!23061 modify test example of probability

Merge pull request !23061 from byweng/master

mindspore/nn/probability/zhusuan/variational/elbo.py

@@ -32,9 +32,12 @@ class ELBO(nn.Cell):
     def construct(self, *inputs, **kwargs):
         if len(inputs) >= 2:
             x, y = inputs[0], inputs[1]
-        else:
+        elif len(inputs) >= 1:
             x = inputs[0]
             y = None
+        else:
+            x = None
+            y = None
 
         z, log_prob_z = self.variational(x, None, y)
         _, log_prob_x_, _, log_prob_z_ = self.generator(x, z, y)

tests/st/ops/gpu/test_determinant_triangle.py

@@ -32,7 +32,7 @@ class Net(nn.Cell):
     def construct(self, x):
         return self.det_triangle(x)
 
-@pytest.mark.level1
+@pytest.mark.level2
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
 def test_net_1D():

tests/st/probability/bnn_layers/dataset.py

@@ -20,7 +20,7 @@ import mindspore.dataset as ds
 import mindspore.dataset.vision.c_transforms as CV
 import mindspore.dataset.transforms.c_transforms as C
 from mindspore.dataset.vision import Inter
-from mindspore.common import dtype as mstype
+from mindspore import dtype as mstype
 
 
 def create_dataset(data_path, batch_size=32, repeat_size=1,

tests/st/probability/bnn_layers/test_bnn_layer.py

@@ -20,7 +20,7 @@ from mindspore.common.initializer import TruncatedNormal
 import mindspore.nn as nn
 from mindspore.nn import TrainOneStepCell
 from mindspore.nn.probability import bnn_layers
-from mindspore.ops import operations as P
+import mindspore.ops as ops
 from mindspore import context
 from dataset import create_dataset
 
@@ -71,7 +71,7 @@ class BNNLeNet5(nn.Cell):
         self.relu = nn.ReLU()
         self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
         self.flatten = nn.Flatten()
-        self.reshape = P.Reshape()
+        self.reshape = ops.Reshape()
 
     def construct(self, x):
         x = self.conv1(x)
@@ -97,7 +97,7 @@ def train_model(train_net, net, dataset):
         label = Tensor(data['label'].astype(np.int32))
         loss = train_net(train_x, label)
         output = net(train_x)
-        log_output = P.LogSoftmax(axis=1)(output)
+        log_output = ops.LogSoftmax(axis=1)(output)
         acc = np.mean(log_output.asnumpy().argmax(axis=1) == label.asnumpy())
         accs.append(acc)
         loss_sum += loss.asnumpy()
@@ -113,7 +113,7 @@ def validate_model(net, dataset):
         train_x = Tensor(data['image'].astype(np.float32))
         label = Tensor(data['label'].astype(np.int32))
         output = net(train_x)
-        log_output = P.LogSoftmax(axis=1)(output)
+        log_output = ops.LogSoftmax(axis=1)(output)
         acc = np.mean(log_output.asnumpy().argmax(axis=1) == label.asnumpy())
         accs.append(acc)
 

tests/st/probability/dpn/test_gpu_svi_cvae.py

@@ -14,12 +14,12 @@
 # ============================================================================
 import os
 
-import mindspore.common.dtype as mstype
+from mindspore import dtype as mstype
 import mindspore.dataset as ds
 import mindspore.dataset.vision.c_transforms as CV
 import mindspore.nn as nn
 from mindspore import context, Tensor
-from mindspore.ops import operations as P
+import mindspore.ops as ops
 from mindspore.nn.probability.dpn import ConditionalVAE
 from mindspore.nn.probability.infer import ELBO, SVI
 
@@ -34,7 +34,7 @@ class Encoder(nn.Cell):
         self.fc1 = nn.Dense(1024 + num_classes, 400)
         self.relu = nn.ReLU()
         self.flatten = nn.Flatten()
-        self.concat = P.Concat(axis=1)
+        self.concat = ops.Concat(axis=1)
         self.one_hot = nn.OneHot(depth=num_classes)
 
     def construct(self, x, y):
@@ -51,7 +51,7 @@ class Decoder(nn.Cell):
         super(Decoder, self).__init__()
         self.fc2 = nn.Dense(400, 1024)
         self.sigmoid = nn.Sigmoid()
-        self.reshape = P.Reshape()
+        self.reshape = ops.Reshape()
 
     def construct(self, z):
         z = self.fc2(z)

tests/st/probability/dpn/test_gpu_svi_vae.py

@@ -14,12 +14,12 @@
 # ============================================================================
 import os
 
-import mindspore.common.dtype as mstype
+from mindspore import dtype as mstype
 import mindspore.dataset as ds
 import mindspore.dataset.vision.c_transforms as CV
 import mindspore.nn as nn
 from mindspore import context, Tensor
-from mindspore.ops import operations as P
+import mindspore.ops as ops
 from mindspore.nn.probability.dpn import VAE
 from mindspore.nn.probability.infer import ELBO, SVI
 
@@ -50,7 +50,7 @@ class Decoder(nn.Cell):
         super(Decoder, self).__init__()
         self.fc1 = nn.Dense(400, 1024)
         self.sigmoid = nn.Sigmoid()
-        self.reshape = P.Reshape()
+        self.reshape = ops.Reshape()
 
     def construct(self, z):
         z = self.fc1(z)

tests/st/probability/dpn/test_gpu_vae_gan.py

@@ -21,8 +21,7 @@ import mindspore.dataset as ds
 import mindspore.dataset.vision.c_transforms as CV
 import mindspore.nn as nn
 from mindspore import context
-from mindspore.ops import operations as P
-from mindspore.ops import composite as C
+import mindspore.ops as ops
 from mindspore.nn.probability.dpn import VAE
 from mindspore.nn.probability.infer import ELBO, SVI
 
@@ -51,7 +50,7 @@ class Decoder(nn.Cell):
         self.fc1 = nn.Dense(400, 1024)
         self.relu = nn.ReLU()
         self.sigmoid = nn.Sigmoid()
-        self.reshape = P.Reshape()
+        self.reshape = ops.Reshape()
 
     def construct(self, z):
         z = self.fc1(z)
@@ -93,9 +92,9 @@ class VaeGan(nn.Cell):
         self.D = Discriminator()
         self.dense = nn.Dense(20, 400)
         self.vae = VAE(self.E, self.G, 400, 20)
-        self.shape = P.Shape()
-        self.normal = C.normal
-        self.to_tensor = P.ScalarToArray()
+        self.shape = ops.Shape()
+        self.normal = ops.normal
+        self.to_tensor = ops.ScalarToArray()
 
     def construct(self, x):
         recon_x, x, mu, std = self.vae(x)
@@ -111,7 +110,7 @@ class VaeGan(nn.Cell):
 class VaeGanLoss(ELBO):
     def __init__(self):
         super(VaeGanLoss, self).__init__()
-        self.zeros = P.ZerosLike()
+        self.zeros = ops.ZerosLike()
         self.mse = nn.MSELoss(reduction='sum')
 
     def construct(self, data, label):

tests/st/probability/toolbox/test_uncertainty.py

@@ -18,11 +18,11 @@ import mindspore.dataset.transforms.c_transforms as C
 import mindspore.dataset.vision.c_transforms as CV
 import mindspore.nn as nn
 from mindspore import context, Tensor
-from mindspore.common import dtype as mstype
+from mindspore import dtype as mstype
 from mindspore.common.initializer import TruncatedNormal
 from mindspore.dataset.vision import Inter
 from mindspore.nn.probability.toolbox.uncertainty_evaluation import UncertaintyEvaluation
-from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from mindspore.train import load_checkpoint, load_param_into_net
 
 context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
 

tests/st/probability/transforms/dataset.py

@@ -20,7 +20,7 @@ import mindspore.dataset as ds
 import mindspore.dataset.vision.c_transforms as CV
 import mindspore.dataset.transforms.c_transforms as C
 from mindspore.dataset.vision import Inter
-from mindspore.common import dtype as mstype
+from mindspore import dtype as mstype
 
 
 def create_dataset(data_path, batch_size=32, repeat_size=1,

tests/st/probability/transforms/test_transform_bnn_layer.py

@@ -20,7 +20,7 @@ from mindspore.common.initializer import TruncatedNormal
 import mindspore.nn as nn
 from mindspore.nn import TrainOneStepCell, WithLossCell
 from mindspore.nn.probability import transforms, bnn_layers
-from mindspore.ops import operations as P
+import mindspore.ops as ops
 from mindspore import context
 from dataset import create_dataset
 
@@ -72,7 +72,7 @@ class LeNet5(nn.Cell):
         self.relu = nn.ReLU()
         self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
         self.flatten = nn.Flatten()
-        self.reshape = P.Reshape()
+        self.reshape = ops.Reshape()
 
     def construct(self, x):
         x = self.conv1(x)
@@ -98,7 +98,7 @@ def train_model(train_net, net, dataset):
         label = Tensor(data['label'].astype(np.int32))
         loss = train_net(train_x, label)
         output = net(train_x)
-        log_output = P.LogSoftmax(axis=1)(output)
+        log_output = ops.LogSoftmax(axis=1)(output)
         acc = np.mean(log_output.asnumpy().argmax(axis=1) == label.asnumpy())
         accs.append(acc)
         loss_sum += loss.asnumpy()
@@ -114,7 +114,7 @@ def validate_model(net, dataset):
         train_x = Tensor(data['image'].astype(np.float32))
         label = Tensor(data['label'].astype(np.int32))
         output = net(train_x)
-        log_output = P.LogSoftmax(axis=1)(output)
+        log_output = ops.LogSoftmax(axis=1)(output)
         acc = np.mean(log_output.asnumpy().argmax(axis=1) == label.asnumpy())
         accs.append(acc)
 

tests/st/probability/transforms/test_transform_bnn_model.py

@@ -19,7 +19,7 @@ from mindspore.common.initializer import TruncatedNormal
 import mindspore.nn as nn
 from mindspore.nn import WithLossCell, TrainOneStepCell
 from mindspore.nn.probability import transforms
-from mindspore.ops import operations as P
+import mindspore.ops as ops
 from mindspore import context
 from dataset import create_dataset
 
@@ -71,7 +71,7 @@ class LeNet5(nn.Cell):
         self.relu = nn.ReLU()
         self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
         self.flatten = nn.Flatten()
-        self.reshape = P.Reshape()
+        self.reshape = ops.Reshape()
 
     def construct(self, x):
         x = self.conv1(x)
@@ -97,7 +97,7 @@ def train_model(train_net, net, dataset):
         label = Tensor(data['label'].astype(np.int32))
         loss = train_net(train_x, label)
         output = net(train_x)
-        log_output = P.LogSoftmax(axis=1)(output)
+        log_output = ops.LogSoftmax(axis=1)(output)
         acc = np.mean(log_output.asnumpy().argmax(axis=1) == label.asnumpy())
         accs.append(acc)
         loss_sum += loss.asnumpy()
@@ -113,7 +113,7 @@ def validate_model(net, dataset):
         train_x = Tensor(data['image'].astype(np.float32))
         label = Tensor(data['label'].astype(np.int32))
         output = net(train_x)
-        log_output = P.LogSoftmax(axis=1)(output)
+        log_output = ops.LogSoftmax(axis=1)(output)
         acc = np.mean(log_output.asnumpy().argmax(axis=1) == label.asnumpy())
         accs.append(acc)