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)
cas_dir = "/disk3/zt/code/4_a/1_ECM_no_inv_drop/output/thumos14/000_thumos_29.13_save_model_Frame_wise_accuracy/save_for_post_process"
epoch = 901
datas = list()
file_name_list = os.listdir(cas_dir)
for file_name in file_name_list:
data = np.load(os.path.join(cas_dir, file_name))
datas.append(data)
output_json_file_cas, test_acc_cas = evaluate_from_offline_cas(
cfg, datas, 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)
base_branch_json = '/disk3/zt/code/4_a/1_ECM_no_inv_drop/output/thumos14/thumos_ablation_only_cas_only_cam_separate_weight_save_model_debug/075_cas.json'
cam_branch_json = '/disk3/zt/code/4_a/1_ECM_no_inv_drop/output/thumos14/thumos_ablation_only_cas_only_cam_separate_weight_save_model_debug/089_cam.json'
evaluate_mAP(cfg, base_branch_json, os.path.join(cfg.BASIC.CKPT_DIR, cfg.DATASET.GT_FILE), cfg.BASIC.VERBOSE)
evaluate_mAP(cfg, cam_branch_json, os.path.join(cfg.BASIC.CKPT_DIR, cfg.DATASET.GT_FILE), cfg.BASIC.VERBOSE)
base_branch_data = json.load(open(base_branch_json, 'r'))
base_branch_results = base_branch_data['results']
cam_branch_data = json.load(open(cam_branch_json, 'r'))
cam_branch_results = cam_branch_data['results']
for vid_name in cam_branch_results.keys():
cam_branch_results[vid_name].extend(base_branch_results[vid_name])
output_dict = {'version': 'VERSION 1.3', 'results': cam_branch_results, 'external_data': {}}
result_file = '/disk3/zt/code/4_a/1_ECM_no_inv_drop/output/thumos14/thumos_ablation_only_cas_only_cam_separate_weight_save_model_debug/cat.json'
outfile = open(result_file, 'w')
json.dump(output_dict, outfile)
outfile.close()
evaluate_mAP(cfg, result_file, 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 = ''
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)
# 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_cas = LocNet(cfg)
# model.apply(weight_init)
model_cam = LocNet(cfg)
model_cas.cuda()
model_cam.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/anet13/anet13_same_as_anet12_seed1_epoch45_TOPK_K_R_0.6_LR_DECAY26_save_every_model/checkpoint_best_cas_epoch35.pth"
weight_file_cas = "/disk3/zt/code/4_a/1_ECM_no_inv_drop/output/thumos14/thumos_ablation_only_cas_only_cam_separate_weight_save_model/checkpoint_best_cas_epoch75_0.2055.pth"
weight_file_cam = "/disk3/zt/code/4_a/1_ECM_no_inv_drop/output/thumos14/thumos_ablation_only_cas_only_cam_separate_weight_save_model/checkpoint_best_cam_epoch89_0.176.pth"
from utils.utils import load_weights
model_cas = load_weights(model_cas, weight_file_cas)
model_cam = load_weights(model_cam, weight_file_cam)
epoch = 911
output_json_file_cas, test_acc_cas = evaluate_fuse_sequence(
cfg, val_loader, model_cas, model_cam, 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 = ""
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/anet12/anet12_0.2350_cam_inv_1_seed7_epoch36_TOPK_K_R_0.25__save_model_LR_decay/anet12_checkpoint_best_cas_0.2394.pth'
weight_file = "/disk3/zt/code/4_a/1_ECM_no_inv_drop/output/anet13/anet13_same_as_anet12_seed1_epoch45_TOPK_K_R_0.6_LR_DECAY26_save_every_model/anet13_checkpoint_best_cas_epoch28_0.2178.pth"
epoch = 603
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 post_process(cfg, actions_json_file, writer, best_mAP, info, epoch, name):
mAP, average_mAP = evaluate_mAP(
cfg, actions_json_file,
os.path.join(cfg.BASIC.CKPT_DIR, cfg.DATASET.GT_FILE),
cfg.BASIC.VERBOSE)
for i in range(len(cfg.TEST.IOU_TH)):
writer.add_scalar('z_mAP@{}/{}'.format(cfg.TEST.IOU_TH[i], name),
mAP[i], epoch)
writer.add_scalar('Average mAP/{}'.format(name), average_mAP, epoch)
if cfg.DATASET.NAME == "THUMOS14":
# use [email protected] as the metric
mAP_5 = mAP[4]
if mAP_5 > best_mAP:
best_mAP = mAP_5
info = [epoch, average_mAP, mAP]
elif cfg.DATASET.NAME == "ActivityNet1.3" or cfg.DATASET.NAME == "ActivityNet1.2":
if average_mAP > best_mAP:
best_mAP = average_mAP
info = [epoch, average_mAP, mAP]
return writer, best_mAP, info
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"
epoch = 601
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)
