def lr_schedule(epoch, lr):
# Drop lr every 2000 episodes
if epoch % drop_lr_every == 0:
return lr / 2
else:
return lr
callbacks = [
EvaluateFewShot(
eval_fn=proto_net_episode,
num_tasks=evaluation_episodes,
n_shot=args.n_test,
k_way=args.k_test,
q_queries=args.q_test,
taskloader=evaluation_taskloader,
prepare_batch=prepare_nshot_task(args.n_test, args.k_test, args.q_test),
distance=args.distance
),
ModelCheckpoint(
filepath=PATH + f'/models/proto_nets/{param_str}.pth',
monitor=f'val_{args.n_test}-shot_{args.k_test}-way_acc'
),
LearningRateScheduler(schedule=lr_schedule),
CSVLogger(PATH + f'/logs/proto_nets/{param_str}.csv'),
]
fit(
model,
optimiser,
# Training #
############
print('Training Matching Network on {}...'.format(args.dataset))
if args.stn:
print('Training with STN')
optimiser = Adam(model.parameters(), lr=1e-3)
loss_fn = torch.nn.NLLLoss().cuda()
callbacks = [
EvaluateFewShot(eval_fn=matching_net_episode,
num_tasks=evaluation_episodes,
n_shot=args.n_test,
k_way=args.k_test,
q_queries=args.q_test,
taskloader=evaluation_taskloader,
prepare_batch=prepare_nshot_task(args.n_test, args.k_test,
args.q_test),
fce=args.fce,
args=args,
stnmodel=None,
stnoptim=None,
distance=args.distance),
ModelCheckpoint(
filepath=PATH + '/models/matching_nets/{}.pth'.format(param_str),
monitor='val_{}-shot_{}-way_acc'.format(args.n_test, args.k_test),
# monitor=f'val_loss',
),
ReduceLROnPlateau(patience=20,
factor=0.5,
monitor='val_{}-shot_{}-way_acc'.format(
args.n_test, args.k_test)),
for i in range(len(meta_optimisers))
]
ReduceLRCallback = CallbackList(ReduceLRCallback)
hash = ''.join([chr(random.randint(97, 122)) for _ in range(3)])
callbacks = [
ModelLoader(names=names),
EvaluateFewShot(
eval_fn=fit_fn,
num_tasks=args.eval_batches,
n_shot=args.n,
k_way=args.k,
q_queries=args.q,
taskloader=evaluation_taskloader,
prepare_batch=prepare_meta_batch(args.n, args.k, args.q,
args.meta_batch_size),
loss_fn=loss_fn,
# MAML kwargs
inner_train_steps=args.inner_val_steps,
inner_lr=args.inner_lr,
device=device,
order=args.order,
model_params=model_params,
pred_fn=test_pred_fn),
EvaluateFewShot(
eval_fn=fit_fn,
num_tasks=args.eval_batches,
n_shot=args.n,
k_way=args.k,
q_queries=args.q,
taskloader=evaluation_taskloader,
def train_proto_net(
args,
model,
device,
n_epochs,
background_taskloader,
evaluation_taskloader,
path='.',
lr=3e-3,
drop_lr_every=100,
evaluation_episodes=100,
episodes_per_epoch=100,
):
# Prepare model
model.to(device, dtype=torch.float)
model.train(True)
# Prepare training etc.
optimizer = Adam(model.parameters(), lr=lr)
loss_fn = torch.nn.NLLLoss().cuda()
ensure_folder(path + '/models')
ensure_folder(path + '/logs')
def lr_schedule(epoch, lr):
if epoch % drop_lr_every == 0:
return lr / 2
else:
return lr
callbacks = [
EvaluateFewShot(eval_fn=proto_net_episode,
num_tasks=evaluation_episodes,
n_shot=args.n_test,
k_way=args.k_test,
q_queries=args.q_test,
taskloader=evaluation_taskloader,
prepare_batch=prepare_nshot_task(
args.n_test, args.k_test, args.q_test),
distance=args.distance),
ModelCheckpoint(
filepath=path + '/models/' + args.param_str + '_e{epoch:02d}.pth',
monitor=args.checkpoint_monitor
or f'val_{args.n_test}-shot_{args.k_test}-way_acc',
period=args.checkpoint_period or 100,
),
LearningRateScheduler(schedule=lr_schedule),
CSVLogger(path + f'/logs/{args.param_str}.csv'),
]
fit(
model,
optimizer,
loss_fn,
epochs=n_epochs,
dataloader=background_taskloader,
prepare_batch=prepare_nshot_task(args.n_train, args.k_train,
args.q_train),
callbacks=callbacks,
metrics=['categorical_accuracy'],
epoch_metrics=[f'val_{args.n_test}-shot_{args.k_test}-way_acc'],
fit_function=proto_net_episode,
fit_function_kwargs={
'n_shot': args.n_train,
'k_way': args.k_train,
'q_queries': args.q_train,
'train': True,
'distance': args.distance
},
)
# Create label
y = create_nshot_task_label(k, q).cuda().repeat(meta_batch_size)
return x, y
return prepare_meta_batch_
callbacks = [
EvaluateFewShot(
eval_fn=meta_gradient_step,
num_tasks=args.eval_batches,
n_shot=args.n,
k_way=args.k,
q_queries=args.q,
taskloader=evaluation_taskloader,
prepare_batch=prepare_meta_batch(args.n, args.k, args.q,
args.meta_batch_size),
# MAML kwargs
inner_train_steps=args.inner_val_steps,
inner_lr=args.inner_lr,
device=device,
order=args.order,
),
ModelCheckpoint(filepath=PATH + f'/models/maml/{param_str}.pth',
monitor=f'val_{args.n}-shot_{args.k}-way_acc'),
#ReduceLROnPlateau(patience=10, factor=0.5, monitor=f'val_loss'),
CSVLogger(PATH + f'/logs/maml/{param_str}.csv'),
]
fit(
meta_model,
def run():
episodes_per_epoch = 600
if args.dataset == 'miniImageNet':
n_epochs = 500
dataset_class = MiniImageNet
num_input_channels = 3
lstm_input_size = 1600
else:
raise(ValueError('need to make other datasets module'))
param_str = f'{args.dataset}_n={args.n_train}_k={args.k_train}_q={args.q_train}_' \
f'nv={args.n_test}_kv={args.k_test}_qv={args.q_test}_' \
f'dist={args.distance}_fce={args.fce}_sampling_method={args.sampling_method}_' \
f'is_diversity={args.is_diversity}_epi_candidate={args.num_s_candidates}'
#########
# Model #
#########
from few_shot.models import MatchingNetwork
model = MatchingNetwork(args.n_train, args.k_train, args.q_train, args.fce, num_input_channels,
lstm_layers=args.lstm_layers,
lstm_input_size=lstm_input_size,
unrolling_steps=args.unrolling_steps,
device=device)
model.to(device, dtype=torch.double)
###################
# Create datasets #
###################
train_dataset = dataset_class('train')
eval_dataset = dataset_class('eval')
# Original_sampling
if not args.sampling_method:
train_dataset_taskloader = DataLoader(
train_dataset,
batch_sampler=NShotTaskSampler(train_dataset, episodes_per_epoch, args.n_train, args.k_train, args.q_train),
num_workers=4
)
eval_dataset_taskloader = DataLoader(
eval_dataset,
batch_sampler=NShotTaskSampler(eval_dataset, episodes_per_epoch, args.n_test, args.k_test, args.q_test),
num_workers=4
)
# Importance sampling
else:
train_dataset_taskloader = DataLoader(
train_dataset,
batch_sampler=ImportanceSampler(train_dataset, model,
episodes_per_epoch, n_epochs, args.n_train, args.k_train, args.q_train,
args.num_s_candidates, args.init_temperature, args.is_diversity),
num_workers=4
)
eval_dataset_taskloader = DataLoader(
eval_dataset,
batch_sampler=NShotTaskSampler(eval_dataset, episodes_per_epoch, args.n_test, args.k_test, args.q_test),
num_workers=4
)
############
# Training #
############
print(f'Training Matching Network on {args.dataset}...')
optimiser = Adam(model.parameters(), lr=1e-3)
loss_fn = torch.nn.NLLLoss().cuda()
callbacks = [
EvaluateFewShot(
eval_fn=matching_net_episode,
n_shot=args.n_test,
k_way=args.k_test,
q_queries=args.q_test,
taskloader=eval_dataset_taskloader,
prepare_batch=prepare_nshot_task(args.n_test, args.k_test, args.q_test),
fce=args.fce,
distance=args.distance
),
ModelCheckpoint(
filepath=PATH + f'/models/matching_nets/{param_str}.pth',
monitor=f'val_{args.n_test}-shot_{args.k_test}-way_acc',
save_best_only=True,
),
ReduceLROnPlateau(patience=20, factor=0.5, monitor=f'val_{args.n_test}-shot_{args.k_test}-way_acc'),
CSVLogger(PATH + f'/logs/matching_nets/{param_str}.csv'),
]
fit(
model,
optimiser,
loss_fn,
epochs=n_epochs,
dataloader=train_dataset_taskloader,
prepare_batch=prepare_nshot_task(args.n_train, args.k_train, args.q_train),
callbacks=callbacks,
metrics=['categorical_accuracy'],
fit_function=matching_net_episode,
fit_function_kwargs={'n_shot': args.n_train, 'k_way': args.k_train, 'q_queries': args.q_train, 'train': True,
'fce': args.fce, 'distance': args.distance}
)
def lr_schedule(epoch, lr):
# Drop lr every 2000 episodes
if epoch % drop_lr_every == 0:
return lr / 2
else:
return lr
callbacks = [
EvaluateFewShot(
eval_fn=proto_net_episode,
num_tasks=evaluation_episodes,
n_shot=args.n_test,
k_way=args.k_test,
q_queries=args.q_test,
taskloader=evaluation_taskloader,
prepare_batch=prepare_nshot_task(
args.n_test, args.k_test, args.q_test
), # n shot task is a simple function that maps classes to [0-k]
distance=args.distance),
ModelCheckpoint(filepath=PATH + f'/models/proto_nets/{param_str}.pth',
monitor=f'val_{args.n_test}-shot_{args.k_test}-way_acc'),
LearningRateScheduler(schedule=lr_schedule),
CSVLogger(PATH + f'/logs/proto_nets/{param_str}.csv'),
]
fit(
model,
optimiser,
loss_fn,
num_workers=4)
############
# Training #
############
print(f'Training Matching Network on {globals.DATASET}...')
optimiser = Adam(model.parameters(), lr=1e-3)
loss_fn = torch.nn.NLLLoss().cuda()
callbacks = [
EvaluateFewShot(eval_fn=matching_net_episode,
num_tasks=evaluation_episodes,
n_shot=globals.N_TEST,
k_way=globals.K_TEST,
q_queries=globals.Q_TEST,
taskloader=evaluation_taskloader,
prepare_batch=prepare_nshot_task(globals.N_TEST,
globals.K_TEST,
globals.Q_TEST),
fce=globals.FCE,
distance=globals.DISTANCE),
ModelCheckpoint(
filepath=PATH + f'/models/matching_nets/{param_str}.pth',
monitor=f'val_{globals.N_TEST}-shot_{globals.K_TEST}-way_acc',
# monitor=f'val_loss',
),
ReduceLROnPlateau(
patience=20,
factor=0.5,
monitor=f'val_{globals.N_TEST}-shot_{globals.K_TEST}-way_acc'),
CSVLogger(PATH + f'/logs/matching_nets/{param_str}.csv'),
batch_sampler=NShotTaskSampler(
evaluation,
episodes_per_epoch,
args.n_test,
args.k_test,
args.q_test,
eval_classes=None
), # why is qtest needed for protonet i think its not rquired for protonet check it
num_workers=4)
callbacks = [
EvaluateFewShot(
eval_fn=eval_fn,
num_tasks=evaluation_episodes,
n_shot=args.n_test,
k_way=args.k_test,
q_queries=args.q_test,
taskloader=evaluation_taskloader,
prepare_batch=prepare_nshot_task(
args.n_test, args.k_test, args.q_test
), # n shot task is a simple function that maps classes to [0-k]
distance=args.distance),
]
elif args.network == 'matching':
from few_shot.models import MatchingNetwork
n_epochs = 200
dataset_class = FashionDataset
num_input_channels = 3
lstm_input_size = 1600
evaluation_taskloader = DataLoader(
evaluation,
def train_sweep():
from torch.optim import Adam
from torch.utils.data import DataLoader
import argparse
from few_shot.datasets import OmniglotDataset, MiniImageNet, ClinicDataset, SNIPSDataset, CustomDataset
from few_shot.models import XLNetForEmbedding
from few_shot.core import NShotTaskSampler, EvaluateFewShot, prepare_nshot_task
from few_shot.proto import proto_net_episode
from few_shot.train_with_prints import fit
from few_shot.callbacks import CallbackList, Callback, DefaultCallback, ProgressBarLogger, CSVLogger, EvaluateMetrics, ReduceLROnPlateau, ModelCheckpoint, LearningRateScheduler
from few_shot.utils import setup_dirs
from few_shot.utils import get_gpu_info
from config import PATH
import wandb
from transformers import AdamW
import torch
gpu_dict = get_gpu_info()
print('Total GPU Mem: {} , Used GPU Mem: {}, Used Percent: {}'.format(
gpu_dict['mem_total'], gpu_dict['mem_used'],
gpu_dict['mem_used_percent']))
setup_dirs()
assert torch.cuda.is_available()
device = torch.device('cuda')
torch.backends.cudnn.benchmark = True
##############
# Parameters #
##############
parser = argparse.ArgumentParser()
parser.add_argument('--dataset', default='Custom')
parser.add_argument('--distance', default='l2')
parser.add_argument('--n-train', default=2, type=int)
parser.add_argument('--n-test', default=2, type=int)
parser.add_argument('--k-train', default=2, type=int)
parser.add_argument('--k-test', default=2, type=int)
parser.add_argument('--q-train', default=2, type=int)
parser.add_argument('--q-test', default=2, type=int)
args = parser.parse_args()
evaluation_episodes = 100
episodes_per_epoch = 10
if args.dataset == 'omniglot':
n_epochs = 40
dataset_class = OmniglotDataset
num_input_channels = 1
drop_lr_every = 20
elif args.dataset == 'miniImageNet':
n_epochs = 80
dataset_class = MiniImageNet
num_input_channels = 3
drop_lr_every = 40
elif args.dataset == 'clinic150':
n_epochs = 5
dataset_class = ClinicDataset
num_input_channels = 150
drop_lr_every = 2
elif args.dataset == 'SNIPS':
n_epochs = 5
dataset_class = SNIPSDataset
num_input_channels = 150
drop_lr_every = 2
elif args.dataset == 'Custom':
n_epochs = 20
dataset_class = CustomDataset
num_input_channels = 150
drop_lr_every = 5
else:
raise (ValueError, 'Unsupported dataset')
param_str = f'{args.dataset}_nt={args.n_train}_kt={args.k_train}_qt={args.q_train}_' \
f'nv={args.n_test}_kv={args.k_test}_qv={args.q_test}'
print(param_str)
from sklearn.model_selection import train_test_split
###################
# Create datasets #
###################
train_df = dataset_class('train')
train_taskloader = DataLoader(train_df,
batch_sampler=NShotTaskSampler(
train_df, episodes_per_epoch,
args.n_train, args.k_train,
args.q_train))
val_df = dataset_class('val')
evaluation_taskloader = DataLoader(
val_df,
batch_sampler=NShotTaskSampler(val_df, episodes_per_epoch, args.n_test,
args.k_test, args.q_test))
#train_iter = iter(train_taskloader)
#train_taskloader = next(train_iter)
#val_iter = iter(evaluation_taskloader)
#evaluation_taskloader = next(val_iter)
#########
# Wandb #
#########
config_defaults = {
'lr': 0.00001,
'optimiser': 'adam',
'batch_size': 16,
}
wandb.init(config=config_defaults)
#########
# Model #
#########
torch.cuda.empty_cache()
try:
print('Before Model Move')
gpu_dict = get_gpu_info()
print('Total GPU Mem: {} , Used GPU Mem: {}, Used Percent: {}'.format(
gpu_dict['mem_total'], gpu_dict['mem_used'],
gpu_dict['mem_used_percent']))
except:
pass
#from transformers import XLNetForSequenceClassification, AdamW
#model = XLNetForSequenceClassification.from_pretrained('xlnet-base-cased', num_labels=150)
#model.cuda()
try:
del model
except:
print("Cannot delete model. No model with name 'model' exists")
model = XLNetForEmbedding(num_input_channels)
model.to(device, dtype=torch.double)
#param_optimizer = list(model.named_parameters())
#no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
#optimizer_grouped_parameters = [
# {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01},
# {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay':0.0}
#]
try:
print('After Model Move')
gpu_dict = get_gpu_info()
print('Total GPU Mem: {} , Used GPU Mem: {}, Used Percent: {}'.format(
gpu_dict['mem_total'], gpu_dict['mem_used'],
gpu_dict['mem_used_percent']))
except:
pass
wandb.watch(model)
############
# Training #
############
from transformers import AdamW
print(f'Training Prototypical network on {args.dataset}...')
if wandb.config.optimiser == 'adam':
optimiser = Adam(model.parameters(), lr=wandb.config.lr)
else:
optimiser = AdamW(model.parameters(), lr=wandb.config.lr)
#optimiser = AdamW(optimizer_grouped_parameters, lr=3e-5)
#loss_fn = torch.nn.NLLLoss().cuda()
#loss_fn = torch.nn.CrossEntropyLoss()
#max_grad_norm = 1.0
loss_fn = torch.nn.NLLLoss()
def lr_schedule(epoch, lr):
# Drop lr every 2000 episodes
if epoch % drop_lr_every == 0:
return lr / 2
else:
return lr
callbacks = [
EvaluateFewShot(eval_fn=proto_net_episode,
num_tasks=evaluation_episodes,
n_shot=args.n_test,
k_way=args.k_test,
q_queries=args.q_test,
taskloader=evaluation_taskloader,
prepare_batch=prepare_nshot_task(
args.n_test, args.k_test, args.q_test),
distance=args.distance),
ModelCheckpoint(
filepath=PATH + f'/models/proto_nets/{param_str}.pth',
monitor=f'val_{args.n_test}-shot_{args.k_test}-way_acc'),
LearningRateScheduler(schedule=lr_schedule),
CSVLogger(PATH + f'/logs/proto_nets/{param_str}.csv'),
]
try:
print('Before Fit')
print('optimiser :', optimiser)
print('Learning Rate: ', wandb.config.lr)
gpu_dict = get_gpu_info()
print('Total GPU Mem: {} , Used GPU Mem: {}, Used Percent: {}'.format(
gpu_dict['mem_total'], gpu_dict['mem_used'],
gpu_dict['mem_used_percent']))
except:
pass
fit(
model,
optimiser,
loss_fn,
epochs=n_epochs,
dataloader=train_taskloader,
prepare_batch=prepare_nshot_task(args.n_train, args.k_train,
args.q_train),
callbacks=callbacks,
metrics=['categorical_accuracy'],
fit_function=proto_net_episode,
fit_function_kwargs={
'n_shot': args.n_train,
'k_way': args.k_train,
'q_queries': args.q_train,
'train': True,
'distance': args.distance
},
)
def run():
episodes_per_epoch = 600
'''
###### LearningRateScheduler ######
drop_lr_every = 20
def lr_schedule(epoch, lr):
# Drop lr every 2000 episodes
if epoch % drop_lr_every == 0:
return lr / 2
else:
return lr
# callbacks add: LearningRateScheduler(schedule=lr_schedule)
'''
if args.dataset == 'miniImageNet':
n_epochs = 500
dataset_class = MiniImageNet
num_input_channels = 3
else:
raise (ValueError('need to make other datasets module'))
param_str = f'{args.dataset}_nt={args.n_train}_kt={args.k_train}_qt={args.q_train}_' \
f'nv={args.n_test}_kv={args.k_test}_qv={args.q_test}_' \
f'dist={args.distance}_sampling_method={args.sampling_method}_is_diverisity={args.is_diversity}'
print(param_str)
#########
# Model #
#########
model = get_few_shot_encoder(num_input_channels)
model.to(device, dtype=torch.double)
###################
# Create datasets #
###################
train_dataset = dataset_class('train')
eval_dataset = dataset_class('eval')
# Original sampling
if not args.sampling_method:
train_dataset_taskloader = DataLoader(
train_dataset,
batch_sampler=NShotTaskSampler(train_dataset, episodes_per_epoch,
args.n_train, args.k_train,
args.q_train),
num_workers=4)
eval_dataset_taskloader = DataLoader(
eval_dataset,
batch_sampler=NShotTaskSampler(eval_dataset, episodes_per_epoch,
args.n_test, args.k_test,
args.q_test),
num_workers=4)
# Importance sampling
else:
# ImportanceSampler: Latent space of model
train_dataset_taskloader = DataLoader(
train_dataset,
batch_sampler=ImportanceSampler(
train_dataset, model, episodes_per_epoch, n_epochs,
args.n_train, args.k_train, args.q_train,
args.num_s_candidates, args.init_temperature,
args.is_diversity),
num_workers=4)
eval_dataset_taskloader = DataLoader(
eval_dataset,
batch_sampler=NShotTaskSampler(eval_dataset, episodes_per_epoch,
args.n_test, args.k_test,
args.q_test),
num_workers=4)
############
# Training #
############
print(f'Training Prototypical network on {args.dataset}...')
optimiser = Adam(model.parameters(), lr=1e-3)
loss_fn = torch.nn.NLLLoss().cuda()
callbacks = [
EvaluateFewShot(eval_fn=proto_net_episode,
n_shot=args.n_test,
k_way=args.k_test,
q_queries=args.q_test,
taskloader=eval_dataset_taskloader,
prepare_batch=prepare_nshot_task(
args.n_test, args.k_test, args.q_test),
distance=args.distance),
ModelCheckpoint(
filepath=PATH + f'/models/proto_nets/{param_str}.pth',
monitor=f'val_{args.n_test}-shot_{args.k_test}-way_acc',
save_best_only=True,
),
ReduceLROnPlateau(
patience=40,
factor=0.5,
monitor=f'val_{args.n_test}-shot_{args.k_test}-way_acc'),
CSVLogger(PATH + f'/logs/proto_nets/{param_str}.csv'),
]
fit(
model,
optimiser,
loss_fn,
epochs=n_epochs,
dataloader=train_dataset_taskloader,
prepare_batch=prepare_nshot_task(args.n_train, args.k_train,
args.q_train),
callbacks=callbacks,
metrics=['categorical_accuracy'],
fit_function=proto_net_episode,
fit_function_kwargs={
'n_shot': args.n_train,
'k_way': args.k_train,
'q_queries': args.q_train,
'train': True,
'distance': args.distance
},
)
args.n_test, args.k_test, args.q_test),
num_workers=4)
############
# Training #
############
print(f'Training Matching Network on {args.dataset}...')
optimiser = Adam(model.parameters(), lr=1e-3)
loss_fn = torch.nn.NLLLoss().cuda()
callbacks = [
EvaluateFewShot(eval_fn=matching_net_episode,
num_tasks=evaluation_episodes,
n_shot=args.n_test,
k_way=args.k_test,
q_queries=args.q_test,
taskloader=evaluation_taskloader,
prepare_batch=prepare_nshot_task(args.n_test, args.k_test,
args.q_test),
fce=args.fce,
distance=args.distance),
ModelCheckpoint(filepath=PATH + f'/models/matching_nets/{param_str}.pth',
monitor=f'val_{args.n_test}-shot_{args.k_test}-way_acc',
save_best_only=True
# monitor=f'val_loss',
),
ReduceLROnPlateau(patience=20,
factor=0.5,
monitor=f'val_{args.n_test}-shot_{args.k_test}-way_acc'),
CSVLogger(PATH + f'/logs/matching_nets/{param_str}.csv'),
]
