forked from mindspore-Ecosystem/mindspore
init add vgg16 gpu version
merge the script optimize the script
This commit is contained in:
ms_yan
parent
79225e044a
commit
cf3fff8996
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@ -12,42 +12,204 @@
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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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"""
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##############test vgg16 example on cifar10#################
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python eval.py --data_path=$DATA_HOME --device_id=$DEVICE_ID
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"""
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"""Eval"""
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import os
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import time
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import argparse
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import datetime
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import glob
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import numpy as np
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import mindspore.nn as nn
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from mindspore import context
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from mindspore import Tensor, context
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from mindspore.nn.optim.momentum import Momentum
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from mindspore.train.model import Model
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from src.config import cifar_cfg as cfg
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from src.dataset import vgg_create_dataset
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from mindspore.ops import operations as P
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from mindspore.ops import functional as F
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from mindspore.common import dtype as mstype
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from src.utils.logging import get_logger
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from src.vgg import vgg16
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from src.dataset import vgg_create_dataset
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from src.dataset import classification_dataset
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Cifar10 classification')
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parser.add_argument('--device_target', type=str, default='Ascend', choices=['Ascend', 'GPU'],
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class ParameterReduce(nn.Cell):
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"""ParameterReduce"""
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def __init__(self):
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super(ParameterReduce, self).__init__()
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self.cast = P.Cast()
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self.reduce = P.AllReduce()
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def construct(self, x):
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one = self.cast(F.scalar_to_array(1.0), mstype.float32)
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out = x * one
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ret = self.reduce(out)
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return ret
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def parse_args(cloud_args=None):
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"""parse_args"""
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parser = argparse.ArgumentParser('mindspore classification test')
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parser.add_argument('--device_target', type=str, default='GPU', choices=['Ascend', 'GPU'],
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help='device where the code will be implemented. (Default: Ascend)')
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parser.add_argument('--data_path', type=str, default='./cifar', help='path where the dataset is saved')
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parser.add_argument('--checkpoint_path', type=str, default=None, help='checkpoint file path.')
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parser.add_argument('--device_id', type=int, default=None, help='device id of GPU or Ascend. (Default: None)')
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# dataset related
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parser.add_argument('--dataset', type=str, choices=["cifar10", "imagenet2012"], default="imagenet2012")
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parser.add_argument('--data_path', type=str, default='', help='eval data dir')
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parser.add_argument('--per_batch_size', default=32, type=int, help='batch size for per npu')
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# network related
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parser.add_argument('--graph_ckpt', type=int, default=1, help='graph ckpt or feed ckpt')
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parser.add_argument('--pretrained', default='', type=str, help='fully path of pretrained model to load. '
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'If it is a direction, it will test all ckpt')
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# logging related
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parser.add_argument('--log_path', type=str, default='outputs/', help='path to save log')
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parser.add_argument('--rank', type=int, default=0, help='local rank of distributed')
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parser.add_argument('--group_size', type=int, default=1, help='world size of distributed')
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# roma obs
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parser.add_argument('--train_url', type=str, default="", help='train url')
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args_opt = parser.parse_args()
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args_opt = merge_args(args_opt, cloud_args)
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context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target)
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context.set_context(device_id=args_opt.device_id)
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if args_opt.dataset == "cifar10":
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from src.config import cifar_cfg as cfg
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else:
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from src.config import imagenet_cfg as cfg
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net = vgg16(num_classes=cfg.num_classes)
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opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, cfg.momentum,
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weight_decay=cfg.weight_decay)
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loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean', is_grad=False)
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model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'})
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args_opt.image_size = cfg.image_size
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args_opt.num_classes = cfg.num_classes
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args_opt.per_batch_size = cfg.batch_size
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args_opt.buffer_size = cfg.buffer_size
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args_opt.pad_mode = cfg.pad_mode
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args_opt.padding = cfg.padding
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args_opt.has_bias = cfg.has_bias
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args_opt.batch_norm = cfg.batch_norm
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param_dict = load_checkpoint(args_opt.checkpoint_path)
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load_param_into_net(net, param_dict)
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net.set_train(False)
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dataset = vgg_create_dataset(args_opt.data_path, 1, False)
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res = model.eval(dataset)
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print("result: ", res)
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args_opt.image_size = list(map(int, args_opt.image_size.split(',')))
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return args_opt
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def get_top5_acc(top5_arg, gt_class):
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sub_count = 0
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for top5, gt in zip(top5_arg, gt_class):
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if gt in top5:
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sub_count += 1
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return sub_count
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def merge_args(args, cloud_args):
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"""merge_args"""
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args_dict = vars(args)
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if isinstance(cloud_args, dict):
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for key in cloud_args.keys():
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val = cloud_args[key]
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if key in args_dict and val:
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arg_type = type(args_dict[key])
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if arg_type is not type(None):
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val = arg_type(val)
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args_dict[key] = val
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return args
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def test(cloud_args=None):
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"""test"""
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args = parse_args(cloud_args)
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context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True,
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device_target=args.device_target, save_graphs=False)
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if os.getenv('DEVICE_ID', "not_set").isdigit():
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context.set_context(device_id=int(os.getenv('DEVICE_ID')))
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args.outputs_dir = os.path.join(args.log_path,
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datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
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args.logger = get_logger(args.outputs_dir, args.rank)
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args.logger.save_args(args)
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if args.dataset == "cifar10":
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net = vgg16(num_classes=args.num_classes)
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opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, cfg.momentum,
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weight_decay=args.weight_decay)
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loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean', is_grad=False)
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model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'})
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param_dict = load_checkpoint(args.checkpoint_path)
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load_param_into_net(net, param_dict)
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net.set_train(False)
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dataset = vgg_create_dataset(args.data_path, 1, False)
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res = model.eval(dataset)
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print("result: ", res)
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else:
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# network
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args.logger.important_info('start create network')
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if os.path.isdir(args.pretrained):
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models = list(glob.glob(os.path.join(args.pretrained, '*.ckpt')))
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print(models)
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if args.graph_ckpt:
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f = lambda x: -1 * int(os.path.splitext(os.path.split(x)[-1])[0].split('-')[-1].split('_')[0])
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else:
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f = lambda x: -1 * int(os.path.splitext(os.path.split(x)[-1])[0].split('_')[-1])
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args.models = sorted(models, key=f)
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else:
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args.models = [args.pretrained,]
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for model in args.models:
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if args.dataset == "cifar10":
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dataset = vgg_create_dataset(args.data_path, args.image_size, args.per_batch_size, training=False)
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else:
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dataset = classification_dataset(args.data_path, args.image_size, args.per_batch_size)
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eval_dataloader = dataset.create_tuple_iterator()
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network = vgg16(args.num_classes, args, phase="test")
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# pre_trained
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load_param_into_net(network, load_checkpoint(model))
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network.add_flags_recursive(fp16=True)
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img_tot = 0
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top1_correct = 0
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top5_correct = 0
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network.set_train(False)
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t_end = time.time()
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it = 0
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for data, gt_classes in eval_dataloader:
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output = network(Tensor(data, mstype.float32))
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output = output.asnumpy()
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top1_output = np.argmax(output, (-1))
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top5_output = np.argsort(output)[:, -5:]
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t1_correct = np.equal(top1_output, gt_classes).sum()
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top1_correct += t1_correct
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top5_correct += get_top5_acc(top5_output, gt_classes)
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img_tot += args.per_batch_size
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if args.rank == 0 and it == 0:
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t_end = time.time()
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it = 1
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if args.rank == 0:
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time_used = time.time() - t_end
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fps = (img_tot - args.per_batch_size) * args.group_size / time_used
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args.logger.info('Inference Performance: {:.2f} img/sec'.format(fps))
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results = [[top1_correct], [top5_correct], [img_tot]]
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args.logger.info('before results={}'.format(results))
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results = np.array(results)
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args.logger.info('after results={}'.format(results))
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top1_correct = results[0, 0]
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top5_correct = results[1, 0]
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img_tot = results[2, 0]
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acc1 = 100.0 * top1_correct / img_tot
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acc5 = 100.0 * top5_correct / img_tot
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args.logger.info('after allreduce eval: top1_correct={}, tot={},'
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'acc={:.2f}%(TOP1)'.format(top1_correct, img_tot, acc1))
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args.logger.info('after allreduce eval: top5_correct={}, tot={},'
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'acc={:.2f}%(TOP5)'.format(top5_correct, img_tot, acc5))
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if __name__ == "__main__":
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test()
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@ -13,21 +13,56 @@
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# limitations under the License.
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# ============================================================================
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"""
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network config setting, will be used in main.py
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network config setting, will be used in train.py and eval.py
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"""
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from easydict import EasyDict as edict
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# config for vgg16, cifar10
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cifar_cfg = edict({
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'num_classes': 10,
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"lr": 0.01,
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'lr_init': 0.01,
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'lr_max': 0.1,
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"lr_epochs": '30,60,90,120',
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"lr_scheduler": "step",
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'warmup_epochs': 5,
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'batch_size': 64,
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'epoch_size': 70,
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'max_epoch': 70,
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'momentum': 0.9,
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'weight_decay': 5e-4,
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"loss_scale": 1.0,
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"label_smooth": 0,
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"label_smooth_factor": 0,
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'buffer_size': 10,
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'image_height': 224,
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'image_width': 224,
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"image_size": '224,224',
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'pad_mode': 'same',
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'padding': 0,
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'has_bias': False,
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"batch_norm": True,
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'keep_checkpoint_max': 10
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})
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# config for vgg16, imagenet2012
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imagenet_cfg = edict({
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'num_classes': 1000,
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"lr": 0.01,
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'lr_init': 0.01,
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'lr_max': 0.1,
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"lr_epochs": '30,60,90,120',
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"lr_scheduler": 'cosine_annealing',
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'warmup_epochs': 0,
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'batch_size': 32,
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'max_epoch': 150,
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'momentum': 0.9,
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'weight_decay': 1e-4,
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"loss_scale": 1024,
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"label_smooth": 1,
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"label_smooth_factor": 0.1,
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'buffer_size': 10,
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"image_size": '224,224',
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'pad_mode': 'pad',
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'padding': 1,
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'has_bias': True,
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"batch_norm": False,
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'keep_checkpoint_max': 10
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})
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@ -0,0 +1,39 @@
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# 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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"""define loss function for network"""
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from mindspore.nn.loss.loss import _Loss
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from mindspore.ops import operations as P
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from mindspore.ops import functional as F
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from mindspore import Tensor
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from mindspore.common import dtype as mstype
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import mindspore.nn as nn
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class CrossEntropy(_Loss):
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"""the redefined loss function with SoftmaxCrossEntropyWithLogits"""
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def __init__(self, smooth_factor=0., num_classes=1001):
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super(CrossEntropy, self).__init__()
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self.onehot = P.OneHot()
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self.on_value = Tensor(1.0 - smooth_factor, mstype.float32)
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self.off_value = Tensor(1.0 * smooth_factor / (num_classes - 1), mstype.float32)
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self.ce = nn.SoftmaxCrossEntropyWithLogits()
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self.mean = P.ReduceMean(False)
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def construct(self, logit, label):
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one_hot_label = self.onehot(label, F.shape(logit)[1], self.on_value, self.off_value)
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loss = self.ce(logit, one_hot_label)
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loss = self.mean(loss, 0)
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return loss
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# limitations under the License.
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# ============================================================================
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"""
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Data operations, will be used in train.py and eval.py
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dataset processing.
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"""
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import os
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import mindspore.common.dtype as mstype
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import mindspore.dataset as ds
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from mindspore.common import dtype as mstype
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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.transforms.vision.c_transforms as vision
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from .config import cifar_cfg as cfg
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from PIL import Image, ImageFile
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from src.utils.sampler import DistributedSampler
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ImageFile.LOAD_TRUNCATED_IMAGES = True
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def vgg_create_dataset(data_home, repeat_num=1, training=True):
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def vgg_create_dataset(data_home, image_size, batch_size, rank_id=0, rank_size=1, repeat_num=1, training=True):
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"""Data operations."""
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ds.config.set_seed(1)
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de.config.set_seed(1)
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data_dir = os.path.join(data_home, "cifar-10-batches-bin")
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if not training:
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data_dir = os.path.join(data_home, "cifar-10-verify-bin")
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rank_size = int(os.environ.get("RANK_SIZE")) if os.environ.get("RANK_SIZE") else None
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rank_id = int(os.environ.get("RANK_ID")) if os.environ.get("RANK_ID") else None
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data_set = ds.Cifar10Dataset(data_dir, num_shards=rank_size, shard_id=rank_id)
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data_set = de.Cifar10Dataset(data_dir, num_shards=rank_size, shard_id=rank_id)
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resize_height = cfg.image_height
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resize_width = cfg.image_width
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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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resize_op = vision.Resize(image_size) # 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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@ -66,6 +64,134 @@ def vgg_create_dataset(data_home, repeat_num=1, training=True):
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data_set = data_set.shuffle(buffer_size=10)
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# apply batch operations
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data_set = data_set.batch(batch_size=cfg.batch_size, drop_remainder=True)
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data_set = data_set.batch(batch_size=batch_size, drop_remainder=True)
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return data_set
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def classification_dataset(data_dir, image_size, per_batch_size, rank=0, group_size=1,
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mode='train',
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input_mode='folder',
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root='',
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num_parallel_workers=None,
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shuffle=None,
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sampler=None,
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repeat_num=1,
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class_indexing=None,
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drop_remainder=True,
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transform=None,
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target_transform=None):
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"""
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A function that returns a dataset for classification. The mode of input dataset could be "folder" or "txt".
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If it is "folder", all images within one folder have the same label. If it is "txt", all paths of images
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are written into a textfile.
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Args:
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data_dir (str): Path to the root directory that contains the dataset for "input_mode="folder"".
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Or path of the textfile that contains every image's path of the dataset.
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image_size (str): Size of the input images.
|
||||
per_batch_size (int): the batch size of evey step during training.
|
||||
rank (int): The shard ID within num_shards (default=None).
|
||||
group_size (int): Number of shards that the dataset should be divided
|
||||
into (default=None).
|
||||
mode (str): "train" or others. Default: " train".
|
||||
input_mode (str): The form of the input dataset. "folder" or "txt". Default: "folder".
|
||||
root (str): the images path for "input_mode="txt"". Default: " ".
|
||||
num_parallel_workers (int): Number of workers to read the data. Default: None.
|
||||
shuffle (bool): Whether or not to perform shuffle on the dataset
|
||||
(default=None, performs shuffle).
|
||||
sampler (Sampler): Object used to choose samples from the dataset. Default: None.
|
||||
repeat_num (int): the num of repeat dataset.
|
||||
class_indexing (dict): A str-to-int mapping from folder name to index
|
||||
(default=None, the folder names will be sorted
|
||||
alphabetically and each class will be given a
|
||||
unique index starting from 0).
|
||||
|
||||
Examples:
|
||||
>>> from mindvision.common.datasets.classification import classification_dataset
|
||||
>>> # path to imagefolder directory. This directory needs to contain sub-directories which contain the images
|
||||
>>> dataset_dir = "/path/to/imagefolder_directory"
|
||||
>>> de_dataset = classification_dataset(train_data_dir, image_size=[224, 244],
|
||||
>>> per_batch_size=64, rank=0, group_size=4)
|
||||
>>> # Path of the textfile that contains every image's path of the dataset.
|
||||
>>> dataset_dir = "/path/to/dataset/images/train.txt"
|
||||
>>> images_dir = "/path/to/dataset/images"
|
||||
>>> de_dataset = classification_dataset(train_data_dir, image_size=[224, 244],
|
||||
>>> per_batch_size=64, rank=0, group_size=4,
|
||||
>>> input_mode="txt", root=images_dir)
|
||||
"""
|
||||
|
||||
mean = [0.485 * 255, 0.456 * 255, 0.406 * 255]
|
||||
std = [0.229 * 255, 0.224 * 255, 0.225 * 255]
|
||||
|
||||
if transform is None:
|
||||
if mode == 'train':
|
||||
transform_img = [
|
||||
vision.RandomCropDecodeResize(image_size, scale=(0.08, 1.0)),
|
||||
vision.RandomHorizontalFlip(prob=0.5),
|
||||
vision.Normalize(mean=mean, std=std),
|
||||
vision.HWC2CHW()
|
||||
]
|
||||
else:
|
||||
transform_img = [
|
||||
vision.Decode(),
|
||||
vision.Resize((256, 256)),
|
||||
vision.CenterCrop(image_size),
|
||||
vision.Normalize(mean=mean, std=std),
|
||||
vision.HWC2CHW()
|
||||
]
|
||||
else:
|
||||
transform_img = transform
|
||||
|
||||
if target_transform is None:
|
||||
transform_label = [C.TypeCast(mstype.int32)]
|
||||
else:
|
||||
transform_label = target_transform
|
||||
|
||||
if input_mode == 'folder':
|
||||
de_dataset = de.ImageFolderDatasetV2(data_dir, num_parallel_workers=num_parallel_workers,
|
||||
shuffle=shuffle, sampler=sampler, class_indexing=class_indexing,
|
||||
num_shards=group_size, shard_id=rank)
|
||||
else:
|
||||
dataset = TxtDataset(root, data_dir)
|
||||
sampler = DistributedSampler(dataset, rank, group_size, shuffle=shuffle)
|
||||
de_dataset = de.GeneratorDataset(dataset, ["image", "label"], sampler=sampler)
|
||||
de_dataset.set_dataset_size(len(sampler))
|
||||
|
||||
de_dataset = de_dataset.map(input_columns="image", num_parallel_workers=8, operations=transform_img)
|
||||
de_dataset = de_dataset.map(input_columns="label", num_parallel_workers=8, operations=transform_label)
|
||||
|
||||
columns_to_project = ["image", "label"]
|
||||
de_dataset = de_dataset.project(columns=columns_to_project)
|
||||
|
||||
de_dataset = de_dataset.batch(per_batch_size, drop_remainder=drop_remainder)
|
||||
de_dataset = de_dataset.repeat(repeat_num)
|
||||
|
||||
return de_dataset
|
||||
|
||||
|
||||
class TxtDataset:
|
||||
"""
|
||||
create txt dataset.
|
||||
|
||||
Args:
|
||||
Returns:
|
||||
de_dataset.
|
||||
"""
|
||||
def __init__(self, root, txt_name):
|
||||
super(TxtDataset, self).__init__()
|
||||
self.imgs = []
|
||||
self.labels = []
|
||||
fin = open(txt_name, "r")
|
||||
for line in fin:
|
||||
img_name, label = line.strip().split(' ')
|
||||
self.imgs.append(os.path.join(root, img_name))
|
||||
self.labels.append(int(label))
|
||||
fin.close()
|
||||
|
||||
def __getitem__(self, index):
|
||||
img = Image.open(self.imgs[index]).convert('RGB')
|
||||
return img, self.labels[index]
|
||||
|
||||
def __len__(self):
|
||||
return len(self.imgs)
|
||||
|
|
|
|||
|
|
@ -12,18 +12,12 @@
|
|||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
"""test_vgg"""
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from mindspore import Tensor
|
||||
from model_zoo.official.cv.vgg16.src.vgg import vgg16
|
||||
from ..ut_filter import non_graph_engine
|
||||
"""
|
||||
linear warm up learning rate.
|
||||
"""
|
||||
|
||||
|
||||
@non_graph_engine
|
||||
def test_vgg16():
|
||||
inputs = Tensor(np.random.rand(1, 3, 112, 112).astype(np.float32))
|
||||
net = vgg16()
|
||||
with pytest.raises(ValueError):
|
||||
print(net.construct(inputs))
|
||||
def linear_warmup_lr(current_step, warmup_steps, base_lr, init_lr):
|
||||
lr_inc = (float(base_lr) - float(init_lr)) / float(warmup_steps)
|
||||
lr = float(init_lr) + lr_inc * current_step
|
||||
return lr
|
||||
|
|
@ -0,0 +1,82 @@
|
|||
# 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.
|
||||
# ============================================================================
|
||||
"""
|
||||
get logger.
|
||||
"""
|
||||
import logging
|
||||
import os
|
||||
import sys
|
||||
from datetime import datetime
|
||||
|
||||
class LOGGER(logging.Logger):
|
||||
"""
|
||||
set up logging file.
|
||||
|
||||
Args:
|
||||
logger_name (string): logger name.
|
||||
log_dir (string): path of logger.
|
||||
|
||||
Returns:
|
||||
string, logger path
|
||||
"""
|
||||
def __init__(self, logger_name, rank=0):
|
||||
super(LOGGER, self).__init__(logger_name)
|
||||
if rank % 8 == 0:
|
||||
console = logging.StreamHandler(sys.stdout)
|
||||
console.setLevel(logging.INFO)
|
||||
formatter = logging.Formatter('%(asctime)s:%(levelname)s:%(message)s')
|
||||
console.setFormatter(formatter)
|
||||
self.addHandler(console)
|
||||
|
||||
def setup_logging_file(self, log_dir, rank=0):
|
||||
"""set up log file"""
|
||||
self.rank = rank
|
||||
if not os.path.exists(log_dir):
|
||||
os.makedirs(log_dir, exist_ok=True)
|
||||
log_name = datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S') + '_rank_{}.log'.format(rank)
|
||||
self.log_fn = os.path.join(log_dir, log_name)
|
||||
fh = logging.FileHandler(self.log_fn)
|
||||
fh.setLevel(logging.INFO)
|
||||
formatter = logging.Formatter('%(asctime)s:%(levelname)s:%(message)s')
|
||||
fh.setFormatter(formatter)
|
||||
self.addHandler(fh)
|
||||
|
||||
def info(self, msg, *args, **kwargs):
|
||||
if self.isEnabledFor(logging.INFO):
|
||||
self._log(logging.INFO, msg, args, **kwargs)
|
||||
|
||||
def save_args(self, args):
|
||||
self.info('Args:')
|
||||
args_dict = vars(args)
|
||||
for key in args_dict.keys():
|
||||
self.info('--> %s: %s', key, args_dict[key])
|
||||
self.info('')
|
||||
|
||||
def important_info(self, msg, *args, **kwargs):
|
||||
if self.isEnabledFor(logging.INFO) and self.rank == 0:
|
||||
line_width = 2
|
||||
important_msg = '\n'
|
||||
important_msg += ('*'*70 + '\n')*line_width
|
||||
important_msg += ('*'*line_width + '\n')*2
|
||||
important_msg += '*'*line_width + ' '*8 + msg + '\n'
|
||||
important_msg += ('*'*line_width + '\n')*2
|
||||
important_msg += ('*'*70 + '\n')*line_width
|
||||
self.info(important_msg, *args, **kwargs)
|
||||
|
||||
|
||||
def get_logger(path, rank):
|
||||
logger = LOGGER("mindversion", rank)
|
||||
logger.setup_logging_file(path, rank)
|
||||
return logger
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
# 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.
|
||||
# ============================================================================
|
||||
"""
|
||||
choose samples from the dataset
|
||||
"""
|
||||
import math
|
||||
import numpy as np
|
||||
|
||||
class DistributedSampler():
|
||||
"""
|
||||
sampling the dataset.
|
||||
|
||||
Args:
|
||||
Returns:
|
||||
num_samples, number of samples.
|
||||
"""
|
||||
def __init__(self, dataset, rank, group_size, shuffle=True, seed=0):
|
||||
self.dataset = dataset
|
||||
self.rank = rank
|
||||
self.group_size = group_size
|
||||
self.dataset_length = len(self.dataset)
|
||||
self.num_samples = int(math.ceil(self.dataset_length * 1.0 / self.group_size))
|
||||
self.total_size = self.num_samples * self.group_size
|
||||
self.shuffle = shuffle
|
||||
self.seed = seed
|
||||
|
||||
def __iter__(self):
|
||||
if self.shuffle:
|
||||
self.seed = (self.seed + 1) & 0xffffffff
|
||||
np.random.seed(self.seed)
|
||||
indices = np.random.permutation(self.dataset_length).tolist()
|
||||
else:
|
||||
indices = list(range(len(self.dataset_length)))
|
||||
|
||||
indices += indices[:(self.total_size - len(indices))]
|
||||
indices = indices[self.rank::self.group_size]
|
||||
return iter(indices)
|
||||
|
||||
def __len__(self):
|
||||
return self.num_samples
|
||||
|
||||
|
|
@ -0,0 +1,36 @@
|
|||
# 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.
|
||||
# ============================================================================
|
||||
"""Util class or function."""
|
||||
|
||||
|
||||
def get_param_groups(network):
|
||||
"""Param groups for optimizer."""
|
||||
decay_params = []
|
||||
no_decay_params = []
|
||||
for x in network.trainable_params():
|
||||
parameter_name = x.name
|
||||
if parameter_name.endswith('.bias'):
|
||||
# all bias not using weight decay
|
||||
no_decay_params.append(x)
|
||||
elif parameter_name.endswith('.gamma'):
|
||||
# bn weight bias not using weight decay, be carefully for now x not include BN
|
||||
no_decay_params.append(x)
|
||||
elif parameter_name.endswith('.beta'):
|
||||
# bn weight bias not using weight decay, be carefully for now x not include BN
|
||||
no_decay_params.append(x)
|
||||
else:
|
||||
decay_params.append(x)
|
||||
|
||||
return [{'params': no_decay_params, 'weight_decay': 0.0}, {'params': decay_params}]
|
||||
|
|
@ -0,0 +1,213 @@
|
|||
# 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.
|
||||
# ============================================================================
|
||||
"""
|
||||
Initialize.
|
||||
"""
|
||||
import math
|
||||
from functools import reduce
|
||||
import numpy as np
|
||||
import mindspore.nn as nn
|
||||
from mindspore import Tensor
|
||||
from mindspore.common import initializer as init
|
||||
|
||||
def _calculate_gain(nonlinearity, param=None):
|
||||
r"""
|
||||
Return the recommended gain value for the given nonlinearity function.
|
||||
|
||||
The values are as follows:
|
||||
================= ====================================================
|
||||
nonlinearity gain
|
||||
================= ====================================================
|
||||
Linear / Identity :math:`1`
|
||||
Conv{1,2,3}D :math:`1`
|
||||
Sigmoid :math:`1`
|
||||
Tanh :math:`\frac{5}{3}`
|
||||
ReLU :math:`\sqrt{2}`
|
||||
Leaky Relu :math:`\sqrt{\frac{2}{1 + \text{negative\_slope}^2}}`
|
||||
================= ====================================================
|
||||
|
||||
Args:
|
||||
nonlinearity: the non-linear function
|
||||
param: optional parameter for the non-linear function
|
||||
|
||||
Examples:
|
||||
>>> gain = calculate_gain('leaky_relu', 0.2) # leaky_relu with negative_slope=0.2
|
||||
"""
|
||||
linear_fns = ['linear', 'conv1d', 'conv2d', 'conv3d', 'conv_transpose1d', 'conv_transpose2d', 'conv_transpose3d']
|
||||
if nonlinearity in linear_fns or nonlinearity == 'sigmoid':
|
||||
return 1
|
||||
if nonlinearity == 'tanh':
|
||||
return 5.0 / 3
|
||||
if nonlinearity == 'relu':
|
||||
return math.sqrt(2.0)
|
||||
if nonlinearity == 'leaky_relu':
|
||||
if param is None:
|
||||
negative_slope = 0.01
|
||||
elif not isinstance(param, bool) and isinstance(param, int) or isinstance(param, float):
|
||||
negative_slope = param
|
||||
else:
|
||||
raise ValueError("negative_slope {} not a valid number".format(param))
|
||||
return math.sqrt(2.0 / (1 + negative_slope ** 2))
|
||||
|
||||
raise ValueError("Unsupported nonlinearity {}".format(nonlinearity))
|
||||
|
||||
def _assignment(arr, num):
|
||||
"""Assign the value of `num` to `arr`."""
|
||||
if arr.shape == ():
|
||||
arr = arr.reshape((1))
|
||||
arr[:] = num
|
||||
arr = arr.reshape(())
|
||||
else:
|
||||
if isinstance(num, np.ndarray):
|
||||
arr[:] = num[:]
|
||||
else:
|
||||
arr[:] = num
|
||||
return arr
|
||||
|
||||
def _calculate_in_and_out(arr):
|
||||
"""
|
||||
Calculate n_in and n_out.
|
||||
|
||||
Args:
|
||||
arr (Array): Input array.
|
||||
|
||||
Returns:
|
||||
Tuple, a tuple with two elements, the first element is `n_in` and the second element is `n_out`.
|
||||
"""
|
||||
dim = len(arr.shape)
|
||||
if dim < 2:
|
||||
raise ValueError("If initialize data with xavier uniform, the dimension of data must greater than 1.")
|
||||
|
||||
n_in = arr.shape[1]
|
||||
n_out = arr.shape[0]
|
||||
|
||||
if dim > 2:
|
||||
counter = reduce(lambda x, y: x * y, arr.shape[2:])
|
||||
n_in *= counter
|
||||
n_out *= counter
|
||||
return n_in, n_out
|
||||
|
||||
def _select_fan(array, mode):
|
||||
mode = mode.lower()
|
||||
valid_modes = ['fan_in', 'fan_out']
|
||||
if mode not in valid_modes:
|
||||
raise ValueError("Mode {} not supported, please use one of {}".format(mode, valid_modes))
|
||||
|
||||
fan_in, fan_out = _calculate_in_and_out(array)
|
||||
return fan_in if mode == 'fan_in' else fan_out
|
||||
|
||||
class KaimingInit(init.Initializer):
|
||||
r"""
|
||||
Base Class. Initialize the array with He kaiming algorithm.
|
||||
|
||||
Args:
|
||||
a: the negative slope of the rectifier used after this layer (only
|
||||
used with ``'leaky_relu'``)
|
||||
mode: either ``'fan_in'`` (default) or ``'fan_out'``. Choosing ``'fan_in'``
|
||||
preserves the magnitude of the variance of the weights in the
|
||||
forward pass. Choosing ``'fan_out'`` preserves the magnitudes in the
|
||||
backwards pass.
|
||||
nonlinearity: the non-linear function, recommended to use only with
|
||||
``'relu'`` or ``'leaky_relu'`` (default).
|
||||
"""
|
||||
def __init__(self, a=0, mode='fan_in', nonlinearity='leaky_relu'):
|
||||
super(KaimingInit, self).__init__()
|
||||
self.mode = mode
|
||||
self.gain = _calculate_gain(nonlinearity, a)
|
||||
def _initialize(self, arr):
|
||||
pass
|
||||
|
||||
|
||||
class KaimingUniform(KaimingInit):
|
||||
r"""
|
||||
Initialize the array with He kaiming uniform algorithm. The resulting tensor will
|
||||
have values sampled from :math:`\mathcal{U}(-\text{bound}, \text{bound})` where
|
||||
|
||||
.. math::
|
||||
\text{bound} = \text{gain} \times \sqrt{\frac{3}{\text{fan\_mode}}}
|
||||
|
||||
Input:
|
||||
arr (Array): The array to be assigned.
|
||||
|
||||
Returns:
|
||||
Array, assigned array.
|
||||
|
||||
Examples:
|
||||
>>> w = np.empty(3, 5)
|
||||
>>> KaimingUniform(w, mode='fan_in', nonlinearity='relu')
|
||||
"""
|
||||
|
||||
def _initialize(self, arr):
|
||||
fan = _select_fan(arr, self.mode)
|
||||
bound = math.sqrt(3.0) * self.gain / math.sqrt(fan)
|
||||
np.random.seed(0)
|
||||
data = np.random.uniform(-bound, bound, arr.shape)
|
||||
|
||||
_assignment(arr, data)
|
||||
|
||||
|
||||
class KaimingNormal(KaimingInit):
|
||||
r"""
|
||||
Initialize the array with He kaiming normal algorithm. The resulting tensor will
|
||||
have values sampled from :math:`\mathcal{N}(0, \text{std}^2)` where
|
||||
|
||||
.. math::
|
||||
\text{std} = \frac{\text{gain}}{\sqrt{\text{fan\_mode}}}
|
||||
|
||||
Input:
|
||||
arr (Array): The array to be assigned.
|
||||
|
||||
Returns:
|
||||
Array, assigned array.
|
||||
|
||||
Examples:
|
||||
>>> w = np.empty(3, 5)
|
||||
>>> KaimingNormal(w, mode='fan_out', nonlinearity='relu')
|
||||
"""
|
||||
|
||||
def _initialize(self, arr):
|
||||
fan = _select_fan(arr, self.mode)
|
||||
std = self.gain / math.sqrt(fan)
|
||||
np.random.seed(0)
|
||||
data = np.random.normal(0, std, arr.shape)
|
||||
|
||||
_assignment(arr, data)
|
||||
|
||||
|
||||
def default_recurisive_init(custom_cell):
|
||||
"""default_recurisive_init"""
|
||||
for _, cell in custom_cell.cells_and_names():
|
||||
if isinstance(cell, nn.Conv2d):
|
||||
cell.weight.default_input = init.initializer(KaimingUniform(a=math.sqrt(5)),
|
||||
cell.weight.default_input.shape,
|
||||
cell.weight.default_input.dtype).to_tensor()
|
||||
if cell.bias is not None:
|
||||
fan_in, _ = _calculate_in_and_out(cell.weight.default_input.asnumpy())
|
||||
bound = 1 / math.sqrt(fan_in)
|
||||
np.random.seed(0)
|
||||
cell.bias.default_input = Tensor(np.random.uniform(-bound, bound, cell.bias.default_input.shape),
|
||||
cell.bias.default_input.dtype)
|
||||
elif isinstance(cell, nn.Dense):
|
||||
cell.weight.default_input = init.initializer(KaimingUniform(a=math.sqrt(5)),
|
||||
cell.weight.default_input.shape,
|
||||
cell.weight.default_input.dtype).to_tensor()
|
||||
if cell.bias is not None:
|
||||
fan_in, _ = _calculate_in_and_out(cell.weight.default_input.asnumpy())
|
||||
bound = 1 / math.sqrt(fan_in)
|
||||
np.random.seed(0)
|
||||
cell.bias.default_input = Tensor(np.random.uniform(-bound, bound, cell.bias.default_input.shape),
|
||||
cell.bias.default_input.dtype)
|
||||
elif isinstance(cell, (nn.BatchNorm2d, nn.BatchNorm1d)):
|
||||
pass
|
||||
|
|
@ -12,12 +12,18 @@
|
|||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
"""VGG."""
|
||||
"""
|
||||
Image classifiation.
|
||||
"""
|
||||
import math
|
||||
import mindspore.nn as nn
|
||||
from mindspore.common.initializer import initializer
|
||||
import mindspore.common.dtype as mstype
|
||||
from mindspore.common import initializer as init
|
||||
from mindspore.common.initializer import initializer
|
||||
from .utils.var_init import default_recurisive_init, KaimingNormal
|
||||
|
||||
def _make_layer(base, batch_norm):
|
||||
|
||||
def _make_layer(base, args, batch_norm):
|
||||
"""Make stage network of VGG."""
|
||||
layers = []
|
||||
in_channels = 3
|
||||
|
|
@ -27,11 +33,14 @@ def _make_layer(base, batch_norm):
|
|||
else:
|
||||
weight_shape = (v, in_channels, 3, 3)
|
||||
weight = initializer('XavierUniform', shape=weight_shape, dtype=mstype.float32).to_tensor()
|
||||
if args.dataset == "imagenet2012":
|
||||
weight = 'normal'
|
||||
conv2d = nn.Conv2d(in_channels=in_channels,
|
||||
out_channels=v,
|
||||
kernel_size=3,
|
||||
padding=0,
|
||||
pad_mode='same',
|
||||
padding=args.padding,
|
||||
pad_mode=args.pad_mode,
|
||||
has_bias=args.has_bias,
|
||||
weight_init=weight)
|
||||
if batch_norm:
|
||||
layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU()]
|
||||
|
|
@ -59,17 +68,25 @@ class Vgg(nn.Cell):
|
|||
>>> num_classes=1000, batch_norm=False, batch_size=1)
|
||||
"""
|
||||
|
||||
def __init__(self, base, num_classes=1000, batch_norm=False, batch_size=1):
|
||||
def __init__(self, base, num_classes=1000, batch_norm=False, batch_size=1, args=None, phase="train"):
|
||||
super(Vgg, self).__init__()
|
||||
_ = batch_size
|
||||
self.layers = _make_layer(base, batch_norm=batch_norm)
|
||||
self.layers = _make_layer(base, args, batch_norm=batch_norm)
|
||||
self.flatten = nn.Flatten()
|
||||
dropout_ratio = 0.5
|
||||
if args.dataset == "cifar10" or phase == "test":
|
||||
dropout_ratio = 1.0
|
||||
self.classifier = nn.SequentialCell([
|
||||
nn.Dense(512 * 7 * 7, 4096),
|
||||
nn.ReLU(),
|
||||
nn.Dropout(dropout_ratio),
|
||||
nn.Dense(4096, 4096),
|
||||
nn.ReLU(),
|
||||
nn.Dropout(dropout_ratio),
|
||||
nn.Dense(4096, num_classes)])
|
||||
if args.dataset == "imagenet2012":
|
||||
default_recurisive_init(self)
|
||||
self.custom_init_weight()
|
||||
|
||||
def construct(self, x):
|
||||
x = self.layers(x)
|
||||
|
|
@ -77,6 +94,25 @@ class Vgg(nn.Cell):
|
|||
x = self.classifier(x)
|
||||
return x
|
||||
|
||||
def custom_init_weight(self):
|
||||
"""
|
||||
Init the weight of Conv2d and Dense in the net.
|
||||
"""
|
||||
for _, cell in self.cells_and_names():
|
||||
if isinstance(cell, nn.Conv2d):
|
||||
cell.weight.default_input = init.initializer(
|
||||
KaimingNormal(a=math.sqrt(5), mode='fan_out', nonlinearity='relu'),
|
||||
cell.weight.default_input.shape, cell.weight.default_input.dtype).to_tensor()
|
||||
if cell.bias is not None:
|
||||
cell.bias.default_input = init.initializer(
|
||||
'zeros', cell.bias.default_input.shape, cell.bias.default_input.dtype).to_tensor()
|
||||
elif isinstance(cell, nn.Dense):
|
||||
cell.weight.default_input = init.initializer(
|
||||
init.Normal(0.01), cell.weight.default_input.shape, cell.weight.default_input.dtype).to_tensor()
|
||||
if cell.bias is not None:
|
||||
cell.bias.default_input = init.initializer(
|
||||
'zeros', cell.bias.default_input.shape, cell.bias.default_input.dtype).to_tensor()
|
||||
|
||||
|
||||
cfg = {
|
||||
'11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
|
||||
|
|
@ -86,12 +122,14 @@ cfg = {
|
|||
}
|
||||
|
||||
|
||||
def vgg16(num_classes=1000):
|
||||
def vgg16(num_classes=1000, args=None, phase="train"):
|
||||
"""
|
||||
Get Vgg16 neural network with batch normalization.
|
||||
|
||||
Args:
|
||||
num_classes (int): Class numbers. Default: 1000.
|
||||
args(dict): param for net init.
|
||||
phase(str): train or test mode.
|
||||
|
||||
Returns:
|
||||
Cell, cell instance of Vgg16 neural network with batch normalization.
|
||||
|
|
@ -100,5 +138,5 @@ def vgg16(num_classes=1000):
|
|||
>>> vgg16(num_classes=1000)
|
||||
"""
|
||||
|
||||
net = Vgg(cfg['16'], num_classes=num_classes, batch_norm=True)
|
||||
net = Vgg(cfg['16'], num_classes=num_classes, args=args, batch_norm=True, phase=phase)
|
||||
return net
|
||||
|
|
|
|||
|
|
@ -0,0 +1,40 @@
|
|||
# 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.
|
||||
# ============================================================================
|
||||
"""
|
||||
warm up cosine annealing learning rate.
|
||||
"""
|
||||
import math
|
||||
import numpy as np
|
||||
|
||||
from .linear_warmup import linear_warmup_lr
|
||||
|
||||
|
||||
def warmup_cosine_annealing_lr(lr, steps_per_epoch, warmup_epochs, max_epoch, T_max, eta_min=0):
|
||||
"""warm up cosine annealing learning rate."""
|
||||
base_lr = lr
|
||||
warmup_init_lr = 0
|
||||
total_steps = int(max_epoch * steps_per_epoch)
|
||||
warmup_steps = int(warmup_epochs * steps_per_epoch)
|
||||
|
||||
lr_each_step = []
|
||||
for i in range(total_steps):
|
||||
last_epoch = i // steps_per_epoch
|
||||
if i < warmup_steps:
|
||||
lr = linear_warmup_lr(i + 1, warmup_steps, base_lr, warmup_init_lr)
|
||||
else:
|
||||
lr = eta_min + (base_lr - eta_min) * (1. + math.cos(math.pi*last_epoch / T_max)) / 2
|
||||
lr_each_step.append(lr)
|
||||
|
||||
return np.array(lr_each_step).astype(np.float32)
|
||||
|
|
@ -0,0 +1,84 @@
|
|||
# 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.
|
||||
# ============================================================================
|
||||
"""
|
||||
warm up step learning rate.
|
||||
"""
|
||||
from collections import Counter
|
||||
import numpy as np
|
||||
|
||||
from .linear_warmup import linear_warmup_lr
|
||||
|
||||
|
||||
def lr_steps(global_step, lr_init, lr_max, warmup_epochs, total_epochs, steps_per_epoch):
|
||||
"""Set learning rate."""
|
||||
lr_each_step = []
|
||||
total_steps = steps_per_epoch * total_epochs
|
||||
warmup_steps = steps_per_epoch * warmup_epochs
|
||||
if warmup_steps != 0:
|
||||
inc_each_step = (float(lr_max) - float(lr_init)) / float(warmup_steps)
|
||||
else:
|
||||
inc_each_step = 0
|
||||
for i in range(total_steps):
|
||||
if i < warmup_steps:
|
||||
lr_value = float(lr_init) + inc_each_step * float(i)
|
||||
else:
|
||||
base = (1.0 - (float(i) - float(warmup_steps)) / (float(total_steps) - float(warmup_steps)))
|
||||
lr_value = float(lr_max) * base * base
|
||||
if lr_value < 0.0:
|
||||
lr_value = 0.0
|
||||
lr_each_step.append(lr_value)
|
||||
|
||||
current_step = global_step
|
||||
lr_each_step = np.array(lr_each_step).astype(np.float32)
|
||||
learning_rate = lr_each_step[current_step:]
|
||||
|
||||
return learning_rate
|
||||
|
||||
|
||||
def warmup_step_lr(lr, lr_epochs, steps_per_epoch, warmup_epochs, max_epoch, gamma=0.1):
|
||||
"""warmup_step_lr"""
|
||||
base_lr = lr
|
||||
warmup_init_lr = 0
|
||||
total_steps = int(max_epoch * steps_per_epoch)
|
||||
warmup_steps = int(warmup_epochs * steps_per_epoch)
|
||||
milestones = lr_epochs
|
||||
milestones_steps = []
|
||||
for milestone in milestones:
|
||||
milestones_step = milestone * steps_per_epoch
|
||||
milestones_steps.append(milestones_step)
|
||||
|
||||
lr_each_step = []
|
||||
lr = base_lr
|
||||
milestones_steps_counter = Counter(milestones_steps)
|
||||
for i in range(total_steps):
|
||||
if i < warmup_steps:
|
||||
lr = linear_warmup_lr(i + 1, warmup_steps, base_lr, warmup_init_lr)
|
||||
else:
|
||||
lr = lr * gamma**milestones_steps_counter[i]
|
||||
lr_each_step.append(lr)
|
||||
|
||||
return np.array(lr_each_step).astype(np.float32)
|
||||
|
||||
|
||||
def multi_step_lr(lr, milestones, steps_per_epoch, max_epoch, gamma=0.1):
|
||||
return warmup_step_lr(lr, milestones, steps_per_epoch, 0, max_epoch, gamma=gamma)
|
||||
|
||||
|
||||
def step_lr(lr, epoch_size, steps_per_epoch, max_epoch, gamma=0.1):
|
||||
lr_epochs = []
|
||||
for i in range(1, max_epoch):
|
||||
if i % epoch_size == 0:
|
||||
lr_epochs.append(i)
|
||||
return multi_step_lr(lr, lr_epochs, steps_per_epoch, max_epoch, gamma=gamma)
|
||||
|
|
@ -17,6 +17,8 @@
|
|||
python train.py --data_path=$DATA_HOME --device_id=$DEVICE_ID
|
||||
"""
|
||||
import argparse
|
||||
import datetime
|
||||
import time
|
||||
import os
|
||||
import random
|
||||
|
||||
|
|
@ -25,83 +27,264 @@ import numpy as np
|
|||
import mindspore.nn as nn
|
||||
from mindspore import Tensor
|
||||
from mindspore import context
|
||||
from mindspore.communication.management import init
|
||||
from mindspore.communication.management import init, get_rank, get_group_size
|
||||
from mindspore.nn.optim.momentum import Momentum
|
||||
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
|
||||
from mindspore.train.callback import Callback, ModelCheckpoint, CheckpointConfig
|
||||
from mindspore.train.model import Model, ParallelMode
|
||||
from mindspore.train.serialization import load_param_into_net, load_checkpoint
|
||||
from src.config import cifar_cfg as cfg
|
||||
from mindspore.train.loss_scale_manager import FixedLossScaleManager
|
||||
from src.dataset import vgg_create_dataset
|
||||
from src.dataset import classification_dataset
|
||||
|
||||
from src.crossentropy import CrossEntropy
|
||||
from src.warmup_step_lr import warmup_step_lr
|
||||
from src.warmup_cosine_annealing_lr import warmup_cosine_annealing_lr
|
||||
from src.warmup_step_lr import lr_steps
|
||||
from src.utils.logging import get_logger
|
||||
from src.utils.util import get_param_groups
|
||||
from src.vgg import vgg16
|
||||
|
||||
|
||||
random.seed(1)
|
||||
np.random.seed(1)
|
||||
|
||||
|
||||
def lr_steps(global_step, lr_init, lr_max, warmup_epochs, total_epochs, steps_per_epoch):
|
||||
"""Set learning rate."""
|
||||
lr_each_step = []
|
||||
total_steps = steps_per_epoch * total_epochs
|
||||
warmup_steps = steps_per_epoch * warmup_epochs
|
||||
if warmup_steps != 0:
|
||||
inc_each_step = (float(lr_max) - float(lr_init)) / float(warmup_steps)
|
||||
class ProgressMonitor(Callback):
|
||||
"""monitor loss and time"""
|
||||
def __init__(self, args_param):
|
||||
super(ProgressMonitor, self).__init__()
|
||||
self.me_epoch_start_time = 0
|
||||
self.me_epoch_start_step_num = 0
|
||||
self.args = args_param
|
||||
self.ckpt_history = []
|
||||
|
||||
def begin(self, run_context):
|
||||
self.args.logger.info('start network train...')
|
||||
|
||||
def epoch_begin(self, run_context):
|
||||
pass
|
||||
|
||||
def epoch_end(self, run_context):
|
||||
"""
|
||||
Called after each epoch finished.
|
||||
|
||||
Args:
|
||||
run_context (RunContext): Include some information of the model.
|
||||
"""
|
||||
cb_params = run_context.original_args()
|
||||
me_step = cb_params.cur_step_num - 1
|
||||
|
||||
real_epoch = me_step // self.args.steps_per_epoch
|
||||
time_used = time.time() - self.me_epoch_start_time
|
||||
fps_mean = self.args.per_batch_size * (me_step-self.me_epoch_start_step_num) * self.args.group_size / time_used
|
||||
self.args.logger.info('epoch[{}], iter[{}], loss:{}, mean_fps:{:.2f}'
|
||||
'imgs/sec'.format(real_epoch, me_step, cb_params.net_outputs, fps_mean))
|
||||
|
||||
if self.args.rank_save_ckpt_flag:
|
||||
import glob
|
||||
ckpts = glob.glob(os.path.join(self.args.outputs_dir, '*.ckpt'))
|
||||
for ckpt in ckpts:
|
||||
ckpt_fn = os.path.basename(ckpt)
|
||||
if not ckpt_fn.startswith('{}-'.format(self.args.rank)):
|
||||
continue
|
||||
if ckpt in self.ckpt_history:
|
||||
continue
|
||||
self.ckpt_history.append(ckpt)
|
||||
self.args.logger.info('epoch[{}], iter[{}], loss:{}, ckpt:{},'
|
||||
'ckpt_fn:{}'.format(real_epoch, me_step, cb_params.net_outputs, ckpt, ckpt_fn))
|
||||
|
||||
self.me_epoch_start_step_num = me_step
|
||||
self.me_epoch_start_time = time.time()
|
||||
|
||||
def step_begin(self, run_context):
|
||||
pass
|
||||
|
||||
def step_end(self, run_context, *me_args):
|
||||
pass
|
||||
|
||||
def end(self, run_context):
|
||||
self.args.logger.info('end network train...')
|
||||
|
||||
|
||||
def parse_args(cloud_args=None):
|
||||
"""parameters"""
|
||||
parser = argparse.ArgumentParser('mindspore classification training')
|
||||
parser.add_argument('--device_target', type=str, default='GPU', choices=['Ascend', 'GPU'],
|
||||
help='device where the code will be implemented. (Default: Ascend)')
|
||||
parser.add_argument('--device_id', type=int, default=1, help='device id of GPU or Ascend. (Default: None)')
|
||||
|
||||
# dataset related
|
||||
parser.add_argument('--dataset', type=str, choices=["cifar10", "imagenet2012"], default="cifar10")
|
||||
parser.add_argument('--data_path', type=str, default='', help='train data dir')
|
||||
|
||||
# network related
|
||||
parser.add_argument('--pre_trained', default='', type=str, help='model_path, local pretrained model to load')
|
||||
parser.add_argument('--lr_gamma', type=float, default=0.1,
|
||||
help='decrease lr by a factor of exponential lr_scheduler')
|
||||
parser.add_argument('--eta_min', type=float, default=0., help='eta_min in cosine_annealing scheduler')
|
||||
parser.add_argument('--T_max', type=int, default=150, help='T-max in cosine_annealing scheduler')
|
||||
|
||||
# logging and checkpoint related
|
||||
parser.add_argument('--log_interval', type=int, default=100, help='logging interval')
|
||||
parser.add_argument('--ckpt_path', type=str, default='outputs/', help='checkpoint save location')
|
||||
parser.add_argument('--ckpt_interval', type=int, default=5000, help='ckpt_interval')
|
||||
parser.add_argument('--is_save_on_master', type=int, default=1, help='save ckpt on master or all rank')
|
||||
|
||||
# distributed related
|
||||
parser.add_argument('--is_distributed', type=int, default=0, help='if multi device')
|
||||
parser.add_argument('--rank', type=int, default=0, help='local rank of distributed')
|
||||
parser.add_argument('--group_size', type=int, default=1, help='world size of distributed')
|
||||
args_opt = parser.parse_args()
|
||||
args_opt = merge_args(args_opt, cloud_args)
|
||||
|
||||
if args_opt.dataset == "cifar10":
|
||||
from src.config import cifar_cfg as cfg
|
||||
else:
|
||||
inc_each_step = 0
|
||||
for i in range(total_steps):
|
||||
if i < warmup_steps:
|
||||
lr_value = float(lr_init) + inc_each_step * float(i)
|
||||
else:
|
||||
base = (1.0 - (float(i) - float(warmup_steps)) / (float(total_steps) - float(warmup_steps)))
|
||||
lr_value = float(lr_max) * base * base
|
||||
if lr_value < 0.0:
|
||||
lr_value = 0.0
|
||||
lr_each_step.append(lr_value)
|
||||
from src.config import imagenet_cfg as cfg
|
||||
|
||||
current_step = global_step
|
||||
lr_each_step = np.array(lr_each_step).astype(np.float32)
|
||||
learning_rate = lr_each_step[current_step:]
|
||||
args_opt.label_smooth = cfg.label_smooth
|
||||
args_opt.label_smooth_factor = cfg.label_smooth_factor
|
||||
args_opt.lr_scheduler = cfg.lr_scheduler
|
||||
args_opt.loss_scale = cfg.loss_scale
|
||||
args_opt.max_epoch = cfg.max_epoch
|
||||
args_opt.warmup_epochs = cfg.warmup_epochs
|
||||
args_opt.lr = cfg.lr
|
||||
args_opt.lr_init = cfg.lr_init
|
||||
args_opt.lr_max = cfg.lr_max
|
||||
args_opt.momentum = cfg.momentum
|
||||
args_opt.weight_decay = cfg.weight_decay
|
||||
args_opt.per_batch_size = cfg.batch_size
|
||||
args_opt.num_classes = cfg.num_classes
|
||||
args_opt.buffer_size = cfg.buffer_size
|
||||
args_opt.ckpt_save_max = cfg.keep_checkpoint_max
|
||||
args_opt.pad_mode = cfg.pad_mode
|
||||
args_opt.padding = cfg.padding
|
||||
args_opt.has_bias = cfg.has_bias
|
||||
args_opt.batch_norm = cfg.batch_norm
|
||||
|
||||
return learning_rate
|
||||
args_opt.lr_epochs = list(map(int, cfg.lr_epochs.split(',')))
|
||||
args_opt.image_size = list(map(int, cfg.image_size.split(',')))
|
||||
|
||||
return args_opt
|
||||
|
||||
|
||||
def merge_args(args_opt, cloud_args):
|
||||
"""dictionary"""
|
||||
args_dict = vars(args_opt)
|
||||
if isinstance(cloud_args, dict):
|
||||
for key_arg in cloud_args.keys():
|
||||
val = cloud_args[key_arg]
|
||||
if key_arg in args_dict and val:
|
||||
arg_type = type(args_dict[key_arg])
|
||||
if arg_type is not None:
|
||||
val = arg_type(val)
|
||||
args_dict[key_arg] = val
|
||||
return args_opt
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(description='Cifar10 classification')
|
||||
parser.add_argument('--device_target', type=str, default='Ascend', choices=['Ascend', 'GPU'],
|
||||
help='device where the code will be implemented. (Default: Ascend)')
|
||||
parser.add_argument('--data_path', type=str, default='./cifar', help='path where the dataset is saved')
|
||||
parser.add_argument('--device_id', type=int, default=None, help='device id of GPU or Ascend. (Default: None)')
|
||||
parser.add_argument('--pre_trained', type=str, default=None, help='the pretrained checkpoint file path.')
|
||||
args_opt = parser.parse_args()
|
||||
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target)
|
||||
context.set_context(device_id=args_opt.device_id)
|
||||
args = parse_args()
|
||||
|
||||
device_num = int(os.environ.get("DEVICE_NUM", 1))
|
||||
if device_num > 1:
|
||||
if args.is_distributed:
|
||||
if args.device_target == "Ascend":
|
||||
init()
|
||||
elif args.device_target == "GPU":
|
||||
init("nccl")
|
||||
args.rank = get_rank()
|
||||
args.group_size = get_group_size()
|
||||
device_num = args.group_size
|
||||
|
||||
context.reset_auto_parallel_context()
|
||||
context.set_auto_parallel_context(device_num=device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
|
||||
mirror_mean=True)
|
||||
init()
|
||||
else:
|
||||
context.set_context(device_id=args.device_id)
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
|
||||
|
||||
# select for master rank save ckpt or all rank save, compatiable for model parallel
|
||||
args.rank_save_ckpt_flag = 0
|
||||
if args.is_save_on_master:
|
||||
if args.rank == 0:
|
||||
args.rank_save_ckpt_flag = 1
|
||||
else:
|
||||
args.rank_save_ckpt_flag = 1
|
||||
|
||||
# logger
|
||||
args.outputs_dir = os.path.join(args.ckpt_path,
|
||||
datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
|
||||
args.logger = get_logger(args.outputs_dir, args.rank)
|
||||
|
||||
if args.dataset == "cifar10":
|
||||
dataset = vgg_create_dataset(args.data_path, args.image_size, args.per_batch_size, args.rank, args.group_size)
|
||||
else:
|
||||
dataset = classification_dataset(args.data_path, args.image_size, args.per_batch_size,
|
||||
args.rank, args.group_size)
|
||||
|
||||
dataset = vgg_create_dataset(args_opt.data_path, 1)
|
||||
batch_num = dataset.get_dataset_size()
|
||||
args.steps_per_epoch = dataset.get_dataset_size()
|
||||
args.logger.save_args(args)
|
||||
|
||||
# network
|
||||
args.logger.important_info('start create network')
|
||||
|
||||
# get network and init
|
||||
network = vgg16(args.num_classes, args)
|
||||
|
||||
net = vgg16(num_classes=cfg.num_classes)
|
||||
# pre_trained
|
||||
if args_opt.pre_trained:
|
||||
load_param_into_net(net, load_checkpoint(args_opt.pre_trained))
|
||||
if args.pre_trained:
|
||||
load_param_into_net(network, load_checkpoint(args.pre_trained))
|
||||
|
||||
lr = lr_steps(0, lr_init=cfg.lr_init, lr_max=cfg.lr_max, warmup_epochs=cfg.warmup_epochs,
|
||||
total_epochs=cfg.epoch_size, steps_per_epoch=batch_num)
|
||||
opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), Tensor(lr), cfg.momentum,
|
||||
weight_decay=cfg.weight_decay)
|
||||
loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean', is_grad=False)
|
||||
model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'},
|
||||
amp_level="O2", keep_batchnorm_fp32=False, loss_scale_manager=None)
|
||||
# lr scheduler
|
||||
if args.lr_scheduler == 'exponential':
|
||||
lr = warmup_step_lr(args.lr,
|
||||
args.lr_epochs,
|
||||
args.steps_per_epoch,
|
||||
args.warmup_epochs,
|
||||
args.max_epoch,
|
||||
gamma=args.lr_gamma,
|
||||
)
|
||||
elif args.lr_scheduler == 'cosine_annealing':
|
||||
lr = warmup_cosine_annealing_lr(args.lr,
|
||||
args.steps_per_epoch,
|
||||
args.warmup_epochs,
|
||||
args.max_epoch,
|
||||
args.T_max,
|
||||
args.eta_min)
|
||||
elif args.lr_scheduler == 'step':
|
||||
lr = lr_steps(0, lr_init=args.lr_init, lr_max=args.lr_max, warmup_epochs=args.warmup_epochs,
|
||||
total_epochs=args.max_epoch, steps_per_epoch=batch_num)
|
||||
else:
|
||||
raise NotImplementedError(args.lr_scheduler)
|
||||
|
||||
config_ck = CheckpointConfig(save_checkpoint_steps=batch_num * 5, keep_checkpoint_max=cfg.keep_checkpoint_max)
|
||||
time_cb = TimeMonitor(data_size=batch_num)
|
||||
ckpoint_cb = ModelCheckpoint(prefix="train_vgg_cifar10", directory="./", config=config_ck)
|
||||
loss_cb = LossMonitor()
|
||||
model.train(cfg.epoch_size, dataset, callbacks=[time_cb, ckpoint_cb, loss_cb])
|
||||
print("train success")
|
||||
# optimizer
|
||||
opt = Momentum(params=get_param_groups(network),
|
||||
learning_rate=Tensor(lr),
|
||||
momentum=args.momentum,
|
||||
weight_decay=args.weight_decay,
|
||||
loss_scale=args.loss_scale)
|
||||
|
||||
if args.dataset == "cifar10":
|
||||
loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean', is_grad=False)
|
||||
model = Model(network, loss_fn=loss, optimizer=opt, metrics={'acc'},
|
||||
amp_level="O2", keep_batchnorm_fp32=False, loss_scale_manager=None)
|
||||
else:
|
||||
if not args.label_smooth:
|
||||
args.label_smooth_factor = 0.0
|
||||
loss = CrossEntropy(smooth_factor=args.label_smooth_factor, num_classes=args.num_classes)
|
||||
|
||||
loss_scale_manager = FixedLossScaleManager(args.loss_scale, drop_overflow_update=False)
|
||||
model = Model(network, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale_manager, amp_level="O2")
|
||||
|
||||
# checkpoint save
|
||||
progress_cb = ProgressMonitor(args)
|
||||
callbacks = [progress_cb,]
|
||||
if args.rank_save_ckpt_flag:
|
||||
ckpt_config = CheckpointConfig(save_checkpoint_steps=args.ckpt_interval * args.steps_per_epoch,
|
||||
keep_checkpoint_max=args.ckpt_save_max)
|
||||
ckpt_cb = ModelCheckpoint(config=ckpt_config,
|
||||
directory=args.outputs_dir,
|
||||
prefix='{}'.format(args.rank))
|
||||
callbacks.append(ckpt_cb)
|
||||
|
||||
model.train(args.max_epoch, dataset, callbacks=callbacks)
|
||||
|
|
|
|||
Loading…
Reference in New Issue