forked from mindspore-Ecosystem/mindspore
155 lines
6.1 KiB
Python
155 lines
6.1 KiB
Python
# Copyright 2020 Huawei Technologies Co., Ltd
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# ============================================================================
|
|
import os
|
|
import random
|
|
import argparse
|
|
import numpy as np
|
|
from resnet import resnet50
|
|
|
|
import mindspore.common.dtype as mstype
|
|
import mindspore.ops.functional as F
|
|
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
|
|
from mindspore.train.serialization import load_checkpoint, load_param_into_net
|
|
import mindspore.dataset as ds
|
|
import mindspore.dataset.transforms.c_transforms as C
|
|
import mindspore.dataset.vision.c_transforms as vision
|
|
import mindspore.nn as nn
|
|
from mindspore import Tensor
|
|
from mindspore import context
|
|
from mindspore.communication.management import init
|
|
from mindspore.nn.optim.momentum import Momentum
|
|
from mindspore.ops import operations as P
|
|
from mindspore.train.model import Model
|
|
from mindspore.context import ParallelMode
|
|
|
|
random.seed(1)
|
|
np.random.seed(1)
|
|
ds.config.set_seed(1)
|
|
|
|
parser = argparse.ArgumentParser(description='Image classification')
|
|
parser.add_argument('--run_distribute', type=bool, default=False, help='Run distribute')
|
|
parser.add_argument('--device_num', type=int, default=1, help='Device num.')
|
|
parser.add_argument('--do_train', type=bool, default=True, help='Do train or not.')
|
|
parser.add_argument('--do_eval', type=bool, default=False, help='Do eval or not.')
|
|
parser.add_argument('--epoch_size', type=int, default=1, help='Epoch size.')
|
|
parser.add_argument('--batch_size', type=int, default=32, help='Batch size.')
|
|
parser.add_argument('--num_classes', type=int, default=10, help='Num classes.')
|
|
parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
|
|
parser.add_argument('--dataset_path', type=str, default="/var/log/npu/datasets/cifar", help='Dataset path')
|
|
args_opt = parser.parse_args()
|
|
|
|
device_id = int(os.getenv('DEVICE_ID'))
|
|
|
|
data_home = args_opt.dataset_path
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
|
context.set_context(device_id=device_id)
|
|
|
|
|
|
def create_dataset(repeat_num=1, training=True):
|
|
data_dir = data_home + "/cifar-10-batches-bin"
|
|
if not training:
|
|
data_dir = data_home + "/cifar-10-verify-bin"
|
|
data_set = ds.Cifar10Dataset(data_dir, num_samples=32)
|
|
|
|
if args_opt.run_distribute:
|
|
rank_id = int(os.getenv('RANK_ID'))
|
|
rank_size = int(os.getenv('RANK_SIZE'))
|
|
data_set = ds.Cifar10Dataset(data_dir, num_shards=rank_size, shard_id=rank_id, num_samples=32)
|
|
|
|
resize_height = 224
|
|
resize_width = 224
|
|
rescale = 1.0 / 255.0
|
|
shift = 0.0
|
|
|
|
# define map operations
|
|
random_crop_op = vision.RandomCrop((32, 32), (4, 4, 4, 4)) # padding_mode default CONSTANT
|
|
random_horizontal_op = vision.RandomHorizontalFlip()
|
|
resize_op = vision.Resize((resize_height, resize_width)) # interpolation default BILINEAR
|
|
rescale_op = vision.Rescale(rescale, shift)
|
|
normalize_op = vision.Normalize((0.4465, 0.4822, 0.4914), (0.2010, 0.1994, 0.2023))
|
|
changeswap_op = vision.HWC2CHW()
|
|
type_cast_op = C.TypeCast(mstype.int32)
|
|
|
|
c_trans = []
|
|
if training:
|
|
c_trans = [random_crop_op, random_horizontal_op]
|
|
c_trans += [resize_op, rescale_op, normalize_op,
|
|
changeswap_op]
|
|
|
|
# apply map operations on images
|
|
data_set = data_set.map(operations=type_cast_op, input_columns="label")
|
|
data_set = data_set.map(operations=c_trans, input_columns="image")
|
|
|
|
# apply repeat operations
|
|
data_set = data_set.repeat(repeat_num)
|
|
|
|
# apply shuffle operations
|
|
data_set = data_set.shuffle(buffer_size=10)
|
|
|
|
# apply batch operations
|
|
data_set = data_set.batch(batch_size=args_opt.batch_size, drop_remainder=True)
|
|
|
|
return data_set
|
|
|
|
|
|
class CrossEntropyLoss(nn.Cell):
|
|
def __init__(self):
|
|
super(CrossEntropyLoss, self).__init__()
|
|
self.cross_entropy = P.SoftmaxCrossEntropyWithLogits()
|
|
self.mean = P.ReduceMean()
|
|
self.one_hot = P.OneHot()
|
|
self.one = Tensor(1.0, mstype.float32)
|
|
self.zero = Tensor(0.0, mstype.float32)
|
|
|
|
def construct(self, logits, label):
|
|
label = self.one_hot(label, F.shape(logits)[1], self.one, self.zero)
|
|
loss_func = self.cross_entropy(logits, label)[0]
|
|
loss_func = self.mean(loss_func, (-1,))
|
|
return loss_func
|
|
|
|
|
|
if __name__ == '__main__':
|
|
if not args_opt.do_eval and args_opt.run_distribute:
|
|
context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
|
|
all_reduce_fusion_config=[140])
|
|
init()
|
|
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
epoch_size = args_opt.epoch_size
|
|
net = resnet50(args_opt.batch_size, args_opt.num_classes)
|
|
loss = CrossEntropyLoss()
|
|
opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, 0.9)
|
|
|
|
model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'})
|
|
|
|
if args_opt.do_train:
|
|
dataset = create_dataset(1)
|
|
batch_num = dataset.get_dataset_size()
|
|
config_ck = CheckpointConfig(save_checkpoint_steps=batch_num * 5, keep_checkpoint_max=10)
|
|
ckpoint_cb = ModelCheckpoint(prefix="train_resnet_cifar10", directory="./", config=config_ck)
|
|
time_cb = TimeMonitor(data_size=batch_num)
|
|
loss_cb = LossMonitor()
|
|
model.train(epoch_size, dataset, callbacks=[ckpoint_cb, loss_cb, time_cb])
|
|
|
|
if args_opt.do_eval:
|
|
if args_opt.checkpoint_path:
|
|
param_dict = load_checkpoint(args_opt.checkpoint_path)
|
|
load_param_into_net(net, param_dict)
|
|
net.set_train(False)
|
|
eval_dataset = create_dataset(1, training=False)
|
|
res = model.eval(eval_dataset)
|
|
print("result: ", res)
|