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import argparse
import numpy as np
import os
import sys
import time
from tqdm import tqdm
from collections import OrderedDict
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from config import init_args
import data
import models
from models import *
from utils import utils, torch_utils
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def validation(args, net, criterion, data_loader, device='cuda'):
# import pdb; pdb.set_trace()
net.eval()
pred_all = torch.tensor([]).to(device)
target_all = torch.tensor([]).to(device)
with torch.no_grad():
for step, batch in tqdm(enumerate(data_loader), total=len(data_loader), desc="Validation"):
pred, target = predict(args, net, batch, device)
pred_all = torch.cat([pred_all, pred], dim=0)
target_all = torch.cat([target_all, target], dim=0)
res = criterion.evaluate(pred_all, target_all)
torch.cuda.empty_cache()
net.train()
return res
def predict(args, net, batch, device):
inputs = {
'frames': batch['frames'].to(device)
}
pred = net(inputs)
target = batch['label'].to(device)
return pred, target
def train(args, device):
# save dir
gpus = torch.cuda.device_count()
gpu_ids = list(range(gpus))
# ----- make dirs for checkpoints ----- #
sys.stdout = utils.LoggerOutput(os.path.join('checkpoints', args.exp, 'log.txt'))
os.makedirs('./checkpoints/' + args.exp, exist_ok=True)
writer = SummaryWriter(os.path.join('./checkpoints', args.exp, 'visualization'))
# ------------------------------------- #
tqdm.write('{}'.format(args))
# ------------------------------------ #
# ----- Dataset and Dataloader ----- #
train_dataset = data.GreatestHitDataset(args, split='train')
# train_dataset.getitem_test(1)
train_loader = DataLoader(
train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True,
drop_last=False)
val_dataset = data.GreatestHitDataset(args, split='val')
val_loader = DataLoader(
val_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
pin_memory=True,
drop_last=False)
# --------------------------------- #
# ----- Network ----- #
net = models.VideoOnsetNet(pretrained=False).to(device)
criterion = models.BCLoss(args)
optimizer = torch_utils.make_optimizer(net, args)
# --------------------- #
# -------- Loading checkpoints weights ------------- #
if args.resume:
resume = './checkpoints/' + args.resume
net, args.start_epoch = torch_utils.load_model(resume, net, device=device, strict=True)
if args.resume_optim:
tqdm.write('loading optimizer...')
optim_state = torch.load(resume)['optimizer']
optimizer.load_state_dict(optim_state)
tqdm.write('loaded optimizer!')
else:
args.start_epoch = 0
# -------------------
net = nn.DataParallel(net, device_ids=gpu_ids)
# --------- Random or resume validation ------------ #
res = validation(args, net, criterion, val_loader, device)
writer.add_scalars('VideoOnset' + '/validation', res, args.start_epoch)
tqdm.write("Beginning, Validation results: {}".format(res))
tqdm.write('\n')
# ----------------- Training ---------------- #
# import pdb; pdb.set_trace()
VALID_STEP = args.valid_step
for epoch in range(args.start_epoch, args.epochs):
running_loss = 0.0
torch_utils.adjust_learning_rate(optimizer, epoch, args)
for step, batch in tqdm(enumerate(train_loader), total=len(train_loader), desc="Training"):
pred, target = predict(args, net, batch, device)
loss = criterion(pred, target)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if step % 1 == 0:
tqdm.write("Epoch: {}/{}, step: {}/{}, loss: {}".format(epoch+1, args.epochs, step+1, len(train_loader), loss))
running_loss += loss.item()
current_step = epoch * len(train_loader) + step + 1
BOARD_STEP = 3
if (step+1) % BOARD_STEP == 0:
writer.add_scalar('VideoOnset' + '/training loss', running_loss / BOARD_STEP, current_step)
running_loss = 0.0
# ----------- Validtion -------------- #
if (epoch + 1) % VALID_STEP == 0:
res = validation(args, net, criterion, val_loader, device)
writer.add_scalars('VideoOnset' + '/validation', res, epoch + 1)
tqdm.write("Epoch: {}/{}, Validation results: {}".format(epoch + 1, args.epochs, res))
# ---------- Save model ----------- #
SAVE_STEP = args.save_step
if (epoch + 1) % SAVE_STEP == 0:
path = os.path.join('./checkpoints', args.exp, 'checkpoint_ep' + str(epoch + 1) + '.pth.tar')
torch.save({'epoch': epoch + 1,
'step': current_step,
'state_dict': net.state_dict(),
'optimizer': optimizer.state_dict(),
},
path)
# --------------------------------- #
torch.cuda.empty_cache()
tqdm.write('Training Complete!')
writer.close()
def test(args, device):
# save dir
gpus = torch.cuda.device_count()
gpu_ids = list(range(gpus))
# ----- make dirs for results ----- #
sys.stdout = utils.LoggerOutput(os.path.join('results', args.exp, 'log.txt'))
os.makedirs('./results/' + args.exp, exist_ok=True)
# ------------------------------------- #
tqdm.write('{}'.format(args))
# ------------------------------------ #
# ----- Dataset and Dataloader ----- #
test_dataset = data.GreatestHitDataset(args, split='test')
test_loader = DataLoader(
test_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
pin_memory=True,
drop_last=False)
# --------------------------------- #
# ----- Network ----- #
net = models.VideoOnsetNet(pretrained=False).to(device)
criterion = models.BCLoss(args)
# -------- Loading checkpoints weights ------------- #
if args.resume:
resume = './checkpoints/' + args.resume
net, _ = torch_utils.load_model(resume, net, device=device, strict=True)
# ------------------- #
net = nn.DataParallel(net, device_ids=gpu_ids)
# --------- Testing ------------ #
res = validation(args, net, criterion, test_loader, device)
tqdm.write("Testing results: {}".format(res))
# CUDA_VISIBLE_DEVICES=1 python main.py --exp='EXP1' --epochs=100 --batch_size=12 --num_workers=8 --save_step=10 --valid_step=1 --lr=0.0001 --optim='Adam' --repeat=1 --schedule='cos'
if __name__ == '__main__':
args = init_args()
if args.test_mode:
test(args, DEVICE)
else:
train(args, DEVICE) |