import numpy as np
from functools import partial
sys.path.append('../../')
from utils import dynamic_train,networks,dataloader,util
args,name = util.train_parser()
pm = util.Path_Manager('../../dataset/cub_fewshot',args=args)
config = util.Config(args=args,
name=name,
suffix='stage_1',
train_annot='part')
train_loader = dataloader.normal_train_dataloader(data_path=pm.support,
batch_size=args.batch_size,
annot=config.train_annot,
annot_path=pm.annot_path)
num_class = len(train_loader.dataset.classes)
model = networks.Dynamic_PN_gt(num_class=num_class,
num_part=args.num_part,
resnet=args.resnet)
model.cuda()
train_func = partial(dynamic_train.train_stage_1,train_loader=train_loader)
tm = util.Train_Manager(args,pm,config,
train_func=train_func)
import sys
import torch
import numpy as np
from functools import partial
import torch.nn as nn
sys.path.append('../../')
from utils import transfer_train, transfer_eval, networks, dataloader, util
args, name = util.train_parser()
pm = util.Path_Manager('../../dataset/cub_fewshot', args=args)
config = util.Config(args=args, name=name, suffix='cub')
train_loader = dataloader.normal_train_dataloader(data_path=pm.test_refer,
batch_size=args.batch_size)
num_class = len(train_loader.dataset.classes)
model = networks.Transfer_PN(num_part=args.num_part, resnet=args.resnet)
model.cuda()
model.load_state_dict(torch.load(args.load_path))
model.linear_classifier = nn.Linear(model.dim, num_class).cuda()
train_func = partial(transfer_train.default_train, train_loader=train_loader)
tm = util.TM_transfer_PN_finetune(args, pm, config, train_func=train_func)
tm.train(model)
transfer_eval.eval_test(model, pm, config)
import sys
import torch
import numpy as np
from functools import partial
sys.path.append('../../')
from utils import dynamic_train, networks, dataloader, util
args, name = util.train_parser()
pm = util.Path_Manager('../../dataset/cub_fewshot', args=args)
config = util.Config(args=args, name=name, suffix='stage_1')
train_loader = dataloader.normal_train_dataloader(data_path=pm.support,
batch_size=args.batch_size)
num_class = len(train_loader.dataset.classes)
model = networks.Dynamic(num_class=num_class, resnet=args.resnet)
model.cuda()
train_func = partial(dynamic_train.train_stage_1, train_loader=train_loader)
tm = util.Train_Manager(args, pm, config, train_func=train_func)
tm.train(model)
import torch.nn as nn
sys.path.append('../../')
from utils import transfer_train, transfer_eval, networks, dataloader, util
args, name = util.train_parser()
pm = util.Path_Manager('../../dataset/cub_fewshot', args=args)
config = util.Config(args=args,
name=name,
suffix='cub',
train_annot='part',
eval_annot='part')
train_loader = dataloader.normal_train_dataloader(data_path=pm.test_refer,
batch_size=args.batch_size,
annot=config.train_annot,
annot_path=pm.annot_path)
num_class = len(train_loader.dataset.classes)
model = networks.Transfer_PN_gt(num_part=args.num_part, resnet=args.resnet)
model.cuda()
model.load_state_dict(torch.load(args.load_path))
model.linear_classifier = nn.Linear(model.dim, num_class).cuda()
train_func = partial(transfer_train.default_train, train_loader=train_loader)
tm = util.TM_transfer_finetune(args, pm, config, train_func=train_func)
tm.train(model)
transfer_eval.eval_test(model, pm, config)
