def commit_insert(self):
if self.keys is None:
self.keys = Parameter(self.keys_to_be_inserted)
self.values = Parameter(self.values_to_be_inserted)
elif self.keys_to_be_inserted is not None:
keys = torch.cat([self.keys.detach(),self.keys_to_be_inserted],0)
self.keys = Parameter(keys)
values = [self.values.detach(),self.values_to_be_inserted]
values = torch.cat(values,0)
self.values = Parameter(values)
# Move most recently used key-value pairs to the back
if len(self.move_to_back) != 0:
unmoved_ids = list(set(range(len(self.keys))) - self.move_to_back)
moved_ids = list(self.move_to_back)
unmoved_keys = self.keys.detach()[unmoved_ids]
moved_keys = self.keys.detach()[moved_ids]
self.keys = Parameter(torch.cat([unmoved_keys, moved_keys], 0))
unmoved_values = self.values.detach()[unmoved_ids]
moved_values = self.values.detach()[moved_ids]
self.values = Parameter(torch.cat([unmoved_values,moved_values], 0))
self.move_to_back = set()
if len(self.keys) > self.max_memory:
# Expel oldest key to maintain total memory
for key in self.keys[:-self.max_memory]:
del self.key_cache[tuple(key.detach().cpu().numpy())]
self.keys = Parameter(self.keys[-self.max_memory:].detach())
self.values = Parameter(self.values[-self.max_memory:].detach())
self.keys_to_be_inserted = None
self.values_to_be_inserted = None
params = [self.keys, self.values]
self.optimizer = get_optimizer(self.opt_name,params,self.lr)
self.kdtree.build_index(self.keys.detach().cpu().numpy())
self.stale_index = False
def update(self, value, index):
"""
Set self.values[index] = value
"""
values = self.values.detach()
values[index] = value[0].detach()
self.values = Parameter(values)
params = [self.keys, self.values]
self.optimizer = get_optimizer(self.opt_name,params,self.lr)
def run(cfg):
'''Load save path'''
cfg.log_string('Data save path: %s' % (cfg.save_path))
checkpoint = CheckpointIO(cfg)
'''Load device'''
cfg.log_string('Loading device settings.')
device = load_device(cfg)
'''Load data'''
cfg.log_string('Loading dataset.')
train_loader = get_dataloader(cfg.config, mode='train')
test_loader = get_dataloader(cfg.config, mode='test')
'''Load net'''
cfg.log_string('Loading model.')
net = get_model(cfg.config, device=device)
if isinstance(net, list):
checkpoint.register_modules(voxnet=net[0])
checkpoint.register_modules(refnet=net[1])
else:
checkpoint.register_modules(voxnet=net)
cfg.log_string('loading loss function')
loss_func = get_loss(cfg.config, device)
'''Load optimizer'''
cfg.log_string('Loading optimizer.')
optimizer = get_optimizer(config=cfg.config, net=net)
if isinstance(net, list):
checkpoint.register_modules(voxopt=optimizer[0])
checkpoint.register_modules(refopt=optimizer[1])
else:
checkpoint.register_modules(voxopt=optimizer)
'''Load scheduler'''
cfg.log_string('Loading optimizer scheduler.')
scheduler = load_scheduler(config=cfg.config, optimizer=optimizer)
if isinstance(net, list):
checkpoint.register_modules(voxsch=scheduler[0])
checkpoint.register_modules(refsch=scheduler[1])
else:
checkpoint.register_modules(voxsch=scheduler)
'''Load trainer'''
cfg.log_string('Loading trainer.')
trainer = get_trainer(cfg.config)
'''Start to train'''
cfg.log_string('Start to train.')
#cfg.log_string('Total number of parameters in {0:s}: {1:d}.'.format(cfg.config['method'], sum(p.numel() for p in net.parameters())))
trainer(cfg,
net,
loss_func,
optimizer,
scheduler,
train_loader=train_loader,
test_loader=test_loader,
device=device,
checkpoint=checkpoint)
cfg.log_string('Training finished.')
def __init__(self, env, args, device='cpu'):
"""
Instantiate an NEC Agent
----------
env: gym.Env
gym environment to train on
args: args class from argparser
args are from from train.py: see train.py for help with each arg
device: string
'cpu' or 'cuda:0' depending on use_cuda flag from train.py
"""
self.environment_type = args.environment_type
self.env = env
self.device = device
# Hyperparameters
self.epsilon = args.initial_epsilon
self.final_epsilon = args.final_epsilon
self.epsilon_decay = args.epsilon_decay
self.gamma = args.gamma
self.N = args.N
# Transition queue and replay memory
self.transition_queue = []
self.replay_every = args.replay_every
self.replay_buffer_size = args.replay_buffer_size
self.replay_memory = ReplayMemory(self.replay_buffer_size)
# CNN for state embedding network
self.frames_to_stack = args.frames_to_stack
self.embedding_size = args.embedding_size
self.in_height = args.in_height
self.in_width = args.in_width
self.cnn = CNN(self.frames_to_stack, self.embedding_size,
self.in_height, self.in_width).to(self.device)
# Differentiable Neural Dictionary (DND): one for each action
self.kernel = inverse_distance
self.num_neighbors = args.num_neighbors
self.max_memory = args.max_memory
self.lr = args.lr
self.dnd_list = []
for i in range(env.action_space.n):
self.dnd_list.append(
DND(self.kernel, self.num_neighbors, self.max_memory,
args.optimizer, self.lr))
# Optimizer for state embedding CNN
self.q_lr = args.q_lr
self.batch_size = args.batch_size
self.optimizer = get_optimizer(args.optimizer, self.cnn.parameters(),
self.lr)
def create_experiment(config):
"""Creates an experiment based on config."""
device = torch.device(config.device)
logging.info("using {}".format(config.device))
experiment = Experiment(config.name, config.save_dir)
experiment.register_config(config)
logger = None
if config.use_tflogger:
logger = Logger(config.tflog_dir)
experiment.register_logger(logger)
torch.manual_seed(config.rseed)
model = NRU(device,
config.input_size,
config.output_size,
num_layers=config.num_layers,
layer_size=config.layer_size,
output_activation="linear",
layer_norm=config.layer_norm,
use_relu=config.use_relu,
memory_size=config.memory_size,
k=config.k).to(device)
experiment.register_model(model)
data_iterator = get_data_iterator(config)
experiment.register_data_iterator(data_iterator)
optimizer = get_optimizer(model.parameters(), config)
model.register_optimizer(optimizer)
tr = MyContainer()
tr.updates_done = 0
tr.epochs_done = 0
tr.ce = {}
tr.ce["train"] = []
tr.accuracy = {}
tr.accuracy["valid"] = []
tr.accuracy["test"] = []
tr.grad_norm = []
experiment.register_train_statistics(tr)
return experiment, model, data_iterator, tr, logger, device
def _train(self):
print(f"\n({self.experim_name}) training...\n")
model = get_model(self.args).to(self.device)
optimizer = get_optimizer(self.args, model)
lr_scheduler = get_lr_scheduler(self.args,
optimizer=optimizer,
iters_per_epoch=len(self.dataloader))
for e in range(1, 1 + self.n_epochs):
model, optimizer, lr_scheduler = self._train_epoch(
e, model, optimizer, lr_scheduler)
self._val(e, model)
if self.debug:
break
self.best_miou = -1.0
return model
def train(model, dataloader, device, optimizer_name, loss_name, lr, verbose):
optimizer_object = get_optimizer(optimizer_name)
optimizer = optimizer_object(model.parameters(), lr=lr)
loss_fn = get_loss(loss_name)
model.train()
running_loss = 0.0
running_corrects = 0
for inputs, targets in dataloader:
inputs = inputs.to(device)
targets = targets.to(device)
bs = len(targets)
classes = torch.zeros((bs, 10))
for i in range(bs):
classes[i][targets[i]] = 1
classes = classes.to(device)
outputs = model(inputs)
loss = loss_fn()(outputs, classes)
optimizer.zero_grad()
loss.backward()
optimizer.step()
_, preds = torch.max(outputs.data, 1)
running_loss += loss.item()
running_corrects += torch.sum(preds == targets.data)
loss = running_loss / 60000
acc = running_corrects.data.item() / 60000
if verbose:
print(f'Training results: Loss: {loss:.4f} Acc: {acc:.4f}')
return acc
def train(model, dataloader, device, optimizer_name, loss_name, lr):
optimizer_object = get_optimizer(optimizer_name)
optimizer = optimizer_object(model.parameters(), lr=lr)
loss_fn = get_loss(loss_name)
model.train()
running_loss = 0.0
running_corrects = 0
for inputs, targets in dataloader:
inputs = inputs.to(device)
targets = targets.to(device)
bs = len(targets)
classes = torch.zeros((bs, 10))
for i in range(bs):
classes[i][targets[i]] = 1
classes = classes.to(device)
outputs = model(inputs)
loss = loss_fn()(outputs,
classes) # LeCun & al. used Maximum Log Likehood
optimizer.zero_grad()
loss.backward()
optimizer.step()
_, preds = torch.max(outputs.data, 1)
# statistics
running_loss += loss.item()
running_corrects += torch.sum(preds == targets.data)
loss = running_loss / 60000
acc = running_corrects.data.item() / 60000
print('Training results: Loss: {:.4f} Acc: {:.4f}'.format(loss, acc))
return acc
def main():
args = parse_args()
reset_config(config, args)
logger, final_output_dir, tb_log_dir = create_logger(
config, args.cfg, 'train')
logger.info(pprint.pformat(args))
logger.info(pprint.pformat(config))
cudnn.benchmark = config.CUDNN.BENCHMARK
torch.backends.cudnn.deterministic = config.CUDNN.DETERMINISTIC
torch.backends.cudnn.enabled = config.CUDNN.ENABLED
backbone_model = eval('models.' + config.BACKBONE_MODEL + '.get_pose_net')(
config, is_train=True)
model = eval('models.' + config.MODEL + '.get_multiview_pose_net')(
backbone_model, config)
print(model)
this_dir = os.path.dirname(__file__)
shutil.copy2(
os.path.join(this_dir, '../../lib/models', config.MODEL + '.py'),
final_output_dir)
shutil.copy2(args.cfg, final_output_dir)
logger.info(pprint.pformat(model))
writer_dict = {
'writer': SummaryWriter(log_dir=tb_log_dir),
'train_global_steps': 0,
'valid_global_steps': 0,
}
gpus = [int(i) for i in config.GPUS.split(',')]
model = torch.nn.DataParallel(model, device_ids=gpus).cuda()
criterion = JointsMSELoss(
use_target_weight=config.LOSS.USE_TARGET_WEIGHT).cuda()
optimizer = get_optimizer(config, model)
start_epoch = config.TRAIN.BEGIN_EPOCH
if config.TRAIN.RESUME:
start_epoch, model, optimizer = load_checkpoint(model, optimizer,
final_output_dir)
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer, config.TRAIN.LR_STEP, config.TRAIN.LR_FACTOR)
# Data loading code
normalize = transforms.Normalize(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
train_dataset = eval('dataset.' + config.DATASET.TRAIN_DATASET)(
config, config.DATASET.TRAIN_SUBSET, True,
transforms.Compose([
transforms.ToTensor(),
normalize,
]))
valid_dataset = eval('dataset.' + config.DATASET.TEST_DATASET)(
config, config.DATASET.TEST_SUBSET, False,
transforms.Compose([
transforms.ToTensor(),
normalize,
]))
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=config.TRAIN.BATCH_SIZE * len(gpus),
shuffle=config.TRAIN.SHUFFLE,
num_workers=config.WORKERS,
pin_memory=True)
valid_loader = torch.utils.data.DataLoader(
valid_dataset,
batch_size=config.TEST.BATCH_SIZE * len(gpus),
shuffle=False,
num_workers=config.WORKERS,
pin_memory=True)
best_perf = 0.0
best_model = False
for epoch in range(start_epoch, config.TRAIN.END_EPOCH):
lr_scheduler.step()
train(config, train_loader, model, criterion, optimizer, epoch,
final_output_dir, writer_dict)
perf_indicator = validate(config, valid_loader, valid_dataset, model,
criterion, final_output_dir, writer_dict)
if perf_indicator > best_perf:
best_perf = perf_indicator
best_model = True
else:
best_model = False
logger.info('=> saving checkpoint to {}'.format(final_output_dir))
save_checkpoint({
'epoch': epoch + 1,
'model': get_model_name(config),
'state_dict': model.module.state_dict(),
'perf': perf_indicator,
'optimizer': optimizer.state_dict(),
}, best_model, final_output_dir)
final_model_state_file = os.path.join(final_output_dir,
'final_state.pth.tar')
logger.info('saving final model state to {}'.format(final_model_state_file))
torch.save(model.module.state_dict(), final_model_state_file)
writer_dict['writer'].close()
def main():
args = parse_args()
update_config(cfg, args)
logger, final_output_dir, tb_log_dir = create_logger(
cfg, args.cfg, 'train')
logger.info(pprint.pformat(args))
logger.info(cfg)
# cudnn related setting
cudnn.benchmark = cfg.CUDNN.BENCHMARK
torch.backends.cudnn.deterministic = cfg.CUDNN.DETERMINISTIC
torch.backends.cudnn.enabled = cfg.CUDNN.ENABLED
model_p, model_d = eval('models.' + cfg.MODEL.NAME +
'.get_adaptive_pose_net')(cfg, is_train=True)
if cfg.TRAIN.CHECKPOINT:
logger.info('=> loading model from {}'.format(cfg.TRAIN.CHECKPOINT))
model_p.load_state_dict(torch.load(cfg.TRAIN.CHECKPOINT))
else:
model_state_file = os.path.join(final_output_dir, 'checkpoint.pth')
logger.info('=> loading model from {}'.format(model_state_file))
model_p.load_state_dict(torch.load(model_state_file))
# copy model file
this_dir = os.path.dirname(__file__)
shutil.copy2(
os.path.join(this_dir, '../lib/models', cfg.MODEL.NAME + '.py'),
final_output_dir)
writer_dict = {
'writer': SummaryWriter(log_dir=tb_log_dir),
'pre_train_global_steps': 0,
'train_global_steps': 0,
'valid_global_steps': 0,
}
dump_input = torch.rand(
(1, 3, cfg.MODEL.IMAGE_SIZE[1], cfg.MODEL.IMAGE_SIZE[0]))
writer_dict['writer'].add_graph(model_p, (dump_input, ), verbose=False)
logger.info(get_model_summary(model_p, dump_input))
model_p = torch.nn.DataParallel(model_p, device_ids=cfg.GPUS).cuda()
model_d = torch.nn.DataParallel(model_d, device_ids=cfg.GPUS).cuda()
# define loss function (criterion) and optimizer for pose_net
criterion_p = JointsMSELoss(
use_target_weight=cfg.LOSS.USE_TARGET_WEIGHT).cuda()
optimizer_p = get_optimizer(cfg, model_p)
# define loss function (criterion) and optimizer for domain
criterion_d = torch.nn.BCEWithLogitsLoss().cuda()
optimizer_d = get_optimizer(cfg, model_d)
# Data loading code
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_pre_dataset = eval('dataset.' + cfg.DATASET.DATASET)(
cfg, cfg.DATASET.ROOT, cfg.DATASET.TRAIN_PRE_SET, True,
transforms.Compose([
transforms.ToTensor(),
normalize,
]))
train_dataset = eval('dataset.' + cfg.DATASET.DATASET)(
cfg, cfg.DATASET.ROOT, cfg.DATASET.TRAIN_SET, True,
transforms.Compose([
transforms.ToTensor(),
normalize,
]))
valid_dataset = eval('dataset.' + cfg.DATASET.DATASET)(
cfg, cfg.DATASET.ROOT, cfg.DATASET.TEST_SET, False,
transforms.Compose([
transforms.ToTensor(),
normalize,
]))
train_pre_loader = torch.utils.data.DataLoader(
train_pre_dataset,
batch_size=cfg.TRAIN.PRE_BATCH_SIZE_PER_GPU * len(cfg.GPUS),
shuffle=cfg.TRAIN.SHUFFLE,
num_workers=cfg.WORKERS,
pin_memory=cfg.PIN_MEMORY)
syn_labels = train_dataset._load_syrip_syn_annotations()
train_loader = torch.utils.data.DataLoader(
train_dataset,
sampler=BalancedBatchSampler(train_dataset, syn_labels),
batch_size=cfg.TRAIN.BATCH_SIZE_PER_GPU * len(cfg.GPUS),
num_workers=cfg.WORKERS,
pin_memory=cfg.PIN_MEMORY)
'''
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=cfg.TRAIN.BATCH_SIZE_PER_GPU*len(cfg.GPUS),
shuffle=cfg.TRAIN.SHUFFLE,
num_workers=cfg.WORKERS,
pin_memory=cfg.PIN_MEMORY
)
'''
valid_loader = torch.utils.data.DataLoader(
valid_dataset,
batch_size=cfg.TEST.BATCH_SIZE_PER_GPU * len(cfg.GPUS),
shuffle=False,
num_workers=cfg.WORKERS,
pin_memory=cfg.PIN_MEMORY)
best_perf = 0.0
best_model = False
last_epoch = -1
begin_epoch = cfg.TRAIN.BEGIN_EPOCH
checkpoint_file = os.path.join(final_output_dir, 'checkpoint.pth')
if cfg.AUTO_RESUME and os.path.exists(checkpoint_file):
logger.info("=> loading checkpoint '{}'".format(checkpoint_file))
checkpoint = torch.load(checkpoint_file)
begin_epoch = checkpoint['epoch']
best_perf = checkpoint['perf']
last_epoch = checkpoint['epoch']
model_p.load_state_dict(checkpoint['state_dict'])
optimizer_p.load_state_dict(checkpoint['optimizer'])
logger.info("=> loaded checkpoint '{}' (epoch {})".format(
checkpoint_file, checkpoint['epoch']))
# freeze some layers
idx = 0
print('Parametersssssssssssssss')
for param in model_p.parameters():
if idx <= 108: #fix 108 for stage 2 + bottleneck or fix 483 for stage 3 + stage 2+ bottleneck
param.requires_grad = False
#print(param.data.shape)
idx = idx + 1
lr_scheduler_p = torch.optim.lr_scheduler.MultiStepLR(
optimizer_p,
cfg.TRAIN.LR_STEP,
cfg.TRAIN.LR_FACTOR,
last_epoch=last_epoch)
lr_scheduler_d = torch.optim.lr_scheduler.MultiStepLR(
optimizer_d, cfg.TRAIN.LR_STEP, cfg.TRAIN.LR_FACTOR)
epoch_D = cfg.TRAIN.PRE_EPOCH
losses_D_list = []
acces_D_list = []
acc_num_total = 0
num = 0
losses_d = AverageMeter()
# Pretrained Stage
print('Pretrained Stage:')
print('Start to train Domain Classifier-------')
for epoch_d in range(epoch_D): # epoch
model_d.train()
model_p.train()
for i, (input, target, target_weight,
meta) in enumerate(train_pre_loader): # iteration
# compute output for pose_net
feature_outputs, outputs = model_p(input)
#print(feature_outputs.size())
# compute for domain classifier
domain_logits = model_d(feature_outputs.detach())
domain_label = (meta['synthetic'].unsqueeze(-1) *
1.0).cuda(non_blocking=True)
# print(domain_label)
loss_d = criterion_d(domain_logits, domain_label)
loss_d.backward(retain_graph=True)
optimizer_d.step()
# compute accuracy of classifier
acc_num = 0
for j in range(len(domain_label)):
if (domain_logits[j] > 0 and domain_label[j] == 1.0) or (
domain_logits[j] < 0 and domain_label[j] == 0.0):
acc_num += 1
acc_num_total += 1
num += 1
acc_d = acc_num * 1.0 / input.size(0)
acces_D_list.append(acc_d)
optimizer_d.zero_grad()
losses_d.update(loss_d.item(), input.size(0))
if i % cfg.PRINT_FREQ == 0:
msg = 'Epoch: [{0}][{1}/{2}]\t' \
'Accuracy_d: {3} ({4})\t' \
'Loss_d: {loss_d.val:.5f} ({loss_d.avg:.5f})'.format(
epoch_d, i, len(train_pre_loader), acc_d, acc_num_total * 1.0 / num, loss_d = losses_d)
logger.info(msg)
writer = writer_dict['writer']
pre_global_steps = writer_dict['pre_train_global_steps']
writer.add_scalar('pre_train_loss_D', losses_d.val,
pre_global_steps)
writer.add_scalar('pre_train_acc_D', acc_d, pre_global_steps)
writer_dict['pre_train_global_steps'] = pre_global_steps + 1
losses_D_list.append(losses_d.val)
print('Training Stage (Step I and II):')
losses_P_list = []
acces_P_list = []
losses_p = AverageMeter()
acces_p = AverageMeter()
for epoch in range(begin_epoch, cfg.TRAIN.END_EPOCH):
lr_scheduler_p.step()
# train for one epoch
losses_P_list, losses_D_list, acces_P_list, acces_D_list = train_adaptive(
cfg, train_loader, model_p, model_d, criterion_p, criterion_d,
optimizer_p, optimizer_d, epoch, final_output_dir, tb_log_dir,
writer_dict, losses_P_list, losses_D_list, acces_P_list,
acces_D_list, acc_num_total, num, losses_p, acces_p, losses_d)
# evaluate on validation set
perf_indicator = validate_adaptive(cfg, valid_loader, valid_dataset,
model_p, criterion_p,
final_output_dir, tb_log_dir,
writer_dict)
if perf_indicator > best_perf:
best_perf = perf_indicator
best_model = True
else:
best_model = False
logger.info('=> saving checkpoint to {}'.format(final_output_dir))
save_checkpoint(
{
'epoch': epoch + 1,
'model': cfg.MODEL.NAME,
'state_dict': model_p.state_dict(),
'best_state_dict': model_p.module.state_dict(),
'perf': perf_indicator,
'optimizer': optimizer_p.state_dict(),
}, best_model, final_output_dir)
final_model_state_file = os.path.join(final_output_dir, 'final_state.pth')
logger.info(
'saving final model state to {}'.format(final_model_state_file))
torch.save(model_p.module.state_dict(), final_model_state_file)
writer_dict['writer'].close()
np.save('./losses_D.npy', np.array(losses_D_list)) # Adversarial-D
np.save('./losses_P.npy', np.array(losses_P_list)) # P
np.save('./acces_P.npy', np.array(acces_P_list)) # P
np.save('./acces_D.npy', np.array(acces_D_list)) # D
def train(use_cuda: bool, n_epochs: int, validate_every: int,
use_dropout: bool, partitions: Partitions, optimizer_name: str,
lr: float, wd: float, momentum: bool):
logger = logging.getLogger('logger')
no_test = True
model_path = "./model_output/pairwise/model_{0}"
partitions.generate_partitions(PairPartition, no_test=no_test)
training_data = Balanced(partitions.train)
if validate_every > 0:
balanced_validation = Balanced(partitions.val)
training_pairs = AllPairs(partitions.train)
search_length = training_pairs.n_references
validation_pairs = AllPairs(partitions.val)
testing_pairs = AllPairs(partitions.test) if not no_test else None
else:
balanced_validation = None
training_pairs = None
validation_pairs = None
testing_pairs = None
search_length = None
# get a siamese network, see Siamese class for architecture
siamese = Siamese(dropout=use_dropout)
siamese = initialize_weights(siamese, use_cuda)
if use_cuda:
siamese = siamese.cuda()
criterion = BCELoss()
optimizer = get_optimizer(siamese, optimizer_name, lr, wd, momentum)
try:
logger.info("Training network with pairwise loss...")
progress = TrainingProgress()
models = training.train_siamese_network(siamese, training_data,
criterion, optimizer, n_epochs,
use_cuda)
for epoch, (model, training_batch_losses) in enumerate(models):
utils.network.save_model(model, model_path.format(epoch))
training_loss = training_batch_losses.mean()
if validate_every != 0 and epoch % validate_every == 0:
validation_batch_losses = inference.siamese_loss(
model, balanced_validation, criterion, use_cuda)
validation_loss = validation_batch_losses.mean()
training_mrr, training_rank = inference.mean_reciprocal_ranks(
model, training_pairs, use_cuda)
val_mrr, val_rank = inference.mean_reciprocal_ranks(
model, validation_pairs, use_cuda)
progress.add_mrr(train=training_mrr, val=val_mrr)
progress.add_rank(train=training_rank, val=val_rank)
progress.add_loss(train=training_loss, val=validation_loss)
else:
progress.add_mrr(train=np.nan, val=np.nan)
progress.add_rank(train=np.nan, val=np.nan)
progress.add_loss(train=training_loss, val=np.nan)
progress.graph("Siamese", search_length)
# load weights from best model if we validated throughout
if validate_every > 0:
siamese = siamese.train()
utils.network.load_model(
siamese, model_path.format(np.argmax(progress.val_mrr)))
# otherwise just save most recent model
utils.network.save_model(siamese, model_path.format('best'))
utils.network.save_model(
siamese,
'./output/{0}/pairwise'.format(utilities.get_trial_number()))
if not no_test:
logger.info(
"Results from best model generated during training, evaluated on test data:"
)
rrs = inference.reciprocal_ranks(siamese, testing_pairs, use_cuda)
utilities.log_final_stats(rrs)
progress.pearson(log=True)
progress.save("./output/{0}/pairwise.pickle".format(
utilities.get_trial_number()))
return siamese
except Exception as e:
utils.network.save_model(siamese, model_path.format('crash_backup'))
logger.critical("Exception occurred while training: {0}".format(
str(e)))
logger.critical(traceback.print_exc())
sys.exit()
# Load Weights into the
decoder = copy_weights(hfvae, decoder)
SECOND_STAGE = False
if SECOND_STAGE:
z_train = encoder.predict(x_train)[0][0]
latent_dim = np.prod(z_train.shape[1:])
z_train = np.reshape(z_train, (-1, latent_dim))
second_vae, second_encoder, second_decoder = two_stage.get_second_stage(
latent_dim)
# Compile model
optimizer = utils.get_optimizer(z_train.shape[0] // batch_size,
initial_lr=1e-3)
second_vae.compile(optimizer=optimizer, loss=None, metrics=[utils.cos_sim])
second_vae.fit(z_train, None, batch_size=batch_size, epochs=epochs)
second_vae.save_weights('saved_weights/secondstage_NVAE_' + data + '.h5')
GMM = True
if GMM:
from sklearn.mixture import GaussianMixture
#we may only work on z_mean of the innermost layer
z_train = encoder.predict(x_train)[0][0]
#print("ltatent dim = ",z_train.shape[1])
z_density = GaussianMixture(n_components=10, max_iter=100)
z_density.fit(z_train)
def main_per_worker(process_index, ngpus_per_node, args):
update_config(cfg, args)
# torch seed
torch.cuda.manual_seed(random.random())
# cudnn
cudnn.benchmark = cfg.CUDNN.BENCHMARK
torch.backends.cudnn.enabled = cfg.CUDNN.ENABLED
#proc_rank
proc_rank = args.rank * ngpus_per_node + process_index
#create logger
logger, output_dir = create_logger(cfg, proc_rank)
# logger.info(pprint.pformat(args))
# logger.info(cfg)
model = get_model(cfg, cfg.MODEL.FILE, cfg.MODEL.NAME)
emb = InceptionResnetV1(pretrained='vggface2', classify=False)
assert cfg.MODEL.APPEARANCE.WEIGHTS != ''
load_eval_model(cfg.MODEL.APPEARANCE.WEIGHTS, emb)
# TODO change based on the paper
optimizer = get_optimizer(cfg, model)
model, optimizer, last_iter = load_checkpoint(cfg, model, optimizer)
lr_scheduler = get_lr_scheduler(cfg, optimizer, last_iter)
transform = FacenetInferenceTransform(size=(cfg.TRAIN.INPUT_MIN, cfg.TRAIN.INPUT_MAX))
train_dataset = TrackletpairDataset(cfg.DATASET.ROOT, transform=transform, is_train=True)
eval_dataset = TrackletpairDataset(cfg.DATASET.ROOT, transform=transform, is_train=False)
# distribution
if args.distributed:
logger.info(
f'Init process group: dist_url: {args.dist_url}, '
f'world_size: {args.world_size}, '
f'machine: {args.rank}, '
f'rank:{proc_rank}'
)
dist.init_process_group(
backend=cfg.DIST_BACKEND,
init_method=args.dist_url,
world_size=args.world_size,
rank=proc_rank
)
torch.cuda.set_device(process_index)
model.cuda()
emb.cuda()
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[process_index]
)
emb = torch.nn.parallel.DistributedDataParallel(
emb, device_ids=[process_index]
)
train_sampler = BalancedBatchSampler(
train_dataset
)
batch_size = cfg.DATASET.IMG_NUM_PER_GPU
else:
assert proc_rank == 0, ('proc_rank != 0, it will influence '
'the evaluation procedure')
model = torch.nn.DataParallel(model).cuda()
emb = torch.nn.DataParallel(emb).cuda()
train_sampler = BalancedBatchSampler(
train_dataset
)
batch_size = cfg.DATASET.IMG_NUM_PER_GPU * ngpus_per_node
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=batch_size,
shuffle=(train_sampler is None),
drop_last=False,
collate_fn=tracklet_pair_collect,
num_workers=cfg.WORKERS,
pin_memory=True,
sampler=train_sampler
)
eval_loader = torch.utils.data.DataLoader(
eval_dataset,
batch_size=batch_size,
shuffle=False,
drop_last=False,
collate_fn=tracklet_pair_collect,
num_workers=cfg.WORKERS
)
criterion = nn.CrossEntropyLoss()
Trainer = trackletpairConnectTrainer(
cfg,
model,
optimizer,
lr_scheduler,
criterion,
output_dir,
'acc',
last_iter,
proc_rank,
pre_ap_model=emb,
)
while True:
Trainer.train(train_loader, eval_loader)
# eval
Trainer.evaluate(eval_loader)
args = parser.parse_args()
with open(args.config) as f:
config = yaml.load(f)
config['config_file'] = args.config.replace('/','.').split('.')[-2]
seed = config['seed']
np.random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
n_epochs = config['optimization']['n_epochs']
if not args.disable_cuda and torch.cuda.is_available():
device = torch.device('cuda:{}'.format(args.gpu))
else:
device = torch.device('cpu')
logger = Logger(config)
model = get_model(config['model'])
optim = get_optimizer(model.parameters(),config['optimization'])
train_loader, valid_loader, test_loader = get_data(config['data'])
## Train
for i in range(n_epochs):
for data, label in train_loader:
break
def main():
args = parse_args()
update_config(cfg, args)
logger, final_output_dir, tb_log_dir = create_logger(
cfg, args.cfg, 'train')
logger.info(pprint.pformat(args))
logger.info(cfg)
# cudnn related setting
cudnn.benchmark = cfg.CUDNN.BENCHMARK
torch.backends.cudnn.deterministic = cfg.CUDNN.DETERMINISTIC
torch.backends.cudnn.enabled = cfg.CUDNN.ENABLED
model_builder = importlib.import_module("models." +
cfg.MODEL.NAME).get_fovea_net
model = model_builder(cfg, is_train=True)
# xiaofeng add for load parameter
if cfg.TEST.MODEL_FILE:
logger.info('=> loading model from {}'.format(cfg.TEST.MODEL_FILE))
model.load_state_dict(torch.load(cfg.TEST.MODEL_FILE), strict=False)
# copy model file -- xiaofeng comment it
# this_dir = os.path.dirname(__file__)
# shutil.copy2(os.path.join(this_dir, '../models', cfg.MODEL.NAME + '.py'), final_output_dir)
writer_dict = {
'writer': SummaryWriter(log_dir=tb_log_dir),
'train_global_steps': 0,
'valid_global_steps': 0,
}
dump_input = torch.rand(
(1, 3, cfg.MODEL.IMAGE_SIZE[1], cfg.MODEL.IMAGE_SIZE[0]))
model = torch.nn.DataParallel(model, device_ids=cfg.GPUS).cuda()
# define loss function (criterion) and optimizer
criterion = HybridLoss(roi_weight=cfg.LOSS.ROI_WEIGHT,
regress_weight=cfg.LOSS.REGRESS_WEIGHT,
use_target_weight=cfg.LOSS.USE_TARGET_WEIGHT,
hrnet_only=cfg.TRAIN.HRNET_ONLY).cuda()
# Data loading code
# normalize = transforms.Normalize(
# mean=[0.134, 0.207, 0.330], std=[0.127, 0.160, 0.239]
# )
# train_dataset = importlib.import_module('dataset.'+cfg.DATASET.DATASET).Dataset(
# cfg, cfg.DATASET.ROOT, cfg.DATASET.TRAIN_SET, True,
# transforms.Compose([
# transforms.ToTensor(),
# normalize,
# ])
# )
# valid_dataset = importlib.import_module('dataset.'+cfg.DATASET.DATASET).Dataset(
# cfg, cfg.DATASET.ROOT, cfg.DATASET.TEST_SET, False,
# transforms.Compose([
# transforms.ToTensor(),
# normalize,
# ])
# )
#
# train_loader = torch.utils.data.DataLoader(
# train_dataset,
# batch_size=cfg.TRAIN.BATCH_SIZE_PER_GPU*len(cfg.GPUS),
# shuffle=cfg.TRAIN.SHUFFLE,
# num_workers=cfg.WORKERS,
# pin_memory=cfg.PIN_MEMORY
# )
# valid_loader = torch.utils.data.DataLoader(
# valid_dataset,
# batch_size=cfg.TEST.BATCH_SIZE_PER_GPU*len(cfg.GPUS),
# shuffle=False,
# num_workers=cfg.WORKERS,
# pin_memory=cfg.PIN_MEMORY
# )
db_trains = []
db_vals = []
final_full_test = cfg.TRAIN.FULL_DATA
normalize_1 = transforms.Normalize(mean=[0.282, 0.168, 0.084],
std=[0.189, 0.110, 0.062])
train_dataset_1 = importlib.import_module('dataset.' +
cfg.DATASET.DATASET).Dataset(
cfg, cfg.DATASET.ROOT,
cfg.DATASET.TRAIN_SET_1,
True,
transforms.Compose([
transforms.ToTensor(),
normalize_1,
]))
db_trains.append(train_dataset_1)
normalize_2 = transforms.Normalize(mean=[0.409, 0.270, 0.215],
std=[0.288, 0.203, 0.160])
train_dataset_2 = importlib.import_module('dataset.' +
cfg.DATASET.DATASET).Dataset(
cfg, cfg.DATASET.ROOT,
cfg.DATASET.TRAIN_SET_2,
True,
transforms.Compose([
transforms.ToTensor(),
normalize_2,
]))
db_trains.append(train_dataset_2)
if final_full_test is True:
normalize_3 = transforms.Normalize(mean=[0.404, 0.271, 0.222],
std=[0.284, 0.202, 0.163])
train_dataset_3 = importlib.import_module(
'dataset.' + cfg.DATASET.DATASET).Dataset(
cfg, cfg.DATASET.ROOT, cfg.DATASET.TEST_SET, True,
transforms.Compose([
transforms.ToTensor(),
normalize_3,
]))
db_trains.append(train_dataset_3)
train_dataset = ConcatDataset(db_trains)
logger.info("Combined Dataset: Total {} images".format(len(train_dataset)))
train_batch_size = cfg.TRAIN.BATCH_SIZE_PER_GPU * len(cfg.GPUS)
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=train_batch_size,
shuffle=cfg.TRAIN.SHUFFLE,
num_workers=cfg.WORKERS,
pin_memory=cfg.PIN_MEMORY)
normalize = transforms.Normalize(mean=[0.404, 0.271, 0.222],
std=[0.284, 0.202, 0.163])
val_dataset_1 = importlib.import_module('dataset.' +
cfg.DATASET.DATASET).Dataset(
cfg, cfg.DATASET.ROOT,
cfg.DATASET.TEST_SET, False,
transforms.Compose([
transforms.ToTensor(),
normalize,
]))
db_vals.append(val_dataset_1)
if final_full_test is True:
normalize_1 = transforms.Normalize(mean=[0.282, 0.168, 0.084],
std=[0.189, 0.110, 0.062])
val_dataset_2 = importlib.import_module('dataset.' +
cfg.DATASET.DATASET).Dataset(
cfg, cfg.DATASET.ROOT,
cfg.DATASET.TRAIN_SET_1,
False,
transforms.Compose([
transforms.ToTensor(),
normalize_1,
]))
db_vals.append(val_dataset_2)
normalize_2 = transforms.Normalize(mean=[0.409, 0.270, 0.215],
std=[0.288, 0.203, 0.160])
val_dataset_3 = importlib.import_module('dataset.' +
cfg.DATASET.DATASET).Dataset(
cfg, cfg.DATASET.ROOT,
cfg.DATASET.TRAIN_SET_2,
False,
transforms.Compose([
transforms.ToTensor(),
normalize_2,
]))
db_vals.append(val_dataset_3)
valid_dataset = ConcatDataset(db_vals)
logger.info("Val Dataset: Total {} images".format(len(valid_dataset)))
test_batch_size = cfg.TEST.BATCH_SIZE_PER_GPU * len(cfg.GPUS)
valid_loader = torch.utils.data.DataLoader(
valid_dataset,
batch_size=cfg.TEST.BATCH_SIZE_PER_GPU * len(cfg.GPUS),
shuffle=False,
num_workers=cfg.WORKERS,
pin_memory=cfg.PIN_MEMORY)
logger.info("Train len: {}, batch_size: {}; Test len: {}, batch_size: {}" \
.format(len(train_loader), train_batch_size, len(valid_loader), test_batch_size))
best_metric = 1e6
best_model = False
last_epoch = -1
optimizer = get_optimizer(cfg, model)
begin_epoch = cfg.TRAIN.BEGIN_EPOCH
if cfg.TEST.MODEL_FILE:
checkpoint_file = cfg.TEST.MODEL_FILE
else:
checkpoint_file = os.path.join(final_output_dir, 'checkpoint.pth')
if cfg.AUTO_RESUME and os.path.exists(checkpoint_file):
logger.info("=> loading checkpoint '{}'".format(checkpoint_file))
checkpoint = torch.load(checkpoint_file)
# begin_epoch = checkpoint['epoch']
begin_epoch = 0 # xiaofeng change it
best_metric = checkpoint['metric']
last_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
logger.info("=> loaded checkpoint '{}' (epoch {})".format(
checkpoint_file, checkpoint['epoch']))
if cfg.TRAIN.LR_EXP:
# llr=lr∗gamma∗∗epoch
lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer,
cfg.TRAIN.GAMMA1,
last_epoch=-1)
else:
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer,
cfg.TRAIN.LR_STEP,
cfg.TRAIN.LR_FACTOR,
last_epoch=last_epoch)
for epoch in range(begin_epoch, cfg.TRAIN.END_EPOCH):
start_time = timer()
lr_scheduler.step()
# evaluate on validation set
# lr_metric, hr_metric, final_metric = validate(
# cfg, valid_loader, valid_dataset, model, criterion,
# final_output_dir, tb_log_dir, writer_dict, db_vals
# )
# print("validation before training spent time:")
# timer(start_time) # timing ends here for "start_time" variable
# train for one epoch
train(cfg, train_loader, model, criterion, optimizer, epoch,
final_output_dir, tb_log_dir, writer_dict)
print("epoch %d train spent time:" % (epoch))
train_time = timer(
start_time) # timing ends here for "start_time" variable
# if epoch >= int(cfg.TRAIN.END_EPOCH/10):
# evaluate on validation set
lr_metric, hr_metric, final_metric = validate(
cfg, valid_loader, valid_dataset, model, criterion,
final_output_dir, tb_log_dir, writer_dict, db_vals)
print("validation spent time:")
val_time = timer(
train_time) # timing ends here for "start_time" variable
min_metric = min(lr_metric, hr_metric, final_metric)
if min_metric <= best_metric:
best_metric = min_metric
best_model = True
logger.info('=> epoch [{}] best model result: {}'.format(
epoch, best_metric))
else:
best_model = False
# xiaofeng changed it
if best_model is True:
logger.info('=> saving checkpoint to {}'.format(final_output_dir))
# transfer the model to CPU before saving to fix unstable bug:
# github.com/pytorch/pytorch/issues/10577
model = model.cpu()
save_checkpoint(
{
'epoch': epoch + 1,
'model': cfg.MODEL.NAME,
'state_dict': model.state_dict(),
'best_state_dict': model.module.state_dict(),
'metric': final_metric,
'optimizer': optimizer.state_dict(),
}, best_model, final_output_dir)
model = model.cuda()
print("saving spent time:")
end_time = timer(
val_time) # timing ends here for "start_time" variable
elif (epoch % 60 == 0) and (epoch != 0):
logger.info('=> saving epoch {} checkpoint to {}'.format(
epoch, final_output_dir))
# transfer the model to CPU before saving to fix unstable bug:
# github.com/pytorch/pytorch/issues/10577
time_str = time.strftime('%Y-%m-%d-%H-%M')
if cfg.TRAIN.HRNET_ONLY:
checkpoint_filename = 'checkpoint_HRNET_epoch%d_%s.pth' % (
epoch, time_str)
else:
checkpoint_filename = 'checkpoint_Hybrid_epoch%d_%s.pth' % (
epoch, time_str)
model = model.cpu()
save_checkpoint(
{
'epoch': epoch + 1,
'model': cfg.MODEL.NAME,
'state_dict': model.state_dict(),
'best_state_dict': model.module.state_dict(),
'metric': final_metric,
'optimizer': optimizer.state_dict(),
}, best_model, final_output_dir, checkpoint_filename)
model = model.cuda()
# xiaofeng change
time_str = time.strftime('%Y-%m-%d-%H-%M')
if cfg.TRAIN.HRNET_ONLY:
model_name = 'final_state_HRNET_%s.pth' % (time_str)
else:
model_name = 'final_state_Hybrid_%s.pth' % (time_str)
final_model_state_file = os.path.join(final_output_dir, model_name)
logger.info(
'=> saving final model state to {}'.format(final_model_state_file))
torch.save(model.module.state_dict(), final_model_state_file)
writer_dict['writer'].close()
# save a final checkpoint
model = model.cpu()
save_checkpoint(
{
'epoch': epoch + 1,
'model': cfg.MODEL.NAME,
'state_dict': model.state_dict(),
'best_state_dict': model.module.state_dict(),
'metric': final_metric,
'optimizer': optimizer.state_dict(),
}, best_model, final_output_dir, "checkpoint_final_state.pth")
def build_model(self, eval=False):
if self.train_mode:
with tf.variable_scope(tf.get_variable_scope()) as outer_scope:
self.learning_rate = tf.constant(self.hparams.learning_rate)
#self.learning_rate = self._get_learning_rate_warmup(self.hparams)
#self.learning_rate = self._get_learning_rate_decay()
opt = utils.get_optimizer(self.hparams, self.learning_rate)
tower_grads = []
losses = []
controller = "/cpu:0"
self._train_models = []
for i, id in enumerate(gpu_utils.get_available_gpus()):
name = 'tower_%d' % i
with tf.device(gpu_utils.assign_to_device(id, controller)), tf.name_scope(name):
model = self.Model()
model(
self.hparams,
tf.estimator.ModeKeys.TRAIN,
self._batched_input_train)
loss = model.loss
with tf.name_scope("compute_gradients"):
grad_and_vars = opt.compute_gradients(
loss,
var_list=model.trainable_variables(),
colocate_gradients_with_ops=self.hparams.colocate_gradients_with_ops)
vars = [var for _, var in grad_and_vars]
grads, _, _ = model_utils.gradient_clip([grad for grad, var in grad_and_vars], max_gradient_norm=MAX_GRADIENT_NORM)
tower_grads.append(zip(grads, vars))
losses.append(loss)
outer_scope.reuse_variables()
self._train_models.append(model)
self._train_model = model
self.params = model.trainable_variables()
with tf.name_scope("apply_gradients"), tf.device(controller):
average_grads = []
for grad_and_vars in zip(*tower_grads):
grads = [g for g, _ in grad_and_vars]
for g, v in grad_and_vars:
print(g, v)
grad = tf.reduce_mean(grads, 0)
v = grad_and_vars[0][1]
grad_and_var = (grad, v)
average_grads.append(grad_and_var)
self.update = opt.apply_gradients(average_grads, self._global_step)
self.loss = tf.reduce_mean(losses)
self._summary = tf.summary.merge([
tf.summary.scalar('train_loss', self.loss),
tf.summary.scalar("learning_rate", self.learning_rate),
])
# init dev model
if self.hparams.dev_data is not None:
with tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE):
self.dev_model = self.Model()
self.hparams.batch_size = self.hparams.eval_batch_size
self.dev_model(
self.hparams,
tf.estimator.ModeKeys.EVAL,
self._batched_input_dev)
if eval or self.eval_mode:
with tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE):
self.test_model = self.Model()
self.hparams.batch_size = self.hparams.eval_batch_size
self.test_model(
self.hparams,
tf.estimator.ModeKeys.EVAL,
self._batched_input_test)
self._eval_summary = tf.no_op()
self.print_logs()
def main():
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
args = parse_args()
print('out')
print(args)
reset_config(config, args)
logger, final_output_dir, tb_log_dir = create_logger(
config, args.cfg, 'train')
logger.info(pprint.pformat(args))
logger.info(pprint.pformat(config))
# cudnn related setting
cudnn.benchmark = config.CUDNN.BENCHMARK
torch.backends.cudnn.deterministic = config.CUDNN.DETERMINISTIC
torch.backends.cudnn.enabled = config.CUDNN.ENABLED
model = eval('models.' + config.MODEL.NAME + '.get_pose_net')(
config, is_train=True)
# copy model file
this_dir = os.path.dirname(__file__)
shutil.copy2(
os.path.join(this_dir, '../lib/models', config.MODEL.NAME + '.py'),
final_output_dir)
writer_dict = {
'writer': SummaryWriter(log_dir=tb_log_dir),
'train_global_steps': 0,
'valid_global_steps': 0,
}
dump_input = torch.rand(
(config.TRAIN.BATCH_SIZE, 3, config.MODEL.IMAGE_SIZE[1],
config.MODEL.IMAGE_SIZE[0]))
writer_dict['writer'].add_graph(model, (dump_input, ), verbose=False)
gpus = [int(i) for i in config.GPUS.split(',')]
model = torch.nn.DataParallel(model, device_ids=gpus).cuda()
# define loss function (criterion) and optimizer
criterion = JointsMSELoss(
use_target_weight=config.LOSS.USE_TARGET_WEIGHT).cuda()
optimizer = get_optimizer(config, model)
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer, config.TRAIN.LR_STEP, config.TRAIN.LR_FACTOR)
# Data loading code
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_dataset = eval('dataset.' + config.DATASET.DATASET)(
config, config.DATASET.ROOT, config.DATASET.TRAIN_SET, True,
transforms.Compose([
transforms.ToTensor(),
normalize,
]))
valid_dataset = eval('dataset.' + config.DATASET.DATASET)(
config, config.DATASET.ROOT, config.DATASET.TEST_SET, False,
transforms.Compose([
transforms.ToTensor(),
normalize,
]))
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=config.TRAIN.BATCH_SIZE * len(gpus),
shuffle=config.TRAIN.SHUFFLE,
num_workers=config.WORKERS,
pin_memory=True)
valid_loader = torch.utils.data.DataLoader(
valid_dataset,
batch_size=config.TEST.BATCH_SIZE * len(gpus),
shuffle=False,
num_workers=config.WORKERS,
pin_memory=True)
best_perf = 0.0
best_model = False
for epoch in range(config.TRAIN.BEGIN_EPOCH, config.TRAIN.END_EPOCH):
lr_scheduler.step()
#print("model check!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
#for i,p in enumerate(model.parameters()):
# print(p.requires_grad)
# train for one epoch
train(config, train_loader, model, criterion, optimizer, epoch,
final_output_dir, tb_log_dir, writer_dict)
# evaluate on validation set
perf_indicator = validate(config, valid_loader, valid_dataset, model,
criterion, final_output_dir, tb_log_dir,
writer_dict)
if perf_indicator > best_perf:
best_perf = perf_indicator
best_model = True
else:
best_model = False
logger.info('=> saving checkpoint to {}'.format(final_output_dir))
save_checkpoint(
{
'epoch': epoch + 1,
'model': get_model_name(config),
'state_dict': model.state_dict(),
'perf': perf_indicator,
'optimizer': optimizer.state_dict(),
}, best_model, final_output_dir)
final_model_state_file = os.path.join(final_output_dir,
'final_state.pth.tar')
logger.info(
'saving final model state to {}'.format(final_model_state_file))
torch.save(model.module.state_dict(), final_model_state_file)
writer_dict['writer'].close()
def main():
args = parse_args()
update_config(cfg, args)
logger, final_output_dir, tb_log_dir = create_logger(
cfg, args.cfg, 'train')
logger.info(pprint.pformat(args))
logger.info(cfg)
t_checkpoints = cfg.KD.TEACHER #注意是在student配置文件中修改
train_type = cfg.KD.TRAIN_TYPE #注意是在student配置文件中修改
train_type = get_train_type(train_type, t_checkpoints)
logger.info('=> train type is {} '.format(train_type))
if train_type == 'FPD':
cfg_name = 'student_' + os.path.basename(args.cfg).split('.')[0]
else:
cfg_name = os.path.basename(args.cfg).split('.')[0]
save_yaml_file(cfg_name, cfg, final_output_dir)
# cudnn related setting
cudnn.benchmark = cfg.CUDNN.BENCHMARK
torch.backends.cudnn.deterministic = cfg.CUDNN.DETERMINISTIC
torch.backends.cudnn.enabled = cfg.CUDNN.ENABLED
model = eval('models.' + cfg.MODEL.NAME + '.get_pose_net')(cfg,
is_train=True)
# fpd method, default NORMAL
if train_type == 'FPD':
tcfg = cfg.clone()
tcfg.defrost()
tcfg.merge_from_file(args.tcfg)
tcfg.freeze()
tcfg_name = 'teacher_' + os.path.basename(args.tcfg).split('.')[0]
save_yaml_file(tcfg_name, tcfg, final_output_dir)
# teacher model
tmodel = eval('models.' + tcfg.MODEL.NAME + '.get_pose_net')(
tcfg, is_train=False)
load_checkpoint(t_checkpoints,
tmodel,
strict=True,
model_info='teacher_' + tcfg.MODEL.NAME)
tmodel = torch.nn.DataParallel(tmodel, device_ids=cfg.GPUS).cuda()
# define kd_pose loss function (criterion) and optimizer
kd_pose_criterion = JointsMSELoss(
use_target_weight=tcfg.LOSS.USE_TARGET_WEIGHT).cuda()
# copy model file
this_dir = os.path.dirname(__file__)
shutil.copy2(
os.path.join(this_dir, '../lib/models', cfg.MODEL.NAME + '.py'),
final_output_dir)
# logger.info(pprint.pformat(model))
writer_dict = {
'writer': SummaryWriter(log_dir=tb_log_dir),
'train_global_steps': 0,
'valid_global_steps': 0,
}
dump_input = torch.rand(
(1, 3, cfg.MODEL.IMAGE_SIZE[1], cfg.MODEL.IMAGE_SIZE[0]))
writer_dict['writer'].add_graph(model, (dump_input, ))
logger.info(get_model_summary(model, dump_input))
if cfg.TRAIN.CHECKPOINT:
load_checkpoint(cfg.TRAIN.CHECKPOINT,
model,
strict=True,
model_info='student_' + cfg.MODEL.NAME)
model = torch.nn.DataParallel(model, device_ids=cfg.GPUS).cuda()
# you can choose or replace pose_loss and kd_pose_loss type, including mse,kl,ohkm loss ect
# define pose loss function (criterion) and optimizer
pose_criterion = JointsMSELoss(
use_target_weight=cfg.LOSS.USE_TARGET_WEIGHT).cuda()
# Data loading code
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_dataset = eval('dataset.' + cfg.DATASET.DATASET)(
cfg, cfg.DATASET.ROOT, cfg.DATASET.TRAIN_SET, True,
transforms.Compose([
transforms.ToTensor(),
normalize,
]))
valid_dataset = eval('dataset.' + cfg.DATASET.DATASET)(
cfg, cfg.DATASET.ROOT, cfg.DATASET.TEST_SET, False,
transforms.Compose([
transforms.ToTensor(),
normalize,
]))
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=cfg.TRAIN.BATCH_SIZE_PER_GPU * len(cfg.GPUS),
shuffle=cfg.TRAIN.SHUFFLE,
num_workers=cfg.WORKERS,
pin_memory=cfg.PIN_MEMORY)
valid_loader = torch.utils.data.DataLoader(
valid_dataset,
batch_size=cfg.TEST.BATCH_SIZE_PER_GPU * len(cfg.GPUS),
shuffle=False,
num_workers=cfg.WORKERS,
pin_memory=cfg.PIN_MEMORY)
best_perf = 0.0
best_model = False
last_epoch = -1
optimizer = get_optimizer(cfg, model)
begin_epoch = cfg.TRAIN.BEGIN_EPOCH
checkpoint_file = os.path.join(final_output_dir, 'checkpoint.pth')
if cfg.AUTO_RESUME and os.path.exists(checkpoint_file):
logger.info("=> loading checkpoint '{}'".format(checkpoint_file))
checkpoint = torch.load(checkpoint_file)
begin_epoch = checkpoint['epoch']
best_perf = checkpoint['perf']
last_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
logger.info("=> loaded checkpoint '{}' (epoch {})".format(
checkpoint_file, checkpoint['epoch']))
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,
cfg.TRAIN.LR_STEP,
cfg.TRAIN.LR_FACTOR,
last_epoch=last_epoch)
# evaluate on validation set
validate(cfg, valid_loader, valid_dataset, tmodel, pose_criterion,
final_output_dir, tb_log_dir, writer_dict)
validate(cfg, valid_loader, valid_dataset, model, pose_criterion,
final_output_dir, tb_log_dir, writer_dict)
for epoch in range(begin_epoch, cfg.TRAIN.END_EPOCH):
lr_scheduler.step()
# fpd method, default NORMAL
if train_type == 'FPD':
# train for one epoch
fpd_train(cfg, train_loader, model, tmodel, pose_criterion,
kd_pose_criterion, optimizer, epoch, final_output_dir,
tb_log_dir, writer_dict)
else:
# train for one epoch
train(cfg, train_loader, model, pose_criterion, optimizer, epoch,
final_output_dir, tb_log_dir, writer_dict)
# evaluate on validation set
perf_indicator = validate(cfg, valid_loader, valid_dataset, model,
pose_criterion, final_output_dir, tb_log_dir,
writer_dict)
if perf_indicator >= best_perf:
best_perf = perf_indicator
best_model = True
else:
best_model = False
logger.info('=> saving checkpoint to {}'.format(final_output_dir))
save_checkpoint(
{
'epoch': epoch + 1,
'model': cfg.MODEL.NAME,
'state_dict': model.state_dict(),
'best_state_dict': model.module.state_dict(),
'perf': perf_indicator,
'optimizer': optimizer.state_dict(),
}, best_model, final_output_dir)
final_model_state_file = os.path.join(final_output_dir, 'final_state.pth')
logger.info(
'=> saving final model state to {}'.format(final_model_state_file))
torch.save(model.module.state_dict(), final_model_state_file)
writer_dict['writer'].close()
def main_per_worker(process_index, ngpus_per_node, args):
update_config(cfg, args)
# torch seed
torch.cuda.manual_seed(random.random())
# cudnn
cudnn.benchmark = cfg.CUDNN.BENCHMARK
torch.backends.cudnn.enabled = cfg.CUDNN.ENABLED
#proc_rank
proc_rank = args.rank * ngpus_per_node + process_index
#create logger
logger, output_dir = create_logger(cfg, proc_rank)
# logger.info(pprint.pformat(args))
# logger.info(cfg)
model = InceptionResnetV1(pretrained='vggface2', classify=False, path=[cfg.MODEL.FEATURE_PATH, cfg.MODEL.LOGITS_PATH])
optimizer = get_optimizer(cfg, model)
model, optimizer, last_iter = load_checkpoint(cfg, model, optimizer)
lr_scheduler = get_lr_scheduler(cfg, optimizer, last_iter)
train_transform = FacenetTransform(size=(cfg.TRAIN.INPUT_MIN, cfg.TRAIN.INPUT_MAX))
train_dataset = FacenetTripletDataset(cfg.DATASET.ROOT, transform=train_transform, is_train=True)
eval_transform = FacenetTransform(size=cfg.TEST.TEST_SIZE)
eval_dataset = FacenetTripletDataset(cfg.DATASET.ROOT, transform=eval_transform, is_train=False)
# distribution
if args.distributed:
logger.info(
f'Init process group: dist_url: {args.dist_url}, '
f'world_size: {args.world_size}, '
f'machine: {args.rank}, '
f'rank:{proc_rank}'
)
dist.init_process_group(
backend=cfg.DIST_BACKEND,
init_method=args.dist_url,
world_size=args.world_size,
rank=proc_rank
)
torch.cuda.set_device(process_index)
model.cuda()
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[process_index]
)
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset
)
batch_size = cfg.DATASET.IMG_NUM_PER_GPU
else:
assert proc_rank == 0, ('proc_rank != 0, it will influence '
'the evaluation procedure')
model = torch.nn.DataParallel(model).cuda()
train_sampler = None
batch_size = cfg.DATASET.IMG_NUM_PER_GPU * ngpus_per_node
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=batch_size,
shuffle=(train_sampler is None),
drop_last=True,
collate_fn=facenet_triplet_collect,
num_workers=cfg.WORKERS,
pin_memory=True,
sampler=train_sampler
)
eval_loader = torch.utils.data.DataLoader(
eval_dataset,
batch_size=batch_size,
shuffle=False,
drop_last=False,
collate_fn=facenet_triplet_collect,
num_workers=cfg.WORKERS
)
criterion = triplet_loss
Trainer = get_trainer(
cfg,
model,
optimizer,
lr_scheduler,
criterion,
output_dir,
last_iter,
proc_rank,
)
while True:
Trainer.train(train_loader, eval_loader)
# eval
Trainer.evaluate(eval_loader)
def trainIters(args):
epoch_resume = 0
model_dir = os.path.join('../models/',
args.model_name + '_prev_inference_mask')
if args.resume:
# will resume training the model with name args.model_name
encoder_dict, decoder_dict, enc_opt_dict, dec_opt_dict, load_args = load_checkpoint(
args.model_name, args.use_gpu)
epoch_resume = load_args.epoch_resume
encoder = FeatureExtractor(load_args)
decoder = RSISMask(load_args)
encoder_dict, decoder_dict = check_parallel(encoder_dict, decoder_dict)
encoder.load_state_dict(encoder_dict)
decoder.load_state_dict(decoder_dict)
args = load_args
elif args.transfer:
# load model from args and replace last fc layer
encoder_dict, decoder_dict, _, _, load_args = load_checkpoint(
args.transfer_from, args.use_gpu)
encoder = FeatureExtractor(load_args)
decoder = RSISMask(args)
encoder_dict, decoder_dict = check_parallel(encoder_dict, decoder_dict)
encoder.load_state_dict(encoder_dict)
decoder.load_state_dict(decoder_dict)
else:
encoder = FeatureExtractor(args)
decoder = RSISMask(args)
# model checkpoints will be saved here
make_dir(model_dir)
# save parameters for future use
pickle.dump(args, open(os.path.join(model_dir, 'args.pkl'), 'wb'))
encoder_params = get_base_params(args, encoder)
skip_params = get_skip_params(encoder)
decoder_params = list(decoder.parameters()) + list(skip_params)
dec_opt = get_optimizer(args.optim, args.lr, decoder_params,
args.weight_decay)
enc_opt = get_optimizer(args.optim_cnn, args.lr_cnn, encoder_params,
args.weight_decay_cnn)
if args.resume:
enc_opt.load_state_dict(enc_opt_dict)
dec_opt.load_state_dict(dec_opt_dict)
from collections import defaultdict
dec_opt.state = defaultdict(dict, dec_opt.state)
if not args.log_term:
print("Training logs will be saved to:",
os.path.join(model_dir, 'train.log'))
sys.stdout = open(os.path.join(model_dir, 'train.log'), 'w')
sys.stderr = open(os.path.join(model_dir, 'train.err'), 'w')
print(args)
# objective function for mask
mask_siou = softIoULoss()
if args.use_gpu:
encoder.cuda()
decoder.cuda()
mask_siou.cuda()
crits = mask_siou
optims = [enc_opt, dec_opt]
if args.use_gpu:
torch.cuda.synchronize()
start = time.time()
# vars for early stopping
best_val_loss = args.best_val_loss
acc_patience = 0
mt_val = -1
# keep track of the number of batches in each epoch for continuity when plotting curves
loaders = init_dataloaders(args)
num_batches = {'train': 0, 'val': 0}
#area_range = [[0 ** 2, 1e5 ** 2], [0 ** 2, 20 ** 2], [20 ** 2, 59 ** 2], [59 ** 2, 1e5 ** 2]]
area_range = [[0**2, 1e5**2], [0**2, 30**2], [30**2, 90**2],
[90**2, 1e5**2]] #for (287,950))
resolution = 0
for e in range(args.max_epoch):
print("Epoch", e + epoch_resume)
# store losses in lists to display average since beginning
epoch_losses = {
'train': {
'total': [],
'iou': []
},
'val': {
'total': [],
'iou': []
}
}
# total mean for epoch will be saved here to display at the end
total_losses = {'total': [], 'iou': []}
# check if it's time to do some changes here
if e + epoch_resume >= args.finetune_after and not args.update_encoder and not args.finetune_after == -1:
print("Starting to update encoder")
args.update_encoder = True
acc_patience = 0
mt_val = -1
if args.loss_penalization:
if e < 10:
resolution = area_range[2]
else:
resolution = area_range[0]
# we validate after each epoch
for split in ['train', 'val']:
if args.dataset == 'davis2017' or args.dataset == 'youtube' or args.dataset == 'kittimots':
loaders[split].dataset.set_epoch(e)
for batch_idx, (inputs, targets, seq_name,
starting_frame) in enumerate(loaders[split]):
# send batch to GPU
prev_hidden_temporal_list = None
loss = None
last_frame = False
max_ii = min(len(inputs), args.length_clip)
for ii in range(max_ii):
# If are on the last frame from a clip, we will have to backpropagate the loss back to the beginning of the clip.
if ii == max_ii - 1:
last_frame = True
# x: input images (N consecutive frames from M different sequences)
# y_mask: ground truth annotations (some of them are zeros to have a fixed length in number of object instances)
# sw_mask: this mask indicates which masks from y_mask are valid
x, y_mask, sw_mask = batch_to_var(
args, inputs[ii], targets[ii])
if ii == 0:
prev_mask = y_mask
# From one frame to the following frame the prev_hidden_temporal_list is updated.
loss, losses, outs, hidden_temporal_list = runIter(
args, encoder, decoder, x, y_mask, sw_mask,
resolution, crits, optims, split, loss,
prev_hidden_temporal_list, prev_mask, last_frame)
# Hidden temporal state from time instant ii is saved to be used when processing next time instant ii+1
if args.only_spatial == False:
prev_hidden_temporal_list = hidden_temporal_list
prev_mask = outs
# store loss values in dictionary separately
epoch_losses[split]['total'].append(losses[0])
epoch_losses[split]['iou'].append(losses[1])
# print after some iterations
if (batch_idx + 1) % args.print_every == 0:
mt = np.mean(epoch_losses[split]['total'])
mi = np.mean(epoch_losses[split]['iou'])
te = time.time() - start
print("iter %d:\ttotal:%.4f\tiou:%.4f\ttime:%.4f" %
(batch_idx, mt, mi, te))
if args.use_gpu:
torch.cuda.synchronize()
start = time.time()
num_batches[split] = batch_idx + 1
# compute mean val losses within epoch
if split == 'val' and args.smooth_curves:
if mt_val == -1:
mt = np.mean(epoch_losses[split]['total'])
else:
mt = 0.9 * mt_val + 0.1 * np.mean(
epoch_losses[split]['total'])
mt_val = mt
else:
mt = np.mean(epoch_losses[split]['total'])
mi = np.mean(epoch_losses[split]['iou'])
# save train and val losses for the epoch
total_losses['iou'].append(mi)
total_losses['total'].append(mt)
args.epoch_resume = e + epoch_resume
print("Epoch %d:\ttotal:%.4f\tiou:%.4f\t(%s)" % (e, mt, mi, split))
if mt < (best_val_loss - args.min_delta):
print("Saving checkpoint.")
best_val_loss = mt
args.best_val_loss = best_val_loss
# saves model, params, and optimizers
save_checkpoint_prev_inference_mask(args, encoder, decoder,
enc_opt, dec_opt)
acc_patience = 0
else:
acc_patience += 1
if acc_patience > args.patience and not args.update_encoder and not args.finetune_after == -1:
print("Starting to update encoder")
acc_patience = 0
args.update_encoder = True
best_val_loss = 1000 # reset because adding a loss term will increase the total value
mt_val = -1
encoder_dict, decoder_dict, enc_opt_dict, dec_opt_dict, _ = load_checkpoint(
args.model_name, args.use_gpu)
encoder.load_state_dict(encoder_dict)
decoder.load_state_dict(decoder_dict)
enc_opt.load_state_dict(enc_opt_dict)
dec_opt.load_state_dict(dec_opt_dict)
# early stopping after N epochs without improvement
if acc_patience > args.patience_stop:
break
def main():
args = parse_args()
if args.dist_url == "env://" and args.world_size == -1:
args.world_size = int(os.environ["WORLD_SIZE"])
dist.init_process_group(backend=args.dist_backend,
init_method=args.dist_url)
logger, final_output_dir, tb_log_dir = create_logger(
config, args.cfg, 'train')
logger.info(pprint.pformat(args))
logger.info(pprint.pformat(config))
# cudnn related setting
cudnn.benchmark = config.CUDNN.BENCHMARK
torch.backends.cudnn.deterministic = config.CUDNN.DETERMINISTIC
torch.backends.cudnn.enabled = config.CUDNN.ENABLED
model = eval('models.' + config.MODEL.NAME + '.get_cls_net')(config)
dump_input = torch.rand(
(1, 3, config.MODEL.IMAGE_SIZE[1], config.MODEL.IMAGE_SIZE[0]))
logger.info(get_model_summary(model, dump_input))
# copy model file
# this_dir = os.path.dirname(__file__)
# models_dst_dir = os.path.join(final_output_dir, 'models')
# if os.path.exists(models_dst_dir):
# shutil.rmtree(models_dst_dir)
# shutil.copytree(os.path.join(this_dir, '../lib/models'), models_dst_dir)
writer_dict = {
'writer': SummaryWriter(log_dir=tb_log_dir),
'train_global_steps': 0,
'valid_global_steps': 0,
}
gpus = list(config.GPUS)
'''
model = torch.nn.DataParallel(model, device_ids=gpus).cuda()
'''
# Change DP to DDP
torch.cuda.set_device(args.local_rank)
model = model.to(args.local_rank)
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[args.local_rank], output_device=args.local_rank)
# define loss function (criterion) and optimizer
criterion = torch.nn.CrossEntropyLoss().cuda()
optimizer = get_optimizer(config, model)
best_perf = 0.0
best_model = False
last_epoch = config.TRAIN.BEGIN_EPOCH
if config.TRAIN.RESUME:
model_state_file = os.path.join(final_output_dir, 'checkpoint.pth.tar')
if os.path.isfile(model_state_file):
checkpoint = torch.load(model_state_file)
last_epoch = checkpoint['epoch']
best_perf = checkpoint['perf']
model.module.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
logger.info("=> loaded checkpoint (epoch {})".format(
checkpoint['epoch']))
best_model = True
if isinstance(config.TRAIN.LR_STEP, list):
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer, config.TRAIN.LR_STEP, config.TRAIN.LR_FACTOR,
last_epoch - 1)
else:
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
config.TRAIN.LR_STEP,
config.TRAIN.LR_FACTOR,
last_epoch - 1)
# Data loading code
traindir = os.path.join(config.DATASET.ROOT, config.DATASET.TRAIN_SET)
valdir = os.path.join(config.DATASET.ROOT, config.DATASET.TEST_SET)
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
'''
train_dataset = datasets.ImageFolder(
traindir,
transforms.Compose([
transforms.RandomResizedCrop(config.MODEL.IMAGE_SIZE[0]),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
])
)
'''
# Change to TSV dataset instance
train_dataset = TSVInstance(
traindir,
transforms.Compose([
transforms.RandomResizedCrop(config.MODEL.IMAGE_SIZE[0]),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
]))
# DDP requires DistributedSampler
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=config.TRAIN.BATCH_SIZE_PER_GPU,
shuffle=(train_sampler is None),
num_workers=config.WORKERS,
pin_memory=True,
sampler=train_sampler)
valid_loader = torch.utils.data.DataLoader(
TSVInstance(
valdir,
transforms.Compose([
transforms.Resize(int(config.MODEL.IMAGE_SIZE[0] / 0.875)),
transforms.CenterCrop(config.MODEL.IMAGE_SIZE[0]),
transforms.ToTensor(),
normalize,
])),
batch_size=config.TEST.BATCH_SIZE_PER_GPU,
shuffle=False,
num_workers=config.WORKERS,
pin_memory=True)
for epoch in range(last_epoch, config.TRAIN.END_EPOCH):
lr_scheduler.step()
# train for one epoch
train(config, train_loader, model, criterion, optimizer, epoch,
final_output_dir, tb_log_dir, writer_dict)
# evaluate on validation set
perf_indicator = validate(config, valid_loader, model, criterion,
final_output_dir, tb_log_dir, writer_dict)
if perf_indicator > best_perf:
best_perf = perf_indicator
best_model = True
else:
best_model = False
logger.info('=> saving checkpoint to {}'.format(final_output_dir))
save_checkpoint(
{
'epoch': epoch + 1,
'model': config.MODEL.NAME,
'state_dict': model.module.state_dict(),
'perf': perf_indicator,
'optimizer': optimizer.state_dict(),
},
best_model,
final_output_dir,
filename='checkpoint.pth.tar')
final_model_state_file = os.path.join(final_output_dir,
'final_state.pth.tar')
logger.info(
'saving final model state to {}'.format(final_model_state_file))
torch.save(model.module.state_dict(), final_model_state_file)
writer_dict['writer'].close()
from trainer.Trainer import Trainer
from torch.utils.tensorboard import SummaryWriter
from models.loss import PixWiseBCELoss
from datasets.PixWiseDataset import PixWiseDataset
from utils.utils import read_cfg, get_optimizer, build_network, get_device
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
cfg = read_cfg(cfg_file='config/densenet_161_adam_lr1e-3.yaml')
device = get_device(cfg)
network = build_network(cfg)
optimizer = get_optimizer(cfg, network)
loss = PixWiseBCELoss(beta=cfg['train']['loss']['beta'])
writer = SummaryWriter(cfg['log_dir'])
dump_input = torch.randn(1, 3, 224, 224)
writer.add_graph(network, (dump_input, ))
# Without Resize transform, images are of different sizes and it causes an error
train_transform = transforms.Compose([
transforms.Resize(cfg['model']['image_size']),
transforms.RandomRotation(cfg['dataset']['augmentation']['rotation']),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
def main():
args = parse_args()
reset_config(config, args)
logger, final_output_dir, tb_log_dir = create_logger(
config, args.cfg, "train")
logger.info(pprint.pformat(args))
logger.info(pprint.pformat(config))
# cudnn related setting
cudnn.benchmark = config.CUDNN.BENCHMARK
torch.backends.cudnn.deterministic = config.CUDNN.DETERMINISTIC
torch.backends.cudnn.enabled = config.CUDNN.ENABLED
model = eval("models." + config.MODEL.NAME + ".get_pose_net")(
config, is_train=True)
# copy model file
this_dir = os.path.dirname(__file__)
shutil.copy2(
os.path.join(this_dir, "../lib/models", config.MODEL.NAME + ".py"),
final_output_dir,
)
writer_dict = {
"writer": SummaryWriter(log_dir=tb_log_dir),
"train_global_steps": 0,
"valid_global_steps": 0,
}
dump_input = torch.rand((
config.TRAIN.BATCH_SIZE,
3,
config.MODEL.IMAGE_SIZE[1],
config.MODEL.IMAGE_SIZE[0],
))
writer_dict["writer"].add_graph(model, (dump_input, ), verbose=False)
gpus = [int(i) for i in config.GPUS.split(",")]
model = torch.nn.DataParallel(model, device_ids=gpus).cuda()
# define loss function (criterion) and optimizer
criterion = JointsMSELoss(
use_target_weight=config.LOSS.USE_TARGET_WEIGHT).cuda()
optimizer = get_optimizer(config, model)
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer, config.TRAIN.LR_STEP, config.TRAIN.LR_FACTOR)
# Data loading code
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_dataset = eval("dataset." + config.DATASET.DATASET)(
config,
config.DATASET.ROOT,
config.DATASET.TRAIN_SET,
True,
transforms.Compose([
transforms.ToTensor(),
normalize,
]),
)
valid_dataset = eval("dataset." + config.DATASET.DATASET)(
config,
config.DATASET.ROOT,
config.DATASET.TEST_SET,
False,
transforms.Compose([
transforms.ToTensor(),
normalize,
]),
)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=config.TRAIN.BATCH_SIZE * len(gpus),
shuffle=config.TRAIN.SHUFFLE,
num_workers=config.WORKERS,
pin_memory=True,
)
valid_loader = torch.utils.data.DataLoader(
valid_dataset,
batch_size=config.TEST.BATCH_SIZE * len(gpus),
shuffle=False,
num_workers=config.WORKERS,
pin_memory=True,
)
best_perf = 0.0
best_model = False
for epoch in range(config.TRAIN.BEGIN_EPOCH, config.TRAIN.END_EPOCH):
lr_scheduler.step()
# train for one epoch
train(
config,
train_loader,
model,
criterion,
optimizer,
epoch,
final_output_dir,
tb_log_dir,
writer_dict,
)
# evaluate on validation set
perf_indicator = validate(
config,
valid_loader,
valid_dataset,
model,
criterion,
final_output_dir,
tb_log_dir,
writer_dict,
)
if perf_indicator > best_perf:
best_perf = perf_indicator
best_model = True
else:
best_model = False
logger.info("=> saving checkpoint to {}".format(final_output_dir))
save_checkpoint(
{
"epoch": epoch + 1,
"model": get_model_name(config),
"state_dict": model.state_dict(),
"perf": perf_indicator,
"optimizer": optimizer.state_dict(),
},
best_model,
final_output_dir,
)
final_model_state_file = os.path.join(final_output_dir,
"final_state.pth.tar")
logger.info(
"saving final model state to {}".format(final_model_state_file))
torch.save(model.module.state_dict(), final_model_state_file)
writer_dict["writer"].close()
def main_worker(gpu, ngpus_per_node, args, final_output_dir, tb_log_dir):
args.gpu = gpu
args.rank = args.rank * ngpus_per_node + gpu
print('Init process group: dist_url: {}, world_size: {}, rank: {}'.format(cfg.DIST_URL, args.world_size, args.rank))
dist.init_process_group(backend=cfg.DIST_BACKEND, init_method=cfg.DIST_URL, world_size=args.world_size, rank=args.rank)
update_config(cfg, args)
# setup logger
logger, _ = setup_logger(final_output_dir, args.rank, 'train')
model = eval('models.'+cfg.MODEL.NAME+'.get_pose_net')(cfg, is_train=True)
logger.info(get_model_summary(model, torch.zeros(1, 3, *cfg.MODEL.IMAGE_SIZE)))
# copy model file
if not cfg.MULTIPROCESSING_DISTRIBUTED or (cfg.MULTIPROCESSING_DISTRIBUTED and args.rank % ngpus_per_node == 0):
this_dir = os.path.dirname(__file__)
shutil.copy2(os.path.join(this_dir, '../lib/models', cfg.MODEL.NAME + '.py'), final_output_dir)
writer_dict = {
'writer': SummaryWriter(log_dir=tb_log_dir),
'train_global_steps': 0,
'valid_global_steps': 0,
}
if not cfg.MULTIPROCESSING_DISTRIBUTED or (cfg.MULTIPROCESSING_DISTRIBUTED and args.rank % ngpus_per_node == 0):
dump_input = torch.rand((1, 3, cfg.MODEL.IMAGE_SIZE[1], cfg.MODEL.IMAGE_SIZE[0]))
writer_dict['writer'].add_graph(model, (dump_input, ))
# logger.info(get_model_summary(model, dump_input, verbose=cfg.VERBOSE))
if cfg.MODEL.SYNC_BN:
model = nn.SyncBatchNorm.convert_sync_batchnorm(model)
torch.cuda.set_device(args.gpu)
model.cuda(args.gpu)
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
# define loss function (criterion) and optimizer
criterion = JointsMSELoss(use_target_weight=cfg.LOSS.USE_TARGET_WEIGHT).cuda(args.gpu)
# Data loading code
train_dataset = eval('dataset.'+cfg.DATASET.DATASET)(
cfg, cfg.DATASET.ROOT, cfg.DATASET.TRAIN_SET, True,
transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
)
valid_dataset = eval('dataset.'+cfg.DATASET.DATASET)(
cfg, cfg.DATASET.ROOT, cfg.DATASET.TEST_SET, False,
transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
)
train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=cfg.TRAIN.BATCH_SIZE_PER_GPU*len(cfg.GPUS),
shuffle=(train_sampler is None),
num_workers=cfg.WORKERS,
pin_memory=cfg.PIN_MEMORY,
sampler=train_sampler
)
valid_loader = torch.utils.data.DataLoader(
valid_dataset,
batch_size=cfg.TEST.BATCH_SIZE_PER_GPU*len(cfg.GPUS),
shuffle=False,
num_workers=cfg.WORKERS,
pin_memory=cfg.PIN_MEMORY
)
logger.info(train_loader.dataset)
best_perf = -1
best_model = False
last_epoch = -1
optimizer = get_optimizer(cfg, model)
begin_epoch = cfg.TRAIN.BEGIN_EPOCH
checkpoint_file = os.path.join(final_output_dir, 'checkpoint.pth')
if cfg.AUTO_RESUME and os.path.exists(checkpoint_file):
logger.info("=> loading checkpoint '{}'".format(checkpoint_file))
checkpoint = torch.load(checkpoint_file)
begin_epoch = checkpoint['epoch']
best_perf = checkpoint['perf']
last_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
logger.info("=> loaded checkpoint '{}' (epoch {})".format(checkpoint_file, checkpoint['epoch']))
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer, cfg.TRAIN.LR_STEP, cfg.TRAIN.LR_FACTOR,
last_epoch=last_epoch)
for epoch in range(begin_epoch, cfg.TRAIN.END_EPOCH):
# train for one epoch
train(cfg, train_loader, model, criterion, optimizer, epoch,
final_output_dir, tb_log_dir, writer_dict)
# In PyTorch 1.1.0 and later, you should call `lr_scheduler.step()` after `optimizer.step()`.
lr_scheduler.step()
# evaluate on validation set
perf_indicator = validate(
args, cfg, valid_loader, valid_dataset, model, criterion,
final_output_dir, tb_log_dir, writer_dict
)
if perf_indicator >= best_perf:
best_perf = perf_indicator
best_model = True
else:
best_model = False
if not cfg.MULTIPROCESSING_DISTRIBUTED or (
cfg.MULTIPROCESSING_DISTRIBUTED
and args.rank == 0
):
logger.info('=> saving checkpoint to {}'.format(final_output_dir))
save_checkpoint({
'epoch': epoch + 1,
'model': cfg.MODEL.NAME,
'state_dict': model.state_dict(),
'best_state_dict': model.module.state_dict(),
'perf': perf_indicator,
'optimizer': optimizer.state_dict(),
}, best_model, final_output_dir)
final_model_state_file = os.path.join(
final_output_dir, 'final_state{}.pth.tar'.format(gpu)
)
logger.info('saving final model state to {}'.format(
final_model_state_file))
torch.save(model.module.state_dict(), final_model_state_file)
writer_dict['writer'].close()
def main():
args = parse_args()
# cudnn related setting
cudnn.benchmark = config.CUDNN.BENCHMARK
torch.backends.cudnn.deterministic = config.CUDNN.DETERMINISTIC
torch.backends.cudnn.enabled = config.CUDNN.ENABLED
model = eval('models.' + config.MODEL.NAME + '.get_nnb')(config)
writer_dict = {
'writer': SummaryWriter(log_dir='./output/facexray'),
'train_global_steps': 0,
'valid_global_steps': 0,
}
gpus = list(config.GPUS)
model = torch.nn.DataParallel(model)
# define loss function (criterion) and optimizer
criterion = Loss()
optimizer = get_optimizer(config, model)
last_epoch = config.TRAIN.BEGIN_EPOCH
if isinstance(config.TRAIN.LR_STEP, list):
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer, config.TRAIN.LR_STEP, config.TRAIN.LR_FACTOR,
last_epoch - 1)
else:
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
config.TRAIN.LR_STEP,
config.TRAIN.LR_FACTOR,
last_epoch - 1)
# Data loading code
# list_name没有单独标注在.yaml文件
# transform还没能适用于其他规格，应做成[256, 256, 3]
train_dataset = eval('dataset.' + config.DATASET.DATASET + '.' +
config.DATASET.DATASET)(
config.DATASET.ROOT, config.DATASET.TRAIN_SET,
None, transforms.Compose([transforms.ToTensor()]))
valid_dataset = eval('dataset.' + config.DATASET.DATASET + '.' +
config.DATASET.DATASET)(config.DATASET.ROOT,
config.DATASET.TEST_SET, None,
transforms.Compose(
[transforms.ToTensor()]))
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=config.TRAIN.BATCH_SIZE_PER_GPU,
shuffle=config.TRAIN.SHUFFLE,
num_workers=config.WORKERS,
pin_memory=config.PIN_MEMORY)
valid_loader = torch.utils.data.DataLoader(
valid_dataset,
batch_size=config.TEST.BATCH_SIZE_PER_GPU,
shuffle=False,
num_workers=config.WORKERS,
pin_memory=config.PIN_MEMORY)
for epoch in range(last_epoch, config.TRAIN.END_EPOCH):
lr_scheduler.step()
# 前50000次迭代锁定原hrnet层参数训练，后面的迭代训练所有参数
if epoch == 150000:
for k, v in model.named_parameters():
v.requires_grad = True
# train for one epoch
train(config, train_loader, model, criterion, optimizer, epoch,
writer_dict)
# evaluate on validation set
validate(config, valid_loader, model, criterion, writer_dict)
torch.save(model.module.state_dict(), './output/BI_dataset/faceXray.pth')
writer_dict['writer'].close()
model = get_model(cfg, [l1_cls_num, l2_cls_num], device, logger)
if cfg.TRAIN_STAGE == 2:
last_stage_weight_path = os.path.join(model_dir, 'best_model_stage1.pth')
load_weight(model, last_stage_weight_path)
model.module.freeze_backbone()
model.module.freeze_classifer(0)
elif cfg.TRAIN_STAGE == 1:
last_stage_weight_path = os.path.join(args.pretrained_path)
load_weight(model, last_stage_weight_path)
model.module.freeze_backbone()
model.module.freeze_classifer(1)
# load_pretrained_weight(model, args.pretrained_path)
combiner = Combiner(cfg, device)
optimizer = get_optimizer(cfg, model)
scheduler = get_scheduler(cfg, optimizer)
# ----- END MODEL BUILDER -----
trainLoader = DataLoader(
train_set,
batch_size=cfg.TRAIN.BATCH_SIZE,
shuffle=cfg.TRAIN.SHUFFLE,
num_workers=cfg.TRAIN.NUM_WORKERS,
pin_memory=cfg.PIN_MEMORY,
drop_last=True
)
validLoader = DataLoader(
valid_set,
batch_size=cfg.TEST.BATCH_SIZE,
def main_worker(
gpu, ngpus_per_node, args, final_output_dir, tb_log_dir
):
# cudnn related setting
cudnn.benchmark = cfg.CUDNN.BENCHMARK
torch.backends.cudnn.deterministic = cfg.CUDNN.DETERMINISTIC
torch.backends.cudnn.enabled = cfg.CUDNN.ENABLED
if cfg.FP16.ENABLED:
assert torch.backends.cudnn.enabled, "fp16 mode requires cudnn backend to be enabled."
if cfg.FP16.STATIC_LOSS_SCALE != 1.0:
if not cfg.FP16.ENABLED:
print("Warning: if --fp16 is not used, static_loss_scale will be ignored.")
args.gpu = gpu
if args.gpu is not None:
print("Use GPU: {} for training".format(args.gpu))
if args.distributed:
if args.dist_url == "env://" and args.rank == -1:
args.rank = int(os.environ["RANK"])
if cfg.MULTIPROCESSING_DISTRIBUTED:
# For multiprocessing distributed training, rank needs to be the
# global rank among all the processes
args.rank = args.rank * ngpus_per_node + gpu
print('Init process group: dist_url: {}, world_size: {}, rank: {}'.
format(args.dist_url, args.world_size, args.rank))
dist.init_process_group(
backend=cfg.DIST_BACKEND,
init_method=args.dist_url,
world_size=args.world_size,
rank=args.rank
)
update_config(cfg, args)
# setup logger
logger, _ = setup_logger(final_output_dir, args.rank, 'train')
model = eval('models.'+cfg.MODEL.NAME+'.get_pose_net')(
cfg, is_train=True
)
# copy model file
if not cfg.MULTIPROCESSING_DISTRIBUTED or (
cfg.MULTIPROCESSING_DISTRIBUTED
and args.rank % ngpus_per_node == 0
):
this_dir = os.path.dirname(__file__)
shutil.copy2(
os.path.join(this_dir, '../lib/models', cfg.MODEL.NAME + '.py'),
final_output_dir
)
writer_dict = {
'writer': SummaryWriter(log_dir=tb_log_dir),
'train_global_steps': 0,
'valid_global_steps': 0,
}
if not cfg.MULTIPROCESSING_DISTRIBUTED or (
cfg.MULTIPROCESSING_DISTRIBUTED
and args.rank % ngpus_per_node == 0
):
dump_input = torch.rand(
(1, 3, cfg.DATASET.INPUT_SIZE, cfg.DATASET.INPUT_SIZE)
)
writer_dict['writer'].add_graph(model, (dump_input, ))
# logger.info(get_model_summary(model, dump_input, verbose=cfg.VERBOSE))
if cfg.FP16.ENABLED:
model = network_to_half(model)
if args.distributed:
# For multiprocessing distributed, DistributedDataParallel constructor
# should always set the single device scope, otherwise,
# DistributedDataParallel will use all available devices.
if args.gpu is not None:
torch.cuda.set_device(args.gpu)
model.cuda(args.gpu)
# When using a single GPU per process and per
# DistributedDataParallel, we need to divide the batch size
# ourselves based on the total number of GPUs we have
# args.workers = int(args.workers / ngpus_per_node)
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[args.gpu]
)
else:
model.cuda()
# DistributedDataParallel will divide and allocate batch_size to all
# available GPUs if device_ids are not set
model = torch.nn.parallel.DistributedDataParallel(model)
elif args.gpu is not None:
torch.cuda.set_device(args.gpu)
model = model.cuda(args.gpu)
else:
model = torch.nn.DataParallel(model).cuda()
# define loss function (criterion) and optimizer
loss_factory = MultiLossFactory(cfg).cuda()
# Data loading code
train_loader = make_dataloader(
cfg, is_train=True, distributed=args.distributed
)
logger.info(train_loader.dataset)
best_perf = -1
best_model = False
last_epoch = -1
optimizer = get_optimizer(cfg, model)
if cfg.FP16.ENABLED:
optimizer = FP16_Optimizer(
optimizer,
static_loss_scale=cfg.FP16.STATIC_LOSS_SCALE,
dynamic_loss_scale=cfg.FP16.DYNAMIC_LOSS_SCALE
)
begin_epoch = cfg.TRAIN.BEGIN_EPOCH
checkpoint_file = os.path.join(
final_output_dir, 'checkpoint.pth.tar')
if cfg.AUTO_RESUME and os.path.exists(checkpoint_file):
logger.info("=> loading checkpoint '{}'".format(checkpoint_file))
checkpoint = torch.load(checkpoint_file)
begin_epoch = checkpoint['epoch']
best_perf = checkpoint['perf']
last_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
logger.info("=> loaded checkpoint '{}' (epoch {})".format(
checkpoint_file, checkpoint['epoch']))
if cfg.FP16.ENABLED:
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer.optimizer, cfg.TRAIN.LR_STEP, cfg.TRAIN.LR_FACTOR,
last_epoch=last_epoch
)
else:
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer, cfg.TRAIN.LR_STEP, cfg.TRAIN.LR_FACTOR,
last_epoch=last_epoch
)
for epoch in range(begin_epoch, cfg.TRAIN.END_EPOCH):
lr_scheduler.step()
# train one epoch
do_train(cfg, model, train_loader, loss_factory, optimizer, epoch,
final_output_dir, tb_log_dir, writer_dict, fp16=cfg.FP16.ENABLED)
perf_indicator = epoch
if perf_indicator >= best_perf:
best_perf = perf_indicator
best_model = True
else:
best_model = False
if not cfg.MULTIPROCESSING_DISTRIBUTED or (
cfg.MULTIPROCESSING_DISTRIBUTED
and args.rank == 0
):
logger.info('=> saving checkpoint to {}'.format(final_output_dir))
save_checkpoint({
'epoch': epoch + 1,
'model': cfg.MODEL.NAME,
'state_dict': model.state_dict(),
'best_state_dict': model.module.state_dict(),
'perf': perf_indicator,
'optimizer': optimizer.state_dict(),
}, best_model, final_output_dir)
final_model_state_file = os.path.join(
final_output_dir, 'final_state{}.pth.tar'.format(gpu)
)
logger.info('saving final model state to {}'.format(
final_model_state_file))
torch.save(model.module.state_dict(), final_model_state_file)
writer_dict['writer'].close()
def build_optimizer(self):
for name, module in self.named_modules():
optim_name = name.replace('net', 'optimizer')
setattr(self, optim_name, utils.get_optimizer(self.opt, module))
def main():
args = parse_args()
update_config(cfg, args)
logger, final_output_dir, tb_log_dir = create_logger(
cfg, args.cfg, 'train')
logger.info(pprint.pformat(args))
logger.info(cfg)
# cudnn related setting
cudnn.benchmark = cfg.CUDNN.BENCHMARK
# 用于加快训练速度，同时避免benchmark的随机性
torch.backends.cudnn.deterministic = cfg.CUDNN.DETERMINISTIC
torch.backends.cudnn.enabled = cfg.CUDNN.ENABLED
model = eval('models.' + cfg.MODEL.NAME + '.get_pose_net')(
cfg, is_train=True) # eval()函数执行一个字符串表达式，并返回表达式的值
# copy model file
this_dir = os.path.dirname(__file__) # 取当前路径
shutil.copy2(
os.path.join(this_dir, '../lib/models', cfg.MODEL.NAME + '.py'),
final_output_dir)
# logger.info(pprint.pformat(model))
writer_dict = {
'writer': SummaryWriter(log_dir=tb_log_dir),
'train_global_steps': 0,
'valid_global_steps': 0,
}
dump_input = torch.rand(
(1, 3, cfg.MODEL.IMAGE_SIZE[1], cfg.MODEL.IMAGE_SIZE[0]))
writer_dict['writer'].add_graph(model, (dump_input, ))
logger.info(get_model_summary(model, dump_input)) # 记录模型日志
model = torch.nn.DataParallel(model, device_ids=cfg.GPUS).cuda()
#model = torch.nn.DataParallel(model, device_ids=[0]).cuda()
# 多GPU训练
# define loss function (criterion) and optimizer
criterion = JointsMSELoss(
use_target_weight=cfg.LOSS.USE_TARGET_WEIGHT).cuda()
regress_loss = RegLoss(use_target_weight=cfg.LOSS.USE_TARGET_WEIGHT).cuda()
# Data loading code
normalize = transforms.Normalize(
# 使用Imagenet的均值和标准差进行归一化
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_dataset = eval('dataset.' + cfg.DATASET.DATASET)(
cfg, cfg.DATASET.ROOT, cfg.DATASET.TRAIN_SET, True,
transforms.Compose([
transforms.ToTensor(),
normalize,
]))
valid_dataset = eval('dataset.' + cfg.DATASET.DATASET)(
cfg, cfg.DATASET.ROOT, cfg.DATASET.TEST_SET, False,
transforms.Compose([
transforms.ToTensor(),
normalize,
])) # 图像处理
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=cfg.TRAIN.BATCH_SIZE_PER_GPU * len(cfg.GPUS),
shuffle=cfg.TRAIN.SHUFFLE,
num_workers=cfg.WORKERS,
pin_memory=cfg.PIN_MEMORY,
)
valid_loader = torch.utils.data.DataLoader(
valid_dataset,
batch_size=cfg.TEST.BATCH_SIZE_PER_GPU * len(cfg.GPUS),
shuffle=False,
num_workers=cfg.WORKERS,
pin_memory=cfg.PIN_MEMORY,
)
best_perf = 0.0
best_model = False
last_epoch = -1
optimizer = get_optimizer(cfg, model)
begin_epoch = cfg.TRAIN.BEGIN_EPOCH
checkpoint_file = os.path.join(final_output_dir, 'checkpoint.pth')
if cfg.AUTO_RESUME and os.path.exists(checkpoint_file):
logger.info("=> loading checkpoint '{}'".format(checkpoint_file))
checkpoint = torch.load(checkpoint_file)
begin_epoch = checkpoint['epoch']
best_perf = checkpoint['perf']
last_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
logger.info("=> loaded checkpoint '{}' (epoch {})".format(
checkpoint_file, checkpoint['epoch']))
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,
cfg.TRAIN.LR_STEP,
cfg.TRAIN.LR_FACTOR,
last_epoch=last_epoch)
for epoch in range(begin_epoch, cfg.TRAIN.END_EPOCH):
lr_scheduler.step()
# train for one epoch
train(cfg, train_loader, model, criterion, regress_loss, optimizer,
epoch, final_output_dir, tb_log_dir, writer_dict)
# evaluate on validation set
perf_indicator = validate(cfg, valid_loader, valid_dataset, model,
criterion, regress_loss, final_output_dir,
tb_log_dir, writer_dict)
if perf_indicator >= best_perf:
best_perf = perf_indicator
best_model = True
else:
best_model = False
logger.info('=> saving checkpoint to {}'.format(final_output_dir))
save_checkpoint(
{
'epoch': epoch + 1,
'model': cfg.MODEL.NAME,
'state_dict': model.state_dict(),
'best_state_dict': model.module.state_dict(),
'perf': perf_indicator,
'optimizer': optimizer.state_dict(),
}, best_model, final_output_dir)
final_model_state_file = os.path.join(final_output_dir, 'final_state.pth')
logger.info(
'=> saving final model state to {}'.format(final_model_state_file))
torch.save(model.module.state_dict(), final_model_state_file)
writer_dict['writer'].close()
shuffle=config.TEST.SHUFFLE,
num_workers=config.WORKERS,
pin_memory=config.PIN_MEMORY,
)
# get device
if torch.cuda.is_available():
device = torch.device("cuda:{}".format(config.GPUID))
else:
device = torch.device("cpu:0")
model = crnn.get_crnn(config)
model = model.to(device)
model_info(model)
print(model)
optimizer = get_optimizer(config, model)
last_epoch = config.TRAIN.BEGIN_EPOCH
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer, config.TRAIN.LR_STEP, config.TRAIN.LR_FACTOR,
last_epoch - 1)
if config.ATTENTION.ENABLE:
criterion = torch.nn.NLLLoss()
else:
criterion = torch.nn.CTCLoss()
# 训练
best_acc = 0.0
for epoch in range(last_epoch, config.TRAIN.END_EPOCH):
model.train()
for i, (inp, idx) in enumerate(train_loader):
# 前馈,计算loss
inp = inp.to(device)
