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}
)
f'dist={args.distance}_fce={args.fce}'
#########
# 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 #
###################
background = dataset_class('background')
background_taskloader = DataLoader(
background,
batch_sampler=NShotTaskSampler(background, episodes_per_epoch,
args.n_train, args.k_train, args.q_train),
num_workers=4)
evaluation = dataset_class('evaluation')
evaluation_taskloader = DataLoader(evaluation,
batch_sampler=NShotTaskSampler(
evaluation, episodes_per_epoch,
args.n_test, args.k_test, args.q_test),
#####
# experiments/matching_nets.py
model = MatchingNetwork(globals.N_TRAIN, globals.K_TRAIN, globals.Q_TRAIN, globals.FCE,
num_input_channels,
lstm_layers=globals.LSTM_LAYERS,
lstm_input_size=lstm_input_size,
unrolling_steps=globals.UNROLLING_STEPS,
device=device)
model_path = 'models/matching_nets/omniglot_n=1_k=5_q=15_nv=1_kv=5_qv=1_dist=l2_fce=False.pth'
loaded_model = torch.load(model_path)
model.load_state_dict(loaded_model)
model.to(device)
model.double()
# print("###########################")
# for param in model.parameters():
# print(param.data)
# print("###########################")
evaluation = dataset_class('evaluation')
dataloader = DataLoader(
evaluation,
batch_sampler=NShotTaskSampler(evaluation, episodes_per_epoch, n=globals.N_TEST, k=globals.K_TEST,
q=globals.Q_TEST),
num_workers=4
)
prepare_batch = prepare_nshot_task(globals.N_TEST, globals.K_TEST, globals.Q_TEST)