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1_train_TCGL_UCF101_R3D50.py
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1_train_TCGL_UCF101_R3D50.py
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"""Video clip order prediction."""
import os
import math
import itertools
import argparse
import time
import random
import logging
import sys
import pandas as pd
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader, random_split
from torchvision import transforms
import torch.optim as optim
from tensorboardX import SummaryWriter
import adabound
from datasets.ucf101 import UCF101VCOPDataset, UCF101VFCOPDataset, UCF101VCOPDataset_color
from models.c3d import C3D
from models.r3d import R3DNet
#from models.r21d import R2Plus1DNet
from models.r21d import R2Plus1DNet
from models.r3d_50 import generate_model
#from models.i3d import InceptionI3d
from models.vcopn import VCOPN, VCOPN_GCN, VCOPN_GCN_R, VCOPN_GCN_R_Eight, VCOPN_GATN_R, VCOPN_GCN_randomedge, TCG_FourClip,VCOPN_GCN_R3D_R21D
from models.TCG import TCG_triple_R3D_R21D_FCA2
from lib.NCEAverage import NCEAverage, NCEAverage_ori
from lib.LinearAverage import LinearAverage
from lib.NCECriterion import NCECriterion, NCESoftmaxLoss
from lib.utils import AverageMeter#, adjust_learning_rate
import ast
import warnings
from torch_geometric.nn import GCNConv
from torch_geometric.nn import GATConv
warnings.filterwarnings("ignore")
def order_class_index(order):
"""Return the index of the order in its full permutation.
Args:
order (tensor): e.g. [0,1,2]
"""
classes = list(itertools.permutations(list(range(len(order)))))
return classes.index(tuple(order.tolist()))
def train(args, model,criterion, optimizer, device, train_dataloader, writer, epoch):
torch.set_grad_enabled(True)
model.train()
running_loss = 0.0
correct = 0
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
losses_contrast_frame = AverageMeter()
losses_contrast_clip = AverageMeter()
losses_contrast = AverageMeter()
losses_order = AverageMeter()
end = time.time()
#torch.cuda.empty_cache()
for i, data in enumerate(train_dataloader, 1):
data_time.update(time.time() - end)
# get inputs
#tuple_clips, tuple_orders, tuple_clips_random, tuple_orders_random, index = data
tuple_clips, tuple_orders, index = data
bsz = tuple_clips.size(0)
inputs = tuple_clips.to(device)
#inputs_random = tuple_clips_random.to(device)
targets = [order_class_index(order) for order in tuple_orders]
targets = torch.tensor(targets).to(device)
index = index.to(device)
# zero the parameter gradients
#optimizer.zero_grad()
# forward and backward
contrast_loss_1, contrast_loss_2, contrast_loss_3,contrast_loss_clip,outputs = model(inputs,tuple_orders) # return logits here
loss_contrast_frame=(contrast_loss_1.sum()+contrast_loss_2.sum()+contrast_loss_3.sum())/(3*args.bs)
loss_contrast_clip=contrast_loss_clip.sum()/args.bs
loss_order = criterion(outputs, targets)
loss=args.weight_contrast_frame*loss_contrast_frame+args.weight_contrast_clip*loss_contrast_clip+args.weight_order*loss_order
#loss=args.weight_order*loss_order
# ===================backward=====================
optimizer.zero_grad()
loss.backward()
optimizer.step()
# ===================meters=====================
#losses_contrast.update(args.weight_contrast*loss_contrast.item(), bsz)
losses_contrast_frame.update(args.weight_contrast_frame*loss_contrast_frame.item(), bsz)
losses_contrast_clip.update(args.weight_contrast_clip*loss_contrast_clip.item(), bsz)
losses_order.update(args.weight_order*loss_order.item(), bsz)
losses.update(loss.item(), bsz)
batch_time.update(time.time() - end)
end = time.time()
# compute loss and acc
running_loss += loss.item()
pts = torch.argmax(outputs, dim=1)
correct += torch.sum(targets == pts).item()
# print statistics and write summary every N batch
# print info
if i % 20 == 0:
log_str=('Train: [{0}/{1}][{2}/{3}] '
#'BT {batch_time.val:.3f} ({batch_time.avg:.3f}) '
#'DT {data_time.val:.3f} ({data_time.avg:.3f}) '
'loss_contrast_frame {loss_contrast_frame.val:.3f} ({loss_contrast_frame.avg:.3f}) '
'loss_contrast_clip {loss_contrast_clip.val:.3f} ({loss_contrast_clip.avg:.3f}) '
'loss_order {loss_order.val:.3f} ({loss_order.avg:.3f}) '
'loss {loss.val:.3f} ({loss.avg:.3f})'
.format(epoch, args.epochs, i , len(train_dataloader), loss_contrast_frame=losses_contrast_frame, loss_contrast_clip=losses_contrast_clip,loss_order=losses_order, loss=losses))
logging.info(log_str)
if i % args.pf == 0:
avg_loss = running_loss / args.pf
avg_acc = correct / (args.pf * args.bs)
logging.info('[TRAIN] epoch-{}, batch-{}, loss: {:.3f}, acc: {:.3f}'.format(epoch, i, avg_loss, avg_acc))
step = (epoch-1)*len(train_dataloader) + i
writer.add_scalar('train/CrossEntropyLoss', avg_loss, step)
writer.add_scalar('train/Accuracy', avg_acc, step)
running_loss = 0.0
correct = 0
# summary params and grads per eopch
#for name, param in model.named_parameters():
# writer.add_histogram('params/{}'.format(name), param, epoch)
# writer.add_histogram('grads/{}'.format(name), param.grad, epoch)
avg_loss = running_loss / len(train_dataloader)
return avg_loss
def validate(args, model, criterion, device, val_dataloader, writer, epoch):
torch.set_grad_enabled(False)
model.eval()
total_loss = 0.0
correct = 0
for i, data in enumerate(val_dataloader):
# get inputs
tuple_clips, tuple_orders,_ = data
inputs = tuple_clips.to(device)
targets = [order_class_index(order) for order in tuple_orders]
targets = torch.tensor(targets).to(device)
# forward
_,_,_,_,outputs = model(inputs,tuple_orders) # return logits here
loss = criterion(outputs, targets)
# compute loss and acc
total_loss += loss.item()
pts = torch.argmax(outputs, dim=1)
correct += torch.sum(targets == pts).item()
# print('correct: {}, {}, {}'.format(correct, targets, pts))
avg_loss = total_loss / len(val_dataloader)
avg_acc = correct / len(val_dataloader.dataset)
#writer.add_scalar('val/CrossEntropyLoss', avg_loss, epoch)
#writer.add_scalar('val/Accuracy', avg_acc, epoch)
logging.info('[VAL] loss: {:.3f}, acc: {:.3f}'.format(avg_loss, avg_acc))
return avg_loss, avg_acc
def test(args, model, criterion, device, test_dataloader):
torch.set_grad_enabled(False)
model.eval()
total_loss = 0.0
correct = 0
for i, data in enumerate(test_dataloader, 1):
# get inputs
tuple_clips, tuple_orders = data
inputs = tuple_clips.to(device)
targets = [order_class_index(order) for order in tuple_orders]
targets = torch.tensor(targets).to(device)
# forward
outputs = model(inputs)
loss = criterion(outputs, targets)
# compute loss and acc
total_loss += loss.item()
pts = torch.argmax(outputs, dim=1)
correct += torch.sum(targets == pts).item()
# print('correct: {}, {}, {}'.format(correct, targets, pts))
avg_loss = total_loss / len(test_dataloader)
avg_acc = correct / len(test_dataloader.dataset)
logging.info('[TEST] loss: {:.3f}, acc: {:.3f}'.format(avg_loss, avg_acc))
return avg_loss
def parse_args():
parser = argparse.ArgumentParser(description='Temporal Contrast Graph')
parser.add_argument('--mode', type=str, default='train', help='train/test')
parser.add_argument('--model', type=str, default='r3d50', help='c3d/r3d/r21d/i3d/r3d50/s3d')
parser.add_argument('--cl', type=int, default=16, help='clip length')
parser.add_argument('--it', type=int, default=8, help='interval')
parser.add_argument('--tl', type=int, default=3, help='tuple length')
parser.add_argument('--gpu', type=int, default=0, help='GPU id')
parser.add_argument('--lr', type=float, default=1e-3, help='learning rate')
parser.add_argument('--momentum', type=float, default=9e-1, help='momentum')
parser.add_argument('--wd', type=float, default=5e-4, help='weight decay')
parser.add_argument('--log', type=str, default='log', help='log directory')
parser.add_argument('--ckpt', type=str, help='checkpoint path')
parser.add_argument('--desp', type=str, help='additional description')
parser.add_argument('--epochs', type=int, default=300, help='number of total epochs to run')
parser.add_argument('--start-epoch', type=int, default=1, help='manual epoch number (useful on restarts)')
parser.add_argument('--bs', type=int, default=8, help='mini-batch size')
parser.add_argument('--workers', type=int, default=4, help='number of data loading workers')
parser.add_argument('--pf', type=int, default=100, help='print frequency every batch')
parser.add_argument('--seed', type=int, default=32, help='seed for initializing training.')
parser.add_argument('--softmax', type=ast.literal_eval, default=True)
parser.add_argument('--nce_k', type=int, default=512)
parser.add_argument('--nce_t', type=float, default=0.07)
parser.add_argument('--nce_m', type=float, default=0.5)
parser.add_argument('--feat_dim', type=int, default=512, help='dim of feat for inner product')
parser.add_argument('--weight_contrast_frame', type=float, default=1)
parser.add_argument('--weight_contrast_clip', type=float, default=1)
parser.add_argument('--weight_order', type=float, default=1)
parser.add_argument('--save_dir', default=None)
parser.add_argument('--output_dir', type=str, default='log/')
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parse_args()
print(vars(args))
torch.backends.cudnn.benchmark = True
#os.environ['CUDA_LAUNCH_BLOCKING'] = '1'
# Force the pytorch to create context on the specific device
os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3"
device = torch.device('cuda:0' if torch.cuda.is_available() else "cpu")
if args.seed:
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
########### model ##############
if args.model == 'c3d':
base = C3D(with_classifier=False).to(device)
elif args.model == 'r3d':
base = R3DNet(layer_sizes=(3, 4, 6, 3), with_classifier=False).to(device)
elif args.model == 'r21d':
base = R2Plus1DNet(layer_sizes=(1,1,1,1), with_classifier=False).to(device)
elif args.model == 'r3d50':
#base = resnet50(sample_size=224,sample_duration=16,with_classifier=False, return_conv=False).to(device)
#base = r3d50(with_classifier=False, return_conv=False).to(device)
base =generate_model(model_depth=50, with_classifier=False, return_conv=False).to(device)
tcg = TCG_triple_R3D_R21D_FCA2(base_network=base, feature_size=2048, tuple_len=args.tl).cuda()
if args.mode == 'train': ########### Train #############
if args.ckpt: # resume training
pretrained_weights = torch.load(args.ckpt)['model']
tcg.load_state_dict({k.replace('module.',''):v for k,v in pretrained_weights.items()},strict=True)
#tcg.load_state_dict(torch.load(args.ckpt))
log_dir = os.path.dirname(args.ckpt)
else:
if args.desp:
exp_name = 'UCF101_R3D50_TCG224_{}_cl{}_it{}_tl{}_{}_{}'.format(args.model, args.cl, args.it, args.tl, args.desp, time.strftime('%m%d%H%M'))
else:
exp_name = 'UCF101_R3D50_TCG224_{}_cl{}_it{}_tl{}_{}'.format(args.model, args.cl, args.it, args.tl, time.strftime('%m%d%H%M'))
log_dir = os.path.join(args.log, exp_name)
writer = SummaryWriter(log_dir)
if torch.cuda.device_count() > 1:
tcg = torch.nn.DataParallel(tcg, device_ids=[0,1,2,3]).cuda()
log_format = '%(message)s'
logging.basicConfig(stream=sys.stdout, level=logging.INFO, format=log_format)
fh = logging.FileHandler(os.path.join(log_dir, 'log.txt'))
fh.setFormatter(logging.Formatter(log_format))
logging.getLogger().addHandler(fh)
train_transforms = transforms.Compose([
transforms.Resize((256, 256)), # smaller edge to 128
transforms.RandomCrop(224),
#transforms.RandomHorizontalFlip(0.5),
transforms.ToTensor()
])
color_jitter = transforms.ColorJitter(brightness=0.8, contrast=0.8, saturation=0.8, hue=0.2)
color_jitter = transforms.RandomApply([color_jitter], p=0.8)
#train_dataset = UCF101VCOPDataset_color('data/ucf101', args.cl, args.it, args.tl, True, train_transforms,color_jitter_=color_jitter)
train_dataset = UCF101VCOPDataset('data/ucf101', args.cl, args.it, args.tl, True, train_transforms)
# split val for 800 videos
train_dataset, val_dataset = random_split(train_dataset,(len(train_dataset)-800, 800))
logging.info('TRAIN video number: {}, VAL video number: {}.'.format(len(train_dataset), len(val_dataset)))
train_dataloader = DataLoader(train_dataset, batch_size=args.bs, shuffle=True,
num_workers=args.workers, pin_memory=True)
val_dataloader = DataLoader(val_dataset, batch_size=args.bs, shuffle=False,
num_workers=args.workers, pin_memory=True)
if args.ckpt:
pass
else:
# save graph and clips_order samples
for data in train_dataloader:
#tuple_clips, tuple_orders, tuple_clips_random, tuple_orders_random,idx = data
tuple_clips, tuple_orders,idx = data
for i in range(args.tl):
writer.add_video('train/tuple_clips', tuple_clips[:, i, :, :, :, :], i, fps=8)
writer.add_text('train/tuple_orders', str(tuple_orders[:, i].tolist()), i)
tuple_clips = tuple_clips.to(device)
#writer.add_graph(tcg, tuple_clips)
break
# save init params at step 0
for name, param in tcg.named_parameters():
writer.add_histogram('params/{}'.format(name), param, 0)
n_data = train_dataset.__len__()
torch.backends.cudnn.benchmark = True
### loss funciton, optimizer and scheduler ###
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(tcg.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.wd)
#optimizer = optim.Adam(tcg.parameters(), lr=args.lr, weight_decay=args.wd)
#optimizer = adabound.AdaBound(tcg.parameters(), lr=args.lr, final_lr=0.1, weight_decay=args.wd)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min', min_lr=1e-7, patience=50, factor=0.1)
#scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[150], gamma=0.1)
prev_best_val_loss = float('inf')
prev_best_val_acc = 0.0
prev_best_loss_model_path = None
prev_best_acc_model_path = None
for epoch in range(args.start_epoch, args.start_epoch+args.epochs):
time_start = time.time()
train_loss = train(args, tcg, criterion, optimizer, device, train_dataloader, writer, epoch)
print('Epoch time: {:.2f} s.'.format(time.time() - time_start))
val_loss, val_acc = validate(args, tcg, criterion, device, val_dataloader, writer, epoch)
scheduler.step(val_loss)
writer.add_scalar('train/lr', optimizer.param_groups[0]['lr'], epoch)
# save model every 20 epoches
if epoch % 10 == 0:
state = {
'opt': args,
'model': tcg.state_dict(),
'optimizer': optimizer.state_dict(),
'epoch': epoch,
}
torch.save(state, os.path.join(log_dir, 'model_{}.pt'.format(epoch)))
del state
# save model for the best val
if val_loss < prev_best_val_loss:
state = {
'opt': args,
'model': tcg.state_dict(),
'optimizer': optimizer.state_dict(),
'epoch': epoch,
}
model_path = os.path.join(log_dir, 'best_loss_model_{}.pt'.format(epoch))
torch.save(state, model_path)
prev_best_val_loss = val_loss
if prev_best_loss_model_path:
os.remove(prev_best_loss_model_path)
prev_best_loss_model_path = model_path
del state
if val_acc > prev_best_val_acc:
state = {
'opt': args,
'model': tcg.state_dict(),
'optimizer': optimizer.state_dict(),
'epoch': epoch,
}
model_path = os.path.join(log_dir, 'best_acc_model_{}.pt'.format(epoch))
torch.save(state, model_path)
prev_best_val_acc = val_acc
if prev_best_acc_model_path:
os.remove(prev_best_acc_model_path)
prev_best_acc_model_path = model_path
del state
#torch.cuda.empty_cache()
#scheduler.step()
elif args.mode == 'test': ########### Test #############
tcg.load_state_dict(torch.load(args.ckpt))
test_transforms = transforms.Compose([
transforms.Resize((128, 171)),
transforms.CenterCrop(112),
transforms.ToTensor()
])
test_dataset = UCF101VCOPDataset('data/ucf101', args.cl, args.it, args.tl, False, test_transforms)
test_dataloader = DataLoader(test_dataset, batch_size=args.bs, shuffle=False,
num_workers=args.workers, pin_memory=True)
print('TEST video number: {}.'.format(len(test_dataset)))
criterion = nn.CrossEntropyLoss()
test(args, tcg, criterion, device, test_dataloader)