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train.py
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train.py
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""" Defines the Trainer class which handles train/validation/validation_video
"""
import time
import torch
import itertools
import numpy as np
from utils import map
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
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 adjust_learning_rate(startlr, decay_rate, optimizer, epoch):
"""Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
lr = startlr * (0.1 ** (epoch // decay_rate))
for param_group in optimizer.param_groups:
param_group['lr'] = lr
def accuracy(output, target, topk=(1,)):
"""Computes the precision@k for the specified values of k"""
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
def submission_file(ids, outputs, filename):
""" write list of ids and outputs to filename"""
with open(filename, 'w') as f:
for vid, output in zip(ids, outputs):
scores = ['{:g}'.format(x)
for x in output]
f.write('{} {}\n'.format(vid, ' '.join(scores)))
class Trainer():
def train(self, loader, model, criterion, optimizer, epoch, args):
adjust_learning_rate(args.lr, args.lr_decay_rate, optimizer, epoch)
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# switch to train mode
model.train()
optimizer.zero_grad()
def part(x):
return itertools.islice(x, int(len(x)*args.train_size))
end = time.time()
for i, (input, target, meta) in enumerate(part(loader)):
data_time.update(time.time() - end)
target = target.long().cuda(async=True)
input_var = torch.autograd.Variable(input.cuda())
target_var = torch.autograd.Variable(target)
output = model(input_var)
loss = None
# for nets that have multiple outputs such as inception
if isinstance(output, tuple):
loss = sum((criterion(o,target_var) for o in output))
output = output[0]
else:
loss = criterion(output, target_var)
prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
losses.update(loss.data[0], input.size(0))
top1.update(prec1[0], input.size(0))
top5.update(prec5[0], input.size(0))
loss.backward()
if i % args.accum_grad == args.accum_grad-1:
print('updating parameters')
optimizer.step()
optimizer.zero_grad()
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
print('Epoch: [{0}][{1}/{2}({3})]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
epoch, i, int(
len(loader)*args.train_size), len(loader),
batch_time=batch_time, data_time=data_time, loss=losses, top1=top1, top5=top5))
return top1.avg,top5.avg
def validate(self, loader, model, criterion, epoch, args):
batch_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# switch to evaluate mode
model.eval()
def part(x): return itertools.islice(x, int(len(x)*args.val_size))
end = time.time()
for i, (input, target, meta) in enumerate(part(loader)):
target = target.long().cuda(async=True)
input_var = torch.autograd.Variable(input.cuda(), volatile=True)
target_var = torch.autograd.Variable(target, volatile=True)
output = model(input_var)
loss = criterion(output, target_var)
prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
losses.update(loss.data[0], input.size(0))
top1.update(prec1[0], input.size(0))
top5.update(prec5[0], input.size(0))
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
print('Test: [{0}/{1} ({2})]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
i, int(len(loader)*args.val_size), len(loader),
batch_time=batch_time, loss=losses,
top1=top1, top5=top5))
print(' * Prec@1 {top1.avg:.3f} Prec@5 {top5.avg:.3f}'
.format(top1=top1, top5=top5))
return top1.avg,top5.avg
def validate_video(self, loader, model, epoch, args):
""" Run video-level validation on the Charades test set"""
batch_time = AverageMeter()
outputs = []
gts = []
ids = []
# switch to evaluate mode
model.eval()
end = time.time()
for i, (input, target, meta) in enumerate(loader):
target = target.long().cuda(async=True)
assert target[0,:].eq(target[1,:]).all(), "val_video not synced"
input_var = torch.autograd.Variable(input.cuda(), volatile=True)
output = model(input_var)
output = torch.nn.Softmax(dim=1)(output)
# store predictions
output_video = output.mean(dim=0)
outputs.append(output_video.data.cpu().numpy())
gts.append(target[0,:])
ids.append(meta['id'][0])
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
print('Test2: [{0}/{1}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})'.format(
i, len(loader), batch_time=batch_time))
#mAP, _, ap = map.map(np.vstack(outputs), np.vstack(gts))
mAP, _, ap = map.charades_map(np.vstack(outputs), np.vstack(gts))
print(ap)
print(' * mAP {:.3f}'.format(mAP))
submission_file(
ids, outputs, '{}/epoch_{:03d}.txt'.format(args.cache, epoch+1))
return mAP