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run.py
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run.py
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import argparse
import os
import random
import numpy as np
import torch
from loguru import logger
import lthNet
from data.data_loader import load_data
def run():
# Load config
args = load_config()
logger.add('logs/{}_model_{}_code_{}_beta_{}_gamma_{}_batchsize_{}.log'.format(
args.dataset,
args.arch,
args.code_length,
args.beta,
args.gamma,
args.batch_size,
),
rotation='500 MB',
level='INFO',
)
logger.info(args)
# Set seed
torch.backends.cudnn.benchmark = True
random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
np.random.seed(args.seed)
# Load dataset
train_dataloader, query_dataloader, retrieval_dataloader = load_data(
args.dataset,
args.root,
args.batch_size,
args.num_workers,
)
# class-samples mapping (train_dataloader). eg., mapping['0']=500, mapping['1']=100, etc.
class_samples = torch.Tensor(np.zeros(args.num_classes))
for _, targets, _ in train_dataloader:
class_samples += torch.sum(targets, dim=0)
mapping = {}
for i in range(len(class_samples)):
mapping[str(i)] = class_samples[i]
# Training
for code_length in args.code_length:
checkpoint = lthNet.train(
train_dataloader,
query_dataloader,
retrieval_dataloader,
args.arch,
args.feature_dim,
code_length,
args.num_classes,
args.dynamic_meta_embedding,
args.num_prototypes,
args.device,
args.lr,
args.max_iter,
args.beta,
args.gamma,
mapping,
args.topk,
args.evaluate_interval,
)
logger.info('[code_length:{}][map:{:.4f}]'.format(code_length, checkpoint['map']))
# Save checkpoint
torch.save(
checkpoint,
os.path.join('checkpoints', '{}_model_{}_code_{}_beta_{}_gamma_{}_map_{:.4f}_batchsize_{}_maxIter_{}.pt'.format(
args.dataset,
args.arch,
code_length,
args.beta,
args.gamma,
checkpoint['map'],
args.batch_size,
args.max_iter)
)
)
def load_config():
"""
Load configuration.
Args
None
Returns
args(argparse.ArgumentParser): Configuration.
"""
parser = argparse.ArgumentParser(description='LTHNet_PyTorch_LinearLoss')
parser.add_argument('--dataset', default='imagenet-100-IF1', type=str,
help='Dataset name.')
parser.add_argument('--root', default='C:\\Users\\dell\\Desktop\\LTH_linearloss\\data\\imagenet100\\', type=str,
help='Path of dataset')
# parser.add_argument('--root', default='/home/13810427976/notespace/cifar-100/cifar-100-IF20/', type=str,
# help='Path of dataset')
parser.add_argument('--code-length', default='32,48,64,96', type=str,
help='Binary hash code length.(default: 32,48,64,96)')
parser.add_argument('--arch', default='resnet34', type=str,
help='CNN model name.(default: alexnet)')
parser.add_argument('--feature-dim', default=2000, type=int,
help='number of classes.(default: 2000)')
parser.add_argument('--num-classes', default=100, type=int,
help='number of classes.(default: 100)')
parser.add_argument('--num-prototypes', default=100, type=int,
help='number of prototypes.(default: 100)')
parser.add_argument('--batch-size', default=128, type=int,
help='Batch size.(default: 128)')
parser.add_argument('--lr', default=1e-5, type=float,
help='Learning rate.(default: 1e-5)')
parser.add_argument('--max-iter', default=100, type=int,
help='Number of iterations.(default: 300)')
parser.add_argument('--num-workers', default=6, type=int,
help='Number of loading data threads.(default: 6)')
parser.add_argument('--dynamic-meta-embedding', default=True, type=bool,
help='dynamic meta embedding.(default: True)')
parser.add_argument('--topk', default=-1, type=int,
help='Calculate map of top k.(default: all)')
parser.add_argument('--gpu', default=None, type=int,
help='Using gpu.(default: False)')
parser.add_argument('--beta', default=0.0, type=float,
help='Hyper-parameter: class-balanced factor.(default: 0.99)')
parser.add_argument('--gamma', default=1.0, type=float,
help='Hyper-parameter: balance between pointwise and pairwise loss.(default: 1.0)')
parser.add_argument('--seed', default=3367, type=int,
help='Random seed.(default: 3367)')
parser.add_argument('--evaluate-interval', default=1, type=int,
help='Evaluation interval.(default: 10)')
args = parser.parse_args()
# GPU
if args.gpu is None:
args.device = torch.device("cpu")
else:
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)
args.device = torch.device("cuda:%d" % 0)
# Hash code length
args.code_length = list(map(int, args.code_length.split(',')))
return args
if __name__ == '__main__':
run()