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action_classification.py
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action_classification.py
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#!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import argparse
import builtins
import os
import random
import shutil
import time
import warnings
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.multiprocessing as mp
import torch.utils.data
import torch.utils.data.distributed
#import torchvision.transforms as transforms
#import torchvision.datasets as datasets
#import torchvision.models as models
#from model import generate_model
#from models.resnet import get_fine_tuning_parameters
import numpy as np
from sklearn.neighbors import KNeighborsClassifier
from sklearn import preprocessing
from sklearn.metrics import accuracy_score
from moco.GRU import *
from moco.HCN import HCN
from moco.AGCN import Model as AGCN
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# change for action recogniton
from dataset import get_finetune_training_set,get_finetune_validation_set
parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
parser.add_argument('-j', '--workers', default=32, type=int, metavar='N',
help='number of data loading workers (default: 32)')
parser.add_argument('--epochs', default=80, type=int, metavar='N',
help='number of total epochs to run')
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=256, type=int,
metavar='N',
help='mini-batch size (default: 256), this is the total '
'batch size of all GPUs on the current node when '
'using Data Parallel or Distributed Data Parallel')
parser.add_argument('--lr', '--learning-rate', default=30., type=float,
metavar='LR', help='initial learning rate', dest='lr')
parser.add_argument('--schedule', default=[50, 70,], nargs='*', type=int,
help='learning rate schedule (when to drop lr by a ratio)')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
help='momentum')
parser.add_argument('--wd', '--weight-decay', default=0., type=float,
metavar='W', help='weight decay (default: 0.)',
dest='weight_decay')
parser.add_argument('-p', '--print-freq', default=10, 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('--seed', default=None, type=int,
help='seed for initializing training. ')
parser.add_argument('--gpu', default=None, type=int,
help='GPU id to use.')
parser.add_argument('--pretrained', default='', type=str,
help='path to moco pretrained checkpoint')
parser.add_argument('--finetune-dataset', default='ntu60', type=str,
help='which dataset to use for finetuning')
parser.add_argument('--protocol', default='cross_view', type=str,
help='traiining protocol of ntu')
parser.add_argument('--finetune-skeleton-representation', default='seq-based', type=str,
help='which skeleton-representation to use for downstream training')
parser.add_argument('--pretrain-skeleton-representation', default='seq-based', type=str,
help='which skeleton-representation where used for pre-training')
best_acc1 = 0
# initilize weight
def weights_init_gru(model):
with torch.no_grad():
for child in list(model.children()):
print("init ",child)
for param in list(child.parameters()):
if param.dim() == 2:
nn.init.xavier_uniform_(param)
print('PC weight initial finished!')
def load_moco_encoder_q(model,pretrained):
if os.path.isfile(pretrained):
print("=> loading checkpoint '{}'".format(pretrained))
checkpoint = torch.load(pretrained, map_location="cpu")
# rename moco pre-trained keys
state_dict = checkpoint['state_dict']
for k in list(state_dict.keys()):
# retain only encoder_q up to before the embedding layer
if k.startswith('module.encoder_q') and not k.startswith('module.encoder_q.fc'):
# remove prefix
state_dict[k[len("module.encoder_q."):]] = state_dict[k]
# delete renamed or unused k
del state_dict[k]
msg = model.load_state_dict(state_dict, strict=False)
print("message",msg)
assert set(msg.missing_keys) == {"fc.weight", "fc.bias"}
print("=> loaded pre-trained model '{}'".format(pretrained))
else:
print("=> no checkpoint found at '{}'".format(pretrained))
def load_moco_encoder_r(model,pretrained):
if os.path.isfile(pretrained):
print("=> loading checkpoint '{}'".format(pretrained))
checkpoint = torch.load(pretrained, map_location="cpu")
# rename moco pre-trained keys
state_dict = checkpoint['state_dict']
for k in list(state_dict.keys()):
# retain only encoder_r up to before the embedding layer
if k.startswith('module.encoder_r') and not k.startswith('module.encoder_r.fc'):
# remove prefix
state_dict[k[len("module.encoder_r."):]] = state_dict[k]
# delete renamed or unused k
del state_dict[k]
msg = model.load_state_dict(state_dict, strict=False)
print("message",msg)
assert set(msg.missing_keys) == {"fc.weight", "fc.bias"}
print("=> loaded pre-trained model '{}'".format(pretrained))
else:
print("=> no checkpoint found at '{}'".format(pretrained))
def load_pretrained(args, model):
# intra-skeleton contrastive pretrianing
if args.pretrain_skeleton_representation == 'seq-based' or args.pretrain_skeleton_representation == 'image-based' or args.pretrain_skeleton_representation == 'graph-based':
# fine tune only seq-based / graph-based / image-based query encoder of the intra-skeleton framework pretrained using corresponding representation
load_moco_encoder_q(model,args.pretrained)
finetune_encoder_q = True
finetune_encoder_r = False
return finetune_encoder_q,finetune_encoder_r
# inter-skeleton contrastive pretrianing
else:
if args.finetune_skeleton_representation=='seq-based' and (args.pretrain_skeleton_representation == 'seq-based_and_graph-based' or args.pretrain_skeleton_representation == 'seq-based_and_image-based') :
# fine tune only seq-based query encoder of the inter-skeleton framework pretrained using seq-based_and_graph-based or 'seq-based_and_image-based' representations
load_moco_encoder_q(model,args.pretrained)
finetune_encoder_q = True
finetune_encoder_r = False
elif args.finetune_skeleton_representation=='graph-based' and args.pretrain_skeleton_representation == 'seq-based_and_graph-based':
# fine tune only graph-based query encoder of the inter-skeleton framework pretrained using seq-based_and_graph-based representations
load_moco_encoder_r(model,args.pretrained)
finetune_encoder_q = False
finetune_encoder_r = True
elif args.finetune_skeleton_representation=='graph-based' and args.pretrain_skeleton_representation == 'graph-based_and_image-based' :
# fine tune only graph-based query encoder of the inter-skeleton framework pretrained using graph-based_and_image-based representations
load_moco_encoder_q(model,args.pretrained)
finetune_encoder_q = True
finetune_encoder_r = False
elif args.finetune_skeleton_representation=='image-based' and args.pretrain_skeleton_representation == 'seq-based_and_image-based':
# fine tune only image-based query encoder of the inter-skeleton framework pretrained using seq-based_and_image-based representations
load_moco_encoder_r(model,args.pretrained)
finetune_encoder_q = False
finetune_encoder_r = True
elif args.finetune_skeleton_representation=='image-based' and args.pretrain_skeleton_representation == 'graph-based_and_image-based':
# fine tune only image-based query encoder of the inter-skeleton framework pretrained using graph-based_and_image-based representations
load_moco_encoder_r(model,args.pretrained)
finetune_encoder_q = False
finetune_encoder_r = True
return finetune_encoder_q,finetune_encoder_r
def main():
args = parser.parse_args()
if args.seed is not None:
random.seed(args.seed)
torch.manual_seed(args.seed)
cudnn.deterministic = True
warnings.warn('You have chosen to seed training. '
'This will turn on the CUDNN deterministic setting, '
'which can slow down your training considerably! '
'You may see unexpected behavior when restarting '
'from checkpoints.')
if args.gpu is not None:
warnings.warn('You have chosen a specific GPU. This will completely '
'disable data parallelism.')
ngpus_per_node = torch.cuda.device_count()
# Simply call main_worker function
main_worker(0, ngpus_per_node, args)
def main_worker(gpu, ngpus_per_node, args):
global best_acc1
args.gpu = gpu
if args.gpu is not None:
print("Use GPU: {} for training".format(args.gpu))
# create model
# training dataset
from options import options_classification as options
if args.finetune_dataset== 'ntu60' and args.protocol == 'cross_view':
opts = options.opts_ntu_60_cross_view()
elif args.finetune_dataset== 'ntu60' and args.protocol == 'cross_subject':
opts = options.opts_ntu_60_cross_subject()
elif args.finetune_dataset== 'ntu120' and args.protocol == 'cross_setup':
opts = options.opts_ntu_120_cross_setup()
elif args.finetune_dataset== 'ntu120' and args.protocol == 'cross_subject':
opts = options.opts_ntu_120_cross_subject()
opts.train_feeder_args['input_representation'] = args.finetune_skeleton_representation
opts.test_feeder_args['input_representation'] = args.finetune_skeleton_representation
if args.finetune_skeleton_representation == 'seq-based':
# Gru model
model = BIGRU(**opts.bi_gru_model_args)
print(model)
print("options",opts.bi_gru_model_args,opts.train_feeder_args,opts.test_feeder_args)
if not args.pretrained:
weights_init_gru(model)
elif args.finetune_skeleton_representation == 'graph-based':
model = AGCN(**opts.agcn_model_args)
print(model)
print("options",opts.agcn_model_args,opts.train_feeder_args,opts.test_feeder_args)
elif args.finetune_skeleton_representation == 'image-based':
model = HCN(**opts.hcn_model_args)
print(model)
print("options",opts.bi_gru_model_args,opts.train_feeder_args,opts.test_feeder_args)
if args.pretrained:
# freeze all layers but the last fc
for name, param in model.named_parameters():
if name not in ['fc.weight', 'fc.bias']:
param.requires_grad = False
else:
print('params',name)
# init the fc layer
model.fc.weight.data.normal_(mean=0.0, std=0.01)
model.fc.bias.data.zero_()
# load from pre-trained model
finetune_encoder_q, finetune_encoder_r = load_pretrained(args, model)
if args.gpu is not None:
model = model.cuda()
model = nn.DataParallel(model, device_ids=None)
# define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss().cuda(args.gpu)
# optimize only the linear classifier
parameters = list(filter(lambda p: p.requires_grad, model.parameters()))
if args.pretrained:
assert len(parameters) == 2 # fc.weight, fc.bias
optimizer = torch.optim.SGD(parameters, args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
if True:
for parm in optimizer.param_groups:
print ("optimize parameters lr",parm['lr'])
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
if args.gpu is None:
checkpoint = torch.load(args.resume)
else:
# Map model to be loaded to specified single gpu.
loc = 'cuda:{}'.format(args.gpu)
checkpoint = torch.load(args.resume, map_location=loc)
args.start_epoch = checkpoint['epoch']
best_acc1 = checkpoint['best_acc1']
if args.gpu is not None:
# best_acc1 may be from a checkpoint from a different GPU
best_acc1 = best_acc1.to(args.gpu)
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})"
.format(args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
cudnn.benchmark = True
## Data loading code
train_dataset = get_finetune_training_set(opts)
val_dataset = get_finetune_validation_set(opts)
train_sampler = None
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None),
num_workers=args.workers, pin_memory=True, sampler=train_sampler,drop_last=False)
val_loader = torch.utils.data.DataLoader(
val_dataset,
batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True,drop_last=False)
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(optimizer, epoch, args)
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch, args)
# evaluate on validation set
acc1 = validate(val_loader, model, criterion, args)
# remember best acc@1 and save checkpoint
is_best = acc1 > best_acc1
if is_best:
print("found new best accuracy:= ",acc1)
best_acc1 = max(acc1, best_acc1)
save_checkpoint({
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'best_acc1': best_acc1,
'optimizer' : optimizer.state_dict(),
}, is_best,filename = args.finetune_skeleton_representation + '_checkpoint.pth.tar' )
# sanity check
if epoch == args.start_epoch:
if finetune_encoder_q:
sanity_check_encoder_q(model.state_dict(), args.pretrained)
elif finetune_encoder_r:
sanity_check_encoder_r(model.state_dict(), args.pretrained)
print("Final best accuracy",best_acc1)
def train(train_loader, model, criterion, optimizer, epoch, args):
batch_time = AverageMeter('Time', ':6.3f')
data_time = AverageMeter('Data', ':6.3f')
losses = AverageMeter('Loss', ':.4e')
top1 = AverageMeter('Acc@1', ':6.2f')
top5 = AverageMeter('Acc@5', ':6.2f')
progress = ProgressMeter(
len(train_loader),
[batch_time, data_time, losses, top1, top5],
prefix="Epoch: [{}]".format(epoch))
"""
Switch to eval mode:
Under the protocol of linear classification on frozen features/models,
it is not legitimate to change any part of the pre-trained model.
BatchNorm in train mode may revise running mean/std (even if it receives
no gradient), which are part of the model parameters too.
"""
model.eval()
end = time.time()
for i, (images, target) in enumerate(train_loader):
# measure data loading time
data_time.update(time.time() - end)
if args.gpu is not None:
images = images.cuda(args.gpu, non_blocking=True)
target = target.cuda(args.gpu, non_blocking=True)
# compute output
output = model(images)
loss = criterion(output, target)
# measure accuracy and record loss
acc1, acc5 = accuracy(output, target, topk=(1, 5))
losses.update(loss.item(), images.size(0))
top1.update(acc1[0], images.size(0))
top5.update(acc5[0], images.size(0))
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
progress.display(i)
def validate(val_loader, model, criterion, args):
batch_time = AverageMeter('Time', ':6.3f')
losses = AverageMeter('Loss', ':.4e')
top1 = AverageMeter('Acc@1', ':6.2f')
top5 = AverageMeter('Acc@5', ':6.2f')
progress = ProgressMeter(
len(val_loader),
[batch_time, losses, top1, top5],
prefix='Test: ')
# switch to evaluate mode
model.eval()
with torch.no_grad():
end = time.time()
for i, (images, target) in enumerate(val_loader):
if args.gpu is not None:
images = images.cuda(args.gpu, non_blocking=True)
target = target.cuda(args.gpu, non_blocking=True)
# compute output
output = model(images)
loss = criterion(output, target)
# measure accuracy and record loss
acc1, acc5 = accuracy(output, target, topk=(1, 5))
losses.update(loss.item(), images.size(0))
top1.update(acc1[0], images.size(0))
top5.update(acc5[0], images.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
progress.display(i)
# TODO: this should also be done with the ProgressMeter
print(' * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}'
.format(top1=top1, top5=top5))
return top1.avg
def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
torch.save(state, filename)
if is_best:
shutil.copyfile(filename, filename+'model_best.pth.tar')
def sanity_check_encoder_q(state_dict, pretrained_weights):
"""
Linear classifier should not change any weights other than the linear layer.
This sanity check asserts nothing wrong happens (e.g., BN stats updated).
"""
print("=> loading '{}' for sanity check".format(pretrained_weights))
checkpoint = torch.load(pretrained_weights, map_location="cpu")
state_dict_pre = checkpoint['state_dict']
for k in list(state_dict.keys()):
# only ignore fc layer
if 'fc.weight' in k or 'fc.bias' in k:
continue
# name in pretrained model
k_pre = 'module.encoder_q.' + k[len('module.'):] \
if k.startswith('module.') else 'module.encoder_q.' + k
assert ((state_dict[k].cpu() == state_dict_pre[k_pre]).all()), \
'{} is changed in linear classifier training.'.format(k)
print("=> sanity check passed.")
def sanity_check_encoder_r(state_dict, pretrained_weights):
"""
Linear classifier should not change any weights other than the linear layer.
This sanity check asserts nothing wrong happens (e.g., BN stats updated).
"""
print("=> loading '{}' for sanity check".format(pretrained_weights))
checkpoint = torch.load(pretrained_weights, map_location="cpu")
state_dict_pre = checkpoint['state_dict']
for k in list(state_dict.keys()):
# only ignore fc layer
if 'fc.weight' in k or 'fc.bias' in k:
continue
# name in pretrained model
k_pre = 'module.encoder_r.' + k[len('module.'):] \
if k.startswith('module.') else 'module.encoder_r.' + k
assert ((state_dict[k].cpu() == state_dict_pre[k_pre]).all()), \
'{} is changed in linear classifier training.'.format(k)
print("=> sanity check passed.")
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self, name, fmt=':f'):
self.name = name
self.fmt = fmt
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def __str__(self):
fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})'
return fmtstr.format(**self.__dict__)
class ProgressMeter(object):
def __init__(self, num_batches, meters, prefix=""):
self.batch_fmtstr = self._get_batch_fmtstr(num_batches)
self.meters = meters
self.prefix = prefix
def display(self, batch):
entries = [self.prefix + self.batch_fmtstr.format(batch)]
entries += [str(meter) for meter in self.meters]
print('\t'.join(entries))
def _get_batch_fmtstr(self, num_batches):
num_digits = len(str(num_batches // 1))
fmt = '{:' + str(num_digits) + 'd}'
return '[' + fmt + '/' + fmt.format(num_batches) + ']'
def adjust_learning_rate(optimizer, epoch, args):
"""Decay the learning rate based on schedule"""
lr = args.lr
for milestone in args.schedule:
lr *= 0.1 if epoch >= milestone else 1.
for param_group in optimizer.param_groups:
param_group['lr'] = lr
def accuracy(output, target, topk=(1,)):
"""Computes the accuracy over the k top predictions for the specified values of k"""
with torch.no_grad():
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
res.append(correct_k.mul_(100.0 / batch_size))
return res
if __name__ == '__main__':
main()