def _train(self) -> Optional[float]:
record_keeper, _, _ = logging_presets.get_record_keeper(
"example_logs", "example_tensorboard")
hooks = logging_presets.get_hook_container(record_keeper)
dataset_dict = {"val": self.val_dataset}
model_folder = "example_saved_models"
def visualizer_hook(umapper, umap_embeddings, labels, split_name,
keyname, *args):
logging.info("UMAP plot for the {} split and label set {}".format(
split_name, keyname))
label_set = np.unique(labels)
num_classes = len(label_set)
fig = plt.figure(figsize=(20, 15))
plt.gca().set_prop_cycle(
cycler("color", [
plt.cm.nipy_spectral(i)
for i in np.linspace(0, 0.9, num_classes)
]))
for i in range(num_classes):
idx = labels == label_set[i]
plt.plot(umap_embeddings[idx, 0],
umap_embeddings[idx, 1],
".",
markersize=1)
#plt.show()
#plt.show(block=False)
file_name = './plots/metric_{0}.png'.format(args[0])
plt.savefig(file_name, dpi=300)
#
# Create the tester
tester = testers.GlobalEmbeddingSpaceTester(
end_of_testing_hook=hooks.end_of_testing_hook,
visualizer=umap.UMAP(),
visualizer_hook=visualizer_hook,
dataloader_num_workers=32)
end_of_epoch_hook = hooks.end_of_epoch_hook(tester,
dataset_dict,
model_folder,
test_interval=1,
patience=200)
trainer = trainers.MetricLossOnly(
self.models_dict,
self.optimizers,
self._train_cfg.batch_per_gpu,
self.loss_funcs,
self.mining_funcs,
#self._train_loader,
self.train_set,
sampler=self.sampler,
dataloader_num_workers=self._train_cfg.workers - 1,
end_of_iteration_hook=hooks.end_of_iteration_hook,
end_of_epoch_hook=end_of_epoch_hook)
#trainer.train(num_epochs=self._train_cfg.epochs)
trainer.train(num_epochs=500)
def mk_tester(self, *args, **kwargs):
return testers.GlobalEmbeddingSpaceTester(
*args,
dataloader_num_workers=4,
data_device=self.tester_device
if self.tester_device is not None
else self.device,
end_of_testing_hook=self.end_of_testing_hook,
**kwargs,
)
def get_testing_hooks(experiment_id, val_dataset, test_interval, patience):
experiment_dir = os.path.join('experiment_logs', experiment_id)
record_keeper, _, _ = logging_presets.get_record_keeper(
experiment_dir,
os.path.join('experiment_logs', 'tensorboard', experiment_id))
hooks = logging_presets.get_hook_container(record_keeper)
dataset_dict = {"val": val_dataset}
model_folder = experiment_dir
def visualizer_hook(umapper, umap_embeddings, labels, split_name, keyname,
*args):
logging.info("UMAP plot for the {} split and label set {}".format(
split_name, keyname))
label_set = np.unique(labels)
num_classes = len(label_set)
fig = plt.figure(figsize=(20, 15))
plt.gca().set_prop_cycle(
cycler("color", [
plt.cm.nipy_spectral(i)
for i in np.linspace(0, 0.9, num_classes)
]))
for i in range(num_classes):
idx = labels == label_set[i]
plt.plot(umap_embeddings[idx, 0],
umap_embeddings[idx, 1],
".",
markersize=1)
plt.show()
writer = SummaryWriter(log_dir=os.path.join(
'experiment_logs', 'tensorboard', experiment_id))
writer.add_embedding(umap_embeddings, metadata=labels)
writer.close()
# Create the tester
tester = testers.GlobalEmbeddingSpaceTester(
end_of_testing_hook=hooks.end_of_testing_hook,
visualizer=umap.UMAP(),
visualizer_hook=visualizer_hook,
dataloader_num_workers=6)
end_of_epoch_hook = hooks.end_of_epoch_hook(tester,
dataset_dict,
model_folder,
test_interval=test_interval,
patience=patience)
return end_of_epoch_hook, hooks.end_of_iteration_hook
loss_weights = {
"metric_loss": 1,
"synth_loss": 0.1,
"g_adv_loss": 0.1,
"g_hard_loss": 0.1,
"g_reg_loss": 0.1
}
record_keeper, _, _ = logging_presets.get_record_keeper(
"example_logs", "example_tensorboard")
hooks = logging_presets.get_hook_container(record_keeper)
dataset_dict = {"val": val_dataset}
model_folder = "example_saved_models"
# Create the tester
tester = testers.GlobalEmbeddingSpaceTester(
end_of_testing_hook=hooks.end_of_testing_hook)
end_of_epoch_hook = hooks.end_of_epoch_hook(tester, dataset_dict, model_folder)
trainer = trainers.DeepAdversarialMetricLearning(
models=models,
optimizers=optimizers,
batch_size=batch_size,
loss_funcs=loss_funcs,
mining_funcs=mining_funcs,
iterations_per_epoch=iterations_per_epoch,
dataset=train_dataset,
loss_weights=loss_weights,
sampler=sampler,
end_of_iteration_hook=hooks.end_of_iteration_hook,
end_of_epoch_hook=end_of_epoch_hook,
metric_alone_epochs=0,
g_alone_epochs=0,
logger=logging.getLogger())
constructors = MODEL_DEF.get(CONF, best_trial, param_gen)
train_dataset, dev_dataset, train_sampler, batch_size = \
next(constructors["fold_generator"]())
trainer_kwargs = constructors["modules"]()
# logging
record_keeper, _, _ = logging_presets.get_record_keeper(
csv_folder=os.path.join(args.log_dir, f"csv"),
tensorboard_folder=os.path.join(args.log_dir, f"tensorboard"))
hooks = logging_presets.get_hook_container(record_keeper)
# tester
tester = testers.GlobalEmbeddingSpaceTester(
end_of_testing_hook=hooks.end_of_testing_hook, dataloader_num_workers=32)
end_of_epoch_hook = hooks.end_of_epoch_hook(tester, {"val": dev_dataset},
os.path.join(
args.log_dir, f"model"),
test_interval=1,
patience=args.patience)
# train
if args.trainer == "MetricLossOnly":
trainer = trainers.MetricLossOnly(
batch_size=batch_size,
mining_funcs={},
dataset=train_dataset,
sampler=train_sampler,
dataloader_num_workers=32,
end_of_iteration_hook=hooks.end_of_iteration_hook,
}
loss_funcs = {"metric_loss": loss}
mining_funcs = {"post_gradient_miner": miner}
trainer = trainers.MetricLossOnly(models,
optimizers,
batch_size,
loss_funcs,
mining_funcs,
iterations_per_epoch,
train_dataset,
record_keeper=record_keeper)
trainer.train(num_epochs=num_epochs)
#############################
########## Testing ##########
#############################
# The testing module requires faiss and scikit-learn
# So if you don't have these, then this import will break
from pytorch_metric_learning import testers
tester = testers.GlobalEmbeddingSpaceTester(record_keeper=record_keeper)
dataset_dict = {"train": train_dataset, "val": val_dataset}
epoch = 2
tester.test(dataset_dict, epoch, trunk, embedder)
if record_keeper is not None:
record_keeper.pickler_and_csver.save_records()
def main():
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
print('Running on device: {}'.format(device))
# Data transformations
trans_train = transforms.Compose([
transforms.RandomApply(transforms=[
transforms.AutoAugment(transforms.AutoAugmentPolicy.IMAGENET),
# transforms.RandomPerspective(distortion_scale=0.6, p=1.0),
transforms.RandomRotation(degrees=(0, 180)),
transforms.RandomHorizontalFlip(),
]),
np.float32,
transforms.ToTensor(),
fixed_image_standardization,
])
trans_val = transforms.Compose([
# transforms.CenterCrop(120),
np.float32,
transforms.ToTensor(),
fixed_image_standardization,
])
train_dataset = datasets.ImageFolder(os.path.join(data_dir,
"train_aligned"),
transform=trans_train)
val_dataset = datasets.ImageFolder(os.path.join(data_dir, "val_aligned"),
transform=trans_val)
# Prepare the model
model = InceptionResnetV1(classify=False,
pretrained="vggface2",
dropout_prob=0.5).to(device)
# for param in list(model.parameters())[:-8]:
# param.requires_grad = False
trunk_optimizer = torch.optim.SGD(model.parameters(), lr=LR)
# Set the loss function
loss = losses.ArcFaceLoss(len(train_dataset.classes), 512)
# Package the above stuff into dictionaries.
models = {"trunk": model}
optimizers = {"trunk_optimizer": trunk_optimizer}
loss_funcs = {"metric_loss": loss}
mining_funcs = {}
lr_scheduler = {
"trunk_scheduler_by_plateau":
torch.optim.lr_scheduler.ReduceLROnPlateau(trunk_optimizer)
}
# Create the tester
record_keeper, _, _ = logging_presets.get_record_keeper(
"logs", "tensorboard")
hooks = logging_presets.get_hook_container(record_keeper)
dataset_dict = {"val": val_dataset, "train": train_dataset}
model_folder = "training_saved_models"
def visualizer_hook(umapper, umap_embeddings, labels, split_name, keyname,
*args):
logging.info("UMAP plot for the {} split and label set {}".format(
split_name, keyname))
label_set = np.unique(labels)
num_classes = len(label_set)
fig = plt.figure(figsize=(8, 7))
plt.gca().set_prop_cycle(
cycler("color", [
plt.cm.nipy_spectral(i)
for i in np.linspace(0, 0.9, num_classes)
]))
for i in range(num_classes):
idx = labels == label_set[i]
plt.plot(umap_embeddings[idx, 0],
umap_embeddings[idx, 1],
".",
markersize=1)
plt.show()
tester = testers.GlobalEmbeddingSpaceTester(
end_of_testing_hook=hooks.end_of_testing_hook,
dataloader_num_workers=4,
accuracy_calculator=AccuracyCalculator(
include=['mean_average_precision_at_r'], k="max_bin_count"))
end_of_epoch_hook = hooks.end_of_epoch_hook(tester,
dataset_dict,
model_folder,
splits_to_eval=[('val',
['train'])])
# Create the trainer
trainer = trainers.MetricLossOnly(
models,
optimizers,
batch_size,
loss_funcs,
mining_funcs,
train_dataset,
lr_schedulers=lr_scheduler,
dataloader_num_workers=8,
end_of_iteration_hook=hooks.end_of_iteration_hook,
end_of_epoch_hook=end_of_epoch_hook)
trainer.train(num_epochs=num_epochs)
def train(train_data, test_data, save_model, num_epochs, lr, embedding_size,
batch_size):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Set trunk model and replace the softmax layer with an identity function
trunk = torchvision.models.resnet18(pretrained=True)
trunk_output_size = trunk.fc.in_features
trunk.fc = common_functions.Identity()
trunk = torch.nn.DataParallel(trunk.to(device))
# Set embedder model. This takes in the output of the trunk and outputs 64 dimensional embeddings
embedder = torch.nn.DataParallel(
MLP([trunk_output_size, embedding_size]).to(device))
# Set optimizers
trunk_optimizer = torch.optim.Adam(trunk.parameters(),
lr=lr / 10,
weight_decay=0.0001)
embedder_optimizer = torch.optim.Adam(embedder.parameters(),
lr=lr,
weight_decay=0.0001)
# Set the loss function
loss = losses.TripletMarginLoss(margin=0.1)
# Set the mining function
miner = miners.MultiSimilarityMiner(epsilon=0.1)
# Set the dataloader sampler
sampler = samplers.MPerClassSampler(train_data.targets,
m=4,
length_before_new_iter=len(train_data))
save_dir = os.path.join(
save_model, ''.join(str(lr).split('.')) + '_' + str(batch_size) + '_' +
str(embedding_size))
os.makedirs(save_dir, exist_ok=True)
# Package the above stuff into dictionaries.
models = {"trunk": trunk, "embedder": embedder}
optimizers = {
"trunk_optimizer": trunk_optimizer,
"embedder_optimizer": embedder_optimizer
}
loss_funcs = {"metric_loss": loss}
mining_funcs = {"tuple_miner": miner}
record_keeper, _, _ = logging_presets.get_record_keeper(
os.path.join(save_dir, "example_logs"),
os.path.join(save_dir, "example_tensorboard"))
hooks = logging_presets.get_hook_container(record_keeper)
dataset_dict = {"val": test_data, "train": train_data}
model_folder = "example_saved_models"
def visualizer_hook(umapper, umap_embeddings, labels, split_name, keyname,
*args):
logging.info("UMAP plot for the {} split and label set {}".format(
split_name, keyname))
label_set = np.unique(labels)
num_classes = len(label_set)
fig = plt.figure(figsize=(20, 15))
plt.title(str(split_name) + '_' + str(num_embeddings))
plt.gca().set_prop_cycle(
cycler("color", [
plt.cm.nipy_spectral(i)
for i in np.linspace(0, 0.9, num_classes)
]))
for i in range(num_classes):
idx = labels == label_set[i]
plt.plot(umap_embeddings[idx, 0],
umap_embeddings[idx, 1],
".",
markersize=1)
plt.show()
# Create the tester
tester = testers.GlobalEmbeddingSpaceTester(
end_of_testing_hook=hooks.end_of_testing_hook,
visualizer=umap.UMAP(),
visualizer_hook=visualizer_hook,
dataloader_num_workers=32,
accuracy_calculator=AccuracyCalculator(k="max_bin_count"))
end_of_epoch_hook = hooks.end_of_epoch_hook(tester,
dataset_dict,
model_folder,
test_interval=1,
patience=1)
trainer = trainers.MetricLossOnly(
models,
optimizers,
batch_size,
loss_funcs,
mining_funcs,
train_data,
sampler=sampler,
dataloader_num_workers=32,
end_of_iteration_hook=hooks.end_of_iteration_hook,
end_of_epoch_hook=end_of_epoch_hook)
trainer.train(num_epochs=num_epochs)
if save_model is not None:
torch.save(models["trunk"].state_dict(),
os.path.join(save_dir, 'trunk.pth'))
torch.save(models["embedder"].state_dict(),
os.path.join(save_dir, 'embedder.pth'))
print('Model saved in ', save_dir)
def objective(trial):
param_gen = ParameterGenerator(trial, CONF["_fix_params"], logger=logger)
# Average results of multiple folds.
print("New parameter.")
metrics = []
constructors = MODEL_DEF.get(CONF, trial, param_gen)
for i_fold, (train_dataset, dev_dataset, train_sampler,
batch_size) in enumerate(constructors["fold_generator"]()):
print(f"Fold {i_fold}")
trainer_kwargs = constructors["modules"]()
# logging
record_keeper, _, _ = logging_presets.get_record_keeper(
csv_folder=os.path.join(args.log_dir,
f"trial_{trial.number}_{i_fold}_csv"),
tensorboard_folder=os.path.join(
args.log_dir, f"trial_{trial.number}_{i_fold}_tensorboard"))
hooks = logging_presets.get_hook_container(record_keeper)
# tester
tester = testers.GlobalEmbeddingSpaceTester(
end_of_testing_hook=hooks.end_of_testing_hook,
dataloader_num_workers=args.n_test_loader)
end_of_epoch_hook = hooks.end_of_epoch_hook(
tester, {"val": dev_dataset},
os.path.join(args.log_dir, f"trial_{trial.number}_{i_fold}_model"),
test_interval=1,
patience=args.patience)
CHECKPOINT_FN = os.path.join(
args.log_dir, f"trial_{trial.number}_{i_fold}_last.pth")
def actual_end_of_epoch_hook(trainer):
continue_training = end_of_epoch_hook(trainer)
torch.save(
({k: m.state_dict()
for k, m in trainer.models.items()},
{k: m.state_dict()
for k, m in trainer.optimizers.items()},
{k: m.state_dict()
for k, m in trainer.loss_funcs.items()}, trainer.epoch),
CHECKPOINT_FN)
return continue_training
# train
if args.trainer == "MetricLossOnly":
trainer = trainers.MetricLossOnly(
batch_size=batch_size,
mining_funcs={},
dataset=train_dataset,
sampler=train_sampler,
dataloader_num_workers=args.n_train_loader,
end_of_iteration_hook=hooks.end_of_iteration_hook,
end_of_epoch_hook=actual_end_of_epoch_hook,
**trainer_kwargs)
elif args.trainer == "TrainWithClassifier":
trainer = trainers.TrainWithClassifier(
batch_size=batch_size,
mining_funcs={},
dataset=train_dataset,
sampler=train_sampler,
dataloader_num_workers=args.n_train_loader,
end_of_iteration_hook=hooks.end_of_iteration_hook,
end_of_epoch_hook=actual_end_of_epoch_hook,
**trainer_kwargs)
while True:
start_epoch = 1
if os.path.exists(CHECKPOINT_FN):
model_dicts, optimizer_dicts, loss_dicts, last_epoch = \
torch.load(CHECKPOINT_FN)
for k, d in model_dicts.items():
trainer.models[k].load_state_dict(d)
for k, d in optimizer_dicts.items():
trainer.optimizers[k].load_state_dict(d)
for k, d in loss_dicts.items():
trainer.loss_funcs[k].load_state_dict(d)
start_epoch = last_epoch + 1
logger.critical(f"Start from old epoch: {last_epoch + 1}")
try:
trainer.train(num_epochs=args.max_epoch,
start_epoch=start_epoch)
except Exception as err:
logger.critical(f"Error: {err}")
if not args.ignore_error:
break
else:
raise err
else:
break
rslt = hooks.get_accuracy_history(
tester, "val", metrics=["mean_average_precision_at_r"])
metrics.append(max(rslt["mean_average_precision_at_r_level0"]))
return np.mean(metrics)
