def __init__(self, cfg):
self.cfg = cfg
assert self.cfg.MODEL.TYPE
assert self.cfg.TRAIN.DATASETS
assert self.cfg.OUTPUT.DIR
Logger.init(cfg.OUTPUT.DIR, comm.get_local_rank())
Logger.log(cfg)
self.train_loader, self.val_loader = build_train_dataloader(cfg)
Logger.log('训练集数据量：{} 验证集数据量：{}'.format(
len(self.train_loader.dataset),
(len(self.val_loader.dataset) if self.val_loader else 0)))
self.model = build_models(cfg)
self.load_weights()
self.model = self.model.cuda()
Logger.log(self.model)
self.optimer = build_optimer(cfg, self.model)
self.lr_scheduler = build_lr_scheduler(cfg, self.optimer,
len(self.train_loader.dataset))
self.criterion = build_criterion(cfg)
if cfg.TRAIN.APEX:
self.model, self.optimer = apex.amp.initialize(
self.model, self.optimer)
if comm.get_world_size() > 1:
if cfg.TRAIN.APEX:
self.model = apex.parallel.convert_syncbn_model(self.model)
self.model = apex.parallel.DistributedDataParallel(self.model)
else:
self.model = DistributedDataParallel(
self.model,
device_ids=[comm.get_local_rank()],
broadcast_buffers=False)
def tmp(cfg):
weight_path = cfg.MODEL.WEIGHTS
if weight_path.endswith('.pth'):
model = build_models(cfg)
model.load_state_dict(torch.load(cfg.MODEL.WEIGHTS,
map_location='cpu'),
strict=True)
else:
model = torch.jit.load(cfg.MODEL.WEIGHTS, map_location='cpu')
model.eval().cuda()
floder = cfg.TEST.METRIC.TEMPLATE_DATAPATH
if not cfg.TEST.METRIC.TEMPLATE_DATAPATH or not os.path.exists(floder):
print("请指定正确的数据集路径：TEMPLATE_DATAPATH")
return None
dataloader = create_infer_dataloader(floder, cfg)
labels = None
features = None
features_template = {}
for image, target in tqdm(dataloader):
with torch.no_grad():
feature = model(image.cuda())
if labels is None:
labels = target
features = feature
else:
labels = torch.cat([labels, target], dim=0)
features = torch.cat([features, feature], dim=0)
labels_set = set(labels.numpy())
for label in labels_set:
pos = labels == label
target_embeddings = features[pos]
features_template[
dataloader.dataset.classes[label]] = target_embeddings
for k in features_template.keys():
feature = features_template[k].mean(dim=0)
denom = feature.norm(2, 0, True).clamp_min(1e-12)
feature = feature / denom
features_template[k] = feature
torch.save(
features_template,
os.path.join(
cfg.OUTPUT.DIR,
os.path.basename(cfg.MODEL.WEIGHTS).split('.')[0] +
'_features_template.pth'))
def cluster_tmp(cfg):
weight_path = cfg.MODEL.WEIGHTS
if weight_path.endswith('.pth'):
model = build_models(cfg)
model.load_state_dict(torch.load(cfg.MODEL.WEIGHTS,
map_location='cpu'),
strict=True)
else:
model = torch.jit.load(cfg.MODEL.WEIGHTS, map_location='cpu')
model.eval().cuda()
floder = cfg.TEST.METRIC.TEMPLATE_DATAPATH
if not cfg.TEST.METRIC.TEMPLATE_DATAPATH or not os.path.exists(floder):
print("请指定正确的数据集路径：TEMPLATE_DATAPATH")
return None
transform = TRANSFORMS_REGISTRY.get(cfg.INFERENCE.TRANSFORM)(
cfg.TRAIN.INPUT_WIDTH, cfg.TRAIN.INPUT_HEIGHT)
dataset = ImageFolder(floder, transform)
cluster_map = {}
for image, target in tqdm(dataset):
target = dataset.classes[target]
with torch.no_grad():
features = model(image.unsqueeze(dim=0).cuda())
if target in cluster_map.keys():
cluster_map[target] = torch.cat((cluster_map[target], features),
dim=0)
else:
cluster_map[target] = features
features_template = {}
features_template['labels'] = []
features_template['features'] = torch.zeros(
(1, cfg.MODEL.METRIC.DIM)).cuda()
for key, value in cluster_map.items():
#对单一类别聚类，返回聚类中心数量和聚类中心特征
cluster_num, features = cluster(cluster_map[key])
for i in range(cluster_num):
features_template['labels'].append(key)
features_template['features'] = torch.cat(
(features_template['features'], features), dim=0)
features_template['features'] = features_template['features'][1:]
print(features_template['features'].shape)
torch.save(
features_template,
os.path.join(
cfg.OUTPUT.DIR,
os.path.basename(cfg.MODEL.WEIGHTS).split('.')[0] +
'_features_template.pth'))
def __init__(self, cfg):
self.cfg = cfg
weight_path = cfg.MODEL.WEIGHTS
if weight_path.endswith('.pth'):
self.model = build_models(cfg)
self.model.load_state_dict(torch.load(cfg.MODEL.WEIGHTS,
map_location='cpu'),
strict=True)
else:
self.model = torch.jit.load(cfg.MODEL.WEIGHTS, map_location='cpu')
self.model.eval().cuda()
if self.cfg.TEST.OUTPATH:
if not os.path.exists(self.cfg.TEST.OUTPATH):
os.mkdir(self.cfg.TEST.OUTPATH)
def instance_tmp(cfg):
weight_path = cfg.MODEL.WEIGHTS
if weight_path.endswith('.pth'):
model = build_models(cfg)
model.load_state_dict(torch.load(cfg.MODEL.WEIGHTS,
map_location='cpu'),
strict=True)
else:
model = torch.jit.load(cfg.MODEL.WEIGHTS, map_location='cpu')
model.eval().cuda()
floder = cfg.TEST.METRIC.TEMPLATE_DATAPATH
if not cfg.TEST.METRIC.TEMPLATE_DATAPATH or not os.path.exists(floder):
print("请指定正确的数据集路径：TEMPLATE_DATAPATH")
return None
transform = TRANSFORMS_REGISTRY.get(cfg.INFERENCE.TRANSFORM)(
cfg.TRAIN.INPUT_WIDTH, cfg.TRAIN.INPUT_HEIGHT)
dataset = ImageFolder(floder, transform)
features_template = {}
features_template['labels'] = []
features_template['features'] = torch.zeros(
(1, cfg.MODEL.METRIC.DIM)).cuda()
for image, target in tqdm(dataset):
target = dataset.classes[target]
with torch.no_grad():
features = model(image.unsqueeze(dim=0).cuda())
simi = torch.matmul(features, torch.t(features_template['features']))
sorted_simi, sorted_idx = torch.sort(simi, 1, True)
if (sorted_simi[0, 0].cpu().item() < 0.99):
features_template['labels'].append(target)
features_template['features'] = torch.cat(
(features_template['features'], features), dim=0)
features_template['features'] = features_template['features'][1:]
print(features_template['features'].shape)
torch.save(
features_template,
os.path.join(
cfg.OUTPUT.DIR,
os.path.basename(cfg.MODEL.WEIGHTS).split('.')[0] +
'_features_template.pth'))