forked from mindspore-Ecosystem/mindspore
fix gcn import error
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14868eb2b0
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a04d497118
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@ -15,13 +15,9 @@
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"""GCN."""
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import numpy as np
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from mindspore import nn
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from mindspore.common.parameter import ParameterTuple
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from mindspore.ops import composite as C
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from mindspore.ops import functional as F
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from mindspore.ops import operations as P
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from mindspore import Tensor
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from mindspore.nn.layer.activation import get_activation
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from model_zoo.gcn.src.metrics import Loss, Accuracy
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def glorot(shape):
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@ -105,116 +101,3 @@ class GCN(nn.Cell):
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output0 = self.layer0(self.adj, self.feature)
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output1 = self.layer1(self.adj, output0)
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return output1
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class LossAccuracyWrapper(nn.Cell):
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"""
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Wraps the GCN model with loss and accuracy cell.
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Args:
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network (Cell): GCN network.
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label (numpy.ndarray): Dataset labels.
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mask (numpy.ndarray): Mask for training, evaluation or test.
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weight_decay (float): Weight decay parameter for weight of the first convolution layer.
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"""
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def __init__(self, network, label, mask, weight_decay):
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super(LossAccuracyWrapper, self).__init__()
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self.network = network
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self.loss = Loss(label, mask, weight_decay, network.trainable_params()[0])
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self.accuracy = Accuracy(label, mask)
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def construct(self):
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preds = self.network()
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loss = self.loss(preds)
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accuracy = self.accuracy(preds)
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return loss, accuracy
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class LossWrapper(nn.Cell):
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"""
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Wraps the GCN model with loss.
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Args:
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network (Cell): GCN network.
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label (numpy.ndarray): Dataset labels.
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mask (numpy.ndarray): Mask for training.
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weight_decay (float): Weight decay parameter for weight of the first convolution layer.
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"""
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def __init__(self, network, label, mask, weight_decay):
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super(LossWrapper, self).__init__()
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self.network = network
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self.loss = Loss(label, mask, weight_decay, network.trainable_params()[0])
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def construct(self):
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preds = self.network()
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loss = self.loss(preds)
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return loss
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class TrainOneStepCell(nn.Cell):
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r"""
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Network training package class.
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Wraps the network with an optimizer. The resulting Cell be trained without inputs.
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Backward graph will be created in the construct function to do parameter updating. Different
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parallel modes are available to run the training.
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Args:
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network (Cell): The training network.
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optimizer (Cell): Optimizer for updating the weights.
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sens (Number): The scaling number to be filled as the input of backpropagation. Default value is 1.0.
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Outputs:
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Tensor, a scalar Tensor with shape :math:`()`.
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Examples:
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>>> net = Net()
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>>> loss_fn = nn.SoftmaxCrossEntropyWithLogits()
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>>> optim = nn.Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
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>>> loss_net = nn.WithLossCell(net, loss_fn)
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>>> train_net = nn.TrainOneStepCell(loss_net, optim)
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"""
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def __init__(self, network, optimizer, sens=1.0):
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super(TrainOneStepCell, self).__init__(auto_prefix=False)
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self.network = network
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self.network.add_flags(defer_inline=True)
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self.weights = ParameterTuple(network.trainable_params())
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self.optimizer = optimizer
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self.grad = C.GradOperation('grad', get_by_list=True, sens_param=True)
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self.sens = sens
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def construct(self):
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weights = self.weights
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loss = self.network()
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sens = P.Fill()(P.DType()(loss), P.Shape()(loss), self.sens)
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grads = self.grad(self.network, weights)(sens)
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return F.depend(loss, self.optimizer(grads))
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class TrainNetWrapper(nn.Cell):
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"""
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Wraps the GCN model with optimizer.
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Args:
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network (Cell): GCN network.
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label (numpy.ndarray): Dataset labels.
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mask (numpy.ndarray): Mask for training, evaluation or test.
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config (ConfigGCN): Configuration for GCN.
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"""
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def __init__(self, network, label, mask, config):
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super(TrainNetWrapper, self).__init__(auto_prefix=True)
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self.network = network
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loss_net = LossWrapper(network, label, mask, config.weight_decay)
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optimizer = nn.Adam(loss_net.trainable_params(),
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learning_rate=config.learning_rate)
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self.loss_train_net = TrainOneStepCell(loss_net, optimizer)
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self.accuracy = Accuracy(label, mask)
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def construct(self):
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loss = self.loss_train_net()
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accuracy = self.accuracy(self.network())
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return loss, accuracy
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@ -17,6 +17,9 @@ from mindspore import nn
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from mindspore import Tensor
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from mindspore.common import dtype as mstype
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from mindspore.ops import operations as P
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from mindspore.common.parameter import ParameterTuple
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from mindspore.ops import composite as C
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from mindspore.ops import functional as F
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class Loss(nn.Cell):
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@ -68,3 +71,116 @@ class Accuracy(nn.Cell):
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mask = mask / mask_reduce
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accuracy_all *= mask
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return self.mean(accuracy_all)
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class LossAccuracyWrapper(nn.Cell):
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"""
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Wraps the GCN model with loss and accuracy cell.
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Args:
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network (Cell): GCN network.
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label (numpy.ndarray): Dataset labels.
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mask (numpy.ndarray): Mask for training, evaluation or test.
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weight_decay (float): Weight decay parameter for weight of the first convolution layer.
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"""
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def __init__(self, network, label, mask, weight_decay):
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super(LossAccuracyWrapper, self).__init__()
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self.network = network
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self.loss = Loss(label, mask, weight_decay, network.trainable_params()[0])
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self.accuracy = Accuracy(label, mask)
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def construct(self):
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preds = self.network()
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loss = self.loss(preds)
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accuracy = self.accuracy(preds)
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return loss, accuracy
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class LossWrapper(nn.Cell):
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"""
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Wraps the GCN model with loss.
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Args:
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network (Cell): GCN network.
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label (numpy.ndarray): Dataset labels.
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mask (numpy.ndarray): Mask for training.
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weight_decay (float): Weight decay parameter for weight of the first convolution layer.
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"""
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def __init__(self, network, label, mask, weight_decay):
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super(LossWrapper, self).__init__()
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self.network = network
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self.loss = Loss(label, mask, weight_decay, network.trainable_params()[0])
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def construct(self):
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preds = self.network()
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loss = self.loss(preds)
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return loss
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class TrainOneStepCell(nn.Cell):
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r"""
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Network training package class.
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Wraps the network with an optimizer. The resulting Cell be trained without inputs.
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Backward graph will be created in the construct function to do parameter updating. Different
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parallel modes are available to run the training.
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Args:
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network (Cell): The training network.
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optimizer (Cell): Optimizer for updating the weights.
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sens (Number): The scaling number to be filled as the input of backpropagation. Default value is 1.0.
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Outputs:
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Tensor, a scalar Tensor with shape :math:`()`.
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Examples:
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>>> net = Net()
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>>> loss_fn = nn.SoftmaxCrossEntropyWithLogits()
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>>> optim = nn.Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
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>>> loss_net = nn.WithLossCell(net, loss_fn)
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>>> train_net = nn.TrainOneStepCell(loss_net, optim)
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"""
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def __init__(self, network, optimizer, sens=1.0):
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super(TrainOneStepCell, self).__init__(auto_prefix=False)
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self.network = network
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self.network.add_flags(defer_inline=True)
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self.weights = ParameterTuple(network.trainable_params())
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self.optimizer = optimizer
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self.grad = C.GradOperation('grad', get_by_list=True, sens_param=True)
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self.sens = sens
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def construct(self):
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weights = self.weights
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loss = self.network()
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sens = P.Fill()(P.DType()(loss), P.Shape()(loss), self.sens)
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grads = self.grad(self.network, weights)(sens)
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return F.depend(loss, self.optimizer(grads))
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class TrainNetWrapper(nn.Cell):
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"""
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Wraps the GCN model with optimizer.
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Args:
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network (Cell): GCN network.
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label (numpy.ndarray): Dataset labels.
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mask (numpy.ndarray): Mask for training, evaluation or test.
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config (ConfigGCN): Configuration for GCN.
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"""
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def __init__(self, network, label, mask, config):
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super(TrainNetWrapper, self).__init__(auto_prefix=True)
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self.network = network
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loss_net = LossWrapper(network, label, mask, config.weight_decay)
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optimizer = nn.Adam(loss_net.trainable_params(),
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learning_rate=config.learning_rate)
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self.loss_train_net = TrainOneStepCell(loss_net, optimizer)
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self.accuracy = Accuracy(label, mask)
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def construct(self):
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loss = self.loss_train_net()
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accuracy = self.accuracy(self.network())
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return loss, accuracy
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@ -26,9 +26,10 @@ from matplotlib import animation
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from sklearn import manifold
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from mindspore import context
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from model_zoo.gcn.src.gcn import GCN, LossAccuracyWrapper, TrainNetWrapper
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from model_zoo.gcn.src.config import ConfigGCN
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from model_zoo.gcn.src.dataset import get_adj_features_labels, get_mask
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from src.gcn import GCN
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from src.metrics import LossAccuracyWrapper, TrainNetWrapper
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from src.config import ConfigGCN
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from src.dataset import get_adj_features_labels, get_mask
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def t_SNE(out_feature, dim):
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@ -17,7 +17,8 @@ import time
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import pytest
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import numpy as np
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from mindspore import context
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from model_zoo.gcn.src.gcn import GCN, LossAccuracyWrapper, TrainNetWrapper
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from model_zoo.gcn.src.gcn import GCN
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from model_zoo.gcn.src.metrics import LossAccuracyWrapper, TrainNetWrapper
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from model_zoo.gcn.src.config import ConfigGCN
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from model_zoo.gcn.src.dataset import get_adj_features_labels, get_mask
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