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671 lines
22 KiB
671 lines
22 KiB
# Copyright 2019 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import numpy as np
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import mindspore as ms
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore import context
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from mindspore.common import dtype as mstype
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from mindspore.common.api import _executor
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from mindspore.common.parameter import Parameter
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from mindspore.common.parameter import ParameterTuple
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from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
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from mindspore.nn.optim.momentum import Momentum
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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.ops.operations.comm_ops import _VirtualDataset
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from mindspore.parallel import set_algo_parameters
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from mindspore.train import Model, ParallelMode
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from tests.dataset_mock import MindData
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from tests.ut.python.ops.test_math_ops import VirtualLoss
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context.set_context(mode=context.GRAPH_MODE)
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context.reset_auto_parallel_context()
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class Dataset(MindData):
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def __init__(self, predict, label, length=3, input_num=2):
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super(Dataset, self).__init__(size=length)
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self.predict = predict
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self.label = label
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self.index = 0
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self.length = length
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self.input_num = input_num
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def __iter__(self):
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return self
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def __next__(self):
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if self.index >= self.length:
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raise StopIteration
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self.index += 1
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if self.input_num == 2:
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return self.predict, self.label
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else:
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return self.predict,
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def reset(self):
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self.index = 0
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class ReshapeNet(nn.Cell):
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def __init__(self, strategy0, strategy1, strategy2):
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super(ReshapeNet, self).__init__()
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self.relu = P.ReLU().set_strategy(strategy0)
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self.reshape = P.Reshape().set_strategy(strategy1)
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self.matmul = P.MatMul().set_strategy(strategy2)
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self.matmul_weight = Parameter(Tensor(np.ones([25088, 256]), dtype=ms.float32), name="weight")
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def construct(self, x):
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x = self.relu(x)
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x = self.reshape(x, (256, 25088))
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x = self.matmul(x, self.matmul_weight)
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return x
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def reshape_net(strategy0, strategy1, strategy2):
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return ReshapeNet(strategy0=strategy0, strategy1=strategy1, strategy2=strategy2)
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def reshape_common(parallel_mode, strategy0, strategy1, strategy2, strategy_loss):
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batch_size = 32
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learning_rate = 0.1
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momentum = 0.9
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epoch_size = 2
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context.reset_auto_parallel_context()
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context.set_auto_parallel_context(parallel_mode=parallel_mode, device_num=8)
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predict = Tensor(np.ones([32, 512, 7, 7]), dtype=ms.float32)
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label = Tensor(np.ones([32]), dtype=ms.int32)
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dataset = Dataset(predict, label, 2)
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net = reshape_net(strategy0, strategy1, strategy2)
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loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
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loss.softmax_cross_entropy.set_strategy(strategy_loss)
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loss.one_hot.set_strategy(((8, 1), (), ()))
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opt = Momentum(net.trainable_params(), learning_rate, momentum)
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model = Model(net, loss, opt)
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model.train(epoch_size, dataset, dataset_sink_mode=False)
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def test_reshape1():
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strategy0 = ((8, 1, 1, 1),)
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strategy1 = None
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strategy2 = ((8, 1), (1, 1))
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strategy_loss = ((8, 1), (8, 1))
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reshape_common(ParallelMode.SEMI_AUTO_PARALLEL, strategy0, strategy1, strategy2, strategy_loss)
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def test_reshape1_strategy_1():
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strategy0 = ((8, 1, 1, 1),)
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strategy1 = ((8, 1, 1, 1),)
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strategy2 = ((8, 1), (1, 1))
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strategy_loss = ((8, 1), (8, 1))
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try:
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reshape_common(ParallelMode.SEMI_AUTO_PARALLEL, strategy0, strategy1, strategy2, strategy_loss)
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except:
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pass
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def test_reshape1_strategy_2():
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strategy0 = ((8, 1, 1, 1),)
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strategy1 = ((8, 1, 1, 1),)
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strategy2 = ((8, 1), (1, 1))
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strategy_loss = ((8, 1), (8, 1))
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try:
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reshape_common(ParallelMode.AUTO_PARALLEL, strategy0, strategy1, strategy2, strategy_loss)
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except:
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pass
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def test_reshape2():
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strategy0 = ((8, 1, 1, 1),)
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strategy1 = None
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strategy2 = ((8, 1), (1, 1))
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strategy_loss = ((8, 1), (8, 1))
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reshape_common(ParallelMode.SEMI_AUTO_PARALLEL, strategy0, strategy1, strategy2, strategy_loss)
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def test_reshape3():
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strategy0 = ((2, 1, 1, 1),)
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strategy1 = None
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strategy2 = ((8, 1), (1, 1))
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strategy_loss = ((8, 1), (8, 1))
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reshape_common(ParallelMode.SEMI_AUTO_PARALLEL, strategy0, strategy1, strategy2, strategy_loss)
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def test_reshape4():
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strategy0 = ((1, 1, 1, 1),)
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strategy1 = None
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strategy2 = ((8, 1), (1, 1))
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strategy_loss = ((8, 1), (8, 1))
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reshape_common(ParallelMode.SEMI_AUTO_PARALLEL, strategy0, strategy1, strategy2, strategy_loss)
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def test_reshape5():
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strategy0 = ((2, 1, 1, 1),)
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strategy1 = None
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strategy2 = ((1, 8), (8, 1))
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strategy_loss = ((8, 1), (8, 1))
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reshape_common(ParallelMode.SEMI_AUTO_PARALLEL, strategy0, strategy1, strategy2, strategy_loss)
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def test_reshape_auto():
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strategy0 = None
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strategy1 = None
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strategy2 = None
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strategy_loss = None
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reshape_common(ParallelMode.AUTO_PARALLEL, strategy0, strategy1, strategy2, strategy_loss)
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class NetWithLoss(nn.Cell):
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def __init__(self, network):
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super(NetWithLoss, self).__init__()
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self.loss = VirtualLoss()
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self.network = network
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def construct(self, x):
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predict = self.network(x)
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return self.loss(predict)
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class GradWrap(nn.Cell):
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def __init__(self, network):
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super(GradWrap, self).__init__()
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self.network = network
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def construct(self, x):
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return C.grad_all(self.network)(x)
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class ReshapeNet1(nn.Cell):
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def __init__(self, strategy0):
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super(ReshapeNet1, self).__init__()
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self.virtual_dataset = _VirtualDataset()
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self.reshape = P.Reshape()
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self.matmul = P.MatMul().set_strategy(strategy0)
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self.matmul_weight = Parameter(Tensor(np.ones([25088, 256]), dtype=ms.float32), name="weight")
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self.reshape2 = P.Reshape()
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def construct(self, x):
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x = self.virtual_dataset(x)
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x = self.reshape(x, (256, 25088))
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x = self.matmul(x, self.matmul_weight)
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x = self.reshape2(x, (256 * 256,))
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return x
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class ReshapeNet2(nn.Cell):
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def __init__(self, strategy0):
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super(ReshapeNet2, self).__init__()
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self.virtual_dataset = _VirtualDataset()
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self.reshape = P.Reshape()
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self.matmul = P.MatMul().set_strategy(strategy0)
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self.matmul_weight = Parameter(Tensor(np.ones([25088, 256]), dtype=ms.float32), name="weight")
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self.reshape2 = P.Reshape()
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self.reduce_sum = P.ReduceSum(keep_dims=True)
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self.reshape3 = P.Reshape()
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def construct(self, x):
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x = self.virtual_dataset(x)
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x = self.reshape(x, (256, 25088))
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x = self.matmul(x, self.matmul_weight)
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x = self.reshape2(x, (256 * 256,))
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x = self.reduce_sum(x, -1)
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x = self.reshape3(x, ())
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return x
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class ReshapeNet3(nn.Cell):
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def __init__(self, strategy0):
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super(ReshapeNet3, self).__init__()
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self.virtual_dataset = _VirtualDataset()
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self.reshape = P.Reshape()
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self.matmul = P.MatMul().set_strategy(strategy0)
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self.matmul_weight = Parameter(Tensor(np.ones([25088, 256]), dtype=ms.float32), name="weight")
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self.reshape2 = P.Reshape()
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self.reduce_sum = P.ReduceSum(keep_dims=False)
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self.reshape3 = P.Reshape()
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def construct(self, x):
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x = self.virtual_dataset(x)
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x = self.reshape(x, (256, 25088))
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x = self.matmul(x, self.matmul_weight)
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x = self.reshape2(x, (256 * 256,))
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x = self.reduce_sum(x, -1)
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x = self.reshape3(x, (1, 1))
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return x
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class ReshapeNet4(nn.Cell):
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def __init__(self, strategy0):
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super(ReshapeNet4, self).__init__()
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self.virtual_dataset = _VirtualDataset()
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self.reshape = P.Reshape()
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self.reshape2 = P.Reshape()
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self.matmul = P.MatMul().set_strategy(strategy0)
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self.matmul_weight = Parameter(Tensor(np.ones([25088, 256]), dtype=ms.float32), name="weight")
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def construct(self, x):
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x = self.virtual_dataset(x)
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x = self.reshape(x, (256, 25088))
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w = self.reshape2(self.matmul_weight, (25088, 256))
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x = self.matmul(x, w)
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return x
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class ReshapeNet5(nn.Cell):
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def __init__(self, strategy0):
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super(ReshapeNet5, self).__init__()
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self.virtual_dataset = _VirtualDataset()
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self.reshape = P.Reshape()
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self.matmul1 = P.MatMul().set_strategy(strategy0)
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self.matmul1_weight = Parameter(Tensor(np.ones([25088, 256]), dtype=ms.float32), name="weight")
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self.matmul2 = P.MatMul().set_strategy(strategy0)
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def construct(self, x):
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x = self.virtual_dataset(x)
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x = self.reshape(x, (256, 25088))
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matmul1_o = self.matmul1(x, self.matmul1_weight)
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matmul2_o = self.matmul2(matmul1_o, x)
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return matmul2_o
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class ReshapeNet6(nn.Cell):
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def __init__(self, strategy0):
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super(ReshapeNet6, self).__init__()
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self.virtual_dataset = _VirtualDataset()
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self.reshape = P.Reshape()
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self.matmul1_1 = P.MatMul().set_strategy(strategy0)
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self.matmul1_2 = P.MatMul().set_strategy(strategy0)
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self.matmul1_weight = Parameter(Tensor(np.ones([25088, 256]), dtype=ms.float32), name="weight")
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self.matmul2 = P.MatMul().set_strategy(strategy0)
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self.add = P.TensorAdd()
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def construct(self, x):
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x = self.virtual_dataset(x)
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x = self.reshape(x, (256, 25088))
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matmul1_1_o = self.matmul1_1(x, self.matmul1_weight)
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matmul1_2_o = self.matmul1_2(x, self.matmul1_weight)
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matmul1_o = self.add(matmul1_1_o, matmul1_2_o)
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matmul2_o = self.matmul2(matmul1_o, x)
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return matmul2_o
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def compile(net, input):
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net.set_auto_parallel()
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_executor.compile(net, input)
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def reshape_net2(backbone):
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batch_size = 16
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device_num = 16
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context.set_auto_parallel_context(device_num=device_num, global_rank=0)
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input = Tensor(np.ones([batch_size * device_num, 512, 7, 7]).astype(np.float32) * 0.01)
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net = GradWrap(NetWithLoss(backbone))
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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compile(net, input)
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def test_reshape_net1_1():
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reshape_net2(ReshapeNet1(((1, 8), (8, 1))))
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def test_reshape_net1_2():
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reshape_net2(ReshapeNet1(((1, 8), (8, 2))))
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def test_reshape_net2_1():
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reshape_net2(ReshapeNet2(((1, 8), (8, 1))))
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def test_reshape_net2_2():
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reshape_net2(ReshapeNet2(((1, 8), (8, 2))))
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def test_reshape_net3_1():
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reshape_net2(ReshapeNet3(((1, 8), (8, 1))))
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def test_reshape_net3_2():
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reshape_net2(ReshapeNet3(((1, 8), (8, 2))))
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def test_reshape_net4_1():
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try:
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reshape_net2(ReshapeNet4(((1, 8), (8, 1))))
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except:
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pass
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def test_reshape_net4_2():
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try:
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reshape_net2(ReshapeNet4(((1, 8), (8, 2))))
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except:
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pass
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def test_reshape_net5_1():
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reshape_net2(ReshapeNet5(((1, 8), (8, 1))))
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def test_reshape_net5_2():
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reshape_net2(ReshapeNet5(((1, 8), (8, 2))))
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def test_reshape_net6_1():
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reshape_net2(ReshapeNet6(((1, 8), (8, 1))))
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def test_reshape_net6_2():
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reshape_net2(ReshapeNet6(((1, 8), (8, 2))))
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class TrainOneStepCell(nn.Cell):
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"""
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Network training package class.
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Append an optimizer to the training network after that the construct function
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can be called to create the backward graph.
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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 adjust parameter. Default: 1.0.
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Examples:
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>>> net = Net()
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>>> loss_fn = nn.SoftmaxCrossEntropyWithLogits()
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>>> optim = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
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>>> loss_net = WithLossCell(net, loss_fn)
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>>> train_net = 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',
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get_by_list=True,
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sens_param=True)
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self.sens = sens
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def construct(self, data):
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weights = self.weights
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loss = self.network(data)
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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)(data, sens)
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return F.depend(loss, self.optimizer(grads))
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def reshape_common2(parallel_mode, net):
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batch_size = 16
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learning_rate = 0.1
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momentum = 0.9
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epoch_size = 2
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predict = Tensor(np.ones([batch_size, 512, 7, 7]), dtype=ms.float32)
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label = Tensor(np.ones([batch_size]), dtype=ms.int32)
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dataset = Dataset(predict, label, 2, input_num=1)
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context.reset_auto_parallel_context()
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context.set_auto_parallel_context(parallel_mode=parallel_mode, device_num=16)
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opt = Momentum(net.trainable_params(), learning_rate, momentum)
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train_net = TrainOneStepCell(net, opt).set_train()
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model = Model(train_net)
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model.train(epoch_size, dataset, dataset_sink_mode=False)
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def test_reshape_common2_0():
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reshape_common2(ParallelMode.SEMI_AUTO_PARALLEL, ReshapeNet1(((1, 8), (8, 1))))
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def test_reshape_common2_1():
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reshape_common2(ParallelMode.SEMI_AUTO_PARALLEL, ReshapeNet1(((1, 8), (8, 2))))
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def test_reshape_common2_2():
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reshape_common2(ParallelMode.SEMI_AUTO_PARALLEL, ReshapeNet2(((1, 8), (8, 1))))
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def test_reshape_common2_3():
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reshape_common2(ParallelMode.SEMI_AUTO_PARALLEL, ReshapeNet2(((1, 8), (8, 2))))
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def test_reshape_common2_4():
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reshape_common2(ParallelMode.SEMI_AUTO_PARALLEL, ReshapeNet3(((1, 8), (8, 1))))
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def test_reshape_common2_5():
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reshape_common2(ParallelMode.SEMI_AUTO_PARALLEL, ReshapeNet3(((1, 8), (8, 2))))
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class BatchNormReshapeNet(nn.Cell):
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def __init__(self):
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super(BatchNormReshapeNet, self).__init__()
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self.vd = P._VirtualDataset()
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self.batch_norm = nn.BatchNorm1d(512, affine=False)
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self.reshape = P.Reshape()
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self.prelu = nn.PReLU(channel=256)
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def construct(self, x):
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x = self.vd(x)
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x = self.batch_norm(x)
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x = self.reshape(x, (512, 256))
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x = self.prelu(x)
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return x
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def test_batchnorm_reshape_train():
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batch_size = 16
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device_num = 16
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context.set_auto_parallel_context(device_num=device_num, global_rank=0)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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input = Tensor(np.ones([batch_size * device_num, 512]).astype(np.float32) * 0.01)
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net = GradWrap(NetWithLoss(BatchNormReshapeNet()))
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compile(net, input)
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|
|
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def bn_with_initialize(out_channels):
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bn = nn.BatchNorm2d(out_channels, momentum=0.3, eps=1e-5).add_flags_recursive(fp32=True)
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return bn
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|
|
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def fc_with_initialize(input_channels, out_channels):
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return nn.Dense(input_channels, out_channels).add_flags_recursive(fp16=True)
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|
|
|
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class BNReshapeDenseBNNet(nn.Cell):
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def __init__(self):
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super(BNReshapeDenseBNNet, self).__init__()
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self.batch_norm = bn_with_initialize(2)
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self.reshape = P.Reshape()
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self.cast = P.Cast()
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self.batch_norm2 = nn.BatchNorm1d(512, affine=False)
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self.fc = fc_with_initialize(2 * 32 * 32, 512)
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|
|
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def construct(self, x):
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x = self.batch_norm(x)
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x = self.reshape(x, (16, 2 * 32 * 32))
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x = self.fc(x)
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x = self.batch_norm2(x)
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return x
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|
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def test_bn_reshape_dense_bn_train():
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batch_size = 16
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device_num = 16
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context.set_auto_parallel_context(device_num=device_num, global_rank=0)
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|
input = Tensor(np.ones([batch_size, 2, 32, 32]).astype(np.float32) * 0.01)
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|
|
|
net = GradWrap(NetWithLoss(BNReshapeDenseBNNet()))
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|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
|
|
|
|
compile(net, input)
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|
|
|
|
|
class ParallelReduceMeanNet(nn.Cell):
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|
def __init__(self, conv_in_channel, conv_out_channel,
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|
reducemean_keep_dims=False, reducemean_axis=-1, strategy=None):
|
|
super().__init__()
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|
self.conv = nn.Conv2d(in_channels=conv_in_channel, out_channels=conv_out_channel,
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|
kernel_size=1, stride=1, pad_mode='valid', has_bias=True,
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|
weight_init='ones', bias_init='ones')
|
|
self.reduce_mean = P.ReduceMean(keep_dims=reducemean_keep_dims)
|
|
self.flat = nn.Flatten()
|
|
self.reducemean_axis = reducemean_axis
|
|
if strategy is not None:
|
|
self.reduce_mean.set_strategy(strategy)
|
|
|
|
def construct(self, inputs):
|
|
x = self.conv(inputs)
|
|
x = self.reduce_mean(x, self.reducemean_axis)
|
|
x = self.flat(x)
|
|
return x
|
|
|
|
|
|
class CrossEntropyLoss(nn.Cell):
|
|
def __init__(self, reduction='mean'):
|
|
super(CrossEntropyLoss, self).__init__()
|
|
|
|
self.reduce_mean = P.ReduceMean()
|
|
self.cross_entropy = SoftmaxCrossEntropyWithLogits()
|
|
self.reduction = reduction
|
|
|
|
def construct(self, logits, label):
|
|
loss = self.cross_entropy(logits, label)
|
|
if self.reduction == 'mean':
|
|
loss = self.reduce_mean(loss, (-1,))
|
|
return loss
|
|
|
|
|
|
def test_flatten_reshape(parallel_mode="auto_parallel"):
|
|
batch_size = 16
|
|
learning_rate = 0.1
|
|
momentum = 0.9
|
|
epoch_size = 2
|
|
context.reset_auto_parallel_context()
|
|
context.set_auto_parallel_context(parallel_mode=parallel_mode, device_num=8)
|
|
net = ParallelReduceMeanNet(conv_in_channel=3, conv_out_channel=64, reducemean_axis=(2, 3),
|
|
strategy=((4, 2, 1, 1),))
|
|
loss = CrossEntropyLoss()
|
|
predict = Tensor(np.ones([batch_size, 3, 32, 32]), dtype=ms.float32)
|
|
label = Tensor(np.ones([batch_size, 64]), dtype=ms.float32)
|
|
dataset = Dataset(predict, label, 2, input_num=2)
|
|
|
|
opt = Momentum(net.trainable_params(), learning_rate, momentum)
|
|
model = Model(net, loss_fn=loss, optimizer=opt)
|
|
model.train(epoch_size, dataset, dataset_sink_mode=False)
|
|
|
|
|
|
def test_flatten_reshape2(parallel_mode="auto_parallel"):
|
|
batch_size = 16
|
|
learning_rate = 0.1
|
|
momentum = 0.9
|
|
epoch_size = 2
|
|
context.reset_auto_parallel_context()
|
|
context.set_auto_parallel_context(parallel_mode=parallel_mode, device_num=8)
|
|
set_algo_parameters(fully_use_devices=False)
|
|
net = ParallelReduceMeanNet(conv_in_channel=3, conv_out_channel=64, reducemean_axis=(2, 3),
|
|
strategy=((4, 1, 1, 1),))
|
|
loss = CrossEntropyLoss()
|
|
predict = Tensor(np.ones([batch_size, 3, 32, 32]), dtype=ms.float32)
|
|
label = Tensor(np.ones([batch_size, 64]), dtype=ms.float32)
|
|
dataset = Dataset(predict, label, 2, input_num=2)
|
|
|
|
opt = Momentum(net.trainable_params(), learning_rate, momentum)
|
|
model = Model(net, loss_fn=loss, optimizer=opt)
|
|
model.train(epoch_size, dataset, dataset_sink_mode=False)
|
|
|
|
|
|
class ParallelReshapeNet(nn.Cell):
|
|
def __init__(self, dense_in_channel, dense_out_channel, shape, strategy=None):
|
|
super().__init__()
|
|
self.flat = nn.Flatten()
|
|
self.dense = nn.Dense(in_channels=dense_in_channel,
|
|
out_channels=dense_out_channel,
|
|
weight_init='ones',
|
|
bias_init='ones',
|
|
has_bias=True)
|
|
self.reshape = P.Reshape()
|
|
self.shape = shape
|
|
self.reshape.set_strategy(strategy)
|
|
|
|
def construct(self, inputs):
|
|
x = self.flat(inputs)
|
|
x = self.dense(x)
|
|
x = self.reshape(x, self.shape)
|
|
return x
|
|
|
|
|
|
# the shape of input and output of reshape is the same
|
|
# reshape is optimized before step_parallel
|
|
def test_flatten_reshape3(parallel_mode="auto_parallel"):
|
|
batch_size = 16
|
|
learning_rate = 0.1
|
|
momentum = 0.9
|
|
epoch_size = 2
|
|
context.reset_auto_parallel_context()
|
|
context.set_auto_parallel_context(parallel_mode=parallel_mode, device_num=8)
|
|
set_algo_parameters(fully_use_devices=False)
|
|
net = ParallelReshapeNet(dense_in_channel=2048, dense_out_channel=1000, shape=(128, 1000), strategy=((16, 1),))
|
|
loss = CrossEntropyLoss()
|
|
predict = Tensor(np.ones([batch_size, 1, 2, 1024]), dtype=ms.float32)
|
|
label = Tensor(np.ones([batch_size, 1000]), dtype=ms.float32)
|
|
dataset = Dataset(predict, label, 2, input_num=2)
|
|
|
|
opt = Momentum(net.trainable_params(), learning_rate, momentum)
|
|
model = Model(net, loss_fn=loss, optimizer=opt)
|
|
model.train(epoch_size, dataset, dataset_sink_mode=False)
|
|
|
|
|
|
class CrossEntropyLoss2(nn.Cell):
|
|
def __init__(self, reduction='mean'):
|
|
super(CrossEntropyLoss2, self).__init__()
|
|
self.cross_entropy = SoftmaxCrossEntropyWithLogits(reduction=reduction)
|
|
|
|
def construct(self, logits, label):
|
|
loss = self.cross_entropy(logits, label)
|
|
return loss
|
|
|
|
|
|
def test_flatten_reshape4(parallel_mode="semi_auto_parallel"):
|
|
batch_size = 16
|
|
learning_rate = 0.1
|
|
momentum = 0.9
|
|
epoch_size = 2
|
|
context.reset_auto_parallel_context()
|
|
context.set_auto_parallel_context(parallel_mode=parallel_mode, device_num=8)
|
|
set_algo_parameters(fully_use_devices=False)
|
|
net = ParallelReduceMeanNet(conv_in_channel=3, conv_out_channel=64, reducemean_keep_dims=True,
|
|
strategy=((4, 1, 1, 1),))
|
|
loss = CrossEntropyLoss2()
|
|
predict = Tensor(np.ones([batch_size, 3, 32, 32]), dtype=ms.float32)
|
|
label = Tensor(np.ones([batch_size, 2048]), dtype=ms.float32)
|
|
dataset = Dataset(predict, label, 2, input_num=2)
|
|
|
|
opt = Momentum(net.trainable_params(), learning_rate, momentum)
|
|
model = Model(net, loss_fn=loss, optimizer=opt)
|
|
model.train(epoch_size, dataset, dataset_sink_mode=False)
|