from __future__ import division, print_function, absolute_import
from torchreid import metrics
from torchreid.losses import CrossEntropyLoss
from ..engine import Engine
[docs]class ImageSoftmaxEngine(Engine):
r"""Softmax-loss engine for image-reid.
Args:
datamanager (DataManager): an instance of ``torchreid.data.ImageDataManager``
or ``torchreid.data.VideoDataManager``.
model (nn.Module): model instance.
optimizer (Optimizer): an Optimizer.
scheduler (LRScheduler, optional): if None, no learning rate decay will be performed.
use_gpu (bool, optional): use gpu. Default is True.
label_smooth (bool, optional): use label smoothing regularizer. Default is True.
Examples::
import torchreid
datamanager = torchreid.data.ImageDataManager(
root='path/to/reid-data',
sources='market1501',
height=256,
width=128,
combineall=False,
batch_size=32
)
model = torchreid.models.build_model(
name='resnet50',
num_classes=datamanager.num_train_pids,
loss='softmax'
)
model = model.cuda()
optimizer = torchreid.optim.build_optimizer(
model, optim='adam', lr=0.0003
)
scheduler = torchreid.optim.build_lr_scheduler(
optimizer,
lr_scheduler='single_step',
stepsize=20
)
engine = torchreid.engine.ImageSoftmaxEngine(
datamanager, model, optimizer, scheduler=scheduler
)
engine.run(
max_epoch=60,
save_dir='log/resnet50-softmax-market1501',
print_freq=10
)
"""
def __init__(
self,
datamanager,
model,
optimizer,
scheduler=None,
use_gpu=True,
label_smooth=True
):
super(ImageSoftmaxEngine, self).__init__(datamanager, use_gpu)
self.model = model
self.optimizer = optimizer
self.scheduler = scheduler
self.register_model('model', model, optimizer, scheduler)
self.criterion = CrossEntropyLoss(
num_classes=self.datamanager.num_train_pids,
use_gpu=self.use_gpu,
label_smooth=label_smooth
)
def forward_backward(self, data):
imgs, pids = self.parse_data_for_train(data)
if self.use_gpu:
imgs = imgs.cuda()
pids = pids.cuda()
outputs = self.model(imgs)
loss = self.compute_loss(self.criterion, outputs, pids)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
loss_summary = {
'loss': loss.item(),
'acc': metrics.accuracy(outputs, pids)[0].item()
}
return loss_summary