#########
# 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)
if args.retrain:
checkpoint = torch.load(PATH + f'/models/matching_nets/{param_str}.pth')
model.load_state_dict(checkpoint)
model.to(device, dtype=torch.double)
###################
# Create datasets #
###################
# background = dataset_class('images_background') #imagenet
background = dataset_class('datasets/in32_all')
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('images_evaluation') #imagenet
evaluation = dataset_class('datasets/test')
evaluation_taskloader = DataLoader(evaluation,
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)
num_tasks=eval_batches,
n_shot=args.n_test,
k_way=args.k_test,
q_queries=args.q_test,
taskloader=evaluation_taskloader,
prepare_batch=prepare_meta_batch(args.n_test, args.k_test,
args.q_test, meta_batch_size),
# MAML kwargs
inner_train_steps=1,
inner_lr=0.4,
device=device,
order=1,
)
]
model.load_state_dict(torch.load(args.model_path))
model.to(device, dtype=torch.double)
############
# Training #
############
#print(f'Training Prototypical network on {args.dataset}...')
def lr_schedule(epoch, lr):
# Drop lr every 2000 episodes
if epoch % drop_lr_every == 0:
return lr / 2
else:
return lr
evaluation_episodes = 1000
episodes_per_epoch = 100
#####
# 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),