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Steffen Illium
2022-02-27 17:56:25 +01:00
parent 78a919395b
commit 926b27b4ef
3 changed files with 167 additions and 104 deletions
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170
experiments/meta_task_exp.py
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@ -16,7 +16,7 @@ from torch.nn import Flatten
from torch.utils.data import Dataset, DataLoader
from torchvision.datasets import MNIST
from torchvision.transforms import ToTensor, Compose, Resize
from tqdm import tqdm
from tqdm import tqdm, trange
# noinspection DuplicatedCode
if platform.node() == 'CarbonX':
@ -46,7 +46,7 @@ WORKER = 10 if not debug else 2
debug = False
BATCHSIZE = 500 if not debug else 50
EPOCH = 100
VALIDATION_FRQ = 4 if not debug else 1
VALIDATION_FRQ = 3 if not debug else 1
SELF_TRAIN_FRQ = 1 if not debug else 1
DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
@ -292,24 +292,23 @@ if __name__ == '__main__':
train_to_task_first = False
sequential_task_train = True
force_st_for_n_from_last_epochs = 5
n_st_per_batch = 3
activation = None # nn.ReLU()
n_st_per_batch = 10
# activation = None # nn.ReLU()
use_sparse_network = False
use_sparse_network = True
for weight_hidden_size in [8]:
for weight_hidden_size in [4, 5, 6]:
tsk_threshold = 0.85
weight_hidden_size = weight_hidden_size
residual_skip = True
n_seeds = 3
n_seeds = 1
data_path = Path('data')
data_path.mkdir(exist_ok=True, parents=True)
assert not (train_to_task_first and train_to_id_first)
st_str = f'{"" if self_train else "no_"}st{f"_n_{n_st_per_batch}" if n_st_per_batch else ""}'
ac_str = f'_{activation.__class__.__name__}' if activation is not None else ''
# ac_str = f'_{activation.__class__.__name__}' if activation is not None else ''
res_str = f'{"" if residual_skip else "_no_res"}'
# dr_str = f'{f"_dr_{dropout}" if dropout != 0 else ""}'
id_str = f'{f"_StToId" if train_to_id_first else ""}'
@ -318,7 +317,7 @@ if __name__ == '__main__':
f_str = f'_f_{force_st_for_n_from_last_epochs}' if \
force_st_for_n_from_last_epochs and sequential_task_train and train_to_task_first else ""
config_str = f'{res_str}{id_str}{tsk_str}{f_str}{sprs_str}'
exp_path = Path('output') / f'mn_{st_str}_{EPOCH}_{weight_hidden_size}{config_str}{ac_str}'
exp_path = Path('output') / f'mn_{st_str}_{EPOCH}_{weight_hidden_size}{config_str}'
if not training:
# noinspection PyRedeclaration
@ -326,10 +325,12 @@ if __name__ == '__main__':
for seed in range(n_seeds):
seed_path = exp_path / str(seed)
seed_path.mkdir(exist_ok=True, parents=True)
model_path = seed_path / '0000_trained_model.zip'
df_store_path = seed_path / 'train_store.csv'
weight_store_path = seed_path / 'weight_store.csv'
init_st_store_path = seed_path / 'init_st_counter.csv'
srnn_parameters = dict()
if training:
@ -345,92 +346,139 @@ if __name__ == '__main__':
d = DataLoader(dataset, batch_size=BATCHSIZE, shuffle=True, drop_last=True, num_workers=WORKER)
interface = np.prod(dataset[0][0].shape)
dense_metanet = MetaNet(interface, depth=5, width=6, out=10, residual_skip=residual_skip,
weight_hidden_size=weight_hidden_size, activation=activation).to(DEVICE)
sparse_metanet = SparseNetwork(interface, depth=5, width=6, out=10, residual_skip=residual_skip,
weight_hidden_size=weight_hidden_size, activation=activation
dense_metanet = MetaNet(interface, depth=3, width=6, out=10, residual_skip=residual_skip,
weight_hidden_size=weight_hidden_size
).to(DEVICE)
sparse_metanet = SparseNetwork(interface, depth=3, width=6, out=10, residual_skip=residual_skip,
weight_hidden_size=weight_hidden_size
).to(DEVICE) if use_sparse_network else dense_metanet
meta_weight_count = sum(p.numel() for p in next(dense_metanet.particles).parameters())
loss_fn = nn.CrossEntropyLoss()
dense_optimizer = torch.optim.SGD(dense_metanet.parameters(), lr=0.004, momentum=0.9)
sparse_optimizer = torch.optim.SGD(
sparse_metanet.parameters(), lr=0.004, momentum=0.9
) if use_sparse_network else dense_optimizer
optimizer = torch.optim.SGD(sparse_metanet.parameters(), lr=0.004, momentum=0.9)
train_store = new_storage_df('train', None)
weight_store = new_storage_df('weights', meta_weight_count)
init_tsk = train_to_task_first
for epoch in tqdm(range(EPOCH), desc=f'Train - Epochs'):
if train_to_task_first:
dense_metanet = dense_metanet.train()
for epoch in trange(10):
for batch, (batch_x, batch_y) in tqdm(enumerate(d), total=len(d), desc='Train - Batch'):
# Task Train
# Zero your gradients for every batch!
optimizer.zero_grad()
batch_x, batch_y = batch_x.to(DEVICE), batch_y.to(DEVICE)
y_pred = dense_metanet(batch_x)
loss = loss_fn(y_pred, batch_y.to(torch.long))
loss.backward()
# Adjust learning weights
optimizer.step()
step_log = dict(Epoch=epoch, Batch=batch,
Metric='Task Loss', Score=loss.item())
train_store.loc[train_store.shape[0]] = step_log
# Transfer weights
if use_sparse_network:
sparse_metanet = sparse_metanet.replace_weights_by_particles(dense_metanet.particles)
if train_to_id_first:
sparse_metanet = sparse_metanet.train()
init_st_epochs = 1500
init_st_df = pd.DataFrame(columns=['Epoch', 'Func Type', 'Count'])
for st_epoch in trange(init_st_epochs):
_ = sparse_metanet.combined_self_train(optimizer)
if st_epoch % 500 == 0:
counter = defaultdict(lambda: 0)
id_functions = test_for_fixpoints(counter, list(sparse_metanet.particles))
counter = dict(counter)
tqdm.write(f"identity_fn after {st_epoch} self-train epochs: {counter}")
for key, value in counter.items():
init_st_df.loc[init_st_df.shape[0]] = (st_epoch, key, value)
sparse_metanet.reset_diverged_particles()
counter = defaultdict(lambda: 0)
id_functions = test_for_fixpoints(counter, list(sparse_metanet.particles))
counter = dict(counter)
tqdm.write(f"identity_fn after {init_st_epochs} self-train epochs: {counter}")
for key, value in counter.items():
init_st_df.loc[init_st_df.shape[0]] = (init_st_epochs, key, value)
init_st_df.to_csv(init_st_store_path, mode='w', index=False)
c = pd.read_csv(init_st_store_path)
sns.lineplot(data=c, x='Epoch', y='Count', hue='Func Type')
plt.savefig(init_st_store_path.parent / f'{init_st_store_path.stem}.png', dpi=300)
# Transfer weights
if use_sparse_network:
dense_metanet = dense_metanet.replace_particles(sparse_metanet.particle_weights)
for epoch in trange(EPOCH, desc=f'Train - Epochs'):
tqdm.write(f'{seed}: {exp_path}')
is_validation_epoch = epoch % VALIDATION_FRQ == 0 if not debug else True
is_self_train_epoch = epoch % SELF_TRAIN_FRQ == 0 if not debug else True
is_validation_epoch = (epoch % VALIDATION_FRQ == 0) if not debug else True
is_self_train_epoch = (epoch % SELF_TRAIN_FRQ == 0) if not debug else True
sparse_metanet = sparse_metanet.train()
dense_metanet = dense_metanet.train()
if is_validation_epoch:
metric = torchmetrics.Accuracy()
else:
metric = None
init_st = train_to_id_first and not all(
x.is_fixpoint == ft.identity_func for x in dense_metanet.particles
)
force_st = (force_st_for_n_from_last_epochs >= (EPOCH - epoch)
) and sequential_task_train and force_st_for_n_from_last_epochs
for batch, (batch_x, batch_y) in tqdm(enumerate(d), total=len(d), desc='MetaNet Train - Batch'):
for batch, (batch_x, batch_y) in tqdm(enumerate(d), total=len(d), desc='Train - Batch'):
# Self Train
if self_train and ((not init_tsk and (is_self_train_epoch or init_st)) or force_st):
# Transfer weights
if use_sparse_network:
sparse_metanet = sparse_metanet.replace_weights_by_particles(dense_metanet.particles)
if is_self_train_epoch:
for _ in range(n_st_per_batch):
self_train_loss = sparse_metanet.combined_self_train(sparse_optimizer, reduction='mean')
self_train_loss = sparse_metanet.combined_self_train(optimizer)
# noinspection PyUnboundLocalVariable
step_log = dict(Epoch=epoch, Batch=batch,
Metric='Self Train Loss', Score=self_train_loss.item())
train_store.loc[train_store.shape[0]] = step_log
# Clean Divergent
sparse_metanet.reset_diverged_particles()
# Transfer weights
if use_sparse_network:
dense_metanet = dense_metanet.replace_particles(sparse_metanet.particle_weights)
dense_metanet.reset_diverged_particles()
# Task Train
if not init_st:
# Zero your gradients for every batch!
dense_optimizer.zero_grad()
batch_x, batch_y = batch_x.to(DEVICE), batch_y.to(DEVICE)
y_pred = dense_metanet(batch_x)
# loss = loss_fn(y, batch_y.unsqueeze(-1).to(torch.float32))
loss = loss_fn(y_pred, batch_y.to(torch.long))
loss.backward()
# Zero your gradients for every batch!
optimizer.zero_grad()
batch_x, batch_y = batch_x.to(DEVICE), batch_y.to(DEVICE)
y_pred = dense_metanet(batch_x)
# Adjust learning weights
dense_optimizer.step()
loss = loss_fn(y_pred, batch_y.to(torch.long))
loss.backward()
step_log = dict(Epoch=epoch, Batch=batch,
Metric='Task Loss', Score=loss.item())
train_store.loc[train_store.shape[0]] = step_log
if is_validation_epoch:
metric(y_pred.cpu(), batch_y.cpu())
# Adjust learning weights
optimizer.step()
# Transfer weights
if use_sparse_network:
sparse_metanet = sparse_metanet.replace_weights_by_particles(dense_metanet.particles)
step_log = dict(Epoch=epoch, Batch=batch,
Metric='Task Loss', Score=loss.item())
train_store.loc[train_store.shape[0]] = step_log
if is_validation_epoch:
metric(y_pred.cpu(), batch_y.cpu())
if batch >= 3 and debug:
break
if is_validation_epoch:
dense_metanet = dense_metanet.eval()
if not init_st:
validation_log = dict(Epoch=int(epoch), Batch=BATCHSIZE,
Metric='Train Accuracy', Score=metric.compute().item())
train_store.loc[train_store.shape[0]] = validation_log
validation_log = dict(Epoch=int(epoch), Batch=BATCHSIZE,
Metric='Train Accuracy', Score=metric.compute().item())
train_store.loc[train_store.shape[0]] = validation_log
accuracy = checkpoint_and_validate(dense_metanet, seed_path, epoch).item()
validation_log = dict(Epoch=int(epoch), Batch=BATCHSIZE,
Metric='Test Accuracy', Score=accuracy)
train_store.loc[train_store.shape[0]] = validation_log
if init_tsk or (train_to_task_first and sequential_task_train):
init_tsk = accuracy <= tsk_threshold
if init_st or is_validation_epoch:
if is_validation_epoch:
counter_dict = defaultdict(lambda: 0)
# This returns ID-functions
_ = test_for_fixpoints(counter_dict, list(dense_metanet.particles))
@ -439,12 +487,14 @@ if __name__ == '__main__':
step_log = dict(Epoch=int(epoch), Batch=BATCHSIZE, Metric=key, Score=value)
train_store.loc[train_store.shape[0]] = step_log
tqdm.write(f'Fixpoint Tester Results: {counter_dict}')
if init_st or is_validation_epoch:
for particle in dense_metanet.particles:
weight_log = (epoch, particle.name, *flat_for_store(particle.parameters()))
weight_store.loc[weight_store.shape[0]] = weight_log
train_store.to_csv(df_store_path, mode='a', header=not df_store_path.exists(), index=False)
weight_store.to_csv(weight_store_path, mode='a', header=not weight_store_path.exists(), index=False)
train_store.to_csv(df_store_path, mode='a', header=not df_store_path.exists(),
index=False)
weight_store.to_csv(weight_store_path, mode='a', header=not weight_store_path.exists(),
index=False)
train_store = new_storage_df('train', None)
weight_store = new_storage_df('weights', meta_weight_count)