Add files via upload
This commit is contained in:
missQian
GitHub
parent
cafe5a84ad
commit
2d660e7ced
@ -0,0 +1,3 @@
|
||||
#! /bin/bash
|
||||
|
||||
CUDA_VISIBLE_DEVICES='' python run_dee_task.py --skip_train True $*
|
||||
@ -0,0 +1,72 @@
|
||||
#! /bin/bash
|
||||
|
||||
DATA_DIR=./Data
|
||||
EXP_DIR=./Exps
|
||||
COMMON_TASK_NAME=HelloEDAG
|
||||
RESUME_TRAIN=True
|
||||
SAVE_CPT=True
|
||||
N_EPOCH=100
|
||||
TRAIN_BS=64
|
||||
EVAL_BS=2
|
||||
NUM_GPUS=8
|
||||
GRAD_ACC_STEP=8
|
||||
# There is one parameter update for every GRAD_ACC_STEP back-propagation steps,
|
||||
# so the real runtime batch size is TRAIN_BS / GRAD_ACC_STEP.
|
||||
# In this way, we can achieve a large batch training with only a few GPUs
|
||||
|
||||
|
||||
# Doc2EDAG Models: Doc2EDAG, GreedyDec
|
||||
MODEL_TYPE=Doc2EDAG
|
||||
MODEL_STR=Doc2EDAG
|
||||
echo "---> ${MODEL_STR} Run"
|
||||
./train_multi.sh ${NUM_GPUS} --resume_latest_cpt ${RESUME_TRAIN} --save_cpt_flag ${SAVE_CPT} \
|
||||
--data_dir ${DATA_DIR} --exp_dir ${EXP_DIR} --task_name ${COMMON_TASK_NAME} --num_train_epochs ${N_EPOCH} \
|
||||
--train_batch_size ${TRAIN_BS} --gradient_accumulation_steps ${GRAD_ACC_STEP} --eval_batch_size ${EVAL_BS} \
|
||||
--model_type ${MODEL_TYPE} --cpt_file_name ${MODEL_STR} --add_greedy_dec True
|
||||
|
||||
|
||||
# DCFEE Baselines: DCFEE-O, DCFEE-M
|
||||
MODEL_TYPE=DCFEE
|
||||
MODEL_STR=DCFEE
|
||||
echo "---> ${MODEL_STR} Run"
|
||||
./train_multi.sh ${NUM_GPUS} --resume_latest_cpt ${RESUME_TRAIN} --save_cpt_flag ${SAVE_CPT} \
|
||||
--data_dir ${DATA_DIR} --exp_dir ${EXP_DIR} --task_name ${COMMON_TASK_NAME} --num_train_epochs ${N_EPOCH} \
|
||||
--train_batch_size ${TRAIN_BS} --gradient_accumulation_steps ${GRAD_ACC_STEP} --eval_batch_size ${EVAL_BS} \
|
||||
--model_type ${MODEL_TYPE} --cpt_file_name ${MODEL_STR}
|
||||
|
||||
|
||||
# Ablation Tests of Doc2EDAG
|
||||
MODEL_TYPE=Doc2EDAG
|
||||
|
||||
# Ablation Test 1
|
||||
MODEL_STR=Doc2EDAG-NoPathMem
|
||||
echo "---> ${MODEL_STR} Run"
|
||||
./train_multi.sh ${NUM_GPUS} --resume_latest_cpt ${RESUME_TRAIN} --save_cpt_flag ${SAVE_CPT} \
|
||||
--data_dir ${DATA_DIR} --exp_dir ${EXP_DIR} --task_name ${COMMON_TASK_NAME} --num_train_epochs ${N_EPOCH} \
|
||||
--train_batch_size ${TRAIN_BS} --gradient_accumulation_steps ${GRAD_ACC_STEP} --eval_batch_size ${EVAL_BS} \
|
||||
--model_type ${MODEL_TYPE} --cpt_file_name ${MODEL_STR} --use_path_mem False
|
||||
|
||||
# Ablation Test 2
|
||||
MODEL_STR=Doc2EDAG-NoScheduledSampling
|
||||
echo "---> ${MODEL_STR} Run"
|
||||
./train_multi.sh ${NUM_GPUS} --resume_latest_cpt ${RESUME_TRAIN} --save_cpt_flag ${SAVE_CPT} \
|
||||
--data_dir ${DATA_DIR} --exp_dir ${EXP_DIR} --task_name ${COMMON_TASK_NAME} --num_train_epochs ${N_EPOCH} \
|
||||
--train_batch_size ${TRAIN_BS} --gradient_accumulation_steps ${GRAD_ACC_STEP} --eval_batch_size ${EVAL_BS} \
|
||||
--model_type ${MODEL_TYPE} --cpt_file_name ${MODEL_STR} --use_scheduled_sampling False
|
||||
|
||||
# Ablation Test 3
|
||||
MODEL_STR=Doc2EDAG-NoDocEnc
|
||||
echo "---> ${MODEL_STR} Run"
|
||||
./train_multi.sh ${NUM_GPUS} --resume_latest_cpt ${RESUME_TRAIN} --save_cpt_flag ${SAVE_CPT} \
|
||||
--data_dir ${DATA_DIR} --exp_dir ${EXP_DIR} --task_name ${COMMON_TASK_NAME} --num_train_epochs ${N_EPOCH} \
|
||||
--train_batch_size ${TRAIN_BS} --gradient_accumulation_steps ${GRAD_ACC_STEP} --eval_batch_size ${EVAL_BS} \
|
||||
--model_type ${MODEL_TYPE} --cpt_file_name ${MODEL_STR} --use_doc_enc False
|
||||
|
||||
# Ablation Test 4
|
||||
MODEL_STR=Doc2EDAG-NoFPPenalty
|
||||
echo "---> ${MODEL_STR} Run"
|
||||
./train_multi.sh ${NUM_GPUS} --resume_latest_cpt ${RESUME_TRAIN} --save_cpt_flag ${SAVE_CPT} \
|
||||
--data_dir ${DATA_DIR} --exp_dir ${EXP_DIR} --task_name ${COMMON_TASK_NAME} --num_train_epochs ${N_EPOCH} \
|
||||
--train_batch_size ${TRAIN_BS} --gradient_accumulation_steps ${GRAD_ACC_STEP} --eval_batch_size ${EVAL_BS} \
|
||||
--model_type ${MODEL_TYPE} --cpt_file_name ${MODEL_STR} --neg_field_loss_scaling 1.0
|
||||
|
||||
@ -0,0 +1,97 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
# AUTHOR: Shun Zheng
|
||||
# DATE: 19-9-19
|
||||
|
||||
import argparse
|
||||
import os
|
||||
import torch.distributed as dist
|
||||
|
||||
from dee.utils import set_basic_log_config, strtobool
|
||||
from dee.dee_task import DEETask, DEETaskSetting
|
||||
from dee.dee_helper import aggregate_task_eval_info, print_total_eval_info, print_single_vs_multi_performance
|
||||
|
||||
set_basic_log_config()
|
||||
|
||||
|
||||
def parse_args(in_args=None):
|
||||
arg_parser = argparse.ArgumentParser()
|
||||
arg_parser.add_argument('--task_name', type=str, required=True,
|
||||
help='Take Name')
|
||||
arg_parser.add_argument('--data_dir', type=str, default='./Data',
|
||||
help='Data directory')
|
||||
arg_parser.add_argument('--exp_dir', type=str, default='./Exps',
|
||||
help='Experiment directory')
|
||||
arg_parser.add_argument('--save_cpt_flag', type=strtobool, default=True,
|
||||
help='Whether to save cpt for each epoch')
|
||||
arg_parser.add_argument('--skip_train', type=strtobool, default=False,
|
||||
help='Whether to skip training')
|
||||
arg_parser.add_argument('--eval_model_names', type=str, default='DCFEE-O,DCFEE-M,GreedyDec,Doc2EDAG',
|
||||
help="Models to be evaluated, seperated by ','")
|
||||
arg_parser.add_argument('--re_eval_flag', type=strtobool, default=False,
|
||||
help='Whether to re-evaluate previous predictions')
|
||||
|
||||
# add task setting arguments
|
||||
for key, val in DEETaskSetting.base_attr_default_pairs:
|
||||
if isinstance(val, bool):
|
||||
arg_parser.add_argument('--' + key, type=strtobool, default=val)
|
||||
else:
|
||||
arg_parser.add_argument('--'+key, type=type(val), default=val)
|
||||
|
||||
arg_info = arg_parser.parse_args(args=in_args)
|
||||
|
||||
return arg_info
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
in_argv = parse_args()
|
||||
|
||||
task_dir = os.path.join(in_argv.exp_dir, in_argv.task_name)
|
||||
if not os.path.exists(task_dir):
|
||||
os.makedirs(task_dir, exist_ok=True)
|
||||
|
||||
in_argv.model_dir = os.path.join(task_dir, "Model")
|
||||
in_argv.output_dir = os.path.join(task_dir, "Output")
|
||||
|
||||
# in_argv must contain 'data_dir', 'model_dir', 'output_dir'
|
||||
dee_setting = DEETaskSetting(
|
||||
**in_argv.__dict__
|
||||
)
|
||||
|
||||
# build task
|
||||
dee_task = DEETask(dee_setting, load_train=not in_argv.skip_train)
|
||||
|
||||
if not in_argv.skip_train:
|
||||
# dump hyper-parameter settings
|
||||
if dee_task.is_master_node():
|
||||
fn = '{}.task_setting.json'.format(dee_setting.cpt_file_name)
|
||||
dee_setting.dump_to(task_dir, file_name=fn)
|
||||
|
||||
dee_task.train(save_cpt_flag=in_argv.save_cpt_flag)
|
||||
else:
|
||||
dee_task.logging('Skip training')
|
||||
|
||||
if dee_task.is_master_node():
|
||||
if in_argv.re_eval_flag:
|
||||
data_span_type2model_str2epoch_res_list = dee_task.reevaluate_dee_prediction(dump_flag=True)
|
||||
else:
|
||||
data_span_type2model_str2epoch_res_list = aggregate_task_eval_info(in_argv.output_dir, dump_flag=True)
|
||||
data_type = 'test'
|
||||
span_type = 'pred_span'
|
||||
metric_type = 'micro'
|
||||
mstr_bepoch_list = print_total_eval_info(
|
||||
data_span_type2model_str2epoch_res_list, metric_type=metric_type, span_type=span_type,
|
||||
model_strs=in_argv.eval_model_names.split(','),
|
||||
target_set=data_type
|
||||
)
|
||||
print_single_vs_multi_performance(
|
||||
mstr_bepoch_list, in_argv.output_dir, dee_task.test_features,
|
||||
metric_type=metric_type, data_type=data_type, span_type=span_type
|
||||
)
|
||||
|
||||
# ensure every processes exit at the same time
|
||||
if dist.is_initialized():
|
||||
dist.barrier()
|
||||
|
||||
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user