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|
- import warnings
- warnings.filterwarnings("ignore")
- import numpy as np
- import time
- import os
- import glob
- import random
- from utils.io_utils import set_seed, parse_args
- params = parse_args('train')
- os.environ["CUDA_VISIBLE_DEVICES"] = params.device
- import torch
- import torch.nn as nn
- from torch.autograd import Variable
- import torch.optim
- import torch.optim.lr_scheduler as lr_scheduler
- set_seed(params.seed)
- import config.configs as configs
- import models.backbone as backbone
- from data.datamgr_2loss import SimpleDataManager, SetDataManager
- from methods.protonet_2loss import ProtoNet
- from methods.maml import MAML
-
- from utils.io_utils import model_dict, get_resume_file, get_best_file, get_assigned_file
- import json
- from models.model_resnet import *
- from utils.utils import RunningAverage, Logger, wandb_restore_models
- from tqdm import tqdm
- import wandb
- from dagshub import DAGsHubLogger
- try:
- from apex.parallel import DistributedDataParallel as DDP
- from apex.fp16_utils import *
- from apex import amp, optimizers
- from apex.multi_tensor_apply import multi_tensor_applier
- except ImportError:
- print("AMP is not installed. If --amp is True, code will fail.")
- # CDFSL
- from data.cdfsl import Chest_few_shot
- from data.cdfsl import CropDisease_few_shot
- from data.cdfsl import EuroSAT_few_shot
- from data.cdfsl import ISIC_few_shot
- # End CDFSL
- def train(base_loader, val_loader, model, optimizer, start_epoch, stop_epoch, params, base_loader_u, val_loader_u, semi_sup):
-
- if params.amp:
- print("-----------Using mixed precision-----------")
- model, optimizer = amp.initialize(model, optimizer)
- if params.scheduler:
- scheduler = lr_scheduler.StepLR(optimizer, step_size=100, gamma=0.5) # Added by Arjun
-
- eval_interval = params.eval_interval
- max_acc = 0
-
- pbar = tqdm(range(0, stop_epoch*len(base_loader)), total = stop_epoch*len(base_loader))
-
- pbar.update(start_epoch*len(base_loader))
- pbar.refresh()
-
- model.global_count = start_epoch*len(base_loader)
-
- for epoch in range(start_epoch,stop_epoch):
- start_time = time.time()
-
- model.train()
- avg_loss = model.train_loop(epoch, base_loader, optimizer, pbar=pbar, enable_amp=params.amp, base_loader_u=base_loader_u, semi_sup=semi_sup)
- end_time = time.time()
-
- Logger.log({"Epoch": epoch}, step=model.global_count)
- Logger.log({"Epoch Time": end_time-start_time}, step=model.global_count)
-
- pbar.write(u'\u2713' + ' Epoch: %d; Time taken: %d sec.' % (epoch, end_time-start_time))
- if(avg_loss == float('inf') or avg_loss == 0):
- raise Exception("avg_loss is: ", avg_loss)
- if params.scheduler:
- scheduler.step()
-
- if epoch % eval_interval == 0 or epoch == stop_epoch - 1:
- pbar.write("Validating...")
- model.eval()
- if not os.path.isdir(params.checkpoint_dir):
- os.makedirs(params.checkpoint_dir)
- if params.jigsaw and params.rotation:
- acc, acc_jigsaw, acc_rotation = model.test_loop( val_loader, base_loader_u=val_loader_u, semi_sup=semi_sup)
- Logger.log({'val/acc': acc}, step=model.global_count)
- Logger.log({'val/acc_jigsaw': acc_jigsaw}, step=model.global_count)
- Logger.log({'val/acc_rotation': acc_rotation}, step=model.global_count)
- elif params.jigsaw:
- acc, acc_jigsaw = model.test_loop( val_loader, base_loader_u=val_loader_u, semi_sup=semi_sup)
- Logger.log({'val/acc': acc}, step=model.global_count)
- Logger.log({'val/acc_jigsaw': acc_jigsaw}, step=model.global_count)
-
- elif params.rotation:
- acc, acc_rotation = model.test_loop( val_loader, base_loader_u=val_loader_u, semi_sup=semi_sup)
- Logger.log({'val/acc': acc}, step=model.global_count)
- Logger.log({'val/acc_rotation': acc_rotation}, step=model.global_count)
-
- else:
- acc = model.test_loop( val_loader, base_loader_u=val_loader_u, semi_sup=semi_sup)
- Logger.log({'val/acc': acc}, step=model.global_count)
-
- if acc > max_acc and params.save_model:
- max_acc = acc
- outfile = os.path.join(params.checkpoint_dir, 'best_model.tar')
- torch.save({'epoch':epoch, 'state':model.state_dict(), 'optimizer': optimizer.state_dict()}, outfile)
- wandb.save(outfile)
- if (((epoch) % params.save_freq==0) or (epoch==stop_epoch-1)) and params.save_model:
- outfile = os.path.join(params.checkpoint_dir, 'last_model.tar')
- torch.save({'epoch':epoch, 'state':model.state_dict(), 'optimizer': optimizer.state_dict()}, outfile)
- wandb.save(outfile)
- pbar.close()
-
- if __name__=='__main__':
- # torch.cuda.set_device(int(params.device[0]))
-
- isAircraft = False
- if params.sweep:
- params.save_model = 0
- if params.bn_type == 1:
- params.no_bn = False
- params.tracking = True
- elif params.bn_type == 2:
- params.no_bn = False
- params.tracking = False
- elif params.bn_type == 3:
- params.no_bn = True
- params.tracking = False
- else:
- raise Exception("Unrecognized BN Type: ", print(params.bn_type), " of type ", type(params.bn_type))
- suffix = params.dataset_percentage
- base_name = 'base.json' if len(suffix) == 0 else 'base_%s.json' % (suffix)
- base_file = configs.data_dir[params.dataset] + base_name
- val_file = configs.data_dir[params.dataset] + 'val.json'
- test_file = configs.data_dir[params.dataset] + 'novel.json'
- print("Base file: ", base_file)
- if params.demo:
- train_iter_num = 5 # NOTE: should be `100`
- val_iter_num = 5 # NOTE: should be `100`
- test_iter_num = 5 # NOTE: should be `600`
- params.stop_epoch = 1
- else:
- train_iter_num = 100 # NOTE: should be `100`
- val_iter_num = 100 # NOTE: should be `100`
- test_iter_num = 600 # NOTE: should be `600`
- image_size = params.image_size
- if params.dataset in ['omniglot', 'cross_char']:
- assert params.model == 'Conv4' and not params.train_aug ,'omniglot only support Conv4 without augmentation'
- params.model = 'Conv4S'
- if params.method in ['protonet','matchingnet','relationnet', 'relationnet_softmax', 'maml', 'maml_approx']:
- n_query = max(1, int(params.n_query * params.test_n_way/params.train_n_way)) #if test_n_way is smaller than train_n_way, reduce n_query to keep batch size small
- print('n_query:',n_query)
- print("semi-sup is: ", params.semi_sup)
- base_datamgr_u = SimpleDataManager(image_size, batch_size = params.train_n_way * (params.n_shot + n_query), jigsaw=params.jigsaw, rotation=params.rotation, isAircraft=isAircraft, grey=params.grey, shuffle=True, drop_last=True)
- val_datamgr_u = SimpleDataManager(image_size, batch_size = params.test_n_way * (params.n_shot + n_query), jigsaw=params.jigsaw, rotation=params.rotation, isAircraft=isAircraft, grey=params.grey, shuffle=True, drop_last=True)
- if params.dataset_unlabel is not None and (params.jigsaw or params.rotation):
- if "," in params.dataset_unlabel:
- params.dataset_unlabel = params.dataset_unlabel.split(",")
- suffix = params.dataset_unlabel_percentage
- base_name = 'base.json' if len(suffix) == 0 else 'base_%s.json' % (suffix)
- val_name = 'val.json'
- if type(params.dataset_unlabel) is list:
- # a list of datasets will be there, and we need to fuse them inside get_data_loader
- print('datasets for self-supervision are: ', params.dataset_unlabel)
- base_file_u = [os.path.join('filelists', x, base_name) for x in params.dataset_unlabel]
- print("base files for self-supervision is:", base_file_u)
- val_file_u = [os.path.join('filelists', x, val_name) for x in params.dataset_unlabel]
- print("val files for self-supervision is:", val_file_u)
- else:
- print('dataset for self-supervision is: ', params.dataset_unlabel)
- if params.dataset_unlabel in ["ISIC"]:
- base_datamgr_u = ISIC_few_shot.SimpleDataManager(image_size, batch_size = params.train_n_way * (params.n_shot + n_query), jigsaw=params.jigsaw, rotation=params.rotation)
- base_loader_u = base_datamgr_u.get_data_loader(aug = params.train_aug, drop_last=True )
- val_datamgr_u = ISIC_few_shot.SimpleDataManager(image_size, batch_size = params.test_n_way * (params.n_shot + n_query), jigsaw=params.jigsaw, rotation=params.rotation)
- val_loader_u = val_datamgr_u.get_data_loader(aug = False, drop_last=True )
- elif params.dataset_unlabel in ["EuroSAT"]:
- base_datamgr_u = EuroSAT_few_shot.SimpleDataManager(image_size, batch_size = params.train_n_way * (params.n_shot + n_query), jigsaw=params.jigsaw, rotation=params.rotation)
- base_loader_u = base_datamgr_u.get_data_loader(aug = params.train_aug, drop_last=True )
- val_datamgr_u = EuroSAT_few_shot.SimpleDataManager(image_size, batch_size = params.test_n_way * (params.n_shot + n_query), jigsaw=params.jigsaw, rotation=params.rotation)
- val_loader_u = val_datamgr_u.get_data_loader(aug = False, drop_last=True )
- elif params.dataset_unlabel in ["CropDisease"]:
- base_datamgr_u = CropDisease_few_shot.SimpleDataManager(image_size, batch_size = params.train_n_way * (params.n_shot + n_query), jigsaw=params.jigsaw, rotation=params.rotation)
- base_loader_u = base_datamgr_u.get_data_loader(aug = params.train_aug, drop_last=True )
- val_datamgr_u = CropDisease_few_shot.SimpleDataManager(image_size, batch_size = params.test_n_way * (params.n_shot + n_query), jigsaw=params.jigsaw, rotation=params.rotation)
- val_loader_u = val_datamgr_u.get_data_loader(aug = False, drop_last=True )
- elif params.dataset_unlabel in ["ChestX"]:
- base_datamgr_u = Chest_few_shot.SimpleDataManager(image_size, batch_size = params.train_n_way * (params.n_shot + n_query), jigsaw=params.jigsaw, rotation=params.rotation)
- base_loader_u = base_datamgr_u.get_data_loader(aug = params.train_aug, drop_last=True )
- val_datamgr_u = Chest_few_shot.SimpleDataManager(image_size, batch_size = params.test_n_way * (params.n_shot + n_query), jigsaw=params.jigsaw, rotation=params.rotation)
- val_loader_u = val_datamgr_u.get_data_loader(aug = False, drop_last=True )
- else:
- base_file_u = os.path.join('filelists', params.dataset_unlabel, base_name)
- val_file_u = os.path.join('filelists', params.dataset_unlabel, val_name)
- base_loader_u = base_datamgr_u.get_data_loader( base_file_u , aug = params.train_aug)
- val_loader_u = val_datamgr_u.get_data_loader( val_file_u , aug = False)
- else:
- base_loader_u = None
- val_loader_u = None
- # train_few_shot_params = dict(n_way = params.train_n_way, n_support = params.n_shot)
- train_few_shot_params = dict(n_way = params.train_n_way, n_support = params.n_shot, \
- jigsaw=params.jigsaw, lbda=params.lbda, rotation=params.rotation, lbda_jigsaw=params.lbda_jigsaw, lbda_rotation=params.lbda_rotation)
- base_datamgr = SetDataManager(image_size, n_query = n_query, n_eposide = train_iter_num, **train_few_shot_params, isAircraft=isAircraft, grey=params.grey, low_res=params.low_res, sup_ratio=params.sup_ratio, semi_sup=params.semi_sup)
- base_loader = base_datamgr.get_data_loader( base_file , aug = params.train_aug )
-
- val_few_shot_params = dict(n_way = params.test_n_way, n_support = params.n_shot, \
- jigsaw=params.jigsaw, lbda=params.lbda, rotation=params.rotation, lbda_jigsaw=params.lbda_jigsaw, lbda_rotation=params.lbda_rotation)
- val_datamgr = SetDataManager(image_size, n_query = n_query, n_eposide = val_iter_num, **val_few_shot_params, isAircraft=isAircraft, grey=params.grey, low_res=params.low_res, semi_sup=params.semi_sup)
- val_loader = val_datamgr.get_data_loader( val_file, aug = False)
- #a batch for SetDataManager: a [n_way, n_support + n_query, dim, w, h] tensor
- if params.dataset_unlabel in ["ISIC"]:
- test_datamgr = ISIC_few_shot.SetDataManager(image_size, n_query = n_query, n_eposide = test_iter_num, n_way = params.test_n_way, n_support = params.n_shot)
- test_loader = test_datamgr.get_data_loader( aug = False)
- elif params.dataset_unlabel in ["EuroSAT"]:
- test_datamgr = EuroSAT_few_shot.SetDataManager(image_size, n_query = n_query, n_eposide = test_iter_num, n_way = params.test_n_way, n_support = params.n_shot)
- test_loader = test_datamgr.get_data_loader( aug = False)
- elif params.dataset_unlabel in ["CropDisease"]:
- test_datamgr = CropDisease_few_shot.SetDataManager(image_size, n_query = n_query, n_eposide = test_iter_num, n_way = params.test_n_way, n_support = params.n_shot)
- test_loader = test_datamgr.get_data_loader( aug = False)
- elif params.dataset_unlabel in ["ChestX"]:
- test_datamgr = Chest_few_shot.SetDataManager(image_size, n_query = n_query, n_eposide = test_iter_num, n_way = params.test_n_way, n_support = params.n_shot)
- test_loader = test_datamgr.get_data_loader( aug = False)
- else:
- test_few_shot_params = dict(n_way = params.test_n_way, n_support = params.n_shot, \
- jigsaw=params.jigsaw, lbda=params.lbda, rotation=params.rotation, lbda_jigsaw=params.lbda_jigsaw, lbda_rotation=params.lbda_rotation)
- test_datamgr = SetDataManager(image_size, n_query = n_query, n_eposide = test_iter_num, **test_few_shot_params, isAircraft=isAircraft, grey=params.grey, low_res=params.low_res, semi_sup=params.semi_sup)
- test_loader = test_datamgr.get_data_loader( test_file, aug = False)
- if params.method == 'protonet':
- model = ProtoNet( model_dict[params.model], **train_few_shot_params , use_bn=(not params.no_bn), pretrain=params.pretrain, tracking=params.tracking, model=params.model)
- elif params.method in ['maml' , 'maml_approx']:
- backbone.ConvBlock.maml = True
- backbone.SimpleBlock.maml = True
- backbone.BottleneckBlock.maml = True
- backbone.ResNet.maml = True
- BasicBlock.maml = True
- Bottleneck.maml = True
- ResNet.maml = True
- model = MAML( model_dict[params.model], approx = (params.method == 'maml_approx') , tracking=params.tracking, **train_few_shot_params , use_bn=(not params.no_bn), pretrain=params.pretrain, model=params.model)
- if params.dataset in ['omniglot', 'cross_char']: #maml use different parameter in omniglot
- model.n_task = 32
- model.task_update_num = 1
- model.train_lr = 0.1
- else:
- raise ValueError('Unknown method')
- # import ipdb; ipdb.set_trace()
- # import ipdb; ipdb.set_trace()
- if params.json_seed is not None:
- params.checkpoint_dir = '%s/results/%s_%s/%s_%s_%s' %(configs.save_dir, params.dataset, params.json_seed, params.date, params.model, params.method)
- else:
- params.checkpoint_dir = '%s/results/%s/%s_%s_%s' %(configs.save_dir, params.dataset, params.date, params.model, params.method)
- if params.train_aug:
- params.checkpoint_dir += '_aug'
- params.checkpoint_dir += '_%dway_%dshot_%dquery' %( params.train_n_way, params.n_shot, params.n_query)
- params.checkpoint_dir += '_%d'%image_size
-
- ## Track bn stats
- if params.tracking:
- params.checkpoint_dir += '_tracking'
- ## Use subset of training data
- if params.firstk > 0:
- params.checkpoint_dir += ('_first'+str(params.firstk))
- ## Use grey image
- if params.grey:
- params.checkpoint_dir += '_grey'
- ## Use low_res image
- if params.low_res:
- params.checkpoint_dir += '_low_res'
- ## Add jigsaw and rotation
- if params.jigsaw and params.rotation:
- params.checkpoint_dir += '_jigsaw_lbda%.2f_rotation_lbda%.2f'%(params.lbda_jigsaw, params.lbda_rotation)
- ## Add jigsaw
- elif params.jigsaw:
- params.checkpoint_dir += '_jigsaw_lbda%.2f'%(params.lbda)
- ## Add rotation
- elif params.rotation:
- params.checkpoint_dir += '_rotation_lbda%.2f'%(params.lbda)
- if params.semi_sup:
- params.checkpoint_dir += '_semi_sup%.2f'%(params.lbda)
- if params.dataset_unlabel:
- params.checkpoint_dir += '_dataset_unlabel=%s'%("".join(params.dataset_unlabel))
- params.checkpoint_dir += '_sup_ratio=%d'%(params.sup_ratio)
- params.checkpoint_dir += params.optimization
- params.checkpoint_dir += '_lr%.4f'%(params.lr)
- if params.finetune:
- params.checkpoint_dir += '_finetune'
- if params.random:
- params.checkpoint_dir += 'results/'+params.dataset+'/random'
- if params.debug:
- params.checkpoint_dir += 'results/'+params.dataset+'/debug'
- params.checkpoint_dir += 'dset_percent='+params.dataset_percentage
- params.checkpoint_dir += 'dset_unlabel_percent='+params.dataset_unlabel_percentage
- print('Checkpoint path:',params.checkpoint_dir)
- if not os.path.isdir(params.checkpoint_dir):
- os.makedirs(params.checkpoint_dir)
- start_epoch = params.start_epoch
- stop_epoch = params.stop_epoch
- if params.method == 'maml' or params.method == 'maml_approx' :
- stop_epoch = params.stop_epoch * model.n_task #maml use multiple tasks in one update
- if params.resume:
- wandb.init(project=params.project, entity="meta-learners", id=params.run_id, resume=True)
- wandb.watch(model)
- Logger.init(wandb=True, DAGsHub=True, ckpt_dir=params.checkpoint_dir)
- old_ckpt_dir = wandb.config["checkpoint_dir"]
- old_ckpt_dir = old_ckpt_dir[old_ckpt_dir.index("results"):]
- params.checkpoint_dir = old_ckpt_dir
- resume_file = wandb_restore_models(old_ckpt_dir)
- # resume_file = get_resume_file(old_ckpt_dir)
- if resume_file is not None:
- tmp = torch.load(resume_file, map_location="cpu")
- start_epoch = tmp['epoch']+1
- model.load_state_dict(tmp['state'])
- print("Resuming run %s from epoch %d" % (params.run_name, start_epoch))
- elif params.loadfile != '':
- print('Loading model from: ' + params.loadfile)
- checkpoint = torch.load(params.loadfile, map_location='cpu')
- model.load_state_dict(checkpoint['state'])
- model = model.cuda()
- model.feature = model.feature.cuda()
- if params.optimization == 'Adam':
- optimizer = torch.optim.Adam(model.parameters(), lr=params.lr)
- elif params.optimization == 'SGD':
- optimizer = torch.optim.SGD(model.parameters(), lr=params.lr)
- elif params.optimization == 'Nesterov':
- optimizer = torch.optim.SGD(model.parameters(), lr=params.lr, nesterov=True, momentum=0.9, weight_decay=params.wd)
- else:
- raise ValueError('Unknown optimization, please define by yourself')
- if params.resume:
- optimizer.load_state_dict(tmp['optimizer'])
- if not params.only_test:
- if not params.resume:
- json.dump(vars(params), open(params.checkpoint_dir+'/configs.json','w'))
- wandb.init(config=vars(params), project=params.project, entity="meta-learners")
- wandb.run.name = wandb.run.id if not params.run_name else params.run_name
- wandb.watch(model)
- Logger.init(wandb=True, DAGsHub=True, ckpt_dir=params.checkpoint_dir)
- Logger.log_hyperparams(vars(params))
- train(base_loader, val_loader, model, optimizer, start_epoch, stop_epoch, params, base_loader_u, val_loader_u, params.semi_sup)
-
- if not params.only_train:
- test_accs = []
- for i,fn in enumerate([get_resume_file, get_best_file]):
- print(fn)
- split = 'novel'
- if params.save_iter != -1:
- split_str = split + "_" +str(params.save_iter)
- else:
- split_str = split
- few_shot_params = dict(n_way = params.test_n_way , n_support = params.n_shot)
- acc_all = []
- if params.loadfile != '':
- modelfile = params.loadfile
- checkpoint_dir = params.loadfile
- else:
- checkpoint_dir = params.checkpoint_dir
- if params.save_iter != -1:
- modelfile = get_assigned_file(checkpoint_dir,params.save_iter)
- else:
- modelfile = fn(checkpoint_dir)
- if params.method in ['maml', 'maml_approx']:
- if modelfile is not None:
- tmp = torch.load(modelfile)
- state = tmp['state']
- state_keys = list(state.keys())
- for i, key in enumerate(state_keys):
- if "feature." in key:
- newkey = key.replace("feature.","") # an architecture model has attribute 'feature', load architecture feature to backbone by casting name from 'feature.trunk.xx' to 'trunk.xx'
- state[newkey] = state.pop(key)
- else:
- state.pop(key)
- # model.load_state_dict(tmp['state'], strict=False)
- model.feature.load_state_dict(tmp['state'])
- datamgr = SetDataManager(image_size, n_eposide = test_iter_num, n_query = params.n_query , **few_shot_params, isAircraft=isAircraft, grey=params.grey, low_res=params.low_res)
- loadfile = configs.data_dir[params.dataset] + split + '.json'
- novel_loader = datamgr.get_data_loader( loadfile, aug = False)
- if params.adaptation:
- model.task_update_num = 100 #We perform adaptation on MAML simply by updating more times.
- model.eval()
- acc_mean, acc_std = model.test_loop( test_loader, semi_sup=params.semi_sup, proto_only=True, std_also=True)
- Logger.log({"test/acc_%s" % ("resume" if fn==get_resume_file else "best"): acc_mean})
- test_accs.append(acc_mean)
- out_dir = os.path.join( checkpoint_dir)
- os.makedirs(out_dir, exist_ok=True)
- with open(os.path.join( checkpoint_dir, split_str +"_test.txt") , 'a') as f:
- timestamp = time.strftime("%Y%m%d-%H%M%S", time.localtime())
- aug_str = '-aug' if params.train_aug else ''
- aug_str += '-adapted' if params.adaptation else ''
- exp_setting = '%s-%s-%s-%s%s %sshot %sway_train %sway_test' %(params.dataset, split_str, params.model, params.method, aug_str , params.n_shot , params.train_n_way, params.test_n_way )
- acc_str = '%d Test Acc = %4.2f%% +- %4.2f%%' %(test_iter_num, acc_mean, 1.96* acc_std/np.sqrt(test_iter_num))
- f.write( 'Time: %s, Setting: %s, Acc: %s \n' %(timestamp,exp_setting,acc_str) )
- else:
- tmp = torch.load(modelfile)
- state = tmp['state']
- state_keys = list(state.keys())
- for i, key in enumerate(state_keys):
- if "feature." in key:
- newkey = key.replace("feature.","") # an architecture model has attribute 'feature', load architecture feature to backbone by casting name from 'feature.trunk.xx' to 'trunk.xx'
- state[newkey] = state.pop(key)
- else:
- state.pop(key)
- model.feature.load_state_dict(state)
- model.feature.eval()
- model = model.cuda()
- model.feature = model.feature.cuda()
- model.eval()
- if params.semi_sup:
- print("Performing supervised + semi-supervised inference...")
- else:
- print("Performing inference...")
- acc_mean, acc_std = model.test_loop( test_loader, semi_sup=params.semi_sup, proto_only=True)
- Logger.log({"test/acc_%s" % ("resume" if fn==get_resume_file else "best"): acc_mean})
- test_accs.append(acc_mean)
- out_dir = os.path.join( checkpoint_dir)
- os.makedirs(out_dir, exist_ok=True)
- with open(os.path.join( checkpoint_dir, split_str +"_test.txt") , 'a') as f:
- timestamp = time.strftime("%Y%m%d-%H%M%S", time.localtime())
- aug_str = '-aug' if params.train_aug else ''
- aug_str += '-adapted' if params.adaptation else ''
- exp_setting = '%s-%s-%s-%s%s %sshot %sway_train %sway_test' %(params.dataset, split_str, params.model, params.method, aug_str , params.n_shot , params.train_n_way, params.test_n_way )
- acc_str = '%d Test Acc = %4.2f%% +- %4.2f%%' %(test_iter_num, acc_mean, 1.96* acc_std/np.sqrt(test_iter_num))
- f.write( 'Time: %s, Setting: %s, Acc: %s \n' %(timestamp,exp_setting,acc_str) )
-
- Logger.log({"test/acc": test_accs[1]})
- wandb.save(os.path.join(params.checkpoint_dir, "last_model.tar"))
- wandb.save(os.path.join(params.checkpoint_dir, "best_model.tar"))
- wandb.finish()
- if params.save_model:
- os.remove(os.path.join(params.checkpoint_dir, "last_model.tar"))
- os.remove(os.path.join(params.checkpoint_dir, "best_model.tar"))
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