def load_model(self, args, config):
if args['model_path'] is not None:
net_old = Learner.Learner(config)
# logger.info("Loading model from path %s", args["model_path"])
self.net = torch.load(args['model_path'] + "/net.model",
map_location="cpu")
for (n1, old_model), (n2, loaded_model) in zip(
net_old.named_parameters(), self.net.named_parameters()):
print(n1, n2, old_model.learn, old_model.meta)
loaded_model.learn = old_model.learn
loaded_model.meta = old_model.meta
else:
self.net = Learner.Learner(config)
def load_model(args, config):
if args['model_path'] is not None:
net_old = Learner.Learner(config)
# logger.info("Loading model from path %s", args["model_path"])
net = torch.load(args['model_path'],
map_location="cpu")
for (n1, old_model), (n2, loaded_model) in zip(net_old.named_parameters(), net.named_parameters()):
# print(n1, n2, old_model.adaptation, old_model.meta)
loaded_model.adaptation = old_model.adaptation
loaded_model.meta = old_model.meta
net.reset_vars()
else:
net = Learner.Learner(config)
return net
def load_model(self, args, config, context_config):
if args['model_path'] is not None and False:
pass
assert (False)
else:
self.net = Learner.Learner(config, context_config)
def load_model(self, args, config, context_config, device="cpu"):
if args['model_path'] is not None and False:
pass
assert (False)
else:
self.net = Learner.Learner(config,
context_config,
type="representation",
device=device)
def __init__(self, args, config):
super(MetaLearingClassification, self).__init__()
self.update_lr = args.update_lr
self.meta_lr = args.meta_lr
self.update_step = args.update_step
self.net = Learner.Learner(config)
self.optimizer = optim.Adam(self.net.parameters(), lr=self.meta_lr)
def __init__(self, args, config):
"""
:param args:
"""
super(MetaLearnerRegression, self).__init__()
self.update_lr = args.update_lr
self.meta_lr = args.meta_lr
self.update_step = args.update_step
self.net = Learner.Learner(config)
self.optimizer = optim.Adam(self.net.parameters(), lr=self.meta_lr)
self.meta_optim = torch.optim.lr_scheduler.MultiStepLR(self.optimizer, [1500, 2500, 3500], 0.1)
def __init__(self, args, config):
super(MetaLearnerRegression, self).__init__()
self.init_stuff(args)
self.net = Learner.Learner(config)
#print(self.net.parameters())
#print('hey')
#print(self.net.vars)
#sys.exit()
self.init_opt()
def __init__(self, args, config):
"""
:param args:
"""
super(MetaLearnerRegression, self).__init__()
self.update_lr = args.update_lr
self.meta_lr = args.meta_lr
self.update_step = args.update_step
self.net = Learner.Learner(config) #this is the actual network architecture
self.optimizer = optim.Adam(self.net.parameters(), lr=self.meta_lr) #use Adam to optimie OML objetive
self.meta_optim = torch.optim.lr_scheduler.MultiStepLR(self.optimizer, [1500, 2500, 3500], 0.3) #decay learning rate based on epoch number
def __init__(self, args, config):
print('initializing MLearner!')
super(MetaLearingClassification, self).__init__()
self.init_stuff(args)
self.net = Learner.Learner(config)
#print(self.net.parameters())
#print('hey')
#print(self.net.vars)
#sys.exit()
self.init_opt()
def __init__(self, args, config):
#print('hey Im starting')
super(MetaLearingClassification, self).__init__()
self.init_stuff(args)
self.net = Learner.Learner(config, args.init_plasticity)
#print(self.net.parameters())
#print('hey')
#print(self.net.vars)
#sys.exit()
self.init_opt()
def __init__(self, args, config, treatment):
super(MetaLearingClassification, self).__init__()
self.update_lr = args.update_lr
self.meta_lr = args.meta_lr
self.update_step = args.update_step
if treatment == "Neuromodulation":
neuromodulation = True
else:
neuromodulation = False
self.net = Learner.Learner(config, neuromodulation)
self.optimizer = optim.Adam(self.net.parameters(), lr=self.meta_lr)
self.meta_iteration = 0
self.inputNM = True
self.nodeNM = False
self.layers_to_fix = []
def main(args):
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
np.random.seed(args.seed)
random.seed(args.seed)
my_experiment = experiment(args.name, args, "../results/", args.commit)
logger = logging.getLogger('experiment')
logger.setLevel(logging.INFO)
total_clases = [900]
keep = list(range(total_clases[0]))
dataset = utils.remove_classes_omni(
df.DatasetFactory.get_dataset("omniglot",
train=True,
path=args.data_path,
all=True), keep)
iterator_sorted = torch.utils.data.DataLoader(utils.iterator_sorter_omni(
dataset, False, classes=total_clases),
batch_size=128,
shuffle=True,
num_workers=2)
iterator = iterator_sorted
print(args)
if torch.cuda.is_available():
device = torch.device('cuda')
else:
device = torch.device('cpu')
maml = torch.load(args.model, map_location='cpu')
if args.scratch:
config = mf.ModelFactory.get_model("na", args.dataset)
maml = learner.Learner(config)
maml = maml.to(device)
reps = []
counter = 0
fig, axes = plt.subplots(9, 4)
with torch.no_grad():
for img, target in iterator:
print(counter)
img = img.to(device)
target = target.to(device)
# print(target)
rep = maml(img, vars=None, bn_training=False, feature=True)
rep = rep.view((-1, 32, 72)).detach().cpu().numpy()
rep_instance = rep[0]
if args.binary:
rep_instance = (rep_instance > 0).astype(int)
if args.max:
rep = rep / np.max(rep)
else:
rep = (rep > 0).astype(int)
if counter < 36:
print("Adding plot")
axes[int(counter / 4), counter % 4].imshow(rep_instance,
cmap=args.color)
axes[int(counter / 4), counter % 4].set_yticklabels([])
axes[int(counter / 4), counter % 4].set_xticklabels([])
axes[int(counter / 4), counter % 4].set_aspect('equal')
counter += 1
reps.append(rep)
plt.subplots_adjust(wspace=0.0, hspace=0.0)
plt.savefig(my_experiment.path + "instance_" + str(counter) + ".pdf",
format="pdf")
plt.clf()
rep = np.concatenate(reps)
averge_activation = np.mean(rep, 0)
plt.imshow(averge_activation, cmap=args.color)
plt.colorbar()
plt.clim(0, np.max(averge_activation))
plt.savefig(my_experiment.path + "average_activation.pdf", format="pdf")
plt.clf()
instance_sparsity = np.mean((np.sum(np.sum(rep, 1), 1)) / (64 * 36))
print("Instance sparisty = ", instance_sparsity)
my_experiment.results["instance_sparisty"] = str(instance_sparsity)
lifetime_sparsity = (np.sum(rep, 0) / len(rep)).flatten()
mean_lifetime = np.mean(lifetime_sparsity)
print("Lifetime sparsity = ", mean_lifetime)
my_experiment.results["lifetime_sparisty"] = str(mean_lifetime)
dead_neuros = float(np.sum(
(lifetime_sparsity == 0).astype(int))) / len(lifetime_sparsity)
print("Dead neurons percentange = ", dead_neuros)
my_experiment.results["dead_neuros"] = str(dead_neuros)
plt.hist(lifetime_sparsity)
plt.savefig(my_experiment.path + "histogram.pdf", format="pdf")
my_experiment.store_json()
def main(args):
torch.manual_seed(args.seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(args.seed)
if torch.cuda.is_available():
device = torch.device('cuda')
else:
device = torch.device('cpu')
np.random.seed(args.seed)
my_experiment = experiment(args.name, args, "../results/")
dataset = df.DatasetFactory.get_dataset(args.dataset)
if args.dataset == "CIFAR100":
args.classes = list(range(50))
if args.dataset == "omniglot":
iterator = torch.utils.data.DataLoader(utils.remove_classes_omni(
dataset, list(range(963))),
batch_size=256,
shuffle=True,
num_workers=1)
else:
iterator = torch.utils.data.DataLoader(utils.remove_classes(
dataset, args.classes),
batch_size=256,
shuffle=True,
num_workers=1)
logger.info(str(args))
config = mf.ModelFactory.get_model("na", args.dataset)
maml = learner.Learner(config).to(device)
opt = torch.optim.Adam(maml.parameters(), lr=args.lr)
for e in range(args.epoch):
correct = 0
for img, y in iterator:
if e == 20:
opt = torch.optim.Adam(maml.parameters(), lr=0.00001)
logger.info("Changing LR from %f to %f", 0.0001, 0.00001)
img = img.to(device)
y = y.to(device)
pred = maml(img)
feature = F.relu(maml(img, feature=True))
avg_feature = feature.mean(0)
beta = args.beta
beta_hat = avg_feature
loss_rec = ((beta / (beta_hat + 0.0001)) -
torch.log(beta / (beta_hat + 0.0001)) - 1)
# loss_rec = (beta / (beta_hat)
loss_rec = loss_rec * (beta_hat > beta).float()
loss_sparse = loss_rec
if args.l1:
loss_sparse = feature.mean(0)
loss_sparse = loss_sparse.mean()
opt.zero_grad()
loss = F.cross_entropy(pred, y)
loss_sparse.backward(retain_graph=True)
loss.backward()
opt.step()
correct += (pred.argmax(1) == y).sum().float() / len(y)
logger.info("Accuracy at epoch %d = %s", e,
str(correct / len(iterator)))
torch.save(maml, my_experiment.path + "model.net")
def main(args):
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
np.random.seed(args.seed)
random.seed(args.seed)
my_experiment = experiment(args.name, args, "/data5/jlindsey/continual/results", args.commit)
writer = SummaryWriter(my_experiment.path + "tensorboard")
logger = logging.getLogger('experiment')
logger.setLevel(logging.INFO)
total_clases = 10
frozen_layers = []
for temp in range(args.rln * 2):
frozen_layers.append("vars." + str(temp))
logger.info("Frozen layers = %s", " ".join(frozen_layers))
#
final_results_all = []
total_clases = [10, 50, 75, 100, 150, 200]
if args.twentyclass:
total_clases = [20, 50]
if args.fiveclass:
total_clases = [5]
for tot_class in total_clases:
avg_perf = 0.0
lr_list = [0.03]
for aoo in range(0, args.runs):
keep = np.random.choice(list(range(200)), tot_class, replace=False)
print('keep', keep)
if args.dataset == "omniglot":
dataset = utils.remove_classes_omni(
df.DatasetFactory.get_dataset("omniglot", train=True, background=False), keep)
print('lenbefore', len(dataset.data))
iterator_sorted = torch.utils.data.DataLoader(
utils.iterator_sorter_omni(dataset, False, classes=total_clases),
batch_size=1,
shuffle=args.iid, num_workers=2)
print("LEN", len(iterator_sorted), len(dataset.data))
dataset = utils.remove_classes_omni(
df.DatasetFactory.get_dataset("omniglot", train=not args.test, background=False), keep)
iterator = torch.utils.data.DataLoader(dataset, batch_size=32,
shuffle=False, num_workers=1)
elif args.dataset == "CIFAR100":
keep = np.random.choice(list(range(50, 100)), tot_class)
dataset = utils.remove_classes(df.DatasetFactory.get_dataset(args.dataset, train=True), keep)
iterator_sorted = torch.utils.data.DataLoader(
utils.iterator_sorter(dataset, False, classes=tot_class),
batch_size=16,
shuffle=args.iid, num_workers=2)
dataset = utils.remove_classes(df.DatasetFactory.get_dataset(args.dataset, train=False), keep)
iterator = torch.utils.data.DataLoader(dataset, batch_size=128,
shuffle=False, num_workers=1)
# sampler = ts.MNISTSampler(list(range(0, total_clases)), dataset)
#
print(args)
if torch.cuda.is_available():
device = torch.device('cuda')
else:
device = torch.device('cpu')
results_mem_size = {}
for mem_size in [args.memory]:
max_acc = -10
max_lr = -10
for lr in lr_list:
print(lr)
# for lr in [0.001, 0.0003, 0.0001, 0.00003, 0.00001]:
maml = torch.load(args.model, map_location='cpu')
if args.scratch:
config = mf.ModelFactory.get_model("na", args.dataset)
maml = learner.Learner(config)
# maml = MetaLearingClassification(args, config).to(device).net
maml = maml.to(device)
for name, param in maml.named_parameters():
param.learn = True
for name, param in maml.named_parameters():
# logger.info(name)
if name in frozen_layers:
# logger.info("Freeezing name %s", str(name))
param.learn = False
# logger.info(str(param.requires_grad))
else:
if args.reset:
w = nn.Parameter(torch.ones_like(param))
# logger.info("W shape = %s", str(len(w.shape)))
if len(w.shape) > 1:
torch.nn.init.kaiming_normal_(w)
else:
w = nn.Parameter(torch.zeros_like(param))
param.data = w
param.learn = True
frozen_layers = []
for temp in range(args.rln * 2):
frozen_layers.append("vars." + str(temp))
'''
torch.nn.init.kaiming_normal_(maml.parameters()[-2])
w = nn.Parameter(torch.zeros_like(maml.parameters()[-1]))
maml.parameters()[-1].data = w
for n, a in maml.named_parameters():
n = n.replace(".", "_")
# logger.info("Name = %s", n)
if n == "vars_14":
w = nn.Parameter(torch.ones_like(a))
# logger.info("W shape = %s", str(w.shape))
torch.nn.init.kaiming_normal_(w)
a.data = w
if n == "vars_15":
w = nn.Parameter(torch.zeros_like(a))
a.data = w
'''
correct = 0
for img, target in iterator:
with torch.no_grad():
img = img.to(device)
target = target.to(device)
logits_q = maml(img, vars=None, bn_training=False, feature=False)
pred_q = (logits_q).argmax(dim=1)
correct += torch.eq(pred_q, target).sum().item() / len(img)
logger.info("Pre-epoch accuracy %s", str(correct / len(iterator)))
filter_list = ["vars.0", "vars.1", "vars.2", "vars.3", "vars.4", "vars.5"]
logger.info("Filter list = %s", ",".join(filter_list))
list_of_names = list(
map(lambda x: x[1], list(filter(lambda x: x[0] not in filter_list, maml.named_parameters()))))
list_of_params = list(filter(lambda x: x.learn, maml.parameters()))
list_of_names = list(filter(lambda x: x[1].learn, maml.named_parameters()))
if args.scratch or args.no_freeze:
print("Empty filter list")
list_of_params = maml.parameters()
#
for x in list_of_names:
logger.info("Unfrozen layer = %s", str(x[0]))
opt = torch.optim.Adam(list_of_params, lr=lr)
fast_weights = maml.vars
if args.randomize_plastic_weights:
maml.randomize_plastic_weights()
if args.zero_plastic_weights:
maml.zero_plastic_weights()
for iter in range(0, args.epoch):
iter_count = 0
imgs = []
ys = []
for img, y in iterator_sorted:
#print(iter_count, y)
if iter_count % 15 >= args.shots:
iter_count += 1
continue
iter_count += 1
img = img.to(device)
y = y.to(device)
imgs.append(img)
ys.append(y)
if not args.batch_learning:
pred = maml(img, vars=fast_weights)
opt.zero_grad()
loss = F.cross_entropy(pred, y)
grad = torch.autograd.grad(loss, fast_weights)
# fast_weights = list(map(lambda p: p[1] - self.update_lr * p[0], zip(grad, self.net.parameters())))
if args.plastic_update:
fast_weights = list(
map(lambda p: p[1] - p[0] * p[2] if p[1].learn else p[1], zip(grad, fast_weights, maml.vars_plasticity)))
else:
fast_weights = list(
map(lambda p: p[1] - args.update_lr * p[0] if p[1].learn else p[1], zip(grad, fast_weights)))
for params_old, params_new in zip(maml.parameters(), fast_weights):
params_new.learn = params_old.learn
if args.batch_learning:
y = torch.cat(ys, 0)
img = torch.cat(imgs, 0)
pred = maml(img, vars=fast_weights)
opt.zero_grad()
loss = F.cross_entropy(pred, y)
grad = torch.autograd.grad(loss, fast_weights)
# fast_weights = list(map(lambda p: p[1] - self.update_lr * p[0], zip(grad, self.net.parameters())))
if args.plastic_update:
fast_weights = list(
map(lambda p: p[1] - p[0] * p[2] if p[1].learn else p[1], zip(grad, fast_weights, maml.vars_plasticity)))
else:
fast_weights = list(
map(lambda p: p[1] - args.update_lr * p[0] if p[1].learn else p[1], zip(grad, fast_weights)))
for params_old, params_new in zip(maml.parameters(), fast_weights):
params_new.learn = params_old.learn
#loss.backward()
#opt.step()
logger.info("Result after one epoch for LR = %f", lr)
correct = 0
for img, target in iterator:
img = img.to(device)
target = target.to(device)
logits_q = maml(img, vars=fast_weights, bn_training=False, feature=False)
pred_q = (logits_q).argmax(dim=1)
correct += torch.eq(pred_q, target).sum().item() / len(img)
logger.info(str(correct / len(iterator)))
if (correct / len(iterator) > max_acc):
max_acc = correct / len(iterator)
max_lr = lr
lr_list = [max_lr]
results_mem_size[mem_size] = (max_acc, max_lr)
avg_perf += max_acc / args.runs
print('avg perf', avg_perf * args.runs / (1+aoo))
logger.info("Final Max Result = %s", str(max_acc))
writer.add_scalar('/finetune/best_' + str(aoo), max_acc, tot_class)
final_results_all.append((tot_class, results_mem_size))
print("A= ", results_mem_size)
logger.info("Final results = %s", str(results_mem_size))
my_experiment.results["Final Results"] = final_results_all
my_experiment.store_json()
np.save('evals/final_results_'+args.orig_name+'.npy', final_results_all)
print("FINAL RESULTS = ", final_results_all)
writer.close()
def main():
p = params.Parser()
total_seeds = len(p.parse_known_args()[0].seed)
rank = p.parse_known_args()[0].rank
all_args = vars(p.parse_known_args()[0])
print("All args = ", all_args)
args = utils.get_run(vars(p.parse_known_args()[0]), rank)
utils.set_seed(args['seed'])
my_experiment = experiment(args['name'],
args,
"../results/",
commit_changes=False,
rank=0,
seed=1)
gpu_to_use = rank % args["gpus"]
if torch.cuda.is_available():
device = torch.device('cuda:' + str(gpu_to_use))
logger.info("Using gpu : %s", 'cuda:' + str(gpu_to_use))
else:
device = torch.device('cpu')
dataset = df.DatasetFactory.get_dataset(args['dataset'],
background=True,
train=True,
path=args["path"],
all=True)
iterator = torch.utils.data.DataLoader(dataset,
batch_size=256,
shuffle=True,
num_workers=0)
logger.info(str(args))
config = mf.ModelFactory.get_model("na", args["dataset"])
maml = learner.Learner(config).to(device)
for k, v in maml.named_parameters():
print(k, v.requires_grad)
opt = torch.optim.Adam(maml.parameters(), lr=args["lr"])
for e in range(args["epoch"]):
correct = 0
for img, y in tqdm(iterator):
img = img.to(device)
y = y.to(device)
pred = maml(img)
opt.zero_grad()
loss = F.cross_entropy(pred, y.long())
loss.backward()
opt.step()
correct += (pred.argmax(1) == y).sum().float() / len(y)
logger.info("Accuracy at epoch %d = %s", e,
str(correct / len(iterator)))
torch.save(maml, my_experiment.path + "model.net")
def main(args):
torch.manual_seed(args.seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(args.seed)
if torch.cuda.is_available():
device = torch.device('cuda')
else:
device = torch.device('cpu')
np.random.seed(args.seed)
my_experiment = experiment(args.name, args, "./results/")
args.classes = list(range(64))
# args.traj_classes = list(range(int(64 / 2), 963))
dataset = imgnet.MiniImagenet(args.dataset_path, mode='train')
dataset_test = imgnet.MiniImagenet(args.dataset_path, mode='test')
# Iterators used for evaluation
iterator_test = torch.utils.data.DataLoader(dataset_test,
batch_size=5,
shuffle=True,
num_workers=1)
iterator = torch.utils.data.DataLoader(dataset,
batch_size=128,
shuffle=True,
num_workers=1)
#
logger.info(str(args))
config = mf.ModelFactory.get_model("na", args.dataset)
maml = learner.Learner(config).to(device)
opt = torch.optim.Adam(maml.parameters(), lr=args.lr)
for e in range(args.epoch):
correct = 0
for img, y in tqdm(iterator):
if e == 50:
opt = torch.optim.Adam(maml.parameters(), lr=0.00001)
logger.info("Changing LR from %f to %f", 0.0001, 0.00001)
img = img.to(device)
y = y.to(device)
pred = maml(img)
feature = maml(img, feature=True)
loss_rep = torch.abs(feature).sum()
opt.zero_grad()
loss = F.cross_entropy(pred, y)
# loss_rep.backward(retain_graph=True)
# logger.info("L1 norm = %s", str(loss_rep.item()))
loss.backward()
opt.step()
correct += (pred.argmax(1) == y).sum().float() / len(y)
logger.info("Accuracy at epoch %d = %s", e,
str(correct / len(iterator)))
# correct = 0
# with torch.no_grad():
# for img, y in tqdm(iterator_test):
#
# img = img.to(device)
# y = y.to(device)
# pred = maml(img)
# feature = maml(img, feature=True)
# loss_rep = torch.abs(feature).sum()
#
# correct += (pred.argmax(1) == y).sum().float() / len(y)
# logger.info("Accuracy Test at epoch %d = %s", e, str(correct / len(iterator_test)))
torch.save(maml,
my_experiment.path + "baseline_pretraining_imagenet.net")
def main(args):
torch.manual_seed(args.seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(args.seed)
if torch.cuda.is_available():
device = torch.device('cuda')
else:
device = torch.device('cpu')
np.random.seed(args.seed)
my_experiment = experiment(args.name, args, "../results/")
dataset = df.DatasetFactory.get_dataset(args.dataset)
dataset_test = df.DatasetFactory.get_dataset(args.dataset, train=False)
if args.dataset == "CUB":
args.classes = list(range(100))
if args.dataset == "CIFAR100":
args.classes = list(range(50))
if args.dataset == "omniglot":
iterator = torch.utils.data.DataLoader(utils.remove_classes_omni(
dataset, list(range(963))),
batch_size=256,
shuffle=True,
num_workers=1)
iterator_test = torch.utils.data.DataLoader(utils.remove_classes_omni(
dataset_test, list(range(963))),
batch_size=256,
shuffle=True,
num_workers=1)
else:
iterator = torch.utils.data.DataLoader(utils.remove_classes(
dataset, args.classes),
batch_size=12,
shuffle=True,
num_workers=1)
iterator_test = torch.utils.data.DataLoader(utils.remove_classes(
dataset_test, args.classes),
batch_size=12,
shuffle=True,
num_workers=1)
logger.info(str(args))
config = mf.ModelFactory.get_model("na", args.dataset)
maml = learner.Learner(config).to(device)
opt = torch.optim.Adam(maml.parameters(), lr=args.lr)
for e in range(args.epoch):
correct = 0
for img, y in tqdm(iterator):
if e == 50:
opt = torch.optim.Adam(maml.parameters(), lr=0.00001)
logger.info("Changing LR from %f to %f", 0.0001, 0.00001)
img = img.to(device)
y = y.to(device)
pred = maml(img)
feature = maml(img, feature=True)
loss_rep = torch.abs(feature).sum()
opt.zero_grad()
loss = F.cross_entropy(pred, y)
# loss_rep.backward(retain_graph=True)
# logger.info("L1 norm = %s", str(loss_rep.item()))
loss.backward()
opt.step()
correct += (pred.argmax(1) == y).sum().float() / len(y)
logger.info("Accuracy at epoch %d = %s", e,
str(correct / len(iterator)))
correct = 0
with torch.no_grad():
for img, y in tqdm(iterator_test):
img = img.to(device)
y = y.to(device)
pred = maml(img)
feature = maml(img, feature=True)
loss_rep = torch.abs(feature).sum()
correct += (pred.argmax(1) == y).sum().float() / len(y)
logger.info("Accuracy Test at epoch %d = %s", e,
str(correct / len(iterator_test)))
torch.save(maml, my_experiment.path + "model.net")
def load_model(self, args, config, context_config, device="cpu"):
self.net = Learner.Learner(config, context_config, device=device)
def main(args):
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
np.random.seed(args.seed)
random.seed(args.seed)
my_experiment = experiment(args.name, args, "../results/", args.commit)
writer = SummaryWriter(my_experiment.path + "tensorboard")
logger = logging.getLogger('experiment')
logger.setLevel(logging.INFO)
total_clases = 10
frozen_layers = []
for temp in range(args.rln * 2):
frozen_layers.append("vars." + str(temp))
logger.info("Frozen layers = %s", " ".join(frozen_layers))
#for v in range(6):
# frozen_layers.append("vars_bn.{0}".format(v))
final_results_all = []
temp_result = []
total_clases = args.schedule
for tot_class in total_clases:
lr_list = [
0.001, 0.0006, 0.0004, 0.00035, 0.0003, 0.00025, 0.0002, 0.00015,
0.0001, 0.00009, 0.00008, 0.00006, 0.00003, 0.00001
]
lr_all = []
for lr_search in range(10):
keep = np.random.choice(list(range(650)), tot_class, replace=False)
dataset = utils.remove_classes_omni(
df.DatasetFactory.get_dataset("omniglot",
train=True,
background=False,
path=args.dataset_path), keep)
iterator_sorted = torch.utils.data.DataLoader(
utils.iterator_sorter_omni(dataset,
False,
classes=total_clases),
batch_size=1,
shuffle=args.iid,
num_workers=2)
dataset = utils.remove_classes_omni(
df.DatasetFactory.get_dataset("omniglot",
train=not args.test,
background=False,
path=args.dataset_path), keep)
iterator = torch.utils.data.DataLoader(dataset,
batch_size=1,
shuffle=False,
num_workers=1)
print(args)
if torch.cuda.is_available():
device = torch.device('cuda')
else:
device = torch.device('cpu')
results_mem_size = {}
for mem_size in [args.memory]:
max_acc = -10
max_lr = -10
for lr in lr_list:
print(lr)
maml = torch.load(args.model, map_location='cpu')
if args.scratch:
config = mf.ModelFactory.get_model("OML", args.dataset)
maml = learner.Learner(config)
# maml = MetaLearingClassification(args, config).to(device).net
maml = maml.to(device)
for name, param in maml.named_parameters():
param.learn = True
for name, param in maml.named_parameters():
# logger.info(name)
if name in frozen_layers:
param.learn = False
else:
if args.reset:
w = nn.Parameter(torch.ones_like(param))
# logger.info("W shape = %s", str(len(w.shape)))
if len(w.shape) > 1:
torch.nn.init.kaiming_normal_(w)
else:
w = nn.Parameter(torch.zeros_like(param))
param.data = w
param.learn = True
frozen_layers = []
for temp in range(args.rln * 2):
frozen_layers.append("vars." + str(temp))
torch.nn.init.kaiming_normal_(maml.parameters()[-2])
w = nn.Parameter(torch.zeros_like(maml.parameters()[-1]))
maml.parameters()[-1].data = w
if args.neuromodulation:
weights2reset = ["vars_26"]
biases2reset = ["vars_27"]
else:
weights2reset = ["vars_14"]
biases2reset = ["vars_15"]
for n, a in maml.named_parameters():
n = n.replace(".", "_")
if n in weights2reset:
w = nn.Parameter(torch.ones_like(a)).to(device)
torch.nn.init.kaiming_normal_(w)
a.data = w
if n in biases2reset:
w = nn.Parameter(torch.zeros_like(a)).to(device)
a.data = w
filter_list = ["vars.{0}".format(v) for v in range(6)]
logger.info("Filter list = %s", ",".join(filter_list))
list_of_names = list(
map(
lambda x: x[1],
list(
filter(lambda x: x[0] not in filter_list,
maml.named_parameters()))))
list_of_params = list(
filter(lambda x: x.learn, maml.parameters()))
list_of_names = list(
filter(lambda x: x[1].learn, maml.named_parameters()))
if args.scratch or args.no_freeze:
print("Empty filter list")
list_of_params = maml.parameters()
for x in list_of_names:
logger.info("Unfrozen layer = %s", str(x[0]))
opt = torch.optim.Adam(list_of_params, lr=lr)
for _ in range(0, args.epoch):
for img, y in iterator_sorted:
img = img.to(device)
y = y.to(device)
pred = maml(img)
opt.zero_grad()
loss = F.cross_entropy(pred, y)
loss.backward()
opt.step()
logger.info("Result after one epoch for LR = %f", lr)
correct = 0
for img, target in iterator:
img = img.to(device)
target = target.to(device)
logits_q = maml(img,
vars=None,
bn_training=False,
feature=False)
pred_q = (logits_q).argmax(dim=1)
correct += torch.eq(pred_q,
target).sum().item() / len(img)
logger.info(str(correct / len(iterator)))
if (correct / len(iterator) > max_acc):
max_acc = correct / len(iterator)
max_lr = lr
lr_all.append(max_lr)
results_mem_size[mem_size] = (max_acc, max_lr)
logger.info("Final Max Result = %s", str(max_acc))
writer.add_scalar('/finetune/best_' + str(lr_search), max_acc,
tot_class)
temp_result.append((tot_class, results_mem_size))
print("A= ", results_mem_size)
logger.info("Temp Results = %s", str(results_mem_size))
my_experiment.results["Temp Results"] = temp_result
my_experiment.store_json()
print("LR RESULTS = ", temp_result)
from scipy import stats
best_lr = float(stats.mode(lr_all)[0][0])
logger.info("BEST LR %s= ", str(best_lr))
for aoo in range(args.runs):
keep = np.random.choice(list(range(650)), tot_class, replace=False)
if args.dataset == "omniglot":
dataset = utils.remove_classes_omni(
df.DatasetFactory.get_dataset("omniglot",
train=True,
background=False), keep)
iterator_sorted = torch.utils.data.DataLoader(
utils.iterator_sorter_omni(dataset,
False,
classes=total_clases),
batch_size=1,
shuffle=args.iid,
num_workers=2)
dataset = utils.remove_classes_omni(
df.DatasetFactory.get_dataset("omniglot",
train=not args.test,
background=False), keep)
iterator = torch.utils.data.DataLoader(dataset,
batch_size=1,
shuffle=False,
num_workers=1)
elif args.dataset == "CIFAR100":
keep = np.random.choice(list(range(50, 100)), tot_class)
dataset = utils.remove_classes(
df.DatasetFactory.get_dataset(args.dataset, train=True),
keep)
iterator_sorted = torch.utils.data.DataLoader(
utils.iterator_sorter(dataset, False, classes=tot_class),
batch_size=16,
shuffle=args.iid,
num_workers=2)
dataset = utils.remove_classes(
df.DatasetFactory.get_dataset(args.dataset, train=False),
keep)
iterator = torch.utils.data.DataLoader(dataset,
batch_size=128,
shuffle=False,
num_workers=1)
print(args)
if torch.cuda.is_available():
device = torch.device('cuda')
else:
device = torch.device('cpu')
results_mem_size = {}
for mem_size in [args.memory]:
max_acc = -10
max_lr = -10
lr = best_lr
maml = torch.load(args.model, map_location='cpu')
if args.scratch:
config = mf.ModelFactory.get_model("MRCL", args.dataset)
maml = learner.Learner(config)
maml = maml.to(device)
for name, param in maml.named_parameters():
param.learn = True
for name, param in maml.named_parameters():
# logger.info(name)
if name in frozen_layers:
param.learn = False
else:
if args.reset:
w = nn.Parameter(torch.ones_like(param))
if len(w.shape) > 1:
torch.nn.init.kaiming_normal_(w)
else:
w = nn.Parameter(torch.zeros_like(param))
param.data = w
param.learn = True
frozen_layers = []
for temp in range(args.rln * 2):
frozen_layers.append("vars." + str(temp))
torch.nn.init.kaiming_normal_(maml.parameters()[-2])
w = nn.Parameter(torch.zeros_like(maml.parameters()[-1]))
maml.parameters()[-1].data = w
for n, a in maml.named_parameters():
n = n.replace(".", "_")
if args.neuromodulation:
weights2reset = ["vars_26"]
biases2reset = ["vars_27"]
else:
weights2reset = ["vars_14"]
biases2reset = ["vars_15"]
for n, a in maml.named_parameters():
n = n.replace(".", "_")
if n in weights2reset:
w = nn.Parameter(torch.ones_like(a)).to(device)
torch.nn.init.kaiming_normal_(w)
a.data = w
if n in biases2reset:
w = nn.Parameter(torch.zeros_like(a)).to(device)
a.data = w
correct = 0
for img, target in iterator:
with torch.no_grad():
img = img.to(device)
target = target.to(device)
logits_q = maml(img,
vars=None,
bn_training=False,
feature=False)
pred_q = (logits_q).argmax(dim=1)
correct += torch.eq(pred_q,
target).sum().item() / len(img)
logger.info("Pre-epoch accuracy %s",
str(correct / len(iterator)))
filter_list = ["vars.{0}".format(v) for v in range(6)]
logger.info("Filter list = %s", ",".join(filter_list))
list_of_names = list(
map(
lambda x: x[1],
list(
filter(lambda x: x[0] not in filter_list,
maml.named_parameters()))))
list_of_params = list(
filter(lambda x: x.learn, maml.parameters()))
list_of_names = list(
filter(lambda x: x[1].learn, maml.named_parameters()))
if args.scratch or args.no_freeze:
print("Empty filter list")
list_of_params = maml.parameters()
for x in list_of_names:
logger.info("Unfrozen layer = %s", str(x[0]))
opt = torch.optim.Adam(list_of_params, lr=lr)
for _ in range(0, args.epoch):
for img, y in iterator_sorted:
img = img.to(device)
y = y.to(device)
pred = maml(img)
opt.zero_grad()
loss = F.cross_entropy(pred, y)
loss.backward()
opt.step()
logger.info("Result after one epoch for LR = %f", lr)
correct = 0
for img, target in iterator:
img = img.to(device)
target = target.to(device)
logits_q = maml(img,
vars=None,
bn_training=False,
feature=False)
pred_q = (logits_q).argmax(dim=1)
correct += torch.eq(pred_q, target).sum().item() / len(img)
logger.info(str(correct / len(iterator)))
if (correct / len(iterator) > max_acc):
max_acc = correct / len(iterator)
max_lr = lr
lr_list = [max_lr]
results_mem_size[mem_size] = (max_acc, max_lr)
logger.info("Final Max Result = %s", str(max_acc))
writer.add_scalar('/finetune/best_' + str(aoo), max_acc,
tot_class)
final_results_all.append((tot_class, results_mem_size))
print("A= ", results_mem_size)
logger.info("Final results = %s", str(results_mem_size))
my_experiment.results["Final Results"] = final_results_all
my_experiment.store_json()
print("FINAL RESULTS = ", final_results_all)
writer.close()
def main(args):
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
np.random.seed(args.seed)
random.seed(args.seed)
my_experiment = experiment(args.name, args, "../results/", args.commit)
writer = SummaryWriter(my_experiment.path + "tensorboard")
logger = logging.getLogger('experiment')
logger.setLevel(logging.INFO)
total_clases = 10
frozen_layers = []
for temp in range(args.rln * 2):
frozen_layers.append("vars." + str(temp))
logger.info("Frozen layers = %s", " ".join(frozen_layers))
#
final_results_all = []
total_clases = [10, 50, 75, 100, 150, 200]
for tot_class in total_clases:
lr_list = [0.03, 0.01, 0.003, 0.001, 0.0003, 0.0001, 0.00003, 0.00001]
for aoo in range(0, 20):
keep = np.random.choice(list(range(200)), tot_class)
if args.dataset == "omniglot":
dataset = utils.remove_classes_omni(
df.DatasetFactory.get_dataset("omniglot",
train=True,
background=False), keep)
iterator_sorted = torch.utils.data.DataLoader(
utils.iterator_sorter_omni(dataset,
False,
classes=total_clases),
batch_size=1,
shuffle=args.iid,
num_workers=2)
dataset = utils.remove_classes_omni(
df.DatasetFactory.get_dataset("omniglot",
train=not args.test,
background=False), keep)
iterator = torch.utils.data.DataLoader(dataset,
batch_size=32,
shuffle=False,
num_workers=1)
elif args.dataset == "CIFAR100":
keep = np.random.choice(list(range(50, 100)), tot_class)
dataset = utils.remove_classes(
df.DatasetFactory.get_dataset(args.dataset, train=True),
keep)
iterator_sorted = torch.utils.data.DataLoader(
utils.iterator_sorter(dataset, False, classes=tot_class),
batch_size=16,
shuffle=args.iid,
num_workers=2)
dataset = utils.remove_classes(
df.DatasetFactory.get_dataset(args.dataset, train=False),
keep)
iterator = torch.utils.data.DataLoader(dataset,
batch_size=128,
shuffle=False,
num_workers=1)
# sampler = ts.MNISTSampler(list(range(0, total_clases)), dataset)
#
print(args)
if torch.cuda.is_available():
device = torch.device('cuda')
else:
device = torch.device('cpu')
results_mem_size = {}
for mem_size in [args.memory]:
max_acc = -10
max_lr = -10
for lr in lr_list:
print(lr)
# for lr in [0.001, 0.0003, 0.0001, 0.00003, 0.00001]:
maml = torch.load(args.model, map_location='cpu')
if args.scratch:
config = mf.ModelFactory.get_model("na", args.dataset)
maml = learner.Learner(config)
# maml = MetaLearingClassification(args, config).to(device).net
maml = maml.to(device)
for name, param in maml.named_parameters():
param.learn = True
for name, param in maml.named_parameters():
# logger.info(name)
if name in frozen_layers:
# logger.info("Freeezing name %s", str(name))
param.learn = False
# logger.info(str(param.requires_grad))
else:
if args.reset:
w = nn.Parameter(torch.ones_like(param))
# logger.info("W shape = %s", str(len(w.shape)))
if len(w.shape) > 1:
torch.nn.init.kaiming_normal_(w)
else:
w = nn.Parameter(torch.zeros_like(param))
param.data = w
param.learn = True
frozen_layers = []
for temp in range(args.rln * 2):
frozen_layers.append("vars." + str(temp))
torch.nn.init.kaiming_normal_(maml.parameters()[-2])
w = nn.Parameter(torch.zeros_like(maml.parameters()[-1]))
maml.parameters()[-1].data = w
for n, a in maml.named_parameters():
n = n.replace(".", "_")
# logger.info("Name = %s", n)
if n == "vars_14":
w = nn.Parameter(torch.ones_like(a))
# logger.info("W shape = %s", str(w.shape))
torch.nn.init.kaiming_normal_(w)
a.data = w
if n == "vars_15":
w = nn.Parameter(torch.zeros_like(a))
a.data = w
correct = 0
for img, target in iterator:
with torch.no_grad():
img = img.to(device)
target = target.to(device)
logits_q = maml(img,
vars=None,
bn_training=False,
feature=False)
pred_q = (logits_q).argmax(dim=1)
correct += torch.eq(pred_q,
target).sum().item() / len(img)
logger.info("Pre-epoch accuracy %s",
str(correct / len(iterator)))
filter_list = [
"vars.0", "vars.1", "vars.2", "vars.3", "vars.4",
"vars.5"
]
logger.info("Filter list = %s", ",".join(filter_list))
list_of_names = list(
map(
lambda x: x[1],
list(
filter(lambda x: x[0] not in filter_list,
maml.named_parameters()))))
list_of_params = list(
filter(lambda x: x.learn, maml.parameters()))
list_of_names = list(
filter(lambda x: x[1].learn, maml.named_parameters()))
if args.scratch or args.no_freeze:
print("Empty filter list")
list_of_params = maml.parameters()
#
for x in list_of_names:
logger.info("Unfrozen layer = %s", str(x[0]))
opt = torch.optim.Adam(list_of_params, lr=lr)
import module.replay as rep
res_sampler = rep.ReservoirSampler(mem_size)
for _ in range(0, args.epoch):
for img, y in iterator_sorted:
if mem_size > 0:
res_sampler.update_buffer(zip(img, y))
res_sampler.update_observations(len(img))
img = img.to(device)
y = y.to(device)
img2, y2 = res_sampler.sample_buffer(16)
img2 = img2.to(device)
y2 = y2.to(device)
img = torch.cat([img, img2], dim=0)
y = torch.cat([y, y2], dim=0)
else:
img = img.to(device)
y = y.to(device)
pred = maml(img)
opt.zero_grad()
loss = F.cross_entropy(pred, y)
loss.backward()
opt.step()
logger.info("Result after one epoch for LR = %f", lr)
correct = 0
for img, target in iterator:
img = img.to(device)
target = target.to(device)
logits_q = maml(img,
vars=None,
bn_training=False,
feature=False)
pred_q = (logits_q).argmax(dim=1)
correct += torch.eq(pred_q,
target).sum().item() / len(img)
logger.info(str(correct / len(iterator)))
if (correct / len(iterator) > max_acc):
max_acc = correct / len(iterator)
max_lr = lr
lr_list = [max_lr]
results_mem_size[mem_size] = (max_acc, max_lr)
logger.info("Final Max Result = %s", str(max_acc))
writer.add_scalar('/finetune/best_' + str(aoo), max_acc,
tot_class)
# quit()
final_results_all.append((tot_class, results_mem_size))
print("A= ", results_mem_size)
logger.info("Final results = %s", str(results_mem_size))
my_experiment.results["Final Results"] = final_results_all
my_experiment.store_json()
print("FINAL RESULTS = ", final_results_all)
writer.close()
def main():
p = params.Parser()
total_seeds = len(p.parse_known_args()[0].seed)
_args = p.parse_args()
# rank = p.parse_known_args()[0].rank
rank = _args.rank
# all_args = vars(p.parse_known_args()[0])
print("All args = ", _args)
args = utils.get_run(vars(_args), rank)
utils.set_seed(args["seed"])
if args["log_root"]:
log_root = osp.join("./results", args["log_root"]) + "/"
else:
log_root = osp.join("./results/")
my_experiment = experiment(
args["name"],
args,
log_root,
commit_changes=False,
rank=0,
seed=args["seed"],
)
writer = SummaryWriter(my_experiment.path + "tensorboard")
gpu_to_use = rank % args["gpus"]
if torch.cuda.is_available():
device = torch.device("cuda:" + str(gpu_to_use))
logger.info("Using gpu : %s", "cuda:" + str(gpu_to_use))
else:
device = torch.device("cpu")
print("Train dataset")
dataset = df.DatasetFactory.get_dataset(
args["dataset"],
background=True,
train=True,
path=args["path"],
all=True,
resize=args["resize"],
augment=args["augment"],
prefetch_gpu=args["prefetch_gpu"],
)
print("Val dataset")
val_dataset = df.DatasetFactory.get_dataset(
args["dataset"],
background=True,
train=True,
path=args["path"],
all=True,
resize=args["resize"],
prefetch_gpu=args["prefetch_gpu"],
# augment=args["augment"],
)
train_labels = np.arange(664)
# class_labels = np.array(dataset.targets)
class_labels = np.array(np.asarray(torch.as_tensor(dataset.targets, device="cpu")))
labels_mapping = {
tl: (class_labels == tl).astype(int).nonzero()[0] for tl in train_labels
}
train_indices = [tl[:15] for tl in labels_mapping.values()]
val_indices = [tl[15:] for tl in labels_mapping.values()]
train_indices = [i for sublist in train_indices for i in sublist]
val_indices = [i for sublist in val_indices for i in sublist]
# indices = np.zeros_like(class_labels)
# for a in train_labels:
# indices = indices + (class_labels == a).astype(int)
# val_indices = (indices == 0).astype(int)
# indices = np.nonzero(indices)[0]
trainset = torch.utils.data.Subset(dataset, train_indices)
# print(indices)
print("Total samples:", len(class_labels))
print("Train samples:", len(train_indices))
print("Val samples:", len(val_indices))
# val_labels = np.arange(664)
# class_labels = np.array(dataset.targets)
# val_indices = np.zeros_like(class_labels)
# for a in train_labels:
# val_indices = val_indices + (class_labels != a).astype(int)
# val_indices = np.nonzero(val_indices)[0]
valset = torch.utils.data.Subset(val_dataset, val_indices)
train_iterator = torch.utils.data.DataLoader(
trainset,
batch_size=64,
shuffle=True,
num_workers=0,
drop_last=True,
)
val_iterator = torch.utils.data.DataLoader(
valset,
batch_size=256,
shuffle=True,
num_workers=0,
drop_last=False,
)
logger.info("Args:")
logger.info(str(vars(_args)))
logger.info(str(args))
config = mf.ModelFactory.get_model("na", args["dataset"], resize=args["resize"])
maml = learner.Learner(config).to(device)
for k, v in maml.named_parameters():
print(k, v.requires_grad)
# opt = torch.optim.Adam(maml.parameters(), lr=args["lr"])
opt = torch.optim.SGD(
maml.parameters(),
lr=args["lr"],
momentum=0.9,
weight_decay=5e-4,
)
scheduler = torch.optim.lr_scheduler.MultiStepLR(
opt,
milestones=_args.schedule,
gamma=0.1,
)
best_val_acc = 0
# print(learner)
# print(learner.eval(False))
histories = {
"train": {"acc": [], "loss": [], "step": []},
"val": {"acc": [], "loss": [], "step": []},
}
for e in range(args["epoch"]):
correct = 0
total_loss = 0.0
maml.train()
for img, y in tqdm(train_iterator):
img = img.to(device)
y = y.to(device)
pred = maml(img)
opt.zero_grad()
loss = F.cross_entropy(pred, y.long())
loss.backward()
opt.step()
correct += (pred.argmax(1) == y).float().mean()
total_loss += loss
correct = correct.item()
total_loss = total_loss.item()
scheduler.step()
val_correct = 0
val_total_loss = 0.0
maml.eval()
for img, y in tqdm(val_iterator):
img = img.to(device)
y = y.to(device)
with torch.no_grad():
pred = maml(img)
opt.zero_grad()
loss = F.cross_entropy(pred, y.long())
# loss.backward()
# opt.step()
val_correct += (pred.argmax(1) == y).sum().float()
val_total_loss += loss * y.size(0)
val_correct = val_correct.item()
val_total_loss = val_total_loss.item()
val_acc = val_correct / len(val_indices)
val_loss = val_total_loss / len(val_indices)
train_correct = correct / len(train_iterator)
train_loss = total_loss / len(train_iterator)
logger.info("Accuracy at epoch %d = %s", e, str(train_correct))
logger.info("Loss at epoch %d = %s", e, str(train_loss))
logger.info("Val Accuracy at epoch %d = %s", e, str(val_acc))
logger.info("Val Loss at epoch %d = %s", e, str(val_loss))
histories["train"]["acc"].append(train_correct)
histories["train"]["loss"].append(train_loss)
histories["val"]["acc"].append(val_acc)
histories["val"]["loss"].append(val_loss)
histories["train"]["step"].append(e + 1)
histories["val"]["step"].append(e + 1)
writer.add_scalar(
"/train/accuracy",
train_correct,
e + 1,
)
writer.add_scalar(
"/train/loss",
train_loss,
e + 1,
)
writer.add_scalar(
"/val/accuracy",
val_acc,
e + 1,
)
writer.add_scalar(
"/train/loss",
val_loss,
e + 1,
)
if val_acc > best_val_acc:
best_val_acc = val_acc
logger.info(f"\nNew best validation accuracy: {str(best_val_acc)}\n")
torch.save(maml, my_experiment.path + "model_best.net")
with open(my_experiment.path + "results.json", "w") as f:
json.dump(histories, f)
torch.save(maml, my_experiment.path + "last_model.net")
