forked from mindspore-Ecosystem/mindspore
151 lines
5.8 KiB
Python
151 lines
5.8 KiB
Python
# Copyright 2020 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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# ============================================================================
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import os
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import random
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import argparse
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import numpy as np
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from resnet import resnet50
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import mindspore.common.dtype as mstype
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import mindspore.context as context
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import mindspore.dataset as de
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import mindspore.dataset.transforms.c_transforms as C
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import mindspore.dataset.vision.c_transforms as vision
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.communication.management import init
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from mindspore.nn.optim.momentum import Momentum
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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.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor
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from mindspore.train.model import Model
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from mindspore.context import ParallelMode
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random.seed(1)
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np.random.seed(1)
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de.config.set_seed(1)
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parser = argparse.ArgumentParser(description='Image classification')
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parser.add_argument('--run_distribute', type=bool, default=False, help='Run distribute')
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parser.add_argument('--device_num', type=int, default=1, help='Device num.')
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parser.add_argument('--do_train', type=bool, default=True, help='Do train or not.')
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parser.add_argument('--do_eval', type=bool, default=False, help='Do eval or not.')
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parser.add_argument('--epoch_size', type=int, default=1, help='Epoch size.')
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parser.add_argument('--batch_size', type=int, default=4, help='Batch size.')
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parser.add_argument('--num_classes', type=int, default=10, help='Num classes.')
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parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
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parser.add_argument('--dataset_path', type=str, default="/var/log/npu/datasets/cifar", help='Dataset path')
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args_opt = parser.parse_args()
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device_id = int(os.getenv('DEVICE_ID'))
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data_home = args_opt.dataset_path
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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context.set_context(device_id=device_id)
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def create_dataset(repeat_num=1, training=True):
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data_dir = data_home + "/cifar-10-batches-bin"
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if not training:
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data_dir = data_home + "/cifar-10-verify-bin"
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ds = de.Cifar10Dataset(data_dir)
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if args_opt.run_distribute:
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rank_id = int(os.getenv('RANK_ID'))
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rank_size = int(os.getenv('RANK_SIZE'))
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ds = de.Cifar10Dataset(data_dir, num_shards=rank_size, shard_id=rank_id)
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resize_height = 224
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resize_width = 224
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rescale = 1.0 / 255.0
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shift = 0.0
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# define map operations
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random_crop_op = vision.RandomCrop((32, 32), (4, 4, 4, 4)) # padding_mode default CONSTANT
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random_horizontal_op = vision.RandomHorizontalFlip()
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resize_op = vision.Resize((resize_height, resize_width)) # interpolation default BILINEAR
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rescale_op = vision.Rescale(rescale, shift)
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normalize_op = vision.Normalize((0.4465, 0.4822, 0.4914), (0.2010, 0.1994, 0.2023))
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changeswap_op = vision.HWC2CHW()
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type_cast_op = C.TypeCast(mstype.int32)
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c_trans = []
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if training:
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c_trans = [random_crop_op, random_horizontal_op]
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c_trans += [resize_op, rescale_op, normalize_op,
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changeswap_op]
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# apply map operations on images
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ds = ds.map(operations=type_cast_op, input_columns="label")
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ds = ds.map(operations=c_trans, input_columns="image")
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# apply repeat operations
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ds = ds.repeat(repeat_num)
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# apply shuffle operations
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ds = ds.shuffle(buffer_size=10)
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# apply batch operations
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ds = ds.batch(batch_size=args_opt.batch_size, drop_remainder=True)
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return ds
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class CrossEntropyLoss(nn.Cell):
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def __init__(self):
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super(CrossEntropyLoss, self).__init__()
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self.cross_entropy = P.SoftmaxCrossEntropyWithLogits()
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self.mean = P.ReduceMean()
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self.one_hot = P.OneHot()
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self.one = Tensor(1.0, mstype.float32)
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self.zero = Tensor(0.0, mstype.float32)
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def construct(self, logits, label):
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label = self.one_hot(label, F.shape(logits)[1], self.one, self.zero)
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loss_func = self.cross_entropy(logits, label)[0]
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loss_func = self.mean(loss_func, (-1,))
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return loss_func
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if __name__ == '__main__':
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if not args_opt.do_eval and args_opt.run_distribute:
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context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL)
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context.set_auto_parallel_context(all_reduce_fusion_split_indices=[140])
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init()
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context.set_context(mode=context.GRAPH_MODE)
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epoch_size = args_opt.epoch_size
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net = resnet50(args_opt.batch_size, args_opt.num_classes)
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loss = CrossEntropyLoss()
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opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, 0.9)
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model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'})
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if args_opt.do_train:
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dataset = create_dataset(epoch_size)
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batch_num = dataset.get_dataset_size()
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config_ck = CheckpointConfig(save_checkpoint_steps=batch_num * 5, keep_checkpoint_max=10)
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ckpoint_cb = ModelCheckpoint(prefix="train_resnet_cifar10", directory="./", config=config_ck)
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loss_cb = LossMonitor()
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model.train(epoch_size, dataset, callbacks=[ckpoint_cb, loss_cb])
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if args_opt.do_eval:
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eval_dataset = create_dataset(1, training=False)
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res = model.eval(eval_dataset)
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print("result: ", res)
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checker = os.path.exists("./memreuse.ir")
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assert checker, True
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