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main.py
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main.py
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#!/usr/bin/env python
from __future__ import print_function
import argparse
import inspect
import os
import pickle
import random
import shutil
import time
from collections import OrderedDict
import numpy as np
# torch
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.optim as optim
import yaml
from tensorboardX import SummaryWriter
from torch.autograd import Variable
from torch.optim.lr_scheduler import _LRScheduler
from tqdm import tqdm
class GradualWarmupScheduler(_LRScheduler):
def __init__(self, optimizer, total_epoch, after_scheduler=None):
self.total_epoch = total_epoch
self.after_scheduler = after_scheduler
self.finished = False
self.last_epoch = -1
super().__init__(optimizer)
def get_lr(self):
return [base_lr * (self.last_epoch + 1) / self.total_epoch for base_lr in self.base_lrs]
def step(self, epoch=None, metric=None):
if self.last_epoch >= self.total_epoch - 1:
if metric is None:
return self.after_scheduler.step(epoch)
else:
return self.after_scheduler.step(metric, epoch)
else:
return super(GradualWarmupScheduler, self).step(epoch)
def init_seed(_):
torch.cuda.manual_seed_all(1)
torch.manual_seed(1)
np.random.seed(1)
random.seed(1)
# torch.backends.cudnn.enabled = False
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
def get_parser():
# parameter priority: command line > config > default
parser = argparse.ArgumentParser(
description='Spatial Temporal Graph Convolution Network')
parser.add_argument(
'--work-dir',
default='./work_dir/temp',
help='the work folder for storing results')
parser.add_argument('-model_saved_name', default='')
parser.add_argument(
'--config',
default='./config/nturgbd-cross-view/test_bone.yaml',
help='path to the configuration file')
# processor
parser.add_argument(
'--phase', default='train', help='must be train or test')
parser.add_argument(
'--save-score',
type=str2bool,
default=False,
help='if ture, the classification score will be stored')
# visulize and debug
parser.add_argument(
'--seed', type=int, default=1, help='random seed for pytorch')
parser.add_argument(
'--log-interval',
type=int,
default=100,
help='the interval for printing messages (#iteration)')
parser.add_argument(
'--save-interval',
type=int,
default=2,
help='the interval for storing models (#iteration)')
parser.add_argument(
'--eval-interval',
type=int,
default=5,
help='the interval for evaluating models (#iteration)')
parser.add_argument(
'--print-log',
type=str2bool,
default=True,
help='print logging or not')
parser.add_argument(
'--show-topk',
type=int,
default=[1, 5],
nargs='+',
help='which Top K accuracy will be shown')
# feeder
parser.add_argument(
'--feeder', default='feeder.feeder', help='data loader will be used')
parser.add_argument(
'--num-worker',
type=int,
default=32,
help='the number of worker for data loader')
parser.add_argument(
'--train-feeder-args',
default=dict(),
help='the arguments of data loader for training')
parser.add_argument(
'--test-feeder-args',
default=dict(),
help='the arguments of data loader for test')
# model
parser.add_argument('--model', default=None, help='the model will be used')
parser.add_argument(
'--model-args',
type=dict,
default=dict(),
help='the arguments of model')
parser.add_argument(
'--weights',
default=None,
help='the weights for network initialization')
parser.add_argument(
'--ignore-weights',
type=str,
default=[],
nargs='+',
help='the name of weights which will be ignored in the initialization')
# optim
parser.add_argument(
'--base-lr', type=float, default=0.01, help='initial learning rate')
parser.add_argument(
'--step',
type=int,
default=[20, 40, 60],
nargs='+',
help='the epoch where optimizer reduce the learning rate')
parser.add_argument(
'--device',
type=int,
default=0,
nargs='+',
help='the indexes of GPUs for training or testing')
parser.add_argument('--optimizer', default='SGD', help='type of optimizer')
parser.add_argument(
'--nesterov', type=str2bool, default=False, help='use nesterov or not')
parser.add_argument(
'--batch-size', type=int, default=256, help='training batch size')
parser.add_argument(
'--test-batch-size', type=int, default=256, help='test batch size')
parser.add_argument(
'--start-epoch',
type=int,
default=0,
help='start training from which epoch')
parser.add_argument(
'--num-epoch',
type=int,
default=80,
help='stop training in which epoch')
parser.add_argument(
'--weight-decay',
type=float,
default=0.0005,
help='weight decay for optimizer')
parser.add_argument('--only_train_part', default=False)
parser.add_argument('--only_train_epoch', default=0)
parser.add_argument('--warm_up_epoch', default=0)
return parser
class Processor():
"""
Processor for Skeleton-based Action Recgnition
"""
def __init__(self, arg):
self.arg = arg
self.save_arg()
if arg.phase == 'train':
if not arg.train_feeder_args['debug']:
if os.path.isdir(arg.model_saved_name):
print('log_dir: ', arg.model_saved_name, 'already exist')
answer = input('delete it? y/n:')
if answer == 'y':
shutil.rmtree(arg.model_saved_name)
print('Dir removed: ', arg.model_saved_name)
input('Refresh the website of tensorboard by pressing any keys')
else:
print('Dir not removed: ', arg.model_saved_name)
self.train_writer = SummaryWriter(os.path.join(arg.model_saved_name, 'train'), 'train')
self.val_writer = SummaryWriter(os.path.join(arg.model_saved_name, 'val'), 'val')
else:
self.train_writer = self.val_writer = SummaryWriter(os.path.join(arg.model_saved_name, 'test'), 'test')
self.global_step = 0
self.load_model()
self.load_optimizer()
self.load_data()
self.lr = self.arg.base_lr
self.best_acc = 0
def load_data(self):
Feeder = import_class(self.arg.feeder)
self.data_loader = dict()
if self.arg.phase == 'train':
self.data_loader['train'] = torch.utils.data.DataLoader(
dataset=Feeder(**self.arg.train_feeder_args),
batch_size=self.arg.batch_size,
shuffle=True,
num_workers=self.arg.num_worker,
drop_last=True,
worker_init_fn=init_seed)
self.data_loader['test'] = torch.utils.data.DataLoader(
dataset=Feeder(**self.arg.test_feeder_args),
batch_size=self.arg.test_batch_size,
shuffle=False,
num_workers=self.arg.num_worker,
drop_last=False,
worker_init_fn=init_seed)
def load_model(self):
output_device = self.arg.device[0] if type(self.arg.device) is list else self.arg.device
self.output_device = output_device
Model = import_class(self.arg.model)
shutil.copy2(inspect.getfile(Model), self.arg.work_dir)
print(Model)
self.model = Model(**self.arg.model_args).cuda(output_device)
print(self.model)
self.loss = nn.CrossEntropyLoss().cuda(output_device)
if self.arg.weights:
self.global_step = int(arg.weights[:-3].split('-')[-1])
self.print_log('Load weights from {}.'.format(self.arg.weights))
if '.pkl' in self.arg.weights:
with open(self.arg.weights, 'r') as f:
weights = pickle.load(f)
else:
weights = torch.load(self.arg.weights)
weights = OrderedDict(
[[k.split('module.')[-1],
v.cuda(output_device)] for k, v in weights.items()])
keys = list(weights.keys())
for w in self.arg.ignore_weights:
for key in keys:
if w in key:
if weights.pop(key, None) is not None:
self.print_log('Sucessfully Remove Weights: {}.'.format(key))
else:
self.print_log('Can Not Remove Weights: {}.'.format(key))
try:
self.model.load_state_dict(weights)
except:
state = self.model.state_dict()
diff = list(set(state.keys()).difference(set(weights.keys())))
print('Can not find these weights:')
for d in diff:
print(' ' + d)
state.update(weights)
self.model.load_state_dict(state)
if type(self.arg.device) is list:
if len(self.arg.device) > 1:
self.model = nn.DataParallel(
self.model,
device_ids=self.arg.device,
output_device=output_device)
def load_optimizer(self):
if self.arg.optimizer == 'SGD':
self.optimizer = optim.SGD(
self.model.parameters(),
lr=self.arg.base_lr,
momentum=0.9,
nesterov=self.arg.nesterov,
weight_decay=self.arg.weight_decay)
elif self.arg.optimizer == 'Adam':
self.optimizer = optim.Adam(
self.model.parameters(),
lr=self.arg.base_lr,
weight_decay=self.arg.weight_decay)
else:
raise ValueError()
lr_scheduler_pre = optim.lr_scheduler.MultiStepLR(
self.optimizer, milestones=self.arg.step, gamma=0.1)
self.lr_scheduler = GradualWarmupScheduler(self.optimizer, total_epoch=self.arg.warm_up_epoch,
after_scheduler=lr_scheduler_pre)
self.print_log('using warm up, epoch: {}'.format(self.arg.warm_up_epoch))
def save_arg(self):
# save arg
arg_dict = vars(self.arg)
if not os.path.exists(self.arg.work_dir):
os.makedirs(self.arg.work_dir)
with open('{}/config.yaml'.format(self.arg.work_dir), 'w') as f:
yaml.dump(arg_dict, f)
def adjust_learning_rate(self, epoch):
if self.arg.optimizer == 'SGD' or self.arg.optimizer == 'Adam':
if epoch < self.arg.warm_up_epoch:
lr = self.arg.base_lr * (epoch + 1) / self.arg.warm_up_epoch
else:
lr = self.arg.base_lr * (
0.1 ** np.sum(epoch >= np.array(self.arg.step)))
for param_group in self.optimizer.param_groups:
param_group['lr'] = lr
return lr
else:
raise ValueError()
def print_time(self):
localtime = time.asctime(time.localtime(time.time()))
self.print_log("Local current time : " + localtime)
def print_log(self, str, print_time=True):
if print_time:
localtime = time.asctime(time.localtime(time.time()))
str = "[ " + localtime + ' ] ' + str
print(str)
if self.arg.print_log:
with open('{}/log.txt'.format(self.arg.work_dir), 'a') as f:
print(str, file=f)
def record_time(self):
self.cur_time = time.time()
return self.cur_time
def split_time(self):
split_time = time.time() - self.cur_time
self.record_time()
return split_time
def train(self, epoch, save_model=False):
self.model.train()
self.print_log('Training epoch: {}'.format(epoch + 1))
loader = self.data_loader['train']
self.adjust_learning_rate(epoch)
# for name, param in self.model.named_parameters():
# self.train_writer.add_histogram(name, param.clone().cpu().data.numpy(), epoch)
loss_value = []
self.train_writer.add_scalar('epoch', epoch, self.global_step)
self.record_time()
timer = dict(dataloader=0.001, model=0.001, statistics=0.001)
process = tqdm(loader)
if self.arg.only_train_part:
if epoch > self.arg.only_train_epoch:
print('only train part, require grad')
for key, value in self.model.named_parameters():
if 'PA' in key:
value.requires_grad = True
# print(key + '-require grad')
else:
print('only train part, do not require grad')
for key, value in self.model.named_parameters():
if 'PA' in key:
value.requires_grad = False
# print(key + '-not require grad')
for batch_idx, (data, label, index) in enumerate(process):
self.global_step += 1
# get data
data = Variable(data.float().cuda(self.output_device), requires_grad=False)
label = Variable(label.long().cuda(self.output_device), requires_grad=False)
timer['dataloader'] += self.split_time()
# forward
output = self.model(data)
# if batch_idx == 0 and epoch == 0:
# self.train_writer.add_graph(self.model, output)
if isinstance(output, tuple):
output, l1 = output
l1 = l1.mean()
else:
l1 = 0
loss = self.loss(output, label) + l1
# backward
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
loss_value.append(loss.data.item())
timer['model'] += self.split_time()
value, predict_label = torch.max(output.data, 1)
acc = torch.mean((predict_label == label.data).float())
self.train_writer.add_scalar('acc', acc, self.global_step)
self.train_writer.add_scalar('loss', loss.data.item(), self.global_step)
self.train_writer.add_scalar('loss_l1', l1, self.global_step)
# self.train_writer.add_scalar('batch_time', process.iterable.last_duration, self.global_step)
# statistics
self.lr = self.optimizer.param_groups[0]['lr']
self.train_writer.add_scalar('lr', self.lr, self.global_step)
# if self.global_step % self.arg.log_interval == 0:
# self.print_log(
# '\tBatch({}/{}) done. Loss: {:.4f} lr:{:.6f}'.format(
# batch_idx, len(loader), loss.data[0], lr))
timer['statistics'] += self.split_time()
# statistics of time consumption and loss
proportion = {
k: '{:02d}%'.format(int(round(v * 100 / sum(timer.values()))))
for k, v in timer.items()
}
self.print_log(
'\tMean training loss: {:.4f}.'.format(np.mean(loss_value)))
self.print_log(
'\tTime consumption: [Data]{dataloader}, [Network]{model}'.format(
**proportion))
if save_model:
state_dict = self.model.state_dict()
weights = OrderedDict([[k.split('module.')[-1],
v.cpu()] for k, v in state_dict.items()])
torch.save(weights, self.arg.model_saved_name + '-' + str(epoch) + '-' + str(int(self.global_step)) + '.pt')
def eval(self, epoch, save_score=False, loader_name=['test'], wrong_file=None, result_file=None):
if wrong_file is not None:
f_w = open(wrong_file, 'w')
if result_file is not None:
f_r = open(result_file, 'w')
self.model.eval()
self.print_log('Eval epoch: {}'.format(epoch + 1))
for ln in loader_name:
loss_value = []
score_frag = []
right_num_total = 0
total_num = 0
loss_total = 0
step = 0
process = tqdm(self.data_loader[ln])
for batch_idx, (data, label, index) in enumerate(process):
with torch.no_grad():
data = Variable(
data.float().cuda(self.output_device),
requires_grad=False,
volatile=True)
label = Variable(
label.long().cuda(self.output_device),
requires_grad=False,
volatile=True)
output = self.model(data)
if isinstance(output, tuple):
output, l1 = output
l1 = l1.mean()
else:
l1 = 0
loss = self.loss(output, label)
score_frag.append(output.data.cpu().numpy())
loss_value.append(loss.data.item())
_, predict_label = torch.max(output.data, 1)
step += 1
if wrong_file is not None or result_file is not None:
predict = list(predict_label.cpu().numpy())
true = list(label.data.cpu().numpy())
for i, x in enumerate(predict):
if result_file is not None:
f_r.write(str(x) + ',' + str(true[i]) + '\n')
if x != true[i] and wrong_file is not None:
f_w.write(str(index[i]) + ',' + str(x) + ',' + str(true[i]) + '\n')
score = np.concatenate(score_frag)
loss = np.mean(loss_value)
accuracy = self.data_loader[ln].dataset.top_k(score, 1)
if accuracy > self.best_acc:
self.best_acc = accuracy
# self.lr_scheduler.step(loss)
print('Accuracy: ', accuracy, ' model: ', self.arg.model_saved_name)
if self.arg.phase == 'train':
self.val_writer.add_scalar('loss', loss, self.global_step)
self.val_writer.add_scalar('loss_l1', l1, self.global_step)
self.val_writer.add_scalar('acc', accuracy, self.global_step)
score_dict = dict(
zip(self.data_loader[ln].dataset.sample_name, score))
self.print_log('\tMean {} loss of {} batches: {}.'.format(
ln, len(self.data_loader[ln]), np.mean(loss_value)))
for k in self.arg.show_topk:
self.print_log('\tTop{}: {:.2f}%'.format(
k, 100 * self.data_loader[ln].dataset.top_k(score, k)))
if save_score:
with open('{}/epoch{}_{}_score.pkl'.format(
self.arg.work_dir, epoch + 1, ln), 'wb') as f:
pickle.dump(score_dict, f)
def start(self):
if self.arg.phase == 'train':
self.print_log('Parameters:\n{}\n'.format(str(vars(self.arg))))
self.global_step = self.arg.start_epoch * len(self.data_loader['train']) / self.arg.batch_size
for epoch in range(self.arg.start_epoch, self.arg.num_epoch):
if self.lr < 1e-3:
break
save_model = ((epoch + 1) % self.arg.save_interval == 0) or (
epoch + 1 == self.arg.num_epoch)
self.train(epoch, save_model=save_model)
self.eval(
epoch,
save_score=self.arg.save_score,
loader_name=['test'])
print('best accuracy: ', self.best_acc, ' model_name: ', self.arg.model_saved_name)
elif self.arg.phase == 'test':
if not self.arg.test_feeder_args['debug']:
wf = self.arg.model_saved_name + '_wrong.txt'
rf = self.arg.model_saved_name + '_right.txt'
else:
wf = rf = None
if self.arg.weights is None:
raise ValueError('Please appoint --weights.')
self.arg.print_log = False
self.print_log('Model: {}.'.format(self.arg.model))
self.print_log('Weights: {}.'.format(self.arg.weights))
self.eval(epoch=0, save_score=self.arg.save_score, loader_name=['test'], wrong_file=wf, result_file=rf)
self.print_log('Done.\n')
def str2bool(v):
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise argparse.ArgumentTypeError('Boolean value expected.')
def import_class(name):
components = name.split('.')
mod = __import__(components[0]) # import return model
for comp in components[1:]:
mod = getattr(mod, comp)
return mod
if __name__ == '__main__':
parser = get_parser()
# load arg form config file
p = parser.parse_args()
if p.config is not None:
with open(p.config, 'r') as f:
default_arg = yaml.load(f)
key = vars(p).keys()
for k in default_arg.keys():
if k not in key:
print('WRONG ARG: {}'.format(k))
assert (k in key)
parser.set_defaults(**default_arg)
arg = parser.parse_args()
init_seed(0)
processor = Processor(arg)
processor.start()