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train.py
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train.py
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#!/usr/bin/env python3
from trojanzoo.utils.fim import KFAC, EKFAC
from trojanzoo.utils.logger import MetricLogger
from trojanzoo.utils.memory import empty_cache
from trojanzoo.utils.model import accuracy, activate_params
from trojanzoo.utils.output import ansi, get_ansi_len, output_iter, prints
from trojanzoo.environ import env
import torch
import torch.nn as nn
import torch.nn.functional as F
from tqdm import tqdm
from trojanzoo.utils.model import ExponentialMovingAverage
from collections.abc import Callable
from torch.optim.optimizer import Optimizer
from torch.optim.lr_scheduler import _LRScheduler
import torch.utils.data
def train(module: nn.Module, num_classes: int,
epochs: int, optimizer: Optimizer, lr_scheduler: _LRScheduler = None,
lr_warmup_epochs: int = 0,
model_ema: ExponentialMovingAverage = None,
model_ema_steps: int = 32,
grad_clip: float = None, pre_conditioner: None | KFAC | EKFAC = None,
print_prefix: str = 'Train', start_epoch: int = 0, resume: int = 0,
validate_interval: int = 10, save: bool = False, amp: bool = False,
loader_train: torch.utils.data.DataLoader = None,
loader_valid: torch.utils.data.DataLoader = None,
epoch_fn: Callable[..., None] = None,
get_data_fn: Callable[..., tuple[torch.Tensor, torch.Tensor]] = None,
forward_fn: Callable[..., torch.Tensor] = None,
loss_fn: Callable[..., torch.Tensor] = None,
after_loss_fn: Callable[..., None] = None,
validate_fn: Callable[..., tuple[float, float]] = None,
save_fn: Callable[..., None] = None, file_path: str = None,
folder_path: str = None, suffix: str = None,
writer=None, main_tag: str = 'train', tag: str = '',
accuracy_fn: Callable[..., list[float]] = None,
verbose: bool = True, output_freq: str = 'iter', indent: int = 0,
change_train_eval: bool = True, lr_scheduler_freq: str = 'epoch',
backward_and_step: bool = True,
**kwargs):
r"""Train the model"""
if epochs <= 0:
return
get_data_fn = get_data_fn or (lambda x: x)
forward_fn = forward_fn or module.__call__
loss_fn = loss_fn or (lambda _input, _label, _output=None: F.cross_entropy(_output or forward_fn(_input), _label))
validate_fn = validate_fn or validate
accuracy_fn = accuracy_fn or accuracy
scaler: torch.cuda.amp.GradScaler = None
if not env['num_gpus']:
amp = False
if amp:
scaler = torch.cuda.amp.GradScaler()
best_validate_result = (0.0, float('inf'))
if validate_interval != 0:
best_validate_result = validate_fn(loader=loader_valid, get_data_fn=get_data_fn,
forward_fn=forward_fn, loss_fn=loss_fn,
writer=writer, tag=tag, _epoch=start_epoch,
verbose=verbose, indent=indent, **kwargs)
best_acc = best_validate_result[0]
params: list[nn.Parameter] = []
for param_group in optimizer.param_groups:
params.extend(param_group['params'])
len_loader_train = len(loader_train)
total_iter = (epochs - resume) * len_loader_train
logger = MetricLogger()
logger.create_meters(loss=None, top1=None, top5=None)
if resume and lr_scheduler:
for _ in range(resume):
lr_scheduler.step()
iterator = range(resume, epochs)
if verbose and output_freq == 'epoch':
header: str = '{blue_light}{0}: {reset}'.format(print_prefix, **ansi)
header = header.ljust(max(len(header), 30) + get_ansi_len(header))
iterator = logger.log_every(range(resume, epochs),
header=print_prefix,
tqdm_header='Epoch',
indent=indent)
for _epoch in iterator:
_epoch += 1
logger.reset()
if callable(epoch_fn):
activate_params(module, [])
epoch_fn(optimizer=optimizer, lr_scheduler=lr_scheduler,
_epoch=_epoch, epochs=epochs, start_epoch=start_epoch)
loader_epoch = loader_train
if verbose and output_freq == 'iter':
header: str = '{blue_light}{0}: {1}{reset}'.format(
'Epoch', output_iter(_epoch, epochs), **ansi)
header = header.ljust(max(len('Epoch'), 30) + get_ansi_len(header))
loader_epoch = logger.log_every(loader_train, header=header,
tqdm_header='Batch',
indent=indent)
if change_train_eval:
module.train()
activate_params(module, params)
for i, data in enumerate(loader_epoch):
_iter = _epoch * len_loader_train + i
# data_time.update(time.perf_counter() - end)
_input, _label = get_data_fn(data, mode='train')
if pre_conditioner is not None and not amp:
pre_conditioner.track.enable()
_output = forward_fn(_input, amp=amp, parallel=True)
loss = loss_fn(_input, _label, _output=_output, amp=amp)
if backward_and_step:
optimizer.zero_grad()
if amp:
scaler.scale(loss).backward()
if callable(after_loss_fn) or grad_clip is not None:
scaler.unscale_(optimizer)
if callable(after_loss_fn):
after_loss_fn(_input=_input, _label=_label,
_output=_output,
loss=loss, optimizer=optimizer,
loss_fn=loss_fn,
amp=amp, scaler=scaler,
_iter=_iter, total_iter=total_iter)
if grad_clip is not None:
nn.utils.clip_grad_norm_(params, grad_clip)
scaler.step(optimizer)
scaler.update()
else:
loss.backward()
if callable(after_loss_fn):
after_loss_fn(_input=_input, _label=_label,
_output=_output,
loss=loss, optimizer=optimizer,
loss_fn=loss_fn,
amp=amp, scaler=scaler,
_iter=_iter, total_iter=total_iter)
# start_epoch=start_epoch, _epoch=_epoch, epochs=epochs)
if pre_conditioner is not None:
pre_conditioner.track.disable()
pre_conditioner.step()
if grad_clip is not None:
nn.utils.clip_grad_norm_(params, grad_clip)
optimizer.step()
if model_ema and i % model_ema_steps == 0:
model_ema.update_parameters(module)
if _epoch <= lr_warmup_epochs:
# Reset ema buffer to keep copying weights
# during warmup period
model_ema.n_averaged.fill_(0)
if lr_scheduler and lr_scheduler_freq == 'iter':
lr_scheduler.step()
acc1, acc5 = accuracy_fn(
_output, _label, num_classes=num_classes, topk=(1, 5))
batch_size = int(_label.size(0))
logger.update(n=batch_size, loss=float(loss), top1=acc1, top5=acc5)
empty_cache()
optimizer.zero_grad()
if lr_scheduler and lr_scheduler_freq == 'epoch':
lr_scheduler.step()
if change_train_eval:
module.eval()
activate_params(module, [])
loss, acc = (logger.meters['loss'].global_avg,
logger.meters['top1'].global_avg)
if writer is not None:
from torch.utils.tensorboard import SummaryWriter
assert isinstance(writer, SummaryWriter)
writer.add_scalars(main_tag='Loss/' + main_tag,
tag_scalar_dict={tag: loss},
global_step=_epoch + start_epoch)
writer.add_scalars(main_tag='Acc/' + main_tag,
tag_scalar_dict={tag: acc},
global_step=_epoch + start_epoch)
if validate_interval != 0 and (_epoch % validate_interval == 0 or _epoch == epochs):
validate_result = validate_fn(module=module,
num_classes=num_classes,
loader=loader_valid,
get_data_fn=get_data_fn,
forward_fn=forward_fn,
loss_fn=loss_fn,
writer=writer, tag=tag,
_epoch=_epoch + start_epoch,
verbose=verbose, indent=indent,
**kwargs)
cur_acc = validate_result[0]
if cur_acc >= best_acc:
best_validate_result = validate_result
if verbose:
prints('{purple}best result update!{reset}'.format(
**ansi), indent=indent)
prints(f'Current Acc: {cur_acc:.3f} '
f'Previous Best Acc: {best_acc:.3f}',
indent=indent)
best_acc = cur_acc
if save:
save_fn(file_path=file_path, folder_path=folder_path,
suffix=suffix, verbose=verbose)
if verbose:
prints('-' * 50, indent=indent)
module.zero_grad()
return best_validate_result
def validate(module: nn.Module, num_classes: int,
loader: torch.utils.data.DataLoader,
print_prefix: str = 'Validate', indent: int = 0,
verbose: bool = True,
get_data_fn: Callable[
..., tuple[torch.Tensor, torch.Tensor]] = None,
forward_fn: Callable[..., torch.Tensor] = None,
loss_fn: Callable[..., torch.Tensor] = None,
writer=None, main_tag: str = 'valid',
tag: str = '', _epoch: int = None,
accuracy_fn: Callable[..., list[float]] = None,
**kwargs) -> tuple[float, float]:
r"""Evaluate the model.
Returns:
(float, float): Accuracy and loss.
"""
module.eval()
get_data_fn = get_data_fn or (lambda x: x)
forward_fn = forward_fn or module.__call__
loss_fn = loss_fn or nn.CrossEntropyLoss()
accuracy_fn = accuracy_fn or accuracy
logger = MetricLogger()
logger.create_meters(loss=None, top1=None, top5=None)
loader_epoch = loader
if verbose:
header: str = '{yellow}{0}{reset}'.format(print_prefix, **ansi)
header = header.ljust(max(len(print_prefix), 30) + get_ansi_len(header))
loader_epoch = logger.log_every(loader, header=header,
tqdm_header='Batch',
indent=indent)
for data in loader_epoch:
_input, _label = get_data_fn(data, mode='valid', **kwargs)
with torch.no_grad():
_output = forward_fn(_input)
loss = float(loss_fn(_input, _label, _output=_output, **kwargs))
acc1, acc5 = accuracy_fn(
_output, _label, num_classes=num_classes, topk=(1, 5))
batch_size = int(_label.size(0))
logger.update(n=batch_size, loss=float(loss), top1=acc1, top5=acc5)
acc, loss = (logger.meters['top1'].global_avg,
logger.meters['loss'].global_avg)
if writer is not None and _epoch is not None and main_tag:
from torch.utils.tensorboard import SummaryWriter
assert isinstance(writer, SummaryWriter)
writer.add_scalars(main_tag='Acc/' + main_tag,
tag_scalar_dict={tag: acc}, global_step=_epoch)
writer.add_scalars(main_tag='Loss/' + main_tag,
tag_scalar_dict={tag: loss}, global_step=_epoch)
return acc, loss
@torch.no_grad()
def compare(module1: nn.Module, module2: nn.Module,
loader: torch.utils.data.DataLoader,
print_prefix='Validate', indent=0, verbose=True,
get_data_fn: Callable[...,
tuple[torch.Tensor, torch.Tensor]] = None,
criterion: Callable[[torch.Tensor, torch.Tensor], torch.Tensor] = nn.CrossEntropyLoss(),
**kwargs) -> float:
module1.eval()
module2.eval()
get_data_fn = get_data_fn if get_data_fn is not None else lambda x: x
logger = MetricLogger()
logger.create_meters(loss=None)
loader_epoch = loader
if verbose:
header: str = '{yellow}{0}{reset}'.format(print_prefix, **ansi)
header = header.ljust(
max(len(print_prefix), 30) + get_ansi_len(header))
if env['tqdm']:
loader_epoch = tqdm(loader_epoch, leave=False)
loader_epoch = logger.log_every(
loader_epoch, header=header, indent=indent)
for data in loader_epoch:
_input, _label = get_data_fn(data, **kwargs)
_output1: torch.Tensor = module1(_input)
_output2: torch.Tensor = module2(_input)
loss = criterion(_output1, _output2.softmax(1)).item()
batch_size = int(_label.size(0))
logger.update(n=batch_size, loss=loss)
return logger.meters['loss'].global_avg