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test.py
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import torch
import numpy as np
from torch.autograd import Variable
import torch.nn as nn
import torch.optim
import json
import torch.utils.data.sampler
import os
import glob
import random
import time
import backbone
import data.feature_loader as feat_loader
from data.datamgr import SetDataManager
from methods.baselinetrain import BaselineTrain
from methods.baselinefinetune import BaselineFinetune
from methods.protonet import ProtoNet
from methods.matchingnet import MatchingNet
from methods.relationnet import RelationNet
from methods.maml import MAML
from io_utils import model_dict, parse_args, get_resume_file, get_best_file , get_assigned_file
def feature_evaluation(cl_data_file, model, n_way = 5, n_support = 5, n_query = 15, adaptation = False):
class_list = cl_data_file.keys()
select_class = random.sample(class_list,n_way)
z_all = []
for cl in select_class:
img_feat = cl_data_file[cl]
perm_ids = np.random.permutation(len(img_feat)).tolist()
z_all.append( [ np.squeeze( img_feat[perm_ids[i]]) for i in range(n_support+n_query) ] ) # stack each batch
z_all = torch.from_numpy(np.array(z_all) )
model.n_query = n_query
if adaptation:
scores = model.set_forward_adaptation(z_all, is_feature = True)
else:
scores = model.set_forward(z_all, is_feature = True)
pred = scores.data.cpu().numpy().argmax(axis = 1)
y = np.repeat(range( n_way ), n_query )
acc = np.mean(pred == y)*100
return acc
if __name__ == '__main__':
params = parse_args('test')
isAircraft = (params.dataset == 'aircrafts')
acc_all = []
iter_num = 600
few_shot_params = dict(n_way = params.test_n_way , n_support = params.n_shot)
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 == 'baseline':
model = BaselineFinetune( model_dict[params.model], **few_shot_params )
elif params.method == 'baseline++':
model = BaselineFinetune( model_dict[params.model], loss_type = 'dist', **few_shot_params )
elif params.method == 'protonet':
model = ProtoNet( model_dict[params.model], **few_shot_params )
elif params.method == 'matchingnet':
model = MatchingNet( model_dict[params.model], **few_shot_params )
elif params.method in ['relationnet', 'relationnet_softmax']:
if params.model == 'Conv4':
feature_model = backbone.Conv4NP
elif params.model == 'Conv6':
feature_model = backbone.Conv6NP
elif params.model == 'Conv4S':
feature_model = backbone.Conv4SNP
else:
feature_model = lambda: model_dict[params.model]( flatten = False )
loss_type = 'mse' if params.method == 'relationnet' else 'softmax'
model = RelationNet( feature_model, loss_type = loss_type , **few_shot_params )
elif params.method in ['maml' , 'maml_approx']:
backbone.ConvBlock.maml = True
backbone.SimpleBlock.maml = True
backbone.BottleneckBlock.maml = True
backbone.ResNet.maml = True
model = MAML( model_dict[params.model], approx = (params.method == 'maml_approx') , **few_shot_params )
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')
model = model.cuda()
model.feature = model.feature.cuda()
if params.json_seed is not None:
checkpoint_dir = 'checkpoints/%s_%s/%s_%s_%s' %(params.dataset, params.json_seed, params.date, params.model, params.method)
else:
checkpoint_dir = 'checkpoints/%s/%s_%s_%s' %(params.dataset, params.date, params.model, params.method)
if params.train_aug:
checkpoint_dir += '_aug'
if not params.method in ['baseline', 'baseline++'] :
checkpoint_dir += '_%dway_%dshot_%dquery' %( params.train_n_way, params.n_shot, params.n_query)
checkpoint_dir += '_%d'%params.image_size
## Use another dataset (dataloader) for unlabeled data
if params.dataset_unlabel is not None:
checkpoint_dir += params.dataset_unlabel
checkpoint_dir += str(params.bs)
## Use grey image
if params.grey:
checkpoint_dir += '_grey'
## Add jigsaw
if params.jigsaw:
checkpoint_dir += '_jigsaw_lbda%.2f'%(params.lbda)
checkpoint_dir += params.optimization
## Add rotation
if params.rotation:
checkpoint_dir += '_rotation_lbda%.2f'%(params.lbda)
checkpoint_dir += params.optimization
checkpoint_dir += '_lr%.4f'%(params.lr)
if params.finetune:
checkpoint_dir += '_finetune'
if params.loadfile != '':
checkpoint_dir = params.loadfile
else:
if not params.method in ['baseline', 'baseline++'] :
if params.save_iter != -1:
modelfile = get_assigned_file(checkpoint_dir,params.save_iter)
else:
modelfile = get_best_file(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.feature.load_state_dict(tmp['state'], strict=False)
print('modelfile:',modelfile)
split = params.split
if params.save_iter != -1:
split_str = split + "_" +str(params.save_iter)
else:
split_str = split
if params.method in ['maml', 'maml_approx']: #maml do not support testing with feature
image_size = params.image_size
datamgr = SetDataManager(image_size, n_eposide = iter_num, n_query = 15 , **few_shot_params, isAircraft=isAircraft, grey=params.grey)
loadfile = os.path.join('filelists', params.dataset, 'novel.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( novel_loader, return_std = True)
else:
novel_file = os.path.join( checkpoint_dir.replace("checkpoints","features"), split_str +".hdf5") #defaut split = novel, but you can also test base or val classes
print('novel_file',novel_file)
cl_data_file = feat_loader.init_loader(novel_file)
for i in range(iter_num):
acc = feature_evaluation(cl_data_file, model, n_query = 15, adaptation = params.adaptation, **few_shot_params)
acc_all.append(acc)
acc_all = np.asarray(acc_all)
acc_mean = np.mean(acc_all)
acc_std = np.std(acc_all)
print('%d Test Acc = %4.2f%% +- %4.2f%%' %(iter_num, acc_mean, 1.96* acc_std/np.sqrt(iter_num)))
if params.method in ['maml', 'maml_approx']:
if not os.path.isdir(checkpoint_dir.replace("checkpoints","features")):
os.mkdir(checkpoint_dir.replace("checkpoints","features"))
with open(os.path.join( checkpoint_dir.replace("checkpoints","features"), 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 ''
if params.method in ['baseline', 'baseline++'] :
exp_setting = '%s-%s-%s-%s%s %sshot %sway_test' %(params.dataset, split_str, params.model, params.method, aug_str, params.n_shot, params.test_n_way )
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%%' %(iter_num, acc_mean, 1.96* acc_std/np.sqrt(iter_num))
f.write( 'Time: %s, Setting: %s, Acc: %s \n' %(timestamp,exp_setting,acc_str) )