fix gcn import error

model_zoo/gcn/src/gcn.py

@@ -15,13 +15,9 @@
 """GCN."""
 import numpy as np
 from mindspore import nn
-from mindspore.common.parameter import ParameterTuple
-from mindspore.ops import composite as C
-from mindspore.ops import functional as F
 from mindspore.ops import operations as P
 from mindspore import Tensor
 from mindspore.nn.layer.activation import get_activation
-from model_zoo.gcn.src.metrics import Loss, Accuracy
 
 
 def glorot(shape):
@@ -105,116 +101,3 @@ class GCN(nn.Cell):
         output0 = self.layer0(self.adj, self.feature)
         output1 = self.layer1(self.adj, output0)
         return output1
-
-
-class LossAccuracyWrapper(nn.Cell):
-    """
-    Wraps the GCN model with loss and accuracy cell.
-
-    Args:
-        network (Cell): GCN network.
-        label (numpy.ndarray): Dataset labels.
-        mask (numpy.ndarray): Mask for training, evaluation or test.
-        weight_decay (float): Weight decay parameter for weight of the first convolution layer.
-    """
-
-    def __init__(self, network, label, mask, weight_decay):
-        super(LossAccuracyWrapper, self).__init__()
-        self.network = network
-        self.loss = Loss(label, mask, weight_decay, network.trainable_params()[0])
-        self.accuracy = Accuracy(label, mask)
-
-    def construct(self):
-        preds = self.network()
-        loss = self.loss(preds)
-        accuracy = self.accuracy(preds)
-        return loss, accuracy
-
-
-class LossWrapper(nn.Cell):
-    """
-    Wraps the GCN model with loss.
-
-    Args:
-        network (Cell): GCN network.
-        label (numpy.ndarray): Dataset labels.
-        mask (numpy.ndarray): Mask for training.
-        weight_decay (float): Weight decay parameter for weight of the first convolution layer.
-    """
-
-    def __init__(self, network, label, mask, weight_decay):
-        super(LossWrapper, self).__init__()
-        self.network = network
-        self.loss = Loss(label, mask, weight_decay, network.trainable_params()[0])
-
-    def construct(self):
-        preds = self.network()
-        loss = self.loss(preds)
-        return loss
-
-
-class TrainOneStepCell(nn.Cell):
-    r"""
-    Network training package class.
-
-    Wraps the network with an optimizer. The resulting Cell be trained without inputs.
-    Backward graph will be created in the construct function to do parameter updating. Different
-    parallel modes are available to run the training.
-
-    Args:
-        network (Cell): The training network.
-        optimizer (Cell): Optimizer for updating the weights.
-        sens (Number): The scaling number to be filled as the input of backpropagation. Default value is 1.0.
-
-    Outputs:
-        Tensor, a scalar Tensor with shape :math:`()`.
-
-    Examples:
-        >>> net = Net()
-        >>> loss_fn = nn.SoftmaxCrossEntropyWithLogits()
-        >>> optim = nn.Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
-        >>> loss_net = nn.WithLossCell(net, loss_fn)
-        >>> train_net = nn.TrainOneStepCell(loss_net, optim)
-    """
-
-    def __init__(self, network, optimizer, sens=1.0):
-        super(TrainOneStepCell, self).__init__(auto_prefix=False)
-        self.network = network
-        self.network.add_flags(defer_inline=True)
-        self.weights = ParameterTuple(network.trainable_params())
-        self.optimizer = optimizer
-        self.grad = C.GradOperation('grad', get_by_list=True, sens_param=True)
-        self.sens = sens
-
-    def construct(self):
-        weights = self.weights
-        loss = self.network()
-        sens = P.Fill()(P.DType()(loss), P.Shape()(loss), self.sens)
-        grads = self.grad(self.network, weights)(sens)
-        return F.depend(loss, self.optimizer(grads))
-
-
-class TrainNetWrapper(nn.Cell):
-    """
-    Wraps the GCN model with optimizer.
-
-    Args:
-        network (Cell): GCN network.
-        label (numpy.ndarray): Dataset labels.
-        mask (numpy.ndarray): Mask for training, evaluation or test.
-        config (ConfigGCN): Configuration for GCN.
-    """
-
-    def __init__(self, network, label, mask, config):
-        super(TrainNetWrapper, self).__init__(auto_prefix=True)
-        self.network = network
-        loss_net = LossWrapper(network, label, mask, config.weight_decay)
-        optimizer = nn.Adam(loss_net.trainable_params(),
-                            learning_rate=config.learning_rate)
-        self.loss_train_net = TrainOneStepCell(loss_net, optimizer)
-        self.accuracy = Accuracy(label, mask)
-
-    def construct(self):
-        loss = self.loss_train_net()
-        accuracy = self.accuracy(self.network())
-        return loss, accuracy

model_zoo/gcn/src/metrics.py

@@ -17,6 +17,9 @@ from mindspore import nn
 from mindspore import Tensor
 from mindspore.common import dtype as mstype
 from mindspore.ops import operations as P
+from mindspore.common.parameter import ParameterTuple
+from mindspore.ops import composite as C
+from mindspore.ops import functional as F
 
 
 class Loss(nn.Cell):
@@ -68,3 +71,116 @@ class Accuracy(nn.Cell):
         mask = mask / mask_reduce
         accuracy_all *= mask
         return self.mean(accuracy_all)
+
+
+class LossAccuracyWrapper(nn.Cell):
+    """
+    Wraps the GCN model with loss and accuracy cell.
+
+    Args:
+        network (Cell): GCN network.
+        label (numpy.ndarray): Dataset labels.
+        mask (numpy.ndarray): Mask for training, evaluation or test.
+        weight_decay (float): Weight decay parameter for weight of the first convolution layer.
+    """
+
+    def __init__(self, network, label, mask, weight_decay):
+        super(LossAccuracyWrapper, self).__init__()
+        self.network = network
+        self.loss = Loss(label, mask, weight_decay, network.trainable_params()[0])
+        self.accuracy = Accuracy(label, mask)
+
+    def construct(self):
+        preds = self.network()
+        loss = self.loss(preds)
+        accuracy = self.accuracy(preds)
+        return loss, accuracy
+
+
+class LossWrapper(nn.Cell):
+    """
+    Wraps the GCN model with loss.
+
+    Args:
+        network (Cell): GCN network.
+        label (numpy.ndarray): Dataset labels.
+        mask (numpy.ndarray): Mask for training.
+        weight_decay (float): Weight decay parameter for weight of the first convolution layer.
+    """
+
+    def __init__(self, network, label, mask, weight_decay):
+        super(LossWrapper, self).__init__()
+        self.network = network
+        self.loss = Loss(label, mask, weight_decay, network.trainable_params()[0])
+
+    def construct(self):
+        preds = self.network()
+        loss = self.loss(preds)
+        return loss
+
+
+class TrainOneStepCell(nn.Cell):
+    r"""
+    Network training package class.
+
+    Wraps the network with an optimizer. The resulting Cell be trained without inputs.
+    Backward graph will be created in the construct function to do parameter updating. Different
+    parallel modes are available to run the training.
+
+    Args:
+        network (Cell): The training network.
+        optimizer (Cell): Optimizer for updating the weights.
+        sens (Number): The scaling number to be filled as the input of backpropagation. Default value is 1.0.
+
+    Outputs:
+        Tensor, a scalar Tensor with shape :math:`()`.
+
+    Examples:
+        >>> net = Net()
+        >>> loss_fn = nn.SoftmaxCrossEntropyWithLogits()
+        >>> optim = nn.Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
+        >>> loss_net = nn.WithLossCell(net, loss_fn)
+        >>> train_net = nn.TrainOneStepCell(loss_net, optim)
+    """
+
+    def __init__(self, network, optimizer, sens=1.0):
+        super(TrainOneStepCell, self).__init__(auto_prefix=False)
+        self.network = network
+        self.network.add_flags(defer_inline=True)
+        self.weights = ParameterTuple(network.trainable_params())
+        self.optimizer = optimizer
+        self.grad = C.GradOperation('grad', get_by_list=True, sens_param=True)
+        self.sens = sens
+
+    def construct(self):
+        weights = self.weights
+        loss = self.network()
+        sens = P.Fill()(P.DType()(loss), P.Shape()(loss), self.sens)
+        grads = self.grad(self.network, weights)(sens)
+        return F.depend(loss, self.optimizer(grads))
+
+
+class TrainNetWrapper(nn.Cell):
+    """
+    Wraps the GCN model with optimizer.
+
+    Args:
+        network (Cell): GCN network.
+        label (numpy.ndarray): Dataset labels.
+        mask (numpy.ndarray): Mask for training, evaluation or test.
+        config (ConfigGCN): Configuration for GCN.
+    """
+
+    def __init__(self, network, label, mask, config):
+        super(TrainNetWrapper, self).__init__(auto_prefix=True)
+        self.network = network
+        loss_net = LossWrapper(network, label, mask, config.weight_decay)
+        optimizer = nn.Adam(loss_net.trainable_params(),
+                            learning_rate=config.learning_rate)
+        self.loss_train_net = TrainOneStepCell(loss_net, optimizer)
+        self.accuracy = Accuracy(label, mask)
+
+    def construct(self):
+        loss = self.loss_train_net()
+        accuracy = self.accuracy(self.network())
+        return loss, accuracy

model_zoo/gcn/train.py

@@ -26,9 +26,10 @@ from matplotlib import animation
 from sklearn import manifold
 from mindspore import context
 
-from model_zoo.gcn.src.gcn import GCN, LossAccuracyWrapper, TrainNetWrapper
+from src.gcn import GCN
-from model_zoo.gcn.src.config import ConfigGCN
+from src.metrics import LossAccuracyWrapper, TrainNetWrapper
-from model_zoo.gcn.src.dataset import get_adj_features_labels, get_mask
+from src.config import ConfigGCN
+from src.dataset import get_adj_features_labels, get_mask
 
 
 def t_SNE(out_feature, dim):

tests/st/gnn/gcn/test_gcn.py

@@ -17,7 +17,8 @@ import time
 import pytest
 import numpy as np
 from mindspore import context
-from model_zoo.gcn.src.gcn import GCN, LossAccuracyWrapper, TrainNetWrapper
+from model_zoo.gcn.src.gcn import GCN
+from model_zoo.gcn.src.metrics import LossAccuracyWrapper, TrainNetWrapper
 from model_zoo.gcn.src.config import ConfigGCN
 from model_zoo.gcn.src.dataset import get_adj_features_labels, get_mask