def save_epoch_status(status_dict, config):
epoch_str = _get_epoch_str(status_dict, config)
step_str = _get_step_str(status_dict, config)
train_loss, train_acc, eval_loss, eval_acc = _get_values("epoch", status_dict)
acc_str = status_dict["acc_str"]
best_epoch_str = _get_best_values_str("epoch", status_dict)
best_step_str = _get_best_values_str("step", status_dict)
epoch_elapsed_time = status_dict["epoch_elapsed_time"]
so_far_elapsed_time = status_dict["so_far_elapsed_time"]
# pylint: disable=bad-continuation
save_str = '[E:%s] [S:%s] [Train Loss:%.6f Acc:%.6f] [Val Loss:%.6f Acc:%.6f %s] [Best Epoch:%s] [Best Step:%s] [%.2fs %.1fs] Epoch status' % (
epoch_str,
step_str,
train_loss,
train_acc,
eval_loss,
eval_acc,
acc_str,
best_epoch_str,
best_step_str,
epoch_elapsed_time,
so_far_elapsed_time,
)
# pylint: enable=bad-continuation
record.save_content(config, save_str)
def save_step_status(status_dict, config):
category_name = "step"
epoch_str = _get_epoch_str(status_dict, config)
step_str = _get_step_str(status_dict, config)
train_loss, train_acc, eval_loss, eval_acc = _get_values(category_name, status_dict)
acc_str = status_dict["acc_str"]
progress_str = status_dict["progress_str"]
best_epoch_str = _get_best_values_str("epoch", status_dict)
best_step_str = _get_best_values_str("step", status_dict)
# pylint: disable=bad-continuation
save_str = '[E:%s] [S:%s] [Train Loss:%.6f Acc:%.6f%s] [Val Loss:%.6f Acc:%.6f %s] [Best Epoch:%s] [Best Step:%s] Step status' % (
epoch_str,
step_str,
train_loss,
train_acc,
progress_str,
eval_loss,
eval_acc,
acc_str,
best_epoch_str,
best_step_str,
)
# pylint: enable=bad-continuation
record.save_content(config, save_str)
def train_the_model(self):
model = self.graph_dict["model"]
record.save_content(self.config, model)
num_epochs = self.config["epochs"]
self.status_dict["best_model_wts"] = copy.deepcopy(model.state_dict())
epoch_start = self.status_dict["epoch"]
for epoch in range(epoch_start + 1, num_epochs):
self._run_a_epoch(epoch)
def without_finetune():
print_str = "run the model with finetune=False"
config["finetune"] = False
record.save_content(config, print_str)
record.record_dict(config, config)
data_dict = ants_bees_data_set.get_dataset_info_dict(config)
g = TrainTransferLearnGraph(data_dict, config)
g.train_the_model()
test_g = TestTransferLearnGraph(data_dict, config)
test_g.test_the_model()
def _check_max_stop(self, value, stop_category):
if self.early_stop:
return True
config = self.config
step = self.status_dict[stop_category]
if step >= value:
record.save_content(
config, " Stop With Max %s: %s" % (stop_category, step))
self.early_stop = True
return True
return False
def _check_early_stop(self, stop_category):
if self.early_stop:
return True
config = self.config
if config["early_stop_%s" % stop_category]:
step = self.status_dict[stop_category]
best_step = self.status_dict["best_%s" % stop_category]
if step - best_step > config["early_stop_%s_limit" %
stop_category]:
record.save_content(
config, "Early Stop With %s: %s" % (stop_category, step))
self.early_stop = True
return True
return False
def predict(self, input_line, rnn=None, n_predictions=3):
if rnn is None:
rnn = self.graph_dict["model"]
all_categories = self.data_dict["all_categories"]
config = self.config
record.save_content(config, '\n> %s' % input_line)
with torch.no_grad():
output = self.get_model_output(
name_dataset.line_to_tensor(input_line, config))
record.save_content(config, output.numpy())
#record.save_content(config, output.tolist())
record.save_content(config, all_categories)
topv, topi = output.topk(n_predictions, 1, True)
predictions = []
for i in range(n_predictions):
value = topv[0][i].item()
category_index = topi[0][i].item()
record.save_content(
config,
'(%.2f) %s' % (value, all_categories[category_index]))
predictions.append([value, all_categories[category_index]])
def _create_output(self):
if (not record.check_output_file(self.config)) or (
not self.config["train_load_check_point_file"]):
model = self.graph_dict["model"]
record.save_content(self.config, model)
record.record_dict(self.config, self.config)
def samples(self, category, start_letters='ABC'):
for start_letter in start_letters:
record.save_content(self.config, "%s start:%s" % (category, start_letter))
con = self.sample(category, start_letter)
record.save_content(self.config, con)
def _init_best_epoch_model(self):
record.save_content(self.config, "best epoch model")
model = self.graph_dict["model"]
model.load_state_dict(self.status_dict["best_epoch_model_wts"])
def save_epoch_status(status_dict, config):
save_str = _get_running_status_str(status_dict, "step", config)
record.save_content(config, "%s Epoch Status" % save_str)