333 lines
13 KiB
Python
333 lines
13 KiB
Python
from contextlib import contextmanager
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import hashlib
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import math
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from pathlib import Path
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import shutil
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import urllib
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import warnings
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from PIL import Image
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import torch
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from torch import nn, optim
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from torch.utils import data
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from torchvision.transforms import functional as TF
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def from_pil_image(x):
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"""Converts from a PIL image to a tensor."""
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x = TF.to_tensor(x)
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if x.ndim == 2:
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x = x[..., None]
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return x * 2 - 1
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def to_pil_image(x):
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"""Converts from a tensor to a PIL image."""
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if x.ndim == 4:
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assert x.shape[0] == 1
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x = x[0]
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if x.shape[0] == 1:
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x = x[0]
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return TF.to_pil_image((x.clamp(-1, 1) + 1) / 2)
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def hf_datasets_augs_helper(examples, transform, image_key, mode='RGB'):
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"""Apply passed in transforms for HuggingFace Datasets."""
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images = [transform(image.convert(mode)) for image in examples[image_key]]
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return {image_key: images}
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def append_dims(x, target_dims):
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"""Appends dimensions to the end of a tensor until it has target_dims dimensions."""
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dims_to_append = target_dims - x.ndim
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if dims_to_append < 0:
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raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less')
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expanded = x[(...,) + (None,) * dims_to_append]
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# MPS will get inf values if it tries to index into the new axes, but detaching fixes this.
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# https://github.com/pytorch/pytorch/issues/84364
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return expanded.detach().clone() if expanded.device.type == 'mps' else expanded
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def n_params(module):
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"""Returns the number of trainable parameters in a module."""
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return sum(p.numel() for p in module.parameters())
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def download_file(path, url, digest=None):
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"""Downloads a file if it does not exist, optionally checking its SHA-256 hash."""
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path = Path(path)
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path.parent.mkdir(parents=True, exist_ok=True)
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if not path.exists():
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with urllib.request.urlopen(url) as response, open(path, 'wb') as f:
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shutil.copyfileobj(response, f)
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if digest is not None:
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file_digest = hashlib.sha256(open(path, 'rb').read()).hexdigest()
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if digest != file_digest:
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raise OSError(f'hash of {path} (url: {url}) failed to validate')
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return path
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@contextmanager
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def train_mode(model, mode=True):
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"""A context manager that places a model into training mode and restores
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the previous mode on exit."""
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modes = [module.training for module in model.modules()]
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try:
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yield model.train(mode)
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finally:
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for i, module in enumerate(model.modules()):
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module.training = modes[i]
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def eval_mode(model):
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"""A context manager that places a model into evaluation mode and restores
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the previous mode on exit."""
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return train_mode(model, False)
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@torch.no_grad()
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def ema_update(model, averaged_model, decay):
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"""Incorporates updated model parameters into an exponential moving averaged
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version of a model. It should be called after each optimizer step."""
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model_params = dict(model.named_parameters())
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averaged_params = dict(averaged_model.named_parameters())
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assert model_params.keys() == averaged_params.keys()
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for name, param in model_params.items():
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averaged_params[name].mul_(decay).add_(param, alpha=1 - decay)
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model_buffers = dict(model.named_buffers())
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averaged_buffers = dict(averaged_model.named_buffers())
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assert model_buffers.keys() == averaged_buffers.keys()
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for name, buf in model_buffers.items():
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averaged_buffers[name].copy_(buf)
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class EMAWarmup:
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"""Implements an EMA warmup using an inverse decay schedule.
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If inv_gamma=1 and power=1, implements a simple average. inv_gamma=1, power=2/3 are
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good values for models you plan to train for a million or more steps (reaches decay
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factor 0.999 at 31.6K steps, 0.9999 at 1M steps), inv_gamma=1, power=3/4 for models
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you plan to train for less (reaches decay factor 0.999 at 10K steps, 0.9999 at
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215.4k steps).
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Args:
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inv_gamma (float): Inverse multiplicative factor of EMA warmup. Default: 1.
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power (float): Exponential factor of EMA warmup. Default: 1.
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min_value (float): The minimum EMA decay rate. Default: 0.
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max_value (float): The maximum EMA decay rate. Default: 1.
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start_at (int): The epoch to start averaging at. Default: 0.
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last_epoch (int): The index of last epoch. Default: 0.
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"""
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def __init__(self, inv_gamma=1., power=1., min_value=0., max_value=1., start_at=0,
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last_epoch=0):
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self.inv_gamma = inv_gamma
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self.power = power
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self.min_value = min_value
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self.max_value = max_value
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self.start_at = start_at
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self.last_epoch = last_epoch
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def state_dict(self):
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"""Returns the state of the class as a :class:`dict`."""
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return dict(self.__dict__.items())
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def load_state_dict(self, state_dict):
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"""Loads the class's state.
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Args:
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state_dict (dict): scaler state. Should be an object returned
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from a call to :meth:`state_dict`.
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"""
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self.__dict__.update(state_dict)
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def get_value(self):
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"""Gets the current EMA decay rate."""
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epoch = max(0, self.last_epoch - self.start_at)
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value = 1 - (1 + epoch / self.inv_gamma) ** -self.power
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return 0. if epoch < 0 else min(self.max_value, max(self.min_value, value))
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def step(self):
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"""Updates the step count."""
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self.last_epoch += 1
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class InverseLR(optim.lr_scheduler._LRScheduler):
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"""Implements an inverse decay learning rate schedule with an optional exponential
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warmup. When last_epoch=-1, sets initial lr as lr.
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inv_gamma is the number of steps/epochs required for the learning rate to decay to
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(1 / 2)**power of its original value.
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Args:
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optimizer (Optimizer): Wrapped optimizer.
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inv_gamma (float): Inverse multiplicative factor of learning rate decay. Default: 1.
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power (float): Exponential factor of learning rate decay. Default: 1.
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warmup (float): Exponential warmup factor (0 <= warmup < 1, 0 to disable)
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Default: 0.
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min_lr (float): The minimum learning rate. Default: 0.
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last_epoch (int): The index of last epoch. Default: -1.
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verbose (bool): If ``True``, prints a message to stdout for
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each update. Default: ``False``.
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"""
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def __init__(self, optimizer, inv_gamma=1., power=1., warmup=0., min_lr=0.,
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last_epoch=-1, verbose=False):
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self.inv_gamma = inv_gamma
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self.power = power
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if not 0. <= warmup < 1:
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raise ValueError('Invalid value for warmup')
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self.warmup = warmup
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self.min_lr = min_lr
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super().__init__(optimizer, last_epoch, verbose)
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def get_lr(self):
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if not self._get_lr_called_within_step:
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warnings.warn("To get the last learning rate computed by the scheduler, "
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"please use `get_last_lr()`.")
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return self._get_closed_form_lr()
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def _get_closed_form_lr(self):
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warmup = 1 - self.warmup ** (self.last_epoch + 1)
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lr_mult = (1 + self.last_epoch / self.inv_gamma) ** -self.power
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return [warmup * max(self.min_lr, base_lr * lr_mult)
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for base_lr in self.base_lrs]
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class ExponentialLR(optim.lr_scheduler._LRScheduler):
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"""Implements an exponential learning rate schedule with an optional exponential
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warmup. When last_epoch=-1, sets initial lr as lr. Decays the learning rate
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continuously by decay (default 0.5) every num_steps steps.
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Args:
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optimizer (Optimizer): Wrapped optimizer.
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num_steps (float): The number of steps to decay the learning rate by decay in.
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decay (float): The factor by which to decay the learning rate every num_steps
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steps. Default: 0.5.
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warmup (float): Exponential warmup factor (0 <= warmup < 1, 0 to disable)
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Default: 0.
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min_lr (float): The minimum learning rate. Default: 0.
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last_epoch (int): The index of last epoch. Default: -1.
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verbose (bool): If ``True``, prints a message to stdout for
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each update. Default: ``False``.
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"""
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def __init__(self, optimizer, num_steps, decay=0.5, warmup=0., min_lr=0.,
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last_epoch=-1, verbose=False):
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self.num_steps = num_steps
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self.decay = decay
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if not 0. <= warmup < 1:
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raise ValueError('Invalid value for warmup')
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self.warmup = warmup
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self.min_lr = min_lr
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super().__init__(optimizer, last_epoch, verbose)
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def get_lr(self):
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if not self._get_lr_called_within_step:
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warnings.warn("To get the last learning rate computed by the scheduler, "
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"please use `get_last_lr()`.")
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return self._get_closed_form_lr()
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def _get_closed_form_lr(self):
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warmup = 1 - self.warmup ** (self.last_epoch + 1)
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lr_mult = (self.decay ** (1 / self.num_steps)) ** self.last_epoch
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return [warmup * max(self.min_lr, base_lr * lr_mult)
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for base_lr in self.base_lrs]
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def rand_log_normal(shape, loc=0., scale=1., device='cpu', dtype=torch.float32):
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"""Draws samples from an lognormal distribution."""
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return (torch.randn(shape, device=device, dtype=dtype) * scale + loc).exp()
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def rand_log_logistic(shape, loc=0., scale=1., min_value=0., max_value=float('inf'), device='cpu', dtype=torch.float32):
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"""Draws samples from an optionally truncated log-logistic distribution."""
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min_value = torch.as_tensor(min_value, device=device, dtype=torch.float64)
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max_value = torch.as_tensor(max_value, device=device, dtype=torch.float64)
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min_cdf = min_value.log().sub(loc).div(scale).sigmoid()
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max_cdf = max_value.log().sub(loc).div(scale).sigmoid()
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u = torch.rand(shape, device=device, dtype=torch.float64) * (max_cdf - min_cdf) + min_cdf
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return u.logit().mul(scale).add(loc).exp().to(dtype)
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def rand_log_uniform(shape, min_value, max_value, device='cpu', dtype=torch.float32):
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"""Draws samples from an log-uniform distribution."""
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min_value = math.log(min_value)
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max_value = math.log(max_value)
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return (torch.rand(shape, device=device, dtype=dtype) * (max_value - min_value) + min_value).exp()
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def rand_v_diffusion(shape, sigma_data=1., min_value=0., max_value=float('inf'), device='cpu', dtype=torch.float32):
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"""Draws samples from a truncated v-diffusion training timestep distribution."""
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min_cdf = math.atan(min_value / sigma_data) * 2 / math.pi
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max_cdf = math.atan(max_value / sigma_data) * 2 / math.pi
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u = torch.rand(shape, device=device, dtype=dtype) * (max_cdf - min_cdf) + min_cdf
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return torch.tan(u * math.pi / 2) * sigma_data
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def rand_split_log_normal(shape, loc, scale_1, scale_2, device='cpu', dtype=torch.float32):
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"""Draws samples from a split lognormal distribution."""
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n = torch.randn(shape, device=device, dtype=dtype).abs()
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u = torch.rand(shape, device=device, dtype=dtype)
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n_left = n * -scale_1 + loc
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n_right = n * scale_2 + loc
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ratio = scale_1 / (scale_1 + scale_2)
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return torch.where(u < ratio, n_left, n_right).exp()
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class FolderOfImages(data.Dataset):
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"""Recursively finds all images in a directory. It does not support
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classes/targets."""
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IMG_EXTENSIONS = {'.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif', '.tiff', '.webp'}
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def __init__(self, root, transform=None):
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super().__init__()
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self.root = Path(root)
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self.transform = nn.Identity() if transform is None else transform
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self.paths = sorted(path for path in self.root.rglob('*') if path.suffix.lower() in self.IMG_EXTENSIONS)
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def __repr__(self):
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return f'FolderOfImages(root="{self.root}", len: {len(self)})'
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def __len__(self):
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return len(self.paths)
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def __getitem__(self, key):
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path = self.paths[key]
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with open(path, 'rb') as f:
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image = Image.open(f).convert('RGB')
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image = self.transform(image)
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return image,
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class CSVLogger:
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def __init__(self, filename, columns):
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self.filename = Path(filename)
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self.columns = columns
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if self.filename.exists():
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self.file = open(self.filename, 'a')
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else:
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self.file = open(self.filename, 'w')
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self.write(*self.columns)
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def write(self, *args):
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print(*args, sep=',', file=self.file, flush=True)
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@contextmanager
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def tf32_mode(cudnn=None, matmul=None):
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"""A context manager that sets whether TF32 is allowed on cuDNN or matmul."""
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cudnn_old = torch.backends.cudnn.allow_tf32
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matmul_old = torch.backends.cuda.matmul.allow_tf32
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try:
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if cudnn is not None:
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torch.backends.cudnn.allow_tf32 = cudnn
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if matmul is not None:
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torch.backends.cuda.matmul.allow_tf32 = matmul
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yield
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finally:
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if cudnn is not None:
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torch.backends.cudnn.allow_tf32 = cudnn_old
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if matmul is not None:
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torch.backends.cuda.matmul.allow_tf32 = matmul_old
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