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configs.py
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# -*- coding: utf-8 -*-
import argparse
from pathlib import Path
import pprint
import json
from datetime import datetime
import socket
import os
project_dir = Path(__file__).resolve().parent
dataset_dir = Path('./out_v2.h5').resolve()
video_list = ["demo"]
save_dir = Path('')
score_dir = Path('')
class TestConfig(object):
def __init__(self, args):
self.dataset_type = "summe"
for k, v in args.items():
setattr(self, k, v)
self.video_path = Path(self.dataset_dir).joinpath("{}.h5".format(self.dataset_name))
if self.split_path != "":
self.split_path = Path(self.split_path)
else:
self.split_path = Path(self.dataset_dir).joinpath("{}_splits.json".format(self.dataset_name))
with open(self.split_path) as fp:
self.splits = json.load(fp)
class Config(object):
def __init__(self, **kwargs):
"""Configuration Class: set kwargs as class attributes with setattr"""
for k, v in kwargs.items():
setattr(self, k, v)
self.save_dir = Path(self.save_dir)
# self.log_dir = self.save_dir
self.ckpt_path = self.save_dir.joinpath(f'epoch-{self.epoch}.pkl')
# self.video_path = Path(self.dataset_dir).joinpath("eccv16_dataset_{}_google_pool5.h5".format(self.dataset_name))
self.video_path = Path(self.dataset_dir).joinpath("{}.h5".format(self.dataset_name))
if self.split_path != "":
self.split_path = Path(self.split_path)
else:
self.split_path = Path(self.dataset_dir).joinpath("{}_splits.json".format(self.dataset_name))
with open(self.split_path) as fp:
self.splits = json.load(fp)
# self.train_split = splits[0]["train_keys"]
# self.test_split = splits[0]['test_keys']
now = datetime.now()
dt_string = now.strftime("%Y-%m-%d-%H-%M-%S")
hostname = socket.gethostname()
self.run_name = "_".join([self.model_name, self.dataset_name, dt_string, hostname])
self.run_save_dir = Path(self.save_dir).joinpath(self.run_name)
self.run_score_dir = Path(self.score_dir).joinpath(self.run_name)
self.run_log_dir = Path(self.log_dir).joinpath(self.run_name)
if kwargs["mode"] == "train":
os.mkdir(self.run_save_dir)
os.mkdir(self.run_score_dir)
os.mkdir(self.run_log_dir)
split_ids = self.split_ids
if split_ids == "all":
self.split_ids = [0, 1, 2, 3, 4]
else:
split_ids = split_ids.split("+")
split_ids = list(map(lambda x: int(x), split_ids))
self.split_ids = split_ids
# self.ckpt_path = self.save_dir.joinpath(f'epoch-{self.epoch}.pkl')
def __repr__(self):
"""Pretty-print configurations in alphabetical order"""
config_str = 'Configurations\n'
config_str += pprint.pformat(self.__dict__)
return config_str
def get_config(parse=True, **optional_kwargs):
"""
Get configurations as attributes of class
1. Parse configurations with argparse.
2. Create Config class initilized with parsed kwargs.
3. Return Config class.
"""
parser = argparse.ArgumentParser()
# Mode
parser.add_argument('--mode', type=str, default='train')
parser.add_argument('--verbose', type=bool, default='true')
parser.add_argument('--preprocessed', type=bool, default='True')
parser.add_argument('--video_type', type=str, default='summe')
parser.add_argument("--model_name", type=str, default="WGAN")
parser.add_argument("--debug", type=bool, default=False)
# Data
parser.add_argument("--dataset_dir", type=str, default="")
parser.add_argument("--dataset_name", type=str, default="eccv16_dataset_summe_google_pool5")
parser.add_argument("--dataset_type", type=str, default="summe")
parser.add_argument("--split_path", type=str, default="")
parser.add_argument("--with_images", type=bool, default=False)
parser.add_argument("--video_dir", type=str, default=None)
parser.add_argument("--image_dir", type=str, default=None)
parser.add_argument("--mapping_file", type=str, default=None)
# Path
parser.add_argument("--log_dir", type=str, default="")
parser.add_argument("--save_dir", type=str, default="")
parser.add_argument("--score_dir", type=str, default="")
# Model
parser.add_argument('--input_size', type=int, default=1024)
parser.add_argument('--hidden_size', type=int, default=500)
parser.add_argument('--num_layers', type=int, default=2)
parser.add_argument('--summary_rate', type=float, default=0.15)
parser.add_argument("--noise_dim", type=int, default=0)
parser.add_argument("--best_k", type=int, default=1)
# Model Type
parser.add_argument("--summarizer", type=str, default="SUM-GAN")
parser.add_argument("--discriminator", type=str, default="SUM-GAN")
parser.add_argument("--compressor", type=str, default="SUM-GAN")
parser.add_argument("--critic", type=str, default="SUM-GAN")
parser.add_argument("--solver", type=str, default="GAN")
parser.add_argument("--compressing_features", type=bool, default=False)
parser.add_argument("--evaluation_methods", nargs="+", default=["ground_truth_avg", "ground_truth_max"])
parser.add_argument("--optimal_criteria", type=str, default="f_measure_by_avg")
parser.add_argument("--cs_m", type=int, default=4)
parser.add_argument("--stgcn_shortcut", type=int, default=1)
# Loss
parser.add_argument("--variance_loss", type=str, default="median") # median ssum, starget, normal
parser.add_argument("--sparsity_loss", type=str, default="dpp") # dpp, slen
# Train
parser.add_argument('--n_epochs', type=int, default=120)
parser.add_argument('--clip', type=float, default=5.0)
parser.add_argument('--lr', type=float, default=1e-4)
parser.add_argument('--discriminator_lr', type=float, default=1e-5)
parser.add_argument('--discriminator_slow_start', type=int, default=15)
parser.add_argument('--gt_evaluate', type=bool, default=0)
parser.add_argument("--weight_decay", type=float, default=1)
parser.add_argument("--discriminator_weight_decay", type=float, default=1)
parser.add_argument("--discriminator_scheduler_step", type=int, default=10)
parser.add_argument("--scheduler_step", type=int, default=10)
parser.add_argument("--scheduler_gamma", type=float, default=0.1)
parser.add_argument("--discriminator_scheduler_gamma", default=0.1)
# load epoch
parser.add_argument('--epoch', type=int, default=2)
parser.add_argument("--split_ids", type=str, default="all")
# Knowledge Distillation
parser.add_argument("--teacher_checkpoint", type=str, default="")
parser.add_argument("--temperature", type=str, default=10)
kwargs = parser.parse_args()
# Namespace => Dictionary
kwargs = vars(kwargs)
kwargs.update(optional_kwargs)
return Config(**kwargs)
if __name__ == '__main__':
config = get_config()
import ipdb
ipdb.set_trace()