File size: 8,059 Bytes
197d4ca |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 |
import torch
import os
import h5py
from methods import backbone
from methods.backbone import model_dict
from data.datamgr import SimpleDataManager
from options import parse_args, get_best_file, get_assigned_file
#from methods.protonet import ProtoNet
#from methods.matchingnet import MatchingNet
from methods.gnnnet import GnnNet
#from methods.relationnet import RelationNet
import data.feature_loader as feat_loader
import random
import numpy as np
from data import ISIC_few_shot, EuroSAT_few_shot, CropDisease_few_shot, Chest_few_shot
# extract and save image features
def save_features(model, data_loader, featurefile):
f = h5py.File(featurefile, 'w')
max_count = len(data_loader)*data_loader.batch_size
all_labels = f.create_dataset('all_labels',(max_count,), dtype='i')
all_feats=None
count=0
for i, (x,y) in enumerate(data_loader):
if (i % 10) == 0:
print(' {:d}/{:d}'.format(i, len(data_loader)))
x = x.cuda()
feats = model(x)
if all_feats is None:
all_feats = f.create_dataset('all_feats', [max_count] + list( feats.size()[1:]) , dtype='f')
all_feats[count:count+feats.size(0)] = feats.data.cpu().numpy()
all_labels[count:count+feats.size(0)] = y.cpu().numpy()
count = count + feats.size(0)
count_var = f.create_dataset('count', (1,), dtype='i')
count_var[0] = count
f.close()
# evaluate using features
def feature_evaluation(cl_data_file, model, n_way = 5, n_support = 5, n_query = 15):
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) ] )
z_all = torch.from_numpy(np.array(z_all) )
model.n_query = n_query
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
def test_bestmodel_bscdfsl(acc_file, name, method, dataset,n_shot, save_epoch=-1):
# parse argument
print('hi, test model 1')
params = parse_args('test')
print (' hi, test model 2')
params.n_shot = n_shot
params.dataset = dataset
params.method = method
params.name = name
params.save_epoch = save_epoch #-1 = best
print('Testing! {} shots on {} dataset with {} epochs of {}({})'.format(params.n_shot, params.dataset, params.save_epoch, params.name, params.method))
remove_featurefile = True
print('\nStage 1: saving features')
# dataset
print(' build dataset')
if 'Conv' in params.model:
image_size = 84
else:
image_size = 224
split = params.split
print(split)
if(params.dataset in ["miniImagenet", "cub", "cars", "places", "plantae"]):
loadfile = os.path.join(params.data_dir, params.dataset, split + '.json')
print('load file:', loadfile)
datamgr = SimpleDataManager(image_size, batch_size = 64)
data_loader = datamgr.get_data_loader(loadfile, aug = False)
else:
if params.dataset in ["ISIC"]:
datamgr = ISIC_few_shot.SimpleDataManager(image_size, batch_size = 64)
data_loader = datamgr.get_data_loader(aug = False )
elif params.dataset in ["EuroSAT"]:
datamgr = EuroSAT_few_shot.SimpleDataManager(image_size, batch_size = 64)
data_loader = datamgr.get_data_loader(aug = False )
elif params.dataset in ["CropDisease"]:
datamgr = CropDisease_few_shot.SimpleDataManager(image_size, batch_size = 64)
data_loader = datamgr.get_data_loader(aug = False )
elif params.dataset in ["ChestX"]:
datamgr = Chest_few_shot.SimpleDataManager(image_size, batch_size = 64)
data_loader = datamgr.get_data_loader(aug = False )
print(' build feature encoder')
# feature encoder
checkpoint_dir = '%s/checkpoints/%s'%(params.save_dir, params.name)
if params.save_epoch != -1:
modelfile = get_assigned_file(checkpoint_dir,params.save_epoch)
else:
modelfile = get_best_file(checkpoint_dir)
if params.method in ['relationnet', 'relationnet_softmax']:
if params.model == 'Conv4':
model = backbone.Conv4NP()
elif params.model == 'Conv6':
model = backbone.Conv6NP()
else:
model = model_dict[params.model]( flatten = False )
else:
model = model_dict[params.model]()
model = model.cuda()
tmp = torch.load(modelfile)
try:
state = tmp['state']
except KeyError:
state = tmp['model_state']
except:
raise
state_keys = list(state.keys())
print('state_keys:', state_keys, len(state_keys))
for i, key in enumerate(state_keys):
if "feature." in key and not 'gamma' in key and not 'beta' in key:
newkey = key.replace("feature.","")
state[newkey] = state.pop(key)
else:
state.pop(key)
print('state keys:', list(state.keys()), len(list(state.keys())))
model.load_state_dict(state)
model.eval()
# save feature file
print(' extract and save features...')
if params.save_epoch != -1:
featurefile = os.path.join( checkpoint_dir.replace("checkpoints","features"), split + "_" + str(params.save_epoch)+ ".hdf5")
else:
featurefile = os.path.join( checkpoint_dir.replace("checkpoints","features"), split + ".hdf5")
dirname = os.path.dirname(featurefile)
if not os.path.isdir(dirname):
os.makedirs(dirname)
save_features(model, data_loader, featurefile)
print('\nStage 2: evaluate')
acc_all = []
iter_num = 1000
few_shot_params = dict(n_way = params.test_n_way , n_support = params.n_shot)
# model
print(' build metric-based model')
if 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 == 'gnnnet':
model = GnnNet( 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
else:
feature_model = model_dict[params.model]
loss_type = 'mse' if params.method == 'relationnet' else 'softmax'
model = RelationNet( feature_model, loss_type = loss_type , **few_shot_params )
else:
raise ValueError('Unknown method')
model = model.cuda()
model.eval()
# load model
checkpoint_dir = '%s/checkpoints/%s'%(params.save_dir, params.name)
if params.save_epoch != -1:
modelfile = get_assigned_file(checkpoint_dir, params.save_epoch)
else:
modelfile = get_best_file(checkpoint_dir)
if modelfile is not None:
tmp = torch.load(modelfile)
try:
model.load_state_dict(tmp['state'])
except RuntimeError:
print('warning! RuntimeError when load_state_dict()!')
model.load_state_dict(tmp['state'], strict=False)
except KeyError:
for k in tmp['model_state']: ##### revise latter
if 'running' in k:
tmp['model_state'][k] = tmp['model_state'][k].squeeze()
model.load_state_dict(tmp['model_state'], strict=False)
except:
raise
# load feature file
print(' load saved feature file')
cl_data_file = feat_loader.init_loader(featurefile)
#print('cl_data_file:', cl_data_file)
# start evaluate
print(' evaluate')
for i in range(iter_num):
acc = feature_evaluation(cl_data_file, model, n_query=15, **few_shot_params)
acc_all.append(acc)
# statics
print(' get statics')
acc_all = np.asarray(acc_all)
acc_mean = np.mean(acc_all)
acc_std = np.std(acc_all)
print(' %d test iterations: Acc = %4.2f%% +- %4.2f%%' %(iter_num, acc_mean, 1.96* acc_std/np.sqrt(iter_num)))
print(' %d test iterations: Acc = %4.2f%% +- %4.2f%%' %(iter_num, acc_mean, 1.96* acc_std/np.sqrt(iter_num)), file = acc_file)
# remove feature files [optional]
if remove_featurefile:
os.remove(featurefile)
|