def main():
args = args_parser()
update_config(args.cfg)
if cfg.BASIC.SHOW_CFG:
pprint.pprint(cfg)
# prepare running environment for the whole project
prepare_env(cfg)
# dataloader
val_dset = WtalDataset(cfg, cfg.DATASET.VAL_SPLIT)
val_loader = DataLoader(val_dset,
batch_size=cfg.TEST.BATCH_SIZE,
shuffle=False,
num_workers=cfg.BASIC.WORKERS,
pin_memory=cfg.BASIC.PIN_MEMORY)
# network
model = LocNet(cfg)
# model.apply(weight_init)
model.cuda()
# weight_file = '/disk3/zt/code/4_a/1_ECM_no_inv_drop/output/0_NeurIPS2020_code_ok/results_and_model/thumos14_checkpoint_best_cas_epoch125_iou0.5__0.2928.pth'
# weight_file = '/disk3/zt/code/4_a/1_ECM_no_inv_drop/output/0_NeurIPS2020_code_ok/results_and_model/anet12_checkpoint_best_cas_epoch30_map_0.2545.pth'
# weight_file = '/disk3/zt/code/4_a/1_ECM_no_inv_drop/output/0_NeurIPS2020_code_ok/results_and_model/anet13_checkpoint_best_cas_epoch35_map_0.2348.pth'
# weight_file = ''
weight_file = '/disk3/zt/code/4_a/1_ECM_no_inv_drop/output/thumos14/thumos_ablation_inv_0_save_model/checkpoint_best_cas_inv0_epoch69_0.2636.pth'
# weight_file = '/disk3/zt/code/4_a/1_ECM_no_inv_drop/output/thumos14/thumos_ablation_only_cas_save_model/checkpoint_best_cas_only_cas_epoch134_0.1957.pth'
# weight_file = '/disk3/zt/code/4_a/1_ECM_no_inv_drop/output/thumos14/thumos_ablation_individual_attention_2048k1_2048k1_2048k1_only_cam_svae_model/checkpoint_best_cas_only_cam_epoch96_0.1714.pth'
res_dir = os.path.join(cfg.BASIC.CKPT_DIR, cfg.TEST.RESULT_DIR,
'vis/cas_gt_idx_minmax_norm_std')
if not os.path.exists(res_dir):
os.makedirs(res_dir)
from utils.utils import load_weights
model = load_weights(model, weight_file)
epoch = 600
output_json_file_cas, output_json_file_cam, test_acc_cas, test_acc_cam = evaluate(
cfg, val_loader, model, epoch)
evaluate_mAP(cfg, output_json_file_cas,
os.path.join(cfg.BASIC.CKPT_DIR, cfg.DATASET.GT_FILE),
cfg.BASIC.VERBOSE)
evaluate_mAP(cfg, output_json_file_cam,
os.path.join(cfg.BASIC.CKPT_DIR, cfg.DATASET.GT_FILE),
cfg.BASIC.VERBOSE)
is_minmax_norm = True
evaluate_vis_cas_minmax_norm_std(cfg, val_loader, model, res_dir,
is_minmax_norm)
def main():
args = args_parser()
update_config(args.cfg)
if cfg.BASIC.SHOW_CFG:
pprint.pprint(cfg)
# prepare running environment for the whole project
prepare_env(cfg)
# dataloader
val_dset = WtalDataset(cfg, cfg.DATASET.VAL_SPLIT)
val_loader = DataLoader(val_dset,
batch_size=cfg.TEST.BATCH_SIZE,
shuffle=False,
num_workers=cfg.BASIC.WORKERS,
pin_memory=cfg.BASIC.PIN_MEMORY)
# network
model = LocNet(cfg)
# model.apply(weight_init)
model.cuda()
# weight_file = ""
weight_file = "/disk3/zt/code/4_a/1_ECM_no_inv_drop/output/0_NeurIPS2020_code_ok/results_and_model/thumos14_checkpoint_best_cas_epoch125_iou0.5__0.2928.pth"
# weight_file = "/disk3/zt/code/4_a/1_ECM_no_inv_drop/output/0_NeurIPS2020_code_ok/results_and_model/anet12_checkpoint_best_cas_epoch30_map_0.2394.pth"
# weight_file = "/disk3/zt/code/4_a/1_ECM_no_inv_drop/output/0_NeurIPS2020_code_ok/results_and_model/anet13_checkpoint_best_cas_epoch35_map_0.2348.pth"
epoch = 801
from utils.utils import load_weights
model = load_weights(model, weight_file)
# actions_json_file = evaluate(cfg, val_loader, model, epoch)
#
# evaluate_mAP(cfg, actions_json_file, os.path.join(cfg.BASIC.CKPT_DIR, cfg.DATASET.GT_FILE))
# output_json_file_cas, output_json_file_cam, test_acc_cas, test_acc_cam = evaluate(cfg, val_loader, model, epoch)
output_json_file_cas, test_acc_cas = evaluate(cfg, val_loader, model,
epoch)
if cfg.BASIC.VERBOSE:
print('test_acc, cas %f' % (test_acc_cas))
mAP, average_mAP = evaluate_mAP(
cfg, output_json_file_cas,
os.path.join(cfg.BASIC.CKPT_DIR, cfg.DATASET.GT_FILE),
cfg.BASIC.VERBOSE)
def main():
args = args_parser()
update_config(args.cfg)
if cfg.BASIC.SHOW_CFG:
pprint.pprint(cfg)
# prepare running environment for the whole project
prepare_env(cfg)
# dataloader
val_dset = WtalDataset(cfg, cfg.DATASET.VAL_SPLIT)
val_loader = DataLoader(val_dset, batch_size=cfg.TEST.BATCH_SIZE, shuffle=False,
num_workers=cfg.BASIC.WORKERS, pin_memory=cfg.BASIC.PIN_MEMORY)
# network
model = LocNet(cfg)
# model.apply(weight_init)
model.cuda()
# weight_file = '/disk3/zt/code/4_a/1_ECM_no_inv_drop/output/0_NeurIPS2020_code_ok/results_and_model/thumos14_checkpoint_best_cas_epoch125_iou0.5__0.2928.pth'
# weight_file = '/disk3/zt/code/4_a/1_ECM_no_inv_drop/output/0_NeurIPS2020_code_ok/results_and_model/anet12_checkpoint_best_cas_epoch30_map_0.2545.pth'
weight_file = '/disk3/zt/code/4_a/1_ECM_no_inv_drop/output/0_NeurIPS2020_code_ok/results_and_model/anet13_checkpoint_best_cas_epoch35_map_0.2348.pth'
res_dir = os.path.join(cfg.BASIC.CKPT_DIR, cfg.TEST.RESULT_DIR,'vis/cas_weight_gt_idx_iou_0.85_mul_instance')
if not os.path.exists(res_dir):
os.makedirs(res_dir)
from utils.utils import load_weights
model = load_weights(model, weight_file)
# epoch = 600
# actions_json_file, _ = evaluate(cfg, val_loader, model, epoch)
# evaluate_mAP(cfg, actions_json_file, os.path.join(cfg.BASIC.CKPT_DIR, cfg.DATASET.GT_FILE), cfg.BASIC.VERBOSE)
is_minmax_norm = True
evaluate_vis_cas_select_specific(cfg, val_loader, model, res_dir, is_minmax_norm)
def main():
args = args_parser()
update_config(args.cfg)
if cfg.BASIC.SHOW_CFG:
pprint.pprint(cfg)
# prepare running environment for the whole project
prepare_env(cfg)
# dataloader
val_dset = WtalDataset(cfg, cfg.DATASET.VAL_SPLIT)
val_loader = DataLoader(val_dset,
batch_size=cfg.TEST.BATCH_SIZE,
shuffle=False,
num_workers=cfg.BASIC.WORKERS,
pin_memory=cfg.BASIC.PIN_MEMORY)
# network
model = LocNet(cfg)
# model.apply(weight_init)
model.cuda()
weight_file = '/disk3/zt/code/4_a/1_ECM_no_inv_drop/output/anet12/anet12_no_inv_base_LR_0.0002_BATCH_128_save_model/checkpoint_best_cas_0.2243.pth'
epoch = 600
res_dir = '/disk3/zt/code/4_a/1_ECM_no_inv_drop/output/anet12/anet12_no_inv_base_LR_0.0002_BATCH_128_save_model/vis/cas_minmax_norm'
if not os.path.exists(res_dir):
os.makedirs(res_dir)
from utils.utils import load_weights
model = load_weights(model, weight_file)
is_minmax_norm = True
# actions_json_file = evaluate(cfg, val_loader, model, epoch)
#
# evaluate_mAP(cfg, actions_json_file, os.path.join(cfg.BASIC.CKPT_DIR, cfg.DATASET.GT_FILE))
# output_json_file_cas, output_json_file_cam, test_acc_cas, test_acc_cam = evaluate(cfg, val_loader, model, epoch)
evaluate_vis_cas(cfg, val_loader, model, epoch, res_dir, is_minmax_norm)
def main():
args = args_parser()
update_config(args.cfg)
if cfg.BASIC.SHOW_CFG:
pprint.pprint(cfg)
# prepare running environment for the whole project
prepare_env(cfg)
# dataloader
val_dset = WtalDataset(cfg, cfg.DATASET.VAL_SPLIT)
val_loader = DataLoader(val_dset,
batch_size=cfg.TEST.BATCH_SIZE,
shuffle=False,
num_workers=cfg.BASIC.WORKERS,
pin_memory=cfg.BASIC.PIN_MEMORY)
# network
model = LocNet(cfg)
# model.apply(weight_init)
model.cuda()
# weight_file = ''
weight_file = '/disk/yangle/Short-Actions/ECM/output/thumos14/ECM_baseline/checkpoint_best_150.pth'
res_dir = os.path.join(cfg.BASIC.CKPT_DIR, cfg.TEST.RESULT_DIR,
'vis/ECM_thumos_score')
if not os.path.exists(res_dir):
os.makedirs(res_dir)
from utils.utils import load_weights
model = load_weights(model, weight_file)
epoch = 600
# output_json_file_cas, test_acc_cas = evaluate(cfg, val_loader, model, epoch)
output_json_file_cas = '/disk/yangle/Short-Actions/ECM/output/thumos14/ECM_baseline/vis/ecm.json'
evaluate_mAP(cfg, output_json_file_cas,
os.path.join(cfg.BASIC.CKPT_DIR, cfg.DATASET.GT_FILE),
cfg.BASIC.VERBOSE)
def main():
args = args_parser()
update_config(args.cfg)
if cfg.BASIC.SHOW_CFG:
pprint.pprint(cfg)
# prepare running environment for the whole project
prepare_env(cfg)
# log
writer = SummaryWriter(
log_dir=os.path.join(cfg.BASIC.CKPT_DIR, cfg.BASIC.LOG_DIR))
# dataloader
train_dset = WtalDataset(cfg, cfg.DATASET.TRAIN_SPLIT)
train_loader = DataLoader(train_dset,
batch_size=cfg.TRAIN.BATCH_SIZE,
shuffle=True,
num_workers=cfg.BASIC.WORKERS,
pin_memory=cfg.BASIC.PIN_MEMORY)
val_dset = WtalDataset(cfg, cfg.DATASET.VAL_SPLIT)
val_loader = DataLoader(val_dset,
batch_size=cfg.TEST.BATCH_SIZE,
shuffle=False,
num_workers=cfg.BASIC.WORKERS,
pin_memory=cfg.BASIC.PIN_MEMORY)
# network
model = LocNet(cfg)
# model.apply(weight_init)
model.cuda()
# weight_file = "/disk3/zt/code/actloc/thumos/17_CAS_CAM_fast_tuning/output/20class_seed_0_save_model/checkpoint_best_cas_0.2701.pth"
weight_file = '/disk3/zt/code/actloc/thumos/20_0.2701_try/output/debug_save_epoch30/checkpoint_best_cas.pth'
from utils.utils import load_weights
model = load_weights(model, weight_file)
# optimizer
optimizer = optim.Adam(model.parameters(),
lr=cfg.TRAIN.LR,
betas=cfg.TRAIN.BETAS,
weight_decay=cfg.TRAIN.WEIGHT_DECAY)
optimizer.load_state_dict(torch.load(weight_file)['optimizer'])
# criterion
criterion = BasNetLoss()
for epoch in range(1, cfg.TRAIN.EPOCH_NUM + 1):
print('Epoch: %d:' % epoch)
loss_average_cas, loss_average_cam, loss_average_consistency, loss_average_norm, loss_average_cas_inv, loss_average_cam_inv = train(
cfg, train_loader, model, optimizer, criterion)
writer.add_scalar('train_loss/cas', loss_average_cas, epoch)
writer.add_scalar('train_loss/cam', loss_average_cam, epoch)
writer.add_scalar('train_loss/consistency', loss_average_consistency,
epoch)
writer.add_scalar('train_loss/norm', loss_average_norm, epoch)
writer.add_scalar('train_loss/cas_inv', loss_average_cas_inv, epoch)
writer.add_scalar('train_loss/cam_inv', loss_average_cam_inv, epoch)
if cfg.BASIC.VERBOSE:
print(
'loss: cas %f, cam %f, consistency %f, norm %f, cas_inv %f, cam_inv %f'
% (loss_average_cas, loss_average_cam,
loss_average_consistency, loss_average_norm,
loss_average_cas_inv, loss_average_cam_inv))
# decay learning rate
if epoch in cfg.TRAIN.LR_DECAY_EPOCHS:
decay_lr(optimizer, factor=cfg.TRAIN.LR_DECAY_FACTOR)
if epoch % cfg.TEST.EVAL_INTERVAL == 0:
_, _, test_acc_cas, test_acc_cam = evaluate(
cfg, val_loader, model, epoch)
if cfg.BASIC.VERBOSE:
print('test_acc, cas %f, cam %f' %
(test_acc_cas, test_acc_cam))
writer.add_scalar('test_acc/cas', test_acc_cas, epoch)
writer.add_scalar('test_acc/cam', test_acc_cam, epoch)
writer.close()
def main():
args = args_parser()
update_config(args.cfg)
if cfg.BASIC.SHOW_CFG:
pprint.pprint(cfg)
# prepare running environment for the whole project
prepare_env(cfg)
# log
writer = SummaryWriter(
log_dir=os.path.join(cfg.BASIC.CKPT_DIR, cfg.BASIC.LOG_DIR))
# dataloader
train_dset = WtalDataset(cfg, cfg.DATASET.TRAIN_SPLIT)
train_loader = DataLoader(train_dset,
batch_size=cfg.TRAIN.BATCH_SIZE,
shuffle=True,
num_workers=cfg.BASIC.WORKERS,
pin_memory=cfg.BASIC.PIN_MEMORY)
val_dset = WtalDataset(cfg, cfg.DATASET.VAL_SPLIT)
val_loader = DataLoader(val_dset,
batch_size=cfg.TEST.BATCH_SIZE,
shuffle=False,
num_workers=cfg.BASIC.WORKERS,
pin_memory=cfg.BASIC.PIN_MEMORY)
# network
model = LocNet(cfg)
model.cuda()
# optimizer
optimizer = optim.Adam(model.parameters(),
lr=cfg.TRAIN.LR,
betas=cfg.TRAIN.BETAS,
weight_decay=cfg.TRAIN.WEIGHT_DECAY)
# criterion
criterion = BasNetLoss()
for epoch in range(1, cfg.TRAIN.EPOCH_NUM + 1):
print('Epoch: %d:' % epoch)
loss_average_cas, loss_average_cam, loss_average_consistency, loss_average_norm, loss_average_cam_inv = train(
cfg, train_loader, model, optimizer, criterion)
writer.add_scalar('train_loss/cas', loss_average_cas, epoch)
writer.add_scalar('train_loss/cam', loss_average_cam, epoch)
writer.add_scalar('train_loss/consistency', loss_average_consistency,
epoch)
writer.add_scalar('train_loss/norm', loss_average_norm, epoch)
writer.add_scalar('train_loss/cam_inv', loss_average_cam_inv, epoch)
if cfg.BASIC.VERBOSE:
print(
'loss: cas %f, cam %f, consistency %f, norm %f, cam_inv %f' %
(loss_average_cas, loss_average_cam, loss_average_consistency,
loss_average_norm, loss_average_cam_inv))
# decay learning rate
if epoch in cfg.TRAIN.LR_DECAY_EPOCHS:
decay_lr(optimizer, factor=cfg.TRAIN.LR_DECAY_FACTOR)
if epoch % cfg.TEST.EVAL_INTERVAL == 0:
_, test_acc_cas = evaluate(cfg, val_loader, model, epoch)
if cfg.BASIC.VERBOSE:
print('test_acc, cas %f' % (test_acc_cas))
writer.add_scalar('test_acc/cas', test_acc_cas, epoch)
save_best_model(cfg,
epoch=epoch,
model=model,
optimizer=optimizer,
name='cas')
writer.close()
