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train_cam_multi.py
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import os
import time
import sys
import random
import logging
import argparse
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
import data.transforms_bbox as Tr
from data.voc import VOC_box
from configs.defaults import _C
from models.ClsNet_RepVGG_multi_0421 import Labeler
from misc import pyutils, torchutils
logger = logging.getLogger("stage1")
def my_collate(batch):
'''
This is to assign a batch-wise index for each box.
'''
sample = {}
img = []
bboxes = []
bg_mask = []
batchID_of_box = []
for batch_id, item in enumerate(batch):
img.append(item[0])
bboxes.append(item[1])
bg_mask.append(item[2])
for _ in range(len(item[1])):
batchID_of_box += [batch_id]
sample["img"] = torch.stack(img, dim=0)
sample["bboxes"] = torch.cat(bboxes, dim=0)
sample["bg_mask"] = torch.stack(bg_mask, dim=0)[:,None]
sample["batchID_of_box"] = torch.tensor(batchID_of_box, dtype=torch.long)
return sample
def main(cfg):
logger.setLevel(logging.DEBUG)
logger.propagate = False
formatter = logging.Formatter("[%(asctime)s] %(levelname)s: %(message)s", datefmt="%m/%d %H:%M:%S")
ch = logging.StreamHandler(stream=sys.stdout)
ch.setLevel(logging.DEBUG)
ch.setFormatter(formatter)
logger.addHandler(ch)
fh = logging.FileHandler(f"./logs/multi/{args.NAME}.txt")
fh.setLevel(logging.DEBUG)
fh.setFormatter(formatter)
logger.addHandler(fh)
logger.info(" ".join(["\n{}: {}".format(k, v) for k,v in cfg.items()]))
if cfg.SEED:
np.random.seed(cfg.SEED)
torch.manual_seed(cfg.SEED)
random.seed(cfg.SEED)
os.environ["PYTHONHASHSEED"] = str(cfg.SEED)
tr_transforms = Tr.Compose([
Tr.RandomScale(0.5, 1.5),
Tr.ResizeRandomCrop(cfg.DATA.CROP_SIZE),
Tr.RandomHFlip(0.5),
Tr.ColorJitter(0.5,0.5,0.5,0),
Tr.Normalize_Caffe(),
])
trainset = VOC_box(cfg, tr_transforms)
train_loader = DataLoader(trainset, batch_size=cfg.DATA.BATCH_SIZE, collate_fn=my_collate, shuffle=True, num_workers=4, pin_memory=True, drop_last=True)
start = time.time()
ROI_SIZE = list(map(int,list(args.ROI_SIZE)))
Features = [args.Features1, args.Features2, args.Features3, args.Features4, args.Features5]
model = Labeler(cfg.DATA.NUM_CLASSES, f"./weights/{cfg.MODEL.WEIGHTS}", ROI_SIZE, args.GRID_SIZE, Features, pretrained = False).cuda()
############################################################
params = model.get_params()
lr = cfg.SOLVER.LR
wd = cfg.SOLVER.WEIGHT_DECAY
optimizer = optim.SGD(
[{"params":params[0], "lr":lr, "weight_decay":wd},
{"params":params[1], "lr":2*lr, "weight_decay":0 },
{"params":params[2], "lr":10*lr, "weight_decay":wd},
{"params":params[3], "lr":20*lr, "weight_decay":0 }],
momentum=cfg.SOLVER.MOMENTUM
)
############################################################
scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=cfg.SOLVER.MILESTONES, gamma=0.1)
criterion = nn.CrossEntropyLoss()
model.train()
iterator = iter(train_loader)
storages = {"CE": 0,}
interval_verbose = cfg.SOLVER.MAX_ITER // 800
logger.info(f"START {args.NAME} -->")
for it in range(1, cfg.SOLVER.MAX_ITER+1):
try:
sample = next(iterator)
except:
iterator = iter(train_loader)
sample = next(iterator)
img = sample["img"]
bboxes = sample["bboxes"]
bg_mask = sample["bg_mask"]
batchID_of_box = sample["batchID_of_box"]
ind_valid_bg_mask = bg_mask.mean(dim=(1,2,3)) > 0.0625 # This is because VGG16 has output stride of 8.
logits_list = model(img.cuda(), bboxes, batchID_of_box, bg_mask.cuda(), ind_valid_bg_mask)
fg_t = bboxes[:,-1][:,None].expand(bboxes.shape[0], np.prod(ROI_SIZE))
fg_t = fg_t.flatten().long() # [199*1]
loss = 0
wths = [0, 0, 1/2, 1/2, 1]
for index, logits in enumerate(logits_list):
if logits != None:
logits = logits[...,0,0]
target = torch.zeros(logits.shape[0], dtype=torch.long) # [212]
target[:fg_t.shape[0]] = fg_t
loss_single = criterion(logits, target.cuda())
loss += loss_single * wths[index]
optimizer.zero_grad()
loss.backward()
optimizer.step()
scheduler.step()
storages["CE"] += loss.item()
if it % interval_verbose == 0:
for k in storages.keys(): storages[k] /= interval_verbose
logger.info("{:3d}/{:3d} Loss (CE): {:.4f} lr: {}".format(it, cfg.SOLVER.MAX_ITER, storages["CE"], optimizer.param_groups[0]["lr"]))
for k in storages.keys(): storages[k] = 0
if it -1 == 0:
for k in storages.keys(): storages[k] /= 1
logger.info("{:3d}/{:3d} Loss (CE): {:.4f} lr: {}".format(it, cfg.SOLVER.MAX_ITER, storages["CE"], optimizer.param_groups[0]["lr"]))
for k in storages.keys(): storages[k] = 0
torch.save(model.state_dict(), f"./weights/multi/{args.NAME}.pt")
logger.info("--- SAVED ---")
logger.info(f"END {args.NAME} -->")
def str2bool(v):
if isinstance(v, bool):
return v
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise argparse.ArgumentTypeError('Boolean value expected.')
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument("--config-file")
parser.add_argument("--gpu-id", type=str, default="0", help="select a GPU index")
parser.add_argument("--NAME", type=str, default="train_cam_repvgg_grid_5", help="select a GPU index")
parser.add_argument("--GRID_SIZE", type=int, default="4", help="select a GPU index")
parser.add_argument("--ROI_SIZE", type=str, default="22", help="select a GPU index")
parser.add_argument("--Features1", type=str2bool, default=False)
parser.add_argument("--Features2", type=str2bool, default=False)
parser.add_argument("--Features3", type=str2bool, default=False)
parser.add_argument("--Features4", type=str2bool, default=False)
parser.add_argument("--Features5", type=str2bool, default=False)
return parser.parse_args()
if __name__ == "__main__":
args = get_args()
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_id
cfg = _C.clone()
cfg.merge_from_file(args.config_file)
cfg.freeze()
main(cfg)