Fix dropout accuracy by setting seed in forward function (#2933)

* fix dropout accuracy by setting seed in forward function

* fix

* change seed to 33

framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/DeiT_DeiT_tiny_distilled_patch16_224/SIR_4.py

@@ -35,6 +35,7 @@ def forward(
         self,
         var_0,    # (shape: [86, 198, 192], dtype: paddle.float32, stop_gradient: False)
     ):
+        paddle.seed(33)
         var_1 = paddle.nn.functional.norm.layer_norm(var_0, normalized_shape=[192], weight=self.parameter_0, bias=self.parameter_1, epsilon=1e-06)
         var_2 = paddle.nn.functional.common.linear(x=var_1, weight=self.parameter_5, bias=self.parameter_3, name=None)
         var_3 = var_2.reshape((-1, 198, 3, 3, 64,))
@@ -89,7 +90,7 @@ def train(self, net, to_static, with_prim=False, with_cinn=False):
                 net = paddle.jit.to_static(net, build_strategy=build_strategy, full_graph=True)
             else:
                 net = paddle.jit.to_static(net, full_graph=True)
-        paddle.seed(123)
+        paddle.seed(33)
         outs = net(*self.inputs)
         return outs
     def test_ast_prim_cinn(self):

framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/DeiT_DeiT_tiny_distilled_patch16_224/SIR_56.py

@@ -35,6 +35,7 @@ def forward(
         self,
         var_0,    # (shape: [54, 198, 192], dtype: paddle.float32, stop_gradient: False)
     ):
+        paddle.seed(33)
         var_1 = paddle.nn.functional.norm.layer_norm(var_0, normalized_shape=[192], weight=self.parameter_3, bias=self.parameter_4, epsilon=1e-06)
         var_2 = paddle.nn.functional.common.linear(x=var_1, weight=self.parameter_0, bias=self.parameter_5, name=None)
         var_3 = var_2.reshape((-1, 198, 3, 3, 64,))
@@ -89,7 +90,7 @@ def train(self, net, to_static, with_prim=False, with_cinn=False):
                 net = paddle.jit.to_static(net, build_strategy=build_strategy, full_graph=True)
             else:
                 net = paddle.jit.to_static(net, full_graph=True)
-        paddle.seed(123)
+        paddle.seed(33)
         outs = net(*self.inputs)
         return outs
     def test_ast_prim_cinn(self):

framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/DeiT_DeiT_tiny_patch16_224/SIR_4.py

@@ -35,6 +35,7 @@ def forward(
         self,
         var_0,    # (shape: [86, 197, 192], dtype: paddle.float32, stop_gradient: False)
     ):
+        paddle.seed(33)
         var_1 = paddle.nn.functional.norm.layer_norm(var_0, normalized_shape=[192], weight=self.parameter_1, bias=self.parameter_4, epsilon=1e-06)
         var_2 = paddle.nn.functional.common.linear(x=var_1, weight=self.parameter_2, bias=self.parameter_0, name=None)
         var_3 = var_2.reshape((-1, 197, 3, 3, 64,))
@@ -89,7 +90,7 @@ def train(self, net, to_static, with_prim=False, with_cinn=False):
                 net = paddle.jit.to_static(net, build_strategy=build_strategy, full_graph=True)
             else:
                 net = paddle.jit.to_static(net, full_graph=True)
-        paddle.seed(123)
+        paddle.seed(33)
         outs = net(*self.inputs)
         return outs
     def test_ast_prim_cinn(self):

framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/DeiT_DeiT_tiny_patch16_224/SIR_56.py

@@ -35,6 +35,7 @@ def forward(
         self,
         var_0,    # (shape: [54, 197, 192], dtype: paddle.float32, stop_gradient: False)
     ):
+        paddle.seed(33)
         var_1 = paddle.nn.functional.norm.layer_norm(var_0, normalized_shape=[192], weight=self.parameter_4, bias=self.parameter_1, epsilon=1e-06)
         var_2 = paddle.nn.functional.common.linear(x=var_1, weight=self.parameter_3, bias=self.parameter_2, name=None)
         var_3 = var_2.reshape((-1, 197, 3, 3, 64,))
@@ -89,7 +90,7 @@ def train(self, net, to_static, with_prim=False, with_cinn=False):
                 net = paddle.jit.to_static(net, build_strategy=build_strategy, full_graph=True)
             else:
                 net = paddle.jit.to_static(net, full_graph=True)
-        paddle.seed(123)
+        paddle.seed(33)
         outs = net(*self.inputs)
         return outs
     def test_ast_prim_cinn(self):

framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/EfficientNet_EfficientNetB0/SIR_140.py

@@ -19,6 +19,7 @@ def forward(
         self,
         var_0,    # (shape: [11, 1280, 7, 7], dtype: paddle.float32, stop_gradient: False)
     ):
+        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_3 = paddle.tensor.manipulation.squeeze(var_2, axis=[2, 3])
@@ -60,7 +61,7 @@ def train(self, net, to_static, with_prim=False, with_cinn=False):
                 net = paddle.jit.to_static(net, build_strategy=build_strategy, full_graph=True)
             else:
                 net = paddle.jit.to_static(net, full_graph=True)
-        paddle.seed(123)
+        paddle.seed(33)
         outs = net(*self.inputs)
         return outs
     def test_ast_prim_cinn(self):

framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/EfficientNet_EfficientNetB0/SIR_96.py

@@ -19,6 +19,7 @@ def forward(
         self,
         var_0,    # (shape: [43, 1280, 7, 7], dtype: paddle.float32, stop_gradient: False)
     ):
+        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_3 = paddle.tensor.manipulation.squeeze(var_2, axis=[2, 3])
@@ -60,7 +61,7 @@ def train(self, net, to_static, with_prim=False, with_cinn=False):
                 net = paddle.jit.to_static(net, build_strategy=build_strategy, full_graph=True)
             else:
                 net = paddle.jit.to_static(net, full_graph=True)
-        paddle.seed(123)
+        paddle.seed(33)
         outs = net(*self.inputs)
         return outs
     def test_ast_prim_cinn(self):

framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/Inception_InceptionV4/SIR_39.py

@@ -19,6 +19,7 @@ def forward(
         self,
         var_0,    # (shape: [22, 1536, 8, 8], dtype: paddle.float32, stop_gradient: False)
     ):
+        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)
@@ -60,7 +61,7 @@ def train(self, net, to_static, with_prim=False, with_cinn=False):
                 net = paddle.jit.to_static(net, build_strategy=build_strategy, full_graph=True)
             else:
                 net = paddle.jit.to_static(net, full_graph=True)
-        paddle.seed(123)
+        paddle.seed(33)
         outs = net(*self.inputs)
         return outs
     def test_ast_prim_cinn(self):

framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/Inception_InceptionV4/SIR_40.py

@@ -19,6 +19,7 @@ def forward(
         self,
         var_0,    # (shape: [10, 1536, 8, 8], dtype: paddle.float32, stop_gradient: False)
     ):
+        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)
@@ -60,7 +61,7 @@ def train(self, net, to_static, with_prim=False, with_cinn=False):
                 net = paddle.jit.to_static(net, build_strategy=build_strategy, full_graph=True)
             else:
                 net = paddle.jit.to_static(net, full_graph=True)
-        paddle.seed(123)
+        paddle.seed(33)
         outs = net(*self.inputs)
         return outs
     def test_ast_prim_cinn(self):

framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/MobileViT_MobileViT_XXS/SIR_26.py

@@ -19,6 +19,7 @@ def forward(
         self,
         var_0,    # (shape: [43, 320, 8, 8], dtype: paddle.float32, stop_gradient: False)
     ):
+        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)
@@ -60,7 +61,7 @@ def train(self, net, to_static, with_prim=False, with_cinn=False):
                 net = paddle.jit.to_static(net, build_strategy=build_strategy, full_graph=True)
             else:
                 net = paddle.jit.to_static(net, full_graph=True)
-        paddle.seed(123)
+        paddle.seed(33)
         outs = net(*self.inputs)
         return outs
     def test_ast_prim_cinn(self):

framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/MobileViT_MobileViT_XXS/SIR_27.py

@@ -19,6 +19,7 @@ def forward(
         self,
         var_0,    # (shape: [11, 320, 8, 8], dtype: paddle.float32, stop_gradient: False)
     ):
+        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)
@@ -60,7 +61,7 @@ def train(self, net, to_static, with_prim=False, with_cinn=False):
                 net = paddle.jit.to_static(net, build_strategy=build_strategy, full_graph=True)
             else:
                 net = paddle.jit.to_static(net, full_graph=True)
-        paddle.seed(123)
+        paddle.seed(33)
         outs = net(*self.inputs)
         return outs
     def test_ast_prim_cinn(self):

framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/PeleeNet_PeleeNet/SIR_41.py

@@ -19,6 +19,7 @@ def forward(
         self,
         var_0,    # (shape: [43, 704, 7, 7], dtype: paddle.float32, stop_gradient: False)
     ):
+        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)
@@ -60,7 +61,7 @@ def train(self, net, to_static, with_prim=False, with_cinn=False):
                 net = paddle.jit.to_static(net, build_strategy=build_strategy, full_graph=True)
             else:
                 net = paddle.jit.to_static(net, full_graph=True)
-        paddle.seed(123)
+        paddle.seed(33)
         outs = net(*self.inputs)
         return outs
     def test_ast_prim_cinn(self):

framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/PeleeNet_PeleeNet/SIR_42.py

@@ -19,6 +19,7 @@ def forward(
         self,
         var_0,    # (shape: [11, 704, 7, 7], dtype: paddle.float32, stop_gradient: False)
     ):
+        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)
@@ -60,7 +61,7 @@ def train(self, net, to_static, with_prim=False, with_cinn=False):
                 net = paddle.jit.to_static(net, build_strategy=build_strategy, full_graph=True)
             else:
                 net = paddle.jit.to_static(net, full_graph=True)
-        paddle.seed(123)
+        paddle.seed(33)
         outs = net(*self.inputs)
         return outs
     def test_ast_prim_cinn(self):

framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/ReXNet_ReXNet_3_0/SIR_60.py

@@ -19,6 +19,7 @@ def forward(
         self,
         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_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)
@@ -60,7 +61,7 @@ def train(self, net, to_static, with_prim=False, with_cinn=False):
                 net = paddle.jit.to_static(net, build_strategy=build_strategy, full_graph=True)
             else:
                 net = paddle.jit.to_static(net, full_graph=True)
-        paddle.seed(123)
+        paddle.seed(33)
         outs = net(*self.inputs)
         return outs
     def test_ast_prim_cinn(self):

framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/ReXNet_ReXNet_3_0/SIR_61.py

@@ -19,6 +19,7 @@ def forward(
         self,
         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_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)
@@ -60,7 +61,7 @@ def train(self, net, to_static, with_prim=False, with_cinn=False):
                 net = paddle.jit.to_static(net, build_strategy=build_strategy, full_graph=True)
             else:
                 net = paddle.jit.to_static(net, full_graph=True)
-        paddle.seed(123)
+        paddle.seed(33)
         outs = net(*self.inputs)
         return outs
     def test_ast_prim_cinn(self):

framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/SqueezeNet_SqueezeNet1_0/SIR_0.py

@@ -219,6 +219,7 @@ def forward(
         self,
         var_0,    # (shape: [43, 3, 224, 224], dtype: paddle.float32, stop_gradient: True)
     ):
+        paddle.seed(33)
         var_1 = paddle.nn.functional.conv._conv_nd(var_0, self.parameter_13, bias=self.parameter_10, stride=[2, 2], padding=[0, 0], padding_algorithm='EXPLICIT', dilation=[1, 1], groups=1, data_format='NCHW', channel_dim=1, op_type='conv2d', use_cudnn=True)
         var_2 = paddle.nn.functional.activation.relu(var_1)
         var_3 = paddle.nn.functional.pooling.max_pool2d(var_2, kernel_size=3, stride=2, padding=0, return_mask=False, ceil_mode=False, data_format='NCHW', name=None)
@@ -322,7 +323,7 @@ def train(self, net, to_static, with_prim=False, with_cinn=False):
                 net = paddle.jit.to_static(net, build_strategy=build_strategy, full_graph=True)
             else:
                 net = paddle.jit.to_static(net, full_graph=True)
-        paddle.seed(123)
+        paddle.seed(33)
         outs = net(*self.inputs)
         return outs
     def test_ast_prim_cinn(self):

framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/SqueezeNet_SqueezeNet1_0/SIR_1.py

@@ -219,6 +219,7 @@ def forward(
         self,
         var_0,    # (shape: [11, 3, 224, 224], dtype: paddle.float32, stop_gradient: True)
     ):
+        paddle.seed(33)
         var_1 = paddle.nn.functional.conv._conv_nd(var_0, self.parameter_34, bias=self.parameter_2, stride=[2, 2], padding=[0, 0], padding_algorithm='EXPLICIT', dilation=[1, 1], groups=1, data_format='NCHW', channel_dim=1, op_type='conv2d', use_cudnn=True)
         var_2 = paddle.nn.functional.activation.relu(var_1)
         var_3 = paddle.nn.functional.pooling.max_pool2d(var_2, kernel_size=3, stride=2, padding=0, return_mask=False, ceil_mode=False, data_format='NCHW', name=None)
@@ -322,7 +323,7 @@ def train(self, net, to_static, with_prim=False, with_cinn=False):
                 net = paddle.jit.to_static(net, build_strategy=build_strategy, full_graph=True)
             else:
                 net = paddle.jit.to_static(net, full_graph=True)
-        paddle.seed(123)
+        paddle.seed(33)
         outs = net(*self.inputs)
         return outs
     def test_ast_prim_cinn(self):

framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/SqueezeNet_SqueezeNet1_0/SIR_2.py

@@ -219,6 +219,7 @@ def forward(
         self,
         var_0,    # (shape: [22, 3, 224, 224], dtype: paddle.float32, stop_gradient: True)
     ):
+        paddle.seed(33)
         var_1 = paddle.nn.functional.conv._conv_nd(var_0, self.parameter_1, bias=self.parameter_42, stride=[2, 2], padding=[0, 0], padding_algorithm='EXPLICIT', dilation=[1, 1], groups=1, data_format='NCHW', channel_dim=1, op_type='conv2d', use_cudnn=True)
         var_2 = paddle.nn.functional.activation.relu(var_1)
         var_3 = paddle.nn.functional.pooling.max_pool2d(var_2, kernel_size=3, stride=2, padding=0, return_mask=False, ceil_mode=False, data_format='NCHW', name=None)
@@ -322,7 +323,7 @@ def train(self, net, to_static, with_prim=False, with_cinn=False):
                 net = paddle.jit.to_static(net, build_strategy=build_strategy, full_graph=True)
             else:
                 net = paddle.jit.to_static(net, full_graph=True)
-        paddle.seed(123)
+        paddle.seed(33)
         outs = net(*self.inputs)
         return outs
     def test_ast_prim_cinn(self):

framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/SqueezeNet_SqueezeNet1_0/SIR_3.py

@@ -219,6 +219,7 @@ def forward(
         self,
         var_0,    # (shape: [10, 3, 224, 224], dtype: paddle.float32, stop_gradient: True)
     ):
+        paddle.seed(33)
         var_1 = paddle.nn.functional.conv._conv_nd(var_0, self.parameter_24, bias=self.parameter_46, stride=[2, 2], padding=[0, 0], padding_algorithm='EXPLICIT', dilation=[1, 1], groups=1, data_format='NCHW', channel_dim=1, op_type='conv2d', use_cudnn=True)
         var_2 = paddle.nn.functional.activation.relu(var_1)
         var_3 = paddle.nn.functional.pooling.max_pool2d(var_2, kernel_size=3, stride=2, padding=0, return_mask=False, ceil_mode=False, data_format='NCHW', name=None)
@@ -322,7 +323,7 @@ def train(self, net, to_static, with_prim=False, with_cinn=False):
                 net = paddle.jit.to_static(net, build_strategy=build_strategy, full_graph=True)
             else:
                 net = paddle.jit.to_static(net, full_graph=True)
-        paddle.seed(123)
+        paddle.seed(33)
         outs = net(*self.inputs)
         return outs
     def test_ast_prim_cinn(self):

framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/Twins_alt_gvt_base/SIR_108.py

@@ -27,6 +27,7 @@ def forward(
         self,
         var_0,    # (shape: [11, 192, 28, 28], dtype: paddle.float32, stop_gradient: False)
     ):
+        paddle.seed(33)
         var_1 = paddle.nn.functional.conv._conv_nd(var_0, self.parameter_0, bias=self.parameter_3, stride=[2, 2], padding=[0, 0], padding_algorithm='EXPLICIT', dilation=[1, 1], groups=1, data_format='NCHW', channel_dim=1, op_type='conv2d', use_cudnn=True)
         var_2 = var_1.flatten(2)
         var_3 = var_2.transpose([0, 2, 1])
@@ -69,7 +70,7 @@ def train(self, net, to_static, with_prim=False, with_cinn=False):
                 net = paddle.jit.to_static(net, build_strategy=build_strategy, full_graph=True)
             else:
                 net = paddle.jit.to_static(net, full_graph=True)
-        paddle.seed(123)
+        paddle.seed(33)
         outs = net(*self.inputs)
         return outs
     def test_ast_prim_cinn(self):

framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/Twins_alt_gvt_base/SIR_136.py

@@ -27,6 +27,7 @@ def forward(
         self,
         var_0,    # (shape: [11, 384, 14, 14], dtype: paddle.float32, stop_gradient: False)
     ):
+        paddle.seed(33)
         var_1 = paddle.nn.functional.conv._conv_nd(var_0, self.parameter_1, bias=self.parameter_2, stride=[2, 2], padding=[0, 0], padding_algorithm='EXPLICIT', dilation=[1, 1], groups=1, data_format='NCHW', channel_dim=1, op_type='conv2d', use_cudnn=True)
         var_2 = var_1.flatten(2)
         var_3 = var_2.transpose([0, 2, 1])
@@ -69,7 +70,7 @@ def train(self, net, to_static, with_prim=False, with_cinn=False):
                 net = paddle.jit.to_static(net, build_strategy=build_strategy, full_graph=True)
             else:
                 net = paddle.jit.to_static(net, full_graph=True)
-        paddle.seed(123)
+        paddle.seed(33)
         outs = net(*self.inputs)
         return outs
     def test_ast_prim_cinn(self):

framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/Twins_alt_gvt_base/SIR_14.py

@@ -27,6 +27,7 @@ def forward(
         self,
         var_0,    # (shape: [43, 96, 56, 56], dtype: paddle.float32, stop_gradient: False)
     ):
+        paddle.seed(33)
         var_1 = paddle.nn.functional.conv._conv_nd(var_0, self.parameter_1, bias=self.parameter_0, stride=[2, 2], padding=[0, 0], padding_algorithm='EXPLICIT', dilation=[1, 1], groups=1, data_format='NCHW', channel_dim=1, op_type='conv2d', use_cudnn=True)
         var_2 = var_1.flatten(2)
         var_3 = var_2.transpose([0, 2, 1])
@@ -69,7 +70,7 @@ def train(self, net, to_static, with_prim=False, with_cinn=False):
                 net = paddle.jit.to_static(net, build_strategy=build_strategy, full_graph=True)
             else:
                 net = paddle.jit.to_static(net, full_graph=True)
-        paddle.seed(123)
+        paddle.seed(33)
         outs = net(*self.inputs)
         return outs
     def test_ast_prim_cinn(self):

framework/e2e/PaddleLT_new/layercase/sublayer1000/Clas_cases/Twins_alt_gvt_base/SIR_2.py

@@ -27,6 +27,7 @@ def forward(
         self,
         var_0,    # (shape: [43, 3, 224, 224], dtype: paddle.float32, stop_gradient: True)
     ):
+        paddle.seed(33)
         var_1 = paddle.nn.functional.conv._conv_nd(var_0, self.parameter_3, bias=self.parameter_2, stride=[4, 4], padding=[0, 0], padding_algorithm='EXPLICIT', dilation=[1, 1], groups=1, data_format='NCHW', channel_dim=1, op_type='conv2d', use_cudnn=True)
         var_2 = var_1.flatten(2)
         var_3 = var_2.transpose([0, 2, 1])
@@ -69,7 +70,7 @@ def train(self, net, to_static, with_prim=False, with_cinn=False):
                 net = paddle.jit.to_static(net, build_strategy=build_strategy, full_graph=True)
             else:
                 net = paddle.jit.to_static(net, full_graph=True)
-        paddle.seed(123)
+        paddle.seed(33)
         outs = net(*self.inputs)
         return outs
     def test_ast_prim_cinn(self):