def on_train_begin(self, logs=None):
self.train_writer = SummaryWriter(
os.path.join(self.logdir, "train"), purge_step=self.purge_step
)
self.val_writer = SummaryWriter(
os.path.join(self.logdir, "val"), purge_step=self.purge_step
)
self.steps = self.params["steps"]
self.global_step = 0
def load(self, f=None):
if f is None:
f = self.ckpt
if isinstance(self.net, nn.DataParallel):
self.net.module.load_state_dict(
torch.load(f, map_location=self.device))
else:
self.net.load_state_dict(torch.load(f, map_location=self.device))
if os.path.exists(f + ".config"):
with open(f + ".config", "r") as fp:
dic = json.loads(fp.read())
self.config = defaultdict(float, dic)
print("Loaded,", self.config)
if os.path.exists(f + ".optimizer"):
self.optimizer.load_state_dict(torch.load(f + ".optimizer"))
if os.path.exists(f + ".scheduler") and self.lr_scheduler is not None:
self.lr_scheduler.load_state_dict(torch.load(f + ".scheduler"))
if os.path.exists(f + ".csv"):
self.dataframe = pd.read_csv(f + ".csv")
if self.config["tensorboard_dir"] is not None:
self.tensorboard = SummaryWriter(self.config["tensorboard_dir"])
else:
self.tensorboard = None
def test_write_scalar(self):
summary_writer = SummaryWriter(self._log_dir)
tag_name = "learning_rate"
learning_rate = torch.tensor(.01)
for i in range(10):
summary_writer.add_scalar(tag_name, learning_rate, i)
learning_rate -= 0.005
def __init__(
self,
net,
criterion=None,
metric=cal_accuracy,
train_dataloader=None,
val_dataloader=None,
test_dataloader=None,
optimizer=None,
lr_scheduler=None,
tensorboard_dir="./pinkblack_tb/",
ckpt="./ckpt/ckpt.pth",
experiment_id=None,
clip_gradient_norm=False,
is_data_dict=False,
):
"""
:param net: nn.Module Network
:param criterion: loss function. __call__(prediction, *batch_y)
:param metric: metric function __call__(prediction, *batch_y).
*note* : bigger is better. (Early Stopping할 때 metric이 더 큰 값을 선택한다)
:param train_dataloader:
:param val_dataloader:
:param test_dataloader:
:param optimizer: torch.optim
:param lr_scheduler:
:param tensorboard_dir: tensorboard log
:param ckpt: weight path
:param experiment_id: be shown on tensorboard
:param clip_gradient_norm: False or Scalar value (숫자를 입력하면 gradient clipping한다.)
:param is_data_dict: whether dataloaders return dict.
(dataloader에서 주는 데이터가 dict인지 - 아니라면 (x, y pair tuple로 주는 데이터이다.)
"""
self.net = net
self.criterion = nn.CrossEntropyLoss(
) if criterion is None else criterion
self.metric = metric
self.dataloader = dict()
if train_dataloader is not None:
self.dataloader["train"] = train_dataloader
if val_dataloader is not None:
self.dataloader["val"] = val_dataloader
if test_dataloader is not None:
self.dataloader["test"] = test_dataloader
if train_dataloader is None or val_dataloader is None:
logging.warning("Init Trainer :: Two dataloaders are needed!")
self.optimizer = (Adam(
filter(lambda p: p.requires_grad, self.net.parameters()))
if optimizer is None else optimizer)
self.lr_scheduler = lr_scheduler
self.ckpt = ckpt
self.config = defaultdict(float)
self.config["max_train_metric"] = -1e8
self.config["max_val_metric"] = -1e8
self.config["max_test_metric"] = -1e8
self.config["tensorboard_dir"] = tensorboard_dir
self.config["timestamp"] = datetime.now().strftime("%Y%m%d_%H%M%S")
self.config["clip_gradient_norm"] = clip_gradient_norm
self.config["is_data_dict"] = is_data_dict
if experiment_id is None:
self.config["experiment_id"] = self.config["timestamp"]
else:
self.config["experiment_id"] = experiment_id
self.dataframe = pd.DataFrame()
self.device = Trainer.get_model_device(self.net)
if self.device == torch.device("cpu"):
logging.warning(
"Init Trainer :: Do you really want to train the network on CPU instead of GPU?"
)
if self.config["tensorboard_dir"] is not None:
self.tensorboard = SummaryWriter(self.config["tensorboard_dir"])
else:
self.tensorboard = None
self.callbacks = defaultdict(list)
def main(
debug: bool = False,
eager: bool = False,
logdir: str = "runs",
steps_per_epoch: int = 200,
batch_size: int = 64,
epochs: int = 100,
size: int = 32,
num_layers: int = 3,
num_heads: int = 8,
dropout: float = 0.0,
):
if debug:
import debugpy
print("Waiting for debugger...")
debugpy.listen(5678)
debugpy.wait_for_client()
current_time = datetime.now().strftime("%b%d_%H-%M-%S")
logdir = os.path.join(logdir, current_time)
X_train, y_train, X_test, y_test = dataget.image.mnist(
global_cache=True).get()
print("X_train:", X_train.shape, X_train.dtype)
print("y_train:", y_train.shape, y_train.dtype)
print("X_test:", X_test.shape, X_test.dtype)
print("y_test:", y_test.shape, y_test.dtype)
model = elegy.Model(
module=ViT(
size=size,
num_layers=num_layers,
num_heads=num_heads,
dropout=dropout,
),
loss=[
elegy.losses.SparseCategoricalCrossentropy(from_logits=True),
# elegy.regularizers.GlobalL2(l=1e-4),
],
metrics=elegy.metrics.SparseCategoricalAccuracy(),
optimizer=optax.adamw(1e-3),
run_eagerly=eager,
)
model.init(X_train, y_train)
model.summary(X_train[:64])
history = model.fit(
x=X_train,
y=y_train,
epochs=epochs,
steps_per_epoch=steps_per_epoch,
batch_size=batch_size,
validation_data=(X_test, y_test),
shuffle=True,
callbacks=[elegy.callbacks.TensorBoard(logdir=logdir)],
)
elegy.utils.plot_history(history)
# get random samples
idxs = np.random.randint(0, 10000, size=(9, ))
x_sample = X_test[idxs]
# get predictions
y_pred = model.predict(x=x_sample)
# plot and save results
with SummaryWriter(os.path.join(logdir, "val")) as tbwriter:
figure = plt.figure(figsize=(12, 12))
for i in range(3):
for j in range(3):
k = 3 * i + j
plt.subplot(3, 3, k + 1)
plt.title(f"{np.argmax(y_pred[k])}")
plt.imshow(x_sample[k], cmap="gray")
# tbwriter.add_figure("Predictions", figure, 100)
plt.show()
print(
"\n\n\nMetrics and images can be explored using tensorboard using:",
f"\n \t\t\t tensorboard --logdir {logdir}",
)
def train(self, load_model=False, model_path=None):
if load_model:
if model_path is not None:
self.load_weights(model_path)
## Training utterances
all_input_ids, all_input_len, all_label_ids = convert_examples_to_features(
self.train_examples, self.label_list, args.max_seq_length,
self.tokenizer, args.max_turn_length)
num_train_batches = all_input_ids.size(0)
num_train_steps = int(num_train_batches / args.train_batch_size /
args.gradient_accumulation_steps *
args.num_train_epochs)
logger.info("***** training *****")
logger.info(" Num examples = %d", len(self.train_examples))
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Num steps = %d", num_train_steps)
all_input_ids, all_input_len, all_label_ids = all_input_ids.to(
DEVICE), all_input_len.to(DEVICE), all_label_ids.to(DEVICE)
train_data = TensorDataset(all_input_ids, all_input_len, all_label_ids)
train_sampler = RandomSampler(train_data)
train_dataloader = DataLoader(train_data,
sampler=train_sampler,
batch_size=args.train_batch_size)
all_input_ids_dev, all_input_len_dev, all_label_ids_dev = convert_examples_to_features(
self.dev_examples, self.label_list, args.max_seq_length,
self.tokenizer, args.max_turn_length)
logger.info("***** validation *****")
logger.info(" Num examples = %d", len(self.dev_examples))
logger.info(" Batch size = %d", args.dev_batch_size)
all_input_ids_dev, all_input_len_dev, all_label_ids_dev = \
all_input_ids_dev.to(DEVICE), all_input_len_dev.to(DEVICE), all_label_ids_dev.to(DEVICE)
dev_data = TensorDataset(all_input_ids_dev, all_input_len_dev,
all_label_ids_dev)
dev_sampler = SequentialSampler(dev_data)
dev_dataloader = DataLoader(dev_data,
sampler=dev_sampler,
batch_size=args.dev_batch_size)
logger.info("Loaded data!")
if args.fp16:
self.sumbt_model.half()
self.sumbt_model.to(DEVICE)
## Get domain-slot-type embeddings
slot_token_ids, slot_len = \
get_label_embedding(self.processor.target_slot, args.max_label_length, self.tokenizer, DEVICE)
# for slot_idx, slot_str in zip(slot_token_ids, self.processor.target_slot):
# self.idx2slot[slot_idx] = slot_str
## Get slot-value embeddings
label_token_ids, label_len = [], []
for slot_idx, labels in zip(slot_token_ids, self.label_list):
# self.idx2value[slot_idx] = {}
token_ids, lens = get_label_embedding(labels,
args.max_label_length,
self.tokenizer, DEVICE)
label_token_ids.append(token_ids)
label_len.append(lens)
# for label, token_id in zip(labels, token_ids):
# self.idx2value[slot_idx][token_id] = label
logger.info('embeddings prepared')
if USE_CUDA and N_GPU > 1:
self.sumbt_model.module.initialize_slot_value_lookup(
label_token_ids, slot_token_ids)
else:
self.sumbt_model.initialize_slot_value_lookup(
label_token_ids, slot_token_ids)
def get_optimizer_grouped_parameters(model):
param_optimizer = [(n, p) for n, p in model.named_parameters()
if p.requires_grad]
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [
{
'params': [
p for n, p in param_optimizer
if not any(nd in n for nd in no_decay)
],
'weight_decay':
0.01,
'lr':
args.learning_rate
},
{
'params': [
p for n, p in param_optimizer
if any(nd in n for nd in no_decay)
],
'weight_decay':
0.0,
'lr':
args.learning_rate
},
]
return optimizer_grouped_parameters
if not USE_CUDA or N_GPU == 1:
optimizer_grouped_parameters = get_optimizer_grouped_parameters(
self.sumbt_model)
else:
optimizer_grouped_parameters = get_optimizer_grouped_parameters(
self.sumbt_model.module)
t_total = num_train_steps
scheduler = None
if args.fp16:
try:
from apex.optimizers import FP16_Optimizer
from apex.optimizers import FusedAdam
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training."
)
optimizer = FusedAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
bias_correction=False,
max_grad_norm=1.0)
if args.fp16_loss_scale == 0:
optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)
else:
optimizer = FP16_Optimizer(
optimizer, static_loss_scale=args.fp16_loss_scale)
else:
optimizer = AdamW(optimizer_grouped_parameters,
lr=args.learning_rate,
correct_bias=False)
scheduler = get_linear_schedule_with_warmup(
optimizer,
num_warmup_steps=args.warmup_proportion * t_total,
num_training_steps=t_total)
logger.info(optimizer)
# Training code
###############################################################################
logger.info("Training...")
global_step = 0
last_update = None
best_loss = None
model = self.sumbt_model
if not args.do_not_use_tensorboard:
summary_writer = None
else:
summary_writer = SummaryWriter("./tensorboard_summary/logs_1214/")
for epoch in trange(int(args.num_train_epochs), desc="Epoch"):
# Train
model.train()
tr_loss = 0
nb_tr_examples = 0
nb_tr_steps = 0
for step, batch in enumerate(tqdm(train_dataloader)):
batch = tuple(t.to(DEVICE) for t in batch)
input_ids, input_len, label_ids = batch
# Forward
if N_GPU == 1:
loss, loss_slot, acc, acc_slot, _ = model(
input_ids, input_len, label_ids, N_GPU)
else:
loss, _, acc, acc_slot, _ = model(input_ids, input_len,
label_ids, N_GPU)
# average to multi-gpus
loss = loss.mean()
acc = acc.mean()
acc_slot = acc_slot.mean(0)
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
# Backward
if args.fp16:
optimizer.backward(loss)
else:
loss.backward()
# tensrboard logging
if summary_writer is not None:
summary_writer.add_scalar("Epoch", epoch, global_step)
summary_writer.add_scalar("Train/Loss", loss, global_step)
summary_writer.add_scalar("Train/JointAcc", acc,
global_step)
if N_GPU == 1:
for i, slot in enumerate(self.processor.target_slot):
summary_writer.add_scalar(
"Train/Loss_%s" % slot.replace(' ', '_'),
loss_slot[i], global_step)
summary_writer.add_scalar(
"Train/Acc_%s" % slot.replace(' ', '_'),
acc_slot[i], global_step)
tr_loss += loss.item()
nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1
if (step + 1) % args.gradient_accumulation_steps == 0:
# modify lealrning rate with special warm up BERT uses
lr_this_step = args.learning_rate * warmup_linear(
global_step / t_total, args.warmup_proportion)
if summary_writer is not None:
summary_writer.add_scalar("Train/LearningRate",
lr_this_step, global_step)
for param_group in optimizer.param_groups:
param_group['lr'] = lr_this_step
if scheduler is not None:
torch.nn.utils.clip_grad_norm_(
optimizer_grouped_parameters, 1.0)
optimizer.step()
if scheduler is not None:
scheduler.step()
optimizer.zero_grad()
global_step += 1
# Perform evaluation on validation dataset
model.eval()
dev_loss = 0
dev_acc = 0
dev_loss_slot, dev_acc_slot = None, None
nb_dev_examples, nb_dev_steps = 0, 0
for step, batch in enumerate(
tqdm(dev_dataloader, desc="Validation")):
batch = tuple(t.to(DEVICE) for t in batch)
input_ids, input_len, label_ids = batch
if input_ids.dim() == 2:
input_ids = input_ids.unsqueeze(0)
input_len = input_len.unsqueeze(0)
label_ids = label_ids.unsuqeeze(0)
with torch.no_grad():
if N_GPU == 1:
loss, loss_slot, acc, acc_slot, _ = model(
input_ids, input_len, label_ids, N_GPU)
else:
loss, _, acc, acc_slot, _ = model(
input_ids, input_len, label_ids, N_GPU)
# average to multi-gpus
loss = loss.mean()
acc = acc.mean()
acc_slot = acc_slot.mean(0)
num_valid_turn = torch.sum(label_ids[:, :, 0].view(-1) > -1,
0).item()
dev_loss += loss.item() * num_valid_turn
dev_acc += acc.item() * num_valid_turn
if N_GPU == 1:
if dev_loss_slot is None:
dev_loss_slot = [l * num_valid_turn for l in loss_slot]
dev_acc_slot = acc_slot * num_valid_turn
else:
for i, l in enumerate(loss_slot):
dev_loss_slot[
i] = dev_loss_slot[i] + l * num_valid_turn
dev_acc_slot += acc_slot * num_valid_turn
nb_dev_examples += num_valid_turn
dev_loss = dev_loss / nb_dev_examples
dev_acc = dev_acc / nb_dev_examples
if N_GPU == 1:
dev_acc_slot = dev_acc_slot / nb_dev_examples
# tensorboard logging
if summary_writer is not None:
summary_writer.add_scalar("Validate/Loss", dev_loss,
global_step)
summary_writer.add_scalar("Validate/Acc", dev_acc, global_step)
if N_GPU == 1:
for i, slot in enumerate(self.processor.target_slot):
summary_writer.add_scalar(
"Validate/Loss_%s" % slot.replace(' ', '_'),
dev_loss_slot[i] / nb_dev_examples, global_step)
summary_writer.add_scalar(
"Validate/Acc_%s" % slot.replace(' ', '_'),
dev_acc_slot[i], global_step)
dev_loss = round(dev_loss, 6)
output_model_file = os.path.join(
os.path.join(SUMBT_PATH, args.output_dir), "pytorch_model.bin")
if last_update is None or dev_loss < best_loss:
if not USE_CUDA or N_GPU == 1:
torch.save(model.state_dict(), output_model_file)
else:
torch.save(model.module.state_dict(), output_model_file)
last_update = epoch
best_loss = dev_loss
best_acc = dev_acc
logger.info(
"*** Model Updated: Epoch=%d, Validation Loss=%.6f, Validation Acc=%.6f, global_step=%d ***"
% (last_update, best_loss, best_acc, global_step))
else:
logger.info(
"*** Model NOT Updated: Epoch=%d, Validation Loss=%.6f, Validation Acc=%.6f, global_step=%d ***"
% (epoch, dev_loss, dev_acc, global_step))
if last_update + args.patience <= epoch:
break
def train(agents, params, num_processes):
"""Training Loop for value-based RL methods.
Params
======
agent (object) --- the agent to train
params (dict) --- the dictionary of parameters
"""
n_episodes = params['episodes']
maxlen = params['maxlen']
name = params['agent_params']['name']
brain_name = params['brain_name']
env = params['environment']
add_noise = params['agent_params']['add_noise']
pretrain = params['pretrain']
pretrain_length = params['pretrain_length']
num_agents = num_processes
scores = np.zeros(num_agents) # list containing scores from each episode
scores_window = deque(maxlen=maxlen) # last N scores
scores_episode = []
writer = SummaryWriter(log_dir=params['log_dir'] + name)
env_info = env.reset(train_mode=True)[brain_name]
tic = time.time()
timesteps = 0
achievement_length = 0
episode_start = 1
if params['load_agent']:
episode_start, timesteps = agents.load_agent()
for i_episode in range(episode_start, n_episodes + 1):
tic = time.time()
states = env_info.vector_observations
scores = np.zeros(num_agents)
env.reset()
while True:
states = torch.tensor(states)
if pretrain and pretrain_length < len(agents.memory.memory):
pretrain = False
actions, noise_epsilon = agents.act(states,
add_noise,
pretrain=pretrain)
env_info = env.step(actions)[
brain_name] # send the action to the environment
next_states = env_info.vector_observations # get the next state
rewards = env_info.rewards # get the reward
dones = env_info.local_done # see if episode has finished
adjusted_rewards = np.array(env_info.rewards)
if params['hack_rewards']:
if adjusted_rewards[0] != 0:
adjusted_rewards[1] = adjusted_rewards[0] * params[
'alternative_reward_scalar']
elif adjusted_rewards[1] != 0:
adjusted_rewards[0] = adjusted_rewards[1] * params[
'alternative_reward_scalar']
actor_loss, critic_loss = agents.step(states,
actions,
adjusted_rewards,
next_states,
dones,
pretrain=pretrain)
if actor_loss != None and critic_loss != None:
if params['agent_params']['schedule_lr']:
actor_lr, critic_lr = agents.get_lr()
else:
actor_lr, critic_lr = params['agent_params'][
'actor_params']['lr'], params['agent_params'][
'critic_params']['lr']
writer.add_scalar('noise_epsilon', noise_epsilon, timesteps)
writer.add_scalar('actor_loss', actor_loss, timesteps)
writer.add_scalar('critic_loss', critic_loss, timesteps)
writer.add_scalar('actor_lr', actor_lr, timesteps)
writer.add_scalar('critic_lr', critic_lr, timesteps)
print('\rTimestep {}\tMax: {:.2f}'.format(timesteps,
np.max(scores)),
end="")
scores += rewards # update the scores
states = next_states # roll over the state to next time step
if np.any(dones): # exit loop if episode finished
break
timesteps += 1
# Fills the buffer with experiences resulting from random actions
# to encourage exploration
if timesteps % params['random_fill_every'] == 0:
pretrain = True
pretrain = params['pretrain_length']
score = np.mean(scores)
scores_episode.append(score)
scores_window.append(score) # save most recent score
print('\rEpisode {}\tMax: {:.2f} \t Time: {:.2f}'.format(
i_episode, np.max(scores),
time.time() - tic),
end="\n")
if i_episode % params['save_every'] == 0:
agents.save_agent(np.mean(scores_window),
i_episode,
timesteps,
save_history=True)
else:
agents.save_agent(np.mean(scores_window),
i_episode,
timesteps,
save_history=False)
writer.add_scalars('scores', {
'mean': np.mean(scores),
'min': np.min(scores),
'max': np.max(scores)
}, timesteps)
update_csv(name, i_episode, np.mean(scores), np.mean(scores))
agents.step_lr(np.mean(scores))
if np.mean(scores) > params['achievement']:
achievement_length += 1
if achievement_length > params['achievement_length']:
toc = time.time()
print(
"\n\n Congratulations! The agent has managed to solve the environment in {} episodes with {} training time\n\n"
.format(i_episode, toc - tic))
writer.close()
return scores
else:
achievement_length = 0
writer.close()
return scores
def main(
debug: bool = False,
eager: bool = False,
logdir: str = "runs",
steps_per_epoch: int = 200,
epochs: int = 100,
batch_size: int = 64,
):
if debug:
import debugpy
print("Waiting for debugger...")
debugpy.listen(5678)
debugpy.wait_for_client()
current_time = datetime.now().strftime("%b%d_%H-%M-%S")
logdir = os.path.join(logdir, current_time)
train_dataset = MNIST(training=True)
test_dataset = MNIST(training=False)
train_loader = eg.data.DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True)
test_loader = eg.data.DataLoader(test_dataset,
batch_size=batch_size,
shuffle=True)
print("X_train:", train_dataset.x.shape, train_dataset.x.dtype)
print("y_train:", train_dataset.y.shape, train_dataset.y.dtype)
print("X_test:", test_dataset.x.shape, test_dataset.x.dtype)
print("y_test:", test_dataset.y.shape, test_dataset.y.dtype)
@dataclass(unsafe_hash=True, repr=False)
class MLP(eg.Module):
"""Standard LeNet-300-100 MLP network."""
n1: int = 300
n2: int = 100
@eg.compact
def __call__(self, x: jnp.ndarray):
x = x.astype(jnp.float32) / 255.0
x = eg.Flatten()(x)
x = eg.Linear(self.n1)(x)
x = jax.nn.relu(x)
x = eg.Linear(self.n2)(x)
x = jax.nn.relu(x)
x = eg.Linear(10)(x)
return x
model = eg.Model(
module=MLP(n1=300, n2=100),
loss=[
eg.losses.Crossentropy(),
eg.regularizers.L2(l=1e-4),
],
metrics=eg.metrics.Accuracy(),
optimizer=optax.adamw(1e-3),
eager=eager,
)
x_sample, y_sample = next(iter(train_loader))
model.summary(x_sample)
history = model.fit(
inputs=train_loader,
epochs=epochs,
steps_per_epoch=steps_per_epoch,
validation_data=test_loader,
shuffle=True,
callbacks=[eg.callbacks.TensorBoard(logdir=logdir)],
)
eg.utils.plot_history(history)
# get random samples
idxs = np.random.randint(0, 10000, size=(9, ))
x_sample, y_sample = next(iter(test_loader))
# get predictions
y_pred = model.predict(x=x_sample)
# plot and save results
def make_plot():
plt.figure(figsize=(12, 12))
for i in range(3):
for j in range(3):
k = 3 * i + j
plt.subplot(3, 3, k + 1)
plt.title(f"{np.argmax(y_pred[k])}")
plt.imshow(x_sample[k], cmap="gray")
with SummaryWriter(os.path.join(logdir, "val")) as tbwriter:
make_plot()
# tbwriter.add_figure("Predictions", plt.gcf(), 100)
make_plot()
plt.show()
print(
"\n\n\nMetrics and images can be explored using tensorboard using:",
f"\n \t\t\t tensorboard --logdir {logdir}",
)
def main(
debug: bool = False,
eager: bool = False,
logdir: str = "runs",
steps_per_epoch: int = 200,
epochs: int = 100,
):
if debug:
import debugpy
print("Waiting for debugger...")
debugpy.listen(5678)
debugpy.wait_for_client()
current_time = datetime.now().strftime("%b%d_%H-%M-%S")
logdir = os.path.join(logdir, current_time)
train_dataset = MNIST(training=True)
test_dataset = MNIST(training=False)
train_loader = elegy.data.DataLoader(train_dataset,
batch_size=64,
shuffle=True)
test_loader = elegy.data.DataLoader(test_dataset,
batch_size=64,
shuffle=True)
print("X_train:", train_dataset.x.shape, train_dataset.x.dtype)
print("y_train:", train_dataset.y.shape, train_dataset.y.dtype)
print("X_test:", test_dataset.x.shape, test_dataset.x.dtype)
print("y_test:", test_dataset.y.shape, test_dataset.y.dtype)
class MLP(elegy.Module):
"""Standard LeNet-300-100 MLP network."""
def __init__(self, n1: int = 300, n2: int = 100, **kwargs):
super().__init__(**kwargs)
self.n1 = n1
self.n2 = n2
def call(self, image: jnp.ndarray):
image = image.astype(jnp.float32) / 255.0
mlp = elegy.nn.sequential(
elegy.nn.Flatten(),
elegy.nn.Linear(self.n1),
jax.nn.relu,
elegy.nn.Linear(self.n2),
jax.nn.relu,
elegy.nn.Linear(10),
)
return mlp(image)
model = elegy.Model(
module=MLP(n1=300, n2=100),
loss=[
elegy.losses.SparseCategoricalCrossentropy(from_logits=True),
elegy.regularizers.GlobalL2(l=1e-4),
],
metrics=elegy.metrics.SparseCategoricalAccuracy(),
optimizer=optax.adamw(1e-3),
run_eagerly=eager,
)
x_sample, y_sample = next(iter(train_loader))
model.summary(x_sample)
history = model.fit(
x=train_loader,
epochs=epochs,
steps_per_epoch=steps_per_epoch,
validation_data=test_loader,
shuffle=True,
callbacks=[elegy.callbacks.TensorBoard(logdir=logdir)],
)
elegy.utils.plot_history(history)
# get random samples
idxs = np.random.randint(0, 10000, size=(9, ))
x_sample, y_sample = next(iter(test_loader))
# get predictions
y_pred = model.predict(x=x_sample)
# plot and save results
def make_plot():
plt.figure(figsize=(12, 12))
for i in range(3):
for j in range(3):
k = 3 * i + j
plt.subplot(3, 3, k + 1)
plt.title(f"{np.argmax(y_pred[k])}")
plt.imshow(x_sample[k], cmap="gray")
with SummaryWriter(os.path.join(logdir, "val")) as tbwriter:
make_plot()
# tbwriter.add_figure("Predictions", plt.gcf(), 100)
make_plot()
plt.show()
print(
"\n\n\nMetrics and images can be explored using tensorboard using:",
f"\n \t\t\t tensorboard --logdir {logdir}",
)
def main(
debug: bool = False,
eager: bool = False,
logdir: str = "runs",
steps_per_epoch: int = 200,
epochs: int = 100,
batch_size: int = 64,
):
if debug:
import debugpy
print("Waiting for debugger...")
debugpy.listen(5678)
debugpy.wait_for_client()
current_time = datetime.now().strftime("%b%d_%H-%M-%S")
logdir = os.path.join(logdir, current_time)
dataset = load_dataset("mnist")
dataset.set_format("np")
X_train = np.stack(dataset["train"]["image"])[..., None]
y_train = dataset["train"]["label"]
X_test = np.stack(dataset["test"]["image"])[..., None]
y_test = dataset["test"]["label"]
print("X_train:", X_train.shape, X_train.dtype)
print("y_train:", y_train.shape, y_train.dtype)
print("X_test:", X_test.shape, X_test.dtype)
print("y_test:", y_test.shape, y_test.dtype)
model = eg.Model(
module=CNN(),
loss=eg.losses.Crossentropy(),
metrics=eg.metrics.Accuracy(),
optimizer=optax.adam(1e-3),
eager=eager,
)
# show summary
model.summary(X_train[:64])
train_dataset = TensorDataset(torch.from_numpy(X_train),
torch.from_numpy(y_train))
train_dataloader = DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True)
test_dataset = TensorDataset(torch.from_numpy(X_test),
torch.from_numpy(y_test))
test_dataloader = DataLoader(test_dataset, batch_size=batch_size)
history = model.fit(
train_dataloader,
epochs=epochs,
steps_per_epoch=steps_per_epoch,
validation_data=test_dataloader,
callbacks=[eg.callbacks.TensorBoard(logdir=logdir)],
)
eg.utils.plot_history(history)
model.save("models/conv")
model = eg.load("models/conv")
print(model.evaluate(x=X_test, y=y_test))
# get random samples
idxs = np.random.randint(0, 10000, size=(9, ))
x_sample = X_test[idxs]
# get predictions
y_pred = model.predict(x=x_sample)
# plot results
with SummaryWriter(os.path.join(logdir, "val")) as tbwriter:
figure = plt.figure(figsize=(12, 12))
for i in range(3):
for j in range(3):
k = 3 * i + j
plt.subplot(3, 3, k + 1)
plt.title(f"{np.argmax(y_pred[k])}")
plt.imshow(x_sample[k], cmap="gray")
# tbwriter.add_figure("Conv classifier", figure, 100)
plt.show()
def main(
debug: bool = False,
eager: bool = False,
logdir: str = "runs",
steps_per_epoch: int = 200,
epochs: int = 100,
batch_size: int = 64,
):
if debug:
import debugpy
print("Waiting for debugger...")
debugpy.listen(5678)
debugpy.wait_for_client()
current_time = datetime.now().strftime("%b%d_%H-%M-%S")
logdir = os.path.join(logdir, current_time)
dataset = load_dataset("mnist")
dataset.set_format("np")
X_train = np.stack(dataset["train"]["image"])
X_test = np.stack(dataset["test"]["image"])
print("X_train:", X_train.shape, X_train.dtype)
print("X_test:", X_test.shape, X_test.dtype)
model = eg.Model(
module=MLP(n1=256, n2=64),
loss=MeanSquaredError(),
optimizer=optax.rmsprop(0.001),
eager=eager,
)
model.summary(X_train[:64])
# Notice we are not passing `y`
history = model.fit(
inputs=X_train,
epochs=epochs,
steps_per_epoch=steps_per_epoch,
batch_size=batch_size,
validation_data=(X_test, ),
shuffle=True,
callbacks=[eg.callbacks.TensorBoard(logdir=logdir, update_freq=300)],
)
eg.utils.plot_history(history)
# get random samples
idxs = np.random.randint(0, 10000, size=(5, ))
x_sample = X_test[idxs]
# get predictions
y_pred = model.predict(x=x_sample)
# plot and save results
with SummaryWriter(os.path.join(logdir, "val")) as tbwriter:
figure = plt.figure(figsize=(12, 12))
for i in range(5):
plt.subplot(2, 5, i + 1)
plt.imshow(x_sample[i], cmap="gray")
plt.subplot(2, 5, 5 + i + 1)
plt.imshow(y_pred[i], cmap="gray")
plt.show()
def main(
steps_per_epoch: int = 200,
epochs: int = 50,
debug: bool = False,
eager: bool = False,
logdir: str = "runs",
):
if debug:
import debugpy
print("Waiting for debugger...")
debugpy.listen(5678)
debugpy.wait_for_client()
current_time = datetime.now().strftime("%b%d_%H-%M-%S")
logdir = os.path.join(logdir, current_time)
X_train, _1, X_test, _2 = dataget.image.mnist(global_cache=True).get()
# Now binarize data
X_train = (X_train > 0).astype(jnp.float32)
X_test = (X_test > 0).astype(jnp.float32)
print("X_train:", X_train.shape, X_train.dtype)
print("X_test:", X_test.shape, X_test.dtype)
vae = VAE(latent_size=LATENT_SIZE)
# model = VariationalAutoEncoder(latent_size=LATENT_SIZE, optimizer=optax.adam(1e-3))
def loss(x, y_pred):
logits, mean, stddev = y_pred
ce_loss = elegy.losses.binary_crossentropy(x, logits,
from_logits=True).mean()
kl_loss = 2e-1 * kl_divergence(mean, stddev)
return ce_loss + kl_loss
model = elegy.Model(
module=vae,
loss=loss,
optimizer=optax.adam(1e-3),
run_eagerly=eager,
)
# Fit with datasets in memory
history = model.fit(
x=X_train,
epochs=epochs,
batch_size=64,
steps_per_epoch=steps_per_epoch,
validation_data=(X_test, ),
shuffle=True,
callbacks=[TensorBoard(logdir)],
)
print(
"\n\n\nMetrics and images can be explored using tensorboard using:",
f"\n \t\t\t tensorboard --logdir {logdir}",
)
elegy.utils.plot_history(history)
# get random samples
idxs = np.random.randint(0, len(X_test), size=(5, ))
x_sample = X_test[idxs]
# get predictions
logits, mean, stddev = model.predict(x=x_sample)
y_pred = jax.nn.sigmoid(logits)
# plot and save results
with SummaryWriter(os.path.join(logdir, "val")) as tbwriter:
figure = plt.figure(figsize=(12, 12))
for i in range(5):
plt.subplot(2, 5, i + 1)
plt.imshow(x_sample[i], cmap="gray")
plt.subplot(2, 5, 5 + i + 1)
plt.imshow(y_pred[i], cmap="gray")
# # tbwriter.add_figure("VAE Example", figure, epochs)
plt.show()
import torch.nn.parallel
import torch.optim
import torch.utils.data
from progress.bar import Bar
from tensorboardX.writer import SummaryWriter
from termcolor import cprint
from model import shape_net
from datasets import SIK1M
from losses import shape_loss
# select proper device to run
from utils import misc
from utils.eval.evalutils import AverageMeter
import numpy as np
writer = SummaryWriter('log')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
cudnn.benchmark = True
steps = 0
def print_args(args):
opts = vars(args)
cprint("{:>30} Options {}".format("=" * 15, "=" * 15), 'yellow')
for k, v in sorted(opts.items()):
print("{:>30} : {}".format(k, v))
cprint("{:>30} Options {}".format("=" * 15, "=" * 15), 'yellow')
def main(args):
def main(
debug: bool = False,
eager: bool = False,
logdir: str = "runs",
steps_per_epoch: int = 200,
batch_size: int = 64,
epochs: int = 100,
):
if debug:
import debugpy
print("Waiting for debugger...")
debugpy.listen(5678)
debugpy.wait_for_client()
current_time = datetime.now().strftime("%b%d_%H-%M-%S")
logdir = os.path.join(logdir, current_time)
dataset = load_dataset("mnist")
dataset.set_format("np")
X_train = np.stack(dataset["train"]["image"])
y_train = dataset["train"]["label"]
X_test = np.stack(dataset["test"]["image"])
y_test = dataset["test"]["label"]
print("X_train:", X_train.shape, X_train.dtype)
print("y_train:", y_train.shape, y_train.dtype)
print("X_test:", X_test.shape, X_test.dtype)
print("y_test:", y_test.shape, y_test.dtype)
model = eg.Model(
module=MLP(n1=300, n2=100),
loss=[
eg.losses.Crossentropy(),
eg.regularizers.L2(l=1e-4),
],
metrics=eg.metrics.Accuracy(),
optimizer=optax.adamw(1e-3),
eager=eager,
)
model.summary(X_train[:64])
history = model.fit(
inputs=X_train,
labels=y_train,
epochs=epochs,
steps_per_epoch=steps_per_epoch,
batch_size=batch_size,
validation_data=(X_test, y_test),
shuffle=True,
callbacks=[eg.callbacks.TensorBoard(logdir=logdir)],
)
eg.utils.plot_history(history)
# get random samples
idxs = np.random.randint(0, 10000, size=(9,))
x_sample = X_test[idxs]
# get predictions
y_pred = model.predict(x=x_sample)
# plot and save results
with SummaryWriter(os.path.join(logdir, "val")) as tbwriter:
figure = plt.figure(figsize=(12, 12))
for i in range(3):
for j in range(3):
k = 3 * i + j
plt.subplot(3, 3, k + 1)
plt.title(f"{np.argmax(y_pred[k])}")
plt.imshow(x_sample[k], cmap="gray")
# tbwriter.add_figure("Predictions", figure, 100)
plt.show()
print(
"\n\n\nMetrics and images can be explored using tensorboard using:",
f"\n \t\t\t tensorboard --logdir {logdir}",
)
def on_train_begin(self, logs=None):
self.writer = SummaryWriter(self.logdir, **self._open_args)
from sklearn.decomposition import PCA
from sklearn.metrics import confusion_matrix
import tf_robustify
import vgg
import carlini_wagner_attack
os.system("taskset -p 0xffffffff %d" % os.getpid())
import sh
sh.rm('-rf', 'logs')
import logging
logging.basicConfig(level=logging.INFO, stream=sys.stdout)
from tensorboardX.writer import SummaryWriter
swriter = SummaryWriter('logs')
add_scalar_old = swriter.add_scalar
def add_scalar_and_log(key, value, global_step=0):
logging.info('{}:{}: {}'.format(global_step, key, value))
add_scalar_old(key, value, global_step)
swriter.add_scalar = add_scalar_and_log
def str2bool(x):
return x.lower() == 'true'
def main(debug: bool = False, eager: bool = False, logdir: str = "runs"):
if debug:
import debugpy
print("Waiting for debugger...")
debugpy.listen(5678)
debugpy.wait_for_client()
current_time = datetime.now().strftime("%b%d_%H-%M-%S")
logdir = os.path.join(logdir, current_time)
X_train, y_train, X_test, y_test = dataget.image.mnist(
global_cache=True).get()
print("X_train:", X_train.shape, X_train.dtype)
print("y_train:", y_train.shape, y_train.dtype)
print("X_test:", X_test.shape, X_test.dtype)
print("y_test:", y_test.shape, y_test.dtype)
class Lambda(elegy.Module):
def __init__(self, f):
super().__init__()
self.f = f
def call(self, x):
return self.f(x)
class MLP(elegy.Module):
"""Standard LeNet-300-100 MLP network."""
def __init__(self, n1: int = 300, n2: int = 100, **kwargs):
super().__init__(**kwargs)
self.n1 = n1
self.n2 = n2
def call(self, image: jnp.ndarray):
image = image.astype(jnp.float32) / 255.0
mlp = elegy.nn.sequential(
elegy.nn.Flatten(),
elegy.nn.Linear(self.n1),
jax.nn.relu,
elegy.nn.Linear(self.n2),
jax.nn.relu,
elegy.nn.Linear(10),
)
return mlp(image)
model = elegy.Model(
module=MLP(n1=300, n2=100),
loss=[
elegy.losses.SparseCategoricalCrossentropy(from_logits=True),
elegy.regularizers.GlobalL2(l=1e-4),
],
metrics=elegy.metrics.SparseCategoricalAccuracy(),
optimizer=optax.adamw(1e-3),
run_eagerly=eager,
)
model.summary(X_train[:64])
history = model.fit(
x=X_train,
y=y_train,
epochs=100,
steps_per_epoch=200,
batch_size=64,
validation_data=(X_test, y_test),
shuffle=True,
callbacks=[elegy.callbacks.TensorBoard(logdir=logdir)],
)
print(model.module.submodules)
plot_history(history)
# get random samples
idxs = np.random.randint(0, 10000, size=(9, ))
x_sample = X_test[idxs]
# get predictions
y_pred = model.predict(x=x_sample)
# plot and save results
with SummaryWriter(os.path.join(logdir, "val")) as tbwriter:
figure = plt.figure(figsize=(12, 12))
for i in range(3):
for j in range(3):
k = 3 * i + j
plt.subplot(3, 3, k + 1)
plt.title(f"{np.argmax(y_pred[k])}")
plt.imshow(x_sample[k], cmap="gray")
tbwriter.add_figure("Predictions", figure, 100)
plt.show()
print(
"\n\n\nMetrics and images can be explored using tensorboard using:",
f"\n \t\t\t tensorboard --logdir {logdir}",
)
def main(
debug: bool = False,
eager: bool = False,
logdir: str = "runs",
steps_per_epoch: int = 200,
epochs: int = 100,
batch_size: int = 64,
):
if debug:
import debugpy
print("Waiting for debugger...")
debugpy.listen(5678)
debugpy.wait_for_client()
current_time = datetime.now().strftime("%b%d_%H-%M-%S")
logdir = os.path.join(logdir, current_time)
X_train, y_train, X_test, y_test = dataget.image.mnist(
global_cache=True).get()
X_train = X_train[..., None]
X_test = X_test[..., None]
print("X_train:", X_train.shape, X_train.dtype)
print("y_train:", y_train.shape, y_train.dtype)
print("X_test:", X_test.shape, X_test.dtype)
print("y_test:", y_test.shape, y_test.dtype)
class CNN(elegy.Module):
def call(self, image: jnp.ndarray, training: bool):
@elegy.to_module
def ConvBlock(x, units, kernel, stride=1):
x = elegy.nn.Conv2D(units,
kernel,
stride=stride,
padding="same")(x)
x = elegy.nn.BatchNormalization()(x, training)
x = elegy.nn.Dropout(0.2)(x, training)
return jax.nn.relu(x)
x: np.ndarray = image.astype(jnp.float32) / 255.0
# base
x = ConvBlock()(x, 32, [3, 3])
x = ConvBlock()(x, 64, [3, 3], stride=2)
x = ConvBlock()(x, 64, [3, 3], stride=2)
x = ConvBlock()(x, 128, [3, 3], stride=2)
# GlobalAveragePooling2D
x = jnp.mean(x, axis=[1, 2])
# 1x1 Conv
x = elegy.nn.Linear(10)(x)
return x
model = elegy.Model(
module=CNN(),
loss=elegy.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=elegy.metrics.SparseCategoricalAccuracy(),
optimizer=optax.adam(1e-3),
run_eagerly=eager,
)
# show model summary
model.summary(X_train[:64], depth=1)
history = model.fit(
x=X_train,
y=y_train,
epochs=epochs,
steps_per_epoch=steps_per_epoch,
batch_size=batch_size,
validation_data=(X_test, y_test),
shuffle=True,
callbacks=[TensorBoard(logdir=logdir)],
)
elegy.utils.plot_history(history)
model.save("models/conv")
model = elegy.load("models/conv")
print(model.evaluate(x=X_test, y=y_test))
# get random samples
idxs = np.random.randint(0, 10000, size=(9, ))
x_sample = X_test[idxs]
# get predictions
y_pred = model.predict(x=x_sample)
# plot results
with SummaryWriter(os.path.join(logdir, "val")) as tbwriter:
figure = plt.figure(figsize=(12, 12))
for i in range(3):
for j in range(3):
k = 3 * i + j
plt.subplot(3, 3, k + 1)
plt.title(f"{np.argmax(y_pred[k])}")
plt.imshow(x_sample[k], cmap="gray")
# tbwriter.add_figure("Conv classifier", figure, 100)
plt.show()
def main(
steps_per_epoch: int = 200,
batch_size: int = 64,
epochs: int = 50,
debug: bool = False,
eager: bool = False,
logdir: str = "runs",
):
if debug:
import debugpy
print("Waiting for debugger...")
debugpy.listen(5678)
debugpy.wait_for_client()
current_time = datetime.now().strftime("%b%d_%H-%M-%S")
logdir = os.path.join(logdir, current_time)
dataset = load_dataset("mnist")
X_train = np.array(np.stack(dataset["train"]["image"]), dtype=np.uint8)
X_test = np.array(np.stack(dataset["test"]["image"]), dtype=np.uint8)
# Now binarize data
X_train = (X_train > 0).astype(jnp.float32)
X_test = (X_test > 0).astype(jnp.float32)
print("X_train:", X_train.shape, X_train.dtype)
print("X_test:", X_test.shape, X_test.dtype)
model = eg.Model(
module=VAE(latent_size=LATENT_SIZE),
loss=[
BinaryCrossEntropy(on="logits"),
KL(weight=0.1),
],
optimizer=optax.adam(1e-3),
eager=eager,
)
model.summary(X_train[:batch_size])
# Fit with datasets in memory
history = model.fit(
inputs=X_train,
epochs=epochs,
batch_size=batch_size,
steps_per_epoch=steps_per_epoch,
validation_data=(X_test, ),
shuffle=True,
callbacks=[eg.callbacks.TensorBoard(logdir)],
)
print(
"\n\n\nMetrics and images can be explored using tensorboard using:",
f"\n \t\t\t tensorboard --logdir {logdir}",
)
eg.utils.plot_history(history)
# get random samples
idxs = np.random.randint(0, len(X_test), size=(5, ))
x_sample = X_test[idxs]
# get predictions
preds = model.predict(x=x_sample)
y_pred = jax.nn.sigmoid(preds["logits"])
# plot and save results
with SummaryWriter(os.path.join(logdir, "val")) as tbwriter:
figure = plt.figure(figsize=(12, 12))
for i in range(5):
plt.subplot(2, 5, i + 1)
plt.imshow(x_sample[i], cmap="gray")
plt.subplot(2, 5, 5 + i + 1)
plt.imshow(y_pred[i], cmap="gray")
# # tbwriter.add_figure("VAE Example", figure, epochs)
plt.show()
def main(debug: bool = False, eager: bool = False, logdir: str = "runs"):
if debug:
import debugpy
print("Waiting for debugger...")
debugpy.listen(5678)
debugpy.wait_for_client()
current_time = datetime.now().strftime("%b%d_%H-%M-%S")
logdir = os.path.join(logdir, current_time)
X_train, _1, X_test, _2 = dataget.image.mnist(global_cache=True).get()
print("X_train:", X_train.shape, X_train.dtype)
print("X_test:", X_test.shape, X_test.dtype)
class MLP(elegy.Module):
"""Standard LeNet-300-100 MLP network."""
def __init__(self, n1: int = 300, n2: int = 100, **kwargs):
super().__init__(**kwargs)
self.n1 = n1
self.n2 = n2
def call(self, image: jnp.ndarray):
image = image.astype(jnp.float32) / 255.0
x = elegy.nn.Flatten()(image)
x = elegy.nn.sequential(
elegy.nn.Linear(self.n1),
jax.nn.relu,
elegy.nn.Linear(self.n2),
jax.nn.relu,
elegy.nn.Linear(self.n1),
jax.nn.relu,
elegy.nn.Linear(x.shape[-1]),
jax.nn.sigmoid,
)(x)
return x.reshape(image.shape) * 255
class MeanSquaredError(elegy.losses.MeanSquaredError):
# we request `x` instead of `y_true` since we are don't require labels in autoencoders
def call(self, x, y_pred):
return super().call(x, y_pred)
model = elegy.Model(
module=MLP(n1=256, n2=64),
loss=MeanSquaredError(),
optimizer=optax.rmsprop(0.001),
run_eagerly=eager,
)
model.summary(X_train[:64])
# Notice we are not passing `y`
history = model.fit(
x=X_train,
epochs=20,
batch_size=64,
validation_data=(X_test,),
shuffle=True,
callbacks=[elegy.callbacks.TensorBoard(logdir=logdir, update_freq=300)],
)
plot_history(history)
# get random samples
idxs = np.random.randint(0, 10000, size=(5,))
x_sample = X_test[idxs]
# get predictions
y_pred = model.predict(x=x_sample)
# plot and save results
with SummaryWriter(os.path.join(logdir, "val")) as tbwriter:
figure = plt.figure(figsize=(12, 12))
for i in range(5):
plt.subplot(2, 5, i + 1)
plt.imshow(x_sample[i], cmap="gray")
plt.subplot(2, 5, 5 + i + 1)
plt.imshow(y_pred[i], cmap="gray")
# tbwriter.add_figure("AutoEncoder images", figure, 20)
plt.show()
print(
"\n\n\nMetrics and images can be explored using tensorboard using:",
f"\n \t\t\t tensorboard --logdir {logdir}",
)
d_iter_count += 1
if d_iter_count == config.max_d_iters:
d_iter_count = 0
mode = 'G'
iter_no = 0
max_iters = 100
mode = 'G'
g_iter_count = 0
d_iter_count = 0
batch_size = config.batch_size
train_writer = SummaryWriter(log_dir='../logs/train')
val_writer = SummaryWriter(log_dir='../logs/val')
with tqdm(total=max_iters) as pbar:
for iter_no in range(max_iters):
train_batch = train_loader.next_batch()
gan.train()
train_step(iter_no, train_batch)
if iter_no % config.validation_interval == 0:
val_batch = val_loader.next_batch()
gan.eval()
validate(val_batch, iter_no)
pbar.update(1)
def main(
steps_per_epoch: tp.Optional[int] = None,
batch_size: int = 32,
epochs: int = 50,
debug: bool = False,
eager: bool = False,
logdir: str = "runs",
):
if debug:
import debugpy
print("Waiting for debugger...")
debugpy.listen(5678)
debugpy.wait_for_client()
current_time = datetime.now().strftime("%b%d_%H-%M-%S")
logdir = os.path.join(logdir, current_time)
dataset = load_dataset("mnist")
dataset.set_format("np")
X_train = np.array(np.stack(dataset["train"]["image"]), dtype=np.uint8)
X_test = np.array(np.stack(dataset["test"]["image"]), dtype=np.uint8)
# Now binarize data
X_train = (X_train / 255.0).astype(jnp.float32)
X_test = (X_test / 255.0).astype(jnp.float32)
print("X_train:", X_train.shape, X_train.dtype)
print("X_test:", X_test.shape, X_test.dtype)
model = eg.Model(
module=VariationalAutoEncoder(latent_size=LATENT_SIZE),
loss=[BinaryCrossEntropy(from_logits=True, on="logits")],
optimizer=optax.adam(1e-3),
eager=eager,
)
assert model.module is not None
model.summary(X_train[:64])
# Fit with datasets in memory
history = model.fit(
inputs=X_train,
epochs=epochs,
batch_size=batch_size,
steps_per_epoch=steps_per_epoch,
validation_data=(X_test, ),
shuffle=True,
callbacks=[eg.callbacks.TensorBoard(logdir)],
)
print(
"\n\n\nMetrics and images can be explored using tensorboard using:",
f"\n \t\t\t tensorboard --logdir {logdir}",
)
eg.utils.plot_history(history)
# get random samples
idxs = np.random.randint(0, len(X_test), size=(5, ))
x_sample = X_test[idxs]
# get predictions
y_pred = model.predict(x=x_sample)
# plot and save results
with SummaryWriter(os.path.join(logdir, "val")) as tbwriter:
figure = plt.figure(figsize=(12, 12))
for i in range(5):
plt.subplot(2, 5, i + 1)
plt.imshow(x_sample[i], cmap="gray")
plt.subplot(2, 5, 5 + i + 1)
plt.imshow(y_pred["det_image"][i], cmap="gray")
# tbwriter.add_figure("VAE Example", figure, epochs)
# sample
model_decoder = eg.Model(model.module.decoder)
z_samples = np.random.normal(size=(12, LATENT_SIZE))
samples = model_decoder.predict(z_samples)
samples = jax.nn.sigmoid(samples)
# plot and save results
# with SummaryWriter(os.path.join(logdir, "val")) as tbwriter:
figure = plt.figure(figsize=(5, 12))
plt.title("Generative Samples")
for i in range(5):
plt.subplot(2, 5, 2 * i + 1)
plt.imshow(samples[i], cmap="gray")
plt.subplot(2, 5, 2 * i + 2)
plt.imshow(samples[i + 1], cmap="gray")
# # tbwriter.add_figure("VAE Generative Example", figure, epochs)
plt.show()
def main(
steps_per_epoch: int = 200,
batch_size: int = 64,
epochs: int = 50,
debug: bool = False,
eager: bool = False,
logdir: str = "runs",
):
if debug:
import debugpy
print("Waiting for debugger...")
debugpy.listen(5678)
debugpy.wait_for_client()
current_time = datetime.now().strftime("%b%d_%H-%M-%S")
logdir = os.path.join(logdir, current_time)
X_train, _1, X_test, _2 = dataget.image.mnist(global_cache=True).get()
# Now binarize data
X_train = (X_train > 0).astype(jnp.float32)
X_test = (X_test > 0).astype(jnp.float32)
print("X_train:", X_train.shape, X_train.dtype)
print("X_test:", X_test.shape, X_test.dtype)
vae = VariationalAutoEncoder(latent_size=LATENT_SIZE)
model = elegy.Model(
module=vae,
loss=[BinaryCrossEntropy(from_logits=True, on="logits")],
optimizer=optax.adam(1e-3),
run_eagerly=eager,
)
model.summary(X_train[:64])
# Fit with datasets in memory
history = model.fit(
x=X_train,
epochs=epochs,
batch_size=batch_size,
steps_per_epoch=steps_per_epoch,
validation_data=(X_test, ),
shuffle=True,
callbacks=[TensorBoard(logdir)],
)
print(
"\n\n\nMetrics and images can be explored using tensorboard using:",
f"\n \t\t\t tensorboard --logdir {logdir}",
)
elegy.utils.plot_history(history)
# get random samples
idxs = np.random.randint(0, len(X_test), size=(5, ))
x_sample = X_test[idxs]
# get predictions
y_pred = model.predict(x=x_sample)
# plot and save results
with SummaryWriter(os.path.join(logdir, "val")) as tbwriter:
figure = plt.figure(figsize=(12, 12))
for i in range(5):
plt.subplot(2, 5, i + 1)
plt.imshow(x_sample[i], cmap="gray")
plt.subplot(2, 5, 5 + i + 1)
plt.imshow(y_pred["det_image"][i], cmap="gray")
# tbwriter.add_figure("VAE Example", figure, epochs)
plt.show()
# call update_modules to enable parameter transfer
# for now only Elegy Modules support this
model.update_modules()
# sample
model_decoder = elegy.Model(vae.decoder)
z_samples = np.random.normal(size=(12, LATENT_SIZE))
samples = model_decoder.predict(z_samples, initialize=True)
samples = jax.nn.sigmoid(samples)
# plot and save results
# with SummaryWriter(os.path.join(logdir, "val")) as tbwriter:
figure = plt.figure(figsize=(5, 12))
plt.title("Generative Samples")
for i in range(5):
plt.subplot(2, 5, 2 * i + 1)
plt.imshow(samples[i], cmap="gray")
plt.subplot(2, 5, 2 * i + 2)
plt.imshow(samples[i + 1], cmap="gray")
# # tbwriter.add_figure("VAE Generative Example", figure, epochs)
plt.show()