def evaluate(args, model, graph, labels, train_idx, val_idx, test_idx, use_labels, loss_fcn, evaluator):
model.eval()
feat = graph.ndata["feat"]
if use_labels:
feat = add_labels(feat, labels, train_idx, n_classes, device)
pred = model(graph, feat)
if args.n_label_iters > 0 and args.use_labels:
unlabel_idx = torch.cat([val_idx, test_idx])
for _ in range(args.n_label_iters):
feat[unlabel_idx, -n_classes:] = F.softmax(pred[unlabel_idx], dim=-1)
pred = model(graph, feat)
train_loss = loss_fcn(pred[train_idx], labels[train_idx])
val_loss = loss_fcn(pred[val_idx], labels[val_idx])
test_loss = loss_fcn(pred[test_idx], labels[test_idx])
return (
compute_acc(pred[train_idx], labels[train_idx], evaluator),
compute_acc(pred[val_idx], labels[val_idx], evaluator),
compute_acc(pred[test_idx], labels[test_idx], evaluator),
train_loss,
val_loss,
test_loss,
pred
)
def validate(epoch, model, device, dataloader, criterion, args, writer):
""" Test loop, print metrics """
progbar = tqdm(total=len(dataloader), desc='Val')
loss_record = utils.RunningAverage()
acc_record = utils.RunningAverage()
model.eval()
with torch.no_grad():
# for batch_idx, (data,label,_,_) in enumerate(tqdm(islice(dataloader,10))):
for batch_idx, (data, label, _, _) in enumerate(tqdm(dataloader)):
data, label = data.to(device), label.to(device)
output = model(data)
loss = criterion(output, label)
# measure accuracy and record loss
acc = utils.compute_acc(output, label)
# acc_record.update(100 * acc[0].item())
acc_record.update(100 * acc[0].item() / data.size(0))
loss_record.update(loss.item())
#print('val Step: {}/{} Loss: {:.4f} \t Acc: {:.4f}'.format(batch_idx,len(dataloader), loss_record(), acc_record()))
progbar.set_description('Val (loss=%.4f)' % (loss_record()))
progbar.update(1)
writer.add_scalar('validation/Loss_epoch', loss_record(), epoch)
writer.add_scalar('validation/Acc_epoch', acc_record(), epoch)
return loss_record(), acc_record()
def train(self,
sess,
dataset,
generate_session=None,
is_train=True,
ftest_name=FLAGS['agn_output_file'].value):
st, ed, loss, acc, acc_1 = 0, 0, [], [], []
if generate_session:
dataset = dataset + generate_session
print("Get %s data:len(dataset) is %d " %
("training" if is_train else "testing", len(dataset)))
if not is_train:
fout = open(ftest_name, "w")
fout.close()
while ed < len(dataset):
st, ed = ed, ed + FLAGS['batch_size'].value if ed + \
FLAGS['batch_size'].value < len(dataset) else len(dataset)
batch_data = gen_batched_data(dataset[st:ed])
outputs = self.step_decoder(
sess, batch_data, forward_only=False if is_train else True)
loss.append(outputs[0])
predict_id = outputs[1] # [batch_size, length, 10]
tmp_acc, tmp_acc_1 = compute_acc(batch_data["aims"],
predict_id,
batch_data["rec_lists"],
batch_data["rec_mask"],
batch_data["purchase"],
ftest_name=ftest_name,
output=(not is_train))
acc.append(tmp_acc)
acc_1.append(tmp_acc_1)
if is_train:
sess.run(self.epoch_add_op)
return np.mean(loss), np.mean(acc), np.mean(acc_1)
def test(self):
self.test_net.load_state_dict(self.train_net.state_dict())
DB = self.gen_batch()
seq = Variable(Tensor(DB[0]))
seq_m = Variable(torch.LongTensor(DB[1].astype('int64')))
target = Variable(Tensor(DB[2]))
label = Variable(torch.LongTensor(DB[3].astype('int64')))
pointer = self.test_net(seq, seq_m, target)
loss = utils.compute_loss(pointer, label, target)
acc = utils.compute_acc(pointer, label)
return pointer, loss, acc, label
def test(val_dir=data_dir, val_text_dir=text_dir):
g = model.Graph(is_training=True)
print('loading validation data, please wait---------------------', 'end= ')
val_feeder = utils.DataIterator2(data_dir=val_dir, text_dir=val_text_dir)
print('***************get image: ', val_feeder.size)
num_val_samples = val_feeder.size
num_val_per_epoch = int(num_val_samples / FLAGS.batch_size)
config = tf.ConfigProto(log_device_placement=False,
allow_soft_placement=False)
config.gpu_options.allow_growth = True
with tf.Session(graph=g.graph, config=config) as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(tf.global_variables(), max_to_keep=100)
ckpt = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
if ckpt:
saver.restore(sess, ckpt)
print('restore from ckpt{}'.format(ckpt))
else:
print('cannot restore')
print(
'=============================begin testing============================='
)
if True:
if True:
if True:
acc_avg = 0.0
for cur_batch_cv in range(num_val_per_epoch):
print(num_val_per_epoch)
index_cv = []
for i in range(FLAGS.batch_size):
index_cv.append(cur_batch_cv * FLAGS.batch_size +
i)
#print("index_cv",index_cv)
val_inputs, val_seq_len, val_labels = val_feeder.input_index_generate_batch(
index_cv)
val_feed = {
g.inputs: val_inputs,
g.labels: val_labels,
g.keep_prob_cv: 1
}
predict_word_index, lr = sess.run(
[g.logits, g.learning_rate], val_feed)
print(val_labels[0], predict_word_index[0])
acc = utils.compute_acc(val_labels, predict_word_index)
acc_avg += acc
acc_avg = acc_avg / num_val_per_epoch
print("acc", acc_avg)
def train(self):
DB = self.gen_batch()
seq = Variable(Tensor(DB[0]))
seq_m = Variable(torch.LongTensor(DB[1].astype('int64')))
target = Variable(Tensor(DB[2]))
label = Variable(torch.LongTensor(DB[3].astype('int64')))
pointer = self.train_net(seq, seq_m, target)
loss = utils.compute_loss(pointer, label, target)
acc = utils.compute_acc(pointer, label)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
return pointer, loss, acc, label
def eval(model, criterion, optimizer, loader, batch_size, epoch_it, rep):
model.eval()
loss_collect = []
metric_collect = []
if rep == 'occ':
sigmoid = torch.nn.Sigmoid()
with tqdm(total=int(len(loader)), ascii=True) as pbar:
with torch.no_grad():
for mbatch in loader:
img_input, points_input, values = mbatch
img_input = Variable(img_input).cuda()
points_input = Variable(points_input).cuda()
values = Variable(values).cuda()
optimizer.zero_grad()
logits = model(points_input, img_input)
loss = criterion(logits, values)
loss_collect.append(loss.data.cpu().item())
if rep == 'occ':
logits = sigmoid(logits)
iou = utils.compute_iou(logits.detach().cpu().numpy(), \
values.detach().cpu().numpy())
metric_collect.append(iou)
elif rep == 'sdf':
# acc_sign is sign IoU
# acc_thres is accuracy within a threshold
# More detail explanation in utils.py
acc_sign, acc_thres, iou = utils.compute_acc(\
logits.detach().cpu().numpy(), \
values.detach().cpu().numpy())
metric_collect.append([acc_sign, acc_thres, iou])
pbar.update(1)
mean_loss = np.mean(np.array(loss_collect))
if rep == 'occ':
mean_metric = np.mean(np.concatenate(metric_collect))
mean_metric = [mean_metric]
else:
mean_metric = np.mean(np.array(metric_collect), axis=0)
return mean_loss, mean_metric
def test(model, device, dataloader, criterion, args):
""" Test loop, print metrics """
loss_record = utils.RunningAverage()
acc_record = utils.RunningAverage()
model.eval()
with torch.no_grad():
# for batch_idx, (data,label,_,_) in enumerate(tqdm(islice(dataloader,10))):
for batch_idx, (data, label, _, _) in enumerate(tqdm(dataloader)):
data, label = data.to(device), label.to(device)
output = model(data)
loss = criterion(output, label)
# measure accuracy and record loss
acc = utils.compute_acc(output, label)
# acc_record.update(100 * acc[0].item())
acc_record.update(100 * acc[0].item() / data.size(0))
loss_record.update(loss.item())
# print('Test Step: {}/{} Loss: {:.4f} \t Acc: {:.4f}'.format(batch_idx,len(dataloader), loss_record(), acc_record()))
return loss_record(), acc_record()
def train_disc(
disc, gen, data, device, batch_size, real_label, nz,
fake_label, criterion_src, criterion_cls, optimD
):
# Real Images
disc.zero_grad()
real_image, real_class = data
real_image = real_image.to(device)
real_class = real_class.to(device)
src_label = torch.full((batch_size,), real_label, device=device)
cls_label = real_class.view(batch_size,)
src, cls = disc(real_image)
errD_src_real = criterion_src(src, src_label)
errD_cls_real = criterion_cls(cls, cls_label)
errD_real = errD_src_real + errD_cls_real
errD_real.backward()
D_x = src.mean().item()
accuracy = compute_acc(cls, cls_label)
# Fake Images
noise = torch.randn(batch_size, nz, 1, 1, device=device)
cls_label = torch.randint(0, 2, (batch_size,), device=device)
src_label.fill_(fake_label)
fake = gen(noise)
src, cls = disc(fake.detach())
errD_src_fake = criterion_src(src, src_label)
errD_cls_fake = criterion_cls(cls, cls_label)
errD_fake = errD_src_fake + errD_cls_fake
errD_fake.backward()
D_G_z1 = src.mean().item()
errD = errD_real + errD_fake
optimD.step()
return accuracy, errD.item(), D_x, D_G_z1, fake
def train(args, model, graph, labels, train_idx, val_idx, test_idx, optimizer, teacher_output, loss_fcn, evaluator, epoch=1):
model.train()
feat = graph.ndata["feat"]
if args.use_labels:
mask = torch.rand(train_idx.shape) < args.mask_rate
train_labels_idx = train_idx[mask]
train_pred_idx = train_idx[~mask]
feat = add_labels(feat, labels, train_labels_idx, n_classes, device)
else:
mask = torch.rand(train_idx.shape) < args.mask_rate
# We change mask to ~mask to match previous definition
train_pred_idx = train_idx[~mask]
optimizer.zero_grad()
pred = model(graph, feat)
if args.n_label_iters > 0 and args.use_labels:
unlabel_idx = torch.cat([train_pred_idx, val_idx, test_idx])
for _ in range(args.n_label_iters):
pred = pred.detach()
torch.cuda.empty_cache()
# unlabel_probs = F.softmax(pred[unlabel_idx], dim=-1)
# unlabel_preds = torch.argmax(unlabel_probs, dim=-1)
# confident_unlabel_idx = unlabel_idx[unlabel_probs.max(dim=-1)[0] > 0.7]
feat[unlabel_idx, -n_classes:] = F.softmax(pred[unlabel_idx], dim=-1)
pred = model(graph, feat)
loss = loss_fcn(pred[train_pred_idx], labels[train_pred_idx])
if args.mode == "student":
loss_kd = loss_kd_only(pred, teacher_output, args.temp)
loss = loss*(1-args.alpha) + loss_kd*args.alpha
loss.backward()
optimizer.step()
return compute_acc(pred[train_idx], labels[train_idx], evaluator), loss
def forward(self, x_shot, y_shot, x_query, y_query):
x_all = torch.cat([x_shot, x_query], dim=0)
x_all = self.encoder(x_all)
x_shot, x_query = x_all[:len(x_shot)], x_all[-len(x_query):]
n_way = int(y_shot.max()) + 1
proto = []
for c in range(n_way):
ind = []
for i, y in enumerate(y_shot):
if int(y) == c:
ind.append(i)
proto.append(x_shot[ind].mean(dim=0))
proto = torch.stack(proto)
logits = utils.compute_logits(x_query,
proto,
metric='cos',
temp=self.temp)
loss = F.cross_entropy(logits, y_query)
acc = utils.compute_acc(logits, y_query)
return loss, acc
def train():
best_acc = 0
for epoch in range(start_epoch, args.num_epochs + 1):
if args.lr_decay < 1:
lr = args.lr * args.lr_decay**max(epoch - 1, 0)
adjust_learning_rate(optimizer, lr)
else:
lr = args.lr
loss_avg, acc_avg = 0, 0
total = len(train_dataloader)
batch_N_prev = torch.zeros((args.batch_size, 1)).long().to(device) + \
train_dataloader.eof_idxN
batch_T_voc_prev = torch.zeros((args.batch_size, 1)).long().to(device) +\
vocab_manager.eof_idxT_in
batch_P_prev = torch.zeros((args.batch_size, 1)).long().to(device)
hs_prev = torch.zeros(
(args.batch_size, 1, model.hidden_size)).to(device)
hc_prev = [torch.zeros((1, 1, 1)).to(device),\
torch.zeros((1, 1, 1)).to(device)]
if args.anonym is not None:
dyn_embs = [torch.zeros((args.batch_size, \
model.decoder.vocab_size_dyn)).long().to(device)-1,\
torch.zeros((0, 1, \
model.decoder.embedding_sizeT_dyn)).float().to(device),\
torch.zeros((0, 1, \
model.decoder.embedding_sizeT_dyn)).float().to(device)]
batch_T_raw_prev = batch_T_voc_prev
model.train()
vocab_manager.reset()
for i, batch in enumerate(tqdm(train_dataloader)):
batch_N, batch_T_raw, batch_T_attn, batch_P = batch
batch_N, batch_T_raw, batch_T_attn, batch_P = \
batch_N.to(device), \
batch_T_raw.to(device), \
batch_T_attn.to(device), \
batch_P.to(device)
batch_T_voc_in, batch_T_voc_out = vocab_manager.get_batch_T_voc(
batch_T_raw)
optimizer.zero_grad()
forward_result = model(batch_N, batch_T_voc_in, batch_T_voc_out, \
batch_T_attn, batch_P, \
batch_N_prev, batch_T_voc_prev, batch_P_prev,\
hs_prev, hc_prev, \
dyn_embs if args.anonym else None)
if args.anonym is None:
loss, ans, hs_, hc_ = forward_result
else:
loss, ans, hs_, hc_, dyn_embs = forward_result
loss_avg += loss.item()
acc = compute_acc(ans, batch_T_raw, batch_T_raw_prev, \
vocab_manager.vocab_size_out,
vocab_manager.vocab_size_out_cl,\
vocab_manager.unk_idx_out,\
vocab_manager.eof_idxT_out)
acc_avg += acc.item()
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), \
args.grad_clip)
optimizer.step()
batch_N_prev = batch_N
batch_T_voc_prev = batch_T_voc_in
batch_P_prev = batch_P
batch_T_raw_prev = batch_T_raw
hs_prev = hs_
hc_prev = hc_
if (i + 1) % 100 == 0 and not args.no_verbose:
prints = 'temp_loss: %f, temp_acc: %f' % (loss_avg /
(i + 1), acc_avg /
(i + 1))
if args.save_verbose:
log.print(prints)
else:
print(prints, flush=True)
if args.test_code and i > 3:
break
train_loss = loss_avg / total
train_acc = acc_avg / total
batch_N_prev = torch.zeros((args.batch_size, 1)).long().to(device) + \
train_dataloader.eof_idxN
batch_T_voc_prev = torch.zeros((args.batch_size, 1)).long().to(device) +\
vocab_manager.eof_idxT_in
batch_P_prev = torch.zeros((args.batch_size, 1)).long().to(device)
hs_prev = torch.zeros(
(args.batch_size, 1, model.hidden_size)).to(device)
hc_prev = [torch.zeros((1, 1, 1)).to(device),\
torch.zeros((1, 1, 1)).to(device)]
if args.anonym is not None:
dyn_embs = [torch.zeros((args.batch_size, \
model.decoder.vocab_size_dyn)).long().to(device)-1,\
torch.zeros((0, 1, \
model.decoder.embedding_sizeT_dyn)).float().to(device),\
torch.zeros((0, 1, \
model.decoder.embedding_sizeT_dyn)).float().to(device)]
batch_T_raw_prev = batch_T_voc_prev
with torch.no_grad():
model.eval()
total_acc = 0.
loss_eval = 0.
vocab_manager.reset()
for i, batch in enumerate(tqdm(test_dataloader)):
batch_N, batch_T_raw, batch_T_attn, batch_P = batch
batch_N, batch_T_raw, batch_T_attn, batch_P = \
batch_N.to(device), \
batch_T_raw.to(device), \
batch_T_attn.to(device), \
batch_P.to(device)
batch_T_voc_in, batch_T_voc_out = vocab_manager.get_batch_T_voc(
batch_T_raw)
forward_result = model(batch_N, batch_T_voc_in, batch_T_voc_out, \
batch_T_attn, batch_P, \
batch_N_prev, batch_T_voc_prev, batch_P_prev,\
hs_prev, hc_prev,\
dyn_embs if args.anonym else None)
if args.anonym is None:
loss, ans, hs_, hc_ = forward_result
else:
loss, ans, hs_, hc_, dyn_embs = forward_result
loss_eval += loss.item()
acc = compute_acc(ans, batch_T_raw, batch_T_raw_prev, \
vocab_manager.vocab_size_out,
vocab_manager.vocab_size_out_cl,\
vocab_manager.unk_idx_out,\
vocab_manager.eof_idxT_out)
total_acc += acc.item()
batch_N_prev = batch_N
batch_T_voc_prev = batch_T_voc_in
batch_P_prev = batch_P
batch_T_raw_prev = batch_T_raw
hs_prev = hs_
hc_prev = hc_
if args.test_code:
break
total_acc /= len(test_dataloader)
loss_eval /= len(test_dataloader)
values = [lr, train_loss, train_acc, \
loss_eval, total_acc]
for (k, _), v in zip(fmt_list, values):
log.add(epoch, **{k: v})
log.iter_info()
log.save(silent=True)
if not args.not_save_weights:
if (epoch + 1) % args.save_fq == 0:
torch.save(
{
'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
'epoch': epoch
}, log.path + '/model_epoch_%04d.cpt' % (epoch))
elif total_acc > best_acc:
torch.save(
{
'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
'epoch': epoch
}, log.path + '/best_model.cpt')
if total_acc > best_acc:
best_acc = total_acc
if args.test_code:
break
def main(config):
svname = args.name
if svname is None:
svname = 'classifier_{}'.format(config['train_dataset'])
svname += '_' + config['model_args']['encoder']
clsfr = config['model_args']['classifier']
if clsfr != 'linear-classifier':
svname += '-' + clsfr
if args.tag is not None:
svname += '_' + args.tag
save_path = os.path.join('./save', svname)
utils.ensure_path(save_path)
utils.set_log_path(save_path)
writer = SummaryWriter(os.path.join(save_path, 'tensorboard'))
yaml.dump(config, open(os.path.join(save_path, 'config.yaml'), 'w'))
#### Dataset ####
# train
train_dataset = datasets.make(config['train_dataset'],
**config['train_dataset_args'])
augmentations = [
transforms.Compose([
transforms.RandomHorizontalFlip(p=0.5),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
]),
transforms.Compose([
transforms.RandomResizedCrop(size=(80, 80),
scale=(0.08, 1.0),
ratio=(0.75, 1.3333)),
transforms.RandomHorizontalFlip(p=0.5),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
]),
transforms.Compose([
transforms.RandomRotation(35),
transforms.RandomHorizontalFlip(p=0.5),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
]),
transforms.Compose([
transforms.ColorJitter(0.4, 0.4, 0.4, 0.1),
transforms.RandomHorizontalFlip(p=0.5),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
]),
transforms.Compose([
transforms.RandomResizedCrop(size=(80, 80),
scale=(0.08, 1.0),
ratio=(0.75, 1.3333)),
transforms.RandomRotation(35),
transforms.RandomHorizontalFlip(p=0.5),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
]),
transforms.Compose([
transforms.RandomRotation(35),
transforms.ColorJitter(0.4, 0.4, 0.4, 0.1),
transforms.RandomHorizontalFlip(p=0.5),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
]),
transforms.Compose([
transforms.RandomResizedCrop(size=(80, 80),
scale=(0.08, 1.0),
ratio=(0.75, 1.3333)),
transforms.ColorJitter(0.4, 0.4, 0.4, 0.1),
transforms.RandomHorizontalFlip(p=0.5),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
]),
transforms.Compose([
transforms.RandomRotation(35),
transforms.RandomResizedCrop(size=(80, 80),
scale=(0.08, 1.0),
ratio=(0.75, 1.3333)),
transforms.ColorJitter(0.4, 0.4, 0.4, 0.1),
transforms.RandomHorizontalFlip(p=0.5),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
]
train_dataset.transform = augmentations[int(config['_a'])]
print(train_dataset.transform)
print("_a", config['_a'])
input("Continue with these augmentations?")
train_loader = DataLoader(train_dataset,
config['batch_size'],
shuffle=True,
num_workers=0,
pin_memory=True)
utils.log('train dataset: {} (x{}), {}'.format(train_dataset[0][0].shape,
len(train_dataset),
train_dataset.n_classes))
if config.get('visualize_datasets'):
utils.visualize_dataset(train_dataset, 'train_dataset', writer)
# val
if config.get('val_dataset'):
eval_val = True
val_dataset = datasets.make(config['val_dataset'],
**config['val_dataset_args'])
val_loader = DataLoader(val_dataset,
config['batch_size'],
num_workers=0,
pin_memory=True)
utils.log('val dataset: {} (x{}), {}'.format(val_dataset[0][0].shape,
len(val_dataset),
val_dataset.n_classes))
if config.get('visualize_datasets'):
utils.visualize_dataset(val_dataset, 'val_dataset', writer)
else:
eval_val = False
# few-shot eval
if config.get('fs_dataset'):
ef_epoch = config.get('eval_fs_epoch')
if ef_epoch is None:
ef_epoch = 5
eval_fs = True
fs_dataset = datasets.make(config['fs_dataset'],
**config['fs_dataset_args'])
utils.log('fs dataset: {} (x{}), {}'.format(fs_dataset[0][0].shape,
len(fs_dataset),
fs_dataset.n_classes))
if config.get('visualize_datasets'):
utils.visualize_dataset(fs_dataset, 'fs_dataset', writer)
n_way = 5
n_query = 15
n_shots = [1, 5]
fs_loaders = []
for n_shot in n_shots:
fs_sampler = CategoriesSampler(fs_dataset.label,
200,
n_way,
n_shot + n_query,
ep_per_batch=4)
fs_loader = DataLoader(fs_dataset,
batch_sampler=fs_sampler,
num_workers=0,
pin_memory=True)
fs_loaders.append(fs_loader)
else:
eval_fs = False
########
#### Model and Optimizer ####
if config.get('load'):
model_sv = torch.load(config['load'])
model = models.load(model_sv)
else:
model = models.make(config['model'], **config['model_args'])
if eval_fs:
fs_model = models.make('meta-baseline', encoder=None)
fs_model.encoder = model.encoder
if config.get('_parallel'):
model = nn.DataParallel(model)
if eval_fs:
fs_model = nn.DataParallel(fs_model)
utils.log('num params: {}'.format(utils.compute_n_params(model)))
optimizer, lr_scheduler = utils.make_optimizer(model.parameters(),
config['optimizer'],
**config['optimizer_args'])
########
max_epoch = config['max_epoch']
save_epoch = config.get('save_epoch')
max_va = 0.
timer_used = utils.Timer()
timer_epoch = utils.Timer()
for epoch in range(1, max_epoch + 1 + 1):
if epoch == max_epoch + 1:
if not config.get('epoch_ex'):
break
train_dataset.transform = train_dataset.default_transform
print(train_dataset.transform)
train_loader = DataLoader(train_dataset,
config['batch_size'],
shuffle=True,
num_workers=0,
pin_memory=True)
timer_epoch.s()
aves_keys = ['tl', 'ta', 'vl', 'va']
if eval_fs:
for n_shot in n_shots:
aves_keys += ['fsa-' + str(n_shot)]
aves = {k: utils.Averager() for k in aves_keys}
# train
model.train()
writer.add_scalar('lr', optimizer.param_groups[0]['lr'], epoch)
for data, label in tqdm(train_loader, desc='train', leave=False):
# for data, label in train_loader:
data, label = data.cuda(), label.cuda()
logits = model(data)
loss = F.cross_entropy(logits, label)
acc = utils.compute_acc(logits, label)
optimizer.zero_grad()
loss.backward()
optimizer.step()
aves['tl'].add(loss.item())
aves['ta'].add(acc)
logits = None
loss = None
# eval
if eval_val:
model.eval()
for data, label in tqdm(val_loader, desc='val', leave=False):
data, label = data.cuda(), label.cuda()
with torch.no_grad():
logits = model(data)
loss = F.cross_entropy(logits, label)
acc = utils.compute_acc(logits, label)
aves['vl'].add(loss.item())
aves['va'].add(acc)
if eval_fs and (epoch % ef_epoch == 0 or epoch == max_epoch + 1):
fs_model.eval()
for i, n_shot in enumerate(n_shots):
np.random.seed(0)
for data, _ in tqdm(fs_loaders[i],
desc='fs-' + str(n_shot),
leave=False):
x_shot, x_query = fs.split_shot_query(data.cuda(),
n_way,
n_shot,
n_query,
ep_per_batch=4)
label = fs.make_nk_label(n_way, n_query,
ep_per_batch=4).cuda()
with torch.no_grad():
logits = fs_model(x_shot, x_query).view(-1, n_way)
acc = utils.compute_acc(logits, label)
aves['fsa-' + str(n_shot)].add(acc)
# post
if lr_scheduler is not None:
lr_scheduler.step()
for k, v in aves.items():
aves[k] = v.item()
t_epoch = utils.time_str(timer_epoch.t())
t_used = utils.time_str(timer_used.t())
t_estimate = utils.time_str(timer_used.t() / epoch * max_epoch)
if epoch <= max_epoch:
epoch_str = str(epoch)
else:
epoch_str = 'ex'
log_str = 'epoch {}, train {:.4f}|{:.4f}'.format(
epoch_str, aves['tl'], aves['ta'])
writer.add_scalars('loss', {'train': aves['tl']}, epoch)
writer.add_scalars('acc', {'train': aves['ta']}, epoch)
if eval_val:
log_str += ', val {:.4f}|{:.4f}'.format(aves['vl'], aves['va'])
writer.add_scalars('loss', {'val': aves['vl']}, epoch)
writer.add_scalars('acc', {'val': aves['va']}, epoch)
if eval_fs and (epoch % ef_epoch == 0 or epoch == max_epoch + 1):
log_str += ', fs'
for n_shot in n_shots:
key = 'fsa-' + str(n_shot)
log_str += ' {}: {:.4f}'.format(n_shot, aves[key])
writer.add_scalars('acc', {key: aves[key]}, epoch)
if epoch <= max_epoch:
log_str += ', {} {}/{}'.format(t_epoch, t_used, t_estimate)
else:
log_str += ', {}'.format(t_epoch)
utils.log(log_str)
if config.get('_parallel'):
model_ = model.module
else:
model_ = model
training = {
'epoch': epoch,
'optimizer': config['optimizer'],
'optimizer_args': config['optimizer_args'],
'optimizer_sd': optimizer.state_dict(),
}
save_obj = {
'file': __file__,
'config': config,
'model': config['model'],
'model_args': config['model_args'],
'model_sd': model_.state_dict(),
'training': training,
}
if epoch <= max_epoch:
torch.save(save_obj, os.path.join(save_path, 'epoch-last.pth'))
if (save_epoch is not None) and epoch % save_epoch == 0:
torch.save(
save_obj,
os.path.join(save_path, 'epoch-{}.pth'.format(epoch)))
if aves['va'] > max_va:
max_va = aves['va']
torch.save(save_obj, os.path.join(save_path, 'max-va.pth'))
else:
torch.save(save_obj, os.path.join(save_path, 'epoch-ex.pth'))
writer.flush()
# top_k = 1
top_k = [1,3,5]
n_samples = [200,500,1000,2000] #TODO: 5,000 in sleep
for n in n_samples:
for k in top_k:
datafile = '../data/training_sub_' + str(n) + '.json'
datafile = '../data/training_sub_' + str(n) + '.json'
datafile = '../data/training_sub_' + str(n) + '.json'
# prepare training subset
with open(datafile, 'r') as f:
data = json.load(f)
# prepare topics
topics = extract_topics(topic_file)
# prepare targets
targets = extract_targets(target_file)
# find best matching segments for all queries
best_segments = find_top_k_segments(topics, data, embedder, k)
# print(best_segments)
# print accuracy
acc = compute_acc(best_segments, targets)
print('n_samples:', n , 'top_k:', k, 'acc:', acc )
def train_batch(self, images, labels):
# optimize D
############################
# (1) Update D network: maximize log(D(x)) + log(1 - D(G(z)))
###########################
for _ in range(1):
# train with real
self.netD.zero_grad()
batch_size = images.size(0)
if params.cuda:
images = images.cuda()
labels = labels.cuda()
self.input.data.resize_as_(images).copy_(images)
self.dis_label.data.resize_(batch_size).fill_(self.real_label)
self.aux_label.data.resize_(batch_size).copy_(labels)
dis_output, aux_output = self.netD(self.input)
dis_errD_real = self.dis_criterion(dis_output, self.dis_label)
aux_errD_real = self.aux_criterion(aux_output, self.aux_label)
errD_real = dis_errD_real + aux_errD_real
errD_real.backward()
D_x = dis_output.data.mean()
# compute the current classification accuracy
accuracy = compute_acc(aux_output, self.aux_label)
# train with fake
self.noise.data.resize_(batch_size, self.nz, 1, 1).normal_(0, 1)
labels = np.random.randint(0, self.num_classes, batch_size)
noise_ = np.random.normal(0, 1, (batch_size, self.nz))
class_onehot = np.zeros((batch_size, self.num_classes))
class_onehot[np.arange(batch_size), labels] = 1
noise_[np.arange(batch_size), :self.num_classes] = class_onehot[
np.arange(batch_size)]
noise_ = (torch.from_numpy(noise_))
self.noise.data.copy_(noise_.view(batch_size, self.nz, 1, 1))
self.aux_label.data.resize_(batch_size).copy_(
torch.from_numpy(labels))
fake = self.netG(self.noise)
self.dis_label.data.fill_(self.fake_label)
dis_output, aux_output = self.netD(fake.detach())
dis_errD_fake = self.dis_criterion(dis_output, self.dis_label)
aux_errD_fake = self.aux_criterion(aux_output, self.aux_label)
errD_fake = dis_errD_fake + aux_errD_fake
errD_fake.backward()
D_G_z1 = dis_output.data.mean()
errD = errD_real + errD_fake
self.optimizerD.step()
# optimize G
############################
# (2) Update G network: maximize log(D(G(z)))
###########################
for _ in range(1):
self.netG.zero_grad()
# fake labels are real for generator cost
self.dis_label.data.fill_(self.real_label)
dis_output, aux_output = self.netD(fake)
dis_errG = self.dis_criterion(dis_output, self.dis_label)
aux_errG = self.aux_criterion(aux_output, self.aux_label)
errG = dis_errG + aux_errG
errG.backward()
D_G_z2 = dis_output.data.mean()
# todo
#dav_loss and pert_loss
# C = 0.1
# loss_perturb = torch.mean(torch.norm(fake.view(fake.shape[0],-1),2,dim=1))
# loss_G = adv_lambda * loss_adv + pert_lambda * loss_perturb
self.optimizerG.step()
return errD, errG, D_x, D_G_z1, D_G_z2, accuracy
def main(config):
svname = args.name
if svname is None:
svname = 'pretrain-multi'
if args.tag is not None:
svname += '_' + args.tag
save_path = os.path.join('./save', svname)
utils.ensure_path(save_path)
utils.set_log_path(save_path)
writer = SummaryWriter(os.path.join(save_path, 'tensorboard'))
yaml.dump(config, open(os.path.join(save_path, 'config.yaml'), 'w'))
#### Dataset ####
def make_dataset(name):
dataset = make_md([name],
'batch', split='train', image_size=126, batch_size=256)
return dataset
ds_names = ['ilsvrc_2012', 'omniglot', 'aircraft', 'cu_birds', 'dtd', \
'quickdraw', 'fungi', 'vgg_flower']
datasets = []
for name in ds_names:
datasets.append(make_dataset(name))
iters = []
for d in datasets:
iters.append(d.make_one_shot_iterator().get_next())
to_torch_labels = lambda a: torch.from_numpy(a).long()
to_pil = transforms.ToPILImage()
augmentation = transforms.Compose([
transforms.Resize(146),
transforms.RandomResizedCrop(128),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
########
#### Model and Optimizer ####
if config.get('load'):
model_sv = torch.load(config['load'])
model = models.load(model_sv)
else:
model = models.make(config['model'], **config['model_args'])
if config.get('_parallel'):
model = nn.DataParallel(model)
utils.log('num params: {}'.format(utils.compute_n_params(model)))
optimizer, lr_scheduler = utils.make_optimizer(
model.parameters(),
config['optimizer'], **config['optimizer_args'])
########
max_epoch = config['max_epoch']
save_epoch = config.get('save_epoch')
max_va = 0.
timer_used = utils.Timer()
timer_epoch = utils.Timer()
for epoch in range(1, max_epoch + 1):
timer_epoch.s()
aves_keys = ['tl', 'ta', 'vl', 'va']
aves = {k: utils.Averager() for k in aves_keys}
# train
model.train()
writer.add_scalar('lr', optimizer.param_groups[0]['lr'], epoch)
n_batch = 915547 // 256
with tf.Session() as sess:
for i_batch in tqdm(range(n_batch)):
if random.randint(0, 1) == 0:
ds_id = 0
else:
ds_id = random.randint(1, len(datasets) - 1)
next_element = iters[ds_id]
e, cfr_id = sess.run(next_element)
data_, label = e[0], to_torch_labels(e[1])
data_ = ((data_ + 1.0) * 0.5 * 255).astype('uint8')
data = torch.zeros(256, 3, 128, 128).float()
for i in range(len(data_)):
x = data_[i]
x = to_pil(x)
x = augmentation(x)
data[i] = x
data = data.cuda()
label = label.cuda()
logits = model(data, cfr_id=ds_id)
loss = F.cross_entropy(logits, label)
acc = utils.compute_acc(logits, label)
optimizer.zero_grad()
loss.backward()
optimizer.step()
aves['tl'].add(loss.item())
aves['ta'].add(acc)
logits = None; loss = None
# post
if lr_scheduler is not None:
lr_scheduler.step()
for k, v in aves.items():
aves[k] = v.item()
t_epoch = utils.time_str(timer_epoch.t())
t_used = utils.time_str(timer_used.t())
t_estimate = utils.time_str(timer_used.t() / epoch * max_epoch)
if epoch <= max_epoch:
epoch_str = str(epoch)
else:
epoch_str = 'ex'
log_str = 'epoch {}, train {:.4f}|{:.4f}'.format(
epoch_str, aves['tl'], aves['ta'])
writer.add_scalars('loss', {'train': aves['tl']}, epoch)
writer.add_scalars('acc', {'train': aves['ta']}, epoch)
if epoch <= max_epoch:
log_str += ', {} {}/{}'.format(t_epoch, t_used, t_estimate)
else:
log_str += ', {}'.format(t_epoch)
utils.log(log_str)
if config.get('_parallel'):
model_ = model.module
else:
model_ = model
training = {
'epoch': epoch,
'optimizer': config['optimizer'],
'optimizer_args': config['optimizer_args'],
'optimizer_sd': optimizer.state_dict(),
}
save_obj = {
'file': __file__,
'config': config,
'model': config['model'],
'model_args': config['model_args'],
'model_sd': model_.state_dict(),
'training': training,
}
if epoch <= max_epoch:
torch.save(save_obj, os.path.join(save_path, 'epoch-last.pth'))
if (save_epoch is not None) and epoch % save_epoch == 0:
torch.save(save_obj, os.path.join(
save_path, 'epoch-{}.pth'.format(epoch)))
if aves['va'] > max_va:
max_va = aves['va']
torch.save(save_obj, os.path.join(save_path, 'max-va.pth'))
else:
torch.save(save_obj, os.path.join(save_path, 'epoch-ex.pth'))
writer.flush()
def main(unused_argv):
loader = Loader(base_path=None, path="/data")
datasets = loader.CUB(ratio=0.2, total_ratio=total_ratio)
model = Resnet18(batch_size=FLAGS.batch_size)
with model.graph.as_default():
model.preload()
vars = [
var for var in tf.global_variables() if var.name.startswith("conv")
]
global_step = tf.Variable(0, name='global_step', trainable=False)
learning_rate = tf.train.exponential_decay(
1e-3,
global_step=global_step,
decay_steps=5 * int(len(datasets["train"]) / FLAGS.batch_size),
decay_rate=0.1,
staircase=True)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
opt = tf.train.AdamOptimizer(learning_rate=learning_rate)
grad_and_vars = opt.compute_gradients(loss=model.loss)
for index, (grad, var) in enumerate(grad_and_vars):
if FLAGS.fine_tune:
if var.op.name.startswith(
"dense") or var.op.name.startswith("conv5"):
grad_and_vars[index] = (grad * 10.0, var)
elif FLAGS.freeze:
if var.op.name.startswith(
"conv1") or var.op.name.startswith("conv2"):
grad_and_vars[index] = (grad * 1e-3, var)
train_op = opt.apply_gradients(grad_and_vars,
global_step=global_step)
# train_op = tf.train.AdamOptimizer(learning_rate=learning_rate)\
# .minimize(loss=model.loss, global_step=global_step)
rest_vars = list(
set([var for var in tf.global_variables()]) - set(vars))
init_rest_vars = tf.variables_initializer(rest_vars)
# writer = tf.summary.FileWriter("logs/", model.graph)
# writer.flush()
# writer.close()
# vars = [var.name for var in vars]
# print('\n'.join(vars))
# import sys
# sys.exit(0)
with tf.Session(graph=model.graph) as sess:
if os.path.exists(utils.path("models/trained")):
tf.train.Saver().restore(
sess,
tf.train.latest_checkpoint(utils.path("models/trained/")))
else:
init_rest_vars.run()
tf.train.Saver(vars).restore(sess,
utils.path("models/init/models.ckpt"))
from BatchLoader import BatchLoader
LOG = utils.Log()
for epoch in range(FLAGS.num_epochs):
for phase in ('train', 'test'):
dataset = datasets[phase]
accs = utils.AverageMeter()
losses = utils.AverageMeter()
start_time = time.time()
bar = progressbar.ProgressBar()
for features, boxes, im_sizes in bar(
BatchLoader(dataset,
batch_size=FLAGS.batch_size,
pre_fetch=FLAGS.pre_fetch,
shuffle=(phase == 'train'),
op_fn=CUB_Dataset.list_to_tuple)):
boxes = utils.crop_boxes(boxes, im_sizes)
boxes = utils.box_transform(boxes, im_sizes)
if phase == 'train':
_, loss, outputs = sess.run(
[train_op, model.loss, model.fc],
feed_dict={
'features:0': features,
'boxes:0': boxes,
'training:0': phase == 'train',
})
else:
loss, outputs = sess.run(
[model.loss, model.fc],
feed_dict={
'features:0': features,
'boxes:0': boxes,
'training:0': phase == 'train',
})
acc = utils.compute_acc(outputs, boxes, im_sizes)
nsample = model.batch_size
accs.update(acc, nsample)
losses.update(loss, nsample)
LOG.add(phase, {"accu": float(acc), "loss": float(loss)})
elapsed_time = time.time() - start_time
print(
'[{}]\tEpoch: {}/{}\tLoss: {:.4f}\tAcc: {:.2%}\tTime: {:.3f}'
.format(phase, epoch, FLAGS.num_epochs, losses.avg,
accs.avg, elapsed_time))
tf.train.Saver().save(sess,
utils.path("models/trained/resnet18.ckpt"),
global_step=global_step)
if FLAGS.log_path is not None:
LOG.dump(FLAGS.log_path)
#Loss_oflstm = EMDLoss()
for i in range(EPOCH2):
TRAIN_ACC = []
VAL_ACC = []
for step, image_DATA1 in enumerate(image_loader1):
input_oflstm = model(image_DATA1['image'].to(device)).view(-1, 1,
1).float()
labels_oflstm = image_DATA1['annotations']
optimizer_oflstm.zero_grad()
output_oflstm, (hn, cn) = rnn(input_oflstm)
if output_oflstm.shape[0] != 16:
continue
TRAIN_ACC.append(
compute_acc(
labels_oflstm,
output_oflstm[FEATURE_ON_X_AXIS * FEATURE_ON_Y_AXIS -
1]).item())
loss_oflstm = Loss_oflstm(
output_oflstm[FEATURE_ON_X_AXIS * FEATURE_ON_Y_AXIS - 1].squeeze(),
labels_oflstm.float().squeeze())
# loss_oflstm = Loss_oflstm(
# output_oflstm[FEATURE_ON_X_AXIS*FEATURE_ON_Y_AXIS-1].squeeze(),
# labels_oflstm.float().squeeze(),
# Mark = 'IN')
# print(loss_oflstm)
print('Latest ACC: %.4f' % (sum(TRAIN_ACC) / len(TRAIN_ACC)))
loss_oflstm.backward()
optimizer_oflstm.step()
for step, image_DATA2 in enumerate(image_loader2):
input_oflstm = model(image_DATA2['image'].to(device)).view(-1, 1,
1).float()
def main(extractor, unique_combos_analysis,
input_data_analysis, input_gold_analysis,
input_system_gold_analysis):
"""
Main analysis function - obtain results on the input data only, input in combination with gold output, and in combination with the system answers.
"""
table_data={'input': defaultdict(dict),
'input_gold': defaultdict(dict),
'system_gold': defaultdict(dict)}
for partition in partitions:
# for partition in ['partial']:
# Setup directories
#input files
gold_prop_dir='../data/input/%s/annotation' % partition
auto_prop_dir='../systems/extracted_data/%s' % partition
# load gold file
gold_file='../data/gold/%s/participants.json' % partition
with open (gold_file, 'rb') as gf:
gold_data=json.load(gf)
# system files
baseline_dir='../data/system/%s/%s' % (extractor, partition)
profiler_dir='../data/system/%s_profiling/%s' % (extractor, partition)
# load system data
sys_data={}
for bdir in glob.glob('%s/*' % baseline_dir):
b=utils.get_filename(bdir)
ext_baseline_dir='%s/%s' % (bdir, property_set)
sys_data[b]=utils.load_system_datasets(ext_baseline_dir)
sys_data['profiler']=utils.load_system_datasets(profiler_dir)
# define input dir and property set
if extractor=='gold':
data_dir=gold_prop_dir
keys=['Ethnicity', 'Gender', 'Age', 'Religion', 'CauseOfDeath',
'Occupation']
else:
data_dir=auto_prop_dir
keys=['Residence', 'Ethnicity', 'EducationLevel', 'MedicalCondition',
'BirthPlace', 'Gender', 'Age', 'Religion', 'PastConviction',
'CauseOfDeath', 'DeathPlace', 'DeathDate', 'Name']
# 1. Analyze numbers of local contexts with unique properties
if unique_combos_analysis:
print_stars()
get_num_unique_combos(data_dir, keys)
print_stars()
input_sets=analyze_distinguishability_of_props(data_dir, keys)
# 2. Analyze distinguishability of properties
if input_data_analysis:
for filename, sets in input_sets.items():
dataset=utils.map_filename(filename, partition)
if not dataset: continue
for k, v in sets.items():
data_part=k.replace('set_', '')
table_data['input'][dataset][data_part]=len(v)
prof_perc=round(len(sets['set_profiler'])*100/len(sets['set_total']))
table_data['input'][dataset]['profiler %']=prof_perc
# 3. Analyze distribution of sameness in each of the distinguishability sets
if input_gold_analysis:
for filename, sets in input_sets.items():
dataset=utils.map_filename(filename, partition)
if not dataset: continue
for set_name, the_set in sets.items():
same, different=utils.analyze_performance_on_pairs(the_set,
gold_data)
k1=(set_name.replace('set_', ''), 'same')
table_data['input_gold'][dataset][k1]=same
k2=(set_name.replace('set_', ''), 'different')
table_data['input_gold'][dataset][k2]=different
# 4. Analyze system performance on each of the sets
if input_system_gold_analysis:
print_stars()
for s, sdata in sys_data.items():
for filename, sets in input_sets.items():
if filename not in sdata.keys():
continue
dataset=utils.map_filename(filename, partition)
if not dataset: continue
sys_predictions=sdata[filename]
for set_name, the_set in sets.items():
acc_counts, total_counts=utils.scores_vs_identity(gold_data,
sys_predictions, the_set)
gold_same_acc=utils.compute_acc(acc_counts, total_counts,
gold_same=True)
gold_diff_acc=utils.compute_acc(acc_counts, total_counts,
gold_same=False)
k1=(set_name.replace('set_', ''), s, 'same')
table_data['system_gold'][dataset][k1]=round(gold_same_acc, 2)
k2=(set_name.replace('set_', ''), s, 'different')
table_data['system_gold'][dataset][k2]=round(gold_diff_acc, 2)
print_stars()
print_tables(table_data)
def main(config):
svname = args.name
if svname is None:
svname = 'meta_{}-{}shot'.format(
config['train_dataset'], config['n_shot'])
svname += '_' + config['model'] + '-' + config['model_args']['encoder']
if args.tag is not None:
svname += '_' + args.tag
save_path = os.path.join('./save', svname)
utils.ensure_path(save_path)
utils.set_log_path(save_path)
writer = SummaryWriter(os.path.join(save_path, 'tensorboard'))
yaml.dump(config, open(os.path.join(save_path, 'config.yaml'), 'w'))
#### Dataset ####
n_way, n_shot = config['n_way'], config['n_shot']
n_query = config['n_query']
if config.get('n_train_way') is not None:
n_train_way = config['n_train_way']
else:
n_train_way = n_way
if config.get('n_train_shot') is not None:
n_train_shot = config['n_train_shot']
else:
n_train_shot = n_shot
if config.get('ep_per_batch') is not None:
ep_per_batch = config['ep_per_batch']
else:
ep_per_batch = 1
# train
train_dataset = datasets.make(config['train_dataset'],
**config['train_dataset_args'])
utils.log('train dataset: {} (x{}), {}'.format(
train_dataset[0][0].shape, len(train_dataset),
train_dataset.n_classes))
if config.get('visualize_datasets'):
utils.visualize_dataset(train_dataset, 'train_dataset', writer)
train_sampler = CategoriesSampler(
train_dataset.label, config['train_batches'],
n_train_way, n_train_shot + n_query,
ep_per_batch=ep_per_batch)
train_loader = DataLoader(train_dataset, batch_sampler=train_sampler,
num_workers=8, pin_memory=True)
# tval
if config.get('tval_dataset'):
tval_dataset = datasets.make(config['tval_dataset'],
**config['tval_dataset_args'])
utils.log('tval dataset: {} (x{}), {}'.format(
tval_dataset[0][0].shape, len(tval_dataset),
tval_dataset.n_classes))
if config.get('visualize_datasets'):
utils.visualize_dataset(tval_dataset, 'tval_dataset', writer)
tval_sampler = CategoriesSampler(
tval_dataset.label, 200,
n_way, n_shot + n_query,
ep_per_batch=4)
tval_loader = DataLoader(tval_dataset, batch_sampler=tval_sampler,
num_workers=8, pin_memory=True)
else:
tval_loader = None
# val
val_dataset = datasets.make(config['val_dataset'],
**config['val_dataset_args'])
utils.log('val dataset: {} (x{}), {}'.format(
val_dataset[0][0].shape, len(val_dataset),
val_dataset.n_classes))
if config.get('visualize_datasets'):
utils.visualize_dataset(val_dataset, 'val_dataset', writer)
val_sampler = CategoriesSampler(
val_dataset.label, 200,
n_way, n_shot + n_query,
ep_per_batch=4)
val_loader = DataLoader(val_dataset, batch_sampler=val_sampler,
num_workers=8, pin_memory=True)
########
#### Model and optimizer ####
if config.get('load'):
model_sv = torch.load(config['load'])
model = models.load(model_sv)
else:
model = models.make(config['model'], **config['model_args'])
if config.get('load_encoder'):
encoder = models.load(torch.load(config['load_encoder'])).encoder
model.encoder.load_state_dict(encoder.state_dict())
if config.get('_parallel'):
model = nn.DataParallel(model)
utils.log('num params: {}'.format(utils.compute_n_params(model)))
optimizer, lr_scheduler = utils.make_optimizer(
model.parameters(),
config['optimizer'], **config['optimizer_args'])
########
max_epoch = config['max_epoch']
save_epoch = config.get('save_epoch')
max_va = 0.
timer_used = utils.Timer()
timer_epoch = utils.Timer()
aves_keys = ['tl', 'ta', 'tvl', 'tva', 'vl', 'va']
trlog = dict()
for k in aves_keys:
trlog[k] = []
for epoch in range(1, max_epoch + 1):
timer_epoch.s()
aves = {k: utils.Averager() for k in aves_keys}
# train
model.train()
if config.get('freeze_bn'):
utils.freeze_bn(model)
writer.add_scalar('lr', optimizer.param_groups[0]['lr'], epoch)
np.random.seed(epoch)
for data, _ in tqdm(train_loader, desc='train', leave=False):
x_shot, x_query = fs.split_shot_query(
data.cuda(), n_train_way, n_train_shot, n_query,
ep_per_batch=ep_per_batch)
label = fs.make_nk_label(n_train_way, n_query,
ep_per_batch=ep_per_batch).cuda()
logits = model(x_shot, x_query).view(-1, n_train_way)
loss = F.cross_entropy(logits, label)
acc = utils.compute_acc(logits, label)
optimizer.zero_grad()
loss.backward()
optimizer.step()
aves['tl'].add(loss.item())
aves['ta'].add(acc)
logits = None; loss = None
# eval
model.eval()
for name, loader, name_l, name_a in [
('tval', tval_loader, 'tvl', 'tva'),
('val', val_loader, 'vl', 'va')]:
if (config.get('tval_dataset') is None) and name == 'tval':
continue
np.random.seed(0)
for data, _ in tqdm(loader, desc=name, leave=False):
x_shot, x_query = fs.split_shot_query(
data.cuda(), n_way, n_shot, n_query,
ep_per_batch=4)
label = fs.make_nk_label(n_way, n_query,
ep_per_batch=4).cuda()
with torch.no_grad():
logits = model(x_shot, x_query).view(-1, n_way)
loss = F.cross_entropy(logits, label)
acc = utils.compute_acc(logits, label)
aves[name_l].add(loss.item())
aves[name_a].add(acc)
_sig = int(_[-1])
# post
if lr_scheduler is not None:
lr_scheduler.step()
for k, v in aves.items():
aves[k] = v.item()
trlog[k].append(aves[k])
t_epoch = utils.time_str(timer_epoch.t())
t_used = utils.time_str(timer_used.t())
t_estimate = utils.time_str(timer_used.t() / epoch * max_epoch)
utils.log('epoch {}, train {:.4f}|{:.4f}, tval {:.4f}|{:.4f}, '
'val {:.4f}|{:.4f}, {} {}/{} (@{})'.format(
epoch, aves['tl'], aves['ta'], aves['tvl'], aves['tva'],
aves['vl'], aves['va'], t_epoch, t_used, t_estimate, _sig))
writer.add_scalars('loss', {
'train': aves['tl'],
'tval': aves['tvl'],
'val': aves['vl'],
}, epoch)
writer.add_scalars('acc', {
'train': aves['ta'],
'tval': aves['tva'],
'val': aves['va'],
}, epoch)
if config.get('_parallel'):
model_ = model.module
else:
model_ = model
training = {
'epoch': epoch,
'optimizer': config['optimizer'],
'optimizer_args': config['optimizer_args'],
'optimizer_sd': optimizer.state_dict(),
}
save_obj = {
'file': __file__,
'config': config,
'model': config['model'],
'model_args': config['model_args'],
'model_sd': model_.state_dict(),
'training': training,
}
torch.save(save_obj, os.path.join(save_path, 'epoch-last.pth'))
torch.save(trlog, os.path.join(save_path, 'trlog.pth'))
if (save_epoch is not None) and epoch % save_epoch == 0:
torch.save(save_obj,
os.path.join(save_path, 'epoch-{}.pth'.format(epoch)))
if aves['va'] > max_va:
max_va = aves['va']
torch.save(save_obj, os.path.join(save_path, 'max-va.pth'))
writer.flush()
def train(self,
sess,
dataset,
is_train=True,
ftest_name=FLAGS['env_output_file'].value):
st, ed, loss, acc, acc_1, pr_loss, pu_loss = 0, 0, [], [], [], [], []
tp, tn, fp, fn = [], [], [], []
print("Get %s data:len(dataset) is %d " %
("training" if is_train else "testing", len(dataset)))
if not is_train:
fout = open(ftest_name, "w")
fout.close()
while ed < len(dataset):
st, ed = ed, ed + FLAGS['batch_size'].value if ed + \
FLAGS['batch_size'].value < len(dataset) else len(dataset)
batch_data = gen_batched_data(dataset[st:ed])
outputs = self.step_decoder(
sess, batch_data, forward_only=False if is_train else True)
loss.append(outputs[0])
predict_index = outputs[1] # [batch_size, length, 10]
pr_loss.append(outputs[2])
pu_loss.append(outputs[3])
purchase_prob = outputs[4][:, :, 1]
tmp_acc, tmp_acc_1 = compute_acc(batch_data["aims"],
predict_index,
batch_data["rec_lists"],
batch_data["rec_mask"],
batch_data["purchase"],
ftest_name=ftest_name,
output=(not is_train))
acc.append(tmp_acc)
acc_1.append(tmp_acc_1)
if not FLAGS['use_simulated_data'].value:
all_num, true_pos, true_neg, false_pos, false_neg = 1e-6, 0., 0., 0., 0.
for b_pu, b_pu_l in zip(batch_data["purchase"], purchase_prob):
for pu, pu_l in zip(b_pu, b_pu_l):
if pu != -1.:
#print pu, pu_l
all_num += 1
if pu == 1. and pu_l > 0.5:
true_pos += 1
if pu == 1. and pu_l <= 0.5:
false_neg += 1
if pu == 0. and pu_l > 0.5:
false_pos += 1
if pu == 0. and pu_l <= 0.5:
true_neg += 1
tp.append(true_pos / all_num)
tn.append(true_neg / all_num)
fp.append(false_pos / all_num)
fn.append(false_neg / all_num)
if not FLAGS['use_simulated_data'].value:
print("Confusion matrix for purchase prediction:")
print("true positive:%.4f" % np.mean(tp),
"true negative:%.4f" % np.mean(tn))
print("false positive:%.4f" % np.mean(fp),
"false negative:%.4f" % np.mean(fn))
print("predict:p@1:%.4f%%" % (np.mean(acc_1) * 100),
"p@%d:%.4f%%" % (FLAGS['metric'].value, np.mean(acc) * 100))
if is_train:
sess.run(self.epoch_add_op)
return np.mean(loss), np.mean(pr_loss), np.mean(pu_loss), np.mean(
acc), np.mean(acc_1)
def main(config):
# dataset
dataset = datasets.make(config['dataset'], **config['dataset_args'])
utils.log('dataset: {} (x{}), {}'.format(dataset[0][0].shape, len(dataset),
dataset.n_classes))
if not args.sauc:
n_way = 5
else:
n_way = 2
n_shot, n_unlabel, n_query = args.shot, 30, 15
n_batch = 200
ep_per_batch = 4
batch_sampler = CategoriesSampler_Semi(dataset.label,
n_batch,
n_way,
n_shot,
n_unlabel,
n_query,
ep_per_batch=ep_per_batch)
loader = DataLoader(dataset,
batch_sampler=batch_sampler,
num_workers=8,
pin_memory=True)
# model
if config.get('load') is None:
model = models.make('meta-baseline', encoder=None)
else:
model = models.load(torch.load(config['load']))
if config.get('load_encoder') is not None:
encoder = models.load(torch.load(config['load_encoder'])).encoder
model.encoder = encoder
if config.get('_parallel'):
model = nn.DataParallel(model)
model.eval()
utils.log('num params: {}'.format(utils.compute_n_params(model)))
# testing
aves_keys = ['vl', 'va']
aves = {k: utils.Averager() for k in aves_keys}
test_epochs = args.test_epochs
np.random.seed(0)
va_lst = []
for epoch in range(1, test_epochs + 1):
for data, _ in tqdm(loader, leave=False):
x_shot, x_unlabel, x_query = fs.split_shot_query_semi(
data.cuda(),
n_way,
n_shot,
n_unlabel,
n_query,
ep_per_batch=ep_per_batch)
with torch.no_grad():
if not args.sauc:
logits = model(x_shot, x_unlabel, x_query).view(-1, n_way)
label = fs.make_nk_label(n_way,
n_query,
ep_per_batch=ep_per_batch).cuda()
loss = F.cross_entropy(logits, label)
acc = utils.compute_acc(logits, label)
aves['vl'].add(loss.item(), len(data))
aves['va'].add(acc, len(data))
va_lst.append(acc)
else:
x_shot = x_shot[:, 0, :, :, :, :].contiguous()
shot_shape = x_shot.shape[:-3]
img_shape = x_shot.shape[-3:]
bs = shot_shape[0]
p = model.encoder(x_shot.view(-1, *img_shape)).reshape(
*shot_shape, -1).mean(dim=1, keepdim=True)
q = model.encoder(x_query.view(-1, *img_shape)).view(
bs, -1, p.shape[-1])
p = F.normalize(p, dim=-1)
q = F.normalize(q, dim=-1)
s = torch.bmm(q, p.transpose(2, 1)).view(bs, -1).cpu()
for i in range(bs):
k = s.shape[1] // 2
y_true = [1] * k + [0] * k
acc = roc_auc_score(y_true, s[i])
aves['va'].add(acc, len(data))
va_lst.append(acc)
print('test epoch {}: acc={:.2f} +- {:.2f} (%), loss={:.4f} (@{})'.
format(epoch, aves['va'].item() * 100,
mean_confidence_interval(va_lst) * 100, aves['vl'].item(),
_[-1]))
print('-' * 5 + '>' + 'Epoch {}/{}'.format(epoch, EPOCH - 1))
print('=' * 40)
train_batch_losses = []
val_batch_losses = []
step = 0
TRAIN_ACC = []
VAL_ACC = []
for i, DATA in enumerate(train_loader):
images = DATA['image'].to(device)
labels = DATA['annotations'].to(device).float()
outputs = model(images)
step += 1
optimizer.zero_grad()
outputs = outputs.view(-1, 10, 1)
TRAIN_ACC.append(compute_acc(labels, outputs).item())
# train_losses = corLoss(labels, outputs)
# train_losses = mseLoss(labels, outputs)
# train_losses = emdLoss(labels, outputs)
# train_losses = cepLoss(labels, outputs)
train_losses = edwklLoss(labels, outputs)
train_batch_losses.append(train_losses.item())
train_losses.backward()
optimizer.step()
if (i + 1) % 1000 == 0:
print('--->')
print('Latest loss: %.4f' % (train_batch_losses))
print('Latest ACC: %.4f' % (sum(TRAIN_ACC) / len(TRAIN_ACC)))
avg_loss = sum(train_batch_losses) / (len(trainset) // TRAIN_BATCH_SIZE +
1)
def main(config):
svname = config.get('sv_name')
if args.tag is not None:
svname += '_' + args.tag
config['sv_name'] = svname
save_path = os.path.join('./save', svname)
utils.ensure_path(save_path)
utils.set_log_path(save_path)
utils.log(svname)
writer = SummaryWriter(os.path.join(save_path, 'tensorboard'))
yaml.dump(config, open(os.path.join(save_path, 'config.yaml'), 'w'))
#### Dataset ####
n_way, n_shot = config['n_way'], config['n_shot']
n_query = config['n_query']
n_pseudo = config['n_pseudo']
ep_per_batch = config['ep_per_batch']
if config.get('test_batches') is not None:
test_batches = config['test_batches']
else:
test_batches = config['train_batches']
for s in ['train', 'val', 'tval']:
if config.get(f"{s}_dataset_args") is not None:
config[f"{s}_dataset_args"]['data_dir'] = os.path.join(os.getcwd(), os.pardir, 'data_root')
# train
train_dataset = CustomDataset(config['train_dataset'], save_dir=config.get('load_encoder'),
**config['train_dataset_args'])
if config['train_dataset_args']['split'] == 'helper':
with open(os.path.join(save_path, 'train_helper_cls.pkl'), 'wb') as f:
pkl.dump(train_dataset.dataset_classes, f)
train_sampler = EpisodicSampler(train_dataset, config['train_batches'], n_way, n_shot, n_query,
n_pseudo, episodes_per_batch=ep_per_batch)
train_loader = DataLoader(train_dataset, batch_sampler=train_sampler,
num_workers=4, pin_memory=True)
# tval
if config.get('tval_dataset'):
tval_dataset = CustomDataset(config['tval_dataset'],
**config['tval_dataset_args'])
tval_sampler = EpisodicSampler(tval_dataset, test_batches, n_way, n_shot, n_query,
n_pseudo, episodes_per_batch=ep_per_batch)
tval_loader = DataLoader(tval_dataset, batch_sampler=tval_sampler,
num_workers=4, pin_memory=True)
else:
tval_loader = None
# val
val_dataset = CustomDataset(config['val_dataset'],
**config['val_dataset_args'])
val_sampler = EpisodicSampler(val_dataset, test_batches, n_way, n_shot, n_query,
n_pseudo, episodes_per_batch=ep_per_batch)
val_loader = DataLoader(val_dataset, batch_sampler=val_sampler,
num_workers=4, pin_memory=True)
#### Model and optimizer ####
if config.get('load'):
model_sv = torch.load(config['load'])
model = models.load(model_sv)
else:
model = models.make(config['model'], **config['model_args'])
if config.get('load_encoder'):
encoder = models.load(torch.load(config['load_encoder'])).encoder
model.encoder.load_state_dict(encoder.state_dict())
if config.get('freeze_encoder'):
for param in model.encoder.parameters():
param.requires_grad = False
if config.get('_parallel'):
model = nn.DataParallel(model)
utils.log('num params: {}'.format(utils.compute_n_params(model)))
optimizer, lr_scheduler = utils.make_optimizer(
model.parameters(),
config['optimizer'], **config['optimizer_args'])
########
max_epoch = config['max_epoch']
save_epoch = config.get('save_epoch')
max_va = 0.
timer_used = utils.Timer()
timer_epoch = utils.Timer()
aves_keys = ['tl', 'ta', 'tvl', 'tva', 'vl', 'va']
trlog = dict()
for k in aves_keys:
trlog[k] = []
for epoch in range(1, max_epoch + 1):
timer_epoch.s()
aves = {k: utils.Averager() for k in aves_keys}
# train
model.train()
if config.get('freeze_bn'):
utils.freeze_bn(model)
writer.add_scalar('lr', optimizer.param_groups[0]['lr'], epoch)
np.random.seed(epoch)
for data in tqdm(train_loader, desc='train', leave=False):
x_shot, x_query, x_pseudo = fs.split_shot_query(
data.cuda(), n_way, n_shot, n_query, n_pseudo,
ep_per_batch=ep_per_batch)
label = fs.make_nk_label(n_way, n_query,
ep_per_batch=ep_per_batch).cuda()
logits = model(x_shot, x_query, x_pseudo)
logits = logits.view(-1, n_way)
loss = F.cross_entropy(logits, label)
acc = utils.compute_acc(logits, label)
optimizer.zero_grad()
loss.backward()
optimizer.step()
aves['tl'].add(loss.item())
aves['ta'].add(acc)
logits = None; loss = None
# eval
model.eval()
for name, loader, name_l, name_a in [
('tval', tval_loader, 'tvl', 'tva'),
('val', val_loader, 'vl', 'va')]:
if (config.get('tval_dataset') is None) and name == 'tval':
continue
np.random.seed(0)
for data in tqdm(loader, desc=name, leave=False):
x_shot, x_query, x_pseudo = fs.split_shot_query(
data.cuda(), n_way, n_shot, n_query, n_pseudo,
ep_per_batch=ep_per_batch)
label = fs.make_nk_label(n_way, n_query,
ep_per_batch=ep_per_batch).cuda()
with torch.no_grad():
logits = model(x_shot, x_query, x_pseudo)
logits = logits.view(-1, n_way)
loss = F.cross_entropy(logits, label)
acc = utils.compute_acc(logits, label)
aves[name_l].add(loss.item())
aves[name_a].add(acc)
# post
if lr_scheduler is not None:
lr_scheduler.step()
for k, v in aves.items():
aves[k] = v.item()
trlog[k].append(aves[k])
t_epoch = utils.time_str(timer_epoch.t())
t_used = utils.time_str(timer_used.t())
t_estimate = utils.time_str(timer_used.t() / epoch * max_epoch)
utils.log('epoch {}, train {:.4f}|{:.4f}, tval {:.4f}|{:.4f}, '
'val {:.4f}|{:.4f}, {} {}/{}'.format(
epoch, aves['tl'], aves['ta'], aves['tvl'], aves['tva'],
aves['vl'], aves['va'], t_epoch, t_used, t_estimate))
writer.add_scalars('loss', {
'train': aves['tl'],
'tval': aves['tvl'],
'val': aves['vl'],
}, epoch)
writer.add_scalars('acc', {
'train': aves['ta'],
'tval': aves['tva'],
'val': aves['va'],
}, epoch)
if config.get('_parallel'):
model_ = model.module
else:
model_ = model
training = {
'epoch': epoch,
'optimizer': config['optimizer'],
'optimizer_args': config['optimizer_args'],
'optimizer_sd': optimizer.state_dict(),
}
save_obj = {
'file': __file__,
'config': config,
'model': config['model'],
'model_args': config['model_args'],
'model_sd': model_.state_dict(),
'training': training,
}
torch.save(save_obj, os.path.join(save_path, 'epoch-last.pth'))
torch.save(trlog, os.path.join(save_path, 'trlog.pth'))
if (save_epoch is not None) and epoch % save_epoch == 0:
torch.save(save_obj,
os.path.join(save_path, 'epoch-{}.pth'.format(epoch)))
if aves['va'] > max_va:
max_va = aves['va']
torch.save(save_obj, os.path.join(save_path, 'max-va.pth'))
writer.flush()
def main(config):
random.seed(0)
np.random.seed(0)
torch.manual_seed(0)
torch.cuda.manual_seed(0)
# torch.backends.cudnn.deterministic = True
# torch.backends.cudnn.benchmark = False
##### Dataset #####
dataset = datasets.make(config['dataset'], **config['test'])
utils.log('meta-test set: {} (x{}), {}'.format(dataset[0][0].shape,
len(dataset),
dataset.n_classes))
loader = DataLoader(dataset,
config['test']['n_episode'],
collate_fn=datasets.collate_fn,
num_workers=1,
pin_memory=True)
##### Model #####
ckpt = torch.load(config['load'])
inner_args = utils.config_inner_args(config.get('inner_args'))
model = models.load(ckpt, load_clf=(not inner_args['reset_classifier']))
if args.efficient:
model.go_efficient()
if config.get('_parallel'):
model = nn.DataParallel(model)
utils.log('num params: {}'.format(utils.compute_n_params(model)))
##### Evaluation #####
model.eval()
aves_va = utils.AverageMeter()
va_lst = []
for epoch in range(1, config['epoch'] + 1):
for data in tqdm(loader, leave=False):
x_shot, x_query, y_shot, y_query = data
x_shot, y_shot = x_shot.cuda(), y_shot.cuda()
x_query, y_query = x_query.cuda(), y_query.cuda()
if inner_args['reset_classifier']:
if config.get('_parallel'):
model.module.reset_classifier()
else:
model.reset_classifier()
logits = model(x_shot,
x_query,
y_shot,
inner_args,
meta_train=False)
logits = logits.view(-1, config['test']['n_way'])
labels = y_query.view(-1)
pred = torch.argmax(logits, dim=1)
acc = utils.compute_acc(pred, labels)
aves_va.update(acc, 1)
va_lst.append(acc)
print('test epoch {}: acc={:.2f} +- {:.2f} (%)'.format(
epoch,
aves_va.item() * 100,
utils.mean_confidence_interval(va_lst) * 100))
weight_tac = 1.
vy, psi, dis, ac, tac = netD(input,
label,
detach=weight_tac < opt.detach_below)
proj_errD_real = dis_criterion(vy + psi, dis_label)
dis_errD_real = dis_criterion(dis, dis_label)
aux_errD_real = aux_criterion(ac, label)
errD_real = proj_errD_real * weight_proj + \
(dis_errD_real + aux_errD_real) * weight_tac
errD_real.backward()
D_x = torch.sigmoid(dis_errD_real).data.mean()
# compute the current classification accuracy
accuracy = compute_acc(ac, label)
# get fake
fake_label.resize_(batch_size).random_(0, num_classes)
noise.resize_(batch_size, nz).normal_(0, 1)
fake = netG(noise, fake_label)
# train with fake
dis_label.resize_(batch_size).fill_(fake_label_const)
vy, psi, dis, ac, tac = netD(fake.detach(),
fake_label,
detach=weight_tac < opt.detach_below)
proj_errD_fake = dis_criterion(vy + psi, dis_label)
dis_errD_fake = dis_criterion(dis, dis_label)
aux_errD_fake = aux_criterion(tac, fake_label)
batch_size = real_cpu.size(0)
if opt.cuda:
real_cpu = real_cpu.cuda()
input.data.resize_as_(real_cpu).copy_(real_cpu)
dis_label.data.resize_(batch_size).fill_(real_label)
aux_label.data.resize_(batch_size).copy_(label)
dis_output, aux_output = netD(input)
dis_errD_real = dis_criterion(dis_output, dis_label)
aux_errD_real = aux_criterion(aux_output, aux_label)
errD_real = dis_errD_real + aux_errD_real
errD_real.backward()
D_x = dis_output.data.mean()
# compute the current classification accuracy
accuracy = compute_acc(aux_output, aux_label)
# train with fake
noise.data.resize_(batch_size, nz, 1, 1).normal_(0, 1)
label = np.random.randint(0, num_classes, batch_size)
noise_ = np.random.normal(0, 1, (batch_size, nz))
class_onehot = np.zeros((batch_size, num_classes))
class_onehot[np.arange(batch_size), label] = 1
noise_[np.arange(batch_size), :num_classes] = class_onehot[np.arange(
batch_size)]
noise_ = (torch.from_numpy(noise_))
noise.data.copy_(noise_.view(batch_size, nz, 1, 1))
aux_label.data.resize_(batch_size).copy_(torch.from_numpy(label))
fake = netG(noise)
dis_label.data.fill_(fake_label)
def main(config):
svname = args.name
if svname is None:
svname = 'meta_{}-{}shot'.format(config['train_dataset'],
config['n_shot'])
svname += '_' + config['model']
if config['model_args'].get('encoder'):
svname += '-' + config['model_args']['encoder']
if config['model_args'].get('prog_synthesis'):
svname += '-' + config['model_args']['prog_synthesis']
svname += '-seed' + str(args.seed)
if args.tag is not None:
svname += '_' + args.tag
save_path = os.path.join(args.save_dir, svname)
utils.ensure_path(save_path, remove=False)
utils.set_log_path(save_path)
writer = SummaryWriter(os.path.join(save_path, 'tensorboard'))
yaml.dump(config, open(os.path.join(save_path, 'config.yaml'), 'w'))
logger = utils.Logger(file_name=os.path.join(save_path, "log_sdout.txt"),
file_mode="a+",
should_flush=True)
#### Dataset ####
n_way, n_shot = config['n_way'], config['n_shot']
n_query = config['n_query']
if config.get('n_train_way') is not None:
n_train_way = config['n_train_way']
else:
n_train_way = n_way
if config.get('n_train_shot') is not None:
n_train_shot = config['n_train_shot']
else:
n_train_shot = n_shot
if config.get('ep_per_batch') is not None:
ep_per_batch = config['ep_per_batch']
else:
ep_per_batch = 1
random_state = np.random.RandomState(args.seed)
print('seed：', args.seed)
# train
train_dataset = datasets.make(config['train_dataset'],
**config['train_dataset_args'])
utils.log('train dataset: {} (x{})'.format(train_dataset[0][0].shape,
len(train_dataset)))
if config.get('visualize_datasets'):
utils.visualize_dataset(train_dataset, 'train_dataset', writer)
train_sampler = BongardSampler(train_dataset.n_tasks,
config['train_batches'], ep_per_batch,
random_state.randint(2**31))
train_loader = DataLoader(train_dataset,
batch_sampler=train_sampler,
num_workers=8,
pin_memory=True)
# tvals
tval_loaders = {}
tval_name_ntasks_dict = {
'tval': 2000,
'tval_ff': 600,
'tval_bd': 480,
'tval_hd_comb': 400,
'tval_hd_novel': 320
} # numbers depend on dataset
for tval_type in tval_name_ntasks_dict.keys():
if config.get('{}_dataset'.format(tval_type)):
tval_dataset = datasets.make(
config['{}_dataset'.format(tval_type)],
**config['{}_dataset_args'.format(tval_type)])
utils.log('{} dataset: {} (x{})'.format(tval_type,
tval_dataset[0][0].shape,
len(tval_dataset)))
if config.get('visualize_datasets'):
utils.visualize_dataset(tval_dataset, 'tval_ff_dataset',
writer)
tval_sampler = BongardSampler(
tval_dataset.n_tasks,
n_batch=tval_name_ntasks_dict[tval_type] // ep_per_batch,
ep_per_batch=ep_per_batch,
seed=random_state.randint(2**31))
tval_loader = DataLoader(tval_dataset,
batch_sampler=tval_sampler,
num_workers=8,
pin_memory=True)
tval_loaders.update({tval_type: tval_loader})
else:
tval_loaders.update({tval_type: None})
# val
val_dataset = datasets.make(config['val_dataset'],
**config['val_dataset_args'])
utils.log('val dataset: {} (x{})'.format(val_dataset[0][0].shape,
len(val_dataset)))
if config.get('visualize_datasets'):
utils.visualize_dataset(val_dataset, 'val_dataset', writer)
val_sampler = BongardSampler(val_dataset.n_tasks,
n_batch=900 // ep_per_batch,
ep_per_batch=ep_per_batch,
seed=random_state.randint(2**31))
val_loader = DataLoader(val_dataset,
batch_sampler=val_sampler,
num_workers=8,
pin_memory=True)
########
#### Model and optimizer ####
if config.get('load'):
print('loading pretrained model: ', config['load'])
model = models.load(torch.load(config['load']))
else:
model = models.make(config['model'], **config['model_args'])
if config.get('load_encoder'):
print('loading pretrained encoder: ', config['load_encoder'])
encoder = models.load(torch.load(config['load_encoder'])).encoder
model.encoder.load_state_dict(encoder.state_dict())
if config.get('load_prog_synthesis'):
print('loading pretrained program synthesis model: ',
config['load_prog_synthesis'])
prog_synthesis = models.load(
torch.load(config['load_prog_synthesis']))
model.prog_synthesis.load_state_dict(prog_synthesis.state_dict())
if config.get('_parallel'):
model = nn.DataParallel(model)
utils.log('num params: {}'.format(utils.compute_n_params(model)))
optimizer, lr_scheduler = utils.make_optimizer(model.parameters(),
config['optimizer'],
**config['optimizer_args'])
########
max_epoch = config['max_epoch']
save_epoch = config.get('save_epoch')
max_va = 0.
timer_used = utils.Timer()
timer_epoch = utils.Timer()
aves_keys = ['tl', 'ta', 'vl', 'va']
tval_tuple_lst = []
for k, v in tval_loaders.items():
if v is not None:
loss_key = 'tvl' + k.split('tval')[-1]
acc_key = ' tva' + k.split('tval')[-1]
aves_keys.append(loss_key)
aves_keys.append(acc_key)
tval_tuple_lst.append((k, v, loss_key, acc_key))
trlog = dict()
for k in aves_keys:
trlog[k] = []
for epoch in range(1, max_epoch + 1):
timer_epoch.s()
aves = {k: utils.Averager() for k in aves_keys}
# train
model.train()
if config.get('freeze_bn'):
utils.freeze_bn(model)
writer.add_scalar('lr', optimizer.param_groups[0]['lr'], epoch)
for data, label in tqdm(train_loader, desc='train', leave=False):
x_shot, x_query = fs.split_shot_query(data.cuda(),
n_train_way,
n_train_shot,
n_query,
ep_per_batch=ep_per_batch)
label_query = fs.make_nk_label(n_train_way,
n_query,
ep_per_batch=ep_per_batch).cuda()
if config['model'] == 'snail': # only use one selected label_query
query_dix = random_state.randint(n_train_way * n_query)
label_query = label_query.view(ep_per_batch, -1)[:, query_dix]
x_query = x_query[:, query_dix:query_dix + 1]
if config['model'] == 'maml': # need grad in maml
model.zero_grad()
logits = model(x_shot, x_query).view(-1, n_train_way)
loss = F.cross_entropy(logits, label_query)
acc = utils.compute_acc(logits, label_query)
optimizer.zero_grad()
loss.backward()
optimizer.step()
aves['tl'].add(loss.item())
aves['ta'].add(acc)
logits = None
loss = None
# eval
model.eval()
for name, loader, name_l, name_a in [('val', val_loader, 'vl', 'va')
] + tval_tuple_lst:
if config.get('{}_dataset'.format(name)) is None:
continue
np.random.seed(0)
for data, _ in tqdm(loader, desc=name, leave=False):
x_shot, x_query = fs.split_shot_query(
data.cuda(),
n_way,
n_shot,
n_query,
ep_per_batch=ep_per_batch)
label_query = fs.make_nk_label(
n_way, n_query, ep_per_batch=ep_per_batch).cuda()
if config[
'model'] == 'snail': # only use one randomly selected label_query
query_dix = random_state.randint(n_train_way)
label_query = label_query.view(ep_per_batch, -1)[:,
query_dix]
x_query = x_query[:, query_dix:query_dix + 1]
if config['model'] == 'maml': # need grad in maml
model.zero_grad()
logits = model(x_shot, x_query, eval=True).view(-1, n_way)
loss = F.cross_entropy(logits, label_query)
acc = utils.compute_acc(logits, label_query)
else:
with torch.no_grad():
logits = model(x_shot, x_query,
eval=True).view(-1, n_way)
loss = F.cross_entropy(logits, label_query)
acc = utils.compute_acc(logits, label_query)
aves[name_l].add(loss.item())
aves[name_a].add(acc)
# post
if lr_scheduler is not None:
lr_scheduler.step()
for k, v in aves.items():
aves[k] = v.item()
trlog[k].append(aves[k])
t_epoch = utils.time_str(timer_epoch.t())
t_used = utils.time_str(timer_used.t())
t_estimate = utils.time_str(timer_used.t() / epoch * max_epoch)
log_str = 'epoch {}, train {:.4f}|{:.4f}, val {:.4f}|{:.4f}'.format(
epoch, aves['tl'], aves['ta'], aves['vl'], aves['va'])
for tval_name, _, loss_key, acc_key in tval_tuple_lst:
log_str += ', {} {:.4f}|{:.4f}'.format(tval_name, aves[loss_key],
aves[acc_key])
writer.add_scalars('loss', {tval_name: aves[loss_key]}, epoch)
writer.add_scalars('acc', {tval_name: aves[acc_key]}, epoch)
log_str += ', {} {}/{}'.format(t_epoch, t_used, t_estimate)
utils.log(log_str)
writer.add_scalars('loss', {
'train': aves['tl'],
'val': aves['vl'],
}, epoch)
writer.add_scalars('acc', {
'train': aves['ta'],
'val': aves['va'],
}, epoch)
if config.get('_parallel'):
model_ = model.module
else:
model_ = model
training = {
'epoch': epoch,
'optimizer': config['optimizer'],
'optimizer_args': config['optimizer_args'],
'optimizer_sd': optimizer.state_dict(),
}
save_obj = {
'file': __file__,
'config': config,
'model': config['model'],
'model_args': config['model_args'],
'model_sd': model_.state_dict(),
'training': training,
}
torch.save(save_obj, os.path.join(save_path, 'epoch-last.pth'))
torch.save(trlog, os.path.join(save_path, 'trlog.pth'))
if (save_epoch is not None) and epoch % save_epoch == 0:
torch.save(save_obj,
os.path.join(save_path, 'epoch-{}.pth'.format(epoch)))
if aves['va'] > max_va:
max_va = aves['va']
torch.save(save_obj, os.path.join(save_path, 'max-va.pth'))
writer.flush()
print('finished training!')
logger.close()
real_cpu, label = data
batch_size = real_cpu.size(0)
if opt.cuda:
real_cpu = real_cpu.cuda()
with torch.no_grad():
input.resize_(real_cpu.size()).copy_(real_cpu)
disr_label.resize_(batch_size).fill_(real_label)
aux_label.resize_(batch_size).copy_(label)
dis_output, aux_output = netD(input)
dis_errD_real = van_loss(dis_output, disr_label)
aux_errD_real = aux_criterion(aux_output, aux_label)
errD_real = dis_errD_real + aux_errD_real
errD_real.backward()
D_x = dis_output.data.mean()
# compute the current classification accuracy
accuracy = compute_acc(aux_output, aux_label)
# train with fake
with torch.no_grad():
noise.resize_(batch_size, nz, 1, 1).normal_(0, 1)
label = np.random.randint(0, num_classes, batch_size)
noise_ = np.random.normal(0, 1, (batch_size, nz))
class_onehot = np.zeros((batch_size, num_classes))
class_onehot[np.arange(batch_size), label] = 1
noise_[np.arange(batch_size), :num_classes] = class_onehot[
np.arange(batch_size)]
noise_ = (torch.from_numpy(noise_))
noise.data.copy_(noise_.view(batch_size, nz, 1, 1))
aux_label.resize_(batch_size).copy_(torch.from_numpy(label))
fake = netG(noise)
def main(config, args):
random.seed(0)
np.random.seed(0)
torch.manual_seed(0)
torch.cuda.manual_seed(0)
# torch.backends.cudnn.deterministic = True
# torch.backends.cudnn.benchmark = False
wandb_auth()
try:
__IPYTHON__
wandb.init(project="NAS", group=f"maml")
except:
wandb.init(project="NAS", group=f"maml", config=config)
ckpt_name = args.name
if ckpt_name is None:
ckpt_name = config['encoder']
ckpt_name += '_' + config['dataset'].replace('meta-', '')
ckpt_name += '_{}_way_{}_shot'.format(config['train']['n_way'],
config['train']['n_shot'])
if args.tag is not None:
ckpt_name += '_' + args.tag
ckpt_path = os.path.join('./save', ckpt_name)
utils.ensure_path(ckpt_path)
utils.set_log_path(ckpt_path)
writer = SummaryWriter(os.path.join(ckpt_path, 'tensorboard'))
yaml.dump(config, open(os.path.join(ckpt_path, 'config.yaml'), 'w'))
##### Dataset #####
# meta-train
train_set = datasets.make(config['dataset'], **config['train'])
utils.log('meta-train set: {} (x{}), {}'.format(train_set[0][0].shape,
len(train_set),
train_set.n_classes))
# meta-val
eval_val = False
if config.get('val'):
eval_val = True
val_set = datasets.make(config['dataset'], **config['val'])
utils.log('meta-val set: {} (x{}), {}'.format(val_set[0][0].shape,
len(val_set),
val_set.n_classes))
val_loader = DataLoader(val_set,
config['val']['n_episode'],
collate_fn=datasets.collate_fn,
num_workers=1,
pin_memory=True)
# if args.split == "traintrain" and config.get('val'): # TODO I dont think this is what they meant by train-train :D
# train_set = torch.utils.data.ConcatDataset([train_set, val_set])
train_loader = DataLoader(train_set,
config['train']['n_episode'],
collate_fn=datasets.collate_fn,
num_workers=1,
pin_memory=True)
##### Model and Optimizer #####
inner_args = utils.config_inner_args(config.get('inner_args'))
if config.get('load') or (args.load is True and
os.path.exists(ckpt_path + '/epoch-last.pth')):
if config.get('load') is None:
config['load'] = ckpt_path + '/epoch-last.pth'
ckpt = torch.load(config['load'])
config['encoder'] = ckpt['encoder']
config['encoder_args'] = ckpt['encoder_args']
config['classifier'] = ckpt['classifier']
config['classifier_args'] = ckpt['classifier_args']
model = models.load(ckpt,
load_clf=(not inner_args['reset_classifier']))
optimizer, lr_scheduler = optimizers.load(ckpt, model.parameters())
start_epoch = ckpt['training']['epoch'] + 1
max_va = ckpt['training']['max_va']
else:
config['encoder_args'] = config.get('encoder_args') or dict()
config['classifier_args'] = config.get('classifier_args') or dict()
config['encoder_args']['bn_args']['n_episode'] = config['train'][
'n_episode']
config['classifier_args']['n_way'] = config['train']['n_way']
model = models.make(config['encoder'], config['encoder_args'],
config['classifier'], config['classifier_args'])
optimizer, lr_scheduler = optimizers.make(config['optimizer'],
model.parameters(),
**config['optimizer_args'])
start_epoch = 1
max_va = 0.
if args.efficient:
model.go_efficient()
if config.get('_parallel'):
model = nn.DataParallel(model)
utils.log('num params: {}'.format(utils.compute_n_params(model)))
timer_elapsed, timer_epoch = utils.Timer(), utils.Timer()
##### Training and evaluation #####
# 'tl': meta-train loss
# 'ta': meta-train accuracy
# 'vl': meta-val loss
# 'va': meta-val accuracy
aves_keys = ['tl', 'ta', 'vl', 'va']
trlog = dict()
for k in aves_keys:
trlog[k] = []
for epoch in tqdm(range(start_epoch, config['epoch'] + 1),
desc="Iterating over epochs"):
timer_epoch.start()
aves = {k: utils.AverageMeter() for k in aves_keys}
# meta-train
model.train()
writer.add_scalar('lr', optimizer.param_groups[0]['lr'], epoch)
np.random.seed(epoch)
all_sotls = 0
all_sovls = 0
for data_idx, data in enumerate(
tqdm(train_loader, desc='meta-train', leave=False)):
x_shot, x_query, y_shot, y_query = data
x_shot, y_shot = x_shot.cuda(), y_shot.cuda()
x_query, y_query = x_query.cuda(), y_query.cuda()
if inner_args['reset_classifier']:
if config.get('_parallel'):
model.module.reset_classifier()
else:
model.reset_classifier()
if args.split == "traintrain":
x_query = x_shot
y_query = y_shot
logits, sotl, all_losses = model(x_shot,
x_query,
y_shot,
inner_args,
meta_train=True)
# print("HAHHA", data_idx, all_losses)
# sotl = sum([l[-1] for l in all_losses])
# for l in all_losses[:-1]:
# for i in range(len(l)-1):
# l[i] = l[i].detach()
logits = logits.flatten(0, 1)
labels = y_query.flatten()
all_sotls += sotl
pred = torch.argmax(logits, dim=-1)
acc = utils.compute_acc(pred, labels)
loss = F.cross_entropy(logits, labels)
# all_sovls += loss # TODO I think this causes blowup because it creates new tensors that never get discarded and it maintains the computational graph after?
if args.split == "trainval" or (
args.split == "sovl"
and not data_idx % args.sotl_freq == 0):
aves['tl'].update(loss.item(), 1)
aves['ta'].update(acc, 1)
optimizer.zero_grad()
loss.backward()
for param in optimizer.param_groups[0]['params']:
nn.utils.clip_grad_value_(param, 10)
optimizer.step()
elif args.split == "traintrain":
aves['tl'].update(loss.item(), 1)
aves['ta'].update(acc, 1)
# sotl = sum(sotl) + loss
optimizer.zero_grad()
# sotl.backward()
loss.backward()
for param in optimizer.param_groups[0]['params']:
nn.utils.clip_grad_value_(param, 10)
optimizer.step()
elif args.split == "sotl" and data_idx % args.sotl_freq == 0:
# TODO doesnt work whatsoever
aves['tl'].update(loss.item(), 1)
aves['ta'].update(acc, 1)
optimizer.zero_grad()
all_sotls.backward()
for param in optimizer.param_groups[0]['params']:
nn.utils.clip_grad_value_(param, 10)
optimizer.step()
all_sotls = 0 # detach
elif args.split == "sovl" and data_idx % args.sotl_freq == 0:
# TODO doesnt work whatsoever
aves['tl'].update(loss.item(), 1)
aves['ta'].update(acc, 1)
optimizer.zero_grad()
all_sovls.backward()
for param in optimizer.param_groups[0]['params']:
nn.utils.clip_grad_value_(param, 10)
optimizer.step()
all_sovls = 0 # detach
# meta-val
if eval_val:
model.eval()
np.random.seed(0)
for data in tqdm(val_loader, desc='meta-val', leave=False):
x_shot, x_query, y_shot, y_query = data
x_shot, y_shot = x_shot.cuda(), y_shot.cuda()
x_query, y_query = x_query.cuda(), y_query.cuda()
if inner_args['reset_classifier']:
if config.get('_parallel'):
model.module.reset_classifier()
else:
model.reset_classifier()
logits, sotl, all_losses = model(x_shot,
x_query,
y_shot,
inner_args,
meta_train=False)
logits = logits.flatten(0, 1)
labels = y_query.flatten()
pred = torch.argmax(logits, dim=-1)
acc = utils.compute_acc(pred, labels)
loss = F.cross_entropy(logits, labels)
aves['vl'].update(loss.item(), 1)
aves['va'].update(acc, 1)
if lr_scheduler is not None:
lr_scheduler.step()
for k, avg in aves.items():
aves[k] = avg.item()
trlog[k].append(aves[k])
t_epoch = utils.time_str(timer_epoch.end())
t_elapsed = utils.time_str(timer_elapsed.end())
t_estimate = utils.time_str(timer_elapsed.end() /
(epoch - start_epoch + 1) *
(config['epoch'] - start_epoch + 1))
# formats output
log_str = 'epoch {}, meta-train {:.4f}|{:.4f}'.format(
str(epoch), aves['tl'], aves['ta'])
writer.add_scalars('loss', {'meta-train': aves['tl']}, epoch)
writer.add_scalars('acc', {'meta-train': aves['ta']}, epoch)
if eval_val:
log_str += ', meta-val {:.4f}|{:.4f}'.format(
aves['vl'], aves['va'])
writer.add_scalars('loss', {'meta-val': aves['vl']}, epoch)
writer.add_scalars('acc', {'meta-val': aves['va']}, epoch)
wandb.log({
"train_loss": aves['tl'],
"train_acc": aves['ta'],
"val_loss": aves['vl'],
"val_acc": aves['va']
})
log_str += ', {} {}/{}'.format(t_epoch, t_elapsed, t_estimate)
utils.log(log_str)
# saves model and meta-data
if config.get('_parallel'):
model_ = model.module
else:
model_ = model
training = {
'epoch':
epoch,
'max_va':
max(max_va, aves['va']),
'optimizer':
config['optimizer'],
'optimizer_args':
config['optimizer_args'],
'optimizer_state_dict':
optimizer.state_dict(),
'lr_scheduler_state_dict':
lr_scheduler.state_dict() if lr_scheduler is not None else None,
}
ckpt = {
'file': __file__,
'config': config,
'encoder': config['encoder'],
'encoder_args': config['encoder_args'],
'encoder_state_dict': model_.encoder.state_dict(),
'classifier': config['classifier'],
'classifier_args': config['classifier_args'],
'classifier_state_dict': model_.classifier.state_dict(),
'training': training,
}
# 'epoch-last.pth': saved at the latest epoch
# 'max-va.pth': saved when validation accuracy is at its maximum
torch.save(ckpt, os.path.join(ckpt_path, 'epoch-last.pth'))
torch.save(trlog, os.path.join(ckpt_path, 'trlog.pth'))
if aves['va'] > max_va:
max_va = aves['va']
torch.save(ckpt, os.path.join(ckpt_path, 'max-va.pth'))
writer.flush()
