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train_i3d_charades.py
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train_i3d_charades.py
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import argparse
import os
import engine
from torch import nn
import torch
from dataset import TSNDataSet
import datasets_video
import torchvision
from transforms import *
import model_zoo.gcn_i3d as modelfile
import time
parser = argparse.ArgumentParser(description='WILDCAT Training')
parser.add_argument('--image-size', '-i', default=448, type=int,
metavar='N', help='image size (default: 224)')
parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
help='number of data loading workers (default: 4)')
parser.add_argument('--epochs', default=20, type=int, metavar='N',
help='number of total epochs to run')
parser.add_argument('--epoch_step', default=[30], type=int, nargs='+',
help='number of epochs to change learning rate')
parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
help='manual epoch number (useful on restarts)')
parser.add_argument('-b', '--batch-size', default=16, type=int,
metavar='N', help='mini-batch size (default: 256)')
parser.add_argument('--lr', '--learning-rate', default=0.1, type=float,
metavar='LR', help='initial learning rate')
parser.add_argument('--weight-decay', '--wd', default=1e-4, type=float,
metavar='W', help='weight decay (default: 1e-4)')
parser.add_argument('--print-freq', '-p', default=0, type=int,
metavar='N', help='print frequency (default: 10)')
parser.add_argument('--resume', default='', type=str, metavar='PATH',
help='path to latest checkpoint (default: none)')
parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',
help='evaluate model on validation set')
parser.add_argument('--save_model_path', default='checkpoint/coco/', type=str,
help='the path of saved models')
parser.add_argument('--log_path', default='./log/', type=str,
help='the path of log files')
parser.add_argument('--data_length', default=1, type=int, help='the length of a segment')
parser.add_argument('--num_segments', default=3, type=int, help='the number of segments')
def get_config_optim(model, lr, weight_decay):
params_dict = dict(model.named_parameters())
params = []
for key, value in params_dict.items():
decay_mult = 0.0 if 'bias' in key or 'adj' == key else 1.0
if key.startswith('conv1') or key.startswith('bn1'):
lr_mult = 0.1
elif 'fc' in key:
lr_mult = 1.0
elif 'adj' == key:
lr_mult = 0.0
elif 'gc' in key:
lr_mult = 1.0
else:
lr_mult = 0.1
params.append({'params': value,
'lr': lr,
'lr_mult': lr_mult,
'weight_decay': weight_decay,
'decay_mult': decay_mult})
return params
def get_optim_fix_conv(model, lr, weight_decay):
params_dict = dict(model.named_parameters())
params = []
for key, value in params_dict.items():
if 'gc' in key:
decay_mult = 0.0 if 'bias' in key or 'adj' == key else 1.0
if key.startswith('conv1') or key.startswith('bn1'):
lr_mult = 0.1
elif 'fc' in key:
lr_mult = 1.0
elif 'A' == key:
lr_mult = 0.0
elif 'gc' in key:
lr_mult = 1.0
else:
lr_mult = 0.1
else:
decay_mult = 0.0
lr_mult = 0.0
params.append({'params': value,
'lr': lr,
'lr_mult': lr_mult,
'weight_decay': weight_decay,
'decay_mult': decay_mult})
return params
def load_pretrained(model, pretrained_path):
pretrained = torch.load(pretrained_path)
total_num = len(pretrained)
model_state = model.state_dict()
load_parameters = {key: value for key, value in pretrained.items()
if key in model_state and value.shape == model_state[key].shape}
load_num = len(load_parameters)
model_state.update(load_parameters)
model.load_state_dict(model_state)
print('pretrained model:{}'.format(pretrained_path))
print('loaded:{}({})\ttotal:{}'.format(load_num, float(load_num) / total_num, total_num))
def main_charades():
global args, best_prec1, use_gpu
use_gpu = torch.cuda.is_available()
categories, args.train_list, args.val_list, args.train_num_list, args.val_num_list, args.root_path, prefix = datasets_video.return_dataset(args.dataset, args.modality, args.root_path)
num_class = len(categories)
crop_size = args.crop_size
scale_size = args.scale_size
input_mean = [0.485, 0.456, 0.406]
input_std = [0.229, 0.224, 0.225]
train_dataset = TSNDataSet(args.root_path, args.train_list, args.train_num_list,
num_class=num_class,
num_segments=args.num_segments,
new_length=args.data_length,
modality=args.modality,
image_tmpl=prefix,
transform=torchvision.transforms.Compose([
GroupMultiScaleCrop(crop_size, [1.0, 0.875, 0.75, 0.66, 0.5], max_distort=2),
GroupRandomHorizontalFlip(is_flow=False),
Stack(roll=False),
ToTorchFormatTensor(div=True),
GroupNormalize(input_mean, input_std),
ChangeToCTHW(modality=args.modality)
]))
val_dataset = TSNDataSet(args.root_path, args.val_list, args.val_num_list,
num_class=num_class,
num_segments=args.num_segments,
new_length=args.data_length,
modality=args.modality,
image_tmpl=prefix,
random_shift=False,
transform=torchvision.transforms.Compose([
GroupScale(int(scale_size)),
GroupCenterCrop(crop_size),
Stack(roll=False),
ToTorchFormatTensor(div=True),
GroupNormalize(input_mean, input_std),
ChangeToCTHW(modality=args.modality)
]))
# model = modelfile.InceptionI3d(num_class, in_channels=3)
model = modelfile.gcn_i3d(num_class=num_class, t=0.4, adj_file='./data/Charades_v1/gcn_info/class_graph_conceptnet_context_0.8.pkl', word_file='./data/Charades_v1/gcn_info/class_word.pkl')
# define loss function (criterion)
criterion = nn.MultiLabelSoftMarginLoss()
# define optimizer
params = get_config_optim(model, lr=args.lr, weight_decay=args.weight_decay)
# params = get_optim_fix_conv(model, lr=args.lr, weight_decay=args.weight_decay)
# optimizer = torch.optim.SGD(params, lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay, nesterov=True)
optimizer = torch.optim.Adam(params, eps=1e-8)
state = {'batch_size': args.batch_size,
'val_batch_size': args.val_batch_size,
'image_size': args.image_size,
'max_epochs': args.epochs,
'evaluate': args.evaluate,
'resume': args.resume,
'num_classes':num_class}
state['difficult_examples'] = False
state['print_freq'] = args.print_freq
state['save_model_path'] = args.save_model_path
state['log_path'] = args.log_path
state['logname'] = args.logname
state['workers'] = args.workers
state['epoch_step'] = args.epoch_step
state['lr'] = args.lr
state['device_ids'] = list(range(torch.cuda.device_count()))
if args.evaluate:
state['evaluate'] = True
mapengine = engine.GCNMultiLabelMAPEngine(state, inp_file='./data/Charades_v1/gcn_info/class_word.pkl')
mapengine.learning(model, criterion, train_dataset, val_dataset, optimizer)
if __name__ == '__main__':
os.environ['CUDA_VISIBLE_DEVICES'] = '0,1,2,3'
gpu_num = len(os.environ['CUDA_VISIBLE_DEVICES'].split(','))
args = parser.parse_args()
args.lr = 0.001
args.weight_decay = 1e-4
args.momentum = 0.9
args.workers = 2 * gpu_num
args.epoch_step = [400, 500, 1000, 2000]
args.epochs = 10000
args.print_freq = 5
args.batch_size = 16
args.val_batch_size = 8
args.crop_size = 224
args.scale_size = 256
args.start_epoch = 0
args.num_segments = 64
args.data_length = 1
args.evaluate = False # Change to True if you want to evaluate a checkpoint.
args.dataset = 'charades'
args.modality = 'RGB'
args.root_path = './data/Charades_v1/Charades_v1_rgb'
args.resume = './checkpoint/charades/model_best.pth.tar'
args.save_model_path = './checkpoint/charades/'
args.log_path = './log/'
args.logname = 'train_charades_gcn_i3d.log'
main_charades()