config = load_config(args.config)
train_gen = EpisodeGenerator(args.dataset_dir, 'train', config)
test_gen = EpisodeGenerator(args.dataset_dir, 'test', config)
if args.train:
max_iter = train_gen.dataset_size[args.dataset_name] * args.max_epoch \
// (nway * qsize)
show_step = args.show_epoch * max_iter // args.max_epoch
save_step = args.save_epoch * max_iter // args.max_epoch
avger = np.zeros([4])
for i in range(1, max_iter + 1):
stt = time.time()
cur_epoch = i * (
nway * qsize) // train_gen.dataset_size[args.dataset_name]
lr = args.lr if i < 0.7 * max_iter else args.lr * .1
sx, sy, qx, qy = train_gen.get_episode(nway, kshot, qsize)
fd = {\
protonet.inputs['sx']: sx,
protonet.inputs['qx']: qx,
protonet.inputs['qy']: qy,
lr_ph: lr}
p1, p2, _ = sess.run([acc, loss, train_op], fd)
avger += [p1, p2, 0, time.time() - stt]
if i % show_step == 0 and i != 0:
avger /= show_step
print ('========= epoch : {:8d}/{} ========='\
.format(cur_epoch, args.max_epoch))
print ('Training - ACC: {:.3f} '
'| LOSS: {:.3f} '
'| lr : {:.3f} '
config = load_config(args.config)
train_gen = EpisodeGenerator(args.dataset_dir, 'train', config)
#test_gen = EpisodeGenerator(args.dataset_dir, 'test', config)
if args.train:
max_iter = train_gen.dataset_size[args.dataset_name] * args.max_epoch \
// (nway * qsize)
show_step = args.show_epoch * max_iter // args.max_epoch
save_step = args.save_epoch * max_iter // args.max_epoch
avger = np.zeros([4])
for i in range(1, max_iter + 1):
stt = time.time()
cur_epoch = i * (
nway * qsize) // train_gen.dataset_size[args.dataset_name]
lr = args.lr if i < 0.7 * max_iter else args.lr * .1
sx, sy, qx, qy = train_gen.get_episode(
nway, kshot, qsize, dataset_name=args.dataset_name)
fd = {\
protonet.inputs['sx']: sx,
protonet.inputs['qx']: qx,
protonet.inputs['qy']: qy,
lr_ph: lr}
p1, p2, _ = sess.run([acc, loss, train_op], fd)
avger += [p1, p2, 0, time.time() - stt]
if i % show_step == 0 and i != 0:
avger /= show_step
print ('========= {:15s} epoch : {:8d}/{} ========='\
.format(args.dataset_name, cur_epoch, args.max_epoch))
print ('Training - ACC: {:.3f} '
'| LOSS: {:.3f} '
'| lr : {:.3f} '
load_loc = os.path.join(args.model_dir,
args.project_name,
args.ens_name + '.ckpt')
saver.restore(sess, load_loc)
ep_gen = EpisodeGenerator(args.dataset_dir, 'test', config)
target_dataset = ep_gen.dataset_list
#target_dataset = ['miniImagenet']
means = np.zeros([len(TRAIN_DATASET)+2, len(target_dataset)])
stds = np.zeros([len(TRAIN_DATASET)+2, len(target_dataset)])
for tdi, dset in enumerate(target_dataset):
print ('==========TARGET : {}=========='.format(dset))
temp_results = [[] for _ in range(len(TRAIN_DATASET)+2)]
for i in range(args.max_iter):
sx, sy, qx, qy = ep_gen.get_episode(nway, kshot, qsize,
dataset_name=dset)
fd = {sx_ph: sx, qx_ph: qx, qy_ph: qy}
ps, p_acc, p_W = sess.run([preds, acc, pemb], fd)
prediction = np.argmax(ps, axis=2) # (5, 150)
for pn, p in enumerate(ps):
temp_p = np.argmax(p, 1)
temp_results[pn].append(np.mean(temp_p == np.argmax(qy, 1)))
temp_results[pn+1].append(np.mean(np.argmax(np.mean(ps,0),1)\
== np.argmax(qy,1)))
temp_results[pn+2].append(p_acc)
for i in range(len(TRAIN_DATASET)):
print ('model trained on {:10s} - Acc {:.3f}'.format(\
TRAIN_DATASET[i], np.mean(temp_results[i])))
means[i, tdi] = np.mean(temp_results[i])
vlist = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
for i, v in enumerate(vlist):
print(i, v)
if args.train:
avger = np.zeros([4])
for i in range(1, args.max_iter + 1):
stt = time.time()
lr = args.meta_lr if i < 0.7 * args.max_iter else args.meta_lr * .1
sx = []
sy = []
qx = []
qy = []
for _ in range(args.meta_batch_size):
sxt, syt, qxt, qyt = ep_train.get_episode(
nway, test_kshot, qsize)
sx.append(sxt)
sy.append(syt)
qx.append(qxt)
qy.append(qyt)
# qx : (metabatchsize, nk, img_size)
# sx : (metabatchsize, nq, img_size)
ip = mamlnet.inputs
feed_dict = {
ip['sx']: sx,
ip['sy']: sy,
ip['qx']: qx,
ip['qy']: qy,
ip['lr_alpha']: args.inner_lr,
ip['lr_beta']: lr
lr = args.lr if i < 0.7 * max_iter else args.lr * .1
sx, sy, qx, qy = ep_queue.get()
fd = {sx_ph: sx, qx_ph: qx, qy_ph: qy, lr_ph: lr, isTr: True}
p1, p2, _ = sess.run([acc, loss, train_op], fd)
avger += [p1, p2, 0, time.time() - stt]
if i % show_step == 0 and i != 0:
avger /= show_step
print('epoch : {:8d}/{} ACC: {:.3f} | LOSS: {:.3f} | lr : {:.3f} | in {:.2f} secs'\
.format(cur_epoch, args.max_epoch, avger[0],
avger[1], lr, avger[3]*show_step))
avger[:] = 0
if i % save_step == 0 and i != 0:
out_loc = os.path.join(
args.model_dir, # models/
args.model_name, # bline/
'all_datasets.ckpt') # cifar100.ckpt
model.save(saver, sess, out_loc)
for test_set_name in TEST_DATASETS:
test_accs = []
for j in range(TEST_NUM_EPISODES):
sx_t, sy_t, qx_t, qy_t = ep_gen_test.get_episode(
nway, kshot, qsize, test_set_name)
fd = {sx_ph: sx_t, qx_ph: qx_t, qy_ph: qy_t, isTr: False}
acc_ = sess.run(acc, fd)
test_accs.append(acc_)
print('datset name: {}, test_acc: {:.3f} {:.5f}'.format(
test_set_name, np.mean(test_accs), np.std(test_accs)))
loaders[tn].restore(sess, saved_loc)
print('model_{} restored from {}'.format(tn, saved_loc))
else:
out_loc = os.path.join(args.model_dir, args.project_name,
args.ens_name + '.ckpt')
saver.restore(sess, out_loc)
ep_gen = EpisodeGenerator(args.dataset_dir, 'val', config)
ep_test = EpisodeGenerator(args.dataset_dir, 'test', config)
avger = np.zeros([4])
np.set_printoptions(precision=3, suppress=False)
for i in range(1, args.max_iter + 1):
stt = time.time()
lr = args.lr if i < 0.7 * args.max_iter else args.lr * .1
sx, sy, qx, qy = ep_gen.get_episode(nway,
kshot,
qsize,
printname=False)
bedt = time.time() - stt
fd = {sx_ph: sx, qx_ph: qx, qy_ph: qy, lr_ph: lr}
loss_val, acc_val, W_val, _ = sess.run([loss, acc, fe, train_op], fd)
avger += [loss_val, acc_val, bedt, time.time() - stt]
if i % args.show_step == 0 and i != 0:
avger /= args.show_step
print ('step : {:8d}/{} loss: {:.3f} | acc: {:.3f} | batchtime: {:.2f} | in {:.2f} secs'\
.format(i, args.max_iter, avger[0],
avger[1], avger[2]*args.show_step, avger[3]*args.show_step))
avger[:] = 0
# print ('+'*30)