modify longtime python ut

mindspore/nn/layer/conv.py

@@ -159,7 +159,7 @@ class Conv2d(_Conv):
         >>> net = nn.Conv2d(120, 240, 4, has_bias=False, weight_init='normal')
         >>> input = mindspore.Tensor(np.ones([1, 120, 1024, 640]), mindspore.float32)
         >>> net(input).shape()
-        (1, 240, 1024, 637)
+        (1, 240, 1024, 640)
     """
     @cell_attr_register
     def __init__(self,

tests/ut/python/parallel/test_auto_parallel_reshape.py

@@ -49,16 +49,16 @@ def test_reshape_matmul():
             super().__init__()
             self.reshape = P.Reshape()
             self.matmul = P.MatMul()
-            self.matmul_weight = Parameter(Tensor(np.ones([25088, 256]), dtype=ms.float32), name="weight")
+            self.matmul_weight = Parameter(Tensor(np.ones([28, 64]), dtype=ms.float32), name="weight")
 
         def construct(self, x):
-            out = self.reshape(x, (256, 25088))
+            out = self.reshape(x, (64, 28))
             out = self.matmul(out, self.matmul_weight)
             return out
 
     size = 8
     context.set_auto_parallel_context(device_num=size, global_rank=0)
-    x = Tensor(np.ones([32*size, 512, 7, 7]), dtype=ms.float32)
+    x = Tensor(np.ones([8*size, 28, 1, 1]), dtype=ms.float32)
 
     net = GradWrap(NetWithLoss(Net()))
     context.set_auto_parallel_context(parallel_mode="auto_parallel")

tests/ut/python/parallel/test_one_hot_net.py

@@ -247,15 +247,15 @@ def fc_with_initialize(input_channels, out_channels):
 class BNReshapeDenseBNNet(nn.Cell):
     def __init__(self):
         super(BNReshapeDenseBNNet, self).__init__()
-        self.batch_norm = bn_with_initialize(512)
+        self.batch_norm = bn_with_initialize(2)
         self.reshape = P.Reshape()
         self.batch_norm2 = nn.BatchNorm1d(512, affine=False)
-        self.fc = fc_with_initialize(512 * 32 * 32, 512)
+        self.fc = fc_with_initialize(2 * 32 * 32, 512)
         self.loss = SemiAutoOneHotNet(args=Args(), strategy=StrategyBatch())
 
     def construct(self, x, label):
         x = self.batch_norm(x)
-        x = self.reshape(x, (16, 512*32*32))
+        x = self.reshape(x, (16, 2*32*32))
         x = self.fc(x)
         x = self.batch_norm2(x)
         loss = self.loss(x, label)
@@ -266,7 +266,7 @@ def test_bn_reshape_dense_bn_train_loss():
     batch_size = 16
     device_num = 16
     context.set_auto_parallel_context(device_num=device_num, global_rank=0)
-    input = Tensor(np.ones([batch_size, 512, 32, 32]).astype(np.float32) * 0.01)
+    input = Tensor(np.ones([batch_size, 2, 32, 32]).astype(np.float32) * 0.01)
     label = Tensor(np.ones([batch_size]), dtype=ms.int32)
 
     net = GradWrap(NetWithLoss(BNReshapeDenseBNNet()))

tests/ut/python/parallel/test_reshape.py

@@ -490,15 +490,15 @@ def fc_with_initialize(input_channels, out_channels):
 class BNReshapeDenseBNNet(nn.Cell):
     def __init__(self):
         super(BNReshapeDenseBNNet, self).__init__()
-        self.batch_norm = bn_with_initialize(512)
+        self.batch_norm = bn_with_initialize(2)
         self.reshape = P.Reshape()
         self.cast = P.Cast()
         self.batch_norm2 = nn.BatchNorm1d(512, affine=False)
-        self.fc = fc_with_initialize(512 * 32 * 32, 512)
+        self.fc = fc_with_initialize(2 * 32 * 32, 512)
 
     def construct(self, x):
         x = self.batch_norm(x)
-        x = self.reshape(x, (16, 512*32*32))
+        x = self.reshape(x, (16, 2*32*32))
         x = self.fc(x)
         x = self.batch_norm2(x)
         return x
@@ -508,7 +508,7 @@ def test_bn_reshape_dense_bn_train():
     batch_size = 16
     device_num = 16
     context.set_auto_parallel_context(device_num=device_num, global_rank=0)
-    input = Tensor(np.ones([batch_size, 512, 32, 32]).astype(np.float32) * 0.01)
+    input = Tensor(np.ones([batch_size, 2, 32, 32]).astype(np.float32) * 0.01)
 
     net = GradWrap(NetWithLoss(BNReshapeDenseBNNet()))
     context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")

tests/ut/python/train/summary/test_summary_performance.py

@@ -43,9 +43,9 @@ def get_test_data(step):
     tag1 = "xt1[:Tensor]"
     tag2 = "xt2[:Tensor]"
     tag3 = "xt3[:Tensor]"
-    np1 = np.random.random((50, 40, 30, 50))
-    np2 = np.random.random((50, 50, 30, 50))
-    np3 = np.random.random((40, 55, 30, 50))
+    np1 = np.random.random((5, 4, 3, 5))
+    np2 = np.random.random((5, 5, 3, 5))
+    np3 = np.random.random((4, 5, 3, 5))
 
     dict1 = {}
     dict1["name"] = tag1