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import os
from scripts.utils.configuration import Configuration
import time
import torch as th
import numpy as np
from einops import rearrange, repeat, reduce
def init_device(cfg):
print(f'Cuda available: {th.cuda.is_available()} Cuda count: {th.cuda.device_count()}')
if th.cuda.is_available():
device = th.device("cuda:0")
verbose = False
cfg.device = "cuda:0"
cfg.model.device = "cuda:0"
print('!!! USING GPU !!!')
else:
device = th.device("cpu")
verbose = True
cfg.device = "cpu"
cfg.model.device = "cpu"
cfg.model.batch_size = 2
cfg.defaults.teacher_forcing = 4
print('!!! USING CPU !!!')
return device,verbose
class Timer:
def __init__(self):
self.last = time.time()
self.passed = 0
self.sum = 0
def __str__(self):
self.passed = self.passed * 0.99 + time.time() - self.last
self.sum = self.sum * 0.99 + 1
passed = self.passed / self.sum
self.last = time.time()
if passed > 1:
return f"{passed:.2f}s/it"
return f"{1.0/passed:.2f}it/s"
class UEMA:
def __init__(self, memory = 100):
self.value = 0
self.sum = 1e-30
self.decay = np.exp(-1 / memory)
def update(self, value):
self.value = self.value * self.decay + value
self.sum = self.sum * self.decay + 1
def __float__(self):
return self.value / self.sum
def model_path(cfg: Configuration, overwrite=False, move_old=True):
"""
Makes the model path, option to not overwrite
:param cfg: Configuration file with the model path
:param overwrite: Overwrites the files in the directory, else makes a new directory
:param move_old: Moves old folder with the same name to an old folder, if not overwrite
:return: Model path
"""
_path = os.path.join('out', cfg.dataset)
path = os.path.join(_path, cfg.model_path)
if not os.path.exists(_path):
os.makedirs(_path)
if not overwrite:
if move_old:
# Moves existing directory to an old folder
if os.path.exists(path):
old_path = os.path.join(_path, f'{cfg.model_path}_old')
if not os.path.exists(old_path):
os.makedirs(old_path)
_old_path = os.path.join(old_path, cfg.model_path)
i = 0
while os.path.exists(_old_path):
i = i + 1
_old_path = os.path.join(old_path, f'{cfg.model_path}_{i}')
os.renames(path, _old_path)
else:
# Increases number after directory name for each new path
i = 0
while os.path.exists(path):
i = i + 1
path = os.path.join(_path, f'{cfg.model_path}_{i}')
return path
class LossLogger:
def __init__(self, writer):
self.avgloss = UEMA()
self.avg_position_loss = UEMA()
self.avg_time_loss = UEMA()
self.avg_latent_loss = UEMA()
self.avg_encoding_loss = UEMA()
self.avg_prediction_loss = UEMA()
self.avg_prediction_loss_long = UEMA(33333)
self.avg_num_objects = UEMA()
self.avg_openings = UEMA()
self.avg_gestalt = UEMA()
self.avg_gestalt2 = UEMA()
self.avg_gestalt_mean = UEMA()
self.avg_update_gestalt = UEMA()
self.avg_update_position = UEMA()
self.avg_num_bounded = UEMA()
self.writer = writer
def update_complete(self, avg_position_loss, avg_time_loss, avg_latent_loss, avg_encoding_loss, avg_prediction_loss, avg_num_objects, avg_openings, avg_gestalt, avg_gestalt2, avg_gestalt_mean, avg_update_gestalt, avg_update_position, avg_num_bounded, lr, num_updates):
self.avg_position_loss.update(avg_position_loss.item())
self.avg_time_loss.update(avg_time_loss.item())
self.avg_latent_loss.update(avg_latent_loss.item())
self.avg_encoding_loss.update(avg_encoding_loss.item())
self.avg_prediction_loss.update(avg_prediction_loss.item())
self.avg_prediction_loss_long.update(avg_prediction_loss.item())
self.avg_num_objects.update(avg_num_objects)
self.avg_openings.update(avg_openings)
self.avg_gestalt.update(avg_gestalt.item())
self.avg_gestalt2.update(avg_gestalt2.item())
self.avg_gestalt_mean.update(avg_gestalt_mean.item())
self.avg_update_gestalt.update(avg_update_gestalt)
self.avg_update_position.update(avg_update_position)
self.avg_num_bounded.update(avg_num_bounded)
self.writer.add_scalar("Train/Position Loss", avg_position_loss.item(), num_updates)
self.writer.add_scalar("Train/Time Loss", avg_time_loss.item(), num_updates)
self.writer.add_scalar("Train/Latent Loss", avg_latent_loss.item(), num_updates)
self.writer.add_scalar("Train/Encoder Loss", avg_encoding_loss.item(), num_updates)
self.writer.add_scalar("Train/Prediction Loss", avg_prediction_loss.item(), num_updates)
self.writer.add_scalar("Train/Number of Objects", avg_num_objects, num_updates)
self.writer.add_scalar("Train/Openings", avg_openings, num_updates)
self.writer.add_scalar("Train/Gestalt", avg_gestalt.item(), num_updates)
self.writer.add_scalar("Train/Gestalt2", avg_gestalt2.item(), num_updates)
self.writer.add_scalar("Train/Gestalt Mean", avg_gestalt_mean.item(), num_updates)
self.writer.add_scalar("Train/Update Gestalt", avg_update_gestalt, num_updates)
self.writer.add_scalar("Train/Update Position", avg_update_position, num_updates)
self.writer.add_scalar("Train/Number Bounded", avg_num_bounded, num_updates)
self.writer.add_scalar("Train/Learning Rate", lr, num_updates)
pass
def update_average_loss(self, avgloss, num_updates):
self.avgloss.update(avgloss)
self.writer.add_scalar("Train/Loss", avgloss, num_updates)
pass
def get_log(self):
info = f'Loss: {np.abs(float(self.avgloss)):.2e}|{float(self.avg_prediction_loss):.2e}|{float(self.avg_prediction_loss_long):.2e}, reg: {float(self.avg_encoding_loss):.2e}|{float(self.avg_time_loss):.2e}|{float(self.avg_latent_loss):.2e}|{float(self.avg_position_loss):.2e}, obj: {float(self.avg_num_objects):.1f}, open: {float(self.avg_openings):.2e}|{float(self.avg_gestalt):.2f}, bin: {float(self.avg_gestalt_mean):.2e}|{np.sqrt(float(self.avg_gestalt2) - float(self.avg_gestalt)**2):.2e} closed: {float(self.avg_update_gestalt):.2e}|{float(self.avg_update_position):.2e}'
return info
class WriterWrapper():
def __init__(self, use_wandb: bool, cfg: Configuration):
if use_wandb:
from torch.utils.tensorboard import SummaryWriter
import wandb
wandb.init(project=f'Loci_Looped_{cfg.dataset}', name= cfg.model_path, sync_tensorboard=True, config=cfg)
self.writer = SummaryWriter()
else:
self.writer = None
def add_scalar(self, name, value, step):
if self.writer is not None:
self.writer.add_scalar(name, value, step)
def add_video(self, name, value, step):
if self.writer is not None:
self.writer.add_video(name, value, step)
def flush(self):
if self.writer is not None:
self.writer.flush()
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