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train_fer_second_stage.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import datetime
import argparse
import torch
import torch.backends.cudnn as cudnn
from torch.cuda.amp import GradScaler
from utils.LossFunctions import *
import clip
from utils.optimizer import build_optimizer
from models.clip_vit import CLIPVIT
from utils.misc import *
from dataloader.data_utils import *
from engine_fer_second_stage import train, test, eval
def main(args):
if not args.eval:
setup_seed(args.seed)
args.is_master = is_main_process()
args.device = "cuda" if torch.cuda.is_available() else "cpu"
torch.cuda.set_device(args.gpu)
######### RECORD SETTING ###########
time = datetime.datetime.now().strftime('%Y-%m-%d-%H:%M:%S')
record_name = time + "_" + args.ckpt_path.split("/")[-2][len("mm-dd-hh:mm:ss"):]+ "-loss_" + args.loss_function + \
"-ep_" + str(args.epochs) + "-lr_" + str(args.lr) + "-bs_" + str(args.batch_size)
args.record_path = os.path.join("outputs", "second_stage", record_name)
if not os.path.exists(args.record_path):
os.makedirs(args.record_path, exist_ok=True)
if args.is_master:
logger = init_log(args, args.record_path)
else:
logger = None
cudnn.benchmark = True # For speed i.e, cudnn autotuner
# Build Dataloader
set_up_datasets(args)
train_dataset, test_dataset, train_dataloader, test_dataloader = get_dataloader(args)
get_labelname(args)
# Init Vision Backbone
clip_model, _ = clip.load(args.clip_path, jit=False)
print("loading clip from {}".format(args.clip_path))
model = CLIPVIT(args, clip_model)
convert_models_to_fp32(model)
if args.ckpt_path:
ckpt = torch.load(args.ckpt_path, map_location="cuda")
msg = model.load_state_dict(ckpt, strict=False)
print(msg)
model = model.to(args.device)
# Build Optimizer
optimizer = build_optimizer(args, model, stage='stage2')
if args.loss_function == 'ce':
criterion = nn.CrossEntropyLoss()
elif args.loss_function == 'focal':
criterion = FocalLoss(class_num=args.classes, device=args.device)
elif args.loss_function == 'balanced':
criterion = BalancedLoss(class_num=args.classes, device=args.device)
elif args.loss_function == 'cosine':
criterion = CosineLoss()
# Dump Params
if is_main_process():
logger.info("------------------------------------------------------------------")
logger.info("USING LR SCHEDULER")
logger.info("------------------------------------------------------------------")
logger.info(("initial learning rate {}".format(args.lr)))
logger.info(optimizer)
write_description_to_folder(os.path.join(args.record_path, "params.txt"), args)
scaler = GradScaler()
for epoch in range(args.epochs):
model.train()
train(model, optimizer, criterion, train_dataloader, logger, scaler, args, epoch)
model.eval()
test(model, criterion, test_dataloader, logger)
else:
args.device = "cuda" if torch.cuda.is_available() else "cpu"
torch.cuda.set_device(args.gpu)
cudnn.benchmark = True # For speed i.e, cudnn autotuner
# Build Dataloader
set_up_datasets(args)
_, test_dataset, _, test_dataloader = get_dataloader(args)
get_labelname(args)
# Init Vision Backbone
clip_model, _ = clip.load(args.clip_path, device=args.device, jit=False)
model = CLIPVIT(args, clip_model)
convert_models_to_fp32(model)
ckpt = torch.load(args.eval_ckpt, map_location="cuda")
msg = model.load_state_dict(ckpt, strict=False)
print("Image Encoder Load Info: ", msg)
model = model.to(args.device)
scaler = GradScaler()
model.eval()
eval(model, test_dataloader)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--name", type=str, default=None )
parser.add_argument("--seed", type=int, default=42 )
parser.add_argument("--record_path", type=str, default='/home/CEPrompt/record')
parser.add_argument("--eval", action="store_true" )
parser.add_argument('--classes', type=int, default=7 )
parser.add_argument('--dataset', type=str, default='rafdb', choices=['rafdb', 'affectnet', 'affectnet_8'])
parser.add_argument('--data-path', type=str, default='/data/RAFDB/basic', choices=['/data/RAFDB/basic/', '/data/AffectNet/'])
parser.add_argument("--ckpt-path", type=str, default='/data/RAFDB/ckpt/model_epoch_34_acc9221.pth' )
parser.add_argument("--clip-path", type=str, default='/data/CEPrompt_ckpt/pre-trained_model/ViT-B-16.pt',
choices=['/data/CEPrompt_ckpt/pre-trained_model/ViT-B-16.pt', '/data/CEPrompt_ckpt/pre-trained_model/ViT-B-32.pt', '/data/CEPrompt_ckpt/pre-trained_model/ViT-L-14.pt'])
parser.add_argument("--eval-ckpt", type=str, default=None )
parser.add_argument("--batch-size", type=int, default=128, )
parser.add_argument("--test-batch-size", type=int, default=30, )
parser.add_argument("--epochs", type=int, default=10, )
parser.add_argument("--warmup_epochs", type=int, default=2, )
parser.add_argument("--loss_function", type=str, default='ce', choices=['ce', 'focal', 'balanced', 'cosine'])
parser.add_argument("--lr", type=float, default=2e-3, )
parser.add_argument("--min_lr", type=float, default=1e-8, )
parser.add_argument("--weight_decay", type=float, default=0.0005, )
parser.add_argument("--workers", type=int, default=8, )
parser.add_argument("--momentum", type=float, default=0.9, )
parser.add_argument("--input_size", type=int, default=224 )
parser.add_argument("--alpha", type=float, default=0.5, )
parser.add_argument("--topk", type=int, default=16 )
parser.add_argument("--stage2_name", type=str, default="cat")
parser.add_argument("--ctxinit", type=str, default="a photo of")
parser.add_argument("--prompts_depth", type=int, default=9 )
parser.add_argument("--use_class_invariant", action='store_true' )
parser.add_argument("--n_ctx", type=int, default=2 )
parser.add_argument("--gpu", type=int, default=0 )
args = parser.parse_args()
main(args)