def infer(valid_queue, model, criterion, verbose=True):
objs = utils.AverageMeter()
top1 = utils.AverageMeter()
top5 = utils.AverageMeter()
model.eval()
with torch.no_grad():
for step, (input, target) in enumerate(valid_queue):
input = Variable(input) #, volatile=True
target = Variable(target) #, volatile=True
if not args.disable_cuda:
input = input.cuda()
target = target.cuda(async=True)
logits = model(input, 0)
loss = criterion(logits, target)
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
n = input.size(0)
objs.update(loss.item(), n)
top1.update(prec1.item(), n)
top5.update(prec5.item(), n)
if verbose and step % args.report_freq == 0:
print('valid %03d %.3e %.3f %.3f' %
(step, objs.avg, top1.avg, top5.avg))
# if step > 10: break
return top1.avg, objs.avg
def train_network(self, params, tbx_writer, checkpointer, train_loader, valLoader, test_loader, model, criterion, optimiser, inferer):
#{{{
print('Epoch,\tLR,\tTrain_Loss,\tTrain_Top1,\tTrain_Top5,\tTest_Loss,\tTest_Top1,\tTest_Top5,\tVal_Loss,\tVal_Top1,\tVal_Top5')
# iterate over the epochs
for epoch in tqdm(range(params.start_epoch, params.epochs), desc='training', leave=False) :
params.curr_epoch = epoch
state = self.update_lr(params, optimiser)
# setup train loss, top1 and top5 averaging units
losses = utils.AverageMeter()
top1 = utils.AverageMeter()
top5 = utils.AverageMeter()
# iterate batches in an epoch
self.batch_iter(model, criterion, optimiser, train_loader, params, losses, top1, top5)
# record train loss
params.train_loss = losses.avg
params.train_top1 = top1.avg
params.train_top5 = top5.avg
# get test loss
params.test_loss, params.test_top1, params.test_top5 = inferer.test_network(params, test_loader, model, criterion, optimiser, verbose=False)
params.val_loss, params.val_top1, params.val_top5 = inferer.test_network(params, valLoader, model, criterion, optimiser, verbose=False)
checkpointer.save_checkpoint(model.state_dict(), optimiser.state_dict(), params)
tqdm.write("{},\t{},\t{:10.5f},\t{:10.5f},\t{:10.5f},\t{:10.5f},\t{:10.5f},\t{:10.5f},\t{:10.5f},\t{:10.5f},\t{:10.5f}".format(epoch, params.lr, params.train_loss, params.train_top1, params.train_top5, params.test_loss, params.test_top1, params.test_top5, params.val_loss, params.val_top1, params.val_top5))
def test_network(self, params, test_loader, model, criterion, optimiser, verbose=True) :
model.eval()
losses = utils.AverageMeter()
top1 = utils.AverageMeter()
top5 = utils.AverageMeter()
for batch_idx, (inputs, targets) in tqdm(enumerate(test_loader), total=len(test_loader)-1, desc='inference', leave=False) :
# move inputs and targets to GPU
with torch.no_grad():
device = 'cuda:' + str(params.gpuList[0])
if params.use_cuda :
inputs, targets = inputs.cuda(device, non_blocking=True), targets.cuda(device, non_blocking=True)
# perform inference
outputs = model(inputs)
loss = criterion(outputs, targets)
prec1, prec5 = utils.accuracy(outputs.data, targets.data)
losses.update(loss.item())
top1.update(prec1.item())
top5.update(prec5.item())
if verbose :
tqdm.write('Loss: {}, Top1: {}, Top5: {}'.format(losses.avg, top1.avg, top5.avg))
return (losses.avg, top1.avg, top5.avg)
def _get_average_meters(self):
error_metric = utils.AverageMeter()
obj = utils.AverageMeter()
main_obj = utils.AverageMeter()
kl = utils.AverageMeter()
ece = utils.AverageMeter()
return error_metric, obj, main_obj, kl, ece
def static_finetune_l1_weights(self, params, pruner, checkpointer,
train_loader, test_loader, valLoader, model,
criterion, optimiser, inferer):
#{{{
print(
'Epoch,\tLR,\tTrain_Loss,\tTrain_Top1,\tTrain_Top5,\tTest_Loss,\tTest_Top1,\tTest_Top5,\tVal_Loss,\tVal_Top1,\tVal_Top5'
)
for epoch in tqdm(range(params.start_epoch, params.finetuneBudget),
desc='training',
leave=False):
params.curr_epoch = epoch
state = self.update_lr(params, optimiser)
# perform pruning
if params.pruneFilters == True and epoch == params.pruneAfter:
checkpointer.save_model_only(model.state_dict(),
params.printOnly, 'pre_pruning')
tqdm.write('Pruning Network')
channelsPruned, model, optimiser = pruner.prune_model(model)
totalPrunedPerc, _, _ = pruner.prune_rate(model)
tqdm.write(
'Pruned Percentage = {:.2f}%'.format(totalPrunedPerc))
summary = pruner.log_pruned_channels(checkpointer.root, params,
totalPrunedPerc,
channelsPruned)
losses = utils.AverageMeter()
top1 = utils.AverageMeter()
top5 = utils.AverageMeter()
self.batch_iter(model, criterion, optimiser, train_loader, params,
losses, top1, top5)
params.train_loss = losses.avg
params.train_top1 = top1.avg
params.train_top5 = top5.avg
# get test loss
params.test_loss, params.test_top1, params.test_top5 = inferer.test_network(
params,
test_loader,
model,
criterion,
optimiser,
verbose=False)
params.val_loss, params.val_top1, params.val_top5 = inferer.test_network(
params, valLoader, model, criterion, optimiser, verbose=False)
checkpointer.save_checkpoint(model.state_dict(),
optimiser.state_dict(), params)
tqdm.write(
"{},\t{:10.5f},\t{:10.5f},\t{:10.5f},\t{:10.5f},\t{:10.5f},\t{:10.5f},\t{:10.5f},\t{:10.5f},\t{:10.5f},\t{:10.5f}"
.format(epoch, params.lr, params.train_loss, params.train_top1,
params.train_top5, params.test_loss, params.test_top1,
params.test_top5, params.val_loss, params.val_top1,
params.val_top5))
def train_batch(self, arch, errors_dict):
args = self.args
if self.steps % len(self.train_queue) == 0:
self.scheduler.step()
self.objs = utils.AverageMeter()
self.top1 = utils.AverageMeter()
self.top5 = utils.AverageMeter()
lr = self.scheduler.get_lr()[0]
weights = self.get_weights_from_arch(arch)
self.set_model_weights(weights)
step = self.steps % len(self.train_queue)
input, target = next(self.train_iter)
self.model.train()
n = input.size(0)
input = Variable(input, requires_grad=False).cuda()
target = Variable(target, requires_grad=False).cuda(async=True)
# get a random minibatch from the search queue with replacement
self.optimizer.zero_grad()
logits = self.model(input, discrete=True)
loss = self.criterion(logits, target)
loss.backward()
nn.utils.clip_grad_norm(self.model.parameters(), args.grad_clip)
self.optimizer.step()
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
self.objs.update(loss.data[0], n)
self.top1.update(prec1.data[0], n)
self.top5.update(prec5.data[0], n)
if step % args.report_freq == 0:
logger.info('train %03d %e %f %f', step, self.objs.avg,
self.top1.avg, self.top5.avg)
self.steps += 1
if self.steps % len(self.train_queue) == 0:
self.epochs += 1
self.train_iter = iter(self.train_queue)
valid_err, valid_obj = self.evaluate(arch)
logger.info('epoch %d | train_acc %f | valid_acc %f' %
(self.epochs, self.top1.avg, 1 - valid_err))
self.save()
errors_dict['train_acc'].append(self.top1.avg)
errors_dict['train_loss'].append(self.objs.avg)
errors_dict['valid_acc'].append(1 - valid_err)
errors_dict['valid_loss'].append(valid_obj)
def train_network(self, params, tbx_writer, checkpointer, train_loader,
test_loader, valLoader, model, criterion, optimiser,
inferer, policy, scaler):
print(
'Epoch,\tLR,\tTrain_Loss,\tTrain_Top1,\tTrain_Top5,\tTest_Loss,\tTest_Top1,\tTest_Top5,\tVal_Loss,\tVal_Top1,\tVal_Top5,\tDataType,\tBitWidth'
)
for epoch in tqdm(range(params.start_epoch, params.epochs),
desc='training',
leave=False):
params.curr_epoch = epoch
state = self.update_lr(params, optimiser)
losses = utils.AverageMeter()
top1 = utils.AverageMeter()
top5 = utils.AverageMeter()
# iterate over the batches in the epoch
self.batch_iter(model, criterion, optimiser, train_loader, params,
losses, top1, top5)
params.train_loss = losses.avg
params.train_top1 = top1.avg
params.train_top5 = top5.avg
# get val and test loss
params.test_loss, params.test_top1, params.test_top5 = inferer.test_network(
params, test_loader, model, criterion, optimiser)
params.val_loss, params.val_top1, params.val_top5 = inferer.test_network(
params, valLoader, model, criterion, optimiser)
if params.runMuppet:
policy.update(model)
if policy.check_violation(epoch, tqdm, checkpointer):
policy.change_precision(scaler, model, optimiser)
tqdm.write(
"GD violation detected, precision changed to {}".
format(params.bitWidth))
if policy.check_stopping_condition(optimiser):
tqdm.write("Ending training")
return
checkpointer.save_checkpoint(model.state_dict(),
optimiser.state_dict(), params)
tqdm.write(
"{},\t{},\t{:10.5f},\t{:10.5f},\t{:10.5f},\t{:10.5f},\t{:10.5f},\t{:10.5f},\t{:10.5f},\t{:10.5f},\t{:10.5f},\t{},\t\t{}"
.format(epoch, params.lr, params.train_loss, params.train_top1,
params.train_top5, params.test_loss, params.test_top1,
params.test_top5, params.val_loss, params.val_top1,
params.val_top5, params.dataType, params.bitWidth))
def train(train_loader,
model,
criterion,
optimizer,
lr_init=None,
lr_now=None,
glob_step=None,
lr_decay=None,
gamma=None,
max_norm=True):
losses = utils.AverageMeter()
model.train()
for i, (inps, tars) in enumerate(tqdm(train_loader)):
glob_step += 1
if glob_step % lr_decay == 0 or glob_step == 1:
lr_now = utils.lr_decay(optimizer, glob_step, lr_init, lr_decay,
gamma)
#make prediction with model
inputs = Variable(inps.cuda())
targets = Variable(tars.cuda(non_blocking=True))
outputs = model(inputs)
# calculate loss
optimizer.zero_grad()
loss = criterion(outputs, targets)
losses.update(loss.item(), inputs.size(0))
loss.backward()
if max_norm:
nn.utils.clip_grad_norm(model.parameters(), max_norm=1)
optimizer.step()
return glob_step, lr_now, losses.avg
def __call__(self, glob_step, epoch):
losses = utils.AverageMeter()
self.model.train()
start = time.time()
batch_time = 0
for i, (inps, tars) in enumerate(self.train_loader):
glob_step += 1
if glob_step % self.cfg.get_lr_decay() == 0 or glob_step == 1:
self.lr_now = self._lr_decay(
glob_step,
self.cfg.get_learning_rate(),
self.cfg.get_lr_decay(),
self.cfg.get_lr_gamma())
inputs = Variable(inps.cuda())
targets = Variable(tars.cuda(async=True))
outputs = self.model(inputs)
# calculate loss
self.optimizer.zero_grad()
loss = self.loss_fuction(outputs, targets)
losses.update(loss.item(), inputs.size(0))
loss.backward()
#losses.update(loss.data[0], inputs.size(0))
if True:
'''
Max norm constraints.
Another form of regularization is to enforce an absolute upper bound
on the magnitude of the weight vector for every neuron and
use projected gradient descent to enforce the constraint.
In practice, this corresponds to performing the parameter update as normal,
and then enforcing the constraint by clamping the weight vector w⃗
of every neuron to satisfy ∥w⃗ ∥2<c.
Typical values of c are on orders of 3 or 4.
Some people report improvements when using this form of regularization.
One of its appealing properties is that network cannot “explode” even
when the learning rates are set too high because the updates are always bounded.
'''
nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=1)
self.optimizer.step()
# update summary
if (i + 1) % 100 == 0:
batch_time = time.time() - start
start = time.time()
print('Epoch: {epoch}/{epochs} | ({batch}/{size}) | batch: {batchtime:.4}ms | loss: {loss:.4f}' \
.format(epoch=epoch+1,
epochs=self.cfg.get_epochs(),
batch=i + 1,
size=len(self.train_loader),
batchtime=batch_time * 10.0,
loss=losses.avg))
return glob_step, self.lr_now, losses.avg, self.optimizer
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!lr_now
def test(test_loader, model, criterion, stat):
losses = utils.AverageMeter()
model.eval()
all_dist, all_output, all_target, all_input = [], [], [], []
for i, (inps, tars) in enumerate(tqdm(test_loader)):
inputs = Variable(inps.cuda())
targets = Variable(tars.cuda(non_blocking=True))
# make prediction with model
outputs = model(inputs)
# outputs = outputs.view(outputs.size(0), -1)
# targets = targets.view(targets.size(0), -1)
# calculate loss
loss = criterion(outputs, targets)
losses.update(loss.item(), inputs.size(0))
# undo normalisation to calculate accuracy in real units
dim = 3
# dimensions = stat['targets_3d']
tar = utils.unNormalizeData(
targets.data.cpu().numpy(), stat["mean"], stat["std"]
)
out = utils.unNormalizeData(
outputs.data.cpu().numpy(), stat["mean"], stat["std"]
)
abserr = np.abs(out - tar)
n_pts = 39 // dim
distance = np.zeros((abserr.shape[0], n_pts))
for k in range(n_pts):
distance[:, k] = np.sum(abserr[:, dim * k : dim * (k + 1)], axis=1)
# group and stack
all_dist.append(distance)
all_output.append(outputs.data.cpu().numpy())
all_target.append(targets.data.cpu().numpy())
all_input.append(inputs.data.cpu().numpy())
all_dist, all_output, all_target, all_input = (
np.vstack(all_dist),
np.vstack(all_output),
np.vstack(all_target),
np.vstack(all_input),
)
# mean errors
all_dist[all_dist == 0] = np.nan
joint_err = np.nanmean(all_dist, axis=0)
ttl_err = np.nanmean(joint_err)
print(">>> error: {} <<<".format(ttl_err))
return losses.avg, ttl_err, joint_err, all_dist, all_output, all_target, all_input
def finetune_entropy(self, params, pruner, checkpointer, train_loader,
test_loader, valLoader, model, criterion, optimiser,
inferer):
#{{{
print(
'Epoch,\tLR,\tTrain_Loss,\tTrain_Top1,\tTrain_Top5,\tTest_Loss,\tTest_Top1,\tTest_Top5,\tVal_Loss,\tVal_Top1,\tVal_Top5'
)
for epoch in tqdm(range(params.start_epoch, params.finetuneBudget),
desc='training',
leave=False):
params.curr_epoch = epoch
state = self.update_lr(params, optimiser)
losses = utils.AverageMeter()
top1 = utils.AverageMeter()
top5 = utils.AverageMeter()
self.batch_iter(model, criterion, optimiser, train_loader, params,
losses, top1, top5)
params.train_loss = losses.avg
params.train_top1 = top1.avg
params.train_top5 = top5.avg
# get test loss
params.test_loss, params.test_top1, params.test_top5 = inferer.test_network(
params,
test_loader,
model,
criterion,
optimiser,
verbose=False)
params.val_loss, params.val_top1, params.val_top5 = inferer.test_network(
params, valLoader, model, criterion, optimiser, verbose=False)
checkpointer.save_checkpoint(model.state_dict(),
optimiser.state_dict(), params)
tqdm.write(
"{},\t{:10.5f},\t{:10.5f},\t{:10.5f},\t{:10.5f},\t{:10.5f},\t{:10.5f},\t{:10.5f},\t{:10.5f},\t{:10.5f},\t{:10.5f}"
.format(epoch, params.lr, params.train_loss, params.train_top1,
params.train_top5, params.test_loss, params.test_top1,
params.test_top5, params.val_loss, params.val_top1,
params.val_top5))
def test(test_loader, model, criterion, stat, predict=False):
losses = utils.AverageMeter()
model.eval()
all_dist, all_output, all_target, all_input, all_bool = [], [], [], [], []
for i, (inps, tars, good_keypts, keys) in enumerate(tqdm(test_loader)):
inputs = Variable(inps.cuda())
targets = Variable(tars.cuda(non_blocking=True))
#make prediction with model
outputs = model(inputs)
all_output.append(outputs.data.cpu().numpy())
all_input.append(inputs.data.cpu().numpy())
if not predict:
# calculate loss
loss = criterion(outputs, targets)
losses.update(loss.item(), inputs.size(0))
outputs[~good_keypts] = 0
targets[~good_keypts] = 0
# undo normalisation to calculate accuracy in real units
dim = 1
dimensions = stat['targets_1d']
out = stats.unNormalize(outputs.data.cpu().numpy(),
stat['mean'][dimensions],
stat['std'][dimensions])
tar = stats.unNormalize(targets.data.cpu().numpy(),
stat['mean'][dimensions],
stat['std'][dimensions])
#compute error
distance = stats.abs_error(tar, out, dim)
all_dist.append(distance)
all_target.append(targets.data.cpu().numpy())
all_bool.append(good_keypts)
all_input = np.vstack(all_input)
all_output = np.vstack(all_output)
if predict:
return None, None, None, None, all_output, None, all_input, None, None
all_target = np.vstack(all_target)
all_dist = np.vstack(all_dist)
all_bool = np.vstack(all_bool)
#mean errors
all_dist[all_dist == 0] = np.nan
joint_err = np.nanmean(all_dist, axis=0)
ttl_err = np.nanmean(joint_err)
print(">>> error: {} <<<".format(ttl_err))
return losses.avg, ttl_err, joint_err, all_dist, all_output, all_target, all_input, all_bool
def validating(self):
losses = utils.AverageMeter()
self.model.eval()
all_dist = []
for i, (inps, tars) in enumerate(self.data_loader):
inputs = Variable(inps.cuda())
tars = Variable(tars.cuda(async=True))
outputs = self.model(inputs)
# calculate loss
outputs_coord = outputs
loss = self.loss_fuction(outputs_coord, tars)
losses.update(loss.item(), inputs.size(0))
# calculate erruracy
targets_unnorm = self.unNormalizeData(tars.data.cpu().numpy(),
self.stat_3d['mean'],
self.stat_3d['std'],
self.stat_3d['dim_use'])
outputs_unnorm = self.unNormalizeData(outputs.data.cpu().numpy(),
self.stat_3d['mean'],
self.stat_3d['std'],
self.stat_3d['dim_use'])
# remove dim ignored
dim_use = np.hstack((np.arange(3), self.stat_3d['dim_use']))
outputs_use = outputs_unnorm[:, dim_use]
targets_use = targets_unnorm[:, dim_use]
#use procrustes analysis at testing
if True:
for ba in range(inps.size(0)):
gt = targets_use[ba].reshape(-1, 3)
out = outputs_use[ba].reshape(-1, 3)
_, Z, T, b, c = get_transformation(gt, out, True)
out = (b * out.dot(T)) + c
outputs_use[ba, :] = out.reshape(1, 51)
sqerr = (outputs_use - targets_use)**2
distance = np.zeros((sqerr.shape[0], 17))
dist_idx = 0
for k in np.arange(0, 17 * 3, 3):
distance[:,
dist_idx] = np.sqrt(np.sum(sqerr[:, k:k + 3], axis=1))
dist_idx += 1
all_dist.append(distance)
all_dist = np.vstack(all_dist)
#joint_err = np.mean(all_dist, axis=0)
ttl_err = np.mean(all_dist)
print(">>> error: {} <<<".format(ttl_err))
return losses.avg, ttl_err
def train(
train_loader,
model,
criterion,
optimizer,
lr_init=None,
lr_now=None,
glob_step=None,
lr_decay=None,
gamma=None,
max_norm=True,
):
losses = utils.AverageMeter()
model.train()
start = time.time()
batch_time = 0
bar = Bar(">>>", fill=">", max=len(train_loader))
for i, (inps, tars) in enumerate(train_loader):
glob_step += 1
if glob_step % lr_decay == 0 or glob_step == 1:
lr_now = utils.lr_decay(optimizer, glob_step, lr_init, lr_decay,
gamma)
inputs = Variable(inps.cuda())
targets = Variable(tars.cuda())
outputs = model(inputs)
# calculate loss
optimizer.zero_grad()
loss = criterion(outputs, targets)
losses.update(loss.item(), inputs.size(0))
loss.backward()
if max_norm:
nn.utils.clip_grad_norm(model.parameters(), max_norm=1)
optimizer.step()
# update summary
if (i + 1) % 100 == 0:
batch_time = time.time() - start
start = time.time()
bar.suffix = "({batch}/{size}) | batch: {batchtime:.4}ms | Total: {ttl} | ETA: {eta:} | loss: {loss:.4f}".format(
batch=i + 1,
size=len(train_loader),
batchtime=batch_time * 10.0,
ttl=bar.elapsed_td,
eta=bar.eta_td,
loss=losses.avg,
)
bar.next()
bar.finish()
return glob_step, lr_now, losses.avg
def evaluate(self, arch, split=None):
# Return error since we want to minimize obj val
logger.info(arch)
objs = utils.AverageMeter()
top1 = utils.AverageMeter()
top5 = utils.AverageMeter()
weights = self.get_weights_from_arch(arch)
self.set_model_weights(weights)
self.model.eval()
if split is None:
n_batches = 10
elif self.args.debug:
n_batches = 1
else:
n_batches = len(self.valid_queue)
for step in range(n_batches):
try:
input, target = next(self.valid_iter)
except Exception as e:
logger.info('looping back over valid set')
self.valid_iter = iter(self.valid_queue)
input, target = next(self.valid_iter)
input = Variable(input, volatile=True).cuda()
target = Variable(target, volatile=True).cuda(async=True)
logits = self.model(input, discrete=True)
loss = self.criterion(logits, target)
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
n = input.size(0)
objs.update(loss.data[0], n)
top1.update(prec1.data[0], n)
top5.update(prec5.data[0], n)
if step % self.args.report_freq == 0:
logger.info('valid %03d %e %f %f', step, objs.avg, top1.avg,
top5.avg)
return 1 - top1.avg, objs.avg
def test(test_loader, model, criterion, joint_num, procrustes=False):
losses = utils.AverageMeter()
model.eval()
all_dist = []
start = time.time()
batch_time = 0
bar = Bar('>>>', fill='>', max=len(test_loader))
for i, data in enumerate(test_loader):
joint2d,truth=data['joint2d'],data['truth']
inputs=Variable(joint2d.cuda().type(torch.cuda.FloatTensor))
targets=Variable(truth.cuda().type(torch.cuda.FloatTensor))
outputs = model(inputs)
outputs=torch.reshape(outputs,(-1,(joint_num)*3))
targets=torch.reshape(targets,(-1,(joint_num)*3))
# calculate loss
loss = criterion(outputs, targets)
losses.update(loss.item(), inputs.size(0))
sqerr = (outputs - targets) ** 2
distance = np.zeros((sqerr.shape[0],joint_num+1))
dist_idx = 0
for k in np.arange(0, (joint_num+1) * 3, 3):
distance[:, dist_idx] = torch.sqrt(torch.sum(sqerr[:, k:k + 3], axis=1)).to('cpu').detach().numpy()
dist_idx += 1
all_dist.append(distance)
# update summary
if (i + 1) % 100 == 0:
batch_time = time.time() - start
start = time.time()
bar.suffix = '({batch}/{size}) | batch: {batchtime:.4}ms | Total: {ttl} | ETA: {eta:} | loss: {loss:.6f}' \
.format(batch=i + 1,
size=len(test_loader),
batchtime=batch_time * 10.0,
ttl=bar.elapsed_td,
eta=bar.eta_td,
loss=losses.avg)
bar.next()
all_dist = np.vstack(all_dist)
# joint_err = np.mean(all_dist, axis=0)
ttl_err = np.mean(all_dist)
bar.finish()
print (">>> error: {} <<<".format(ttl_err))
return targets, losses.avg, ttl_err
def train(train_loader, model, criterion, optimizer, joint_num,
lr_init=None, lr_now=None, glob_step=None, lr_decay=None, gamma=None,
max_norm=True):
losses = utils.AverageMeter()
model.train()
start = time.time()
batch_time = 0
bar = Bar('>>>', fill='>', max=len(train_loader))
for i, data in enumerate(train_loader):
# Turn down Learning Rate
glob_step += 1
if glob_step % lr_decay == 0 or glob_step == 1:
lr_now = utils.lr_decay(optimizer, glob_step, lr_init, lr_decay, gamma)
joint2d, truth = data['joint2d'], data['truth']
inputs=Variable(joint2d.cuda().type(torch.cuda.FloatTensor))
targets=Variable(truth.cuda().type(torch.cuda.FloatTensor))
outputs = model(inputs)
outputs=torch.reshape(outputs,(-1,(joint_num)*3))
targets=torch.reshape(targets,(-1,(joint_num)*3))
# calculate loss
optimizer.zero_grad()
loss = criterion(outputs, targets)
losses.update(loss.item(), inputs.size(0))
loss.backward()
if max_norm:
nn.utils.clip_grad_norm_(model.parameters(), max_norm=1)
optimizer.step()
# update summary
if (i + 1) % 100 == 0:
batch_time = time.time() - start
start = time.time()
bar.suffix = '({batch}/{size}) | batch: {batchtime:.4}ms | Total: {ttl} | ETA: {eta:} | loss: {loss:.4f}' \
.format(batch=i + 1,
size=len(train_loader),
batchtime=batch_time * 10.0,
ttl=bar.elapsed_td,
eta=bar.eta_td,
loss=losses.avg)
bar.next()
bar.finish()
return glob_step, lr_now, losses.avg
def test_network(self,
params,
test_loader,
model,
criterion,
optimiser,
verbose=True):
model.eval()
losses = utils.AverageMeter()
top1 = utils.AverageMeter()
top5 = utils.AverageMeter()
with torch.no_grad():
with tqdm(total=len(test_loader), desc='Inference',
leave=verbose) as t:
for batch_idx, (inputs, targets) in enumerate(test_loader):
device = 'cuda:' + str(params.gpuList[0])
inputs, targets = inputs.cuda(device, non_blocking=True),\
targets.cuda(device, non_blocking=True)
outputs = model(inputs)
loss = criterion(outputs, targets)
prec1, prec5 = utils.accuracy(outputs.data, targets.data)
losses.update(loss.item())
top1.update(prec1.item())
top5.update(prec5.item())
t.set_postfix({
'loss': losses.avg,
'top1': top1.avg,
'top5': top5.avg
})
t.update(1)
return (losses.avg, top1.avg, top5.avg)
def train(train_loader,
model,
criterion,
optimizer,
stat_2d,
stat_3d,
lr_init=None,
lr_now=None,
glob_step=None,
lr_decay=None,
gamma=None,
max_norm=True):
losses = utils.AverageMeter()
model.train()
# for i, (inps, tars) in enumerate(train_loader): # inps = (64, 32)
pbar = tqdm(train_loader)
for i, (inps, tars) in enumerate(pbar): # inps = (64, 32)
glob_step += 1
if glob_step % lr_decay == 0 or glob_step == 1:
lr_now = utils.lr_decay(optimizer, glob_step, lr_init, lr_decay,
gamma)
### Input unnormalization
inputs_unnorm = data_process.unNormalizeData(
inps.data.cpu().numpy(), stat_2d['mean'], stat_2d['std'],
stat_2d['dim_use']) # 64, 64
dim_2d_use = stat_2d['dim_use']
inputs_use = inputs_unnorm[:, dim_2d_use] # (64, 32)
### Input distance normalization
inputs_dist_norm, _ = data_process.input_norm(
inputs_use) # (64, 32) , array
input_dist = torch.tensor(inputs_dist_norm, dtype=torch.float32)
### Targets unnormalization
targets_unnorm = data_process.unNormalizeData(
tars.data.cpu().numpy(), stat_3d['mean'], stat_3d['std'],
stat_3d['dim_use']) # (64, 96)
dim_3d_use = np.array([
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 18, 19, 20, 21, 22, 23, 24,
25, 26, 36, 37, 38, 39, 40, 41, 45, 46, 47, 51, 52, 53, 54, 55, 56,
57, 58, 59, 75, 76, 77, 78, 79, 80, 81, 82, 83
])
targets_use = targets_unnorm[:, dim_3d_use] # (51, )
### Targets distance normalization
targets_dist_norm, _ = data_process.output_norm(targets_use)
targets_dist = torch.tensor(targets_dist_norm, dtype=torch.float32)
inputs = Variable(input_dist.cuda())
targets = Variable(targets_dist.cuda(async=True))
outputs = model(inputs)
# calculate loss
optimizer.zero_grad()
loss = criterion(outputs, targets)
losses.update(loss.item(), inputs.size(0))
loss.backward()
if max_norm:
nn.utils.clip_grad_norm_(model.parameters(), max_norm=1)
optimizer.step()
# tqdm.set_postfix(loss='{:05.6f}'.format(losses.avg))
pbar.set_postfix(tr_loss='{:05.6f}'.format(losses.avg))
return glob_step, lr_now, losses.avg
def job(tuning, params_path, devices, resume, save_interval):
global params
if tuning:
with open(params_path, 'r') as f:
params = json.load(f)
mode_str = 'tuning'
setting = '_'.join(f'{tp}-{params[tp]}'
for tp in params['tuning_params'])
else:
mode_str = 'train'
setting = ''
# パラメーターを変えるときにseedも変えたい(seed averagingの効果を期待)
seed = sum(ord(_) for _ in str(params.values()))
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.benchmark = False
exp_path = ROOT + f'experiments/{params["ex_name"]}/'
os.environ['CUDA_VISIBLE_DEVICES'] = devices
logger, writer = utils.get_logger(
log_dir=exp_path + f'{mode_str}/log/{setting}',
tensorboard_dir=exp_path + f'{mode_str}/tf_board/{setting}')
if params['augmentation'] == 'soft':
params['scale_limit'] = 0.2
params['brightness_limit'] = 0.1
elif params['augmentation'] == 'middle':
params['scale_limit'] = 0.3
params['shear_limit'] = 4
params['brightness_limit'] = 0.1
params['contrast_limit'] = 0.1
else:
raise ValueError
train_transform, eval_transform = data_utils.build_transforms(
scale_limit=params['scale_limit'],
shear_limit=params['shear_limit'],
brightness_limit=params['brightness_limit'],
contrast_limit=params['contrast_limit'],
)
data_loaders = data_utils.make_train_loaders(
params=params,
data_root=ROOT + 'input/' + params['data'],
train_transform=train_transform,
eval_transform=eval_transform,
scale='S',
test_size=0,
class_topk=params['class_topk'],
num_workers=8)
model = models.LandmarkNet(
n_classes=params['class_topk'],
model_name=params['model_name'],
pooling=params['pooling'],
loss_module=params['loss'],
s=params['s'],
margin=params['margin'],
theta_zero=params['theta_zero'],
use_fc=params['use_fc'],
fc_dim=params['fc_dim'],
).cuda()
optimizer = utils.get_optim(params, model)
criterion = nn.CrossEntropyLoss()
scheduler = optim.lr_scheduler.CosineAnnealingLR(
optimizer,
T_max=params['epochs'] * len(data_loaders['train']),
eta_min=3e-6)
start_epoch = 0
if len(devices.split(',')) > 1:
model = nn.DataParallel(model)
for epoch in range(start_epoch, params['epochs']):
logger.info(
f'Epoch {epoch}/{params["epochs"]} | lr: {optimizer.param_groups[0]["lr"]}'
)
# ============================== train ============================== #
model.train(True)
losses = utils.AverageMeter()
prec1 = utils.AverageMeter()
for i, (_, x, y) in tqdm(enumerate(data_loaders['train']),
total=len(data_loaders['train']),
miniters=None,
ncols=55):
x = x.to('cuda')
y = y.to('cuda')
outputs = model(x, y)
loss = criterion(outputs, y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
scheduler.step()
acc = metrics.accuracy(outputs, y)
losses.update(loss.item(), x.size(0))
prec1.update(acc, x.size(0))
if i % 100 == 99:
logger.info(
f'{epoch+i/len(data_loaders["train"]):.2f}epoch | {setting} acc: {prec1.avg}'
)
train_loss = losses.avg
train_acc = prec1.avg
writer.add_scalars('Loss', {'train': train_loss}, epoch)
writer.add_scalars('Acc', {'train': train_acc}, epoch)
writer.add_scalar('LR', optimizer.param_groups[0]['lr'], epoch)
if (epoch + 1) == params['epochs'] or (epoch + 1) % save_interval == 0:
output_file_name = exp_path + f'ep{epoch}_' + setting + '.pth'
utils.save_checkpoint(path=output_file_name,
model=model,
epoch=epoch,
optimizer=optimizer,
params=params)
model = model.module
datasets = ('roxford5k', 'rparis6k')
results = eval_datasets(model,
datasets=datasets,
ms=False,
tta_gem_p=1.0,
logger=logger)
if tuning:
tuning_result = {}
for d in datasets:
for key in ['mapE', 'mapM', 'mapH']:
mapE, mapM, mapH, mpE, mpM, mpH, kappas = results[d]
tuning_result[d + '-' + key] = [eval(key)]
utils.write_tuning_result(params, tuning_result,
exp_path + 'tuning/results.csv')
def evaluate(model, validation_generator, criterion, epoch, writer, log_file, print_every=25):
model.eval()
losses = utils.AverageMeter()
accuracies = utils.AverageMeter()
num_iter_per_epoch = len(validation_generator)
y_true = []
y_pred = []
for iter, batch in tqdm(enumerate(validation_generator), total=num_iter_per_epoch):
features, labels = batch
if torch.cuda.is_available():
features = features.cuda()
labels = labels.cuda()
with torch.no_grad():
predictions = model(features)
loss = criterion(predictions, labels)
y_true += labels.cpu().numpy().tolist()
y_pred += torch.max(predictions, 1)[1].cpu().numpy().tolist()
validation_metrics = utils.get_evaluation(labels.cpu().numpy(),
predictions.cpu().detach().numpy(),
list_metrics=["accuracy", "f1"])
accuracy = validation_metrics['accuracy']
f1 = validation_metrics['f1']
losses.update(loss.data, features.size(0))
accuracies.update(validation_metrics["accuracy"], features.size(0))
writer.add_scalar('Test/Loss',
loss.item(),
epoch * num_iter_per_epoch + iter)
writer.add_scalar('Test/Accuracy',
accuracy,
epoch * num_iter_per_epoch + iter)
writer.add_scalar('Test/f1',
f1,
epoch * num_iter_per_epoch + iter)
if (iter % print_every == 0) and (iter > 0):
print("[Validation - Epoch: {}] , Iteration: {}/{} , Loss: {}, Accuracy: {}".format(
epoch + 1,
iter,
num_iter_per_epoch,
losses.avg,
accuracies.avg
))
f1_test = f1_score(y_true, y_pred, average='weighted')
writer.add_scalar('Test/loss/epoch', losses.avg, epoch + iter)
writer.add_scalar('Test/acc/epoch', accuracies.avg, epoch + iter)
writer.add_scalar('Test/f1/epoch', f1_test, epoch + iter)
report = classification_report(y_true, y_pred)
print(report)
with open(log_file, 'a') as f:
f.write(f'Validation on Epoch {epoch} \n')
f.write(f'Average loss: {losses.avg.item()} \n')
f.write(f'Average accuracy: {accuracies.avg.item()} \n')
f.write(f'F1 score {f1_test} \n\n')
f.write(report)
f.write('=' * 50)
f.write('\n')
return losses.avg.item(), accuracies.avg.item(), f1_test
for _ in range(start_epoch*len(data_loaders['train'])):
scheduler.step()
if len(devices.split(',')) > 1:
model = nn.DataParallel(model)
for epoch in range(start_epoch, end_epoch):
#while epoch <= end_epoch:
logger.info(f'Epoch {epoch}/{end_epoch}')
# ============================== train ============================== #
model.train(True)
losses = utils.AverageMeter()
prec1 = utils.AverageMeter()
for i, (_, x, y) in tqdm(enumerate(data_loaders['train']),
total=len(data_loaders['train']),
miniters=None, ncols=55):
if num_GPU>0:
x = x.to('cuda')
y = y.to('cuda')
outputs = model(x, y)
loss = criterion(outputs, y)
optimizer.zero_grad()
loss.backward()
def train(model, training_generator, optimizer, criterion, epoch, writer, log_file, scheduler, class_names, args, print_every=25):
model.train()
losses = utils.AverageMeter()
accuracies = utils.AverageMeter()
num_iter_per_epoch = len(training_generator)
progress_bar = tqdm(enumerate(training_generator),
total=num_iter_per_epoch)
y_true = []
y_pred = []
for iter, batch in progress_bar:
features, labels = batch
if torch.cuda.is_available():
features = features.cuda()
labels = labels.cuda()
optimizer.zero_grad()
predictions = model(features)
y_true += labels.cpu().numpy().tolist()
y_pred += torch.max(predictions, 1)[1].cpu().numpy().tolist()
loss = criterion(predictions, labels)
loss.backward()
if args.scheduler == 'clr':
scheduler.step()
optimizer.step()
training_metrics = utils.get_evaluation(labels.cpu().numpy(),
predictions.cpu().detach().numpy(),
list_metrics=["accuracy", "f1"])
losses.update(loss.data, features.size(0))
accuracies.update(training_metrics["accuracy"], features.size(0))
f1 = training_metrics['f1']
writer.add_scalar('Train/Loss',
loss.item(),
epoch * num_iter_per_epoch + iter)
writer.add_scalar('Train/Accuracy',
training_metrics['accuracy'],
epoch * num_iter_per_epoch + iter)
writer.add_scalar('Train/f1',
f1,
epoch * num_iter_per_epoch + iter)
lr = optimizer.state_dict()["param_groups"][0]["lr"]
if (iter % print_every == 0) and (iter > 0):
print("[Training - Epoch: {}], LR: {} , Iteration: {}/{} , Loss: {}, Accuracy: {}".format(
epoch + 1,
lr,
iter,
num_iter_per_epoch,
losses.avg,
accuracies.avg
))
if bool(args.log_f1):
intermediate_report = classification_report(
y_true, y_pred, output_dict=True)
f1_by_class = 'F1 Scores by class: '
for class_name in class_names:
f1_by_class += f"{class_name} : {np.round(intermediate_report[class_name]['f1-score'], 4)} |"
print(f1_by_class)
f1_train = f1_score(y_true, y_pred, average='weighted')
writer.add_scalar('Train/loss/epoch', losses.avg, epoch + iter)
writer.add_scalar('Train/acc/epoch', accuracies.avg, epoch + iter)
writer.add_scalar('Train/f1/epoch', f1_train, epoch + iter)
report = classification_report(y_true, y_pred)
print(report)
with open(log_file, 'a') as f:
f.write(f'Training on Epoch {epoch} \n')
f.write(f'Average loss: {losses.avg.item()} \n')
f.write(f'Average accuracy: {accuracies.avg.item()} \n')
f.write(f'F1 score: {f1_train} \n\n')
f.write(report)
f.write('*' * 25)
f.write('\n')
return losses.avg.item(), accuracies.avg.item(), f1_train
def train(train_loader,
model,
criterion,
optimizer,
num_kpts=15,
num_classes=200,
lr_init=None,
lr_now=None,
glob_step=None,
lr_decay=None,
gamma=None,
max_norm=True):
losses = utils.AverageMeter()
model.train()
errs, accs = [], []
start = time.time()
batch_time = 0
bar = Bar('>>>', fill='>', max=len(train_loader))
for i, sample in enumerate(train_loader):
glob_step += 1
if glob_step % lr_decay == 0 or glob_step == 1:
lr_now = utils.lr_decay(optimizer, glob_step, lr_init, lr_decay,
gamma)
inputs = sample['X'].cuda()
# NOTE: PyTorch issue with dim0=1.
if inputs.shape[0] == 1:
continue
targets = sample['Y'].reshape(-1).cuda()
outputs = model(inputs)
# calculate loss
optimizer.zero_grad()
loss = criterion(outputs, targets)
losses.update(loss.item(), inputs.size(0))
loss.backward()
if max_norm:
nn.utils.clip_grad_norm(model.parameters(), max_norm=1)
optimizer.step()
# Set outputs to [0, 1].
softmax = nn.Softmax()
outputs = softmax(outputs)
outputs = outputs.data.cpu().numpy()
targets = one_hot(targets.data.cpu().numpy(), num_classes)
errs.append(np.mean(np.abs(outputs - targets)))
accs.append(
metrics.accuracy_score(np.argmax(targets, axis=1),
np.argmax(outputs, axis=1)))
# update summary
if (i + 1) % 100 == 0:
batch_time = time.time() - start
start = time.time()
bar.suffix = '({batch}/{size}) | batch: {batchtime:.4}ms | Total: {ttl} | ETA: {eta:} | loss: {loss:.6f}' \
.format(batch=i + 1,
size=len(train_loader),
batchtime=batch_time * 10.0,
ttl=bar.elapsed_td,
eta=bar.eta_td,
loss=losses.avg)
bar.next()
bar.finish()
err = np.mean(np.array(errs, dtype=np.float32))
acc = np.mean(np.array(accs, dtype=np.float32))
print(">>> train error: {} <<<".format(err))
print(">>> train accuracy: {} <<<".format(acc))
return glob_step, lr_now, losses.avg, err, acc
def test(test_loader,
model,
criterion,
num_kpts=15,
num_classes=2,
batch_size=64,
inference=False,
log=True):
losses = utils.AverageMeter()
model.eval()
errs, accs = [], []
all_outputs, all_targets = [], []
start = time.time()
batch_time = 0
if log:
bar = Bar('>>>', fill='>', max=len(test_loader))
for i, sample in enumerate(test_loader):
inputs = sample['X'].cuda()
# NOTE: PyTorch issue with dim0=1.
if inputs.shape[0] == 1:
continue
targets = sample['Y'].reshape(-1).cuda()
outputs = model(inputs)
# calculate loss
loss = criterion(outputs, targets)
losses.update(loss.item(), inputs.size(0))
# Set outputs to [0, 1].
softmax = nn.Softmax()
outputs = softmax(outputs)
outputs = outputs.data.cpu().numpy()
targets = targets.data.cpu().numpy()
all_outputs.append(outputs)
all_targets.append(targets)
# errs.append(np.mean(np.abs(outputs - targets)))
# accs.append(accuracy_score(
# np.argmax(targets, axis=1),
# np.argmax(outputs, axis=1))
# )
# update summary
if (i + 1) % 100 == 0:
batch_time = time.time() - start
start = time.time()
if log:
bar.suffix = '({batch}/{size}) | batch: {batchtime:.4}ms | Total: {ttl} | ETA: {eta:} | loss: {loss:.6f}' \
.format(batch=i + 1,
size=len(test_loader),
batchtime=batch_time * 10.0,
ttl=bar.elapsed_td,
eta=bar.eta_td,
loss=losses.avg)
bar.next()
# err = np.mean(np.array(errs))
# acc = np.mean(np.array(accs))
all_outputs = np.concatenate(all_outputs)
all_targets = np.concatenate(all_targets)
pred_values = np.amax(all_outputs, axis=1)
pred_labels = np.argmax(all_outputs, axis=1)
err = np.mean(np.abs(pred_values - all_targets))
acc = np.mean(metrics.accuracy_score(all_targets, pred_labels))
auc = calc_auc(all_targets, pred_values)
prec = metrics.average_precision_score(all_targets, pred_values)
if log:
bar.finish()
print('>>> test error: {} <<<'.format(err))
print('>>> test accuracy: {} <<<'.format(acc))
return losses.avg, err, acc, auc, prec
def job(tuning, params_path, devices, resume, save_interval):
global params
if tuning:
with open(params_path, 'r') as f:
params = json.load(f)
mode_str = 'tuning'
setting = '_'.join(f'{tp}-{params[tp]}'
for tp in params['tuning_params'])
else:
mode_str = 'train'
setting = ''
exp_path = ROOT + f'experiments/{params["ex_name"]}/'
os.environ['CUDA_VISIBLE_DEVICES'] = devices
if resume is None:
# C-AIRとABCIで整合性が取れるようにしている。
params[
'base_ckpt_path'] = f'experiments/v1only/ep4_augmentation-soft_epochs-5_loss-{params["loss"]}.pth'
params[
'clean_path'] = ROOT + f'input/clean/train19_cleaned_verifythresh{params["verifythresh"]}_freqthresh{params["freqthresh"]}.csv'
else:
params = utils.load_checkpoint(path=resume, params=True)['params']
logger, writer = utils.get_logger(
log_dir=exp_path + f'{mode_str}/log/{setting}',
tensorboard_dir=exp_path + f'{mode_str}/tf_board/{setting}')
if params['augmentation'] == 'soft':
params['scale_limit'] = 0.2
params['brightness_limit'] = 0.1
elif params['augmentation'] == 'middle':
params['scale_limit'] = 0.3
params['shear_limit'] = 4
params['brightness_limit'] = 0.1
params['contrast_limit'] = 0.1
else:
raise ValueError
train_transform, eval_transform = data_utils.build_transforms(
scale_limit=params['scale_limit'],
shear_limit=params['shear_limit'],
brightness_limit=params['brightness_limit'],
contrast_limit=params['contrast_limit'],
)
data_loaders = data_utils.make_train_loaders(
params=params,
data_root=ROOT + 'input/' + params['data'],
train_transform=train_transform,
eval_transform=eval_transform,
scale='SS2',
test_size=0,
class_topk=params['class_topk'],
num_workers=8)
model = models.LandmarkNet(
n_classes=params['class_topk'],
model_name=params['model_name'],
pooling=params['pooling'],
loss_module=params['loss'],
s=params['s'],
margin=params['margin'],
theta_zero=params['theta_zero'],
use_fc=params['use_fc'],
fc_dim=params['fc_dim'],
).cuda()
criterion = nn.CrossEntropyLoss()
optimizer = utils.get_optim(params, model)
if resume is None:
sdict = torch.load(ROOT + params['base_ckpt_path'])['state_dict']
if params['loss'] == 'adacos':
del sdict['final.W'] # remove fully-connected layer
elif params['loss'] == 'softmax':
del sdict['final.weight'], sdict[
'final.bias'] # remove fully-connected layer
else:
del sdict['final.weight'] # remove fully-connected layer
model.load_state_dict(sdict, strict=False)
scheduler = optim.lr_scheduler.CosineAnnealingLR(
optimizer,
T_max=params['epochs'] * len(data_loaders['train']),
eta_min=3e-6)
start_epoch, end_epoch = (0,
params['epochs'] - params['scaleup_epochs'])
else:
ckpt = utils.load_checkpoint(path=resume,
model=model,
optimizer=optimizer,
epoch=True)
model, optimizer, start_epoch = ckpt['model'], ckpt[
'optimizer'], ckpt['epoch'] + 1
end_epoch = params['epochs']
scheduler = optim.lr_scheduler.CosineAnnealingLR(
optimizer,
T_max=params['epochs'] * len(data_loaders['train']),
eta_min=3e-6,
last_epoch=start_epoch * len(data_loaders['train']))
setting += 'scaleup_' + resume.split('/')[-1].replace('.pth', '')
data_loaders = data_utils.make_verified_train_loaders(
params=params,
data_root=ROOT + 'input/' + params['data'],
train_transform=train_transform,
eval_transform=eval_transform,
scale='M2',
test_size=0,
num_workers=8)
batch_norm.freeze_bn(model)
if len(devices.split(',')) > 1:
model = nn.DataParallel(model)
for epoch in range(start_epoch, end_epoch):
logger.info(f'Epoch {epoch}/{end_epoch}')
# ============================== train ============================== #
model.train(True)
losses = utils.AverageMeter()
prec1 = utils.AverageMeter()
for i, (_, x, y) in tqdm(enumerate(data_loaders['train']),
total=len(data_loaders['train']),
miniters=None,
ncols=55):
x = x.to('cuda')
y = y.to('cuda')
outputs = model(x, y)
loss = criterion(outputs, y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
scheduler.step()
acc = metrics.accuracy(outputs, y)
losses.update(loss.item(), x.size(0))
prec1.update(acc, x.size(0))
if i % 100 == 99:
logger.info(
f'{epoch+i/len(data_loaders["train"]):.2f}epoch | {setting} acc: {prec1.avg}'
)
train_loss = losses.avg
train_acc = prec1.avg
writer.add_scalars('Loss', {'train': train_loss}, epoch)
writer.add_scalars('Acc', {'train': train_acc}, epoch)
writer.add_scalar('LR', optimizer.param_groups[0]['lr'], epoch)
if (epoch + 1) == end_epoch or (epoch + 1) % save_interval == 0:
output_file_name = exp_path + f'ep{epoch}_' + setting + '.pth'
utils.save_checkpoint(path=output_file_name,
model=model,
epoch=epoch,
optimizer=optimizer,
params=params)
model = model.module
datasets = ('oxford5k', 'paris6k', 'roxford5k', 'rparis6k')
results = eval_datasets(model,
datasets=datasets,
ms=True,
tta_gem_p=1.0,
logger=logger)
if tuning:
tuning_result = {}
for d in datasets:
if d in ('oxford5k', 'paris6k'):
tuning_result[d] = results[d]
else:
for key in ['mapE', 'mapM', 'mapH']:
mapE, mapM, mapH, mpE, mpM, mpH, kappas = results[d]
tuning_result[d + '-' + key] = [eval(key)]
utils.write_tuning_result(params, tuning_result,
exp_path + 'tuning/results.csv')
def perform_search(self, app):
#{{{
# def check_stopping(mode, state, prevPp, currPp):
def check_stopping():
#{{{
if prevPp == currPp:
return True
# if mode == 'memory_opt':
# if prevPp == currPp:
# return True
# elif mode == 'cost_opt':
# if state == 1:
# return True
return False
#}}}
# get unpruned test accuracy
loss, top1, top5 = app.run_inference()
targetAcc = top1
print('Epoch,\tLR,\tTrain_Loss,\tTrain_Top1,\tTrain_Top5,\tTest_Loss,\tTest_Top1,\tTest_Top5,\tVal_Loss,\tVal_Top1,\tVal_Top5')
# perform finetuning once
for epoch in tqdm(range(app.params.start_epoch, app.params.pruneAfter), desc='finetuning', leave=False) :
#{{{
app.params.curr_epoch = epoch
state = self.update_lr(app.params, app.optimiser)
losses = utils.AverageMeter()
top1 = utils.AverageMeter()
top5 = utils.AverageMeter()
self.batch_iter(app.model, app.criterion, app.optimiser, app.train_loader, app.params, losses, top1, top5)
app.params.train_loss = losses.avg
app.params.train_top1 = top1.avg
app.params.train_top5 = top5.avg
# get test loss
app.params.test_loss, app.params.test_top1, app.params.test_top5 = app.inferer.test_network(app.params, app.test_loader, app.model, app.criterion, app.optimiser, verbose=False)
app.params.val_loss, app.params.val_top1, app.params.val_top5 = app.inferer.test_network(app.params, app.valLoader, app.model, app.criterion, app.optimiser, verbose=False)
app.checkpointer.save_checkpoint(app.model.state_dict(), app.optimiser.state_dict(), app.params)
tqdm.write("{},\t{:10.5f},\t{:10.5f},\t{:10.5f},\t{:10.5f},\t{:10.5f},\t{:10.5f},\t{:10.5f},\t{:10.5f},\t{:10.5f},\t{:10.5f}".format(epoch, app.params.lr, app.params.train_loss, app.params.train_top1, app.params.train_top5, app.params.test_loss, app.params.test_top1, app.params.test_top5, app.params.val_loss, app.params.val_top1, app.params.val_top5))
#}}}
# store model to revert to
finetunedModel = app.model
app.checkpointer.save_model_only(app.model.state_dict(), app.params.printOnly, 'pre_pruning')
app.params.curr_epoch += 1
# initialise search
app.params.start_epoch = app.params.pruneAfter
initPp = 50
prevPp = 0
currPp = initPp
uB = 95
lB = 5
bestPp = 0
state = 0
bestTestAcc = targetAcc
# while not check_stopping(mode, state, prevPp, currPp):
while not check_stopping():
# prune model
app.params.pruningPerc = currPp
app.model = finetunedModel
app.setup_pruners()
tqdm.write('Pruning Network')
channelsPruned, prunedModel, app.optimiser = app.pruner.prune_model(finetunedModel)
totalPrunedPerc, _, _ = app.pruner.prune_rate(prunedModel)
tqdm.write('Pruned Percentage = {:.2f}%'.format(totalPrunedPerc))
summary = app.pruner.log_pruned_channels(app.checkpointer.root, app.params, totalPrunedPerc, channelsPruned)
# perform retraining
testAccs = [1]
validAccs = [1]
for epoch in tqdm(range(app.params.curr_epoch, app.params.curr_epoch + app.params.finetuneBudget), desc='training', leave=False) :
#{{{
app.params.curr_epoch = epoch
state = self.update_lr(app.params, app.optimiser)
losses = utils.AverageMeter()
top1 = utils.AverageMeter()
top5 = utils.AverageMeter()
self.batch_iter(prunedModel, app.criterion, app.optimiser, app.train_loader, app.params, losses, top1, top5)
app.params.train_loss = losses.avg
app.params.train_top1 = top1.avg
app.params.train_top5 = top5.avg
# get test loss
app.params.test_loss, app.params.test_top1, app.params.test_top5 = app.inferer.test_network(app.params, app.test_loader, prunedModel, app.criterion, app.optimiser, verbose=False)
app.params.val_loss, app.params.val_top1, app.params.val_top5 = app.inferer.test_network(app.params, app.valLoader, prunedModel, app.criterion, app.optimiser, verbose=False)
testAccs.append(app.params.test_top1)
validAccs.append(app.params.val_top1)
app.checkpointer.save_checkpoint(prunedModel.state_dict(), app.optimiser.state_dict(), app.params)
tqdm.write("{},\t{:10.5f},\t{:10.5f},\t{:10.5f},\t{:10.5f},\t{:10.5f},\t{:10.5f},\t{:10.5f},\t{:10.5f},\t{:10.5f},\t{:10.5f}".format(epoch, app.params.lr, app.params.train_loss, app.params.train_top1, app.params.train_top5, app.params.test_loss, app.params.test_top1, app.params.test_top5, app.params.val_loss, app.params.val_top1, app.params.val_top5))
#}}}
highestTestAcc = testAccs[validAccs.index(max(validAccs))]
if int(highestTestAcc) < int(targetAcc):
state = -1
else:
state = 1
# prune less
if state == -1:
tmp = (lB + currPp) / 2.
uB = currPp
# try to prune more, but return previous model if state goes to -1
elif state == 1:
tmp = (uB + currPp) / 2.
lB = currPp
bestPp = currPp
bestTestAcc = highestTestAcc
prevPp = currPp
currPp = 5 * math.ceil(tmp/5)
return bestPp, bestTestAcc
def test(test_loader, model, criterion, stat_2d, stat_3d, procrustes=False):
losses = utils.AverageMeter()
model.eval()
all_dist = []
pbar = tqdm(test_loader)
for i, (inps, tars) in enumerate(pbar):
### input unnorm
data_coord = data_process.unNormalizeData(inps.data.cpu().numpy(),
stat_2d['mean'],
stat_2d['std'],
stat_2d['dim_use']) # 64, 64
dim_2d_use = stat_2d['dim_use']
data_use = data_coord[:, dim_2d_use] # (64, 32)
### input dist norm
data_dist_norm, data_dist_set = data_process.input_norm(
data_use) # (64, 32) , array
data_dist = torch.tensor(data_dist_norm, dtype=torch.float32)
# target unnorm
label_coord = data_process.unNormalizeData(
tars.data.cpu().numpy(), stat_3d['mean'], stat_3d['std'],
stat_3d['dim_use']) # (64, 96)
dim_3d_use = np.array([
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 18, 19, 20, 21, 22, 23, 24,
25, 26, 36, 37, 38, 39, 40, 41, 45, 46, 47, 51, 52, 53, 54, 55, 56,
57, 58, 59, 75, 76, 77, 78, 79, 80, 81, 82, 83
])
label_use = label_coord[:, dim_3d_use] # (48, )
# target dist norm
label_dist_norm, label_dist_set = data_process.output_norm(label_use)
label_dist = torch.tensor(label_dist_norm, dtype=torch.float32)
inputs = Variable(data_dist.cuda())
targets = Variable(label_dist.cuda(async=True))
outputs = model(inputs)
# calculate loss
pred_coord = outputs
loss = criterion(pred_coord, targets) # 64 losses average
losses.update(loss.item(), inputs.size(0))
tars = targets
pred = outputs
# inputs_dist_set = np.reshape(targets_dist_set, (-1, 1))
# inputs_dist_set = np.repeat(targets_dist_set, 48, axis=1)
targets_dist = np.reshape(label_dist_set, (-1, 1))
targets_dist_set = np.repeat(targets_dist, 48, axis=1)
c = np.reshape(np.asarray([0, 0, 10]), (1, -1))
c = np.repeat(c, 16, axis=0)
c = np.reshape(c, (1, -1))
c = np.repeat(c, inputs.size(0), axis=0)
# c_set = np.repeat(np.asarray([0,0,10]), 16, axis=0)
#### undist -> unnorm
outputs_undist = (pred.data.cpu().numpy() * targets_dist_set) - c
# outputs_undist = outputs_undist - c
targets_undist = (tars.data.cpu().numpy() * targets_dist_set) - c
# targets_undist = targets_undist - c
outputs_use = outputs_undist
targets_use = targets_undist # (64, 48)
if procrustes:
for ba in range(inps.size(0)):
gt = targets_use[ba].reshape(-1, 3)
out = outputs_use[ba].reshape(-1, 3) # (17,3)
_, Z, T, b, c = get_transformation(gt, out, True)
out = (b * out.dot(T)) + c
outputs_use[ba, :] = out.reshape(1, 48)
sqerr = (outputs_use - targets_use)**2
# distance = np.zeros((sqerr.shape[0], 17))
distance = np.zeros((sqerr.shape[0], 16))
dist_idx = 0
for k in np.arange(0, 16 * 3, 3):
# for k in np.arange(0, 17 * 3, 3):
distance[:, dist_idx] = np.sqrt(np.sum(sqerr[:, k:k + 3], axis=1))
dist_idx += 1
all_dist.append(distance)
pbar.set_postfix(tt_loss='{:05.6f}'.format(losses.avg))
all_dist = np.vstack(all_dist)
joint_err = np.mean(all_dist, axis=0)
ttl_err = np.mean(all_dist)
# bar.finish()
print(">>> error: {} <<<".format(ttl_err))
return losses.avg, ttl_err
def test(test_loader,
model,
criterion,
stat_3d,
device,
procrustes=False,
pck_thresholds=[50, 100, 150, 200, 250],
noise_fun=lambda x: x,
refine_dic=None,
refine_coeff_fun=None,
refine_extra_kwargs={},
cache_prefix=None,
visualize=False):
model.eval()
all_outputs = []
all_targets = []
losses = utils.AverageMeter()
for i, (inps, tars) in enumerate(test_loader):
inps_noise = noise_fun(inps)
inputs = Variable(inps_noise.to(device))
targets = Variable(tars.to(device))
outputs = model(inputs)
# calculate loss
outputs_coord = outputs
loss = criterion(outputs_coord, targets)
losses.update(loss.item(), inputs.size(0))
tars = targets
# calculate erruracy
targets_unnorm = data_process.unNormalizeData(tars.data.cpu().numpy(),
stat_3d['mean'],
stat_3d['std'],
stat_3d['dim_use'])
outputs_unnorm = data_process.unNormalizeData(
outputs.data.cpu().numpy(), stat_3d['mean'], stat_3d['std'],
stat_3d['dim_use'])
# remove dim ignored
dim_use = np.hstack((np.arange(3), stat_3d['dim_use']))
outputs_use = outputs_unnorm[:, dim_use]
targets_use = targets_unnorm[:, dim_use]
all_outputs.append(outputs_use)
all_targets.append(targets_use)
accu_frames = np.cumsum(test_loader.dataset.frames)
all_outputs = np.split(np.concatenate(all_outputs, axis=0),
accu_frames)[:-1]
all_targets = np.split(np.concatenate(all_targets, axis=0),
accu_frames)[:-1]
start = time.time()
seq_time = 0
bar = Bar('>>>', fill='>', max=len(all_outputs))
all_dist, all_pck = [], []
for i, (outputs_use,
targets_use) in enumerate(zip(all_outputs, all_targets)):
if refine_dic is not None:
origin = outputs_use
outputs_use, _ = ru.refine(outputs_use, refine_dic,
refine_coeff_fun, **refine_extra_kwargs)
if visualize:
visual = [
ru.convert_to_pose_16(seq.reshape([-1, 17, 3]))
for seq in [outputs_use, origin, targets_use]
]
ru.plot_pose_seq(visual, plot_axis=True, r=1000)
if procrustes:
for frame in range(outputs_use.shape[0]):
gt = targets_use[frame].reshape(-1, 3)
out = outputs_use[frame].reshape(-1, 3)
_, Z, T, b, c = get_transformation(gt,
out,
True,
reflection=False)
out = (b * out.dot(T)) + c
outputs_use[frame, :] = out.reshape(1, 51)
for pred, gt in zip(outputs_use, targets_use):
pred, gt = pred.reshape([-1, 3]), gt.reshape([-1, 3])
all_dist.append(mpjpe_fun(pred, gt))
all_pck.append(pck_fun(pred, gt, thresholds=pck_thresholds))
# update summary
seq_time = time.time() - start
start = time.time()
bar.suffix = '({seq}/{size}) | seq: {seqtime:.4}s | Total: {ttl} | ETA: {eta:} | mpjpe: {loss:.6f}' \
.format(seq=i + 1,
size=len(all_outputs),
seqtime=seq_time,
ttl=bar.elapsed_td,
eta=bar.eta_td,
loss=np.mean(all_dist))
bar.next()
all_dist = np.vstack(all_dist)
all_pck = np.array(all_pck)
mpjpe = np.mean(all_dist)
if cache_prefix:
with open('cache/{}_.pkl'.format(cache_prefix), 'wb') as f:
pickle.dump({'mpjpe': all_dist, 'pck': all_pck}, f)
pck = np.mean(all_pck, axis=0)
bar.finish()
print(">>> error: {:4f}, pck: {} <<<".format(
mpjpe, ' '.join(['{:4f}'.format(val) for val in pck])))
return losses.avg, mpjpe, pck
def test(test_loader, model, criterion, stat_3d, procrustes=False):
losses = utils.AverageMeter()
model.eval()
all_dist = []
start = time.time()
batch_time = 0
bar = Bar(">>>", fill=">", max=len(test_loader))
for i, (inps, tars) in enumerate(test_loader):
inputs = Variable(inps.cuda())
targets = Variable(tars.cuda())
outputs = model(inputs)
# calculate loss
outputs_coord = outputs
loss = criterion(outputs_coord, targets)
losses.update(loss.item(), inputs.size(0))
tars = targets
# calculate erruracy
targets_unnorm = data_process.unNormalizeData(tars.data.cpu().numpy(),
stat_3d["mean"],
stat_3d["std"],
stat_3d["dim_use"])
outputs_unnorm = data_process.unNormalizeData(
outputs.data.cpu().numpy(),
stat_3d["mean"],
stat_3d["std"],
stat_3d["dim_use"],
)
# remove dim ignored
dim_use = np.hstack((np.arange(3), stat_3d["dim_use"]))
outputs_use = outputs_unnorm[:, dim_use]
targets_use = targets_unnorm[:, dim_use]
if procrustes:
for ba in range(inps.size(0)):
gt = targets_use[ba].reshape(-1, 3)
out = outputs_use[ba].reshape(-1, 3)
_, Z, T, b, c = get_transformation(gt, out, True)
out = (b * out.dot(T)) + c
outputs_use[ba, :] = out.reshape(1, 51)
sqerr = (outputs_use - targets_use)**2
distance = np.zeros((sqerr.shape[0], 17))
dist_idx = 0
for k in np.arange(0, 17 * 3, 3):
distance[:, dist_idx] = np.sqrt(np.sum(sqerr[:, k:k + 3], axis=1))
dist_idx += 1
all_dist.append(distance)
# update summary
if (i + 1) % 100 == 0:
batch_time = time.time() - start
start = time.time()
bar.suffix = "({batch}/{size}) | batch: {batchtime:.4}ms | Total: {ttl} | ETA: {eta:} | loss: {loss:.6f}".format(
batch=i + 1,
size=len(test_loader),
batchtime=batch_time * 10.0,
ttl=bar.elapsed_td,
eta=bar.eta_td,
loss=losses.avg,
)
bar.next()
all_dist = np.vstack(all_dist)
joint_err = np.mean(all_dist, axis=0)
ttl_err = np.mean(all_dist)
bar.finish()
print(">>> error: {} <<<".format(ttl_err))
return losses.avg, ttl_err
