class Model():
def __init__(self, configuration, pre_embed=None):
configuration = deepcopy(configuration)
self.configuration = deepcopy(configuration)
configuration['model']['encoder']['pre_embed'] = pre_embed
self.encoder = Encoder.from_params(
Params(configuration['model']['encoder'])).to(device)
configuration['model']['decoder'][
'hidden_size'] = self.encoder.output_size
self.decoder = AttnDecoder.from_params(
Params(configuration['model']['decoder'])).to(device)
self.encoder_params = list(self.encoder.parameters())
self.attn_params = list([
v for k, v in self.decoder.named_parameters() if 'attention' in k
])
self.decoder_params = list([
v for k, v in self.decoder.named_parameters()
if 'attention' not in k
])
self.bsize = configuration['training']['bsize']
weight_decay = configuration['training'].get('weight_decay', 1e-5)
self.encoder_optim = torch.optim.Adam(self.encoder_params,
lr=0.001,
weight_decay=weight_decay,
amsgrad=True)
self.attn_optim = torch.optim.Adam(self.attn_params,
lr=0.001,
weight_decay=0,
amsgrad=True)
self.decoder_optim = torch.optim.Adam(self.decoder_params,
lr=0.001,
weight_decay=weight_decay,
amsgrad=True)
self.adversarymulti = AdversaryMulti(decoder=self.decoder)
self.all_params = self.encoder_params + self.attn_params + self.decoder_params
self.all_optim = torch.optim.Adam(self.all_params,
lr=0.001,
weight_decay=weight_decay,
amsgrad=True)
# self.all_optim = adagrad.Adagrad(self.all_params, weight_decay=weight_decay)
pos_weight = configuration['training'].get('pos_weight', [1.0] *
self.decoder.output_size)
self.pos_weight = torch.Tensor(pos_weight).to(device)
self.criterion = nn.BCEWithLogitsLoss(reduction='none').to(device)
self.swa_settings = configuration['training']['swa']
import time
dirname = configuration['training']['exp_dirname']
basepath = configuration['training'].get('basepath', 'outputs')
self.time_str = time.ctime().replace(' ', '_')
self.dirname = os.path.join(basepath, dirname, self.time_str)
self.temperature = configuration['training']['temperature']
self.train_losses = []
if self.swa_settings[0]:
# self.attn_optim = SWA(self.attn_optim, swa_start=3, swa_freq=1, swa_lr=0.05)
# self.decoder_optim = SWA(self.decoder_optim, swa_start=3, swa_freq=1, swa_lr=0.05)
# self.encoder_optim = SWA(self.encoder_optim, swa_start=3, swa_freq=1, swa_lr=0.05)
self.swa_all_optim = SWA(self.all_optim)
self.running_norms = []
@classmethod
def init_from_config(cls, dirname, **kwargs):
config = json.load(open(dirname + '/config.json', 'r'))
config.update(kwargs)
obj = cls(config)
obj.load_values(dirname)
return obj
def get_param_buffer_norms(self):
for p in self.swa_all_optim.param_groups[0]['params']:
param_state = self.swa_all_optim.state[p]
if 'swa_buffer' not in param_state:
self.swa_all_optim.update_swa()
norms = []
# for p in np.array(self.swa_all_optim.param_groups[0]['params'])[[1, 2, 5, 6, 9]]:
for p in np.array(self.swa_all_optim.param_groups[0]['params'])[[6,
9]]:
param_state = self.swa_all_optim.state[p]
buf = np.squeeze(param_state['swa_buffer'].cpu().numpy())
cur_state = np.squeeze(p.data.cpu().numpy())
norm = np.linalg.norm(buf - cur_state)
norms.append(norm)
if self.swa_settings[3] == 2:
return np.max(norms)
return np.mean(norms)
def total_iter_num(self):
return self.swa_all_optim.param_groups[0]['step_counter']
def iter_for_swa_update(self, iter_num):
return iter_num > self.swa_settings[1] \
and iter_num % self.swa_settings[2] == 0
def check_and_update_swa(self):
if self.iter_for_swa_update(self.total_iter_num()):
cur_step_diff_norm = self.get_param_buffer_norms()
if self.swa_settings[3] == 0:
self.swa_all_optim.update_swa()
return
if not self.running_norms:
running_mean_norm = 0
else:
running_mean_norm = np.mean(self.running_norms)
if cur_step_diff_norm > running_mean_norm:
self.swa_all_optim.update_swa()
self.running_norms = [cur_step_diff_norm]
elif cur_step_diff_norm > 0:
self.running_norms.append(cur_step_diff_norm)
def train(self, data_in, target_in, train=True):
sorting_idx = get_sorting_index_with_noise_from_lengths(
[len(x) for x in data_in], noise_frac=0.1)
data = [data_in[i] for i in sorting_idx]
target = [target_in[i] for i in sorting_idx]
self.encoder.train()
self.decoder.train()
bsize = self.bsize
N = len(data)
loss_total = 0
batches = list(range(0, N, bsize))
batches = shuffle(batches)
for n in tqdm(batches):
torch.cuda.empty_cache()
batch_doc = data[n:n + bsize]
batch_data = BatchHolder(batch_doc)
self.encoder(batch_data)
self.decoder(batch_data)
batch_target = target[n:n + bsize]
batch_target = torch.Tensor(batch_target).to(device)
if len(batch_target.shape) == 1: #(B, )
batch_target = batch_target.unsqueeze(-1) #(B, 1)
bce_loss = self.criterion(batch_data.predict / self.temperature,
batch_target)
weight = batch_target * self.pos_weight + (1 - batch_target)
bce_loss = (bce_loss * weight).mean(1).sum()
loss = bce_loss
self.train_losses.append(bce_loss.detach().cpu().numpy() + 0)
if hasattr(batch_data, 'reg_loss'):
loss += batch_data.reg_loss
if train:
if self.swa_settings[0]:
self.check_and_update_swa()
self.swa_all_optim.zero_grad()
loss.backward()
self.swa_all_optim.step()
else:
# self.encoder_optim.zero_grad()
# self.decoder_optim.zero_grad()
# self.attn_optim.zero_grad()
self.all_optim.zero_grad()
loss.backward()
# self.encoder_optim.step()
# self.decoder_optim.step()
# self.attn_optim.step()
self.all_optim.step()
loss_total += float(loss.data.cpu().item())
if self.swa_settings[0] and self.swa_all_optim.param_groups[0][
'step_counter'] > self.swa_settings[1]:
print("\nSWA swapping\n")
# self.attn_optim.swap_swa_sgd()
# self.encoder_optim.swap_swa_sgd()
# self.decoder_optim.swap_swa_sgd()
self.swa_all_optim.swap_swa_sgd()
self.running_norms = []
return loss_total * bsize / N
def predictor(self, inp_text_permutations):
text_permutations = [
dataset_vec.map2idxs(x.split()) for x in inp_text_permutations
]
outputs = []
bsize = 512
N = len(text_permutations)
for n in range(0, N, bsize):
torch.cuda.empty_cache()
batch_doc = text_permutations[n:n + bsize]
batch_data = BatchHolder(batch_doc)
self.encoder(batch_data)
self.decoder(batch_data)
batch_data.predict = torch.sigmoid(batch_data.predict)
pred = batch_data.predict.cpu().data.numpy()
for i in range(len(pred)):
if math.isnan(pred[i][0]):
pred[i][0] = 0.5
outputs.extend(pred)
ret_val = [[output_i[0], 1 - output_i[0]] for output_i in outputs]
ret_val = np.array(ret_val)
return ret_val
def evaluate(self, data):
self.encoder.eval()
self.decoder.eval()
bsize = self.bsize
N = len(data)
outputs = []
attns = []
for n in tqdm(range(0, N, bsize)):
torch.cuda.empty_cache()
batch_doc = data[n:n + bsize]
batch_data = BatchHolder(batch_doc)
self.encoder(batch_data)
self.decoder(batch_data)
batch_data.predict = torch.sigmoid(batch_data.predict /
self.temperature)
if self.decoder.use_attention:
attn = batch_data.attn.cpu().data.numpy()
attns.append(attn)
predict = batch_data.predict.cpu().data.numpy()
outputs.append(predict)
outputs = [x for y in outputs for x in y]
if self.decoder.use_attention:
attns = [x for y in attns for x in y]
return outputs, attns
def get_lime_explanations(self, data):
explanations = []
explainer = LimeTextExplainer(class_names=["A", "B"])
for data_i in data:
sentence = ' '.join(dataset_vec.map2words(data_i))
exp = explainer.explain_instance(text_instance=sentence,
classifier_fn=self.predictor,
num_features=len(data_i),
num_samples=5000).as_list()
explanations.append(exp)
return explanations
def gradient_mem(self, data):
self.encoder.train()
self.decoder.train()
bsize = self.bsize
N = len(data)
grads = {'XxE': [], 'XxE[X]': [], 'H': []}
for n in tqdm(range(0, N, bsize)):
torch.cuda.empty_cache()
batch_doc = data[n:n + bsize]
grads_xxe = []
grads_xxex = []
grads_H = []
for i in range(self.decoder.output_size):
batch_data = BatchHolder(batch_doc)
batch_data.keep_grads = True
batch_data.detach = True
self.encoder(batch_data)
self.decoder(batch_data)
torch.sigmoid(batch_data.predict[:, i]).sum().backward()
g = batch_data.embedding.grad
em = batch_data.embedding
g1 = (g * em).sum(-1)
grads_xxex.append(g1.cpu().data.numpy())
g1 = (g * self.encoder.embedding.weight.sum(0)).sum(-1)
grads_xxe.append(g1.cpu().data.numpy())
g1 = batch_data.hidden.grad.sum(-1)
grads_H.append(g1.cpu().data.numpy())
grads_xxe = np.array(grads_xxe).swapaxes(0, 1)
grads_xxex = np.array(grads_xxex).swapaxes(0, 1)
grads_H = np.array(grads_H).swapaxes(0, 1)
import ipdb
ipdb.set_trace()
grads['XxE'].append(grads_xxe)
grads['XxE[X]'].append(grads_xxex)
grads['H'].append(grads_H)
for k in grads:
grads[k] = [x for y in grads[k] for x in y]
return grads
def remove_and_run(self, data):
self.encoder.train()
self.decoder.train()
bsize = self.bsize
N = len(data)
outputs = []
for n in tqdm(range(0, N, bsize)):
batch_doc = data[n:n + bsize]
batch_data = BatchHolder(batch_doc)
po = np.zeros(
(batch_data.B, batch_data.maxlen, self.decoder.output_size))
for i in range(1, batch_data.maxlen - 1):
batch_data = BatchHolder(batch_doc)
batch_data.seq = torch.cat(
[batch_data.seq[:, :i], batch_data.seq[:, i + 1:]], dim=-1)
batch_data.lengths = batch_data.lengths - 1
batch_data.masks = torch.cat(
[batch_data.masks[:, :i], batch_data.masks[:, i + 1:]],
dim=-1)
self.encoder(batch_data)
self.decoder(batch_data)
po[:, i] = torch.sigmoid(batch_data.predict).cpu().data.numpy()
outputs.append(po)
outputs = [x for y in outputs for x in y]
return outputs
def permute_attn(self, data, num_perm=100):
self.encoder.train()
self.decoder.train()
bsize = self.bsize
N = len(data)
permutations = []
for n in tqdm(range(0, N, bsize)):
torch.cuda.empty_cache()
batch_doc = data[n:n + bsize]
batch_data = BatchHolder(batch_doc)
batch_perms = np.zeros(
(batch_data.B, num_perm, self.decoder.output_size))
self.encoder(batch_data)
self.decoder(batch_data)
for i in range(num_perm):
batch_data.permute = True
self.decoder(batch_data)
output = torch.sigmoid(batch_data.predict)
batch_perms[:, i] = output.cpu().data.numpy()
permutations.append(batch_perms)
permutations = [x for y in permutations for x in y]
return permutations
def save_values(self,
use_dirname=None,
save_model=True,
append_to_dir_name=''):
if use_dirname is not None:
dirname = use_dirname
else:
dirname = self.dirname + append_to_dir_name
self.last_epch_dirname = dirname
os.makedirs(dirname, exist_ok=True)
shutil.copy2(file_name, dirname + '/')
json.dump(self.configuration, open(dirname + '/config.json', 'w'))
if save_model:
torch.save(self.encoder.state_dict(), dirname + '/enc.th')
torch.save(self.decoder.state_dict(), dirname + '/dec.th')
return dirname
def load_values(self, dirname):
self.encoder.load_state_dict(
torch.load(dirname + '/enc.th', map_location={'cuda:1': 'cuda:0'}))
self.decoder.load_state_dict(
torch.load(dirname + '/dec.th', map_location={'cuda:1': 'cuda:0'}))
def adversarial_multi(self, data):
self.encoder.eval()
self.decoder.eval()
for p in self.encoder.parameters():
p.requires_grad = False
for p in self.decoder.parameters():
p.requires_grad = False
bsize = self.bsize
N = len(data)
adverse_attn = []
adverse_output = []
for n in tqdm(range(0, N, bsize)):
torch.cuda.empty_cache()
batch_doc = data[n:n + bsize]
batch_data = BatchHolder(batch_doc)
self.encoder(batch_data)
self.decoder(batch_data)
self.adversarymulti(batch_data)
attn_volatile = batch_data.attn_volatile.cpu().data.numpy(
) #(B, 10, L)
predict_volatile = batch_data.predict_volatile.cpu().data.numpy(
) #(B, 10, O)
adverse_attn.append(attn_volatile)
adverse_output.append(predict_volatile)
adverse_output = [x for y in adverse_output for x in y]
adverse_attn = [x for y in adverse_attn for x in y]
return adverse_output, adverse_attn
def logodds_attention(self, data, logodds_map: Dict):
self.encoder.eval()
self.decoder.eval()
bsize = self.bsize
N = len(data)
adverse_attn = []
adverse_output = []
logodds = np.zeros((self.encoder.vocab_size, ))
for k, v in logodds_map.items():
if v is not None:
logodds[k] = abs(v)
else:
logodds[k] = float('-inf')
logodds = torch.Tensor(logodds).to(device)
for n in tqdm(range(0, N, bsize)):
torch.cuda.empty_cache()
batch_doc = data[n:n + bsize]
batch_data = BatchHolder(batch_doc)
self.encoder(batch_data)
self.decoder(batch_data)
attn = batch_data.attn #(B, L)
batch_data.attn_logodds = logodds[batch_data.seq]
self.decoder.get_output_from_logodds(batch_data)
attn_volatile = batch_data.attn_volatile.cpu().data.numpy(
) #(B, L)
predict_volatile = torch.sigmoid(
batch_data.predict_volatile).cpu().data.numpy() #(B, O)
adverse_attn.append(attn_volatile)
adverse_output.append(predict_volatile)
adverse_output = [x for y in adverse_output for x in y]
adverse_attn = [x for y in adverse_attn for x in y]
return adverse_output, adverse_attn
def logodds_substitution(self, data, top_logodds_words: Dict):
self.encoder.eval()
self.decoder.eval()
bsize = self.bsize
N = len(data)
adverse_X = []
adverse_attn = []
adverse_output = []
words_neg = torch.Tensor(
top_logodds_words[0][0]).long().cuda().unsqueeze(0)
words_pos = torch.Tensor(
top_logodds_words[0][1]).long().cuda().unsqueeze(0)
words_to_select = torch.cat([words_neg, words_pos], dim=0) #(2, 5)
for n in tqdm(range(0, N, bsize)):
torch.cuda.empty_cache()
batch_doc = data[n:n + bsize]
batch_data = BatchHolder(batch_doc)
self.encoder(batch_data)
self.decoder(batch_data)
predict_class = (torch.sigmoid(batch_data.predict).squeeze(-1) >
0.5) * 1 #(B,)
attn = batch_data.attn #(B, L)
top_val, top_idx = torch.topk(attn, 5, dim=-1)
subs_words = words_to_select[1 - predict_class.long()] #(B, 5)
batch_data.seq.scatter_(1, top_idx, subs_words)
self.encoder(batch_data)
self.decoder(batch_data)
attn_volatile = batch_data.attn.cpu().data.numpy() #(B, L)
predict_volatile = torch.sigmoid(
batch_data.predict).cpu().data.numpy() #(B, O)
X_volatile = batch_data.seq.cpu().data.numpy()
adverse_X.append(X_volatile)
adverse_attn.append(attn_volatile)
adverse_output.append(predict_volatile)
adverse_X = [x for y in adverse_X for x in y]
adverse_output = [x for y in adverse_output for x in y]
adverse_attn = [x for y in adverse_attn for x in y]
return adverse_output, adverse_attn, adverse_X
def predict(self, batch_data, lengths, masks):
batch_holder = BatchHolderIndentity(batch_data, lengths, masks)
self.encoder(batch_holder)
self.decoder(batch_holder)
# batch_holder.predict = torch.sigmoid(batch_holder.predict)
predict = batch_holder.predict
return predict
def train(self):
# prepare data
train_data = self.data('train')
train_steps = int((len(train_data) + self.config.batch_size - 1) /
self.config.batch_size)
train_dataloader = DataLoader(train_data,
batch_size=self.config.batch_size,
collate_fn=self.get_collate_fn('train'),
shuffle=True,
num_workers=2)
# prepare optimizer
params_lr = [{
"params": self.model.bert_parameters,
'lr': self.config.small_lr
}, {
"params": self.model.other_parameters,
'lr': self.config.large_lr
}]
optimizer = torch.optim.Adam(params_lr)
optimizer = SWA(optimizer)
# prepare early stopping
early_stopping = EarlyStopping(self.model,
self.config.best_model_path,
big_server=BIG_GPU,
mode='max',
patience=10,
verbose=True)
# prepare learning schedual
learning_schedual = LearningSchedual(
optimizer, self.config.epochs, train_steps,
[self.config.small_lr, self.config.large_lr])
# prepare other
aux = REModelAux(self.config, train_steps)
moving_log = MovingData(window=500)
ending_flag = False
# self.model.load_state_dict(torch.load(ROOT_SAVED_MODEL + 'temp_model.ckpt'))
#
# with torch.no_grad():
# self.model.eval()
# print(self.eval())
# return
for epoch in range(0, self.config.epochs):
for step, (inputs, y_trues,
spo_info) in enumerate(train_dataloader):
inputs = [aaa.cuda() for aaa in inputs]
y_trues = [aaa.cuda() for aaa in y_trues]
if epoch > 0 or step == 1000:
self.model.detach_bert = False
# train ================================================================================================
preds = self.model(inputs)
loss = self.calculate_loss(preds, y_trues, inputs[1],
inputs[2])
optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm(self.model.parameters(), 1)
optimizer.step()
with torch.no_grad():
logs = {'lr0': 0, 'lr1': 0}
if (epoch > 0 or step > 620) and False:
sbj_f1, spo_f1 = self.calculate_train_f1(
spo_info[0], preds, spo_info[1:3],
inputs[2].cpu().numpy())
metrics_data = {
'loss': loss.cpu().numpy(),
'sampled_num': 1,
'sbj_correct_num': sbj_f1[0],
'sbj_pred_num': sbj_f1[1],
'sbj_true_num': sbj_f1[2],
'spo_correct_num': spo_f1[0],
'spo_pred_num': spo_f1[1],
'spo_true_num': spo_f1[2]
}
moving_data = moving_log(epoch * train_steps + step,
metrics_data)
logs['loss'] = moving_data['loss'] / moving_data[
'sampled_num']
logs['sbj_precise'], logs['sbj_recall'], logs[
'sbj_f1'] = calculate_f1(
moving_data['sbj_correct_num'],
moving_data['sbj_pred_num'],
moving_data['sbj_true_num'],
verbose=True)
logs['spo_precise'], logs['spo_recall'], logs[
'spo_f1'] = calculate_f1(
moving_data['spo_correct_num'],
moving_data['spo_pred_num'],
moving_data['spo_true_num'],
verbose=True)
else:
metrics_data = {
'loss': loss.cpu().numpy(),
'sampled_num': 1
}
moving_data = moving_log(epoch * train_steps + step,
metrics_data)
logs['loss'] = moving_data['loss'] / moving_data[
'sampled_num']
# update lr
logs['lr0'], logs['lr1'] = learning_schedual.update_lr(
epoch, step)
if step == int(train_steps / 2) or step + 1 == train_steps:
self.model.eval()
torch.save(self.model.state_dict(),
ROOT_SAVED_MODEL + 'temp_model.ckpt')
aux.new_line()
# dev ==========================================================================================
dev_result = self.eval()
logs['dev_loss'] = dev_result['loss']
logs['dev_sbj_precise'] = dev_result['sbj_precise']
logs['dev_sbj_recall'] = dev_result['sbj_recall']
logs['dev_sbj_f1'] = dev_result['sbj_f1']
logs['dev_spo_precise'] = dev_result['spo_precise']
logs['dev_spo_recall'] = dev_result['spo_recall']
logs['dev_spo_f1'] = dev_result['spo_f1']
logs['dev_precise'] = dev_result['precise']
logs['dev_recall'] = dev_result['recall']
logs['dev_f1'] = dev_result['f1']
# other thing
early_stopping(logs['dev_f1'])
if logs['dev_f1'] > 0.730:
optimizer.update_swa()
# test =========================================================================================
if (epoch + 1 == self.config.epochs and step + 1
== train_steps) or early_stopping.early_stop:
ending_flag = True
optimizer.swap_swa_sgd()
optimizer.bn_update(train_dataloader, self.model)
torch.save(self.model.state_dict(),
ROOT_SAVED_MODEL + 'swa.ckpt')
self.test(ROOT_SAVED_MODEL + 'swa.ckpt')
self.model.train()
aux.show_log(epoch, step, logs)
if ending_flag:
return
def train(model,
device,
trainloader,
testloader,
optimizer,
criterion,
metric,
epochs,
learning_rate,
swa=True,
enable_scheduler=True,
model_arch=''):
'''
Function to perform model training.
'''
model.to(device)
steps = 0
running_loss = 0
running_metric = 0
print_every = 100
train_losses = []
test_losses = []
train_metrics = []
test_metrics = []
if swa:
# initialize stochastic weight averaging
opt = SWA(optimizer)
else:
opt = optimizer
# learning rate cosine annealing
if enable_scheduler:
scheduler = lr_scheduler.CosineAnnealingLR(optimizer,
len(trainloader),
eta_min=0.0000001)
for epoch in range(epochs):
if enable_scheduler:
scheduler.step()
for inputs, labels in trainloader:
steps += 1
# Move input and label tensors to the default device
inputs, labels = inputs.to(device), labels.to(device)
opt.zero_grad()
outputs = model.forward(inputs)
loss = criterion(outputs, labels.float())
loss.backward()
opt.step()
running_loss += loss
running_metric += metric(outputs, labels.float())
if steps % print_every == 0:
test_loss = 0
test_metric = 0
model.eval()
with torch.no_grad():
for inputs, labels in testloader:
inputs, labels = inputs.to(device), labels.to(device)
outputs = model.forward(inputs)
test_loss += criterion(outputs, labels.float())
test_metric += metric(outputs, labels.float())
print(f"Epoch {epoch+1}/{epochs}.. "
f"Train loss: {running_loss/print_every:.3f}.. "
f"Test loss: {test_loss/len(testloader):.3f}.. "
f"Train metric: {running_metric/print_every:.3f}.. "
f"Test metric: {test_metric/len(testloader):.3f}.. ")
train_losses.append(running_loss / print_every)
test_losses.append(test_loss / len(testloader))
train_metrics.append(running_metric / print_every)
test_metrics.append(test_metric / len(testloader))
running_loss = 0
running_metric = 0
model.train()
if swa:
opt.update_swa()
save_model(model,
model_arch,
learning_rate,
epochs,
train_losses,
test_losses,
train_metrics,
test_metrics,
filepath='models_checkpoints')
if swa:
opt.swap_swa_sgd()
return model, train_losses, test_losses, train_metrics, test_metrics
class Train(object):
"""Train class.
"""
def __init__(self, train_ds, val_ds, fold):
self.fold = fold
self.init_lr = cfg.TRAIN.init_lr
self.warup_step = cfg.TRAIN.warmup_step
self.epochs = cfg.TRAIN.epoch
self.batch_size = cfg.TRAIN.batch_size
self.l2_regularization = cfg.TRAIN.weight_decay_factor
self.device = torch.device(
"cuda" if torch.cuda.is_available() else 'cpu')
self.model = Net().to(self.device)
self.load_weight()
param_optimizer = list(self.model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{
'params': [
p for n, p in param_optimizer
if not any(nd in n for nd in no_decay)
],
'weight_decay':
cfg.TRAIN.weight_decay_factor
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.0
}]
if 'Adamw' in cfg.TRAIN.opt:
self.optimizer = torch.optim.AdamW(self.model.parameters(),
lr=self.init_lr,
eps=1.e-5)
else:
self.optimizer = torch.optim.SGD(self.model.parameters(),
lr=0.001,
momentum=0.9)
if cfg.TRAIN.SWA > 0:
##use swa
self.optimizer = SWA(self.optimizer)
if cfg.TRAIN.mix_precision:
self.model, self.optimizer = amp.initialize(self.model,
self.optimizer,
opt_level="O1")
self.ema = EMA(self.model, 0.999)
self.ema.register()
###control vars
self.iter_num = 0
self.train_ds = train_ds
self.val_ds = val_ds
# self.scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(self.optimizer,mode='max', patience=3,verbose=True)
self.scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
self.optimizer, self.epochs, eta_min=1.e-6)
self.criterion = nn.BCEWithLogitsLoss().to(self.device)
def custom_loop(self):
"""Custom training and testing loop.
Args:
train_dist_dataset: Training dataset created using strategy.
test_dist_dataset: Testing dataset created using strategy.
strategy: Distribution strategy.
Returns:
train_loss, train_accuracy, test_loss, test_accuracy
"""
def distributed_train_epoch(epoch_num):
summary_loss = AverageMeter()
acc_score = ACCMeter()
self.model.train()
if cfg.MODEL.freeze_bn:
for m in self.model.modules():
if isinstance(m, nn.BatchNorm2d):
m.eval()
if cfg.MODEL.freeze_bn_affine:
m.weight.requires_grad = False
m.bias.requires_grad = False
for step in range(self.train_ds.size):
if epoch_num < 10:
###excute warm up in the first epoch
if self.warup_step > 0:
if self.iter_num < self.warup_step:
for param_group in self.optimizer.param_groups:
param_group['lr'] = self.iter_num / float(
self.warup_step) * self.init_lr
lr = param_group['lr']
logger.info('warm up with learning rate: [%f]' %
(lr))
start = time.time()
images, data, target = self.train_ds()
images = torch.from_numpy(images).to(self.device).float()
data = torch.from_numpy(data).to(self.device).float()
target = torch.from_numpy(target).to(self.device).float()
batch_size = data.shape[0]
output = self.model(images, data)
current_loss = self.criterion(output, target)
summary_loss.update(current_loss.detach().item(), batch_size)
acc_score.update(target, output)
self.optimizer.zero_grad()
if cfg.TRAIN.mix_precision:
with amp.scale_loss(current_loss,
self.optimizer) as scaled_loss:
scaled_loss.backward()
else:
current_loss.backward()
self.optimizer.step()
if cfg.MODEL.ema:
self.ema.update()
self.iter_num += 1
time_cost_per_batch = time.time() - start
images_per_sec = cfg.TRAIN.batch_size / time_cost_per_batch
if self.iter_num % cfg.TRAIN.log_interval == 0:
log_message = '[fold %d], '\
'Train Step %d, ' \
'summary_loss: %.6f, ' \
'accuracy: %.6f, ' \
'time: %.6f, '\
'speed %d images/persec'% (
self.fold,
step,
summary_loss.avg,
acc_score.avg,
time.time() - start,
images_per_sec)
logger.info(log_message)
if cfg.TRAIN.SWA > 0 and epoch_num >= cfg.TRAIN.SWA:
self.optimizer.update_swa()
return summary_loss, acc_score
def distributed_test_epoch(epoch_num):
summary_loss = AverageMeter()
acc_score = ACCMeter()
self.model.eval()
t = time.time()
with torch.no_grad():
for step in range(self.val_ds.size):
images, data, target = self.train_ds()
images = torch.from_numpy(images).to(self.device).float()
data = torch.from_numpy(data).to(self.device).float()
target = torch.from_numpy(target).to(self.device).float()
batch_size = data.shape[0]
output = self.model(images, data)
loss = self.criterion(output, target)
summary_loss.update(loss.detach().item(), batch_size)
acc_score.update(target, output)
if step % cfg.TRAIN.log_interval == 0:
log_message = '[fold %d], '\
'Val Step %d, ' \
'summary_loss: %.6f, ' \
'acc: %.6f, ' \
'time: %.6f' % (
self.fold,step, summary_loss.avg, acc_score.avg, time.time() - t)
logger.info(log_message)
return summary_loss, acc_score
for epoch in range(self.epochs):
for param_group in self.optimizer.param_groups:
lr = param_group['lr']
logger.info('learning rate: [%f]' % (lr))
t = time.time()
summary_loss, acc_score = distributed_train_epoch(epoch)
train_epoch_log_message = '[fold %d], '\
'[RESULT]: Train. Epoch: %d,' \
' summary_loss: %.5f,' \
' acuracy: %.5f,' \
' time:%.5f' % (
self.fold,epoch, summary_loss.avg,acc_score.avg, (time.time() - t))
logger.info(train_epoch_log_message)
if cfg.TRAIN.SWA > 0 and epoch >= cfg.TRAIN.SWA:
###switch to avg model
self.optimizer.swap_swa_sgd()
##switch eam weighta
if cfg.MODEL.ema:
self.ema.apply_shadow()
if epoch % cfg.TRAIN.test_interval == 0:
summary_loss, acc_score = distributed_test_epoch(epoch)
val_epoch_log_message = '[fold %d], '\
'[RESULT]: VAL. Epoch: %d,' \
' summary_loss: %.5f,' \
' accuracy: %.5f,' \
' time:%.5f' % (
self.fold,epoch, summary_loss.avg,acc_score.avg, (time.time() - t))
logger.info(val_epoch_log_message)
self.scheduler.step()
# self.scheduler.step(final_scores.avg)
#### save model
if not os.access(cfg.MODEL.model_path, os.F_OK):
os.mkdir(cfg.MODEL.model_path)
###save the best auc model
#### save the model every end of epoch
current_model_saved_name = './models/fold%d_epoch_%d_val_loss%.6f.pth' % (
self.fold, epoch, summary_loss.avg)
logger.info('A model saved to %s' % current_model_saved_name)
torch.save(self.model.state_dict(), current_model_saved_name)
####switch back
if cfg.MODEL.ema:
self.ema.restore()
# save_checkpoint({
# 'state_dict': self.model.state_dict(),
# },iters=epoch,tag=current_model_saved_name)
if cfg.TRAIN.SWA > 0 and epoch > cfg.TRAIN.SWA:
###switch back to plain model to train next epoch
self.optimizer.swap_swa_sgd()
def load_weight(self):
if cfg.MODEL.pretrained_model is not None:
state_dict = torch.load(cfg.MODEL.pretrained_model,
map_location=self.device)
self.model.load_state_dict(state_dict, strict=False)
def train(train_df, CONFIG):
# set-up
seed_everything(CONFIG['SEED'])
torch.manual_seed(CONFIG['TORCH_SEED'])
mlflow.log_params(CONFIG)
TRAIN_LEN = len(train_df)
train_dataset = TweetDataset(train_df, CONFIG)
CRITERION = define_criterion(CONFIG)
folds = StratifiedKFold(n_splits=CONFIG["FOLD"],
shuffle=True,
random_state=CONFIG["SEED"])
for n_fold, (train_idx, valid_idx) in enumerate(
folds.split(train_dataset.df['textID'],
train_dataset.df['sentiment'])):
if n_fold != CONFIG["FOLD_NUM"]:
continue
## DataLoaderの定義
train = torch.utils.data.Subset(train_dataset, train_idx)
valid = torch.utils.data.Subset(train_dataset, valid_idx)
DATA_IN_EPOCH = len(train)
TOTAL_DATA = DATA_IN_EPOCH * CONFIG["EPOCHS"]
T_TOTAL = int(CONFIG["EPOCHS"] * DATA_IN_EPOCH /
CONFIG["TRAIN_BATCH_SIZE"])
## modelとoptimizerの初期化
model = build_model(CONFIG)
model.to(DEVICE)
model.train()
## From 20/05/17
param_optimizer = list(model.named_parameters())
bert_params = [n for n, p in param_optimizer if "bert" in n]
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [
## BERT param
{
'params': [
p for n, p in param_optimizer
if (not any(nd in n
for nd in no_decay)) and (n in bert_params)
],
'weight_decay':
CONFIG["WEIGHT_DECAY"],
'lr':
CONFIG['LR'] * 1,
},
{
'params': [
p for n, p in param_optimizer
if (any(nd in n for nd in no_decay)) and (n in bert_params)
],
'weight_decay':
0.0,
'lr':
CONFIG['LR'] * 1,
},
## Other param
{
'params': [
p for n, p in param_optimizer
if (not any(nd in n
for nd in no_decay)) and (n not in bert_params)
],
'weight_decay':
CONFIG["WEIGHT_DECAY"],
'lr':
CONFIG['LR'] * 1,
},
{
'params': [
p for n, p in param_optimizer
if (any(nd in n
for nd in no_decay)) and (n not in bert_params)
],
'weight_decay':
0.0,
'lr':
CONFIG['LR'] * 1,
},
]
optimizer = torch.optim.AdamW(optimizer_grouped_parameters, eps=4e-5)
if CONFIG['SWA']:
optimizer = SWA(optimizer)
scheduler = get_linear_schedule_with_warmup(
optimizer,
num_warmup_steps=int(CONFIG["WARMUP"] * T_TOTAL),
num_training_steps=T_TOTAL)
train_sampler = SentimentBalanceSampler(train, CONFIG)
train_loader = DataLoader(train,
batch_size=CONFIG["TRAIN_BATCH_SIZE"],
shuffle=False,
sampler=train_sampler,
collate_fn=TweetCollate(CONFIG),
num_workers=1)
valid_loader = DataLoader(
valid,
batch_size=CONFIG["VALID_BATCH_SIZE"],
shuffle=False,
# sampler = valid_sampler,
collate_fn=TweetCollate(CONFIG),
num_workers=1)
n_data = 0
n_e_data = 0
best_val = 0.0
best_val_neu = 0.0
best_val_pos = 0.0
best_val_neg = 0.0
t_batch = 0
while n_data < TOTAL_DATA:
print(f"Epoch : {int(n_data/DATA_IN_EPOCH)}")
n_batch = 0
loss_list = []
jac_token_list = []
jac_text_list = []
jac_sentiment_list = []
jac_cl_text_list = []
output_list = []
target_list = []
for batch in tqdm(train_loader):
textID = batch['textID']
text = batch['text']
sentiment = batch['sentiment']
cl_text = batch['cl_text']
selected_text = batch['selected_text']
cl_selected_text = batch['cl_selected_text']
text_idx = batch['text_idx']
offsets = batch['offsets']
tokenized_text = batch['tokenized_text'].to(DEVICE)
mask = batch['mask'].to(DEVICE)
mask_out = batch['mask_out'].to(DEVICE)
token_type_ids = batch['token_type_ids'].to(DEVICE)
weight = batch['weight'].to(DEVICE)
target = batch['target'].to(DEVICE)
ep = int(n_data / DATA_IN_EPOCH)
n_data += len(textID)
n_e_data += len(textID)
n_batch += 1
t_batch += 1
model.zero_grad()
# optimizer.zero_grad()
output = model(input_ids=tokenized_text,
attention_mask=mask,
token_type_ids=token_type_ids,
mask_out=mask_out)
loss = CRITERION(output, target)
loss = loss * weight
loss.mean().backward()
loss = loss.detach().cpu().numpy().tolist()
optimizer.step()
if t_batch < T_TOTAL * 0.50:
scheduler.step()
loss_list.extend(loss)
output = output.detach().cpu().numpy()
target = target.detach().cpu().numpy()
jac = calc_jaccard(output, batch, CONFIG)
jac_token_list.extend(jac['jaccard_token'].tolist())
jac_cl_text_list.extend(jac['jaccard_cl_text'].tolist())
jac_text_list.extend(jac['jaccard_text'].tolist())
jac_sentiment_list.extend(sentiment)
if ((((ep > 0) &
(n_batch %
(int(5 * 32 / CONFIG["TRAIN_BATCH_SIZE"])) == 0)) |
((n_batch %
(int(50 * 32 / CONFIG["TRAIN_BATCH_SIZE"])) == 0)) |
(n_data >= TOTAL_DATA)) and
(CONFIG['SWA'] and ep > 0 and t_batch >= T_TOTAL * 0.50)):
optimizer.update_swa()
if (
((ep > 0) &
(n_batch %
(int(50 * 32 / CONFIG["TRAIN_BATCH_SIZE"])) == 0)) |
((n_batch %
(int(50 * 32 / CONFIG["TRAIN_BATCH_SIZE"])) == 0)) |
(n_data >= TOTAL_DATA)
): # ((n_data>=0)&(n_data<=1600)|(n_data>=21000)&(n_data<=23000))&
if CONFIG['SWA'] and ep > 0 and t_batch >= T_TOTAL * 0.50:
# optimizer.update_swa()
optimizer.swap_swa_sgd()
val = create_valid(model, valid_loader, CONFIG)
trn_loss = np.array(loss_list).mean()
trn_jac_token = np.array(jac_token_list).mean()
trn_jac_cl_text = np.array(jac_cl_text_list).mean()
trn_jac_text = np.array(jac_text_list).mean()
trn_jac_text_neu = np.array(jac_text_list)[np.array(
jac_sentiment_list) == 'neutral'].mean()
trn_jac_text_pos = np.array(jac_text_list)[np.array(
jac_sentiment_list) == 'positive'].mean()
trn_jac_text_neg = np.array(jac_text_list)[np.array(
jac_sentiment_list) == 'negative'].mean()
val_loss = val['loss'].mean()
val_jac_token = val['jaccard_token'].mean()
val_jac_cl_text = val['jaccard_cl_text'].mean()
val_jac_text = val['jaccard_text'].mean()
val_jac_text_neu = val['jaccard_text'][val['sentiment'] ==
'neutral'].mean()
val_jac_text_pos = val['jaccard_text'][val['sentiment'] ==
'positive'].mean()
val_jac_text_neg = val['jaccard_text'][val['sentiment'] ==
'negative'].mean()
loss_list = []
jac_token_list = []
jac_cl_text_list = []
jac_text_list = []
jac_sentiment_list = []
# mlflow
metrics = {
"lr": optimizer.param_groups[0]['lr'],
"trn_loss": trn_loss,
"trn_jac_text_neu": trn_jac_text_neu,
"trn_jac_text_pos": trn_jac_text_pos,
"trn_jac_text_neg": trn_jac_text_neg,
"trn_jac_text": trn_jac_text,
"val_loss": val_loss,
"val_jac_text_neu": val_jac_text_neu,
"val_jac_text_pos": val_jac_text_pos,
"val_jac_text_neg": val_jac_text_neg,
"val_jac_text": val_jac_text,
}
mlflow.log_metrics(metrics, step=n_data)
if CONFIG['SWA'] and t_batch < T_TOTAL * 0.50:
pass
else:
if best_val < val_jac_text:
best_val = val_jac_text
best_model = copy.deepcopy(model)
if CONFIG['SWA'] and ep > 0 and t_batch >= T_TOTAL * 0.50:
optimizer.swap_swa_sgd()
if n_e_data >= DATA_IN_EPOCH:
n_e_data -= DATA_IN_EPOCH
if n_data >= TOTAL_DATA:
filepath = os.path.join(FILE_DIR, OUTPUT_DIR, "model.pth")
torch.save(best_model.state_dict(), filepath)
# mlflow
mlflow.log_artifact(filepath)
break
def fit(
model,
train_dataset,
val_dataset,
optimizer_name="adam",
samples_per_player=0,
epochs=50,
batch_size=32,
val_bs=32,
warmup_prop=0.1,
lr=1e-3,
acc_steps=1,
swa_first_epoch=50,
num_classes_aux=0,
aux_mode="sigmoid",
verbose=1,
first_epoch_eval=0,
device="cuda",
):
"""
Fitting function for the classification task.
Args:
model (torch model): Model to train.
train_dataset (torch dataset): Dataset to train with.
val_dataset (torch dataset): Dataset to validate with.
optimizer_name (str, optional): Optimizer name. Defaults to 'adam'.
samples_per_player (int, optional): Number of images to use per player. Defaults to 0.
epochs (int, optional): Number of epochs. Defaults to 50.
batch_size (int, optional): Training batch size. Defaults to 32.
val_bs (int, optional): Validation batch size. Defaults to 32.
warmup_prop (float, optional): Warmup proportion. Defaults to 0.1.
lr (float, optional): Learning rate. Defaults to 1e-3.
acc_steps (int, optional): Accumulation steps. Defaults to 1.
swa_first_epoch (int, optional): Epoch to start applying SWA from. Defaults to 50.
num_classes_aux (int, optional): Number of auxiliary classes. Defaults to 0.
aux_mode (str, optional): Mode for auxiliary classification. Defaults to 'sigmoid'.
verbose (int, optional): Period (in epochs) to display logs at. Defaults to 1.
first_epoch_eval (int, optional): Epoch to start evaluating at. Defaults to 0.
device (str, optional): Device for torch. Defaults to "cuda".
Returns:
numpy array [len(val_dataset)]: Last predictions on the validation data.
numpy array [len(val_dataset) x num_classes_aux]: Last aux predictions on the val data.
"""
optimizer = define_optimizer(optimizer_name, model.parameters(), lr=lr)
if swa_first_epoch <= epochs:
optimizer = SWA(optimizer)
loss_fct = nn.BCEWithLogitsLoss()
loss_fct_aux = nn.BCEWithLogitsLoss(
) if aux_mode == "sigmoid" else nn.CrossEntropyLoss()
aux_loss_weight = 1 if num_classes_aux else 0
if samples_per_player:
sampler = PlayerSampler(
RandomSampler(train_dataset),
train_dataset.players,
batch_size=batch_size,
drop_last=True,
samples_per_player=samples_per_player,
)
train_loader = DataLoader(
train_dataset,
batch_sampler=sampler,
num_workers=NUM_WORKERS,
pin_memory=True,
)
print(
f"Using {len(train_loader)} out of {len(train_dataset) // batch_size} "
f"batches by limiting to {samples_per_player} samples per player.\n"
)
else:
train_loader = DataLoader(
train_dataset,
batch_size=batch_size,
shuffle=True,
drop_last=True,
num_workers=NUM_WORKERS,
pin_memory=True,
)
val_loader = DataLoader(
val_dataset,
batch_size=val_bs,
shuffle=False,
num_workers=NUM_WORKERS,
pin_memory=True,
)
num_training_steps = int(epochs * len(train_loader))
num_warmup_steps = int(warmup_prop * num_training_steps)
scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps,
num_training_steps)
for epoch in range(epochs):
model.train()
start_time = time.time()
optimizer.zero_grad()
avg_loss = 0
if epoch + 1 > swa_first_epoch:
optimizer.swap_swa_sgd()
for batch in train_loader:
images = batch[0].to(device)
y_batch = batch[1].to(device).view(-1).float()
y_batch_aux = batch[2].to(device).float()
y_batch_aux = y_batch_aux.float(
) if aux_mode == "sigmoid" else y_batch_aux.long()
y_pred, y_pred_aux = model(images)
loss = loss_fct(y_pred.view(-1), y_batch)
if aux_loss_weight:
loss += aux_loss_weight * loss_fct_aux(y_pred_aux, y_batch_aux)
loss.backward()
avg_loss += loss.item() / len(train_loader)
optimizer.step()
scheduler.step()
for param in model.parameters():
param.grad = None
if epoch + 1 >= swa_first_epoch:
optimizer.update_swa()
optimizer.swap_swa_sgd()
preds = np.empty(0)
preds_aux = np.empty((0, num_classes_aux))
model.eval()
avg_val_loss, auc, scores_aux = 0., 0., 0.
if epoch + 1 >= first_epoch_eval or epoch + 1 == epochs:
with torch.no_grad():
for batch in val_loader:
images = batch[0].to(device)
y_batch = batch[1].to(device).view(-1).float()
y_aux = batch[2].to(device).float()
y_batch_aux = y_aux.float(
) if aux_mode == "sigmoid" else y_aux.long()
y_pred, y_pred_aux = model(images)
loss = loss_fct(y_pred.detach().view(-1), y_batch)
if aux_loss_weight:
loss += aux_loss_weight * loss_fct_aux(
y_pred_aux.detach(), y_batch_aux)
avg_val_loss += loss.item() / len(val_loader)
y_pred = torch.sigmoid(y_pred).view(-1)
preds = np.concatenate(
[preds, y_pred.detach().cpu().numpy()])
if num_classes_aux:
y_pred_aux = (y_pred_aux.sigmoid() if aux_mode
== "sigmoid" else y_pred_aux.softmax(-1))
preds_aux = np.concatenate(
[preds_aux,
y_pred_aux.detach().cpu().numpy()])
auc = roc_auc_score(val_dataset.labels, preds)
if num_classes_aux:
if aux_mode == "sigmoid":
scores_aux = np.round(
[
roc_auc_score(val_dataset.aux_labels[:, i],
preds_aux[:, i])
for i in range(num_classes_aux)
],
3,
).tolist()
else:
scores_aux = np.round(
[
roc_auc_score((val_dataset.aux_labels
== i).astype(int), preds_aux[:, i])
for i in range(num_classes_aux)
],
3,
).tolist()
else:
scores_aux = 0
elapsed_time = time.time() - start_time
if (epoch + 1) % verbose == 0:
elapsed_time = elapsed_time * verbose
lr = scheduler.get_last_lr()[0]
print(
f"Epoch {epoch + 1:02d}/{epochs:02d} \t lr={lr:.1e}\t t={elapsed_time:.0f}s \t"
f"loss={avg_loss:.3f}",
end="\t",
)
if epoch + 1 >= first_epoch_eval:
print(
f"val_loss={avg_val_loss:.3f} \t auc={auc:.3f}\t aucs_aux={scores_aux}"
)
else:
print("")
del val_loader, train_loader, y_pred
torch.cuda.empty_cache()
return preds, preds_aux
def train(self, train_inputs):
config = self.config.fitting
model = train_inputs['model']
train_data = train_inputs['train_data']
dev_data = train_inputs['dev_data']
epoch_start = train_inputs['epoch_start']
train_steps = int((len(train_data) + config.batch_size - 1) / config.batch_size)
train_dataloader = DataLoader(train_data,
batch_size=config.batch_size,
collate_fn=self.get_collate_fn('train'),
shuffle=True)
params_lr = []
for key, value in model.get_params().items():
if key in config.lr:
params_lr.append({"params": value, 'lr': config.lr[key]})
optimizer = torch.optim.Adam(params_lr)
optimizer = SWA(optimizer)
early_stopping = EarlyStopping(model, ROOT_WEIGHT, mode='max', patience=3)
learning_schedual = LearningSchedual(optimizer, config.epochs, config.end_epoch, train_steps, config.lr)
aux = ModelAux(self.config, train_steps)
moving_log = MovingData(window=100)
ending_flag = False
detach_flag = False
swa_flag = False
fgm = FGM(model)
for epoch in range(epoch_start, config.epochs):
for step, (inputs, targets, others) in enumerate(train_dataloader):
inputs = dict([(key, value[0].cuda() if value[1] else value[0]) for key, value in inputs.items()])
targets = dict([(key, value.cuda()) for key, value in targets.items()])
if epoch > 0 and step == 0:
model.detach_ptm(False)
detach_flag = False
if epoch == 0 and step == 0:
model.detach_ptm(True)
detach_flag = True
# train ================================================================================================
preds = model(inputs, en_decode=config.verbose)
loss = model.cal_loss(preds, targets, inputs['mask'])
loss['back'].backward()
# 对抗训练
if (not detach_flag) and config.en_fgm:
fgm.attack(emb_name='word_embeddings') # 在embedding上添加对抗扰动
preds_adv = model(inputs, en_decode=False)
loss_adv = model.cal_loss(preds_adv, targets, inputs['mask'])
loss_adv['back'].backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度
fgm.restore(emb_name='word_embeddings') # 恢复embedding参数
# torch.nn.utils.clip_grad_norm(model.parameters(), 1)
optimizer.step()
optimizer.zero_grad()
with torch.no_grad():
logs = {}
if config.verbose:
pred_entity_point = model.find_entity(preds['pred'], others['raw_text'])
cn, pn, tn = self.calculate_f1(pred_entity_point, others['raw_entity'])
metrics_data = {'loss': loss['show'].cpu().numpy(), 'sampled_num': 1,
'correct_num': cn, 'pred_num': pn,
'true_num': tn}
moving_data = moving_log(epoch * train_steps + step, metrics_data)
logs['loss'] = moving_data['loss'] / moving_data['sampled_num']
logs['precise'], logs['recall'], logs['f1'] = calculate_f1(moving_data['correct_num'],
moving_data['pred_num'],
moving_data['true_num'],
verbose=True)
else:
metrics_data = {'loss': loss['show'].cpu().numpy(), 'sampled_num': 1}
moving_data = moving_log(epoch * train_steps + step, metrics_data)
logs['loss'] = moving_data['loss'] / moving_data['sampled_num']
# update lr
lr_data = learning_schedual.update_lr(epoch, step)
logs.update(lr_data)
if step + 1 == train_steps:
model.eval()
aux.new_line()
# dev ==========================================================================================
eval_inputs = {'model': model,
'data': dev_data,
'type_data': 'dev',
'outfile': train_inputs['dev_res_file']}
dev_result = self.eval(eval_inputs)
logs['dev_loss'] = dev_result['loss']
logs['dev_precise'] = dev_result['precise']
logs['dev_recall'] = dev_result['recall']
logs['dev_f1'] = dev_result['f1']
if logs['dev_f1'] > 0.80:
torch.save(model.state_dict(),
"{}/auto_save_{:.6f}.ckpt".format(ROOT_WEIGHT, logs['dev_f1']))
if (epoch > 3 or swa_flag) and config.en_swa:
optimizer.update_swa()
swa_flag = True
early_stop, best_score = early_stopping(logs['dev_f1'])
# test =========================================================================================
if (epoch + 1 == config.epochs and step + 1 == train_steps) or early_stop:
ending_flag = True
if swa_flag:
optimizer.swap_swa_sgd()
optimizer.bn_update(train_dataloader, model)
model.train()
aux.show_log(epoch, step, logs)
if ending_flag:
return best_score
def main():
maxIOU = 0.0
assert torch.cuda.is_available()
torch.backends.cudnn.benchmark = True
model_fname = '../data/model_swa_8/deeplabv3_{0}_epoch%d.pth'.format(
'crops')
focal_loss = FocalLoss2d()
train_dataset = CropSegmentation(train=True, crop_size=args.crop_size)
# test_dataset = CropSegmentation(train=False, crop_size=args.crop_size)
model = torchvision.models.segmentation.deeplabv3_resnet50(
pretrained=False, progress=True, num_classes=5, aux_loss=True)
if args.train:
weight = np.ones(4)
weight[2] = 5
weight[3] = 5
w = torch.FloatTensor(weight).cuda()
criterion = nn.CrossEntropyLoss() #ignore_index=255 weight=w
model = nn.DataParallel(model).cuda()
for param in model.parameters():
param.requires_grad = True
optimizer1 = optim.SGD(model.parameters(),
lr=config.lr,
momentum=0.9,
weight_decay=1e-4)
scheduler = lr_scheduler.CosineAnnealingLR(optimizer,
T_max=(args.epochs // 9) +
1)
optimizer = SWA(optimizer1)
dataset_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.batch_size,
shuffle=args.train,
pin_memory=True,
num_workers=args.workers)
max_iter = args.epochs * len(dataset_loader)
losses = AverageMeter()
start_epoch = 0
if args.resume:
if os.path.isfile(args.resume):
print('=> loading checkpoint {0}'.format(args.resume))
checkpoint = torch.load(args.resume)
start_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print('=> loaded checkpoint {0} (epoch {1})'.format(
args.resume, checkpoint['epoch']))
else:
print('=> no checkpoint found at {0}'.format(args.resume))
for epoch in range(start_epoch, args.epochs):
scheduler.step(epoch)
model.train()
for i, (inputs, target) in enumerate(dataset_loader):
inputs = Variable(inputs.cuda())
target = Variable(target.cuda())
outputs = model(inputs)
loss1 = focal_loss(outputs['out'], target)
loss2 = focal_loss(outputs['aux'], target)
loss01 = loss1 + 0.1 * loss2
loss3 = lovasz_softmax(outputs['out'], target)
loss4 = lovasz_softmax(outputs['aux'], target)
loss02 = loss3 + 0.1 * loss4
loss = loss01 + loss02
if np.isnan(loss.item()) or np.isinf(loss.item()):
pdb.set_trace()
losses.update(loss.item(), args.batch_size)
loss.backward()
optimizer.step()
optimizer.zero_grad()
if i > 10 and i % 5 == 0:
optimizer.update_swa()
print('epoch: {0}\t'
'iter: {1}/{2}\t'
'loss: {loss.val:.4f} ({loss.ema:.4f})'.format(
epoch + 1, i + 1, len(dataset_loader), loss=losses))
if epoch > 5:
torch.save(
{
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
}, model_fname % (epoch + 1))
optimizer.swap_swa_sgd()
torch.save(
{
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
}, model_fname % (665 + 1))
class Model() :
def __init__(self, configuration, pre_embed=None) :
configuration = deepcopy(configuration)
self.configuration = deepcopy(configuration)
configuration['model']['encoder']['pre_embed'] = pre_embed
encoder_copy = deepcopy(configuration['model']['encoder'])
self.Pencoder = Encoder.from_params(Params(configuration['model']['encoder'])).to(device)
self.Qencoder = Encoder.from_params(Params(encoder_copy)).to(device)
configuration['model']['decoder']['hidden_size'] = self.Pencoder.output_size
self.decoder = AttnDecoderQA.from_params(Params(configuration['model']['decoder'])).to(device)
self.bsize = configuration['training']['bsize']
self.adversary_multi = AdversaryMulti(self.decoder)
weight_decay = configuration['training'].get('weight_decay', 1e-5)
self.params = list(self.Pencoder.parameters()) + list(self.Qencoder.parameters()) + list(self.decoder.parameters())
self.optim = torch.optim.Adam(self.params, weight_decay=weight_decay, amsgrad=True)
# self.optim = torch.optim.Adagrad(self.params, lr=0.05, weight_decay=weight_decay)
self.criterion = nn.CrossEntropyLoss()
import time
dirname = configuration['training']['exp_dirname']
basepath = configuration['training'].get('basepath', 'outputs')
self.time_str = time.ctime().replace(' ', '_')
self.dirname = os.path.join(basepath, dirname, self.time_str)
self.swa_settings = configuration['training']['swa']
if self.swa_settings[0]:
self.swa_all_optim = SWA(self.optim)
self.running_norms = []
@classmethod
def init_from_config(cls, dirname, **kwargs) :
config = json.load(open(dirname + '/config.json', 'r'))
config.update(kwargs)
obj = cls(config)
obj.load_values(dirname)
return obj
def get_param_buffer_norms(self):
for p in self.swa_all_optim.param_groups[0]['params']:
param_state = self.swa_all_optim.state[p]
if 'swa_buffer' not in param_state:
self.swa_all_optim.update_swa()
norms = []
for p in np.array(self.swa_all_optim.param_groups[0]['params'])[
[1, 2, 5, 6, 10, 11, 14, 15, 18, 20, 24, 26]]:
param_state = self.swa_all_optim.state[p]
buf = np.squeeze(
param_state['swa_buffer'].cpu().numpy())
cur_state = np.squeeze(p.data.cpu().numpy())
norm = np.linalg.norm(buf - cur_state)
norms.append(norm)
if self.swa_settings[3] == 2:
return np.max(norms)
return np.mean(norms)
def total_iter_num(self):
return self.swa_all_optim.param_groups[0]['step_counter']
def iter_for_swa_update(self, iter_num):
return iter_num > self.swa_settings[1] \
and iter_num % self.swa_settings[2] == 0
def check_and_update_swa(self):
if self.iter_for_swa_update(self.total_iter_num()):
cur_step_diff_norm = self.get_param_buffer_norms()
if self.swa_settings[3] == 0:
self.swa_all_optim.update_swa()
return
if not self.running_norms:
running_mean_norm = 0
else:
running_mean_norm = np.mean(self.running_norms)
if cur_step_diff_norm > running_mean_norm:
self.swa_all_optim.update_swa()
self.running_norms = [cur_step_diff_norm]
elif cur_step_diff_norm > 0:
self.running_norms.append(cur_step_diff_norm)
def train(self, train_data, train=True) :
docs_in = train_data.P
question_in = train_data.Q
entity_masks_in = train_data.E
target_in = train_data.A
sorting_idx = get_sorting_index_with_noise_from_lengths([len(x) for x in docs_in], noise_frac=0.1)
docs = [docs_in[i] for i in sorting_idx]
questions = [question_in[i] for i in sorting_idx]
entity_masks = [entity_masks_in[i] for i in sorting_idx]
target = [target_in[i] for i in sorting_idx]
self.Pencoder.train()
self.Qencoder.train()
self.decoder.train()
bsize = self.bsize
N = len(questions)
loss_total = 0
batches = list(range(0, N, bsize))
batches = shuffle(batches)
for n in tqdm(batches) :
torch.cuda.empty_cache()
batch_doc = docs[n:n+bsize]
batch_ques = questions[n:n+bsize]
batch_entity_masks = entity_masks[n:n+bsize]
batch_doc = BatchHolder(batch_doc)
batch_ques = BatchHolder(batch_ques)
batch_data = BatchMultiHolder(P=batch_doc, Q=batch_ques)
batch_data.entity_mask = torch.ByteTensor(np.array(batch_entity_masks)).to(device)
self.Pencoder(batch_data.P)
self.Qencoder(batch_data.Q)
self.decoder(batch_data)
batch_target = target[n:n+bsize]
batch_target = torch.LongTensor(batch_target).to(device)
ce_loss = self.criterion(batch_data.predict, batch_target)
loss = ce_loss
if hasattr(batch_data, 'reg_loss') :
loss += batch_data.reg_loss
if train :
if self.swa_settings[0]:
self.check_and_update_swa()
self.swa_all_optim.zero_grad()
loss.backward()
self.swa_all_optim.step()
else:
self.optim.zero_grad()
loss.backward()
self.optim.step()
loss_total += float(loss.data.cpu().item())
if self.swa_settings[0] and self.swa_all_optim.param_groups[0][
'step_counter'] > self.swa_settings[1]:
print("\nSWA swapping\n")
# self.attn_optim.swap_swa_sgd()
# self.encoder_optim.swap_swa_sgd()
# self.decoder_optim.swap_swa_sgd()
self.swa_all_optim.swap_swa_sgd()
self.running_norms = []
return loss_total*bsize/N
def evaluate(self, data) :
docs = data.P
questions = data.Q
entity_masks = data.E
self.Pencoder.train()
self.Qencoder.train()
self.decoder.train()
bsize = self.bsize
N = len(questions)
outputs = []
attns = []
scores = []
for n in tqdm(range(0, N, bsize)) :
torch.cuda.empty_cache()
batch_doc = docs[n:n+bsize]
batch_ques = questions[n:n+bsize]
batch_entity_masks = entity_masks[n:n+bsize]
batch_doc = BatchHolder(batch_doc)
batch_ques = BatchHolder(batch_ques)
batch_data = BatchMultiHolder(P=batch_doc, Q=batch_ques)
batch_data.entity_mask = torch.ByteTensor(np.array(batch_entity_masks)).to(device)
self.Pencoder(batch_data.P)
self.Qencoder(batch_data.Q)
self.decoder(batch_data)
prediction_scores = batch_data.predict.cpu().data.numpy()
batch_data.predict = torch.argmax(batch_data.predict, dim=-1)
if self.decoder.use_attention :
attn = batch_data.attn
attns.append(attn.cpu().data.numpy())
predict = batch_data.predict.cpu().data.numpy()
outputs.append(predict)
scores.append(prediction_scores)
outputs = [x for y in outputs for x in y]
attns = [x for y in attns for x in y]
scores = [x for y in scores for x in y]
return outputs, attns, scores
def save_values(self, use_dirname=None, save_model=True) :
if use_dirname is not None :
dirname = use_dirname
else :
dirname = self.dirname
os.makedirs(dirname, exist_ok=True)
shutil.copy2(file_name, dirname + '/')
json.dump(self.configuration, open(dirname + '/config.json', 'w'))
if save_model :
torch.save(self.Pencoder.state_dict(), dirname + '/encP.th')
torch.save(self.Qencoder.state_dict(), dirname + '/encQ.th')
torch.save(self.decoder.state_dict(), dirname + '/dec.th')
return dirname
def load_values(self, dirname) :
self.Pencoder.load_state_dict(torch.load(dirname + '/encP.th'))
self.Qencoder.load_state_dict(torch.load(dirname + '/encQ.th'))
self.decoder.load_state_dict(torch.load(dirname + '/dec.th'))
def permute_attn(self, data, num_perm=100) :
docs = data.P
questions = data.Q
entity_masks = data.E
self.Pencoder.train()
self.Qencoder.train()
self.decoder.train()
bsize = self.bsize
N = len(questions)
permutations_predict = []
permutations_diff = []
for n in tqdm(range(0, N, bsize)) :
torch.cuda.empty_cache()
batch_doc = docs[n:n+bsize]
batch_ques = questions[n:n+bsize]
batch_entity_masks = entity_masks[n:n+bsize]
batch_doc = BatchHolder(batch_doc)
batch_ques = BatchHolder(batch_ques)
batch_data = BatchMultiHolder(P=batch_doc, Q=batch_ques)
batch_data.entity_mask = torch.ByteTensor(np.array(batch_entity_masks)).to(device)
self.Pencoder(batch_data.P)
self.Qencoder(batch_data.Q)
self.decoder(batch_data)
predict_true = batch_data.predict.clone().detach()
batch_perms_predict = np.zeros((batch_data.P.B, num_perm))
batch_perms_diff = np.zeros((batch_data.P.B, num_perm))
for i in range(num_perm) :
batch_data.permute = True
self.decoder(batch_data)
predict = torch.argmax(batch_data.predict, dim=-1)
batch_perms_predict[:, i] = predict.cpu().data.numpy()
predict_difference = self.adversary_multi.output_diff(batch_data.predict, predict_true)
batch_perms_diff[:, i] = predict_difference.squeeze(-1).cpu().data.numpy()
permutations_predict.append(batch_perms_predict)
permutations_diff.append(batch_perms_diff)
permutations_predict = [x for y in permutations_predict for x in y]
permutations_diff = [x for y in permutations_diff for x in y]
return permutations_predict, permutations_diff
def adversarial_multi(self, data) :
docs = data.P
questions = data.Q
entity_masks = data.E
self.Pencoder.eval()
self.Qencoder.eval()
self.decoder.eval()
print(self.adversary_multi.K)
self.params = list(self.Pencoder.parameters()) + list(self.Qencoder.parameters()) + list(self.decoder.parameters())
for p in self.params :
p.requires_grad = False
bsize = self.bsize
N = len(questions)
batches = list(range(0, N, bsize))
outputs, attns, diffs = [], [], []
for n in tqdm(batches) :
torch.cuda.empty_cache()
batch_doc = docs[n:n+bsize]
batch_ques = questions[n:n+bsize]
batch_entity_masks = entity_masks[n:n+bsize]
batch_doc = BatchHolder(batch_doc)
batch_ques = BatchHolder(batch_ques)
batch_data = BatchMultiHolder(P=batch_doc, Q=batch_ques)
batch_data.entity_mask = torch.ByteTensor(np.array(batch_entity_masks)).to(device)
self.Pencoder(batch_data.P)
self.Qencoder(batch_data.Q)
self.decoder(batch_data)
self.adversary_multi(batch_data)
predict_volatile = torch.argmax(batch_data.predict_volatile, dim=-1)
outputs.append(predict_volatile.cpu().data.numpy())
attn = batch_data.attn_volatile
attns.append(attn.cpu().data.numpy())
predict_difference = self.adversary_multi.output_diff(batch_data.predict_volatile, batch_data.predict.unsqueeze(1))
diffs.append(predict_difference.cpu().data.numpy())
outputs = [x for y in outputs for x in y]
attns = [x for y in attns for x in y]
diffs = [x for y in diffs for x in y]
return outputs, attns, diffs
def gradient_mem(self, data) :
docs = data.P
questions = data.Q
entity_masks = data.E
self.Pencoder.train()
self.Qencoder.train()
self.decoder.train()
bsize = self.bsize
N = len(questions)
grads = {'XxE' : [], 'XxE[X]' : [], 'H' : []}
for n in range(0, N, bsize) :
torch.cuda.empty_cache()
batch_doc = docs[n:n+bsize]
batch_ques = questions[n:n+bsize]
batch_entity_masks = entity_masks[n:n+bsize]
batch_doc = BatchHolder(batch_doc)
batch_ques = BatchHolder(batch_ques)
batch_data = BatchMultiHolder(P=batch_doc, Q=batch_ques)
batch_data.entity_mask = torch.ByteTensor(np.array(batch_entity_masks)).to(device)
batch_data.P.keep_grads = True
batch_data.detach = True
self.Pencoder(batch_data.P)
self.Qencoder(batch_data.Q)
self.decoder(batch_data)
max_predict = torch.argmax(batch_data.predict, dim=-1)
prob_predict = nn.Softmax(dim=-1)(batch_data.predict)
max_class_prob = torch.gather(prob_predict, -1, max_predict.unsqueeze(-1))
max_class_prob.sum().backward()
g = batch_data.P.embedding.grad
em = batch_data.P.embedding
g1 = (g * em).sum(-1)
grads['XxE[X]'].append(g1.cpu().data.numpy())
g1 = (g * self.Pencoder.embedding.weight.sum(0)).sum(-1)
grads['XxE'].append(g1.cpu().data.numpy())
g1 = batch_data.P.hidden.grad.sum(-1)
grads['H'].append(g1.cpu().data.numpy())
for k in grads :
grads[k] = [x for y in grads[k] for x in y]
return grads
def remove_and_run(self, data) :
docs = data.P
questions = data.Q
entity_masks = data.E
self.Pencoder.train()
self.Qencoder.train()
self.decoder.train()
bsize = self.bsize
N = len(questions)
output_diffs = []
for n in tqdm(range(0, N, bsize)) :
torch.cuda.empty_cache()
batch_doc = docs[n:n+bsize]
batch_ques = questions[n:n+bsize]
batch_entity_masks = entity_masks[n:n+bsize]
batch_doc = BatchHolder(batch_doc)
batch_ques = BatchHolder(batch_ques)
batch_data = BatchMultiHolder(P=batch_doc, Q=batch_ques)
batch_data.entity_mask = torch.ByteTensor(np.array(batch_entity_masks)).to(device)
self.Pencoder(batch_data.P)
self.Qencoder(batch_data.Q)
self.decoder(batch_data)
po = np.zeros((batch_data.P.B, batch_data.P.maxlen))
for i in range(1, batch_data.P.maxlen - 1) :
batch_doc = BatchHolder(docs[n:n+bsize])
batch_doc.seq = torch.cat([batch_doc.seq[:, :i], batch_doc.seq[:, i+1:]], dim=-1)
batch_doc.lengths = batch_doc.lengths - 1
batch_doc.masks = torch.cat([batch_doc.masks[:, :i], batch_doc.masks[:, i+1:]], dim=-1)
batch_data_loop = BatchMultiHolder(P=batch_doc, Q=batch_ques)
batch_data_loop.entity_mask = torch.ByteTensor(np.array(batch_entity_masks)).to(device)
self.Pencoder(batch_data_loop.P)
self.decoder(batch_data_loop)
predict_difference = self.adversary_multi.output_diff(batch_data_loop.predict, batch_data.predict)
po[:, i] = predict_difference.squeeze(-1).cpu().data.numpy()
output_diffs.append(po)
output_diffs = [x for y in output_diffs for x in y]
return output_diffs
def train(opt):
model = www_model_jamo_vertical.STR(opt, device)
print(
'model parameters. height {}, width {}, num of fiducial {}, input channel {}, output channel {}, hidden size {}, batch max length {}'
.format(opt.imgH, opt.imgW, opt.num_fiducial, opt.input_channel,
opt.output_channel, opt.hidden_size, opt.batch_max_length))
# weight initialization
for name, param, in model.named_parameters():
if 'localization_fc2' in name:
print(f'Skip {name} as it is already initializaed')
continue
try:
if 'bias' in name:
init.constant_(param, 0.0)
elif 'weight' in name:
init.kaiming_normal_(param)
except Exception as e:
if 'weight' in name:
param.data.fill_(1)
continue
# load pretrained model
if opt.saved_model != '':
base_path = './models'
print(
f'looking for pretrained model from {os.path.join(base_path, opt.saved_model)}'
)
try:
model.load_state_dict(
torch.load(os.path.join(base_path, opt.saved_model)))
print('loading complete ')
except Exception as e:
print(e)
print('coud not find model')
#data parallel for multi GPU
model = torch.nn.DataParallel(model).to(device)
model.train()
# loss
criterion = torch.nn.CrossEntropyLoss(ignore_index=0).to(
device) #ignore [GO] token = ignore index 0
log_avg = utils.Averager()
# filter that only require gradient descent
filtered_parameters = []
params_num = []
for p in filter(lambda p: p.requires_grad, model.parameters()):
filtered_parameters.append(p)
params_num.append(np.prod(p.size()))
print('Tranable params : ', sum(params_num))
# optimizer
# base_opt = optim.Adadelta(filtered_parameters, lr= opt.lr, rho = opt.rho, eps = opt.eps)
base_opt = torch.optim.Adam(filtered_parameters, lr=0.001)
optimizer = SWA(base_opt)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,
mode='max',
verbose=True,
patience=2,
factor=0.5)
# optimizer = adabound.AdaBound(filtered_parameters, lr=1e-3, final_lr=0.1)
# opt log
with open(f'./models/{opt.experiment_name}/opt.txt', 'a') as opt_file:
opt_log = '---------------------Options-----------------\n'
args = vars(opt)
for k, v in args.items():
opt_log += f'{str(k)} : {str(v)}\n'
opt_log += '---------------------------------------------\n'
opt_file.write(opt_log)
#start training
start_time = time.time()
best_accuracy = -1
best_norm_ED = -1
swa_count = 0
for n_epoch, epoch in enumerate(range(opt.num_epoch)):
for n_iter, data_point in enumerate(data_loader):
image_tensors, top, mid, bot = data_point
image = image_tensors.to(device)
text_top, length_top = top_converter.encode(
top, batch_max_length=opt.batch_max_length)
text_mid, length_mid = middle_converter.encode(
mid, batch_max_length=opt.batch_max_length)
text_bot, length_bot = bottom_converter.encode(
bot, batch_max_length=opt.batch_max_length)
batch_size = image.size(0)
pred_top, pred_mid, pred_bot = model(image, text_top[:, :-1],
text_mid[:, :-1],
text_bot[:, :-1])
# cost_top = criterion(pred_top.view(-1, pred_top.shape[-1]), text_top[:, 1:].contiguous().view(-1))
# cost_mid = criterion(pred_mid.view(-1, pred_mid.shape[-1]), text_mid[:, 1:].contiguous().view(-1))
# cost_bot = criterion(pred_bot.view(-1, pred_bot.shape[-1]), text_bot[:, 1:].contiguous().view(-1))
if n_iter % 2 == 0:
cost_top = utils.reduced_focal_loss(
pred_top.view(-1, pred_top.shape[-1]),
text_top[:, 1:].contiguous().view(-1),
ignore_index=0,
gamma=2,
alpha=0.25,
threshold=0.5)
cost_mid = utils.reduced_focal_loss(
pred_mid.view(-1, pred_mid.shape[-1]),
text_mid[:, 1:].contiguous().view(-1),
ignore_index=0,
gamma=2,
alpha=0.25,
threshold=0.5)
cost_bot = utils.reduced_focal_loss(
pred_bot.view(-1, pred_bot.shape[-1]),
text_bot[:, 1:].contiguous().view(-1),
ignore_index=0,
gamma=2,
alpha=0.25,
threshold=0.5)
else:
cost_top = utils.CB_loss(text_top[:, 1:].contiguous().view(-1),
pred_top.view(-1, pred_top.shape[-1]),
top_per_cls, opt.top_n_cls, 'focal',
0.999, 0.5)
cost_mid = utils.CB_loss(text_mid[:, 1:].contiguous().view(-1),
pred_mid.view(-1, pred_mid.shape[-1]),
mid_per_cls, opt.middle_n_cls,
'focal', 0.999, 0.5)
cost_bot = utils.CB_loss(text_bot[:, 1:].contiguous().view(-1),
pred_bot.view(-1, pred_bot.shape[-1]),
bot_per_cls, opt.bottom_n_cls,
'focal', 0.999, 0.5)
cost = cost_top * 0.33 + cost_mid * 0.33 + cost_bot * 0.33
loss_avg = utils.Averager()
loss_avg.add(cost)
model.zero_grad()
cost.backward()
torch.nn.utils.clip_grad_norm_(
model.parameters(), opt.grad_clip) #gradient clipping with 5
optimizer.step()
print(loss_avg.val())
#validation
if (n_iter % opt.valInterval == 0) & (n_iter != 0):
elapsed_time = time.time() - start_time
with open(f'./models/{opt.experiment_name}/log_train.txt',
'a') as log:
model.eval()
with torch.no_grad():
valid_loss, current_accuracy, current_norm_ED, pred_top_str, pred_mid_str, pred_bot_str, label_top, label_mid, label_bot, infer_time, length_of_data = evaluate.validation_jamo(
model, criterion, valid_loader, top_converter,
middle_converter, bottom_converter, opt)
scheduler.step(current_accuracy)
model.train()
present_time = time.localtime()
loss_log = f'[epoch : {n_epoch}/{opt.num_epoch}] [iter : {n_iter*opt.batch_size} / {int(len(data) * 0.95)}]\n' + f'Train loss : {loss_avg.val():0.5f}, Valid loss : {valid_loss:0.5f}, Elapsed time : {elapsed_time:0.5f}, Present time : {present_time[1]}/{present_time[2]}, {present_time[3]+9} : {present_time[4]}'
loss_avg.reset()
current_model_log = f'{"Current_accuracy":17s}: {current_accuracy:0.3f}, {"current_norm_ED":17s}: {current_norm_ED:0.2f}'
#keep the best
if current_accuracy > best_accuracy:
best_accuracy = current_accuracy
torch.save(
model.module.state_dict(),
f'./models/{opt.experiment_name}/best_accuracy_{round(current_accuracy,2)}.pth'
)
if current_norm_ED > best_norm_ED:
best_norm_ED = current_norm_ED
torch.save(
model.module.state_dict(),
f'./models/{opt.experiment_name}/best_norm_ED.pth')
best_model_log = f'{"Best accuracy":17s}: {best_accuracy:0.3f}, {"Best_norm_ED":17s}: {best_norm_ED:0.2f}'
loss_model_log = f'{loss_log}\n{current_model_log}\n{best_model_log}'
print(loss_model_log)
log.write(loss_model_log + '\n')
dashed_line = '-' * 80
head = f'{"Ground Truth":25s} | {"Prediction" :25s}| T/F'
predicted_result_log = f'{dashed_line}\n{head}\n{dashed_line}\n'
random_idx = np.random.choice(range(len(label_top)),
size=5,
replace=False)
label_concat = np.concatenate([
np.asarray(label_top).reshape(1, -1),
np.asarray(label_mid).reshape(1, -1),
np.asarray(label_bot).reshape(1, -1)
],
axis=0).reshape(3, -1)
pred_concat = np.concatenate([
np.asarray(pred_top_str).reshape(1, -1),
np.asarray(pred_mid_str).reshape(1, -1),
np.asarray(pred_bot_str).reshape(1, -1)
],
axis=0).reshape(3, -1)
for i in random_idx:
label_sample = label_concat[:, i]
pred_sample = pred_concat[:, i]
gt_str = utils.str_combine(label_sample[0],
label_sample[1],
label_sample[2])
pred_str = utils.str_combine(pred_sample[0],
pred_sample[1],
pred_sample[2])
predicted_result_log += f'{gt_str:25s} | {pred_str:25s} | \t{str(pred_str == gt_str)}\n'
predicted_result_log += f'{dashed_line}'
print(predicted_result_log)
log.write(predicted_result_log + '\n')
# Stochastic weight averaging
optimizer.update_swa()
swa_count += 1
if swa_count % 5 == 0:
optimizer.swap_swa_sgd()
torch.save(
model.module.state_dict(),
f'./models/{opt.experiment_name}/swa_{swa_count}.pth')
if (n_epoch) % 5 == 0:
torch.save(model.module.state_dict(),
f'./models/{opt.experiment_name}/{n_epoch}.pth')
class TPUFitter:
def __init__(self, model, device, config, base_model_path='/', model_name='unnamed', model_prefix='roberta', model_version='v1', out_path='/', log_path='/'):
self.log_path = Path(log_path, 'log').with_suffix('.txt')
self.log(f'TPUFitter started to initilized.', direct_out=True)
self.config = config
self.epoch = 0
self.base_model_path = base_model_path
self.model_name = model_name
self.model_version = model_version
self.model_path = Path(self.base_model_path, self.model_name, self.model_version)
self.out_path = out_path
self.node_path = Path(self.out_path, 'node_submissions')
self.create_dir_structure()
self.model = model
self.device = device
# whether use stochastic weight avaraging
self.use_SWA = config.use_SWA
# whether use different lr for backbone and classifier head
self.use_diff_lr = config.use_diff_lr
self._set_optimizer_scheduler()
self.criterion = config.criterion
self.best_score = -1.0
self.log(f'Fitter prepared. Device is {self.device}', direct_out=True)
def create_dir_structure(self):
self.node_path.mkdir(parents=True, exist_ok=True)
self.log(f'**** Directory structure created ****', direct_out=True)
def _set_optimizer_scheduler(self):
self.log(f'Optimizer and scheduler started to initilized.', direct_out=True)
def is_backbone(n):
return 'backbone' in n
param_optimizer = list(self.model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
# use different learning rate for backbone transformer and classifier head
if self.use_diff_lr:
backbone_lr, head_lr = self.config.lr*xm.xrt_world_size(), self.config.lr*xm.xrt_world_size()*500
optimizer_grouped_parameters = [
# {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.001},
# {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0},
{"params": [p for n, p in param_optimizer if is_backbone(n)], "lr": backbone_lr},
{"params": [p for n, p in param_optimizer if not is_backbone(n)], "lr": head_lr}
]
self.log(f'Different Learning rate for backbone: {backbone_lr} head:{head_lr}')
else:
optimizer_grouped_parameters = [
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.001},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0},
]
try:
self.optimizer = AdamW(optimizer_grouped_parameters, lr=self.config.lr*xm.xrt_world_size())
# self.optimizer = SGD(optimizer_grouped_parameters, lr=self.config.lr*xm.xrt_world_size(), momentum=0.9)
except:
param_g_1 = [p for n, p in param_optimizer if is_backbone(n)]
param_g_2 = [p for n, p in param_optimizer if not is_backbone(n)]
param_intersect = list(set(param_g_1) & set(param_g_2))
self.log(f'intersect: {param_intersect}', direct_out=True)
if self.use_SWA:
self.optimizer = SWA(self.optimizer)
if 'num_training_steps' in self.config.scheduler_params:
num_training_steps = int(self.config.train_lenght / self.config.batch_size / xm.xrt_world_size() * self.config.n_epochs)
self.log(f'Number of training steps: {num_training_steps}', direct_out=True)
self.config.scheduler_params['num_training_steps'] = num_training_steps
self.scheduler = self.config.SchedulerClass(self.optimizer, **self.config.scheduler_params)
def fit(self, train_loader, validation_loader, n_epochs=None):
self.log(f'**** Fitting process has been started ****', direct_out=True)
if n_epochs is None:
n_epochs = self.config.n_epochs
for e in range(n_epochs):
if self.config.verbose:
lr = self.optimizer.param_groups[0]['lr']
timestamp = datetime.utcnow().isoformat()
self.log(f'\n{timestamp}\nLR: {lr} \nEpoch:{e}')
t = time.time()
para_loader = pl.ParallelLoader(train_loader, [self.device])
losses, final_scores = self.train_one_epoch(para_loader.per_device_loader(self.device), e)
self.log(f'[RESULT]: Train. Epoch: {self.epoch}, loss: {losses.avg:.5f}, final_score: {final_scores.avg:.5f}, time: {(time.time() - t):.5f}')
t = time.time()
para_loader = pl.ParallelLoader(validation_loader, [self.device])
# swap SWA weights for validation
if self.use_SWA:
self.log('Swapping SWA weights for validation', direct_out=True)
self.optimizer.swap_swa_sgd()
losses, final_scores, threshold = self.validation(para_loader.per_device_loader(self.device))
self.log(f'[RESULT]: Validation. Epoch: {self.epoch}, loss: {losses.avg:.5f}, final_score: {final_scores.avg:.5f}, best_th: {threshold.find:.3f}, time: {(time.time() - t):.5f}')
# swap back to normal weights to continue training
if self.use_SWA:
self.log('Swapping back to original weights for validation', direct_out=True)
self.optimizer.swap_swa_sgd()
if final_scores.avg > self.best_score:
self.best_score = final_scores.avg
self.save('best_model')
self.log('Best model has been updated', direct_out=True)
# after one epoch, update SWA model if validation score is increased
if self.use_SWA:
self.optimizer.update_swa()
self.log('SWA model weights have been updated', direct_out=True)
if self.config.validation_scheduler:
# self.scheduler.step(metrics=final_scores.avg)
self.scheduler.step()
self.epoch += 1
def run_tuning_and_inference(self, test_loader, validation_loader, validation_tune_loader, n_epochs):
self.log('******Validation tuning and inference is started*****', direct_out=True)
self.run_validation_tuning(validation_loader, validation_tune_loader, n_epochs)
para_loader = pl.ParallelLoader(test_loader, [self.device])
self.run_inference(para_loader.per_device_loader(self.device))
def run_validation_tuning(self, validation_loader, validation_tune_loader, n_epochs):
self.log('******Validation tuning is started*****', direct_out=True)
# self.optimizer.param_groups[0]['lr'] = self.config.lr*xm.xrt_world_size() / (epoch + 1)
self.fit(validation_tune_loader, validation_loader, n_epochs)
def validation(self, val_loader):
self.log(f'**** Validation process has been started ****', direct_out=True)
self.model.eval()
losses = AverageMeter()
final_scores = RocAucMeter()
threshold = ThresholdMeter()
t = time.time()
for step, (targets, inputs, attention_masks) in enumerate(val_loader):
self.log(
f'Valid Step {step}, loss: ' + \
f'{losses.avg:.5f}, final_score: {final_scores.avg:.5f}, ' + \
f'time: {(time.time() - t):.5f}', step=step
)
with torch.no_grad():
inputs = inputs.to(self.device, dtype=torch.long)
attention_masks = attention_masks.to(self.device, dtype=torch.long)
targets = targets.to(self.device, dtype=torch.float)
outputs = self.model(inputs, attention_masks)
loss = self.criterion(outputs, targets)
batch_size = inputs.size(0)
final_scores.update(targets, outputs)
losses.update(loss.detach().item(), batch_size)
threshold.update(targets, outputs)
return losses, final_scores, threshold
def train_one_epoch(self, train_loader, epoch):
self.log(f'**** Epoch training has started: {epoch} ****', direct_out=True)
self.model.train()
losses = AverageMeter()
final_scores = RocAucMeter()
t = time.time()
for step, (targets, inputs, attention_masks) in enumerate(train_loader):
self.log(
f'Train Step {step}, loss: ' + \
f'{losses.avg:.5f}, final_score: {final_scores.avg:.5f}, ' + \
f'time: {(time.time() - t):.5f}', step=step
)
inputs = inputs.to(self.device, dtype=torch.long)
attention_masks = attention_masks.to(self.device, dtype=torch.long)
targets = targets.to(self.device, dtype=torch.float)
self.optimizer.zero_grad()
outputs = self.model(inputs, attention_masks)
loss = self.criterion(outputs, targets)
batch_size = inputs.size(0)
final_scores.update(targets, outputs)
losses.update(loss.detach().item(), batch_size)
loss.backward()
xm.optimizer_step(self.optimizer)
if self.config.step_scheduler:
self.scheduler.step()
return losses, final_scores
def run_inference(self, test_loader):
self.log(f'**** Inference process has been started ****', direct_out=True)
self.model.eval()
result = {'id': [], 'toxic': []}
t = time.time()
for step, (ids, inputs, attention_masks) in enumerate(test_loader):
self.log(f'Prediction Step {step}, time: {(time.time() - t):.5f}', step=step)
with torch.no_grad():
inputs = inputs.to(self.device, dtype=torch.long)
attention_masks = attention_masks.to(self.device, dtype=torch.long)
outputs = self.model(inputs, attention_masks)
toxics = torch.nn.functional.softmax(outputs, dim=1).data.cpu().numpy()[:,1]
result['id'].extend(ids.cpu().numpy())
result['toxic'].extend(toxics)
result = pd.DataFrame(result)
print(f'Node path is: {self.node_path}')
node_count = len(list(self.node_path.glob('*.csv')))
result.to_csv(self.node_path/f'submission_{node_count}_{datetime.utcnow().microsecond}_{random.random()}.csv', index=False)
def run_pseudolabeling(self, test_loader, epoch):
losses = AverageMeter()
final_scores = RocAucMeter()
self.model.eval()
t = time.time()
for step, (ids, inputs, attention_masks) in enumerate(test_loader):
inputs = inputs.to(self.device, dtype=torch.long)
attention_masks = attention_masks.to(self.device, dtype=torch.long)
outputs = self.model(inputs, attention_masks)
# print(f'Inputs: {inputs} size: {inputs.size()}')
# print(f'outputs: {outputs} size: {outputs.size()}')
toxics = torch.nn.functional.softmax(outputs, dim=1)[:,1]
toxic_mask = (toxics<=0.4) | (toxics>=0.8)
# print(attention_masks.size())
toxics = toxics[toxic_mask]
inputs = inputs[toxic_mask]
attention_masks = attention_masks[toxic_mask]
# print(f'toxics: {toxics.size()}')
# print(f'inputs: {inputs.size()}')
if toxics.nelement() != 0:
targets_int = (toxics>self.config.pseudolabeling_threshold).int()
targets = torch.stack([onehot(2, target) for target in targets_int])
# print(targets_int)
self.model.train()
self.log(
f'Pseudolabeling Step {step}, loss: ' + \
f'{losses.avg:.5f}, final_score: {final_scores.avg:.5f}, ' + \
f'time: {(time.time() - t):.5f}', step=step
)
targets = targets.to(self.device, dtype=torch.float)
self.optimizer.zero_grad()
outputs = self.model(inputs, attention_masks)
loss = self.criterion(outputs, targets)
batch_size = inputs.size(0)
final_scores.update(targets, outputs)
losses.update(loss.detach().item(), batch_size)
loss.backward()
xm.optimizer_step(self.optimizer)
if self.config.step_scheduler:
self.scheduler.step()
self.log(f'[RESULT]: Pseudolabeling. Epoch: {epoch}, loss: {losses.avg:.5f}, final_score: {final_scores.avg:.5f}, time: {(time.time() - t):.5f}')
def get_submission(self, out_dir):
submission = pd.concat([pd.read_csv(path) for path in (out_dir/'node_submissions').glob('*.csv')]).groupby('id').mean()
return submission
def save(self, name):
self.model_path.mkdir(parents=True, exist_ok=True)
path = (self.model_path/name).with_suffix('.bin')
if self.use_SWA:
self.optimizer.swap_swa_sgd()
xm.save(self.model.state_dict(), path)
self.log(f'Model has been saved')
def log(self, message, step=None, direct_out=False):
if direct_out or self.config.verbose:
if direct_out or step is None or (step is not None and step % self.config.verbose_step == 0):
xm.master_print(message)
with open(self.log_path, 'a+') as logger:
xm.master_print(f'{message}', logger)
class Train(object):
"""Train class.
"""
def __init__(self, ):
trainds = AlaskaDataIter(cfg.DATA.root_path,
cfg.DATA.train_txt_path,
training_flag=True)
self.train_ds = DataLoader(trainds,
cfg.TRAIN.batch_size,
num_workers=cfg.TRAIN.process_num,
shuffle=True)
valds = AlaskaDataIter(cfg.DATA.root_path,
cfg.DATA.val_txt_path,
training_flag=False)
self.val_ds = DataLoader(valds,
cfg.TRAIN.batch_size,
num_workers=cfg.TRAIN.process_num,
shuffle=False)
self.init_lr = cfg.TRAIN.init_lr
self.warup_step = cfg.TRAIN.warmup_step
self.epochs = cfg.TRAIN.epoch
self.batch_size = cfg.TRAIN.batch_size
self.l2_regularization = cfg.TRAIN.weight_decay_factor
self.device = torch.device(
"cuda" if torch.cuda.is_available() else 'cpu')
self.model = CenterNet().to(self.device)
self.load_weight()
if 'Adamw' in cfg.TRAIN.opt:
self.optimizer = torch.optim.AdamW(
self.model.parameters(),
lr=self.init_lr,
eps=1.e-5,
weight_decay=self.l2_regularization)
else:
self.optimizer = torch.optim.SGD(
self.model.parameters(),
lr=self.init_lr,
momentum=0.9,
weight_decay=self.l2_regularization)
if cfg.TRAIN.SWA > 0:
##use swa
self.optimizer = SWA(self.optimizer)
if cfg.TRAIN.mix_precision:
self.model, self.optimizer = amp.initialize(self.model,
self.optimizer,
opt_level="O1")
self.model = nn.DataParallel(self.model)
self.ema = EMA(self.model, 0.999)
self.ema.register()
###control vars
self.iter_num = 0
# self.scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(self.optimizer,mode='max', patience=3,verbose=True)
self.scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
self.optimizer, self.epochs, eta_min=1.e-6)
self.criterion = CenterNetLoss().to(self.device)
def custom_loop(self):
"""Custom training and testing loop.
Args:
train_dist_dataset: Training dataset created using strategy.
test_dist_dataset: Testing dataset created using strategy.
strategy: Distribution strategy.
Returns:
train_loss, train_accuracy, test_loss, test_accuracy
"""
def train_epoch(epoch_num):
summary_loss_cls = AverageMeter()
summary_loss_wh = AverageMeter()
self.model.train()
if cfg.MODEL.freeze_bn:
for m in self.model.modules():
if isinstance(m, nn.BatchNorm2d):
m.eval()
if cfg.MODEL.freeze_bn_affine:
m.weight.requires_grad = False
m.bias.requires_grad = False
for image, hm_target, wh_target, weights in self.train_ds:
if epoch_num < 10:
###excute warm up in the first epoch
if self.warup_step > 0:
if self.iter_num < self.warup_step:
for param_group in self.optimizer.param_groups:
param_group['lr'] = self.iter_num / float(
self.warup_step) * self.init_lr
lr = param_group['lr']
logger.info('warm up with learning rate: [%f]' %
(lr))
start = time.time()
if cfg.TRAIN.vis:
for i in range(image.shape[0]):
img = image[i].numpy()
img = np.transpose(img, axes=[1, 2, 0])
hm = hm_target[i].numpy()
wh = wh_target[i].numpy()
if cfg.DATA.use_int8_data:
hm = hm[:, :, 0].astype(np.uint8)
wh = wh[:, :, 0]
else:
hm = hm[:, :, 0].astype(np.float32)
wh = wh[:, :, 0].astype(np.float32)
cv2.namedWindow('s_hm', 0)
cv2.imshow('s_hm', hm)
cv2.namedWindow('s_wh', 0)
cv2.imshow('s_wh', wh + 1)
cv2.namedWindow('img', 0)
cv2.imshow('img', img)
cv2.waitKey(0)
else:
data = image.to(self.device).float()
if cfg.DATA.use_int8_data:
hm_target = hm_target.to(
self.device).float() / cfg.DATA.use_int8_enlarge
else:
hm_target = hm_target.to(self.device).float()
wh_target = wh_target.to(self.device).float()
weights = weights.to(self.device).float()
batch_size = data.shape[0]
cls, wh = self.model(data)
cls_loss, wh_loss = self.criterion(
[cls, wh], [hm_target, wh_target, weights])
current_loss = cls_loss + wh_loss
summary_loss_cls.update(cls_loss.detach().item(),
batch_size)
summary_loss_wh.update(wh_loss.detach().item(), batch_size)
self.optimizer.zero_grad()
if cfg.TRAIN.mix_precision:
with amp.scale_loss(current_loss,
self.optimizer) as scaled_loss:
scaled_loss.backward()
else:
current_loss.backward()
self.optimizer.step()
if cfg.TRAIN.ema:
self.ema.update()
self.iter_num += 1
time_cost_per_batch = time.time() - start
images_per_sec = cfg.TRAIN.batch_size * cfg.TRAIN.num_gpu / time_cost_per_batch
if self.iter_num % cfg.TRAIN.log_interval == 0:
log_message = '[TRAIN], '\
'Epoch %d Step %d, ' \
'summary_loss: %.6f, ' \
'cls_loss: %.6f, '\
'wh_loss: %.6f, ' \
'time: %.6f, '\
'speed %d images/persec'% (
epoch_num,
self.iter_num,
summary_loss_cls.avg+summary_loss_wh.avg,
summary_loss_cls.avg ,
summary_loss_wh.avg,
time.time() - start,
images_per_sec)
logger.info(log_message)
if cfg.TRAIN.SWA > 0 and epoch_num >= cfg.TRAIN.SWA:
self.optimizer.update_swa()
return summary_loss_cls, summary_loss_wh
def test_epoch(epoch_num):
summary_loss_cls = AverageMeter()
summary_loss_wh = AverageMeter()
self.model.eval()
t = time.time()
with torch.no_grad():
for step, (image, hm_target, wh_target,
weights) in enumerate(self.val_ds):
data = image.to(self.device).float()
if cfg.DATA.use_int8_data:
hm_target = hm_target.to(
self.device).float() / cfg.DATA.use_int8_enlarge
else:
hm_target = hm_target.to(self.device).float()
wh_target = wh_target.to(self.device).float()
weights = weights.to(self.device).float()
batch_size = data.shape[0]
with torch.no_grad():
cls, wh = self.model(data)
cls_loss, wh_loss = self.criterion(
[cls, wh], [hm_target, wh_target, weights])
summary_loss_cls.update(cls_loss.detach().item(),
batch_size)
summary_loss_wh.update(wh_loss.detach().item(), batch_size)
if step % cfg.TRAIN.log_interval == 0:
log_message = '[VAL], '\
'Epoch %d Step %d, ' \
'summary_loss: %.6f, ' \
'cls_loss: %.6f, '\
'wh_loss: %.6f, ' \
'time: %.6f' % (epoch_num,
step,
summary_loss_cls.avg+summary_loss_wh.avg,
summary_loss_cls.avg,
summary_loss_wh.avg,
time.time() - t)
logger.info(log_message)
return summary_loss_cls, summary_loss_wh
for epoch in range(self.epochs):
for param_group in self.optimizer.param_groups:
lr = param_group['lr']
logger.info('learning rate: [%f]' % (lr))
t = time.time()
summary_loss_cls, summary_loss_wh = train_epoch(epoch)
train_epoch_log_message = '[centernet], '\
'[RESULT]: Train. Epoch: %d,' \
' summary_loss: %.5f,' \
' cls_loss: %.6f, ' \
' wh_loss: %.6f, ' \
' time:%.5f' % (epoch,
summary_loss_cls.avg+summary_loss_wh.avg,
summary_loss_cls.avg,
summary_loss_wh.avg,
(time.time() - t))
logger.info(train_epoch_log_message)
if cfg.TRAIN.SWA > 0 and epoch >= cfg.TRAIN.SWA:
###switch to avg model
self.optimizer.swap_swa_sgd()
##switch eam weighta
if cfg.TRAIN.ema:
self.ema.apply_shadow()
if epoch % cfg.TRAIN.test_interval == 0:
summary_loss_cls, summary_loss_wh = test_epoch(epoch)
val_epoch_log_message = '[centernet], '\
'[RESULT]: VAL. Epoch: %d,' \
' summary_loss: %.5f,' \
' cls_loss: %.6f, ' \
' wh_loss: %.6f, ' \
' time:%.5f' % (epoch,
summary_loss_cls.avg+summary_loss_wh.avg,
summary_loss_cls.avg,
summary_loss_wh.avg,
(time.time() - t))
logger.info(val_epoch_log_message)
self.scheduler.step()
# self.scheduler.step(final_scores.avg)
#### save model
if not os.access(cfg.MODEL.model_path, os.F_OK):
os.mkdir(cfg.MODEL.model_path)
#### save the model every end of epoch
current_model_saved_name = './model/centernet_epoch_%d_val_loss%.6f.pth' % (
epoch, summary_loss_cls.avg + summary_loss_wh.avg)
logger.info('A model saved to %s' % current_model_saved_name)
torch.save(self.model.module.state_dict(),
current_model_saved_name)
####switch back
if cfg.TRAIN.ema:
self.ema.restore()
if cfg.TRAIN.SWA > 0 and epoch > cfg.TRAIN.SWA:
###switch back to plain model to train next epoch
self.optimizer.swap_swa_sgd()
def load_weight(self):
if cfg.MODEL.pretrained_model is not None:
state_dict = torch.load(cfg.MODEL.pretrained_model,
map_location=self.device)
self.model.load_state_dict(state_dict, strict=False)
