def validate_one_epoch(self, epoch):
# return a list of meters
meter_list = []
with torch.no_grad():
for arc_id in range(self.test_arc_per_epoch):
meters = AverageMeterGroup()
for x, y in self.test_loader:
x, y = to_device(x, self.device), to_device(y, self.device)
self.mutator.reset()
logits = self.model(x)
if isinstance(logits, tuple):
logits, _ = logits
metrics = self.metrics(logits, y)
loss = self.loss(logits, y)
metrics["loss"] = loss.item()
meters.update(metrics)
meter_dict = json.loads(
json.dumps('{' + meters.summary() + '}'))
#print("meter_dict: {}".format(meter_dict))
#print("type of meter_dict: {}".format(type(json.loads(meter_dict))))
meter_list.append(json.loads(meter_dict))
logger.info("Test Epoch [%d/%d] Arc [%d/%d] Summary %s",
epoch + 1, self.num_epochs, arc_id + 1,
self.test_arc_per_epoch, meters.summary())
return meter_list
def train_one_epoch(self, epoch):
# Train sampler (mutator)
self.model.eval()
self.mutator.train()
total_loss=0
meters = AverageMeterGroup()
for mutator_step in range(1, self.mutator_steps + 1):
self.mutator_optim.zero_grad()
for step in range(1, self.mutator_steps_aggregate + 1):
x, y = next(self.valid_loader)
x, y = to_device(x, self.device), to_device(y, self.device)
self.mutator.reset()
with torch.no_grad():
logits = self.model(x)
self._write_graph_status()
jacobian = get_batch_jacobian(self.model, x)
jacobian = jacobian.reshape(jacobian.size(0), -1)
reward = eval_score(jacobian)
total_loss += reward.item()
if self.entropy_weight:
reward += self.entropy_weight * self.mutator.sample_entropy.item()
# https://arxiv.org/pdf/1707.06347.pdf
self.baseline = self.baseline * self.baseline_decay + reward * (1 - self.baseline_decay)
loss = self.mutator.sample_log_prob * (reward - self.baseline)
if self.skip_weight:
loss += self.skip_weight * self.mutator.sample_skip_penalty
loss /= self.mutator_steps_aggregate
loss.backward()
cur_step = step + (mutator_step - 1) * self.mutator_steps_aggregate
if self.log_frequency is not None and cur_step % self.log_frequency == 0:
logger.info("RL Epoch [%d/%d] Step [%d/%d] [%d/%d] %s", epoch + 1, self.num_epochs,
mutator_step, self.mutator_steps, step, self.mutator_steps_aggregate,
reward.int())
nn.utils.clip_grad_norm_(self.mutator.parameters(), 5.)
self.mutator_optim.step()
mlflow.log_metric('Total reward', -total_loss/(self.mutator_steps*self.mutator_steps_aggregate), epoch)
torch.save({
'model':self.mutator.state_dict(),
'optimizer':self.mutator_optim.state_dict()
}, 'mutator_run_stats.pyt')
mlflow.log_artifact('mutator_run_stats.pyt')
def validate_one_epoch(self, epoch):
with torch.no_grad():
for arc_id in range(self.test_arc_per_epoch):
meters = AverageMeterGroup()
for x, y in self.test_loader:
x, y = to_device(x, self.device), to_device(y, self.device)
self.mutator.reset()
logits = self.model(x)
if isinstance(logits, tuple):
logits, _ = logits
metrics = self.metrics(logits, y)
loss = self.loss(logits, y)
metrics["loss"] = loss.item()
meters.update(metrics)
logger.info("Test Epoch [%d/%d] Arc [%d/%d] Summary %s",
epoch + 1, self.num_epochs, arc_id + 1,
self.test_arc_per_epoch, meters.summary())
def train_one_epoch(self, epoch):
# Sample model and train
self.model.train()
self.mutator.eval()
meters = AverageMeterGroup()
for step in range(1, self.child_steps + 1):
x, y = next(self.train_loader)
x, y = to_device(x, self.device), to_device(y, self.device)
self.optimizer.zero_grad()
with torch.no_grad():
self.mutator.reset()
logits = self.model(x)
if isinstance(logits, tuple):
logits, aux_logits = logits
aux_loss = self.loss(aux_logits, y)
else:
aux_loss = 0.
metrics = self.metrics(logits, y)
loss = self.loss(logits, y)
loss = loss + self.aux_weight * aux_loss
loss.backward()
nn.utils.clip_grad_norm_(self.model.parameters(), 5.)
self.optimizer.step()
metrics["loss"] = loss.item()
meters.update(metrics)
if self.log_frequency is not None and step % self.log_frequency == 0:
logger.info("Model Epoch [%d/%d] Step [%d/%d] %s", epoch + 1,
self.num_epochs, step, self.child_steps, meters)
# Train sampler (mutator)
self.model.eval()
self.mutator.train()
meters = AverageMeterGroup()
for mutator_step in range(1, self.mutator_steps + 1):
self.mutator_optim.zero_grad()
for step in range(1, self.mutator_steps_aggregate + 1):
x, y = next(self.valid_loader)
x, y = to_device(x, self.device), to_device(y, self.device)
self.mutator.reset()
with torch.no_grad():
logits = self.model(x)
metrics = self.metrics(logits, y)
reward = self.reward_function(logits, y)
if self.entropy_weight:
reward += self.entropy_weight * self.mutator.sample_entropy.item(
)
self.baseline = self.baseline * self.baseline_decay + reward * (
1 - self.baseline_decay)
loss = self.mutator.sample_log_prob * (reward - self.baseline)
if self.skip_weight:
loss += self.skip_weight * self.mutator.sample_skip_penalty
metrics["reward"] = reward
metrics["loss"] = loss.item()
metrics["ent"] = self.mutator.sample_entropy.item()
metrics["log_prob"] = self.mutator.sample_log_prob.item()
metrics["baseline"] = self.baseline
metrics["skip"] = self.mutator.sample_skip_penalty
loss /= self.mutator_steps_aggregate
loss.backward()
meters.update(metrics)
cur_step = step + (mutator_step -
1) * self.mutator_steps_aggregate
if self.log_frequency is not None and cur_step % self.log_frequency == 0:
logger.info("RL Epoch [%d/%d] Step [%d/%d] [%d/%d] %s",
epoch + 1, self.num_epochs, mutator_step,
self.mutator_steps, step,
self.mutator_steps_aggregate, meters)
nn.utils.clip_grad_norm_(self.mutator.parameters(), 5.)
self.mutator_optim.step()
def train_one_epoch(self, epoch):
# Sample model and train
self.model.train()
self.mutator.eval()
meters = AverageMeterGroup()
# COMMENT: 先训练模型
for step in range(1, self.child_steps + 1):
x, y = next(self.train_loader)
x, y = to_device(x, self.device), to_device(y, self.device)
self.optimizer.zero_grad()
with torch.no_grad():
self.mutator.reset()
self._write_graph_status()
logits = self.model(x)
if isinstance(logits, tuple):
logits, aux_logits = logits
aux_loss = self.loss(aux_logits, y)
else:
aux_loss = 0.
metrics = self.metrics(logits, y)
# 计算acc
loss = self.loss(logits, y)
# 计算loss
loss = loss + self.aux_weight * aux_loss
loss.backward()
nn.utils.clip_grad_norm_(self.model.parameters(), 5.)
self.optimizer.step()
metrics["loss"] = loss.item()
meters.update(metrics)
if self.log_frequency is not None and step % self.log_frequency == 0:
logger.info("Model Epoch [%d/%d] Step [%d/%d] %s", epoch + 1,
self.num_epochs, step, self.child_steps, meters)
# Train sampler (mutator)
self.model.eval()
self.mutator.train()
# 然后训练变化器，突变器
meters = AverageMeterGroup()
for mutator_step in range(1, self.mutator_steps + 1):
self.mutator_optim.zero_grad()
for step in range(1, self.mutator_steps_aggregate + 1):
x, y = next(self.valid_loader)
x, y = to_device(x, self.device), to_device(y, self.device)
self.mutator.reset()
with torch.no_grad():
logits = self.model(x)
self._write_graph_status()
# 得到acc
metrics = self.metrics(logits, y)
# 得到reward
'''
def reward_accuracy(output, target, topk=(1,)):
batch_size = target.size(0)
_, predicted = torch.max(output.data, 1)
return (predicted == target).sum().item() / batch_size
'''
reward = self.reward_function(logits, y) # 当前这个batch正确的个数
if self.entropy_weight: # 交叉熵权重
reward += self.entropy_weight * self.mutator.sample_entropy.item() # 得到样本熵
self.baseline = self.baseline * self.baseline_decay + reward * (1 - self.baseline_decay)
# 有点policy gradient的感觉了
loss = self.mutator.sample_log_prob * (reward - self.baseline)
if self.skip_weight:
loss += self.skip_weight * self.mutator.sample_skip_penalty
metrics["reward"] = reward
metrics["loss"] = loss.item()
metrics["ent"] = self.mutator.sample_entropy.item()
metrics["log_prob"] = self.mutator.sample_log_prob.item()
metrics["baseline"] = self.baseline
metrics["skip"] = self.mutator.sample_skip_penalty
loss /= self.mutator_steps_aggregate
loss.backward()
meters.update(metrics)
cur_step = step + (mutator_step - 1) * self.mutator_steps_aggregate
if self.log_frequency is not None and cur_step % self.log_frequency == 0:
logger.info("RL Epoch [%d/%d] Step [%d/%d] [%d/%d] %s", epoch + 1, self.num_epochs,
mutator_step, self.mutator_steps, step, self.mutator_steps_aggregate,
meters)
nn.utils.clip_grad_norm_(self.mutator.parameters(), 5.)
self.mutator_optim.step()