def validate_eval(val_loader, model, criterion, args, epoch=None, fnames=[]):
batch_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
model.eval()
end = time.time()
scores = np.zeros((len(val_loader.dataset), args.num_class))
labels = np.zeros((len(val_loader.dataset), ))
for i, (frames, objects, target) in enumerate(val_loader):
with torch.no_grad():
target = target.cuda(async=True)
frames = frames.cuda()
objects = objects.cuda()
output = model(frames, objects)
loss = criterion(output, target)
losses.update(loss.item(), target.size(0))
prec1 = accuracy(output.data, target)
top1.update(prec1[0], target.size(0))
batch_time.update(time.time() - end)
end = time.time()
# Record scores.
output_f = F.softmax(output, dim=1) # To [0, 1]
output_np = output_f.data.cpu().numpy()
labels_np = target.data.cpu().numpy()
b_ind = i * args.batch_size
e_ind = b_ind + min(args.batch_size, output_np.shape[0])
scores[b_ind:e_ind, :] = output_np
labels[b_ind:e_ind] = labels_np
print(
'Test [Epoch {0}/{1}]: '
'*Time {2:.2f}mins ({batch_time.avg:.2f}s) '
'*Loss {loss.avg:.4f} '
'*Prec@1 {top1.avg:.3f}'.format(epoch,
args.epoch,
batch_time.sum / 60,
batch_time=batch_time,
top1=top1,
loss=losses))
model.train()
res_scores = multi_scores(scores, labels,
['precision', 'recall', 'average_precision'])
return top1.avg, losses.avg, res_scores['precision'], res_scores[
'recall'], res_scores['average_precision']
def train_eval(train_loader,
val_loader,
model,
criterion,
optimizer,
args,
epoch,
fnames=[]):
batch_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
model.train()
# Fix resnet50.
# model.module.resnet50.eval()
end = time.time()
scores = np.zeros((len(train_loader.dataset), args.num_class))
labels = np.zeros((len(train_loader.dataset), ))
# Checkpoint begin batch.
b_batch = 0
if epoch == cp_recorder.contextual['b_epoch']:
b_batch = cp_recorder.contextual['b_batch'] + 1
for i, (frames, target) in enumerate(train_loader):
# Jump to contextual batch
if i < b_batch:
continue
target = target.cuda(async=True)
frames = frames.cuda()
output = model(frames)
loss = criterion(output, target)
losses.update(loss.item(), target.size(0))
prec1 = accuracy(output.data, target)
top1.update(prec1[0], target.size(0))
optimizer.zero_grad()
loss.backward()
optimizer.step()
batch_time.update(time.time() - end)
end = time.time()
if (i + 1) % args.print_freq == 0:
"""Every 10 batches, print on screen and print train information on tensorboard
"""
niter = epoch * len(train_loader) + i
print(
'Train [Batch {0}/{1}|Epoch {2}/{3}]: '
'Time {batch_time.val:.3f} ({batch_time.avg:.3f}) '
'Loss {loss.val:.4f} ({loss.avg:.4f}) '
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})'.format(
i,
len(train_loader),
epoch,
args.epoch,
batch_time=batch_time,
loss=losses,
top1=top1))
writer.add_scalars('Loss (per batch)', {'train-10b': loss.item()},
niter)
writer.add_scalars('Prec@1 (per batch)', {'train-10b': prec1[0]},
niter)
if (i + 1) % (args.print_freq * 10) == 0:
# Every 100 batches, print on screen and print validation information on tensorboard
top1_avg_val, loss_avg_val, prec, recall, ap = validate_eval(
val_loader, model, criterion, args, epoch)
writer.add_scalars('Loss (per batch)', {'valid': loss_avg_val},
niter)
writer.add_scalars('Prec@1 (per batch)', {'valid': top1_avg_val},
niter)
writer.add_scalars('mAP (per batch)',
{'valid': np.nan_to_num(ap).mean()}, niter)
# Save checkpoint every 100 batches.
cp_recorder.record_contextual({
'b_epoch': epoch,
'b_batch': i,
'prec': top1_avg_val,
'loss': loss_avg_val,
'class_prec': prec,
'class_recall': recall,
'class_ap': ap,
'mAP': np.nan_to_num(ap).mean()
})
cp_recorder.save_checkpoint(model)
# Record scores.
output_f = F.softmax(output, dim=1) # To [0, 1]
output_np = output_f.data.cpu().numpy()
labels_np = target.data.cpu().numpy()
b_ind = i * args.batch_size
e_ind = b_ind + min(args.batch_size, output_np.shape[0])
scores[b_ind:e_ind, :] = output_np
labels[b_ind:e_ind] = labels_np
res_scores = multi_scores(scores, labels,
['precision', 'recall', 'average_precision'])
print(
'Train [Epoch {0}/{1}]: '
'*Time {2:.2f}mins ({batch_time.avg:.2f}s) '
'*Loss {loss.avg:.4f} '
'*Prec@1 {top1.avg:.3f}'.format(epoch,
args.epoch,
batch_time.sum / 60,
batch_time=batch_time,
loss=losses,
top1=top1))
return top1.avg, losses.avg, res_scores['precision'], res_scores[
'recall'], res_scores['average_precision']
def validate_eval(val_loader, model, criterion, args, epoch=None, fnames=[]):
batch_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
model.eval()
end = time.time()
scores = np.zeros((len(val_loader.dataset), args.num_classes))
labels = np.zeros((len(val_loader.dataset), ))
for i, (union, obj1, obj2, bpos, target, full_im, bboxes_14,
categories) in enumerate(val_loader):
with torch.no_grad():
# Create bboxes
batch_size = bboxes_14.size(0)
cur_rois_sum = categories[0, 0].item()
bboxes = bboxes_14[0, 0:categories[0, 0].item(), :]
for b in range(1, batch_size):
bboxes = torch.cat(
(bboxes, bboxes_14[b, 0:categories[b, 0], :]), 0)
cur_rois_sum += categories[b, 0].item()
assert (bboxes.size(0) == cur_rois_sum
), 'Bboxes num must equal to categories num'
target = target.cuda(async=True)
union = union.cuda()
obj1 = obj1.cuda()
obj2 = obj2.cuda()
bpos = bpos.cuda()
full_im = full_im.cuda()
bboxes = bboxes.cuda()
categories = categories.cuda()
output = model(union, obj1, obj2, bpos, full_im, bboxes,
categories)
loss = criterion(output, target)
losses.update(loss.item(), union.size(0))
prec1 = accuracy(output.data, target)
top1.update(prec1[0], union.size(0))
batch_time.update(time.time() - end)
end = time.time()
# Record scores.
output_f = F.softmax(output, dim=1) # To [0, 1]
output_np = output_f.data.cpu().numpy()
labels_np = target.data.cpu().numpy()
b_ind = i * args.batch_size
e_ind = b_ind + min(args.batch_size, output_np.shape[0])
scores[b_ind:e_ind, :] = output_np
labels[b_ind:e_ind] = labels_np
print(
'Test [Epoch {0}/{1}]: '
'*Time {2:.2f}mins ({batch_time.avg:.2f}s) '
'*Loss {loss.avg:.4f} '
'*Prec@1 {top1.avg:.3f}'.format(epoch,
args.epoch,
batch_time.sum / 60,
batch_time=batch_time,
top1=top1,
loss=losses))
res_scores = multi_scores(scores, labels,
['precision', 'recall', 'average_precision'])
return top1.avg, losses.avg, res_scores['precision'], res_scores[
'recall'], res_scores['average_precision']
