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),
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',
batch_sampler=NShotTaskSampler(
evaluation, episodes_per_epoch,
args.n_test, args.k_test, args.q_test),
num_workers=4)
############
# Training #
############
if args.retrain:
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}_experiment={args.experiment_name}_retrain=True'
print(f'Training Matching Network on {args.dataset}...')
optimiser = Adam(model.parameters(), lr=1e-3)
if args.retrain:
optimiser = Adam(model.parameters(), lr=0.000125)
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,
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}
)
args.n_test,
args.k_test,
args.q_test,
eval_classes=eval_classes
), # why is qtest needed for protonet i think its not rquired for protonet check it
num_workers=4)
model = MatchingNetwork(args.n_train,
args.k_train,
args.q_train,
True,
num_input_channels,
lstm_layers=1,
lstm_input_size=lstm_input_size,
unrolling_steps=2,
device=device)
optimiser = Adam(model.parameters(), lr=1e-3)
loss_fn = torch.nn.NLLLoss().cuda()
eval_fn = matching_net_episode
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=True,
distance=args.distance)
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--model_path", type=str,
default="./models/matching_nets/miniImageNet_n=5_k=5_q=10_nv=5_kv=5_qv=10_dist=cosine_fce=True_sampling_method=False_is_diversity=None_epi_candidate=20.pth",
help="model path")
parser.add_argument(
"--result_path", type=str,
default="./results/matching_nets/5shot_training_5shot_inference_randomsampling.csv",
help="Directory for evaluation report result (for experiments)")
parser.add_argument('--dataset', type=str, required=True)
parser.add_argument('--fce', type=lambda x: x.lower()[0] == 't')
parser.add_argument('--distance', default='cosine')
parser.add_argument('--n_train', default=1, type=int)
parser.add_argument('--n_test', default=1, type=int)
parser.add_argument('--k_train', default=5, type=int)
parser.add_argument('--k_test', default=5, type=int)
parser.add_argument('--q_train', default=15, type=int)
parser.add_argument('--q_test', default=15, type=int)
parser.add_argument('--lstm_layers', default=1, type=int)
parser.add_argument('--unrolling_steps', default=2, type=int)
parser.add_argument(
"--debug", action="store_true", help="set logging level DEBUG",
)
args = parser.parse_args()
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.DEBUG if args.debug else logging.INFO,
)
###################
# Create datasets #
###################
episodes_per_epoch = 600
if args.dataset == 'miniImageNet':
n_epochs = 5
dataset_class = MiniImageNet
num_input_channels = 3
lstm_input_size = 1600
else:
raise(ValueError('need to make other datasets module'))
test_dataset = dataset_class('test')
test_dataset_taskloader = DataLoader(
test_dataset,
batch_sampler=NShotTaskSampler(test_dataset, episodes_per_epoch, args.n_test, args.k_test, args.q_test),
num_workers=4
)
#########
# Model #
#########
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
).to(device, dtype=torch.double)
model.load_state_dict(torch.load(args.model_path), strict=False)
model.eval()
#############
# Inference #
#############
logger.info("***** Epochs = %d *****", n_epochs)
logger.info("***** Num episodes per epoch = %d *****", episodes_per_epoch)
result_writer = ResultWriter(args.result_path)
# just argument (function: matching_net_episode)
prepare_batch = prepare_nshot_task(args.n_test, args.k_test, args.q_test)
optimiser = Adam(model.parameters(), lr=1e-3)
loss_fn = torch.nn.NLLLoss().cuda()
train_iterator = trange(0, int(n_epochs), desc="Epoch",)
for i_epoch in train_iterator:
epoch_iterator = tqdm(test_dataset_taskloader, desc="Iteration",)
seen = 0
metric_name = f'test_{args.n_test}-shot_{args.k_test}-way_acc'
metric = {metric_name: 0.0}
for _, batch in enumerate(epoch_iterator):
x, y = prepare_batch(batch)
loss, y_pred = matching_net_episode(
model,
optimiser,
loss_fn,
x,
y,
n_shot=args.n_test,
k_way=args.k_test,
q_queries=args.q_test,
train=False,
fce=args.fce,
distance=args.distance
)
seen += y_pred.shape[0]
metric[metric_name] += categorical_accuracy(y, y_pred) * y_pred.shape[0]
metric[metric_name] = metric[metric_name] / seen
logger.info("epoch: {}, categorical_accuracy: {}".format(i_epoch, metric[metric_name]))
result_writer.update(**metric)
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),
num_workers=4
)
############
# Training #
############
print(f'Training Matching Network on {args.dataset}...')
optimiser = Adam(model.parameters(), lr=0.001)
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(