def main(args):
batch_time = AverageMeter()
end = time.time()
checkpoint = load_checkpoint(args.resume) #loaded
print('pool_features:', args.pool_feature)
epoch = checkpoint['epoch']
gallery_feature, gallery_labels, query_feature, query_labels = \
Model2Feature(data=args.data, root=args.data_root, net=args.net, checkpoint=checkpoint
, batch_size=args.batch_size, nThreads=args.nThreads, pool_feature=args.pool_feature) #output
sim_mat = pairwise_similarity(query_feature, gallery_feature) #成对相似性
if args.gallery_eq_query is True:
sim_mat = sim_mat - torch.eye(sim_mat.size(0))
print('labels', query_labels)
print('feature:', gallery_feature)
recall_ks = Recall_at_ks(sim_mat,
query_ids=query_labels,
gallery_ids=gallery_labels,
data=args.data)
result = ' '.join(['%.4f' % k for k in recall_ks]) # result=recall_ks
print('Epoch-%d' % epoch, result)
batch_time.update(time.time() - end)
print('Epoch-%d\t' % epoch,
'Time {batch_time.avg:.3f}\t'.format(batch_time=batch_time))
import matplotlib.pyplot as plt
import torchvision
import numpy as np
similarity = torch.mm(gallery_feature, gallery_feature.t())
similarity.size()
#draw Feature Map
img = torchvision.utils.make_grid(similarity).numpy()
plt.imshow(np.transpose(img, (1, 2, 0)))
plt.show()
def extract_recalls(data,
data_root,
width,
net,
checkpoint,
dim,
batch_size,
nThreads,
pool_feature,
gallery_eq_query,
model=None,
epoch=0,
org_feature=False,
save_txt="",
args=None):
gallery_feature, gallery_labels, query_feature, query_labels = \
Model2Feature(data=data, root=data_root, width=width, net=net, checkpoint=checkpoint,
dim=dim, batch_size=batch_size, nThreads=nThreads, pool_feature=pool_feature, model=model, org_feature=org_feature, args=args)
sim_mat = pairwise_similarity(query_feature, gallery_feature)
if gallery_eq_query is True:
sim_mat = sim_mat - torch.eye(sim_mat.size(0))
recall_ks = Recall_at_ks(sim_mat,
query_ids=query_labels,
gallery_ids=gallery_labels,
data=data,
args=args,
epoch=epoch)
labels = [x.item() for x in gallery_labels]
nmi = NMI(gallery_feature, gallery_labels, n_cluster=len(set(labels)))
print(recall_ks, nmi)
result = ' '.join(['%.4f' % k for k in (recall_ks.tolist() + [nmi])])
print('Epoch-%d' % epoch, result)
def eval(ckp_path=None, model=None):
args = Config()
if (ckp_path != None):
checkpoint = load_checkpoint(ckp_path, args)
else:
checkpoint = model
checkpoint.eval()
# print(args.pool_feature)
gallery_feature, gallery_labels, query_feature, query_labels = \
Model2Feature(data=args.data,model = checkpoint, batch_size=args.batch_size, nThreads=args.nThreads, pool_feature=args.pool_feature)
sim_mat = pairwise_similarity(query_feature, gallery_feature)
if args.gallery_eq_query is True:
sim_mat = sim_mat - torch.eye(sim_mat.size(0))
recall_ks = Recall_at_ks(sim_mat,
query_ids=query_labels,
gallery_ids=gallery_labels,
data=args.data)
if (ckp_path == None):
checkpoint.train()
return recall_ks
parser.add_argument('--Incremental_flag', default=False, type=bool, help='incremental learning or not')
parser.add_argument('--Retrieval_visualization', default=False, type=bool, help='Visualize the retrieved image for a given img')
parser.add_argument('--batch_size', type=int, default=80)
parser.add_argument('--nThreads', '-j', default=16, type=int, metavar='N', help='number of data loading threads (default: 2)')
parser.add_argument('--pool_feature', type=ast.literal_eval, default=False, required=False, help='if True extract feature from the last pool layer')
args = parser.parse_args()
checkpoint = load_checkpoint(args.resume)
print(args.pool_feature)
epoch = checkpoint['epoch']
print('Training Epoch:', epoch)
gallery_feature, gallery_labels, query_feature, query_labels, img_name, img_name_shuffled = \
Model2Feature(data=args.data, root=args.data_root, width=args.width, net=args.net, checkpoint=checkpoint, Retrieval_visualization =args.Retrieval_visualization,
dim=args.dim, batch_size=args.batch_size, nThreads=args.nThreads, pool_feature=args.pool_feature)
if args.Retrieval_visualization:
specific_query = 599 # the index of a query image
sim_mat = pairwise_similarity(query_feature[specific_query:specific_query+1, :], gallery_feature) #query * gallery
else:
sim_mat = pairwise_similarity(query_feature, gallery_feature) #query * gallery
if args.gallery_eq_query is True:
sim_mat = sim_mat - torch.eye(sim_mat.size(0))
### for retrieval visual. the query image is given and fixed, args.Retrieval_visualization = True
if args.Retrieval_visualization:
topK_visual(sim_mat, img_name, img_name_shuffled, specific_query, query_ids=query_labels[specific_query:specific_query+1], gallery_ids=gallery_labels, data=args.data)
recall_ks = Recall_at_ks(sim_mat, img_name, query_ids=query_labels, gallery_ids=gallery_labels, data=args.data)
'-j',
default=16,
type=int,
metavar='N',
help='number of data loading threads (default: 2)')
parser.add_argument('--pool_feature',
type=ast.literal_eval,
default=False,
required=False,
help='if True extract feature from the last pool layer')
args = parser.parse_args()
checkpoint = load_checkpoint(args.resume)
print(args.pool_feature)
epoch = checkpoint['epoch']
gallery_feature, gallery_labels, query_feature, query_labels = \
Model2Feature(data=args.data, root=args.data_root, width=args.width, net=args.net, checkpoint=checkpoint,
dim=args.dim, batch_size=args.batch_size, nThreads=args.nThreads, pool_feature=args.pool_feature)
sim_mat = pairwise_similarity(query_feature, gallery_feature)
if args.gallery_eq_query is True:
sim_mat = sim_mat - torch.eye(sim_mat.size(0))
recall_ks = Recall_at_ks(sim_mat,
query_ids=query_labels,
gallery_ids=gallery_labels,
data=args.data)
result = ' '.join(['%.4f' % k for k in recall_ks])
print('Epoch-%d' % epoch, result)