Delete train.py

train.py

@@ -1,197 +0,0 @@
-import time, os, torch, argparse, warnings, glob, pandas, json
-
-from utils.tools import *
-from dlhammer import bootstrap
-os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
-import torch.multiprocessing as mp
-import torch.distributed as dist
-
-from xxlib.utils.distributed import all_gather, all_reduce
-from torch import nn
-from dataLoader_multiperson import train_loader, val_loader
-
-from loconet import loconet
-
-
-class MyCollator(object):
-
-    def __init__(self, cfg):
-        self.cfg = cfg
-
-    def __call__(self, data):
-        audiofeatures = [item[0] for item in data]
-        visualfeatures = [item[1] for item in data]
-        labels = [item[2] for item in data]
-        masks = [item[3] for item in data]
-        cut_limit = self.cfg.MODEL.CLIP_LENGTH
-        # pad audio
-        lengths = torch.tensor([t.shape[1] for t in audiofeatures])
-        max_len = max(lengths)
-        padded_audio = torch.stack([
-            torch.cat([i, i.new_zeros((i.shape[0], max_len - i.shape[1], i.shape[2]))], 1)
-            for i in audiofeatures
-        ], 0)
-
-        if max_len > cut_limit * 4:
-            padded_audio = padded_audio[:, :, :cut_limit * 4, ...]
-
-        # pad video
-        lengths = torch.tensor([t.shape[1] for t in visualfeatures])
-        max_len = max(lengths)
-        padded_video = torch.stack([
-            torch.cat(
-                [i, i.new_zeros((i.shape[0], max_len - i.shape[1], i.shape[2], i.shape[3]))], 1)
-            for i in visualfeatures
-        ], 0)
-        padded_labels = torch.stack(
-            [torch.cat([i, i.new_zeros((i.shape[0], max_len - i.shape[1]))], 1) for i in labels], 0)
-        padded_masks = torch.stack(
-            [torch.cat([i, i.new_zeros((i.shape[0], max_len - i.shape[1]))], 1) for i in masks], 0)
-
-        if max_len > cut_limit:
-            padded_video = padded_video[:, :, :cut_limit, ...]
-            padded_labels = padded_labels[:, :, :cut_limit, ...]
-            padded_masks = padded_masks[:, :, :cut_limit, ...]
-
-        return padded_audio, padded_video, padded_labels, padded_masks
-
-
-class DataPrep():
-
-    def __init__(self, cfg, world_size, rank):
-        self.cfg = cfg
-        self.world_size = world_size
-        self.rank = rank
-
-    def train_dataloader(self):
-
-        loader = train_loader(self.cfg, trialFileName = self.cfg.trainTrialAVA, \
-                          audioPath      = os.path.join(self.cfg.audioPathAVA , 'train'), \
-                          visualPath     = os.path.join(self.cfg.visualPathAVA, 'train'), \
-                          num_speakers=self.cfg.MODEL.NUM_SPEAKERS,
-                          )
-        train_sampler = torch.utils.data.distributed.DistributedSampler(
-            loader, num_replicas=self.world_size, rank=self.rank)
-        collator = MyCollator(self.cfg)
-        trainLoader = torch.utils.data.DataLoader(loader,
-                                                  batch_size=self.cfg.TRAIN.BATCH_SIZE,
-                                                  pin_memory=False,
-                                                  num_workers=self.cfg.NUM_WORKERS,
-                                                  collate_fn=collator,
-                                                  sampler=train_sampler)
-        return trainLoader
-
-    def val_dataloader(self):
-        loader = val_loader(self.cfg, trialFileName = self.cfg.evalTrialAVA, \
-                            audioPath     = os.path.join(self.cfg
-                                .audioPathAVA , self.cfg
-                                .evalDataType), \
-                            visualPath    = os.path.join(self.cfg
-                                .visualPathAVA, self.cfg
-                                .evalDataType), \
-                            num_speakers = self.cfg.MODEL.NUM_SPEAKERS
-                                )
-        valLoader = torch.utils.data.DataLoader(loader,
-                                                batch_size=self.cfg.VAL.BATCH_SIZE,
-                                                shuffle=False,
-                                                pin_memory=True,
-                                                num_workers=16)
-
-        return valLoader
-
-
-def prepare_context_files(cfg):
-    path = os.path.join(cfg.DATA.dataPathAVA, "csv")
-    for phase in ["train", "val", "test"]:
-        csv_f = f"{phase}_loader.csv"
-        csv_orig = f"{phase}_orig.csv"
-        entity_f = os.path.join(path, phase + "_entity.json")
-        ts_f = os.path.join(path, phase + "_ts.json")
-        if os.path.exists(entity_f) and os.path.exists(ts_f):
-            continue
-        orig_df = pandas.read_csv(os.path.join(path, csv_orig))
-        entity_data = {}
-        ts_to_entity = {}
-
-        for index, row in orig_df.iterrows():
-
-            entity_id = row['entity_id']
-            video_id = row['video_id']
-            if row['label'] == "SPEAKING_AUDIBLE":
-                label = 1
-            else:
-                label = 0
-            ts = float(row['frame_timestamp'])
-            if video_id not in entity_data.keys():
-                entity_data[video_id] = {}
-            if entity_id not in entity_data[video_id].keys():
-                entity_data[video_id][entity_id] = {}
-            if ts not in entity_data[video_id][entity_id].keys():
-                entity_data[video_id][entity_id][ts] = []
-
-            entity_data[video_id][entity_id][ts] = label
-
-            if video_id not in ts_to_entity.keys():
-                ts_to_entity[video_id] = {}
-            if ts not in ts_to_entity[video_id].keys():
-                ts_to_entity[video_id][ts] = []
-            ts_to_entity[video_id][ts].append(entity_id)
-
-        with open(entity_f) as f:
-            json.dump(entity_data, f)
-
-        with open(ts_f) as f:
-            json.dump(ts_to_entity, f)
-
-
-def main(gpu, world_size):
-    # The structure of this code is learnt from https://github.com/clovaai/voxceleb_trainer
-    cfg = bootstrap(print_cfg=False)
-    rank = gpu
-    dist.init_process_group(backend='nccl', init_method='env://', world_size=world_size, rank=rank)
-
-    make_deterministic(seed=int(cfg.SEED))
-    torch.cuda.set_device(gpu)
-    device = torch.device("cuda:{}".format(gpu))
-
-    warnings.filterwarnings("ignore")
-
-    cfg = init_args(cfg)
-
-    data = DataPrep(cfg, world_size, rank)
-
-    if cfg.downloadAVA == True:
-        preprocess_AVA(cfg)
-        quit()
-
-    prepare_context_files(cfg)
-
-    modelfiles = glob.glob('%s/model_0*.model' % cfg.modelSavePath)
-    modelfiles.sort()
-    if len(modelfiles) >= 1:
-        print("Model %s loaded from previous state!" % modelfiles[-1])
-        epoch = int(os.path.splitext(os.path.basename(modelfiles[-1]))[0][6:]) + 1
-        s = loconet(cfg, rank, device)
-        s.loadParameters(modelfiles[-1])
-    else:
-        epoch = 1
-        s = loconet(cfg, rank, device)
-
-    while (1):
-        loss, lr = s.train_network(epoch=epoch, loader=data.train_dataloader())
-
-        s.saveParameters(cfg.modelSavePath + "/model_%04d.model" % epoch)
-
-        if epoch >= cfg.TRAIN.MAX_EPOCH:
-            quit()
-
-        epoch += 1
-
-
-if __name__ == '__main__':
-
-    cfg = bootstrap()
-    world_size = cfg.NUM_GPUS    #
-    os.environ['MASTER_ADDR'] = '127.0.0.1'    #
-    os.environ['MASTER_PORT'] = str(random.randint(4000, 8888))    #
-    mp.spawn(main, nprocs=cfg.NUM_GPUS, args=(world_size,))