File size: 11,079 Bytes
2d47d90 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 |
import os
import signal
import time
import csv
import sys
import warnings
import random
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP
import torch.multiprocessing as mp
import numpy as np
import time
import pprint
from loguru import logger
import smplx
from torch.utils.tensorboard import SummaryWriter
import wandb
import matplotlib.pyplot as plt
from utils import config, logger_tools, other_tools, metric
from dataloaders import data_tools
from dataloaders.build_vocab import Vocab
from optimizers.optim_factory import create_optimizer
from optimizers.scheduler_factory import create_scheduler
from optimizers.loss_factory import get_loss_func
class BaseTrainer(object):
def __init__(self, args):
self.args = args
self.rank = dist.get_rank()
self.checkpoint_path = args.out_path + "custom/" + args.name + args.notes + "/" #wandb.run.dir #args.cache_path+args.out_path+"/"+args.name
if self.rank==0:
if self.args.stat == "ts":
self.writer = SummaryWriter(log_dir=args.out_path + "custom/" + args.name + args.notes + "/")
else:
wandb.init(project=args.project, entity="liu1997", dir=args.out_path, name=args.name[12:] + args.notes)
wandb.config.update(args)
self.writer = None
#self.test_demo = args.data_path + args.test_data_path + "bvh_full/"
# self.train_data = __import__(f"dataloaders.{args.dataset}", fromlist=["something"]).CustomDataset(args, "train")
# self.train_loader = torch.utils.data.DataLoader(
# self.train_data,
# batch_size=args.batch_size,
# shuffle=False if args.ddp else True,
# num_workers=args.loader_workers,
# drop_last=True,
# sampler=torch.utils.data.distributed.DistributedSampler(self.train_data) if args.ddp else None,
# )
# self.train_length = len(self.train_loader)
# logger.info(f"Init train dataloader success")
# self.val_data = __import__(f"dataloaders.{args.dataset}", fromlist=["something"]).CustomDataset(args, "val")
# self.val_loader = torch.utils.data.DataLoader(
# self.val_data,
# batch_size=args.batch_size,
# shuffle=False,
# num_workers=args.loader_workers,
# drop_last=False,
# sampler=torch.utils.data.distributed.DistributedSampler(self.val_data) if args.ddp else None,
# )
# logger.info(f"Init val dataloader success")
if self.rank == 0:
self.test_data = __import__(f"dataloaders.{args.dataset}", fromlist=["something"]).CustomDataset(args, "test")
self.test_loader = torch.utils.data.DataLoader(
self.test_data,
batch_size=1,
shuffle=False,
num_workers=args.loader_workers,
drop_last=False,
)
logger.info(f"Init test dataloader success")
model_module = __import__(f"models.{args.model}", fromlist=["something"])
if args.ddp:
self.model = getattr(model_module, args.g_name)(args).to(self.rank)
process_group = torch.distributed.new_group()
self.model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(self.model, process_group)
self.model = DDP(self.model, device_ids=[self.rank], output_device=self.rank,
broadcast_buffers=False, find_unused_parameters=False)
else:
self.model = torch.nn.DataParallel(getattr(model_module, args.g_name)(args), args.gpus).cuda()
if self.rank == 0:
logger.info(self.model)
logger.info(f"init {args.g_name} success")
if args.stat == "wandb":
wandb.watch(self.model)
# if args.d_name is not None:
# if args.ddp:
# self.d_model = getattr(model_module, args.d_name)(args).to(self.rank)
# self.d_model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(self.d_model, process_group)
# self.d_model = DDP(self.d_model, device_ids=[self.rank], output_device=self.rank,
# broadcast_buffers=False, find_unused_parameters=False)
# else:
# self.d_model = torch.nn.DataParallel(getattr(model_module, args.d_name)(args), args.gpus).cuda()
# if self.rank == 0:
# logger.info(self.d_model)
# logger.info(f"init {args.d_name} success")
# if args.stat == "wandb":
# wandb.watch(self.d_model)
# self.opt_d = create_optimizer(args, self.d_model, lr_weight=args.d_lr_weight)
# self.opt_d_s = create_scheduler(args, self.opt_d)
if args.e_name is not None:
"""
bugs on DDP training using eval_model, using additional eval_copy for evaluation
"""
eval_model_module = __import__(f"models.{args.eval_model}", fromlist=["something"])
# eval copy is for single card evaluation
if self.args.ddp:
self.eval_model = getattr(eval_model_module, args.e_name)(args).to(self.rank)
self.eval_copy = getattr(eval_model_module, args.e_name)(args).to(self.rank)
else:
self.eval_model = getattr(eval_model_module, args.e_name)(args)
self.eval_copy = getattr(eval_model_module, args.e_name)(args).to(self.rank)
#if self.rank == 0:
other_tools.load_checkpoints(self.eval_copy, args.data_path+args.e_path, args.e_name)
other_tools.load_checkpoints(self.eval_model, args.data_path+args.e_path, args.e_name)
if self.args.ddp:
self.eval_model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(self.eval_model, process_group)
self.eval_model = DDP(self.eval_model, device_ids=[self.rank], output_device=self.rank,
broadcast_buffers=False, find_unused_parameters=False)
self.eval_model.eval()
self.eval_copy.eval()
if self.rank == 0:
logger.info(self.eval_model)
logger.info(f"init {args.e_name} success")
if args.stat == "wandb":
wandb.watch(self.eval_model)
self.smplx = smplx.create(
self.args.data_path_1+"smplx_models/",
model_type='smplx',
gender='NEUTRAL_2020',
use_face_contour=False,
num_betas=300,
num_expression_coeffs=100,
ext='npz',
use_pca=False,
).to(self.rank).eval()
self.alignmenter = metric.alignment(0.3, 7, self.train_data.avg_vel, upper_body=[3,6,9,12,13,14,15,16,17,18,19,20,21]) if self.rank == 0 else None
self.align_mask = 60
self.l1_calculator = metric.L1div() if self.rank == 0 else None
def train_recording(self, epoch, its, t_data, t_train, mem_cost, lr_g, lr_d=None):
pstr = "[%03d][%03d/%03d] "%(epoch, its, self.train_length)
for name, states in self.tracker.loss_meters.items():
metric = states['train']
if metric.count > 0:
pstr += "{}: {:.3f}\t".format(name, metric.avg)
self.writer.add_scalar(f"train/{name}", metric.avg, epoch*self.train_length+its) if self.args.stat == "ts" else wandb.log({name: metric.avg}, step=epoch*self.train_length+its)
pstr += "glr: {:.1e}\t".format(lr_g)
self.writer.add_scalar("lr/glr", lr_g, epoch*self.train_length+its) if self.args.stat == "ts" else wandb.log({'glr': lr_g}, step=epoch*self.train_length+its)
if lr_d is not None:
pstr += "dlr: {:.1e}\t".format(lr_d)
self.writer.add_scalar("lr/dlr", lr_d, epoch*self.train_length+its) if self.args.stat == "ts" else wandb.log({'dlr': lr_d}, step=epoch*self.train_length+its)
pstr += "dtime: %04d\t"%(t_data*1000)
pstr += "ntime: %04d\t"%(t_train*1000)
pstr += "mem: {:.2f} ".format(mem_cost*len(self.args.gpus))
logger.info(pstr)
def val_recording(self, epoch):
pstr_curr = "Curr info >>>> "
pstr_best = "Best info >>>> "
for name, states in self.tracker.loss_meters.items():
metric = states['val']
if metric.count > 0:
pstr_curr += "{}: {:.3f} \t".format(name, metric.avg)
if epoch != 0:
if self.args.stat == "ts":
self.writer.add_scalars(f"val/{name}", {name+"_val":metric.avg, name+"_train":states['train'].avg}, epoch*self.train_length)
else:
wandb.log({name+"_val": metric.avg, name+"_train":states['train'].avg}, step=epoch*self.train_length)
new_best_train, new_best_val = self.tracker.update_and_plot(name, epoch, self.checkpoint_path+f"{name}_{self.args.name+self.args.notes}.png")
if new_best_val:
other_tools.save_checkpoints(os.path.join(self.checkpoint_path, f"{name}.bin"), self.model, opt=None, epoch=None, lrs=None)
for k, v in self.tracker.values.items():
metric = v['val']['best']
if self.tracker.loss_meters[k]['val'].count > 0:
pstr_best += "{}: {:.3f}({:03d})\t".format(k, metric['value'], metric['epoch'])
logger.info(pstr_curr)
logger.info(pstr_best)
def test_recording(self, dict_name, value, epoch):
self.tracker.update_meter(dict_name, "test", value)
_ = self.tracker.update_values(dict_name, 'test', epoch)
@logger.catch
def main_worker(rank, world_size, args):
#os.environ['TRANSFORMERS_CACHE'] = args.data_path_1 + "hub/"
if not sys.warnoptions:
warnings.simplefilter("ignore")
dist.init_process_group(backend="nccl", rank=rank, world_size=world_size)
logger_tools.set_args_and_logger(args, rank)
other_tools.set_random_seed(args)
other_tools.print_exp_info(args)
# return one intance of trainer
trainer = __import__(f"{args.trainer}_trainer", fromlist=["something"]).CustomTrainer(args) if args.trainer != "base" else BaseTrainer(args)
other_tools.load_checkpoints(trainer.model, args.test_ckpt, args.g_name)
trainer.test(999)
if __name__ == "__main__":
os.environ["MASTER_ADDR"]='127.0.0.1'
os.environ["MASTER_PORT"]='8675'
#os.environ["TORCH_DISTRIBUTED_DEBUG"] = "DETAIL"
args = config.parse_args()
if args.ddp:
mp.set_start_method("spawn", force=True)
mp.spawn(
main_worker,
args=(len(args.gpus), args,),
nprocs=len(args.gpus),
)
else:
main_worker(0, 1, args) |