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train_IM.py
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from torch.backends import cudnn
from utils.logger import setup_logger
from model import make_model
from solver import make_optimizer, WarmupMultiStepLR
from loss import make_loss
from datasets.sampler import RandomIdentitySampler
from datasets.bases import ImageDataset
from datasets.make_dataloader import train_collate_fn
import argparse
from config import cfg
from config import cfg_test
from processor import do_train, do_inference_Pseudo_track_rerank
import random
import torch
import numpy as np
from datasets import make_dataloader_Pseudo, make_dataloader
import os
import os.path as osp
from torch.utils.data import DataLoader
from collections import defaultdict
if __name__ == '__main__':
torch.manual_seed(1234)
torch.cuda.manual_seed(1234)
torch.cuda.manual_seed_all(1234)
np.random.seed(1234)
random.seed(1234)
torch.backends.cudnn.deterministic = True
cudnn.benchmark = True
parser = argparse.ArgumentParser(description="ReID Baseline Training")
parser.add_argument(
"--config_file", default="", help="path to config file", type=str
)
parser.add_argument(
"--config_file_test", default="", help="path to config file", type=str
)
parser.add_argument("opts", help="Modify config options using the command-line", default=None,
nargs=argparse.REMAINDER)
args = parser.parse_args()
# Where the query dataset is placed.
imgs_dir_query = '../data/AIC20_track2/AIC20_ReID/image_query'
# Where the test dataset is placed.
imgs_dir_test = '../data/AIC20_track2/AIC20_ReID/image_test'
if args.config_file_test != "":
cfg_test.merge_from_file(args.config_file_test)
cfg_test.freeze()
if args.config_file_test != "":
print("Loaded test configuration file {}".format(args.config_file_test))
with open(args.config_file_test, 'r') as cf:
config_str = "\n" + cf.read()
print(config_str)
os.environ['CUDA_VISIBLE_DEVICES'] = cfg_test.MODEL.DEVICE_ID
print(cfg_test, 'cfg_test')
train_loader, val_loader, num_query, num_classes = make_dataloader(cfg_test)
KNOWN = num_classes
print("num_class in the custom training: {}".format(KNOWN))
model = make_model(cfg_test, num_class=num_classes)
model.load_param(cfg_test.TEST.WEIGHT)
print('Ready for inference')
distmat, img_name_q, image_all_name = do_inference_Pseudo_track_rerank(cfg_test, model, val_loader, num_query)
print(distmat, 'distmat')
print('The shape of distmat is: {}'.format(distmat.shape))
if args.config_file != "":
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
output_dir = cfg.OUTPUT_DIR
if output_dir and not os.path.exists(output_dir):
os.makedirs(output_dir)
logger = setup_logger("reid_baseline", output_dir, if_train=True)
logger.info("Saving model in the path :{}".format(cfg.OUTPUT_DIR))
logger.info(args)
if args.config_file != "":
logger.info("Loaded configuration file {}".format(args.config_file))
with open(args.config_file, 'r') as cf:
config_str = "\n" + cf.read()
logger.info(config_str)
logger.info("Running with config:\n{}".format(cfg))
os.environ['CUDA_VISIBLE_DEVICES'] = cfg.MODEL.DEVICE_ID
indexes = np.argwhere(distmat < cfg.MODEL.THRESH)
print(cfg.MODEL.THRESH,'cfg.MODEL.THRESH')
logger.info('The number of galleries selected at the beginning: {}'.format(len(indexes)))
final_index = defaultdict(list)
gallery_container = set()
for index in indexes:
if index[1] not in gallery_container:
gallery_container.add(index[1])
final_index[index[1]] = index[0]
else:
if distmat[index[0]][index[1]] < distmat[final_index[index[1]]][index[1]]:
final_index.pop(index[1])
final_index[index[1]] = index[0]
logger.info('The number of galleries selected after processing: {}'.format(len(final_index)))
seletcted_data = []
pid_container = set()
for gallery, query in final_index.items():
pid_container.add(query)
pid2label = {pid: label for label, pid in enumerate(pid_container)}
label_convert = defaultdict(list)
for gallery, query in final_index.items():
label_convert[pid2label[query]].append(image_all_name[gallery])
for label, images in label_convert.items():
for image in images:
seletcted_data.append((os.path.join(imgs_dir_test, image), label + KNOWN, 1, 1))
for pid in pid_container:
seletcted_data.append((osp.join(imgs_dir_query,
img_name_q[pid]), pid2label[pid] + KNOWN, 1, 1))
logger.info("the Number of Pseudo-seletcted_data is :{}".format(len(seletcted_data)))
logger.info("the class of Pseudo-label is :{}".format(len(label_convert)))
train_loader, val_loader, num_query, num_classes, dataset, train_set, train_transforms = make_dataloader_Pseudo(cfg)
seletcted_set = ImageDataset(seletcted_data, train_transforms)
new_train_data = train_set + seletcted_set
train_loader_test = DataLoader(
new_train_data, batch_size=cfg.SOLVER.IMS_PER_BATCH,
sampler=RandomIdentitySampler(dataset.train + seletcted_data, cfg.SOLVER.IMS_PER_BATCH,
cfg.DATALOADER.NUM_INSTANCE),
num_workers=cfg.DATALOADER.NUM_WORKERS, collate_fn=train_collate_fn
)
num_classes = KNOWN + len(label_convert)
model = make_model(cfg, num_class=num_classes)
loss_func, center_criterion = make_loss(cfg, num_classes=num_classes)
optimizer, optimizer_center = make_optimizer(cfg, model, center_criterion)
scheduler = WarmupMultiStepLR(optimizer, cfg.SOLVER.STEPS, cfg.SOLVER.GAMMA,
cfg.SOLVER.WARMUP_FACTOR,
cfg.SOLVER.WARMUP_EPOCHS, cfg.SOLVER.WARMUP_METHOD)
if cfg.MODEL.PRETRAIN_CHOICE == 'finetune':
model.load_param_finetune(cfg.MODEL.PRETRAIN_PATH)
print('Loading pretrained model for finetuning......')
do_train(
cfg,
model,
center_criterion,
train_loader_test,
val_loader,
optimizer,
optimizer_center,
scheduler, # modify for using self trained model
loss_func,
num_query
)