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import numpy as np
import torch
import torch.nn as nn
import torch.optim
import random
from methods.backbone import model_dict
from data.datamgr import SetDataManager
from options import parse_args
#from methods.matchingnet import MatchingNet
#from methods.relationnet import RelationNet
#from methods.protonet import ProtoNet
#from methods.gnnnet import GnnNet
#from methods.tpn import TPN
#from PSG import PseudoSampleGenerator
from utils.PSG import PseudoSampleGenerator
from data import ISIC_few_shot, EuroSAT_few_shot, CropDisease_few_shot, Chest_few_shot
#from cvpr2023_startup_20221026 import *
#from cvpr2023_load_models_20221102 import load_ViTsmall
from methods.load_ViT_models import load_ViTsmall
#from models.pmf_protonet import ProtoNet
#from methods.pmf_protonet import ProtoNet
from methods.protonet import ProtoNet
#PMF_metatrained = False
PMF_metatrained = True
FINAL_FEAT_DIM = 384
FINETUNE_ALL = True
#FINETUNE_ALL = False
#tune_lr = 0.01
#tune_lr = 0.001
#tune_lr = 0.0001
tune_lr = 5e-5
def load_model():
vit_model = load_ViTsmall()
model = ProtoNet(vit_model)
if PMF_metatrained:
#pmf_pretrained_ckp = 'outputs/20221103-styleAdv_metatrain_vit_protonet_trainEpoch20_exp0_lr0/checkpoint.pth'
#pmf_pretrained_ckp = 'outputs/20221103-styleAdv_metatrain_vit_protonet_trainEpoch20_exp1_lr1/checkpoint.pth'
#pmf_pretrained_ckp = 'outputs/20221103-styleAdv_metatrain_vit_protonet_trainEpoch20_exp2_lr2/checkpoint.pth'
#pmf_pretrained_ckp = 'outputs/20221103-styleAdv_metatrain_vit_protonet_trainEpoch20_exp3_lr3/checkpoint.pth'
# 1shot
#pmf_pretrained_ckp = 'outputs/20221106-styleAdv_metatrain_vit_protonet_trainEpoch20_1shot_exp0_lr0_saveBestPth/checkpoint.pth'
pmf_pretrained_ckp = 'output/20221106-styleAdv_metatrain_vit_protonet_trainEpoch20_1shot_exp2_lr2_saveBestPth/checkpoint.pth'
#pmf_pretrained_ckp = 'outputs/20221106-styleAdv_metatrain_vit_protonet_trainEpoch20_1shot_exp0_lr0_saveBestPth_PthreDot4/checkpoint.pth'
#pmf_pretrained_ckp = 'outputs/20221106-styleAdv_metatrain_vit_protonet_trainEpoch20_1shot_exp2_lr2_saveBestPth_PthreDot4/checkpoint.pth'
#pmf_pretrained_ckp = 'outputs/20221106-withoutstyleAdv_metatrain_vit_protonet_exp0_1shot/best.pth'
state_pmf = torch.load(pmf_pretrained_ckp)['model']
#
state_new = state_pmf
state_keys = list(state_pmf.keys())
for i, key in enumerate(state_keys):
if 'feature.' in key:
newkey = key.replace("feature.","backbone.")
state_new[newkey] = state_pmf.pop(key)
if 'classifier.' in key:
state_new.pop(key)
else:
pass
model.load_state_dict(state_new)
model.train().cuda()
return model
def set_forward_ViTProtonet(model, x):
n_way = x.size()[0]
n_query = 15
n_support = x.size()[1] - n_query
SupportTensor = x[:, :n_support, :, :, :]
QueryTensor = x[:, n_support:, :, :, :]
SupportLabel = torch.from_numpy(np.repeat(range(n_way), n_support)).cuda()
QueryLabel = torch.from_numpy(np.repeat(range(n_way), n_query)).cuda()
SupportTensor = SupportTensor.contiguous().view(-1, n_way*n_support, 3, 224, 224)
QueryTensor = QueryTensor.contiguous().view(-1, n_way*n_query, 3, 224, 224)
SupportLabel = SupportLabel.contiguous().view(-1, n_way*n_support)
QueryLabel = QueryLabel.contiguous().view(-1, n_way*n_query)
#print(SupportTensor.size(), SupportLabel.size(), QueryTensor.size())
output = model(SupportTensor, SupportLabel, QueryTensor)
output = output.view(n_way*n_query,n_way)
return output
def finetune(novel_loader, n_pseudo=75, n_way=5, n_support=5):
iter_num = len(novel_loader)
acc_all = []
#checkpoint_dir = '%s/checkpoints/%s/best_model.tar' % (params.save_dir, params.name)
#checkpoint_dir = '%s/checkpoints/%s/best_model.tar' % (params.save_dir, params.resume_dir)
#state = torch.load(checkpoint_dir)['state']
for ti, (x, _) in enumerate(novel_loader): # x:(5, 20, 3, 224, 224)
'''
# Model
if params.method == 'MatchingNet':
model = MatchingNet(model_dict[params.model], n_way=n_way, n_support=n_support).cuda()
elif params.method == 'RelationNet':
model = RelationNet(model_dict[params.model], n_way=n_way, n_support=n_support).cuda()
elif params.method == 'ProtoNet':
model = ProtoNet(model_dict[params.model], n_way=n_way, n_support=n_support).cuda()
elif params.method == 'GNN':
model = GnnNet(model_dict[params.model], n_way=n_way, n_support=n_support).cuda()
elif params.method == 'TPN':
model = TPN(model_dict[params.model], n_way=n_way, n_support=n_support).cuda()
else:
print("Please specify the method!")
assert (False)
# Update model
if 'FWT' in params.name:
model_params = model.state_dict()
pretrained_dict = {k: v for k, v in state.items() if k in model_params}
model_params.update(pretrained_dict)
model.load_state_dict(model_params)
else:
model.load_state_dict(state, strict = False)
'''
model = load_model()
x = x.cuda()
# Finetune components initialization
xs = x[:, :n_support].reshape(-1, *x.size()[2:]) # (25, 3, 224, 224)
#print('xs:', xs.size())
pseudo_q_genrator = PseudoSampleGenerator(n_way, n_support, n_pseudo)
loss_fun = nn.CrossEntropyLoss().cuda()
#opt = torch.optim.Adam(model.parameters())
#opt = torch.optim.Adam(model.parameters(), lr=0.0005) #lr version 2
opt = torch.optim.SGD(model.parameters(), lr = tune_lr, momentum=0.9, weight_decay=0,) #pmf opt
# Finetune process
n_query = n_pseudo//n_way
pseudo_set_y = torch.from_numpy(np.repeat(range(n_way), n_query)).cuda()
model.n_query = n_query
model.train()
for epoch in range(params.finetune_epoch):
opt.zero_grad()
pseudo_set = pseudo_q_genrator.generate(xs) # (5, n_support+n_query, 3, 224, 224)
#scores = model.set_forward(pseudo_set) # (5*n_query, 5)
scores = set_forward_ViTProtonet(model, pseudo_set)
loss = loss_fun(scores, pseudo_set_y)
loss.backward()
opt.step()
del pseudo_set, scores, loss
torch.cuda.empty_cache()
# Inference process
n_query = x.size(1) - n_support
model.n_query = n_query
yq = np.repeat(range(n_way), n_query)
with torch.no_grad():
#scores = model.set_forward(x) # (80, 5)
scores = set_forward_ViTProtonet(model, x)
_, topk_labels = scores.data.topk(1, 1, True, True)
topk_ind = topk_labels.cpu().numpy() # (80, 1)
top1_correct = np.sum(topk_ind[:,0]==yq)
acc = top1_correct*100./(n_way*n_query)
acc_all.append(acc)
del scores, topk_labels
torch.cuda.empty_cache()
#print('Task %d : %4.2f%%'%(ti, acc))
#print('Task %d : %4.2f%%, mean Acc: %4.2f'%(ti, acc, np.mean(np.array(acc_all))))
if(ti%50==0):
print('Task %d : %4.2f%%, mean Acc: %4.2f'%(ti, acc, np.mean(np.array(acc_all))))
acc_all = np.asarray(acc_all)
acc_mean = np.mean(acc_all)
acc_std = np.std(acc_all)
print('Test Acc = %4.2f +- %4.2f%%'%(acc_mean, 1.96*acc_std/np.sqrt(iter_num)))
def run_single_testset(params):
seed = 0
#print("set seed = %d" % seed)
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
#np.random.seed(10)
#params = parse_args('train')
#params = parse_args()
image_size = 224
iter_num = 1000
n_query = 15
n_pseudo = 75
#print('n_pseudo: ', n_pseudo)
print('Loading target dataset!:', params.testset)
if params.testset in ['cub', 'cars', 'places', 'plantae']:
novel_file = os.path.join(params.data_dir, params.testset, 'novel.json')
datamgr = SetDataManager(image_size, n_query=n_query, n_way=params.test_n_way, n_support=params.n_shot, n_eposide=iter_num)
novel_loader = datamgr.get_data_loader(novel_file, aug=False)
else:
few_shot_params = dict(n_way = params.test_n_way , n_support = params.n_shot)
if params.testset in ["ISIC"]:
datamgr = ISIC_few_shot.SetDataManager(image_size, n_eposide = iter_num, n_query = n_query, **few_shot_params)
novel_loader = datamgr.get_data_loader(aug = False )
elif params.testset in ["EuroSAT"]:
datamgr = EuroSAT_few_shot.SetDataManager(image_size, n_eposide = iter_num, n_query = n_query, **few_shot_params)
novel_loader = datamgr.get_data_loader(aug = False )
elif params.testset in ["CropDisease"]:
datamgr = CropDisease_few_shot.SetDataManager(image_size, n_eposide = iter_num, n_query = n_query, **few_shot_params)
novel_loader = datamgr.get_data_loader(aug = False )
elif params.testset in ["ChestX"]:
datamgr = Chest_few_shot.SetDataManager(image_size, n_eposide = iter_num, n_query = n_query, **few_shot_params)
novel_loader = datamgr.get_data_loader(aug = False )
finetune(novel_loader, n_pseudo=n_pseudo, n_way=params.test_n_way, n_support=params.n_shot)
if __name__=='__main__':
params = parse_args(script='train')
#for tmp_testset in ['cub', 'cars', 'places', 'plantae', 'ChestX', 'ISIC', 'EuroSAT', 'CropDisease']:
#for tmp_testset in ['EuroSAT', 'CropDisease']:
#for tmp_testset in ['CropDisease']:
#for tmp_testset in ['EuroSAT', 'plantae']:
#for tmp_testset in ['ISIC']:
#for tmp_testset in ['ChestX', 'ISIC']:
for tmp_testset in ['EuroSAT']:
params.testset = tmp_testset
run_single_testset(params)
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