, p=0.1, axis=None, training=True, mode='upscale_in_train', name=None)
+        var_2 = paddle.nn.functional.common.dropout(var_0, p=0.1, axis=None, training=self.training, mode='upscale_in_train', name=None)
         var_3 = var_1.__add__(var_2)
         var_4 = paddle.nn.functional.norm.layer_norm(var_3, normalized_shape=[1024], weight=self.parameter_6, bias=self.parameter_7, epsilon=1e-05)
         var_5 = paddle.nn.functional.common.linear(x=var_4, weight=self.parameter_4, bias=self.parameter_5, name=None)
         var_6 = paddle.nn.functional.activation.gelu(var_5)
-        var_7 = paddle.nn.functional.common.dropout(var_6, p=0.1, axis=None, training=True, mode='upscale_in_train', name=None)
+        var_7 = paddle.nn.functional.common.dropout(var_6, p=0.1, axis=None, training=self.training, mode='upscale_in_train', name=None)
         var_8 = paddle.nn.functional.common.linear(x=var_7, weight=self.parameter_1, bias=self.parameter_0, name=None)
-        var_9 = paddle.nn.functional.common.dropout(var_8, p=0.1, axis=None, training=True, mode='upscale_in_train', name=None)
+        var_9 = paddle.nn.functional.common.dropout(var_8, p=0.1, axis=None, training=self.training, mode='upscale_in_train', name=None)
         var_10 = var_4.__add__(var_9)
         var_11 = paddle.nn.functional.norm.layer_norm(var_10, normalized_shape=[1024], weight=self.parameter_2, bias=self.parameter_3, epsilon=1e-05)
         return var_11

framework/e2e/PaddleLT_new/layercase/sublayer160/Det_cases/vitdet_ppyoloe_vit_base_csppan_cae_36e_coco/SIR_6.py

@@ -60,12 +60,12 @@ def forward(
         var_15 = var_11.matmul(var_14)
         var_16 = var_15.__mul__(0.125)
         var_17 = paddle.nn.functional.activation.softmax(var_16, axis=-1)
-        var_18 = paddle.nn.functional.common.dropout(var_17, p=0.0, axis=None, training=True, mode='upscale_in_train', name=None)
+        var_18 = paddle.nn.functional.common.dropout(var_17, p=0.0, axis=None, training=self.training, mode='upscale_in_train', name=None)
         var_19 = var_18.matmul(var_13)
         var_20 = var_19.transpose((0, 2, 1, 3,))
         var_21 = var_20.reshape((-1, var_3, var_4,))
         var_22 = paddle.nn.functional.common.linear(x=var_21, weight=self.parameter_5, bias=self.parameter_0, name=None)
-        var_23 = paddle.nn.functional.common.dropout(var_22, p=0.0, axis=None, training=True, mode='upscale_in_train', name=None)
+        var_23 = paddle.nn.functional.common.dropout(var_22, p=0.0, axis=None, training=self.training, mode='upscale_in_train', name=None)
         var_24 = self.parameter_3.__mul__(var_23)
         return var_24
 

framework/e2e/PaddleLT_new/layercase/sublayer160/Ocr_cases/rec_rec_svtrnet/SIR_43.py

@@ -48,12 +48,12 @@ def forward(
         var_11 = var_7.matmul(var_10)
         var_12 = var_11.__add__(var_1)
         var_13 = paddle.nn.functional.activation.softmax(var_12, axis=-1)
-        var_14 = paddle.nn.functional.common.dropout(var_13, p=0.0, axis=None, training=True, mode='upscale_in_train', name=None)
+        var_14 = paddle.nn.functional.common.dropout(var_13, p=0.0, axis=None, training=self.training, mode='upscale_in_train', name=None)
         var_15 = var_14.matmul(var_9)
         var_16 = var_15.transpose((0, 2, 1, 3,))
         var_17 = var_16.reshape((0, -1, 128,))
         var_18 = paddle.nn.functional.common.linear(x=var_17, weight=self.parameter_5, bias=self.parameter_2, name=None)
-        var_19 = paddle.nn.functional.common.dropout(var_18, p=0.0, axis=None, training=True, mode='upscale_in_train', name=None)
+        var_19 = paddle.nn.functional.common.dropout(var_18, p=0.0, axis=None, training=self.training, mode='upscale_in_train', name=None)
         return var_19
 
 

framework/e2e/PaddleLT_new/layercase/sublayer160/Ocr_cases/rec_rec_svtrnet/SIR_57.py

@@ -46,12 +46,12 @@ def forward(
         var_9 = var_7.transpose((0, 1, 3, 2,))
         var_10 = var_6.matmul(var_9)
         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_8)
         var_14 = var_13.transpose((0, 2, 1, 3,))
         var_15 = var_14.reshape((0, -1, 128,))
         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
 
 

framework/e2e/PaddleLT_new/layercase/sublayer160/Seg_cases/danet_danet_resnet101_os8_cityscapes_1024x512_80k/SIR_34.py

@@ -38,13 +38,13 @@ def forward(
         var_2,    # (shape: [1, 2048, 64, 128], dtype: paddle.float32, stop_gradient: False)
     ):
         var_3 = var_0.__add__(var_1)
-        var_4 = paddle.nn.functional.common.dropout2d(var_3, p=0.1, training=True, data_format='NCHW', name=None)
+        var_4 = paddle.nn.functional.common.dropout2d(var_3, p=0.1, training=self.training, data_format='NCHW', name=None)
         var_5 = paddle.nn.functional.conv._conv_nd(var_4, 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_6 = paddle.nn.functional.common.dropout2d(var_1, p=0.1, training=True, data_format='NCHW', name=None)
+        var_6 = paddle.nn.functional.common.dropout2d(var_1, p=0.1, training=self.training, data_format='NCHW', name=None)
         var_7 = paddle.nn.functional.conv._conv_nd(var_6, self.parameter_4, bias=self.parameter_5, 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_8 = paddle.nn.functional.common.dropout2d(var_0, p=0.1, training=True, data_format='NCHW', name=None)
+        var_8 = paddle.nn.functional.common.dropout2d(var_0, p=0.1, training=self.training, data_format='NCHW', name=None)
         var_9 = paddle.nn.functional.conv._conv_nd(var_8, self.parameter_4, bias=self.parameter_5, 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_10 = paddle.nn.functional.common.dropout2d(var_2, p=0.1, training=True, data_format='NCHW', name=None)
+        var_10 = paddle.nn.functional.common.dropout2d(var_2, p=0.1, training=self.training, data_format='NCHW', name=None)
         var_11 = paddle.nn.functional.conv._conv_nd(var_10, self.parameter_3, bias=self.parameter_2, 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)
         return var_5, var_7, var_9, var_11
 

framework/e2e/PaddleLT_new/layercase/sublayer160/Seg_cases/enet_enet_cityscapes_1024x512_80k/SIR_40.py

@@ -11,7 +11,7 @@ def forward(
         self,
         var_0,    # (shape: [1, 128, 64, 128], dtype: paddle.float32, stop_gradient: False)
     ):
-        var_1 = paddle.nn.functional.common.dropout2d(var_0, p=0.1, training=True, data_format='NCHW', name=None)
+        var_1 = paddle.nn.functional.common.dropout2d(var_0, p=0.1, training=self.training, data_format='NCHW', name=None)
         return var_1
 
 

framework/e2e/PaddleLT_new/layercase/sublayer160/Seg_cases/enet_enet_cityscapes_1024x512_80k/SIR_46.py

@@ -12,7 +12,7 @@ def forward(
         var_0,    # (shape: [1, 128, 64, 128], dtype: paddle.float32, stop_gradient: False)
         var_1,    # (shape: [1, 128, 64, 128], dtype: paddle.float32, stop_gradient: False)
     ):
-        var_2 = paddle.nn.functional.common.dropout2d(var_0, p=0.1, training=True, data_format='NCHW', name=None)
+        var_2 = paddle.nn.functional.common.dropout2d(var_0, p=0.1, training=self.training, data_format='NCHW', name=None)
         var_3 = var_1.__add__(var_2)
         return var_3
 

framework/e2e/PaddleLT_new/layercase/sublayer160/Seg_cases/espnet_espnet_cityscapes_1024x512_120k/SIR_143.py

@@ -19,7 +19,7 @@ def forward(
     ):
         var_3 = paddle.nn.functional.common.interpolate(var_0, scale_factor=2, mode='bilinear', align_corners=True)
         var_4 = paddle.tensor.manipulation.concat([var_1, var_3], axis=1)
-        var_5 = paddle.nn.functional.common.dropout2d(var_4, p=0.0, training=True, data_format='NCHW', name=None)
+        var_5 = paddle.nn.functional.common.dropout2d(var_4, p=0.0, training=self.training, data_format='NCHW', name=None)
         var_6 = paddle.nn.functional.conv._conv_nd(var_5, self.parameter_0, bias=None, 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_7 = paddle.nn.functional.common.interpolate(var_6, scale_factor=2, mode='bilinear', align_corners=True)
         var_8 = paddle.nn.functional.common.interpolate(var_2, scale_factor=2, mode='bilinear', align_corners=True)

framework/e2e/PaddleLT_new/layercase/sublayer160/Seg_cases/segformer_segformer_b2_cityscapes_1024x1024_160k/SIR_18.py

@@ -83,12 +83,12 @@ def forward(
         var_22 = var_9.__matmul__(var_21)
         var_23 = var_22.__mul__(0.125)
         var_24 = paddle.nn.functional.activation.softmax(var_23, axis=-1)
-        var_25 = paddle.nn.functional.common.dropout(var_24, p=0.0, axis=None, training=True, mode='upscale_in_train', name=None)
+        var_25 = paddle.nn.functional.common.dropout(var_24, p=0.0, axis=None, training=self.training, mode='upscale_in_train', name=None)
         var_26 = var_25.__matmul__(var_20)
         var_27 = var_26.transpose([0, 2, 1, 3])
         var_28 = var_27.reshape([var_5, var_6, 128])
         var_29 = paddle.nn.functional.common.linear(x=var_28, weight=self.parameter_8, bias=self.parameter_6, name=None)
-        var_30 = paddle.nn.functional.common.dropout(var_29, p=0.0, axis=None, training=True, mode='upscale_in_train', name=None)
+        var_30 = paddle.nn.functional.common.dropout(var_29, p=0.0, axis=None, training=self.training, mode='upscale_in_train', name=None)
         return var_30
 
 

framework/e2e/PaddleLT_new/layercase/sublayer160/Seg_cases/segformer_segformer_b2_cityscapes_1024x1024_160k/SIR_66.py

@@ -59,12 +59,12 @@ def forward(
         var_14 = var_7.__matmul__(var_13)
         var_15 = var_14.__mul__(0.125)
         var_16 = paddle.nn.functional.activation.softmax(var_15, axis=-1)
-        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)
         var_18 = var_17.__matmul__(var_12)
         var_19 = var_18.transpose([0, 2, 1, 3])
         var_20 = var_19.reshape([var_3, var_4, 512])
         var_21 = paddle.nn.functional.common.linear(x=var_20, weight=self.parameter_5, bias=self.parameter_2, name=None)
-        var_22 = paddle.nn.functional.common.dropout(var_21, p=0.0, axis=None, training=True, mode='upscale_in_train', name=None)
+        var_22 = paddle.nn.functional.common.dropout(var_21, p=0.0, axis=None, training=self.training, mode='upscale_in_train', name=None)
         return var_22
 
 

framework/e2e/PaddleLT_new/layercase/sublayer160/Seg_cases/segmenter_segmenter_vit_small_mask_ade20k_512x512_160k/SIR_66.py

@@ -46,12 +46,12 @@ def forward(
         var_13 = var_9.matmul(var_12)
         var_14 = var_13.__mul__(0.125)
         var_15 = paddle.nn.functional.activation.softmax(var_14, axis=-1)
-        var_16 = paddle.nn.functional.common.dropout(var_15, p=0.0, axis=None, training=True, mode='upscale_in_train', name=None)
+        var_16 = paddle.nn.functional.common.dropout(var_15, p=0.0, axis=None, training=self.training, mode='upscale_in_train', name=None)
         var_17 = var_16.matmul(var_11)
         var_18 = var_17.transpose((0, 2, 1, 3,))
         var_19 = var_18.reshape((-1, var_3, var_4,))
         var_20 = paddle.nn.functional.common.linear(x=var_19, weight=self.parameter_4, bias=self.parameter_2, name=None)
-        var_21 = paddle.nn.functional.common.dropout(var_20, p=0.0, axis=None, training=True, mode='upscale_in_train', name=None)
+        var_21 = paddle.nn.functional.common.dropout(var_20, p=0.0, axis=None, training=self.training, mode='upscale_in_train', name=None)
         return var_21