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engine.py
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engine.py
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# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
"""
Train and eval functions used in main.py
"""
import math
import sys
from typing import Iterable, Optional
import torch
import timm
from timm.data import Mixup
from timm.utils import accuracy, ModelEma
import utils
def train_one_epoch(model: torch.nn.Module, criterion,
data_loader: Iterable, optimizer: torch.optim.Optimizer,
device: torch.device, epoch: int, loss_scaler, amp_autocast, max_norm: float = 0,
model_ema: Optional[ModelEma] = None, mixup_fn: Optional[Mixup] = None,
set_training_mode=True, args = None):
model.train(set_training_mode)
metric_logger = utils.MetricLogger(delimiter=" ")
metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
header = 'Epoch: [{}]'.format(epoch)
print_freq = 10
accum_iter = args.accum_iter
optimizer.zero_grad()
for step, (samples, targets) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
samples = samples.to(device, non_blocking=True)
targets = targets.to(device, non_blocking=True)
if mixup_fn is not None:
samples, targets = mixup_fn(samples, targets)
if args.bce_loss:
targets = targets.gt(0.0).type(targets.dtype)
with amp_autocast:
outputs = model(samples)
loss = criterion(outputs, targets)
loss_value = loss.item()
if not math.isfinite(loss_value):
print("Loss is {}, stopping training".format(loss_value))
sys.exit(1)
loss /= accum_iter
if loss_scaler != 'none':
is_second_order = hasattr(optimizer, 'is_second_order') and optimizer.is_second_order
grad_norm = loss_scaler(loss, optimizer,
clip_grad=max_norm,
parameters=model.parameters(),
create_graph=is_second_order,
need_update = (step + 1) % accum_iter == 0)
else:
loss.backward()
if max_norm != None:
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm)
optimizer.step()
if (step + 1) % accum_iter == 0:
optimizer.zero_grad()
torch.cuda.synchronize()
if model_ema is not None and (step + 1) % accum_iter == 0:
model_ema.update(model)
metric_logger.update(loss=loss_value)
metric_logger.update(lr=optimizer.param_groups[0]["lr"])
metric_logger.update(grad_norm=grad_norm)
# gather the stats from all processes
metric_logger.synchronize_between_processes()
print("Averaged stats:", metric_logger)
return {k: meter.global_avg for k, meter in metric_logger.meters.items()}
@torch.no_grad()
def evaluate(data_loader, model, device, amp_autocast):
criterion = torch.nn.CrossEntropyLoss()
metric_logger = utils.MetricLogger(delimiter=" ")
header = 'Test:'
# switch to evaluation mode
model.eval()
for images, target in metric_logger.log_every(data_loader, 10, header):
images = images.to(device, non_blocking=True)
target = target.to(device, non_blocking=True)
# compute output
with amp_autocast:
output = model(images)
loss = criterion(output, target)
acc1, acc5 = accuracy(output, target, topk=(1, 5))
batch_size = images.shape[0]
metric_logger.update(loss=loss.item())
metric_logger.meters['acc1'].update(acc1.item(), n=batch_size)
metric_logger.meters['acc5'].update(acc5.item(), n=batch_size)
# gather the stats from all processes
metric_logger.synchronize_between_processes()
print('* Acc@1 {top1.global_avg:.3f} Acc@5 {top5.global_avg:.3f} loss {losses.global_avg:.3f}'
.format(top1=metric_logger.acc1, top5=metric_logger.acc5, losses=metric_logger.loss))
return {k: meter.global_avg for k, meter in metric_logger.meters.items()}
def load_ema(model_ema, model):
ema_state_dict = model_ema.ema.state_dict()
for k, v in model.state_dict().items():
k = k.replace('module.', '')
if not k in ema_state_dict:
if 'total_params' in k or 'total_ops' in k:
continue
raise ValueError(f'{k} not in ema_state_dict')
tmp = v.data.clone()
v.data.copy_(ema_state_dict[k].data)
ema_state_dict[k].data.copy_(tmp)
def recover(model_ema, model):
state_dict = model.state_dict()
ema_state_dict = model_ema.ema.state_dict()
for k, v in ema_state_dict.items():
k_module = 'module.' + k
if not k in state_dict and k_module in state_dict:
k = k_module
if not k in state_dict or not k_module in state_dict:
raise ValueError(f'{k} not in state_dict')
tmp = v.data.clone()
v.data.copy_(state_dict[k].data)
state_dict[k].data.copy_(tmp)
@torch.no_grad()
def evaluate_ema(data_loader, model, device, amp_autocast, model_ema):
criterion = torch.nn.CrossEntropyLoss()
metric_logger = utils.MetricLogger(delimiter=" ")
header = 'Test:'
# switch to evaluation mode
model.eval()
if model_ema is not None:
load_ema(model_ema, model)
for images, target in metric_logger.log_every(data_loader, 10, header):
images = images.to(device, non_blocking=True)
target = target.to(device, non_blocking=True)
# compute output
with amp_autocast:
output = model(images)
loss = criterion(output, target)
acc1, acc5 = accuracy(output, target, topk=(1, 5))
batch_size = images.shape[0]
metric_logger.update(loss=loss.item())
metric_logger.meters['acc1'].update(acc1.item(), n=batch_size)
metric_logger.meters['acc5'].update(acc5.item(), n=batch_size)
# gather the stats from all processes
metric_logger.synchronize_between_processes()
print('* EMA_Acc@1 {top1.global_avg:.3f} EMA_Acc@5 {top5.global_avg:.3f} EMA_loss {losses.global_avg:.3f}'
.format(top1=metric_logger.acc1, top5=metric_logger.acc5, losses=metric_logger.loss))
if model_ema is not None:
recover(model_ema, model)
return {k: meter.global_avg for k, meter in metric_logger.meters.items()}