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import time, os, torch, argparse, warnings, glob, pandas, json
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from utils.tools import *
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from dlhammer import bootstrap
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from dataLoader_multiperson import val_loader
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from loconet import loconet
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class DataPrep():
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def __init__(self, cfg):
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self.cfg = cfg
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def val_dataloader(self):
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cfg = self.cfg
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loader = val_loader(cfg, trialFileName = cfg.evalTrialAVA, \
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audioPath = os.path.join(cfg.audioPathAVA , cfg.evalDataType), \
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visualPath = os.path.join(cfg.visualPathAVA, cfg.evalDataType), \
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num_speakers=cfg.MODEL.NUM_SPEAKERS,
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)
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valLoader = torch.utils.data.DataLoader(loader,
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batch_size=cfg.VAL.BATCH_SIZE,
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shuffle=False,
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num_workers=16)
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return valLoader
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def prepare_context_files(cfg):
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path = os.path.join(cfg.DATA.dataPathAVA, "csv")
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for phase in ["val", "test"]:
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csv_f = f"{phase}_loader.csv"
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csv_orig = f"{phase}_orig.csv"
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entity_f = os.path.join(path, phase + "_entity.json")
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ts_f = os.path.join(path, phase + "_ts.json")
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if os.path.exists(entity_f) and os.path.exists(ts_f):
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continue
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orig_df = pandas.read_csv(os.path.join(path, csv_orig))
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entity_data = {}
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ts_to_entity = {}
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for index, row in orig_df.iterrows():
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entity_id = row['entity_id']
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video_id = row['video_id']
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if row['label'] == "SPEAKING_AUDIBLE":
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label = 1
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else:
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label = 0
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ts = float(row['frame_timestamp'])
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if video_id not in entity_data.keys():
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entity_data[video_id] = {}
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if entity_id not in entity_data[video_id].keys():
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entity_data[video_id][entity_id] = {}
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if ts not in entity_data[video_id][entity_id].keys():
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entity_data[video_id][entity_id][ts] = []
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entity_data[video_id][entity_id][ts] = label
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if video_id not in ts_to_entity.keys():
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ts_to_entity[video_id] = {}
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if ts not in ts_to_entity[video_id].keys():
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ts_to_entity[video_id][ts] = []
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ts_to_entity[video_id][ts].append(entity_id)
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with open(entity_f) as f:
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json.dump(entity_data, f)
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with open(ts_f) as f:
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json.dump(ts_to_entity, f)
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def main():
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cfg = bootstrap(print_cfg=False)
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print(cfg)
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epoch = cfg.RESUME_EPOCH
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warnings.filterwarnings("ignore")
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cfg = init_args(cfg)
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data = DataPrep(cfg)
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prepare_context_files(cfg)
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if cfg.downloadAVA == True:
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preprocess_AVA(cfg)
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quit()
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s = loconet(cfg)
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s.loadParameters(cfg.RESUME_PATH)
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mAP = s.evaluate_network(epoch=epoch, loader=data.val_dataloader())
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print(f"evaluate ckpt: {cfg.RESUME_PATH}")
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print(mAP)
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if __name__ == '__main__':
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main()
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