File size: 8,120 Bytes
9157432 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 |
import gc
import logging
import os
import time
from pathlib import Path
import numpy as np
import pandas as pd
import torch
from config import get_config_regression
from data_loader import MMDataLoader
from trains import ATIO
from utils import assign_gpu, setup_seed
from trains.singleTask.model import DLF
from trains.singleTask.distillnets import get_distillation_kernel, get_distillation_kernel_homo
from trains.singleTask.misc import softmax
import sys
from datetime import datetime
now = datetime.now()
format = "%Y/%m/%d %H:%M:%S"
formatted_now = now.strftime(format)
formatted_now = str(formatted_now)+" - "
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:2"
logger = logging.getLogger('MMSA')
def _set_logger(log_dir, model_name, dataset_name, verbose_level):
# base logger
log_file_path = Path(log_dir) / f"{model_name}-{dataset_name}.log"
logger = logging.getLogger('MMSA')
logger.setLevel(logging.DEBUG)
# file handler
fh = logging.FileHandler(log_file_path)
fh_formatter = logging.Formatter('%(asctime)s - %(name)s [%(levelname)s] - %(message)s')
fh.setLevel(logging.DEBUG)
fh.setFormatter(fh_formatter)
logger.addHandler(fh)
# stream handler
stream_level = {0: logging.ERROR, 1: logging.INFO, 2: logging.DEBUG}
ch = logging.StreamHandler()
ch.setLevel(stream_level[verbose_level])
ch_formatter = logging.Formatter('%(name)s - %(message)s')
ch.setFormatter(ch_formatter)
logger.addHandler(ch)
return logger
def DLF_run(
model_name, dataset_name, config=None, config_file="", seeds=[], is_tune=False,
tune_times=500, feature_T="", feature_A="", feature_V="",
model_save_dir="", res_save_dir="", log_dir="",
gpu_ids=[0], num_workers=1, verbose_level=1, mode = '', is_training = False
):
# Initialization
model_name = model_name.upper()
dataset_name = dataset_name.lower()
if config_file != "":
config_file = Path(config_file)
else: # use default config files
config_file = Path(__file__).parent / "config" / "config.json"
if not config_file.is_file():
raise ValueError(f"Config file {str(config_file)} not found.")
if model_save_dir == "":
model_save_dir = Path.home() / "MMSA" / "saved_models"
Path(model_save_dir).mkdir(parents=True, exist_ok=True)
if res_save_dir == "":
res_save_dir = Path.home() / "MMSA" / "results"
Path(res_save_dir).mkdir(parents=True, exist_ok=True)
if log_dir == "":
log_dir = Path.home() / "MMSA" / "logs"
Path(log_dir).mkdir(parents=True, exist_ok=True)
seeds = seeds if seeds != [] else [1111, 1112, 1113, 1114, 1115]
logger = _set_logger(log_dir, model_name, dataset_name, verbose_level)
args = get_config_regression(model_name, dataset_name, config_file)
args.is_training = is_training
args.mode = mode # train or test
args['model_save_path'] = Path(model_save_dir) / f"{args['model_name']}-{args['dataset_name']}.pth"
args['device'] = assign_gpu(gpu_ids)
args['train_mode'] = 'regression'
args['feature_T'] = feature_T
args['feature_A'] = feature_A
args['feature_V'] = feature_V
if config:
args.update(config)
res_save_dir = Path(res_save_dir) / "normal"
res_save_dir.mkdir(parents=True, exist_ok=True)
model_results = []
for i, seed in enumerate(seeds):
setup_seed(seed)
args['cur_seed'] = i + 1
result = _run(args, num_workers, is_tune)
model_results.append(result)
if args.is_training:
criterions = list(model_results[0].keys())
# save result to csv
csv_file = res_save_dir / f"{dataset_name}.csv"
if csv_file.is_file():
df = pd.read_csv(csv_file)
else:
df = pd.DataFrame(columns=["Time"]+["Model"] + criterions)
# save results
res = [model_name]
for c in criterions:
values = [r[c] for r in model_results]
mean = round(np.mean(values)*100, 2)
std = round(np.std(values)*100, 2)
res.append((mean, std))
res = [formatted_now]+res
df.loc[len(df)] = res
df.to_csv(csv_file, index=None)
logger.info(f"Results saved to {csv_file}.")
def _run(args, num_workers=4, is_tune=False, from_sena=False):
dataloader = MMDataLoader(args, num_workers)
if args.is_training:
print("training for DLF")
args.gd_size_low = 64
args.w_losses_low = [1, 10]
args.metric_low = 'l1'
args.gd_size_high = 32
args.w_losses_high = [1, 10]
args.metric_high = 'l1'
to_idx = [0, 1, 2]
from_idx = [0, 1, 2]
assert len(from_idx) >= 1
model = []
model_DLF = getattr(DLF, 'DLF')(args)
model_distill_homo = getattr(get_distillation_kernel_homo, 'DistillationKernel')(n_classes=1,
hidden_size=
args.dst_feature_dim_nheads[0],
gd_size=args.gd_size_low,
to_idx=to_idx, from_idx=from_idx,
gd_prior=softmax([0, 0, 1, 0, 1, 0], 0.25),
gd_reg=10,
w_losses=args.w_losses_low,
metric=args.metric_low,
alpha=1 / 8,
hyp_params=args)
model_distill_hetero = getattr(get_distillation_kernel, 'DistillationKernel')(n_classes=1,
hidden_size=
args.dst_feature_dim_nheads[0] * 2,
gd_size=args.gd_size_high,
to_idx=to_idx, from_idx=from_idx,
gd_prior=softmax([0, 0, 1, 0, 1, 1], 0.25),
gd_reg=10,
w_losses=args.w_losses_high,
metric=args.metric_high,
alpha=1 / 8,
hyp_params=args)
model_DLF = model_DLF.cuda()
model = [model_DLF]
else:
print("testing phase for DLF")
model = getattr(DLF, 'DLF')(args)
model = model.cuda()
trainer = ATIO().getTrain(args)
#test
if args.mode == 'test':
model.load_state_dict(torch.load('./pt/DLF'+str(args.dataset_name)+'.pth'),strict=False)
results = trainer.do_test(model, dataloader['test'], mode="TEST")
sys.stdout.flush()
input('[Press Any Key to start another run]')
#train
else:
epoch_results = trainer.do_train(model, dataloader, return_epoch_results=from_sena)
model[0].load_state_dict(torch.load('./pt/DLF'+str(args.dataset_name)+'.pth'))
results = trainer.do_test(model[0], dataloader['test'], mode="TEST")
del model
torch.cuda.empty_cache()
gc.collect()
time.sleep(1)
return results |