def train_proxy(opt):
logging.info(opt)
# Set random seed
mx.random.seed(opt.seed)
np.random.seed(opt.seed)
# Setup computation context
context = get_context(opt.gpus, logging)
run_results = []
# Adjust batch size to each compute context
batch_size = opt.batch_size * len(context)
# Prepare feature extractor
if opt.model == 'inception-bn':
feature_net, feature_params = get_feature_model(opt.model, ctx=context)
data_shape = 224
scale_image_data = False
elif opt.model == 'resnet50_v2':
feature_net = mx.gluon.model_zoo.vision.resnet50_v2(
pretrained=True, ctx=context).features
data_shape = 224
scale_image_data = True
else:
raise RuntimeError('Unsupported model: %s' % opt.model)
# Prepare datasets
train_dataset, val_dataset = get_dataset(opt.dataset,
opt.data_path,
data_shape=data_shape,
use_crops=opt.use_crops,
use_aug=True,
with_proxy=True,
scale_image_data=scale_image_data)
logging.info('Training with %d classes, validating with %d classes' %
(train_dataset.num_classes(), val_dataset.num_classes()))
if opt.iteration_per_epoch > 0:
train_dataset, _ = get_dataset_iterator(
opt.dataset,
opt.data_path,
batch_k=(opt.batch_size //
3) if opt.loss == 'xentropy' else opt.batch_k,
batch_size=opt.batch_size,
data_shape=data_shape,
use_crops=opt.use_crops,
scale_image_data=scale_image_data,
batchify=False)
train_dataloader = mx.gluon.data.DataLoader(
DatasetIterator(train_dataset,
opt.iteration_per_epoch,
'next_proxy_sample',
call_params={
'sampled_classes':
(opt.batch_size // opt.batch_k) if
(opt.batch_k is not None) else None,
'chose_classes_randomly':
True,
}),
batch_size=1,
shuffle=False,
num_workers=opt.num_workers,
last_batch='keep')
else:
train_dataloader = mx.gluon.data.DataLoader(
train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=opt.num_workers,
last_batch='rollover')
val_dataloader = mx.gluon.data.DataLoader(val_dataset,
batch_size=batch_size,
shuffle=False,
num_workers=opt.num_workers,
last_batch='keep')
# Prepare proxy model
net = ProxyNet(feature_net,
opt.embed_dim,
num_classes=train_dataset.num_classes())
if opt.lr is None:
logging.info('Using variable learning rate')
opt.lr = max([opt.lr_proxynca, opt.lr_embedding, opt.lr_inception])
for p, v in net.encoder.collect_params().items():
v.lr_mult = opt.lr_embedding / opt.lr
for p, v in net.base_net.collect_params().items():
v.lr_mult = opt.lr_inception / opt.lr
for p, v in net.proxies.collect_params().items():
v.lr_mult = opt.lr_proxynca / opt.lr
else:
logging.info('Using single learning rate: %f' % opt.lr)
for run in range(1, opt.number_of_runs + 1):
logging.info('Starting run %d/%d' % (run, opt.number_of_runs))
# reset networks
if opt.model == 'inception-bn':
net.base_net.collect_params().load(feature_params,
ctx=context,
ignore_extra=True)
if opt.dataset == 'CUB':
for v in net.base_net.collect_params().values():
if v.name in ['batchnorm', 'bn_']:
v.grad_req = 'null'
elif opt.model == 'resnet50_v2':
logging.info('Lowering LR for Resnet backbone')
net.base_net = mx.gluon.model_zoo.vision.resnet50_v2(
pretrained=True, ctx=context).features
# Use a smaller learning rate for pre-trained convolutional layers.
for v in net.base_net.collect_params().values():
if 'conv' in v.name:
setattr(v, 'lr_mult', 0.01)
else:
raise NotImplementedError('Unknown model: %s' % opt.model)
if opt.loss == 'triplet':
net.encoder.initialize(mx.init.Xavier(magnitude=0.2),
ctx=context,
force_reinit=True)
net.proxies.initialize(mx.init.Xavier(magnitude=0.2),
ctx=context,
force_reinit=True)
else:
net.init(TruncNorm(stdev=0.001), ctx=context, init_basenet=False)
if not opt.disable_hybridize:
net.hybridize()
run_result = train(net, opt, train_dataloader, val_dataloader, context,
run)
run_results.append(run_result)
logging.info('Run %d finished with %f' % (run, run_result[0][1]))
logging.info(
'Average validation of %d runs:\n%s' %
(opt.number_of_runs, format_results(average_results(run_results))))
def train_discriminative(opt):
logging.info(opt)
# Settings.
mx.random.seed(opt.seed)
np.random.seed(opt.seed)
# Setup computation context
context = get_context(opt.gpus, logging)
run_results = []
# Get model
if opt.model == 'inception-bn':
feature_net, feature_params = get_feature_model(opt.model, ctx=context)
feature_net.collect_params().load(feature_params,
ctx=context,
ignore_extra=True)
data_shape = 224
scale_image_data = False
elif opt.model == 'resnet50_v2':
feature_net = mx.gluon.model_zoo.vision.resnet50_v2(
pretrained=True, ctx=context).features
data_shape = 224
scale_image_data = True
else:
raise RuntimeError('Unsupported model: %s' % opt.model)
# Get data iterators
train_dataset, val_dataset = get_dataset(opt.dataset,
opt.data_path,
data_shape=data_shape,
use_crops=opt.use_crops,
use_aug=True,
with_proxy=True,
scale_image_data=scale_image_data)
logging.info('Training with %d classes, validating with %d classes' %
(train_dataset.num_classes(), val_dataset.num_classes()))
train_dataloader = mx.gluon.data.DataLoader(train_dataset,
batch_size=opt.batch_size,
shuffle=True,
num_workers=opt.num_workers,
last_batch='rollover')
val_dataloader = mx.gluon.data.DataLoader(val_dataset,
batch_size=opt.batch_size,
shuffle=False,
num_workers=opt.num_workers,
last_batch='keep')
net = EmbeddingNet(
feature_net,
[opt.embed_dim, train_dataset.num_classes()],
normalize=True,
dropout=True)
if opt.model == 'resnet50_v2':
# Use a smaller learning rate for pre-trained convolutional layers.
for v in net.base_net.collect_params().values():
if 'conv' in v.name:
setattr(v, 'lr_mult', 0.01)
# main run loop for multiple training runs
for run in range(1, opt.number_of_runs + 1):
logging.info('Starting run %d/%d' % (run, opt.number_of_runs))
net.init(mx.init.Xavier(magnitude=0.2),
ctx=context,
init_basenet=False)
if opt.model == 'inception-bn':
net.base_net.collect_params().load(feature_params,
ctx=context,
ignore_extra=True)
elif opt.model == 'resnet50_v2':
net.base_net = mx.gluon.model_zoo.vision.resnet50_v2(
pretrained=True, ctx=context).features
else:
raise RuntimeError('Unsupported model: %s' % opt.model)
if not opt.disable_hybridize:
net.hybridize()
run_result = train(net, opt, train_dataloader, val_dataloader, context,
run)
run_results.append(run_result)
logging.info('Run %d finished with %f' % (run, run_result[0][1]))
logging.info(
'Average validation of %d runs:\n%s' %
(opt.number_of_runs, format_results(average_results(run_results))))
def train_normproxy(opt):
logging.info(opt)
# Set random seed
mx.random.seed(opt.seed)
np.random.seed(opt.seed)
# Setup computation context
context = get_context(opt.gpus, logging)
# Adjust batch size to each compute context
batch_size = opt.batch_size * len(context)
run_results = []
# Prepare feature extractor
if opt.model == 'inception-bn':
feature_net, feature_params = get_feature_model(opt.model, ctx=context)
feature_net.collect_params().load(feature_params, ctx=context, ignore_extra=True)
data_shape = 224
scale_image_data = False
feature_size = 1024
elif opt.model == 'resnet50_v2':
feature_params = None
feature_net = mx.gluon.model_zoo.vision.resnet50_v2(pretrained=True, ctx=context).features
data_shape = 224
scale_image_data = True
feature_size = 2048
else:
raise RuntimeError('Unsupported model: %s' % opt.model)
# Prepare datasets
train_dataset, val_dataset = get_dataset(opt.dataset, opt.data_path, data_shape=data_shape, use_crops=opt.use_crops,
use_aug=True, with_proxy=True, scale_image_data=scale_image_data)
logging.info(
'Training with %d classes, validating with %d classes' % (
train_dataset.num_classes(), val_dataset.num_classes()))
if opt.batch_k > 0:
train_dataset, _ = get_dataset_iterator(opt.dataset, opt.data_path, batch_k=opt.batch_k,
batch_size=batch_size, data_shape=data_shape, use_crops=opt.use_crops,
scale_image_data=scale_image_data)
train_dataloader = train_dataset
else:
train_dataloader = mx.gluon.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True,
num_workers=opt.num_workers, last_batch='rollover')
val_dataloader = mx.gluon.data.DataLoader(val_dataset, batch_size=batch_size, shuffle=False,
num_workers=opt.num_workers, last_batch='keep')
num_train_classes = train_dataset.num_classes()
# Prepare proxy model
net = NormProxyNet(feature_net, opt.embed_dim, num_classes=train_dataset.num_classes(),
feature_size=feature_size, no_fc=opt.no_fc, dropout=opt.dropout, static_proxies=opt.static_proxies)
# main run loop for multiple training runs
for run in range(1, opt.number_of_runs + 1):
logging.info('Starting run %d/%d' % (run, opt.number_of_runs))
# reset networks
if opt.model == 'inception-bn':
net.base_net.collect_params().load(feature_params, ctx=context, ignore_extra=True)
elif opt.model == 'resnet50_v2':
net.base_net = mx.gluon.model_zoo.vision.resnet50_v2(pretrained=True, ctx=context).features
# Use a smaller learning rate for pre-trained convolutional layers.
logging.info('Lowering LR for Resnet backbone by 100x')
for v in net.base_net.collect_params().values():
if 'conv' in v.name:
setattr(v, 'lr_mult', 0.01)
else:
raise NotImplementedError('Unknown model: %s' % opt.model)
if opt.start_epoch != 1:
param_file = 'normproxy_model.params'
logging.info('Loading parameters from %s' % param_file)
net.load_parameters(param_file, ctx=context)
else:
if opt.model == 'resnet50_v2':
net.init(mx.init.Xavier(magnitude=2), ctx=context, init_basenet=False)
else:
net.init(TruncNorm(stdev=0.001), ctx=context, init_basenet=False)
if not opt.disable_hybridize:
net.hybridize()
run_result = train(net, opt, train_dataloader, val_dataloader, num_train_classes, context, run)
run_results.append(run_result)
logging.info('Run %d finished with %f' % (run, run_result[0][1]))
logging.info(
'Average validation of %d runs:\n%s' % (opt.number_of_runs, format_results(average_results(run_results))))
def train_angular(opt):
logging.info(opt)
# Settings.
mx.random.seed(opt.seed)
np.random.seed(opt.seed)
# Setup computation context
context = get_context(opt.gpus, logging)
run_results = []
# Get model
if opt.model == 'inception-bn':
feature_net, feature_params = get_feature_model(opt.model, ctx=context)
feature_net.collect_params().load(feature_params,
ctx=context,
ignore_extra=True)
data_shape = 224
scale_image_data = False
elif opt.model == 'resnet50_v2':
feature_net = mx.gluon.model_zoo.vision.resnet50_v2(
pretrained=True, ctx=context).features
feature_params = mx.gluon.model_zoo.model_store.get_model_file(
'resnet%d_v%d' % (50, 2),
root=os.path.join(mx.base.data_dir(), 'models'))
data_shape = 224
scale_image_data = True
else:
raise RuntimeError('Unsupported model: %s' % opt.model)
net = EmbeddingNet(feature_net, opt.embed_dim, normalize=False)
if opt.model == 'resnet50_v2':
# Use a smaller learning rate for pre-trained convolutional layers.
for v in net.base_net.collect_params().values():
if 'conv' in v.name:
setattr(v, 'lr_mult', 0.01)
# Get iterators
train_data, _ = get_npairs_iterators(
opt.dataset,
opt.data_path,
batch_size=opt.batch_size,
data_shape=data_shape,
test_batch_size=len(context) * 32,
use_crops=opt.use_crops,
scale_image_data=scale_image_data,
same_image_sampling=opt.same_image_sampling)
train_it_dataloader = mx.gluon.data.DataLoader(DatasetIterator(
train_data, opt.epoch_length),
batch_size=1,
shuffle=False,
num_workers=opt.num_workers,
last_batch='rollover')
_, val_dataset = get_dataset(opt.dataset,
opt.data_path,
data_shape=data_shape,
use_crops=opt.use_crops,
use_aug=True,
with_proxy=False,
scale_image_data=scale_image_data)
val_data = mx.gluon.data.DataLoader(val_dataset,
batch_size=opt.batch_size,
shuffle=False,
num_workers=opt.num_workers,
last_batch='keep')
for run in range(1, opt.number_of_runs + 1):
logging.info('Starting run %d/%d' % (run, opt.number_of_runs))
net.init(mx.init.Xavier(magnitude=0.2),
ctx=context,
init_basenet=False)
if opt.model == 'inception-bn':
net.base_net.collect_params().load(feature_params,
ctx=context,
ignore_extra=True)
elif opt.model == 'resnet50_v2':
net.base_net.load_parameters(feature_params,
ctx=context,
allow_missing=True,
ignore_extra=True)
if not opt.disable_hybridize:
net.hybridize()
run_result = train(net, opt, train_it_dataloader, val_data, context,
run)
run_results.append(run_result)
logging.info('Run %d finished with %f' % (run, run_result[0][1]))
logging.info(
'Average validation of %d runs:\n%s' %
(opt.number_of_runs, format_results(average_results(run_results))))
def train_rankedlist(opt):
logging.info(opt)
# Settings.
mx.random.seed(opt.seed)
np.random.seed(opt.seed)
# Setup computation context
context = get_context(opt.gpus, logging)
run_results = []
# Get model
if opt.model == 'inception-bn':
feature_net, feature_params = get_feature_model(opt.model, ctx=context)
feature_net.collect_params().load(feature_params,
ctx=context,
ignore_extra=True)
data_shape = 224
scale_image_data = False
elif opt.model == 'resnet50_v2':
feature_net = mx.gluon.model_zoo.vision.resnet50_v2(
pretrained=True, ctx=context).features
data_shape = 224
scale_image_data = True
feature_params = None
else:
raise RuntimeError('Unsupported model: %s' % opt.model)
if opt.bottleneck_layers != '':
embedding_layers = [int(x) for x in opt.bottleneck_layers.split(',')
] + [opt.embed_dim]
else:
embedding_layers = [opt.embed_dim]
logging.info('Embedding layers: [%s]' %
','.join([str(x) for x in embedding_layers]))
if len(embedding_layers) == 1:
embedding_layers = embedding_layers[0]
net = EmbeddingNet(feature_net,
embedding_layers,
normalize=True,
dropout=False)
logging.info(net)
if opt.model == 'resnet50_v2':
# Use a smaller learning rate for pre-trained convolutional layers.
for v in net.base_net.collect_params().values():
if 'conv' in v.name:
setattr(v, 'lr_mult', 0.01)
elif 'batchnorm' in v.name or 'bn_' in v.name:
v.grad_req = 'null'
else:
for v in net.encoder.collect_params().values():
setattr(v, 'lr_mult', 10.)
# Get data iterators
train_dataset = DatasetIterator(
get_dataset_iterator(opt.dataset,
opt.data_path,
batch_k=opt.batch_k,
batch_size=opt.batch_size,
batchify=False,
data_shape=data_shape,
use_crops=opt.use_crops,
scale_image_data=scale_image_data)[0],
opt.iteration_per_epoch, 'next')
train_dataiterator = mx.gluon.data.DataLoader(train_dataset,
batch_size=1,
shuffle=False,
num_workers=opt.num_workers,
last_batch='keep')
val_dataset = get_dataset(opt.dataset,
opt.data_path,
data_shape=data_shape,
use_crops=opt.use_crops,
use_aug=True,
scale_image_data=scale_image_data)[1]
logging.info(
'Training with %d classes, validating with %d classes' %
(train_dataset.data_iterator.num_classes(), val_dataset.num_classes()))
val_dataloader = mx.gluon.data.DataLoader(val_dataset,
batch_size=opt.batch_size,
shuffle=False,
num_workers=opt.num_workers,
last_batch='keep')
# main run loop for multiple training runs
for run in range(1, opt.number_of_runs + 1):
logging.info('Starting run %d/%d' % (run, opt.number_of_runs))
net.init(mx.init.Xavier(magnitude=0.2),
ctx=context,
init_basenet=False)
if opt.model == 'inception-bn':
net.base_net.collect_params().load(feature_params,
ctx=context,
ignore_extra=True)
elif opt.model == 'resnet50_v2':
net.base_net = mx.gluon.model_zoo.vision.resnet50_v2(
pretrained=True, ctx=context).features
else:
raise RuntimeError('Unsupported model: %s' % opt.model)
if not opt.disable_hybridize:
net.hybridize()
run_result = train(net, opt, train_dataiterator, val_dataloader,
context, run)
run_results.append(run_result)
logging.info('Run %d finished with %f' % (run, run_result[0][1]))
logging.info(
'Average validation of %d runs:\n%s' %
(opt.number_of_runs, format_results(average_results(run_results))))
def test_format_results(self):
result = np.array([1, 2])
assert format_results(result) == ' R@1 \t R@2\n1.00\t2.00'
assert format_results(None) == ''
def train_margin(opt):
logging.info(opt)
# Set random seed
mx.random.seed(opt.seed)
np.random.seed(opt.seed)
# Setup computation context
context = get_context(opt.gpus, logging)
# Adjust batch size to each compute context
batch_size = opt.batch_size * len(context)
run_results = []
# Get model
if opt.model == 'inception-bn':
feature_net, feature_params = get_feature_model(opt.model, ctx=context)
feature_net.collect_params().load(feature_params,
ctx=context,
ignore_extra=True)
data_shape = 224
scale_image_data = False
elif opt.model == 'resnet50_v2':
feature_params = None
feature_net = mx.gluon.model_zoo.vision.resnet50_v2(
pretrained=True, ctx=context).features
data_shape = 224
scale_image_data = True
else:
raise RuntimeError('Unsupported model: %s' % opt.model)
net = MarginNet(feature_net, opt.embed_dim)
if opt.model == 'resnet50_v2':
# Use a smaller learning rate for pre-trained convolutional layers.
for v in net.base_net.collect_params().values():
if 'conv' in v.name:
setattr(v, 'lr_mult', 0.01)
# Get data iterators
train_dataset, val_dataset = get_dataset(opt.dataset,
opt.data_path,
data_shape=data_shape,
use_crops=opt.use_crops,
use_aug=True,
scale_image_data=scale_image_data)
train_dataiter, _ = get_dataset_iterator(opt.dataset,
opt.data_path,
batch_k=opt.batch_k,
batch_size=batch_size,
data_shape=data_shape,
use_crops=opt.use_crops,
scale_image_data=scale_image_data,
batchify=False)
train_dataloader = mx.gluon.data.DataLoader(DatasetIterator(
train_dataiter, opt.iteration_per_epoch, 'next'),
batch_size=1,
shuffle=False,
num_workers=opt.num_workers,
last_batch='keep')
val_dataloader = mx.gluon.data.DataLoader(val_dataset,
batch_size=opt.batch_size,
shuffle=False,
num_workers=opt.num_workers,
last_batch='keep')
logging.info('Training with %d classes, validating with %d classes' %
(train_dataset.num_classes(), val_dataset.num_classes()))
# main run loop for multiple training runs
for run in range(1, opt.number_of_runs + 1):
logging.info('Starting run %d/%d' % (run, opt.number_of_runs))
# Re-init embedding layers and reload pretrained layers
if opt.model == 'inception-bn':
net.init(mx.init.Xavier(magnitude=0.2),
ctx=context,
init_basenet=False)
net.base_net.collect_params().load(feature_params,
ctx=context,
ignore_extra=True)
elif opt.model == 'resnet50_v2':
net.init(mx.init.Xavier(magnitude=2),
ctx=context,
init_basenet=False)
net.base_net = mx.gluon.model_zoo.vision.resnet50_v2(
pretrained=True, ctx=context).features
else:
raise RuntimeError('Unknown model type: %s' % opt.model)
if not opt.disable_hybridize:
net.hybridize()
if opt.lr_beta > 0.0:
logging.info('Learning beta margin')
beta = mx.gluon.nn.Embedding(train_dataset.num_classes(), 1)
else:
beta = opt.beta
run_result = train(net, beta, opt, train_dataloader, val_dataloader,
batch_size, context, run)
run_results.append(run_result)
logging.info('Run %d finished with %f' % (run, run_result[0][1]))
logging.info(
'Average validation of %d runs:\n%s' %
(opt.number_of_runs, format_results(average_results(run_results))))
